@inproceedings{bono2024exploration,
  title={An Exploration of Decentralized Moderation on Mastodon},
  author={Bono, Carlo and La Cava, Lucio and Luceri, Luca and Pierri, Francesco},
 booktitle={16th ACM Web Science Conference 2024},
  year={2024}
}

@InProceedings{Rizvi24a,
	author = 	"ASM Rizvi and Tingshan Huang and Rasit	Esrefoglu and John Heidemann",
	title = 	"Anycast Polarization in The Wild",
        booktitle =     "Proceedings of the " # " Passive and Active Measurement Conference",
        year =          2024,
	sortdate = 		"2024-03-11", 
	project = "ant, pimawat, classnet, diiner, sabres",
        month =      mar,
        address =    "Virtual Location",
        publisher =  "Springer",
        jlocation =   "johnh: pafile",
        keywords =   "anycast, polarization, bgp",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi24a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi24a.pdf",
	blogurl =	"https://ant.isi.edu/blog/?p=2048",
	dataseturl = "https://ant.isi.edu/datasets/outage/index.html",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "Springer",
        abstract = "IP anycast is a commonly used method to associate users with services
provided across multiple sites, and if properly used, it can provide
efficient access with low latency.  However, prior work has shown that
\emph{polarization} can occur in global anycast services, where some
users of that service are routed to an anycast site on another
continent, adding 100\,ms or more latency compared to a nearby site.
This paper describes the causes of polarization in real-world anycast
and shows how to observe polarization in third-party anycast services.
We use these methods to look for polarization and its causes in 7986
known anycast prefixes.  We find that polarization occurs in more than
a quarter of anycast prefixes, and identify incomplete connectivity to
Tier-1 transit providers and route leakage by regional ISPs as common
problems.  Finally, working with a commercial CDN, we show how small
routing changes can often address polarization, improving latency for
40\% of clients, by up to 54\%.",
}

IP anycast is a commonly used method to associate users with services provided across multiple sites, and if properly used, it can provide efficient access with low latency. However, prior work has shown that \emphpolarization can occur in global anycast services, where some users of that service are routed to an anycast site on another continent, adding 100\,ms or more latency compared to a nearby site. This paper describes the causes of polarization in real-world anycast and shows how to observe polarization in third-party anycast services. We use these methods to look for polarization and its causes in 7986 known anycast prefixes. We find that polarization occurs in more than a quarter of anycast prefixes, and identify incomplete connectivity to Tier-1 transit providers and route leakage by regional ISPs as common problems. Finally, working with a commercial CDN, we show how small routing changes can often address polarization, improving latency for 40% of clients, by up to 54%.

@inproceedings{shbita2024automatically,
  title={Automatically Constructing Geospatial Feature Taxonomies from OpenStreetMap Data},
  author={Shbita, Basel and Knoblock, Craig A},
  booktitle={2024 IEEE 18th International Conference on Semantic Computing (ICSC)},
  pages={208--211},
  year={2024},
  organization={IEEE},
  urlLink={https://doi.org/10.1109/ICSC59802.2024.00039},
  urlPaper={http://usc-isi-i2.github.io/papers/shbita24-icsc.pdf},
  urlSlides={http://usc-isi-i2.github.io/slides/shbita24-icsc-slides.pdf}
}

@inproceedings{sheng_yu_careforme_MOBILESoft2024,
 author = {Sheng Yu and Narjes Nourzad and Randye J. Semple and Yixue Zhao and Emily Zhou and Bhaskar Krishnamachari},
 title = {CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for Mental Health},
 booktitle = {Proceedings of the 11th IEEE/ACM International Conference on Mobile Software Engineering and Systems},
 series = {MOBILESoft '24},
 year = {2024},
 location = {Portugal},
}

@inproceedings{shbita2024constructing,
  title={Constructing a Knowledge Graph of Historical Mining Data},
  author={Shbita, Basel and Sharma, Namrata and Vu, Binh and Lin, Fandel and Knoblock, Craig A.},
  booktitle={6th International Workshop on Geospatial Linked Data (GeoLD) Co-located with the 21st Extended Semantic Web Conference (ESWC)},
  year={2024},
  organization={CEUR},
  urlPaper={http://usc-isi-i2.github.io/papers/shbita24-geold_ewsc.pdf},
  urlSlides={http://usc-isi-i2.github.io/slides/shbita24-geold_ewsc-slides.pdf}
}

@Article{Moura24a,
	author =	"Giovane C. M. Moura and Marco Davids and
	Caspar Schutijser and Christian Hesselman and John Heidemann
	and Georgios Smaragdakis",
	title =		"Deep Dive into {NTP} Pool's Popularity and Mapping",
	journal =	"{ACM} Proceedings of the ACM on Measurement and Analysis of Computing Systems",
	volume =	8,
	number =	1,
	month =		mar,
	year =		2024,
	project = "ant, inttrafmap, pimawat",
	jsubject = "network_security",
	pages =		"30",
	jlocation =	"johnh: pafile",
	keywords =	"NTP, ntp pool",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Moura24a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Moura24a.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=2035",
        doi =        "https://doi.org/10.1145/3639041",
	abstract = "Time synchronization is of paramount importance on the Internet, with the Network Time Protocol (NTP) serving as the primary synchronization protocol. The NTP Pool, a volunteer-driven initiative launched two decades ago, facilitates connections between clients and NTP servers. Our analysis of root DNS queries reveals that the NTP Pool has consistently been the most popular time service. We further investigate the DNS component (GeoDNS) of the NTP Pool, which is responsible for mapping clients to servers. Our findings indicate that the current algorithm  is heavily skewed, leading to the emergence of time monopolies for entire countries. For instance, clients in the US are served by 551 NTP servers, while clients in Cameroon and Nigeria are served by only one and two servers, respectively, out of the 4k+ servers available in the NTP Pool. We examine the underlying assumption behind GeoDNS for these mappings and discover that time servers located far away can still provide accurate clock time information to clients. We have shared our findings with the NTP Pool operators, who acknowledge them and plan to revise their algorithm to enhance security.
",
}

Time synchronization is of paramount importance on the Internet, with the Network Time Protocol (NTP) serving as the primary synchronization protocol. The NTP Pool, a volunteer-driven initiative launched two decades ago, facilitates connections between clients and NTP servers. Our analysis of root DNS queries reveals that the NTP Pool has consistently been the most popular time service. We further investigate the DNS component (GeoDNS) of the NTP Pool, which is responsible for mapping clients to servers. Our findings indicate that the current algorithm is heavily skewed, leading to the emergence of time monopolies for entire countries. For instance, clients in the US are served by 551 NTP servers, while clients in Cameroon and Nigeria are served by only one and two servers, respectively, out of the 4k+ servers available in the NTP Pool. We examine the underlying assumption behind GeoDNS for these mappings and discover that time servers located far away can still provide accurate clock time information to clients. We have shared our findings with the NTP Pool operators, who acknowledge them and plan to revise their algorithm to enhance security.

@article{james_detecting_2024,
	title = {Detecting {Paleoclimate} {Transitions} {With} {Laplacian} {Eigenmaps} of {Recurrence} {Matrices} ({LERM})},
	volume = {39},
	issn = {2572-4517, 2572-4525},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023PA004700},
	doi = {10.1029/2023PA004700},
	abstract = {Abstract 
            Paleoclimate records can be considered low‐dimensional projections of the climate system that generated them. Understanding what these projections tell us about past climates, and changes in their dynamics, is a main goal of time series analysis on such records. Laplacian eigenmaps of recurrence matrices (LERM) is a novel technique using univariate paleoclimate time series data to indicate when notable shifts in dynamics have occurred. LERM leverages time delay embedding to construct a manifold that is mappable to the attractor of the climate system; this manifold can then be analyzed for significant dynamical transitions. Through numerical experiments with observed and synthetic data, LERM is applied to detect both gradual and abrupt regime transitions. Our paragon for gradual transitions is the Mid‐Pleistocene Transition (MPT). We show that LERM can robustly detect gradual MPT‐like transitions for sufficiently high signal‐to‐noise (S/N) ratios, though with a time lag related to the embedding process. Our paragon of abrupt transitions is the “8.2 ka” event; we find that LERM is generally robust at detecting 8.2 ka‐like transitions for sufficiently high S/N ratios, though edge effects become more influential. We conclude that LERM can usefully detect dynamical transitions in paleogeoscientific time series, with the caveat that false positive rates are high when dynamical transitions are not present, suggesting the importance of using multiple records to confirm the robustness of transitions. We share an open‐source Python package to facilitate the use of LERM in paleoclimatology and paleoceanography. 

Key Points:           
                                
Laplacian eigenmaps of recurrence matrices (LERM) is a novel tool for paleoclimate time series analysis 
                 
                 
LERM can robustly detect the gradual Mid‐Pleistocene Transition in relatively low signal‐to‐noise ratio scenarios 
                 
                 
LERM can also be applied to detect abrupt climate transitions like the 8.2 ka event, though less robustly},
	language = {en},
	number = {1},
	urldate = {2024-01-03},
	journal = {Paleoceanography and Paleoclimatology},
	author = {James, Alexander and Emile‐Geay, Julien and Malik, Nishant and Khider, Deborah},
	month = jan,
	year = {2024},
	pages = {e2023PA004700},
}

Abstract Paleoclimate records can be considered low‐dimensional projections of the climate system that generated them. Understanding what these projections tell us about past climates, and changes in their dynamics, is a main goal of time series analysis on such records. Laplacian eigenmaps of recurrence matrices (LERM) is a novel technique using univariate paleoclimate time series data to indicate when notable shifts in dynamics have occurred. LERM leverages time delay embedding to construct a manifold that is mappable to the attractor of the climate system; this manifold can then be analyzed for significant dynamical transitions. Through numerical experiments with observed and synthetic data, LERM is applied to detect both gradual and abrupt regime transitions. Our paragon for gradual transitions is the Mid‐Pleistocene Transition (MPT). We show that LERM can robustly detect gradual MPT‐like transitions for sufficiently high signal‐to‐noise (S/N) ratios, though with a time lag related to the embedding process. Our paragon of abrupt transitions is the “8.2 ka” event; we find that LERM is generally robust at detecting 8.2 ka‐like transitions for sufficiently high S/N ratios, though edge effects become more influential. We conclude that LERM can usefully detect dynamical transitions in paleogeoscientific time series, with the caveat that false positive rates are high when dynamical transitions are not present, suggesting the importance of using multiple records to confirm the robustness of transitions. We share an open‐source Python package to facilitate the use of LERM in paleoclimatology and paleoceanography. Key Points: Laplacian eigenmaps of recurrence matrices (LERM) is a novel tool for paleoclimate time series analysis LERM can robustly detect the gradual Mid‐Pleistocene Transition in relatively low signal‐to‐noise ratio scenarios LERM can also be applied to detect abrupt climate transitions like the 8.2 ka event, though less robustly

@inproceedings{yixue_zhao_pausenow_MOBILESoft2024,
 author = {Yixue Zhao and Tianyi Li and Michael Sobolev},
 title = {Digital Wellbeing Redefined: Toward User-Centric Approach for Positive Social Media Engagement},
 booktitle = {Proceedings of the 11th IEEE/ACM International Conference on Mobile Software Engineering and Systems},
 series = {MOBILESoft '24},
 year = {2024},
 location = {Portugal}
}

@article{Schmitz2024_extremist,
	abstract = {Social media offers an avenue for like-minded individuals to interact in ways that were previously not possible. Yet, it can also be a breeding ground for hate and extremism to spread. Despite research into hate speech on social media, its influence on users adopting extremist beliefs is less understood. In this study, we use causal analysis to quantify extremist adoption resulting from users becoming active in hate online communities known as subreddits. Using an interrupted time series research design, we compare users who became involved in hate subreddits (treatment group) to those who did not (control group). This analysis is reproduced across ten different subreddits covering four different topics: Alt-Right, Racism, Sexism, and Fat-Shaming. From these analyses, we uncover a causal link between a user becoming active in a hate community and using more hate speech both within hate subreddits and across the wider platform. The results are consistent and replicate across communities. Our findings are tentative evidence that users adopt extremist ideas from exposure to hate subreddits.},
	author = {Schmitz, Matheus and Muric, Goran and Hickey, Daniel and Burghardt, Keith},
	date = {2024/01/11},
	date-added = {2024-01-17 20:03:11 -0800},
	date-modified = {2024-01-17 20:03:11 -0800},
	doi = {10.1007/s13278-023-01184-8},
	id = {Schmitz2024},
	isbn = {1869-5469},
	journal = {Social Network Analysis and Mining},
	number = {1},
	pages = {22},
	title = {Do users adopt extremist beliefs from exposure to hate subreddits?},
	url = {https://doi.org/10.1007/s13278-023-01184-8},
	volume = {14},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1007/s13278-023-01184-8}}

Social media offers an avenue for like-minded individuals to interact in ways that were previously not possible. Yet, it can also be a breeding ground for hate and extremism to spread. Despite research into hate speech on social media, its influence on users adopting extremist beliefs is less understood. In this study, we use causal analysis to quantify extremist adoption resulting from users becoming active in hate online communities known as subreddits. Using an interrupted time series research design, we compare users who became involved in hate subreddits (treatment group) to those who did not (control group). This analysis is reproduced across ten different subreddits covering four different topics: Alt-Right, Racism, Sexism, and Fat-Shaming. From these analyses, we uncover a causal link between a user becoming active in a hate community and using more hate speech both within hate subreddits and across the wider platform. The results are consistent and replicate across communities. Our findings are tentative evidence that users adopt extremist ideas from exposure to hate subreddits.

@InProceedings{Baltra24a,
	author = 	"Guillermo Baltra and Xiao Song and John Heidemann",
	title = 	"Ebb and Flow: Implications of {ISP} Address Dynamics",
        booktitle =     "Proceedings of the " # " Passive and Active Measurement Workshop",
        year =          2024,
	sortdate = 		"2024-03-11", 
	project = "ant, eieio, minceq",
        month =      mar,
        address =    "Virtual Location",
        publisher =  "Springer",
        jlocation =   "johnh: pafile",
        keywords =   "trinocular, outages, ISP address use",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Baltra24a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Baltra24a.pdf",
	blogurl =	"https://ant.isi.edu/blog/?p=2043",
	dataseturl = "https://ant.isi.edu/datasets/outage/index.html",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "Springer",
        abstract = "\emph{Address dynamics} are changes in IP address occupation as users
come and go, ISPs renumber them for privacy or for routing
maintenance.  Address dynamics affect address reputation services, IP
geolocation, network measurement, and outage detection, with
implications of Internet governance, e-commerce, and science.  While
prior work has identified diurnal trends in address use, we show the
effectiveness of Multi-Seasonal-Trend using Loess decomposition to
identify both daily and weekly trends.  We use ISP-wide dynamics to
develop IAS, a new algorithm that is the first to automatically detect
ISP maintenance events that move users in the address space.  We show
that 20\% of such events result in /24 IPv4 address blocks that become
unused for days or more, and correcting nearly 41k false outages per
quarter.  Our analysis provides a new understanding about ISP address
use:  while only about 2.8\% of ASes (1,730) are diurnal, some diurnal
ASes show more than 20\% changes each day.  It also shows greater
fragmentation in IPv4 address use compared to IPv6.",
}

\emphAddress dynamics are changes in IP address occupation as users come and go, ISPs renumber them for privacy or for routing maintenance. Address dynamics affect address reputation services, IP geolocation, network measurement, and outage detection, with implications of Internet governance, e-commerce, and science. While prior work has identified diurnal trends in address use, we show the effectiveness of Multi-Seasonal-Trend using Loess decomposition to identify both daily and weekly trends. We use ISP-wide dynamics to develop IAS, a new algorithm that is the first to automatically detect ISP maintenance events that move users in the address space. We show that 20% of such events result in /24 IPv4 address blocks that become unused for days or more, and correcting nearly 41k false outages per quarter. Our analysis provides a new understanding about ISP address use: while only about 2.8% of ASes (1,730) are diurnal, some diurnal ASes show more than 20% changes each day. It also shows greater fragmentation in IPv4 address use compared to IPv6.

@inproceedings{OperaDLRM,
author = {Imes, Connor and Rittenbach, Andrew and Xie, Peng and Kang, Dong In D. and Walters, John Paul and Crago, Stephen P.},
title = {Evaluating Deep Learning Recommendation Model Training Scalability with the Dynamic Opera Network},
year = {2024},
isbn = {9798400705410},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3642970.3655825},
doi = {10.1145/3642970.3655825},
abstract = {Deep learning is commonly used to make personalized recommendations to users for a wide variety of activities. However, deep learning recommendation model (DLRM) training is increasingly dominated by all-to-all and many-to-many communication patterns. While there are a wide variety of algorithms to efficiently overlap communication and computation for many collective operations, these patterns are strictly limited by network bottlenecks. We propose co-designing DLRM model training with the recently proposed Opera network, which is designed to avoid multiple network hops using time-varying source-to-destination circuits. Using measurements from state-of-the-art NVIDIA A100 GPUs, we simulate DLRM model training on networks ranging from 16 to 1024 nodes and demonstrate up to 1.79\texttimes{} improvement using Opera compared with equivalent fat-tree networks. We identify important parameters affecting training time and demonstrate that careful co-design is needed to optimize training latency.},
booktitle = {Proceedings of the 4th Workshop on Machine Learning and Systems},
pages = {169–175},
numpages = {7},
keywords = {deep learning, dynamic networks, machine learning, networks, recommendation models},
location = {, Athens, Greece, },
series = {EuroMLSys '24},
ISIArea = {ML, CAS, NET}
}

Deep learning is commonly used to make personalized recommendations to users for a wide variety of activities. However, deep learning recommendation model (DLRM) training is increasingly dominated by all-to-all and many-to-many communication patterns. While there are a wide variety of algorithms to efficiently overlap communication and computation for many collective operations, these patterns are strictly limited by network bottlenecks. We propose co-designing DLRM model training with the recently proposed Opera network, which is designed to avoid multiple network hops using time-varying source-to-destination circuits. Using measurements from state-of-the-art NVIDIA A100 GPUs, we simulate DLRM model training on networks ranging from 16 to 1024 nodes and demonstrate up to 1.79× improvement using Opera compared with equivalent fat-tree networks. We identify important parameters affecting training time and demonstrate that careful co-design is needed to optimize training latency.

@inproceedings{vu2024,
  title        = {Exploiting Distant Supervision to Learn Semantic Descriptions of Tables with Overlapping Data},
  author       = {Vu, Binh and Knoblock, Craig A and Shbita, Basel and Lin, Fandel},
  booktitle    = {The Semantic Web--ISWC 2024: 23th International Semantic Web Conference, ISWC 2024, November 11--15, 2024},
  year         = {2024},
  organization = {Springer International Publishing}
}

@InProceedings{ Haroldsen-2024,
author = {Travis Haroldsen, Ting-Yuan Sung, Osaze Shears, Dallon Glick, Jay Danner},
title = {Independent Functional Testing for Intel Stratix 10},
booktitle = {GOMACTech-24},
year = {2024},
note = {To appear.},
ISIArea = {CAS}
}

@inproceedings{cift-gomac:2024,
 author = {T. Haroldsen and T.Y. Sung and O. Shears and D. Glick and J. Danner},
 title = {{Independent Functional Testing for Intel Stratix 10}},
 booktitle = {Government Microcircuit Applications and Critical Technology Conference},
 month = {March},
 year = {2024},
}

@inproceedings{nogara_2024_misinformation,
	author = {Nogara, Gianluca and Pierri, Francesco and Cresci, Stefano and Luceri, Luca and Giordano, Silvia},
	title = {Misinformation and Polarization around COVID-19 vaccines in France, Germany, and Italy},
	year = {2024},
	isbn = {9798400703348},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3614419.3644020},
	doi = {10.1145/3614419.3644020},
	booktitle = {Proceedings of the 16th ACM Web Science Conference},
	pages = {119–128},
	numpages = {10},
	location = {Stuttgart, Germany},
	series = {WEBSCI '24}
}

@misc{jiang2024moral,
  title={Moral Values Underpinning COVID-19 Online Communication Patterns},
  author={Jiang, Julie and Luceri, Luca and Ferrara, Emilio},
   Eprint = {arXiv:2401.08789},
  Howpublished = {arXiv Preprint: https://arxiv.org/abs/2401.08789},
  year={2024}
}

@misc{rfc9490,
    series =    {Request for Comments},
    number =    9490,
    howpublished =  {RFC 9490},
    publisher = {RFC Editor},
    doi =       {10.17487/RFC9490},
    url =       {https://www.rfc-editor.org/info/rfc9490},
    author =    {},
    title =     {{Report from the IAB Workshop on Management Techniques in Encrypted Networks (M-TEN)}},
    pagetotal = 15,
    year =      2024,
    month =     jan,
    abstract =  {The "Management Techniques in Encrypted Networks (M-TEN)" workshop was convened by the Internet Architecture Board (IAB) from 17 October 2022 to 19 October 2022 as a three-day online meeting. The workshop was organized in three parts to discuss ways to improve network management techniques in support of even broader adoption of encryption on the Internet. This report summarizes the workshop's discussion and identifies topics that warrant future work and consideration. Note that this document is a report on the proceedings of the workshop. The views and positions documented in this report are those of the expressed during the workshop by participants and do not necessarily reflect IAB views and positions.},
}

The "Management Techniques in Encrypted Networks (M-TEN)" workshop was convened by the Internet Architecture Board (IAB) from 17 October 2022 to 19 October 2022 as a three-day online meeting. The workshop was organized in three parts to discuss ways to improve network management techniques in support of even broader adoption of encryption on the Internet. This report summarizes the workshop's discussion and identifies topics that warrant future work and consideration. Note that this document is a report on the proceedings of the workshop. The views and positions documented in this report are those of the expressed during the workshop by participants and do not necessarily reflect IAB views and positions.

@misc{mukherjee2024sok,
      title={SoK: The Ghost Trilemma}, 
      author={Sulagna Mukherjee and Srivatsan Ravi and Paul Schmitt and Barath Raghavan},
      year={2024},
      eprint={2308.02202},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

@inproceedings{Burghardt2024_characteristics,
title={Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts},
Author={Keith Burghardt and Ashwin Rao and Siyi Guo and Zihao He and Georgios Chochlakis and Baruah Sabyasachee and Andrew Rojecki and Shri Narayanan and Kristina Lerman},
booktitle={ICWSM},
Year={2024},
pages={(in press)}
}

@inproceedings{ye2023susceptibility,
  title={Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure},
  author={Ye, Jinyi and Luceri, Luca and Jiang, Julie and Ferrara, Emilio},
 booktitle={Proceedings of The 2024 World Wide Web Conference},
  year={2024}
}

@article{Wildfire2024,
  author = {Andrew Rittenbach and Connor Imes and John Paul Walters},
  booktitle = {Optica Imaging Congress 2024 (3D, AOMS, COSI, ISA, pcAOP)},
  journal = {Optica Imaging Congress 2024 (3D, AOMS, COSI, ISA, pcAOP)},
  keywords = {Deep learning; Image processing; Machine vision; Real time imaging; Segmentation; Spatial resolution},
  pages = {FD1.7},
  publisher = {Optica Publishing Group},
  title = {Timely Wildfire Perimeter Mapping for Unmanned Aerial Platforms},
  year = {2024},
  url = {https://opg.optica.org/abstract.cfm?URI=ISA-2024-FD1.7},
  abstract = {Wildfire perimeter mapping currently relies on deferred processing of data from manned and orbital platforms using hand-tuned physics-based models. We demonstrate real-time on-board multispectral data processing on cost-efficient unmanned aerial platforms using ML-based semantic segmentation.},
  ISIArea = {ML, CAS}
}

Wildfire perimeter mapping currently relies on deferred processing of data from manned and orbital platforms using hand-tuned physics-based models. We demonstrate real-time on-board multispectral data processing on cost-efficient unmanned aerial platforms using ML-based semantic segmentation.

@inproceedings{suresh2023tracking,
  title={Tracking Fringe and Coordinated Activity on Twitter Leading Up To the US Capitol Attack},
  author={Suresh, Vishnuprasad Padinjaredath and Nogara, Gianluca and Cardoso, Felipe and Cresci, Stefano and Giordano, Silvia and Luceri, Luca},
 booktitle={Proceedings of the 2024 International Conference of Web and Social Media (ICWSM)},
  year={2024}
}

@inproceedings{luceri2023unmasking,
  title={Unmasking the web of deceit: Uncovering coordinated activity to expose information operations on twitter},
  author={Luceri, Luca and Pant{\`e}, Valeria and Burghardt, Keith and Ferrara, Emilio},
  booktitle={Proceedings of The 2024 World Wide Web Conference},
  year={2024}
}

@article{salinas2023not,
  title={" Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Models},
  author={Salinas, Abel and Penafiel, Louis and McCormack, Robert and Morstatter, Fred},
  journal={arXiv preprint arXiv:2310.08780},
  year={2023}
}

@article{DBLP:journals/corr/abs-2302-01439,
  author       = {Rong{-}Ching Chang and
                  Ashwin Rao and
                  Qiankun Zhong and
                  Magdalena Wojcieszak and
                  Kristina Lerman},
  title        = {{\#}RoeOverturned: Twitter Dataset on the Abortion Rights Controversy},
  journal      = {CoRR},
  volume       = {abs/2302.01439},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2302.01439},
  doi          = {10.48550/ARXIV.2302.01439},
  eprinttype    = {arXiv},
  eprint       = {2302.01439},
  timestamp    = {Thu, 09 Feb 2023 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2302-01439.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{xie2023critical-view-dynamics,
  title={A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment},
  author={Xie, Hanchen and Zhu, Jiageng and Khayatkhoei, Mahyar and Li, Jiazhi and Hussein, Mohamed E. and Abdalmageed, Wael},
  booktitle={Proceedings of the 40th International Conference on Machine Learning},
  pages={38258--38271},
  year={2023},
  month={July},
  volume={202},
  publisher={PMLR},
  url_Paper={https://arxiv.org/pdf/2305.07648.pdf},
  url_Link={https://icml.cc/virtual/2023/poster/23866},
  ISIArea = {ML, VISTA}
}

@article{DBLP:journals/corr/abs-2304-02144,
  author       = {Siyi Guo and
                  Negar Mokhberian and
                  Kristina Lerman},
  title        = {A Data Fusion Framework for Multi-Domain Morality Learning},
  journal      = {CoRR},
  volume       = {abs/2304.02144},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2304.02144},
  doi          = {10.48550/ARXIV.2304.02144},
  eprinttype    = {arXiv},
  eprint       = {2304.02144},
  timestamp    = {Mon, 17 Apr 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2304-02144.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{mohd_razak_dynamic_2023-1,
	title = {A {Dynamic} {Residual} {Learning} {Approach} to {Improve} {Physics}-{Constrained} {Neural} {Network} {Predictions} in {Unconventional} {Reservoirs}},
	url = {https://onepetro.org/SPEMEOS/proceedings/23MEOS/D021S084R005/517253},
	urldate = {2024-02-12},
	booktitle = {Middle {East} {Oil}, {Gas} and {Geosciences} {Show}},
	publisher = {OnePetro},
	author = {Mohd Razak, Syamil and Cornelio, Jodel and Cho, Young and Liu, Hui-Hai and Vaidya, Ravimadhav and Jafarpour, Behnam},
	year = {2023},
}

@article{tksk08,
author={Ang Li and Peter Stuckey and Sven Koenig and Satish Thittamaranahalli},
title={A FastMap-Based Framework for Efficiently Computing Top-K Projected Centrality},
journal={Proceedings of the Ninth International Conference on Machine Learning, Optimization, and Data Science (LOD-2023)},
year={2023}
}

@article{shen2023formalism,
  title={A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores},
  author={Shen, Ke and Kejriwal, Mayank},
  journal={arXiv preprint arXiv:2310.03283},
  year={2023}
}

@article{schaffhauser-etal-cgj23,
  title =  {A Framework for Broad Dissemination of Hydrological Models to Non-Expert Users},
  author = {Timo Schaffhauser and Daniel Garijo and Maximiliano Osorio and Daniel Bittner and Suzanne Pierce and Hernan Vargas and Markus Disse and Yolanda Gil},
  journal = {Computers and Geosciences},
  year = {2023}
}

@article{kejriwal2023knowledge,
  title={A Knowledge Graph-Based Search Engine for Robustly Finding Doctors and Locations in the Healthcare Domain},
  author={Kejriwal, Mayank and Haidarian, Hamid and Chiu, Min-Hsueh and Xiang, Andy and Shrestha, Deep and Javed, Faizan},
  journal={arXiv preprint arXiv:2310.05258},
  year={2023}
}

@article{tang2023pilot,
  title={A Pilot Evaluation of ChatGPT and DALL-E 2 on Decision Making and Spatial Reasoning},
  author={Tang, Zhisheng and Kejriwal, Mayank},
  journal={arXiv e-prints},
  pages={arXiv--2302},
  year={2023}
}

@article{tksk11,
author={Kushal Sharma and Ang Li and Malcolm White and Satish Thittamaranahalli},
title={A Study of Distance Functions in FastMapSVM for Classifying Seismograms},
journal={Proceedings of the Twenty-Second International Conference on Machine Learning and Applications (ICMLA-2023)},
year={2023}
}

@article{santos2023theoretically,
  title={A Theoretically Grounded Question Answering Data Set for Evaluating Machine Common Sense},
  author={Santos, Henrique and Shen, Ke and Mulvehill, Alice M and Kejriwal, Mayank and McGuinness, Deborah L},
  journal={Data Intelligence},
  pages={1--29},
  year={2023},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@misc{sun2023structural,
      title={A structural study of Big Tech firm-switching of inventors in the post-recession era}, 
      author={Yidan Sun and Mayank Kejriwal},
      year={2023},
      eprint={2307.07920},
      archivePrefix={arXiv},
      primaryClass={cs.SI}
}

@InProceedings{	  rynge-pearc-2023,
  Author	= {Rynge, Mats and Vahi, Karan and Alam, Mohammad Zaiyan and
		  Deelman, Ewa and Miller, Todd and Livny, Miron and Knuth,
		  Shelley and Griffioen, James and Goodhue, John and Hudak,
		  David and Ma, Julie and Pasquale, Andrew and Fein, Lissie},
  Title		= {ACCESS Pegasus: Bringing Workflows to the ACCESS Masses},
  Year		= {2023},
  ISBN		= {9781450399852},
  Publisher	= {Association for Computing Machinery},
  Address	= {New York, NY, USA},
  URL		= {https://doi.org/10.1145/3569951.3597590},
  DOI		= {10.1145/3569951.3597590},
  BookTitle	= {Practice and Experience in Advanced Research Computing},
  Pages		= {478–480},
  numpages	= {3},
  Location	= {Portland, OR, USA},
  Series	= {PEARC '23}
}

@inproceedings{wu-etal-2023-acquired,
    title = "{ACQUIRED}: A Dataset for Answering Counterfactual Questions In Real-Life Videos",
    author = "Wu, Te-Lin  and
      Dou, Zi-Yi  and
      Hu, Qingyuan  and
      Hou, Yu  and
      Chandra, Nischal  and
      Freedman, Marjorie  and
      Weischedel, Ralph  and
      Peng, Nanyun",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.719",
    doi = "10.18653/v1/2023.emnlp-main.719",
    pages = "11753--11770",
}

@inproceedings{DBLP:conf/emnlp/HeHLCLS23,
  author       = {Zihao He and
                  Weituo Hao and
                  Wei Tsung Lu and
                  Changyou Chen and
                  Kristina Lerman and
                  Xuchen Song},
  editor       = {Houda Bouamor and
                  Juan Pino and
                  Kalika Bali},
  title        = {{ALCAP:} Alignment-Augmented Music Captioner},
  booktitle    = {Proceedings of the 2023 Conference on Empirical Methods in Natural
                  Language Processing, {EMNLP} 2023, Singapore, December 6-10, 2023},
  pages        = {16501--16512},
  publisher    = {Association for Computational Linguistics},
  year         = {2023},
  url          = {https://doi.org/10.18653/v1/2023.emnlp-main.1028},
  doi          = {10.18653/V1/2023.EMNLP-MAIN.1028},
  timestamp    = {Fri, 12 Apr 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/emnlp/HeHLCLS23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{avgust_demo_ICSE2023,
 author = {Saghar Talebipour and Hyojae Park and Kesina Baral and Leon Yee and Safwat Ali Khan and Kevin Moran and Yuriy Brun and Nenad Medvidovic and Yixue Zhao},
 title = {AVGUST: A Tool for Generating Usage-Based Tests from Videos of App Executions},
 booktitle = {Proceedings of the 45th IEEE/ACM International Conference on Software Engineering},
 series = {ICSE '23},
 year = {2023},
 location = {Australia}
}

@inproceedings{THZ_Device_2023_March_Meeting,
author = {Sachin Sharma, Zhi Cai,  Haley A Weinstein, Indu Amma, Stephen B Cronin, and Ioannis Chatzakis},
    booktitle = {APS March Meeting Abstracts},
         year = 2023,
       series = {APS Meeting Abstracts},
       volume = {2023},
title = {Active THz Optoelectronics Using Multilayer Graphene: tunable filters, phase modulators, and resonators},
year = {2023},
url = {https://meetings.aps.org/Meeting/MAR23/Session/M21.11},

}

@misc{leong2023adapting,
      title={Adapting to the Low-Resource Double-Bind: Investigating Low-Compute Methods on Low-Resource African Languages},
      author={Colin Leong and Herumb Shandilya and Bonaventure F. P. Dossou and Atnafu Lambebo Tonja and Joel Mathew and Abdul-Hakeem Omotayo and Oreen Yousuf and Zainab Akinjobi and Chris Chinenye Emezue and Shamsudeen Muhammad and Steven Kolawole and Younwoo Choi and Tosin Adewumi},
      year={2023},
      eprint={2303.16985},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{DBLP:journals/corr/abs-2310-18553,
  author       = {Dan Feldman and
                  Ashwin Rao and
                  Zihao He and
                  Kristina Lerman},
  title        = {Affective Polarization in Social Networks},
  journal      = {CoRR},
  volume       = {abs/2310.18553},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2310.18553},
  doi          = {10.48550/ARXIV.2310.18553},
  eprinttype    = {arXiv},
  eprint       = {2310.18553},
  timestamp    = {Thu, 02 Nov 2023 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2310-18553.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{https://doi.org/10.1002/iis2.13055,
    author = {Dove, Rick and Lunney, Kerry and Orosz, Michael and Yokell, Mike},
    title = {Agile Systems Engineering – Eight Core Aspects},
    journal = {INCOSE International Symposium},
    volume = {33},
    number = {1},
    pages = {823-837},
    doi = {https://doi.org/10.1002/iis2.13055},
    url = {https://incose.onlinelibrary.wiley.com/doi/abs/10.1002/iis2.13055},
    eprint = {https://incose.onlinelibrary.wiley.com/doi/pdf/10.1002/iis2.13055},
    abstract = {Abstract Agile engineering, of any kind, employs strategies for designing, building, sustaining, and evolving purpose-fulfilling creations when knowledge is uncertain and operational environments are dynamic. Strategies address what needs to be accomplished and why, without constraints or directions on how. How those strategies manifest as operational methods depends upon the engineering context. For instance, though single-domain software engineering is different than multi-domain systems engineering, both share the same goals and strategies. This article describes eight agility-supporting strategic aspects with application discussions and examples relevant to systems engineering and exposes common myths and misunderstandings. Each of the eight aspects can individually improve capability to deal with uncertain knowledge and dynamic environments.},
    year = {2023}
}

Abstract Agile engineering, of any kind, employs strategies for designing, building, sustaining, and evolving purpose-fulfilling creations when knowledge is uncertain and operational environments are dynamic. Strategies address what needs to be accomplished and why, without constraints or directions on how. How those strategies manifest as operational methods depends upon the engineering context. For instance, though single-domain software engineering is different than multi-domain systems engineering, both share the same goals and strategies. This article describes eight agility-supporting strategic aspects with application discussions and examples relevant to systems engineering and exposes common myths and misunderstandings. Each of the eight aspects can individually improve capability to deal with uncertain knowledge and dynamic environments.

@article{shen2023experimental,
  title={An experimental study measuring the generalization of fine-tuned language representation models across commonsense reasoning benchmarks},
  author={Shen, Ke and Kejriwal, Mayank},
  journal={Expert Systems},
  pages={e13243},
  year={2023},
  publisher={Wiley Online Library}
}

@inproceedings{moon-etal-2023-analyzing,
    title = "Analyzing Norm Violations in Live-Stream Chat",
    author = "Moon, Jihyung  and
      Lee, Dong-Ho  and
      Cho, Hyundong  and
      Jin, Woojeong  and
      Park, Chan  and
      Kim, Minwoo  and
      May, Jonathan  and
      Pujara, Jay  and
      Park, Sungjoon",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.55",
    doi = "10.18653/v1/2023.emnlp-main.55",
    pages = "852--868",
    abstract = "Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35{\%}.",
}

Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35%.

@inproceedings{DBLP:conf/sbp/ChenHCML23,
  author       = {Kai Chen and
                  Zihao He and
                  Rong{-}Ching Chang and
                  Jonathan May and
                  Kristina Lerman},
  editor       = {Robert Thomson and
                  Samer Al{-}khateeb and
                  Annetta Burger and
                  Patrick S. Park and
                  Aryn A. Pyke},
  title        = {Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit},
  booktitle    = {Social, Cultural, and Behavioral Modeling - 16th International Conference,
                  SBP-BRiMS 2023, Pittsburgh, PA, USA, September 20-22, 2023, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14161},
  pages        = {44--53},
  publisher    = {Springer},
  year         = {2023},
  url          = {https://doi.org/10.1007/978-3-031-43129-6\_5},
  doi          = {10.1007/978-3-031-43129-6\_5},
  timestamp    = {Wed, 20 Sep 2023 15:51:04 +0200},
  biburl       = {https://dblp.org/rec/conf/sbp/ChenHCML23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@book{DBLP:books/sp/Kejriwal23,
  author    = {Mayank Kejriwal},
  title     = {Artificial Intelligence for Industries of the Future - Beyond Facebook,
               Amazon, Microsoft and Google},
  publisher = {Springer},
  year      = {2023},
  url       = {https://doi.org/10.1007/978-3-031-19039-1},
  doi       = {10.1007/978-3-031-19039-1},
  isbn      = {978-3-031-19038-4},
  timestamp = {Thu, 22 Dec 2022 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/books/sp/Kejriwal23.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Hickey2023_musk,
	abstractnote = {On October 27th, 2022, Elon Musk purchased Twitter, becoming its new CEO and firing many top executives in the process. Musk listed fewer restrictions on content moderation and removal of spam bots among his goals for the platform. Given findings of prior research on moderation and hate speech in online communities, the promise of less strict content moderation poses the concern that hate will rise on Twitter. We examine the levels of hate speech and prevalence of bots before and after Musk's acquisition of the platform. We find that hate speech rose dramatically upon Musk purchasing Twitter and the prevalence of most types of bots increased, while the prevalence of astroturf bots decreased.},
	author = {Hickey, Daniel and Schmitz, Matheus and Fessler, Daniel and Smaldino, Paul E. and Muric, Goran and Burghardt, Keith},
	doi = {10.1609/icwsm.v17i1.22222},
	journal = {Proceedings of the International AAAI Conference on Web and Social Media},
	month = {Jun.},
	number = {1},
	pages = {1133-1137},
	title = {Auditing Elon Musk's Impact on Hate Speech and Bots},
	url = {https://ojs.aaai.org/index.php/ICWSM/article/view/22222},
	volume = {17},
	year = {2023},
	bdsk-url-1 = {https://ojs.aaai.org/index.php/ICWSM/article/view/22222},
	bdsk-url-2 = {https://doi.org/10.1609/icwsm.v17i1.22222}}

@inproceedings{lee:eacl23,
    Author = "Lee, Dong-Ho and Selvam, Ravi Kiran and Sarwar, Sheikh Muhammad and Lin, Bill Yuchen and Morstatter, Fred and Pujara, Jay and Boschee, Elizabeth and Allan, James and Ren, Xiang",
    acceptrate = "25\%",
    bib_url = "/pubs/bib/lee-eacl23.bib",
    booktitle = "European Chapter of the Association for Computational Linguistics",
    doi_url = "http://dx.doi.org/10.18653/v1/2023.eacl-main.219",
    pdf_url = "/pubs/2023/lee-eacl23/lee-eacl23.pdf",
    sec = "conf",
    title = "{A}uto{T}rigg{ER}: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction",
    year = "2023"
}

@inproceedings{gopher-gomac:2023,
 author = {C. R. Clark and W. Stuckley and N. Braswell and T. Haroldsen and T.Y. Sung and and O. Shears},
 title = {{Automated Formal Equivalency Checking for FPGA Tools}},
 booktitle = {Government Microcircuit Applications and Critical Technology Conference},
 month = {March},
 year = {2023},
}

@InProceedings{ Clark-2023,
author = {Christopher Clark, William Stuckey, Nathan Braswell, Travis Haroldsen, Ting-Yean Sung, Osaze Shears},
title = {Automated Formal equivalency checking for FPGA Tools},
booktitle = {GOMACTech-23},
year = {2023},
ISIArea = {CAS}
}

@misc{kou2023automated,
  abstract = {Software developers often resort to Stack Overflow (SO) to fill their
programming needs. Given the abundance of relevant posts, navigating them and
comparing different solutions is tedious and time-consuming. Recent work has
proposed to automatically summarize SO posts to concise text to facilitate the
navigation of SO posts. However, these techniques rely only on information
retrieval methods or heuristics for text summarization, which is insufficient
to handle the ambiguity and sophistication of natural language. This paper
presents a deep learning based framework called ASSORT for SO post
summarization. ASSORT includes two complementary learning methods, ASSORT_S and
ASSORT_{IS}, to address the lack of labeled training data for SO post
summarization. ASSORT_S is designed to directly train a novel ensemble learning
model with BERT embeddings and domainspecific features to account for the
unique characteristics of SO posts. By contrast, ASSORT_{IS} is designed to
reuse pre-trained models while addressing the domain shift challenge when no
training data is present (i.e., zero-shot learning). Both ASSORT_S and
ASSORT_{IS} outperform six existing techniques by at least 13% and 7%
respectively in terms of the F1 score. Furthermore, a human study shows that
participants significantly preferred summaries generated by ASSORT_S and
ASSORT_{IS} over the best baseline, while the preference difference between
ASSORT_S and ASSORT_{IS} was small.},
  added-at = {2023-07-11T04:02:52.000+0200},
  author = {Kou, Bonan and Chen, Muhao and Zhang, Tianyi},
  biburl = {https://www.bibsonomy.org/bibtex/2169cbd6c1b27b97b7c3721b785d97420/woobanseok},
  description = {Automated Summarization of Stack Overflow Posts},
  interhash = {e1fe7ec4b2a5869e29d4bf9ee58934fb},
  intrahash = {169cbd6c1b27b97b7c3721b785d97420},
  keywords = {javascript},
  note = {cite arxiv:2305.16680Comment: ICSE 2023},
  timestamp = {2023-07-11T04:02:52.000+0200},
  title = {Automated Summarization of Stack Overflow Posts},
  url = {http://arxiv.org/abs/2305.16680},
  year = 2023
}

Software developers often resort to Stack Overflow (SO) to fill their programming needs. Given the abundance of relevant posts, navigating them and comparing different solutions is tedious and time-consuming. Recent work has proposed to automatically summarize SO posts to concise text to facilitate the navigation of SO posts. However, these techniques rely only on information retrieval methods or heuristics for text summarization, which is insufficient to handle the ambiguity and sophistication of natural language. This paper presents a deep learning based framework called ASSORT for SO post summarization. ASSORT includes two complementary learning methods, ASSORT_S and ASSORT_IS, to address the lack of labeled training data for SO post summarization. ASSORT_S is designed to directly train a novel ensemble learning model with BERT embeddings and domainspecific features to account for the unique characteristics of SO posts. By contrast, ASSORT_IS is designed to reuse pre-trained models while addressing the domain shift challenge when no training data is present (i.e., zero-shot learning). Both ASSORT_S and ASSORT_IS outperform six existing techniques by at least 13% and 7% respectively in terms of the F1 score. Furthermore, a human study shows that participants significantly preferred summaries generated by ASSORT_S and ASSORT_IS over the best baseline, while the preference difference between ASSORT_S and ASSORT_IS was small.

@inproceedings{liu2023automatic,
  title={Automatic Semantic Typing of Pet E-commerce Products Using Crowdsourced Reviews: An Experimental Study},
  author={Liu, Xinyu and Sun, Tiancheng and Fu, Diantian and Li, Zijue and Qian, Sheng and Meng, Ruyue and Kejriwal, Mayank},
  booktitle={Iberoamerican Knowledge Graphs and Semantic Web Conference},
  pages={151--167},
  year={2023},
  organization={Springer}
}

@INPROCEEDINGS{10195505,
  author={Sunder, Sugeet and Jaiswal, Akhilesh and Jacob, Ajey},
  booktitle={2023 IEEE 73rd Electronic Components and Technology Conference (ECTC)}, 
  title={Backside Optical Coupler : A Novel I/O for Mechanically Stable High Efficiency Fiber Coupling}, 
  year={2023},
  volume={},
  number={},
  pages={727-731},
  keywords={Couplings;Optical fiber polarization;Technological innovation;Optical polarization;Optical fiber couplers;Couplers;Optical coupling;Couplers;Optical Fiber;Photonic Integrated Circuits;Grating couplers;Distributed Bragg Reflector},
  doi={10.1109/ECTC51909.2023.00126}}

@InProceedings{Chiu_2023_ICCV,
    author    = {Chiu, Ming-Chang and Chen, Pin-Yu and Ma, Xuezhe},
    title     = {Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {4956-4966}
}

@conference {Schmidt2023,
    title = {Bitstream Assurance Checking Engine for Undocumented Functionality (BRACE)},
    organization = {Government Microcircuit Applications and Critical Technology Conference (GOMACTech)},
    year = {2023},
    month = {March},
    author = {A. Schmidt, J. Wilford, B. Reynwar, T. Sung, M. French, },
    ISIArea = {CAS, MES}
    }

@inproceedings{10.1145/3543873.3587629,
author = {Bonab, Hamed and Joshi, Ashutosh and Bhatia, Ravi and Gandhi, Ankit and Huddar, Vijay and Naik, Juhi and Al-Darabsah, Mutasem and Teo, Choon Hui and May, Jonathan and Agarwal, Tarun and Petricek, Vaclav},
title = {Blend and Match: Distilling Semantic Search Models with Different Inductive Biases and Model Architectures},
year = {2023},
isbn = {9781450394192},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3543873.3587629},
doi = {10.1145/3543873.3587629},
abstract = {Commercial search engines use different semantic models to augment lexical matches. These models provide candidate items for a user’s query from a target space of millions to billions of items. Models with different inductive biases provide relatively different predictions, making it desirable to launch multiple semantic models in production. However, latency and resource constraints make simultaneously deploying multiple models impractical. In this paper, we introduce a distillation approach, called Blend and Match (BM), to unify two different semantic search models into a single model. We use a Bi-encoder semantic matching model as our primary model and propose a novel loss function to incorporate eXtreme Multi-label Classification (XMC) predictions as the secondary model. Our experiments conducted on two large-scale datasets, collected from a popular e-commerce store, show that our proposed approach significantly improves the recall of the primary Bi-encoder model by 11\% to 17\% with a minimal loss in precision. We show that traditional knowledge distillation approaches result in a sub-optimal performance for our problem setting, and our BM approach yields comparable rankings with strong Rank Fusion (RF) methods used only if one could deploy multiple models.},
booktitle = {Companion Proceedings of the ACM Web Conference 2023},
pages = {869–877},
numpages = {9},
keywords = {Semantic Search, Ranking Distillation, Product Search, Model Blending},
location = {Austin, TX, USA},
series = {WWW '23 Companion}
}

Commercial search engines use different semantic models to augment lexical matches. These models provide candidate items for a user’s query from a target space of millions to billions of items. Models with different inductive biases provide relatively different predictions, making it desirable to launch multiple semantic models in production. However, latency and resource constraints make simultaneously deploying multiple models impractical. In this paper, we introduce a distillation approach, called Blend and Match (BM), to unify two different semantic search models into a single model. We use a Bi-encoder semantic matching model as our primary model and propose a novel loss function to incorporate eXtreme Multi-label Classification (XMC) predictions as the secondary model. Our experiments conducted on two large-scale datasets, collected from a popular e-commerce store, show that our proposed approach significantly improves the recall of the primary Bi-encoder model by 11% to 17% with a minimal loss in precision. We show that traditional knowledge distillation approaches result in a sub-optimal performance for our problem setting, and our BM approach yields comparable rankings with strong Rank Fusion (RF) methods used only if one could deploy multiple models.

@inproceedings{yu-etal-2023-bridging,
    title = "Bridging the Gap between Native Text and Translated Text through Adversarial Learning: A Case Study on Cross-Lingual Event Extraction",
    author = "Yu, Pengfei  and
      May, Jonathan  and
      Ji, Heng",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-eacl.57",
    doi = "10.18653/v1/2023.findings-eacl.57",
    pages = "754--769",
    abstract = "Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance. We explore this idea for cross-lingual event extraction with a new model architecture that jointly encodes a source language input sentence with its translation to the target language during training, and takes a target language sentence with its translation back to the source language as input during evaluation. However, we observe significant representational gap between the native source language texts during training and the texts translated into source language during evaluation, as well as the texts translated into target language during training and the native target language texts during evaluation. This representational gap undermines the effectiveness of cross-lingual transfer learning for event extraction with machine-translated data. In order to mitigate this problem, we propose an adversarial training framework that encourages the language model to produce more similar representations for the translated text and the native text. To be specific, we train the language model such that its hidden representations are able to fool a jointly trained discriminator that distinguishes translated texts{'} representations from native texts{'} representations. We conduct experiments on cross-lingual for event extraction across three languages. Results demonstrate that our proposed adversarial training can effectively incorporate machine translation to improve event extraction, while simply adding machine-translated data yields unstable performance due to the representational gap.",
}

Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance. We explore this idea for cross-lingual event extraction with a new model architecture that jointly encodes a source language input sentence with its translation to the target language during training, and takes a target language sentence with its translation back to the source language as input during evaluation. However, we observe significant representational gap between the native source language texts during training and the texts translated into source language during evaluation, as well as the texts translated into target language during training and the native target language texts during evaluation. This representational gap undermines the effectiveness of cross-lingual transfer learning for event extraction with machine-translated data. In order to mitigate this problem, we propose an adversarial training framework that encourages the language model to produce more similar representations for the translated text and the native text. To be specific, we train the language model such that its hidden representations are able to fool a jointly trained discriminator that distinguishes translated texts' representations from native texts' representations. We conduct experiments on cross-lingual for event extraction across three languages. Results demonstrate that our proposed adversarial training can effectively incorporate machine translation to improve event extraction, while simply adding machine-translated data yields unstable performance due to the representational gap.

@InProceedings{	  murillo-pearc-2023,
  Author	= {Murillo, Angela and Brower, Don and Hossain, Sarowar and
		  Kee, Kerk and Mandal, Anirban and Nabrzyski, Jarek and
		  Scott, Erik and Virdone, Nicole and Ewing, Rodney and
		  Deelman, Ewa},
  Title		= {Broadening Student Engagement To Build the Next Generation
		  of Cyberinfrastructure Professionals},
  Year		= {2023},
  ISBN		= {9781450399852},
  Publisher	= {Association for Computing Machinery},
  Address	= {New York, NY, USA},
  URL		= {https://doi.org/10.1145/3569951.3597567},
  DOI		= {10.1145/3569951.3597567},
  BookTitle	= {Practice and Experience in Advanced Research Computing},
  Pages		= {470–473},
  numpages	= {4},
  Keywords	= {workforce development, cyberinfrastructure, Student
		  fellowships, Major Facilities},
  Location	= {Portland, OR, USA},
  Series	= {PEARC '23},
  Note		= {Funding Acknowledgments: NSF 2127548}
}

@inbook{ilievski:cnsai23,
    Author = "Ilievski, Filip and Ma, Kaixin and Oltramari, Alessandro and Wang, Peifeng and Pujara, Jay",
    address = "Nieuwe Hemweg 6B, 1013 BG Amsterdam, NL",
    booktitle = "Compendium of Neurosymbolic Artificial Intelligence",
    isbn = "978-1-64368-406-2",
    pages = "178 - 209",
    publisher = "IOS Press",
    title = "Building Robust and Explainable AI with Commonsense Knowledge Graphs and Neural Models",
    year = "2023"
}

@article{shbita2023building,
  title={Building Spatio-Temporal Knowledge Graphs from Vectorized Topographic Historical Maps},
  author={Shbita, Basel and Knoblock, Craig A and Duan, Weiwei and Chiang, Yao-Yi and Uhl, Johannes H and Leyk, Stefan},
  journal={Semantic Web},
  volume={14},
  number={3},
  pages={527--549},
  year={2023},
  publisher={IOS Press},
  urlLink={https://content.iospress.com/articles/semantic-web/sw222918},
  urlPaper={http://usc-isi-i2.github.io/papers/shbita23-swj.pdf},
  urlSlides={http://usc-isi-i2.github.io/slides/shbita23-swj-slides.pdf}
}

@techreport{khider_building_2023,
	title = {Building {Upon} the {EarthCube} {Community}: a geoscience and cyberinfrastructure workshop - {Report}},
	copyright = {Creative Commons Attribution 4.0 International},
	shorttitle = {Building {Upon} the {EarthCube} {Community}},
	url = {https://figshare.com/articles/conference_contribution/Building_Upon_the_EarthCube_Community_a_geoscience_and_cyberinfrastructure_workshop_-_Report/23949168/2},
	abstract = {This report summarizes the discussion at the "Building Upon the EarthCube Community: A geoscience and cyberinfrastructure workshop" which was held at the University of Southern California Information Sciences Institute, June 27-28th 2023.},
	urldate = {2024-01-25},
	author = {Khider, Deborah and {Mike Daniels} and Jarboe, Nick},
	year = {2023},
	keywords = {Earth and space science informatics},
	pages = {9095054 Bytes},
}

This report summarizes the discussion at the "Building Upon the EarthCube Community: A geoscience and cyberinfrastructure workshop" which was held at the University of Southern California Information Sciences Institute, June 27-28th 2023.

@article{DBLP:journals/corr/abs-2305-09846,
  author       = {Zihao He and
                  Jonathan May and
                  Kristina Lerman},
  title        = {CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation
                  Detection in Online Communities},
  journal      = {CoRR},
  volume       = {abs/2305.09846},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2305.09846},
  doi          = {10.48550/ARXIV.2305.09846},
  eprinttype    = {arXiv},
  eprint       = {2305.09846},
  timestamp    = {Wed, 24 May 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2305-09846.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{tang2023can1,
  title={Can Language Models Be Used in Multistep Commonsense Planning Domains?},
  author={Tang, Zhisheng and Kejriwal, Mayank},
  booktitle={International Conference on Artificial General Intelligence},
  pages={276--285},
  year={2023},
  organization={Springer}
}

@article{tang2023can,
  title={Can language representation models think in bets?},
  author={Tang, Zhisheng and Kejriwal, Mayank},
  journal={Royal Society Open Science},
  volume={10},
  number={3},
  pages={221585},
  year={2023},
  publisher={The Royal Society}
}

@Article{	  dongarra-osti-2023,
  Author	= {Dongarra, Jack and Deelman, Ewa and Hey, Tony and
		  Matsuoka, Satoshi and Sarakar, Vivek and Bell, Greg and
		  Foster, Ian and Keyes, David, and Kranzlmueller, Dieter
		  et-al},
  Journal	= {OSTI},
  Title		= {Can the United States Maintain Its Leadership in
		  High-Performance Computing?-A report from the ASCAC
		  Subcommittee on American Competitiveness and Innovation to
		  the ASCR Office},
  Year		= {2023},
  Volume	= {},
  Number	= {},
  Pages		= {},
  DOI		= {10.2172/1989107}
}

@article{DBLP:journals/corr/abs-2311-09743,
  author       = {Negar Mokhberian and
                  Myrl G. Marmarelis and
                  Frederic R. Hopp and
                  Valerio Basile and
                  Fred Morstatter and
                  Kristina Lerman},
  title        = {Capturing Perspectives of Crowdsourced Annotators in Subjective Learning
                  Tasks},
  journal      = {CoRR},
  volume       = {abs/2311.09743},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2311.09743},
  doi          = {10.48550/ARXIV.2311.09743},
  eprinttype    = {arXiv},
  eprint       = {2311.09743},
  timestamp    = {Tue, 21 Nov 2023 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2311-09743.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{jin-etal-2023-challenges,
    title = "Challenges in Context-Aware Neural Machine Translation",
    author = "Jin, Linghao  and
      He, Jacqueline  and
      May, Jonathan  and
      Ma, Xuezhe",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.943",
    doi = "10.18653/v1/2023.emnlp-main.943",
    pages = "15246--15263",
    abstract = "Context-aware neural machine translation, a paradigm that involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, has given rise to a number of recent techniques. However, despite well-reasoned intuitions, most context-aware translation models show only modest improvements over sentence-level systems. In this work, we investigate and present several core challenges that impede progress within the field, relating to discourse phenomena, context usage, model architectures, and document-level evaluation. To address these problems, we propose a more realistic setting for document-level translation, called paragraph-to-paragraph (PARA2PARA) translation, and collect a new dataset of Chinese-English novels to promote future research.",
}

Context-aware neural machine translation, a paradigm that involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, has given rise to a number of recent techniques. However, despite well-reasoned intuitions, most context-aware translation models show only modest improvements over sentence-level systems. In this work, we investigate and present several core challenges that impede progress within the field, relating to discourse phenomena, context usage, model architectures, and document-level evaluation. To address these problems, we propose a more realistic setting for document-level translation, called paragraph-to-paragraph (PARA2PARA) translation, and collect a new dataset of Chinese-English novels to promote future research.

@inproceedings{wang:issi23,
    Author = "Wang, Ziao and Ahrabian, Kian and Rusti, Casandra and Pujara, Jay and Lerman, Kristina",
    bib_url = "/pubs/bib/wang-issi23.bib",
    booktitle = "International Society of Scientometrics and Informetrics Conference",
    pdf_url = "/pubs/2023/wang-issi23/wang-issi23.pdf",
    sec = "ws",
    title = "Changes in Research Collaborations During the Pandemic",
    year = "2023"
}

@article{tksk10,
author={Malcolm White and Kushal Sharma and Ang Li and Satish Thittamaranahalli and Nori Nakata},
title={Classifying Seismograms Using the FastMap Algorithm and Support-Vector Machines},
journal={Nature Communications Engineering (NATURE-COMMSENG-2023)},
year={2023}
}

@article{DBLP:journals/corr/abs-2304-03479,
  author       = {Haochen Pi and
                  Keith Burghardt and
                  Allon G. Percus and
                  Kristina Lerman},
  title        = {Clique Densification in Networks},
  journal      = {CoRR},
  volume       = {abs/2304.03479},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2304.03479},
  doi          = {10.48550/ARXIV.2304.03479},
  eprinttype    = {arXiv},
  eprint       = {2304.03479},
  timestamp    = {Tue, 18 Apr 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2304-03479.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{molina:iswc23,
    Author = "Iglesias-Molina, Ana and Ahrabian, Kian and Ilievski, Filip and Pujara, Jay and Corcho, Oscar",
    acceptrate = "16\%",
    bib_url = "/pubs/bib/molina-iswc23.bib",
    booktitle = "International Semantic Web Conference",
    pdf_url = "/pubs/2023/molina-iswc23/molina-iswc23.pdf",
    sec = "conf",
    title = "Comparison of Knowledge Graph Representations for Consumer Scenarios",
    year = "2023"
}

@inproceedings{kejriwal2023context,
  title={Context-Rich Evaluation of Machine Common Sense},
  author={Kejriwal, Mayank and Santos, Henrique and Shen, Ke and Mulvehill, Alice M and McGuinness, Deborah L},
  booktitle={International Conference on Artificial General Intelligence},
  pages={167--176},
  year={2023},
  organization={Springer}
}

@article{deshpande2023contextualizing,
  title={Contextualizing Argument Quality Assessment with Relevant Knowledge},
  author={Deshpande, Darshan and Sourati, Zhivar and Ilievski, Filip and Morstatter, Fred},
  journal={arXiv preprint arXiv:2305.12280},
  year={2023}
}

@misc{joshi2023contextualizing,
  title={Contextualizing Internet Memes Across Social Media Platforms},
  author={Joshi, Saurav and Ilievski, Filip and Luceri, Luca},
  Eprint = {arXiv:2311.11157},
  Howpublished = {arXiv Preprint: https://arxiv.org/abs/2311.11157},
  year={2023}
}

@inproceedings{cho-etal-2023-continual,
    title = "Continual Dialogue State Tracking via Example-Guided Question Answering",
    author = "Cho, Hyundong  and
      Madotto, Andrea  and
      Lin, Zhaojiang  and
      Chandu, Khyathi  and
      Kottur, Satwik  and
      Xu, Jing  and
      May, Jonathan  and
      Sankar, Chinnadhurai",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.235",
    doi = "10.18653/v1/2023.emnlp-main.235",
    pages = "3873--3886",
    abstract = "Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services. Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user{'}s goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning. Our approach alleviates service-specific memorization and teaches a model to contextualize the given question and example to extract the necessary information from the conversation. We find that a model with just 60M parameters can achieve a significant boost by learning to learn from in-context examples retrieved by a retriever trained to identify turns with similar dialogue state changes. Combining our method with dialogue-level memory replay, our approach attains state of the art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods.",
}

Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services. Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user's goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning. Our approach alleviates service-specific memorization and teaches a model to contextualize the given question and example to extract the necessary information from the conversation. We find that a model with just 60M parameters can achieve a significant boost by learning to learn from in-context examples retrieved by a retriever trained to identify turns with similar dialogue state changes. Combining our method with dialogue-level memory replay, our approach attains state of the art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods.

@inproceedings{mhamdi-etal-2023-cross,
    title = "Cross-lingual Continual Learning",
    author = "M{'}hamdi, Meryem  and
      Ren, Xiang  and
      May, Jonathan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.217",
    doi = "10.18653/v1/2023.acl-long.217",
    pages = "3908--3943",
    abstract = "The longstanding goal of multi-lingual learning has been to develop a universal cross-lingual model that can withstand the changes in multi-lingual data distributions. There has been a large amount of work to adapt such multi-lingual models to unseen target languages. However, the majority of work in this direction focuses on the standard one-hop transfer learning pipeline from source to target languages, whereas in realistic scenarios, new languages can be incorporated at any time in a sequential manner. In this paper, we present a principled Cross-lingual Continual Learning (CCL) evaluation paradigm, where we analyze different categories of approaches used to continually adapt to emerging data from different languages. We provide insights into what makes multilingual sequential learning particularly challenging. To surmount such challenges, we benchmark a representative set of cross-lingual continual learning algorithms and analyze their knowledge preservation, accumulation, and generalization capabilities compared to baselines on carefully curated datastreams. The implications of this analysis include a recipe for how to measure and balance different cross-lingual continual learning desiderata, which go beyond conventional transfer learning.",
}

The longstanding goal of multi-lingual learning has been to develop a universal cross-lingual model that can withstand the changes in multi-lingual data distributions. There has been a large amount of work to adapt such multi-lingual models to unseen target languages. However, the majority of work in this direction focuses on the standard one-hop transfer learning pipeline from source to target languages, whereas in realistic scenarios, new languages can be incorporated at any time in a sequential manner. In this paper, we present a principled Cross-lingual Continual Learning (CCL) evaluation paradigm, where we analyze different categories of approaches used to continually adapt to emerging data from different languages. We provide insights into what makes multilingual sequential learning particularly challenging. To surmount such challenges, we benchmark a representative set of cross-lingual continual learning algorithms and analyze their knowledge preservation, accumulation, and generalization capabilities compared to baselines on carefully curated datastreams. The implications of this analysis include a recipe for how to measure and balance different cross-lingual continual learning desiderata, which go beyond conventional transfer learning.

@article{DBLP:journals/corr/abs-2312-17527,
  author       = {Yuan Xia and
                  Jyotirmoy V. Deshmukh and
                  Mukund Raghothaman and
                  Srivatsan Ravi},
  title        = {Data-Driven Template-Free Invariant Generation},
  journal      = {CoRR},
  volume       = {abs/2312.17527},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2312.17527},
  doi          = {10.48550/ARXIV.2312.17527},
  eprinttype    = {arXiv},
  eprint       = {2312.17527},
  timestamp    = {Fri, 19 Jan 2024 13:27:20 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2312-17527.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Schuler2023,
	abstract = {Database management systems have been used to great advantage for industry usage scenarios. As science becomes increasingly dependent on carefully organized and curated data to inform and drive new discoveries, the need for database management systems has grown significantly. The long standing ``20 questions'' method was advocated in early studies of applying relational databases for science in order to elicit requirements for designing and developing information systems for scientific data. It has been observed, however, that database designs become outdated within months of usage leading to degradation in the quality of the database schema. In addition, there is limited evidence that scientists themselves have the tools and processes necessary to develop and maintain scientific databases without reliance on database administrators. Beyond learning to query databases, scientists need tools to create and evolve databases and guidance on how to apply those tools to develop information systems. In this paper, we present a simplified methodology for database evolution for scientists and a case study of database evolution by a scientist in the context of a research database for cell modeling. We include a detailed analysis of the activities and processes employed by the scientist during the schema evolution. Our results show that a scientist can successfully evolve a complex information system driven by new research requirements.},
	address = {{Limassol, Cyprus}},
	author = {Schuler, Robert and Singla, Jitin and Vallat, Brinda and White, Kate L. and Berman, Helen M. and Kesselman, Carl},
	booktitle = {2023 {{IEEE}} 19th {{International Conference}} on E-{{Science}} (e-{{Science}})},
	date-added = {2024-01-22 12:05:54 -0800},
	date-modified = {2024-01-22 12:05:54 -0800},
	doi = {10.1109/e-Science58273.2023.10254872},
	file = {/Users/schuler/Zotero/storage/L493H8AJ/Schuler et al. - 2022 - Relational Database Schema Evolution, by Scientist.pdf;/Users/schuler/Zotero/storage/PD8URSG9/CHiSEL_CaseStudy_2022.pdf},
	keywords = {in review},
	month = oct,
	pages = {1--10},
	publisher = {{IEEE}},
	title = {Database {{Evolution}}, by {{Scientists}}, for {{Scientists}}: {{A Case Study}}},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1109/e-Science58273.2023.10254872}}

Database management systems have been used to great advantage for industry usage scenarios. As science becomes increasingly dependent on carefully organized and curated data to inform and drive new discoveries, the need for database management systems has grown significantly. The long standing ``20 questions'' method was advocated in early studies of applying relational databases for science in order to elicit requirements for designing and developing information systems for scientific data. It has been observed, however, that database designs become outdated within months of usage leading to degradation in the quality of the database schema. In addition, there is limited evidence that scientists themselves have the tools and processes necessary to develop and maintain scientific databases without reliance on database administrators. Beyond learning to query databases, scientists need tools to create and evolve databases and guidance on how to apply those tools to develop information systems. In this paper, we present a simplified methodology for database evolution for scientists and a case study of database evolution by a scientist in the context of a research database for cell modeling. We include a detailed analysis of the activities and processes employed by the scientist during the schema evolution. Our results show that a scientist can successfully evolve a complex information system driven by new research requirements.

@article{imesHPEC2023extabs,
  title={Deep Learning Recommendation Model Training Co-design with the Dynamic Opera Network},
  author={Imes, Connor and Rittenbach, Andrew and Xie, Peng and Kang, Dong In D and Walters, John Paul and Crago, Stephen P},
  journal={HPEC},
  year={2023},
  ISIArea = {ML, CAS, NET}
}

@inproceedings{Rizvi23a,
  author = {Rizvi, {A S M} and Mirkovic, Jelena and adn Wes Hardaker, John Heidemann and Story, Robert},
  title = {Defending Root {DNS} Servers Against {DDoS} Using Layered Defenses},
  booktitle = {Proceedings of the {IEEE} International Conference on Communications Systems and Netowrks (COMSNETS)},
  year = {2023},
  sortdate = {2023-01-03},
  project = {ant, ddidd, paaddos},
  jsubject = {network_security},
  note = {Awarded best paper},
  pages = {to appear},
  month = jan,
  address = {Bengaluru, India},
  publisher = {IEEE},
  location = {johnh: pafile},
  keywords = {ddidd, ddos, filtering, frade},
  xdoi = {tbd},
  blogurl = {https://ant.isi.edu/blog/?p=1948},
  url = {https://www.isi.edu/%7ejohnh/PAPERS/Rizvi23a.html},
  pdfurl = {https://www.isi.edu/%7ejohnh/PAPERS/Rizvi23a.pdf}
}

@inproceedings{zvi87632023,
author={A S M Rizvi and Jelena Mirkovic and John Heidemann and Wesley Hardaker and Robert Story},
title={Defending Root DNS Servers Against DDoS Using Layered Defenses},
booktitle={IEEE COMSNETS},
year={2023}}

@Article{Rizvi23b,
        author =        "{A S M} Rizvi and Jelena Mirkovic and John
 Heidemann and Wes Hardaker and Robert Story",
 title = "Defending Root {DNS} Servers Against {DDoS} Using Layered
                  Defenses (Extended)",
        journal =       "Ad Hoc Networks Journal",
        year =          2023,
        volume =     151,
        xpages =      "no pages",
        month =      dec,
	sortdate = 		"2023-12-01", 
	project = "ant, ddidd, paaddos",
	jsubject = "network_security",
        publisher =  "Elsevier Science Publishing Co., Inc.",
        jlocation =   "johnh: pafile",
        keywords =   "ddidd, ddos, filtering, frade",
        doi =        "https://doi.org/10.1016/j.adhoc.2023.103259",
	xblogurl = "https://ant.isi.edu/blog/?p=tbd",
	url =	"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi23b.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi23b.pdf",
	abstract = "Distributed Denial-of-Service (DDoS) attacks exhaust resources, leaving a server unavailable to legitimate clients. The Domain Name System (DNS) is a frequent target of DDoS attacks. Since DNS is a critical infrastructure service, protecting it from DoS is imperative. Many prior approaches have focused on specific filters or anti-spoofing techniques to protect generic services. DNS root nameservers are more challenging to protect, since they use fixed IP addresses, serve very diverse clients and requests, receive predominantly UDP traffic that can be spoofed, and must guarantee high quality of service. In this paper we propose a layered DDoS defense for DNS root nameservers. Our defense uses a \emph{library} of defensive filters, which can be optimized for different attack types, with different levels of selectivity. We further propose a method that \emph{automatically and continuously evaluates and selects} the best combination of filters throughout the attack. We show that this layered defense approach provides exceptional protection against all attack types using traces of ten real attacks from a DNS root nameserver. Our automated system can select the best defense within seconds and quickly reduces traffic to the server within a manageable range, while keeping collateral damage lower than 2\%. We show our system can successfully mitigate resource exhaustion using replay of a real-world attack. We can handle millions of filtering rules without noticeable operational overhead."
,}

Distributed Denial-of-Service (DDoS) attacks exhaust resources, leaving a server unavailable to legitimate clients. The Domain Name System (DNS) is a frequent target of DDoS attacks. Since DNS is a critical infrastructure service, protecting it from DoS is imperative. Many prior approaches have focused on specific filters or anti-spoofing techniques to protect generic services. DNS root nameservers are more challenging to protect, since they use fixed IP addresses, serve very diverse clients and requests, receive predominantly UDP traffic that can be spoofed, and must guarantee high quality of service. In this paper we propose a layered DDoS defense for DNS root nameservers. Our defense uses a \emphlibrary of defensive filters, which can be optimized for different attack types, with different levels of selectivity. We further propose a method that \emphautomatically and continuously evaluates and selects the best combination of filters throughout the attack. We show that this layered defense approach provides exceptional protection against all attack types using traces of ten real attacks from a DNS root nameserver. Our automated system can select the best defense within seconds and quickly reduces traffic to the server within a manageable range, while keeping collateral damage lower than 2%. We show our system can successfully mitigate resource exhaustion using replay of a real-world attack. We can handle millions of filtering rules without noticeable operational overhead.

@article{minoofar2023demonstration,
  title={Demonstration of optically-assisted reconfigurable average of two 20-Gbaud 4-phase-encoded data channels using nonlinear wave mixing},
  author={Minoofar, Amir and Karapetyan, Narek and Almaiman, Ahmed and Zhou, Huibin and Song, Hao and Zou, Kaiheng and Ko, Wing and Ramakrishnan, Muralekrishnan and Annavaram, Murali and Habif, Jonathan L and others},
  journal={Optics Letters},
  volume={48},
  number={17},
  pages={4617--4620},
  year={2023},
  publisher={Optica Publishing Group}
}

@INPROCEEDINGS{10386206,
  author={Yin, Zihan and Datta, Gourav and Kaiser, Md Abdullah-Al and Beerel, Peter and Jacob, Ajey and Jaiswal, Akhilesh},
  booktitle={2023 IEEE International Conference on Rebooting Computing (ICRC)}, 
  title={Design Considerations for 3D Heterogeneous Integration Driven Analog Processing-in-Pixel for Extreme-Edge Intelligence}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  keywords={Computer vision;Three-dimensional displays;Multichip modules;CMOS image sensors;Data processing;Trajectory;Circuit synthesis;3D integration;in-pixel computing;edge computing;CMOS image sensors;Cu–Cu connections},
  doi={10.1109/ICRC60800.2023.10386206}}

@inproceedings{kejriwal2023designing,
  title={Designing Artificial Intelligence for Open Worlds},
  author={Kejriwal, Mayank},
  booktitle={2023 Annual Meeting},
  year={2023},
  organization={AAAS}
}

@article{Schmitz2023_avaxtar,
	abstractnote = {Vaccine hesitancy, which has recently been driven by online narratives, significantly degrades the efficacy of vaccination strategies, such as those for COVID-19. Despite broad agreement in the medical community about the safety and efficacy of available vaccines, a large number of social media users continue to be inundated with false information about vaccines and are indecisive or unwilling to be vaccinated. The goal of this study is to better understand anti-vaccine sentiment by developing a system capable of automatically identifying the users responsible for spreading anti-vaccine narratives. We introduce a publicly available Python package capable of analyzing Twitter profiles to assess how likely that profile is to share anti-vaccine sentiment in the future. The software package is built using text embedding methods, neural networks, and automated dataset generation and is trained on several million tweets. We find this model can accurately detect anti-vaccine users up to a year before they tweet anti-vaccine hashtags or keywords. We also show examples of how text analysis helps us understand anti-vaccine discussions by detecting moral and emotional differences between anti-vaccine spreaders on Twitter and regular users. Our results will help researchers and policy-makers understand how users become anti-vaccine and what they discuss on Twitter. Policy-makers can utilize this information for better targeted campaigns that debunk harmful anti-vaccination myths.},
	author = {Schmitz, Matheus and Muric, Goran and Burghardt, Keith},
	doi = {10.1609/icwsm.v17i1.22188},
	journal = {Proceedings of the International AAAI Conference on Web and Social Media},
	month = {Jun.},
	number = {1},
	pages = {787-795},
	title = {Detecting Anti-vaccine Users on Twitter},
	url = {https://ojs.aaai.org/index.php/ICWSM/article/view/22188},
	volume = {17},
	year = {2023},
	bdsk-url-1 = {https://ojs.aaai.org/index.php/ICWSM/article/view/22188},
	bdsk-url-2 = {https://doi.org/10.1609/icwsm.v17i1.22188}}

@article{fan2023detecting,
author = {Fan, Rui and Alipour, Kamran and Bowd, Christopher and Christopher, Mark and Brye, Nicole and Proudfoot, James and Goldbaum, Michael and Belghith, Akram and Girkin, Christopher and Fazio, Massimo and Liebmann, Jeffrey and Weinreb, Robert and Pazzani, Michael and Kriegman, David and Zangwill, Linda},
title = {Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization},
  volume={3},
  number={1},
  pages={100233},
  year={2023},
  publisher={Elsevier}
}

@INPROCEEDINGS{10386632,
  author={Pham, Minh and Knoblock, Craig A. and Chen, Muhao},
  booktitle={Proceedings of the 2023 IEEE International Conference on Big Data (BigData)}, 
  title={Detecting Semantic Errors in Tables using Textual Evidence}, 
  year={2023},
  volume={},
  number={},
  pages={292-303},
  keywords={Semantics;Self-supervised learning;Syntactics;Big Data;Data models;Semantic error detection;contrastive learning;language models;textual evidence},
  doi={10.1109/BigData59044.2023.10386632}}

@inproceedings{haider2020detecting,
  title={Detecting social media manipulation in low-resource languages},
  author={Haider, Samar and Luceri, Luca and Deb, Ashok and Badawy, Adam and Peng, Nanyun and Ferrara, Emilio},
 booktitle={Companion Proceedings of The 2023 World Wide Web Conference},
  year={2023}
}

@article{Belzer:23,
author = {Helena Belzer and Andrew Rittenbach and Jonathan L. Habif},
journal = {Opt. Continuum},
keywords = {Heterodyne detection; Infrared lasers; Laser light; Laser sources; Near infrared; Tunable lasers},
number = {4},
pages = {751--757},
publisher = {Optica Publishing Group},
title = {Detection of laser light scattered from aerosols in a bright background using a balanced coherent receiver},
volume = {2},
month = {Apr},
year = {2023},
url = {https://opg.optica.org/optcon/abstract.cfm?URI=optcon-2-4-751},
doi = {10.1364/OPTCON.484917},
abstract = {We experimentally demonstrate the detection of laser light, which has been scattered from micron-scale atmospheric particulates, using a balanced heterodyne detection system with a non-cooperative tunable laser as a local oscillator source. The signal generated by the coherent detection receiver is provided to a signal processing algorithm designed to discriminate scattered laser light entering the receiver from the incoherent background and internal receiver noise. A receiver operating characteristic quantifies the performance of the receiver and demonstrates that optical coherence can be used as a parameter for laser detection even after the process of optical scattering from a disordered media.},
}

We experimentally demonstrate the detection of laser light, which has been scattered from micron-scale atmospheric particulates, using a balanced heterodyne detection system with a non-cooperative tunable laser as a local oscillator source. The signal generated by the coherent detection receiver is provided to a signal processing algorithm designed to discriminate scattered laser light entering the receiver from the incoherent background and internal receiver noise. A receiver operating characteristic quantifies the performance of the receiver and demonstrates that optical coherence can be used as a parameter for laser detection even after the process of optical scattering from a disordered media.

@article{DBLP:journals/corr/abs-2307-08541,
  author       = {Wanying Zhao and
                  Fiona Guo and
                  Kristina Lerman and
                  Yong{-}Yeol Ahn},
  title        = {Discovering collective narratives shifts in online discussions},
  journal      = {CoRR},
  volume       = {abs/2307.08541},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2307.08541},
  doi          = {10.48550/ARXIV.2307.08541},
  eprinttype    = {arXiv},
  eprint       = {2307.08541},
  timestamp    = {Tue, 25 Jul 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2307-08541.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@INPROCEEDINGS{PipeEdgeRevAuct,
  author={Imes, Connor and King, David W. and Walters, John Paul},
  booktitle={2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC)},
  title={Distributed Edge Machine Learning Pipeline Scheduling with Reverse Auctions},
  year={2023},
  volume={},
  number={},
  pages={196-203},
  doi={10.1109/FMEC59375.2023.10306169},
  ISIArea = {ML, CAS}
}

@article{tksk03,
author={Omkar Thakoor and Satish Thittamaranahalli},
title={Dynamic FastMap: An Efficient Algorithm for Spatiotemporal Embedding of Dynamic Graphs},
journal={Proceedings of the Thirty-Sixth International FLAIRS Conference (FLAIRS-2023)},
year={2023}
}

@InBook{	  papadimitriou-dddas-2023,
  Author	= "Papadimitriou, George and Wang, Cong and Lyons, Eric and
		  Thareja, Komal and Ruth, Paul and Villalobos, J. J. and
		  Rodero, Ivan and Deelman, Ewa and Zink, Michael and Mandal,
		  Anirban",
  Editor	= "Darema, Frederica and Blasch, Erik P. and Ravela, Sai and
		  Aved, Alex J.",
  Title		= "Dynamic Network-Centric Multi-cloud Platform for Real-Time
		  and Data-Intensive Science Workflows",
  BookTitle	= "Handbook of Dynamic Data Driven Applications Systems:
		  Volume 2",
  Year		= "2023",
  Publisher	= "Springer International Publishing",
  Address	= "Cham",
  Pages		= "835--868",
  ISBN		= "978-3-031-27986-7",
  DOI		= "10.1007/978-3-031-27986-7_32",
  URL		= "https://doi.org/10.1007/978-3-031-27986-7_32",
  Note		= {Funding Acknowledgments: NSF 1826997}
}

@inproceedings{mohd_razak_dynamic_2023,
	title = {Dynamic {Physics}-{Guided} {Deep} {Learning} for {Production} {Forecasting} in {Unconventional} {Reservoirs}},
	url = {https://onepetro.org/SPEWRM/proceedings-abstract/23WRM/2-23WRM/519649},
	urldate = {2024-02-12},
	booktitle = {{SPE} {Western} {Regional} {Meeting}},
	publisher = {SPE},
	author = {Mohd Razak, Syamil and Cornelio, Jodel and Cho, Young and Liu, Hui-Hai and Vaidya, Ravimadhav and Jafarpour, Behnam},
	year = {2023},
	pages = {D021S004R002},
}

@article{cai2023dynamic,
  title={Dynamic Study of Intercalation/Deintercalation of Ionic Liquids in Multilayer Graphene Using an Alternating Current Raman Spectroscopy Technique},
  author={Cai, Zhi and Weinstein, Haley and Aravind, Indu and Li, Ruoxi and Weng, Sizhe and Zhang, Boxin and Habif, Jonathan L and Cronin, Stephen B},
  journal={The Journal of Physical Chemistry Letters},
  volume={14},
  number={32},
  pages={7223--7228},
  year={2023},
  publisher={ACS Publications}
}

@Article{Cai2023,
author={Zhi Cai, Haley Weinstein, Indu Aravind, Ruoxi Li, Sizhe Weng, Boxin Zhang, Jonathan L. Habif, and Stephen B. Cronin},
title={Dynamic Study of Intercalation/Deintercalation of Ionic Liquids in Multilayer Graphene Using an Alternating Current Raman Spectroscopy Technique},
journal={The Journal of Physical Chemistry Letters},
year={2023},
month={Aug},
day={17},
publisher={American Chemical Society},
volume={14},
number={32},
pages={7223-7228},
doi={10.1021/acs.jpclett.3c01686},
url={https://doi.org/10.1021/acs.jpclett.3c01686}
}

@article{rodier2023effectiveness,
  title={Effectiveness of Nonpharmaceutical Interventions to Avert the Second COVID-19 Surge in Los Angeles County: A Simulation Study},
  author={Rodier, Caroline and Horn, Abigail and Zhang, Yunwan and Kaddoura, Ihab and M{\"u}ller, Sebastian},
  year={2023},
  url={https://escholarship.org/uc/item/5f78h654}
}

@article{treviso-etal-2023-efficient,
	abstract = {Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.},
	address = {Cambridge, MA},
	author = {Treviso, Marcos and Lee, Ji-Ung and Ji, Tianchu and van Aken, Betty and Cao, Qingqing and Ciosici, Manuel R. and Hassid, Michael and Heafield, Kenneth and Hooker, Sara and Raffel, Colin and Martins, Pedro H. and Martins, Andr{\'e} F. T. and Forde, Jessica Zosa and Milder, Peter and Simpson, Edwin and Slonim, Noam and Dodge, Jesse and Strubell, Emma and Balasubramanian, Niranjan and Derczynski, Leon and Gurevych, Iryna and Schwartz, Roy},
	date-added = {2024-01-29 13:18:11 -0800},
	date-modified = {2024-01-29 13:18:11 -0800},
	doi = {10.1162/tacl_a_00577},
	journal = {Transactions of the Association for Computational Linguistics},
	pages = {826--860},
	publisher = {MIT Press},
	title = {Efficient Methods for Natural Language Processing: A Survey},
	url = {https://aclanthology.org/2023.tacl-1.48},
	volume = {11},
	year = {2023},
	bdsk-url-1 = {https://aclanthology.org/2023.tacl-1.48},
	bdsk-url-2 = {https://doi.org/10.1162/tacl_a_00577}}

Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.

@article{tksk01,
author={Han Zhang and Oren Salzman and Ariel Felner and Satish Thittamaranahalli and Carlos Hernandez and Sven Koenig},
title={Efficient Multi-Query Bi-Objective Search via Contraction Hierarchies},
journal={Proceedings of the Thirty-Third International Conference on Automated Planning and Scheduling (ICAPS-2023)},
year={2023}
}

@article{tksk07,
author={Han Zhang and Oren Salzman and Ariel Felner and Satish Thittamaranahalli and Carlos Hernandez and Sven Koenig},
title={Efficient Multi-Query Bi-Objective Search via Contraction Hierarchies},
journal={Proceedings of the First International Workshop on Search and Planning with Complex Objectives (WoSePCO-2023)},
year={2023}
}

@inproceedings{cift-gomac:2023,
 author = {T. Haroldsen and T.Y. Sung and O. Shears and D. Glick and J. Danner},
 title = {{Efficient Porting of FPGA Independent Testing Within and Across Vendors}},
 booktitle = {Government Microcircuit Applications and Critical Technology Conference},
 month = {March},
 year = {2023},
}

@InProceedings{ Haroldsen-2023,
author = {Travis Haroldsen, Ting-Yuan Sung, Osaze Shears, Dallon Glick, Jay Danner},
title = {Efficient Porting of FPGA independent Testing Within and Across Vendors},
booktitle = {GOMACTech-23},
year = {2023},
ISIArea = {CAS}
}

@InProceedings{	  mahmud-indis-2023,
  Author	= {Mahmud, Imtiaz and Papadimitriou, George and Wang, Cong
		  and Kiran, Mariam and Mandal, Anirban and Deelman, Ewa},
  Title		= {Elephants Sharing the Highway: Studying TCP Fairness in
		  Large Transfers over High Throughput Links},
  Year		= {2023},
  ISBN		= {9798400707858},
  Publisher	= {Association for Computing Machinery},
  Address	= {New York, NY, USA},
  URL		= {https://doi.org/10.1145/3624062.3624594},
  DOI		= {10.1145/3624062.3624594},
  BookTitle	= {Proceedings of the SC '23 Workshops of The International
		  Conference on High Performance Computing, Network, Storage,
		  and Analysis},
  Pages		= {806–818},
  numpages	= {13},
  Keywords	= {Buffer Size, FABRIC, High-Speed Internet, TCP Congestion
		  Control, Elephant Flows, Active Queue Management,
		  High-Bandwidth, Fairness},
  Location	= {Denver, CO, USA},
  Series	= {SC-W '23},
  Note		= {Funding Acknowledgments: DOE DE-SC0022328. Best Paper
		  Award.}
}

@InProceedings{khayatkhoei2023emergent-asymmetry-pr,
  title={Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions},
  author={Khayatkhoei, Mahyar and Abdalmageed, Wael},
  booktitle={Proceedings of the 40th International Conference on Machine Learning},
  pages={16326--16343},
  year={2023},
  month={July},
  volume={202},
  publisher={PMLR},
  url_Paper={https://arxiv.org/pdf/2306.09618.pdf},
  url_Link={https://icml.cc/virtual/2023/poster/23612},
  ISIArea = {ML, VISTA}
}

@InProceedings{	  roa-ucc-2023,
  Author	= {Roa, Camila and Rynge, Mats and Olaya, Paula and Vahi,
		  Karan and Miller, Todd and Goodhue, John and Griffioen,
		  James and Hudak, David and Knuth, Shelley and Llamas,
		  Ricardo and Vargas, Rodrigo and Livny, Miron, and Deelman,
		  Ewa and Taufer, Michela},
  Title		= {End-to-end Integration of Scientific Workflows on
		  Distributed Cyberinfrastructures: Challenges and Lessons
		  Learned with an Earth Science Application},
  Year		= {2023},
  BookTitle	= {2023 IEEE/ACM 16th International Conference on Utility and
		  Cloud Computing (UCC)},
  Location	= {Taormina (Messina), Italy},
  Series	= {UCC '23}
}

@inproceedings{sun-etal-2023-evaluating,
    title = "Evaluating Large Language Models on Controlled Generation Tasks",
    author = "Sun, Jiao and Tian, Yufei and Zhou, Wangchunshu and Xu, Nan and Hu, Qian and Gupta, Rahul  
      and Wieting, John  and Peng, Nanyun  and Ma, Xuezhe",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    pages = "3155--3168",
}

@article{tang2023evaluating,
  title={Evaluating deep generative models on cognitive tasks: a case study},
  author={Tang, Zhisheng and Kejriwal, Mayank},
  journal={Discover Artificial Intelligence},
  volume={3},
  number={1},
  pages={21},
  year={2023},
  publisher={Springer}
}

@inproceedings{dhinagar2023:sipaim,
	journal={18th International Symposium on Medical Information Processing and Analysis},
	doi={10.1117/12.2670457},
	publisher={SPIE},
	title={Evaluation of transfer learning methods for detecting Alzheimer’s disease with brain MRI},
	url={https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12567/125671L/Evaluation-of-transfer-learning-methods-for-detecting-Alzheimers-disease-with/10.1117/12.2670457.full},
	volume=12567,
	author={Dhinagar, Nikhil J. and Thomopoulos, Sophia I. and Rajagopalan, Priya and Stripelis, Dimitris and Ambite, Jose Luis and Steeg, Greg Ver and Thompson, Paul M.},
	note={[Online; accessed 2023-04-18]},
	pages={504--513},
	date={2023-03-06},
	year=2023,
	month=3,
	day=6,
}

@article{PRXQuantum.4.020318,
  title = {Experimental Observation of Thermalization with Noncommuting Charges},
  author = {Kranzl, Florian and Lasek, Aleksander and Joshi, Manoj K. and Kalev, Amir and Blatt, Rainer and Roos, Christian F. and Yunger Halpern, Nicole},
  journal = {PRX Quantum},
  volume = {4},
  issue = {2},
  pages = {020318},
  numpages = {19},
  year = {2023},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/PRXQuantum.4.020318},
  url = {https://link.aps.org/doi/10.1103/PRXQuantum.4.020318}
}

@article{song2023experimental,
  title={Experimental demonstration of an optics-based 4-PSK half-adder using nonlinear wave mixing},
  author={Song, Hao and Zou, Kaiheng and Zhou, Huibin and Karapetyan, Narek and Minoofar, Amir and Su, Xinzhou and Almaiman, Ahmed and Habif, Jonathan L and Tur, Moshe and Willner, Alan E},
  journal={Optics Letters},
  volume={48},
  number={13},
  pages={3475--3478},
  year={2023},
  publisher={Optica Publishing Group}
}

@InProceedings{	  morel-infocom-2023,
  Author	= {Morel, Alicia Esquivel and Gafurov, Durbek and Calyam,
		  Prasad and Wang, Cong and Thareja, Komal and Mandal,
		  Anirban and Lyons, Eric and Zink, Michael and
		  Papadimitriou, George and Deelman, Ewa},
  BookTitle	= {IEEE INFOCOM 2023 - IEEE Conference on Computer
		  Communications Workshops (INFOCOM WKSHPS)},
  Title		= {Experiments on Network Services for Video Transmission
		  using FABRIC Instrument Resources},
  Year		= {2023},
  Volume	= {},
  Number	= {},
  Pages		= {1-6},
  DOI		= {10.1109/INFOCOMWKSHPS57453.2023.10225817},
  Note		= {Funding Acknowledgments: NSF CNS-1950873, CNS-1647182 and
		  OAC-2018074}
}

@inproceedings{thakur2023explainable,
  title={Explainable Classification of Internet Memes},
  author={Thakur, Abhinav Kumar and Ilievski, Filip and Sandlin, H{\^o}ng-{\^A}n and Sourati, Zhivar and Luceri, Luca and Tommasini, Riccardo and Mermoud, Alain},
   booktitle={NeSy 2023, 17th International Workshop on Neural-Symbolic Learning and Reasoning},
  year={2023}
}

@inproceedings{10.1145/3589132.3625659,
author = {Lin, Fandel and Knoblock, Craig A. and Shbita, Basel and Vu, Binh and Li, Zekun and Chiang, Yao-Yi},
title = {Exploiting Polygon Metadata to Understand Raster Maps - Accurate Polygonal Feature Extraction},
year = {2023},
isbn = {9798400701689},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589132.3625659},
doi = {10.1145/3589132.3625659},
abstract = {Locating undiscovered deposits of critical minerals requires accurate geological data. However, most of the 100,000 historical geological maps of the United States Geological Survey (USGS) are in raster format. This hinders critical mineral assessment. We target the problem of extracting geological features represented as polygons from raster maps. We exploit the polygon metadata that provides information on the geological features, such as the map keys indicating how the polygon features are represented, to extract the features. We present a metadata-driven machine-learning approach that encodes the raster map and map key into a series of bitmaps and uses a convolutional model to learn to recognize the polygon features. We evaluated our approach on USGS geological maps; our approach achieves a median F1 score of 0.809 and outperforms state-of-the-art methods by 4.52\%.},
booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},
articleno = {109},
numpages = {12},
keywords = {raster map, polygon extraction, image processing},
location = {, Hamburg, Germany, },
series = {SIGSPATIAL '23}
}

Locating undiscovered deposits of critical minerals requires accurate geological data. However, most of the 100,000 historical geological maps of the United States Geological Survey (USGS) are in raster format. This hinders critical mineral assessment. We target the problem of extracting geological features represented as polygons from raster maps. We exploit the polygon metadata that provides information on the geological features, such as the map keys indicating how the polygon features are represented, to extract the features. We present a metadata-driven machine-learning approach that encodes the raster map and map key into a series of bitmaps and uses a convolutional model to learn to recognize the polygon features. We evaluated our approach on USGS geological maps; our approach achieves a median F1 score of 0.809 and outperforms state-of-the-art methods by 4.52%.

@article{ezzeddine2023exposing,
  title={Exposing influence campaigns in the age of LLMs: a behavioral-based AI approach to detecting state-sponsored trolls},
  author={Ezzeddine, Fatima and Ayoub, Omran and Giordano, Silvia and Nogara, Gianluca and Sbeity, Ihab and Ferrara, Emilio and Luceri, Luca},
  journal={EPJ Data Science},
  volume={12},
  number={1},
  pages={46},
  year={2023},
  publisher={Springer Berlin Heidelberg}
}

@inproceedings{wang2023extracting,
  title={Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction},
  author={Wang, Haoyu and Zhang, Hongming and Deng, Yuqian and Gardner, Jacob R and Roth, Dan and Chen, Muhao},
  booktitle={The 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL)},
  year={2023}
}

@unpublished{jandaghi:arxiv23,
    author = "Jandaghi, Pegah and Sheng, XiangHai and Bai, Xinyi and Pujara, Jay and Sidahmed, Hakim",
    arxiv_url = "https://arxiv.org/pdf/2310.03051.pdf",
    sec = "preprint",
    title = "Faithful Persona-based Conversational Dataset Generation with Large Language Models",
    year = "2023"
}

@Article{photonics10020116,
AUTHOR = {Kim, Junhyung Lyle and Kollias, George and Kalev, Amir and Wei, Ken X. and Kyrillidis, Anastasios},
TITLE = {Fast Quantum State Reconstruction via Accelerated Non-Convex Programming},
JOURNAL = {Photonics},
VOLUME = {10},
YEAR = {2023},
NUMBER = {2},
ARTICLE-NUMBER = {116},
URL = {https://www.mdpi.com/2304-6732/10/2/116},
ISSN = {2304-6732},
DOI = {10.3390/photonics10020116}
}
%%%%%% YEAR 2021%%%%%%%%

@article{tksk09,
author={Kexin Zheng and Ang Li and Han Zhang and Satish Thittamaranahalli},
title={FastMapSVM for Predicting CSP Satisfiability},
journal={Proceedings of the Twenty-Ninth International Conference on Principles and Practice of Constraint Programming (CP-2023)},
year={2023}
}

@article{hasenstab2023feature,
  title={Feature Interpretation using Generative Adversarial Networks (FIGAN): A Framework for Visualizing a CNNs Learned Features},
  author={Hasenstab, Kyle A and Huynh, Justin and Masoudi, Samira and Cunha, Guilherme M and Pazzani, Michael and Hsiao, Albert},
  journal={IEEE Access},
  year={2023},
  publisher={IEEE}
}

@article{DBLP:journals/corr/abs-2303-10837,
  author       = {Weizhao Jin and
                  Yuhang Yao and
                  Shanshan Han and
                  Carlee Joe{-}Wong and
                  Srivatsan Ravi and
                  Salman Avestimehr and
                  Chaoyang He},
  title        = {FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving
                  Federated Learning System},
  journal      = {CoRR},
  volume       = {abs/2303.10837},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2303.10837},
  doi          = {10.48550/ARXIV.2303.10837},
  eprinttype    = {arXiv},
  eprint       = {2303.10837},
  timestamp    = {Wed, 22 Mar 2023 14:41:36 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2303-10837.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{stripelis2023:AD,
	title = {Federated {Deep} {Learning} for {Detecting} {Alzheimer}’s {Disease} in {Multi}-{Cohort} {Brain} {MRI}},
	volume = {19},
	copyright = {© 2023 the Alzheimer's Association.},
	issn = {1552-5279},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/alz.065998},
	doi = {10.1002/alz.065998},
	language = {en},
	number = {S1},
	urldate = {2024-01-27},
	journal = {Alzheimer's \& Dementia},
	author = {Stripelis, Dimitris and Dhinagar, Nikhil J and Romero, Rafael V. Sanchez and Thomopoulos, Sophia I and Thompson, Paul M and Ambite, Jose Luis},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/alz.065998},
	pages = {e065998},
	file = {Snapshot:/Users/ambite/Zotero/storage/NRYZMLVB/alz.html:text/html},
}

@InProceedings{stripelis2023:W3PHIAI,
  author = 	 {Dimitris Stripelis and Jos\'{e} Luis Ambite},
  title = 	 {Federated Learning over Harmonized Data Silos},
  booktitle =    {7th International Workshop on Health Intelligence {(W3PHIAI-23)}},
  year = 	 {2023},
  address = 	 {Washington, D.C.},
  organization = {AAAI},
}

@inproceedings{DBLP:conf/sbp/ChangML23,
  author       = {Rong{-}Ching Chang and
                  Jonathan May and
                  Kristina Lerman},
  editor       = {Robert Thomson and
                  Samer Al{-}khateeb and
                  Annetta Burger and
                  Patrick S. Park and
                  Aryn A. Pyke},
  title        = {Feedback Loops and Complex Dynamics of Harmful Speech in Online Discussions},
  booktitle    = {Social, Cultural, and Behavioral Modeling - 16th International Conference,
                  SBP-BRiMS 2023, Pittsburgh, PA, USA, September 20-22, 2023, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14161},
  pages        = {85--94},
  publisher    = {Springer},
  year         = {2023},
  url          = {https://doi.org/10.1007/978-3-031-43129-6\_9},
  doi          = {10.1007/978-3-031-43129-6\_9},
  timestamp    = {Tue, 07 May 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/sbp/ChangML23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{10.1007/978-3-031-43129-6_9,
author = {Chang, Rong-Ching and May, Jonathan and Lerman, Kristina},
title = {Feedback Loops and Complex Dynamics of Harmful Speech in Online Discussions},
year = {2023},
isbn = {978-3-031-43128-9},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-43129-6_9},
doi = {10.1007/978-3-031-43129-6_9},
abstract = {Harmful and toxic speech contribute to an unwelcoming online environment that suppresses participation and conversation. Efforts have focused on detecting and mitigating harmful speech; however, the mechanisms by which toxicity degrades online discussions are not well understood. This paper makes two contributions. First, to comprehensively model harmful comments, we introduce a multilingual misogyny and sexist speech detection model (). Second, we model the complex dynamics of online discussions as feedback loops in which harmful comments lead to negative emotions which prompt even more harmful comments. To quantify the feedback loops, we use a combination of mutual Granger causality and regression to analyze discussions on two political forums on Reddit: the moderated political forum r/Politics and the moderated neutral political forum r/NeutralPolitics. Our results suggest that harmful comments and negative emotions create self-reinforcing feedback loops in forums. Contrarily, moderation with neutral discussion appears to tip interactions into self-extinguishing feedback loops that reduce harmful speech and negative emotions. Our study sheds more light on the complex dynamics of harmful speech and the role of moderation and neutral discussion in mitigating these dynamics.},
booktitle = {Social, Cultural, and Behavioral Modeling: 16th International Conference, SBP-BRiMS 2023, Pittsburgh, PA, USA, September 20–22, 2023, Proceedings},
pages = {85–94},
numpages = {10},
keywords = {Feedback Loop, Moderation, Granger Causality},
location = {Pittsburgh, PA, USA}
}

Harmful and toxic speech contribute to an unwelcoming online environment that suppresses participation and conversation. Efforts have focused on detecting and mitigating harmful speech; however, the mechanisms by which toxicity degrades online discussions are not well understood. This paper makes two contributions. First, to comprehensively model harmful comments, we introduce a multilingual misogyny and sexist speech detection model (). Second, we model the complex dynamics of online discussions as feedback loops in which harmful comments lead to negative emotions which prompt even more harmful comments. To quantify the feedback loops, we use a combination of mutual Granger causality and regression to analyze discussions on two political forums on Reddit: the moderated political forum r/Politics and the moderated neutral political forum r/NeutralPolitics. Our results suggest that harmful comments and negative emotions create self-reinforcing feedback loops in forums. Contrarily, moderation with neutral discussion appears to tip interactions into self-extinguishing feedback loops that reduce harmful speech and negative emotions. Our study sheds more light on the complex dynamics of harmful speech and the role of moderation and neutral discussion in mitigating these dynamics.

@Proceedings{spangher23.djc,
  title =        {First Steps Towards a Source Recommendation Engine:
Investigating How Sources Are Used in News Articles},
  year =         2023,
  url={https://www.datajconf.com/papers/CJ_DataJConf_2023_paper_74.pdf},
  booktitle = {Proc. The Joint Computation + Journalism European Data \& Computational Journalism Conference},
  address =   {Zurich, Switzerland},
  month =     {June}}

@Article{	  flow-bench,
  Author	= {Papadimitriou, George and Jin, Hongwei and Wang, Cong and
		  Raghavan, Krishnan and Mandal, Anirban and Balaprakash,
		  Prasanna and Deelman, Ewa},
  Title		= {Flow-Bench: A Dataset for Computational Workflow Anomaly
		  Detection},
  Publisher	= {arXiv},
  Year		= {2023},
  DOI		= {10.48550/ARXIV.2306.09930},
  URL		= {https://arxiv.org/abs/2306.09930},
  copyright	= {arXiv.org perpetual, non-exclusive license}
}

@Article{	  morel-mic-2023,
  Author	= {Morel, Alicia Esquivel and Qu, Chengyi and Calyam, Prasad
		  and Wang, Cong and Thareja, Komal and Mandal, Anirban and
		  Lyons, Eric and Zink, Michael and Papadimitriou, George and
		  Deelman, Ewa},
  Journal	= {IEEE Internet Computing},
  Title		= {FlyNet: Drones on the Horizon},
  Year		= {2023},
  Volume	= {27},
  Number	= {3},
  Pages		= {35-43},
  DOI		= {10.1109/MIC.2023.3260440},
  Note		= {Funding Acknowledgments: NSF 1950873, 1647182, 2018074}
}

@InProceedings{	  grote-escience-2023,
  Author	= {Grote, Andrew and Lyons, Eric and Thareja, Komal and
		  Papadimitriou, George and Deelman, Ewa and Mandal, Anirban
		  and Calyam, Prasad and Zink, Michael},
  BookTitle	= {2023 IEEE 19th International Conference on e-Science
		  (e-Science)},
  Title		= {FlyPaw: Optimized Route Planning for Scientific
		  UAVMissions},
  Year		= {2023},
  Volume	= {},
  Number	= {},
  Pages		= {1-10},
  DOI		= {10.1109/e-Science58273.2023.10254831},
  Note		= {Funding Acknowledgments: NSF 2018074, 1939334}
}

@InProceedings{song2023formal-fingerprints,
  title={Formal Definition of Fingerprints Improves Attribution of Generative Models},
  author={Song, Hae Jin and Khayatkhoei, Mahyar and AbdAlmageed, Wael},
  booktitle={Advances in Neural Information Processing Systems Workshop on Attributing Model Behavior at Scale},
  year={2023},
  month={December},
  url_Paper={https://openreview.net/pdf?id=Zqj3YQ1QAC},
  url_Link={https://neurips.cc/virtual/2023/83414},
  ISIArea = {ML, VISTA}
}

@article{DBLP:journals/corr/abs-2301-11994,
  author       = {Rebecca Dorn and
                  Yiwen Ma and
                  Fred Morstatter and
                  Kristina Lerman},
  title        = {Gender and Prestige Bias in Coronavirus News Reporting},
  journal      = {CoRR},
  volume       = {abs/2301.11994},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2301.11994},
  doi          = {10.48550/ARXIV.2301.11994},
  eprinttype    = {arXiv},
  eprint       = {2301.11994},
  timestamp    = {Tue, 31 Jan 2023 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2301-11994.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@incollection{chiang2023geoai,
  title={GeoAI for the Digitization of Historical Maps},
  author={Chiang, Yao-Yi and Chen, Muhao and Duan, Weiwei and Kim, Jina and Knoblock, Craig A and Leyk, Stefan and Li, Zekun and Lin, Yijun and Namgung, Min and Shbita, Basel and others},
  booktitle={Handbook of Geospatial Artificial Intelligence},
  pages={217--247},
  year={2023},
  publisher={CRC Press}
}

@Article{	  jin-ijhpca-2023,
  Author	= {Hongwei Jin and Krishnan Raghavan and George Papadimitriou
		  and Cong Wang and Anirban Mandal and Mariam Kiran and Ewa
		  Deelman and Prasanna Balaprakash},
  Title		= {Graph neural networks for detecting anomalies in
		  scientific workflows},
  Journal	= {The International Journal of High Performance Computing
		  Applications},
  Volume	= {},
  Number	= {},
  Pages		= {},
  Year		= {2023},
  DOI		= {10.1177/10943420231172140},
  URL		= {https://doi.org/10.1177/10943420231172140},
  EPrint	= {https://doi.org/10.1177/10943420231172140},
  Note		= {Funding Acknowledgments: DOE DE-SC0022328,
		  DE-AC02-06CH11357}
}

@inproceedings{du:aaai23ws,
    Author = "Du, Xinwei and Ahrabian, Kian and Ananthan, Arun Baalaaji Sankar and Myloth, Richard Delwin and Pujara, Jay",
    bib_url = "/pubs/bib/du-aaai23ws.bib",
    booktitle = "Workshop on Scientific Document Understanding at AAAI",
    pdf_url = "/pubs/2023/du-aaai23ws/du-aaai23ws.pdf",
    sec = "ws",
    title = "Graph-Based Structure Aware Citation Intent Classification",
    year = "2023"
}

@article{nananukul2023halo,
  title={HALO: An Ontology for Representing Hallucinations in Generative Models},
  author={Nananukul, Navapat and Kejriwal, Mayank},
  journal={arXiv preprint arXiv:2312.05209},
  year={2023}
}

@article{Uhl2023_hisdaces,
	author = {Uhl, J. H. and Roy\'e, D. and Burghardt, K. and Aldrey V\'azquez, J. A. and Borobio Sanchiz, M. and Leyk, S.},
	doi = {10.5194/essd-15-4713-2023},
	journal = {Earth System Science Data},
	number = {10},
	pages = {4713--4747},
	title = {HISDAC-ES: historical settlement data compilation for Spain (1900--2020)},
	url = {https://essd.copernicus.org/articles/15/4713/2023/},
	volume = {15},
	year = {2023},
	bdsk-url-1 = {https://essd.copernicus.org/articles/15/4713/2023/},
	bdsk-url-2 = {https://doi.org/10.5194/essd-15-4713-2023}}



	

@inproceedings{Graphene_Device_2023_March_Meeting,
author = {Haley Weinstein, Zhi Cai, Stephen Cronin, and Jonathan L. Habif},
    booktitle = {APS March Meeting Abstracts},
         year = 2023,
       series = {APS Meeting Abstracts},
       volume = {2023},
title = {Harvesting Planck radiation for free-space optical communications in the LWIR band},
year = {2023},
url = {https://meetings.aps.org/Meeting/MAR23/Session/N42.10},

}

@InProceedings{Imana23a,
        author =        "Basileal Imana and Aleksandra Korolova and John Heidemann",
        title =         "Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest",
        booktitle =     " Computer Supported Cooperative Work ",
        year =          2023,
	sortdate = 	"2023-10-13", 
	project = "ant",
	jsubject = "network_observation",
        pages =      "to appear",
        month =      oct,
        address =    "Minneapolis, Minnesota, USA",
        publisher =  "ACM",
        keywords =   "linkedin, facebook, ad delivery algorithm, bias,
                  skew, discrimination, platform-supported auditing,
                  differential privacy",
        doi =     "https://doi.org/10.1145/3579610",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Imana23a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Imana23a.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1889",
	abstract = "Social media platforms curate access to information and opportunities,
and so play a critical role in shaping public discourse today.  The
opaque nature of the algorithms these platforms use to curate content
raises societal questions.  Prior studies have used black-box methods
led by experts or collaborative audits driven by everyday users to
show that these algorithms can lead to biased or discriminatory
outcomes.  However, existing auditing methods face fundamental
limitations because they function independent of the platforms.
Concerns of potential harmful outcomes have prompted proposal of
legislation in both the U.S.~and the E.U.~to mandate a new form of
auditing where vetted external researchers get privileged access to
social media platforms.  Unfortunately, to date there have been no
concrete technical proposals to provide such auditing, because
auditing at scale risks disclosure of users' private data and
platforms' proprietary algorithms.  We propose a new method for
\emph{platform-supported auditing} that can meet the goals of the
proposed legislation.  The first contribution of our work is to
enumerate the challenges and the limitations of existing auditing
methods to implement these policies at scale.  Second, we suggest that
limited, privileged access to \emph{relevance estimators} is the key
to enabling generalizable platform-supported auditing of social media
platforms by external researchers.  Third, we show platform-supported
auditing need not risk user privacy nor disclosure of platforms'
business interests by proposing an auditing framework that protects
against these risks.  For a particular fairness metric, we show that
ensuring privacy imposes only a small constant factor increase
($6.34\times$ as an upper bound, and $4\times$ for typical parameters)
in the number of samples required for accurate auditing.  Our
technical contributions, combined with ongoing legal and policy
efforts, can enable public oversight into how social media platforms
affect individuals and society by moving past the
privacy-vs-transparency hurdle.
",
}

Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse today. The opaque nature of the algorithms these platforms use to curate content raises societal questions. Prior studies have used black-box methods led by experts or collaborative audits driven by everyday users to show that these algorithms can lead to biased or discriminatory outcomes. However, existing auditing methods face fundamental limitations because they function independent of the platforms. Concerns of potential harmful outcomes have prompted proposal of legislation in both the U.S. and the E.U. to mandate a new form of auditing where vetted external researchers get privileged access to social media platforms. Unfortunately, to date there have been no concrete technical proposals to provide such auditing, because auditing at scale risks disclosure of users' private data and platforms' proprietary algorithms. We propose a new method for \emphplatform-supported auditing that can meet the goals of the proposed legislation. The first contribution of our work is to enumerate the challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to \emphrelevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms' business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase ($6.34×$ as an upper bound, and $4×$ for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle.

@unpublished{zhou:arxiv23,
    author = "Zhou, Pei and Madaan, Aman and Potharaju, Srividya Pranavi and Gupta, Aditya and McKee, Kevin R. and Holtzman, Ari and Pujara, Jay and Ren, Xiang and Mishra, Swaroop and Nematzadeh, Aida and Upadhyay, Shyam and Faruqui, Manaal",
    arxiv_url = "https://arxiv.org/pdf/2310.03051.pdf",
    sec = "preprint",
    title = "How FaR Are Large Language Models From Agents with Theory-of-Mind?",
    year = "2023"
}

@article{pierri2023does,
  title={How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events},
  author={Pierri, Francesco and Luceri, Luca and Chen, Emily and Ferrara, Emilio},
  journal={EPJ Data Science},
  volume={12},
  number={1},
  pages={43},
  year={2023},
  publisher={Springer Berlin Heidelberg}
}

@article{sisaengsuwanchai2023does,
  title={How does prompt engineering affect ChatGPT performance on unsupervised entity resolution?},
  author={Sisaengsuwanchai, Khanin and Nananukul, Navapat and Kejriwal, Mayank},
  journal={arXiv preprint arXiv:2310.06174},
  year={2023}
}

@InProceedings{	  deelman-escience-2023,
  Author	= {Deelman, Ewa},
  BookTitle	= {2023 IEEE 19th International Conference on e-Science
		  (e-Science)},
  Title		= {How is Artificial Intelligence Changing Science?},
  Year		= {2023},
  Volume	= {},
  Number	= {},
  Pages		= {1-4},
  DOI		= {10.1109/e-Science58273.2023.10254913},
  Note		= {Funding Acknowledgments: DOE DE-SC0022328 and NSF 1664162,
		  2138286}
}

@article{benjamin2023hybrid,
  title={Hybrid forecasting of geopolitical events},
  author={Benjamin, Daniel M and Morstatter, Fred and Abbas, Ali E and Abeliuk, Andres and Atanasov, Pavel and Bennett, Stephen and Beger, Andreas and Birari, Saurabh and Budescu, David V and Catasta, Michele and others},
  journal={AI Magazine},
  year={2023}
}

@inproceedings{zhou:acl23,
    Author = "Zhou, Pei and Zhu, Andrew and Hu, Jennifer and Pujara, Jay and Ren, Xiang and Callison-Burch, Chris and Choi, Yejin and Ammanabrolu, Prithviraj",
    acceptrate = "25\%",
    bib_url = "/pubs/bib/zhou-acl23.bib",
    booktitle = "Association for Computational Linguistics",
    doi_url = "http://dx.doi.org/10.18653/v1/2023.acl-long.624",
    pages = "11136--11155",
    pdf_url = "/pubs/2023/zhou-acl23/zhou-acl23.pdf",
    sec = "conf",
    title = "{I} Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons",
    year = "2023"
}

@inproceedings{spangher-etal-2023-identifying,
    title = "Identifying Informational Sources in News Articles",
    author = "Spangher, Alexander  and
      Peng, Nanyun  and
      Ferrara, Emilio  and
      May, Jonathan",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.221",
    doi = "10.18653/v1/2023.emnlp-main.221",
    pages = "3626--3639",
    abstract = "News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing. We first show that our dataset can be used to train high-performing models for information detection and source attribution. Then, we introduce a novel task, source prediction, to study the compositionality of sources in news articles {--} i.e. how they are chosen to complement each other. We show good modeling performance on this task, indicating that there is a pattern to the way different sources are used \textit{together} in news storytelling. This insight opens the door for a focus on sources in narrative science (i.e. planning-based language generation) and computational journalism (i.e. a source-recommendation system to aid journalists writing stories). All data and model code can be found at https://github.com/alex2awesome/source-exploration.",
}

News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing. We first show that our dataset can be used to train high-performing models for information detection and source attribution. Then, we introduce a novel task, source prediction, to study the compositionality of sources in news articles – i.e. how they are chosen to complement each other. We show good modeling performance on this task, indicating that there is a pattern to the way different sources are used together in news storytelling. This insight opens the door for a focus on sources in narrative science (i.e. planning-based language generation) and computational journalism (i.e. a source-recommendation system to aid journalists writing stories). All data and model code can be found at https://github.com/alex2awesome/source-exploration.

@inproceedings{jandaghi:acl23ws,
    author = "Jandaghi, Pegah and Pujara, Jay",
    bib_url = "/pubs/bib/jandaghi-acl23ws.bib",
    doi_url = "https://doi.org/10.18653/v1/2023.matching-1.2",
    pdf_url = "/pubs/2023/jandaghi-acl23ws/jandaghi-acl23ws.pdf",
    title = "Identifying Quantifiably Verifiable Statements from Text",
    booktitle = "ACL Workshop on Matching From Unstructured and Structured Data ",
    sec = "ws",
    year = "2023"
}

@inproceedings{cornelio_identifying_2023,
	title = {Identifying and {Ranking} {Multiple} {Source} {Models} for {Transfer} {Learning} in {Unconventional} {Reservoirs}.},
	url = {https://onepetro.org/SPEMEOS/proceedings-abstract/23MEOS/2-23MEOS/517277},
	urldate = {2024-02-12},
	booktitle = {{SPE} {Middle} {East} {Oil} and {Gas} {Show} and {Conference}},
	publisher = {SPE},
	author = {Cornelio, Jodel and Mohd Razak, Syamil and Cho, Young and Liu, Hui-Hai and Vaidya, Ravimadhav and Jafarpour, Behnam},
	year = {2023},
	pages = {D021S084R003},
}

@inproceedings{wang2022identifying,
  title={Identifying and characterizing behavioral classes of radicalization within the QAnon conspiracy on Twitter},
  author={Wang, Emily L and Luceri, Luca and Pierri, Francesco and Ferrara, Emilio},
  booktitle={Proceedings of the 2023 International Conference of Web and Social Media (ICWSM)},
  year={2023}
}

@article{DBLP:journals/corr/abs-2302-11365,
  author       = {Shantanu Agarwal and
                  Steven Fincke and
                  Chris Jenkins and
                  Scott Miller and
                  Elizabeth Boschee},
  title        = {Impact of Subword Pooling Strategy on Cross-lingual Event Detection},
  journal      = {CoRR},
  volume       = {abs/2302.11365},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2302.11365},
  doi          = {10.48550/ARXIV.2302.11365},
  eprinttype    = {arXiv},
  eprint       = {2302.11365},
  timestamp    = {Tue, 28 Feb 2023 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2302-11365.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{tksk05,
author={Yi Zheng and Srivatsan Ravi and Erik Kline and Lincoln Thurlow and Sven Koenig and Satish Thittamaranahalli},
title={Improved Conflict-Based Search for the Virtual Network Embedding Problem},
journal={Proceedings of the Thirty-Second International Conference on Computer Communications and Networks (ICCCN-2023)},
year={2023}
}

@inproceedings{zheng2023improved,
  title={Improved Conflict-Based Search for the Virtual Network Embedding Problem},
  author={Zheng, Yi and Ravi, Srivatsan and Kline, Erik and Thurlow, Lincoln and Koenig, Sven and Kumar, TK Satish},
  booktitle={2023 32nd International Conference on Computer Communications and Networks (ICCCN)},
  pages={1--10},
  year={2023},
  organization={IEEE}
}

@INPROCEEDINGS{10094902,
  author={Datta, Gourav and Liu, Zeyu and Kaiser, Md Abdullah-Al and Kundu, Souvik and Mathai, Joe and Yin, Zihan and Jacob, Ajey P. and Jaiswal, Akhilesh R. and Beerel, Peter A.},
  booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient Computer Vision}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  keywords={Wireless communication;Energy consumption;Wireless sensor networks;Signal processing algorithms;Bandwidth;Signal processing;Data transfer},
  doi={10.1109/ICASSP49357.2023.10094902}}

@inproceedings{10.1145/3589132.3625571,
author = {Lin, Fandel and Knoblock, Craig A.},
title = {Indirect Cooperation in Distributed Stationary-Resource Searching with Predefined Destinations},
year = {2023},
isbn = {9798400701689},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589132.3625571},
doi = {10.1145/3589132.3625571},
abstract = {Private vehicles are a direct means to bring people from one place to their desired destinations. However, no omniscient dispatcher is handling the origin-destination of vehicles and the availability of stationary resources, such as parking spaces or charging stations. Competitive cruising for stationary resources leads to environmental pollution and is a waste of drivers' time. We focus on the problem of distributed stationary-resource searching with predefined destinations under a multi-agent scenario. It is a distributed route planning problem with global optimization objectives. We present a probabilistic approach to achieving indirect resource coordination and latent agent cooperation in a distributed manner. Our approach treats the estimated availability of stationary resources as a reference and guides each agent based on their preferences. We evaluate our approach on four real-world datasets. Our approach outperforms state-of-the-art methods by 5\% in multi-criteria optimization.},
booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},
articleno = {26},
numpages = {12},
keywords = {stationary-resource searching, distributed route planning, multi-criteria optimization},
location = {, Hamburg, Germany, },
series = {SIGSPATIAL '23}
}

Private vehicles are a direct means to bring people from one place to their desired destinations. However, no omniscient dispatcher is handling the origin-destination of vehicles and the availability of stationary resources, such as parking spaces or charging stations. Competitive cruising for stationary resources leads to environmental pollution and is a waste of drivers' time. We focus on the problem of distributed stationary-resource searching with predefined destinations under a multi-agent scenario. It is a distributed route planning problem with global optimization objectives. We present a probabilistic approach to achieving indirect resource coordination and latent agent cooperation in a distributed manner. Our approach treats the estimated availability of stationary resources as a reference and guides each agent based on their preferences. We evaluate our approach on four real-world datasets. Our approach outperforms state-of-the-art methods by 5% in multi-criteria optimization.

@InProceedings{Song23a,
        author =        "Xiao Song and Guillermo Baltra and John Heidemann",
        title =         "Inferring Changes in Daily Human Activity from Internet Response",
        booktitle =     "Proceedings of the " # "ACM Internet Measurement Conference",
        year =          2023,
	sortdate = "2023-10-26",
	project = "ant, eieio, internetmap, minceq",
	jsubject = "network_topology",
        pages =      "to appear",
        month =      oct,
        address =    "Montreal, QC, Canada",
        publisher =  "ACM",
        jlocation =   "johnh: pafile",
        keywords =   "internet, address scans, covid-19, poster, trinocular",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Song23a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Song23a.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1998",
	dataseturl =	"https://ant.isi.edu/datasets/ip_accumulation/",
        doi =        "https://doi.org/10.1145/3618257.3624796",
	abstract = "Network traffic is often diurnal, with some networks peaking during
the workday and many homes during evening streaming hours.  Monitoring
systems consider diurnal trends for capacity planning and anomaly
detection.  In this paper, we reverse this inference and
use \emph{diurnal network trends and their absence to infer human activity}.
We draw on existing and new ICMP echo-request scans of
more than 5.2M /24 IPv4 networks to identify diurnal trends in IP
address responsiveness.  Some of these networks 
are \emph{change-sensitive}, with diurnal patterns correlating with human
activity.  We develop algorithms to clean this data, extract
underlying trends from diurnal and weekly fluctuation, and detect
changes in that activity.  Although firewalls hide many networks, and
Network Address Translation often hides human trends, we show about
168k to 330k (3.3--6.4\% of the 5.2M) /24 IPv4 networks are
change-sensitive.  These blocks are spread globally, representing some
of the most active 60\% of \twotwodegree geographic gridcells, regions
that include 98.5\% of ping-responsive blocks.  Finally, we detect
interesting changes in human activity.  Reusing existing data allows
our new algorithm to identify changes, such as Work-from-Home due to
the global reaction to the emergence of Covid-19 in 2020.  We also see
other changes in human activity, such as national holidays and
government-mandated curfews.  This ability to detect trends in human
activity from the Internet data provides a new ability to understand
our world, complementing other sources of public information such as
news reports and wastewater virus observation.",
}

Network traffic is often diurnal, with some networks peaking during the workday and many homes during evening streaming hours. Monitoring systems consider diurnal trends for capacity planning and anomaly detection. In this paper, we reverse this inference and use \emphdiurnal network trends and their absence to infer human activity. We draw on existing and new ICMP echo-request scans of more than 5.2M /24 IPv4 networks to identify diurnal trends in IP address responsiveness. Some of these networks are \emphchange-sensitive, with diurnal patterns correlating with human activity. We develop algorithms to clean this data, extract underlying trends from diurnal and weekly fluctuation, and detect changes in that activity. Although firewalls hide many networks, and Network Address Translation often hides human trends, we show about 168k to 330k (3.3–6.4% of the 5.2M) /24 IPv4 networks are change-sensitive. These blocks are spread globally, representing some of the most active 60% of \twotwodegree geographic gridcells, regions that include 98.5% of ping-responsive blocks. Finally, we detect interesting changes in human activity. Reusing existing data allows our new algorithm to identify changes, such as Work-from-Home due to the global reaction to the emergence of Covid-19 in 2020. We also see other changes in human activity, such as national holidays and government-mandated curfews. This ability to detect trends in human activity from the Internet data provides a new ability to understand our world, complementing other sources of public information such as news reports and wastewater virus observation.

@InProceedings{li2023information-bounds-bias-removal,
  title={Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks},
  author={Li, Jiazhi and Khayatkhoei, Mahyar and Zhu, Jiageng and Xie, Hanchen and Hussein, Mohamed E and AbdAlmageed, Wael},
  booktitle={Advances in Neural Information Processing Systems Workshop on Algorithmic Fairness through the Lens of Time},
  year={2023},
  month={December},
  url_Paper={https://arxiv.org/pdf/2310.04955.pdf},
  url_Link={https://neurips.cc/virtual/2023/77745},
  ISIArea = {ML, VISTA}
}

@inproceedings{schmidt2023_1,
	author = {Andrew G. Schmidt and Justinand Benedict Reynwar and Kellie Canida and Matthew French},
	booktitle = {Government Microcircuit Applications \& Critical Techology Conference (GOMACTech)},
	title = {Integrity and Assurance Tools Targeting FPGA Hard IP Block Bitstream Undocumented Functionality},
	year = {2023}}

@inproceedings{myloth:aaai23ws,
    Author = "Myloth, Richard Delwin and Ahrabian, Kian and Ananthan, Arun Baalaaji Sankar and Du, Xinwei and Pujara, Jay",
    bib_url = "/pubs/bib/myloth-aaai23ws.bib",
    booktitle = "Workshop on Scientific Document Understanding at AAAI",
    pdf_url = "/pubs/2023/myloth-aaai23ws/myloth-aaai23ws.pdf",
    sec = "ws",
    title = "Is Dynamicity All You Need?",
    year = "2023"
}

@inproceedings{pfeffer2023just,
  title={Just another day on Twitter: a complete 24 hours of Twitter data},
  author={Pfeffer, Juergen and Matter, Daniel and Jaidka, Kokil and Varol, Onur and Mashhadi, Afra and Lasser, Jana and Assenmacher, Dennis and Wu, Siqi and Yang, Diyi and Brantner, Cornelia and others},
  booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
  volume={17},
  pages={1073--1081},
  year={2023}
}

@inproceedings{gheini-etal-2023-know,
    title = "Know Where You{'}re Going: Meta-Learning for Parameter-Efficient Fine-Tuning",
    author = "Gheini, Mozhdeh  and
      Ma, Xuezhe  and
      May, Jonathan",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.737",
    doi = "10.18653/v1/2023.findings-acl.737",
    pages = "11602--11612",
    abstract = "A recent family of techniques, dubbed lightweight fine-tuning methods, facilitates parameter-efficient transfer by updating only a small set of additional parameters while keeping the parameters of the original model frozen. While proven to be an effective approach, there are no existing studies on if and how such knowledge of the downstream fine-tuning approach calls for complementary measures after pre-training and before fine-tuning. In this work, we show that taking the ultimate choice of fine-tuning into consideration boosts the performance of parameter-efficient fine-tuning. By relying on optimization-based meta-learning using MAML with certain modifications for our distinct purpose, we prime the pre-trained model specifically for parameter-efficient fine-tuning, resulting in gains of up to 4.96 points on cross-lingual NER fine-tuning. Our ablation settings and analyses further reveal that the specific approach we take to meta-learning is crucial for the attained gains.",
}

A recent family of techniques, dubbed lightweight fine-tuning methods, facilitates parameter-efficient transfer by updating only a small set of additional parameters while keeping the parameters of the original model frozen. While proven to be an effective approach, there are no existing studies on if and how such knowledge of the downstream fine-tuning approach calls for complementary measures after pre-training and before fine-tuning. In this work, we show that taking the ultimate choice of fine-tuning into consideration boosts the performance of parameter-efficient fine-tuning. By relying on optimization-based meta-learning using MAML with certain modifications for our distinct purpose, we prime the pre-trained model specifically for parameter-efficient fine-tuning, resulting in gains of up to 4.96 points on cross-lingual NER fine-tuning. Our ablation settings and analyses further reveal that the specific approach we take to meta-learning is crucial for the attained gains.

@inproceedings{cheng2023knowledge,
  title={Knowledge Graph-based Embedding for Connecting Scholars in Academic Social Networks},
  author={Cheng, Xiyao and Zhang, Yuanxun and Joshi, Harsh and Kejriwal, Mayank and Calyam, Prasad},
  booktitle={2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)},
  pages={1--10},
  year={2023},
  organization={IEEE}
}

@inproceedings{zhou2023lima,
title={{LIMA}: Less Is More for Alignment},
author={Chunting Zhou and Pengfei Liu and Puxin Xu and Srini Iyer and Jiao Sun and Yuning Mao and Xuezhe Ma and Avia Efrat and Ping Yu and LILI YU and Susan Zhang and Gargi Ghosh and Mike Lewis and Luke Zettlemoyer and Omer Levy},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023}
}

@article{DBLP:journals/corr/abs-2308-03734,
  author       = {Yixiang Yao and
                  Weizhao Jin and
                  Srivatsan Ravi},
  title        = {Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity
                  Resolution},
  journal      = {CoRR},
  volume       = {abs/2308.03734},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2308.03734},
  doi          = {10.48550/ARXIV.2308.03734},
  eprinttype    = {arXiv},
  eprint       = {2308.03734},
  timestamp    = {Tue, 22 Aug 2023 13:58:15 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2308-03734.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{tandon2023leader,
  title={Leader: Defense Against Exploit-Based Denial-of-Service Attacks on Web Applications},
  author={Tandon, Rajat and Wang, Haoda and Weideman, Nicolaas and Arakelyan, Shushan and Bartlett, Genevieve and Hauser, Christophe and Mirkovic, Jelena},
  booktitle={Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses},
  pages={744--758},
  year={2023}
}

@inproceedings{thawani:emnlpf23,
    author = "Thawani, Avijit and Ghanekar, Saurabh and Zhu, Xiaoyuan and Pujara, Jay",
    acceptrate = "46.2\%",
    arxiv_url = "https://arxiv.org/pdf/2310.11628",
    bib_url = "/pubs/bib/thawani-emnlpf23.bib",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP",
    doi_url = "https://doi.org/10.18653/v1/2023.findings-emnlp.662",
    pdf_url = "/pubs/2023/thawani-emnlpf23/thawani-emnlpf23.pdf",
    sec = "conf",
    title = "Learn Your Tokens: Word-Pooled Tokenization for Language Modeling",
    year = "2023"
}

@inproceedings{DBLP:conf/eurosp/GhaiYR23,
  author       = {Tanmay Ghai and
                  Yixiang Yao and
                  Srivatsan Ravi},
  title        = {Lessons Learned: Building a Privacy-Preserving Entity Resolution Adaptation
                  of PPJoin using End-to-End Homomorphic Encryption},
  booktitle    = {{IEEE} European Symposium on Security and Privacy, EuroS{\&}P
                  2023 - Workshops, Delft, Netherlands, July 3-7, 2023},
  pages        = {117--124},
  publisher    = {{IEEE}},
  year         = {2023},
  url          = {https://doi.org/10.1109/EuroSPW59978.2023.00018},
  doi          = {10.1109/EUROSPW59978.2023.00018},
  timestamp    = {Mon, 07 Aug 2023 15:56:24 +0200},
  biburl       = {https://dblp.org/rec/conf/eurosp/GhaiYR23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Kesselman2023,
	address = {{Limassol, Cyprus}},
	author = {Kesselman, Carl and Schuler, Robert and Foster, Ian},
	booktitle = {2023 {{IEEE}} 19th {{International Conference}} on E-{{Science}} (e-{{Science}})},
	date-added = {2024-01-22 12:05:54 -0800},
	date-modified = {2024-01-22 12:05:54 -0800},
	doi = {10.1109/e-Science58273.2023.10254914},
	month = oct,
	pages = {1--3},
	publisher = {{IEEE}},
	title = {Let's {{Put}} the {{Science}} in {{eScience}}},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1109/e-Science58273.2023.10254914}}

@inproceedings{DBLP:conf/icassp/ChochlakisMBBLN23,
  author       = {Georgios Chochlakis and
                  Gireesh Mahajan and
                  Sabyasachee Baruah and
                  Keith Burghardt and
                  Kristina Lerman and
                  Shrikanth Narayanan},
  title        = {Leveraging Label Correlations in a Multi-Label Setting: a Case Study
                  in Emotion},
  booktitle    = {{IEEE} International Conference on Acoustics, Speech and Signal Processing
                  {ICASSP} 2023, Rhodes Island, Greece, June 4-10, 2023},
  pages        = {1--5},
  publisher    = {{IEEE}},
  year         = {2023},
  url          = {https://doi.org/10.1109/ICASSP49357.2023.10096864},
  doi          = {10.1109/ICASSP49357.2023.10096864},
  timestamp    = {Sun, 05 Nov 2023 16:51:21 +0100},
  biburl       = {https://dblp.org/rec/conf/icassp/ChochlakisMBBLN23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{luceri2023leveraging,
  title={Leveraging Large Language Models to Detect Influence Campaigns in Social Media},
  author={Luceri, Luca and Boniardi, Eric and Ferrara, Emilio},
  Eprint = {arXiv:2311.07816},
  Howpublished = {arXiv Preprint: https://arxiv.org/abs/2311.07816},
  year={2023}
}

@inproceedings{fornaciari2023leveraging,
  title={Leveraging Social Interactions to Detect Misinformation on Social Media},
  author={Fornaciari, Tommaso and Luceri, Luca and Ferrara, Emilio and Hovy, Dirk},
  booktitle={AAAI ICWSM Workshop},
  year={2023}
}

@inproceedings{xu-etal-2023-look,
    title = "Look-back Decoding for Open-Ended Text Generation",
    author = "Xu, Nan  and Zhou, Chunting and Celikyilmaz, Asli and Ma, Xuezhe",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    pages = "1039--1050",
}

@inproceedings{sun:kdd23ws,
    Author = "Sun, Kexuan and Pujara, Jay",
    bib_url = "/pubs/bib/sun-kdd23ws.bib",
    booktitle = "KDD Workshop on Robust NLP for Finance",
    pdf_url = "/pubs/2023/sun-kdd23ws/sun-kdd23ws.pdf",
    sec = "ws",
    title = "Low-Resource Financial QA with Case-based Reasoning",
    year = "2023"
}

@inproceedings{lee:emnlp23b,
    author = "Lee, Dong-Ho and Pujara, Jay and Sewak, Mohit and White, Ryen and Jauhar, Sujay",
    acceptrate = "23.3\%",
    arxiv_url = "https://arxiv.org/pdf/2310.20111",
    bib_url = "/pubs/bib/lee-emnlp23b.bib",
    booktitle = "Conference on Empirical Methods in Natural Language Processing",
    code_url = "https://github.com/microsoft/llm-data-creation",
    doi_url = "https://doi.org/10.18653/v1/2023.emnlp-main.948",
    pdf_url = "/pubs/2023/lee-emnlp23b/lee-emnlp23b.pdf",
    sec = "conf",
    title = "Making Large Language Models Better Data Creators",
    year = "2023"
}

@inbook{doi:https://doi.org/10.1002/9781394203314.ch21,
    author = {Orosz, Michael},
    publisher = {John Wiley & Sons, Ltd},
    isbn = {9781394203314},
    title = {Managing Risk},
    booktitle = {Systems Engineering for the Digital Age},
    chapter = {21},
    pages = {463-470},
    doi = {https://doi.org/10.1002/9781394203314.ch21},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781394203314.ch21},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781394203314.ch21},
    year = {2023},
    keywords = {risk, technical risk, system risk, obsolescence, risk management, agile risk management},
    abstract = {Summary Risk is generally defined as the measure of the potential inability to achieve overall program objectives within defined cost, schedule, and technical constraints. What is missing from this definition are risks associated with failing to identify and capture evolving user, system, and technical requirements, which are often driven by evolving end-user or marketplace demands. Ignoring these evolving needs introduces risks, meaning you may be developing an obsolete product or one that only partially meets end-user needs. There are many different flavors of risk management, but each of these includes considerations for defining and assessing risk (including the likelihood it will occur and the potential impact if it does), determining how to decide which risks to mitigate and how to do so, and risk monitoring. This chapter focuses on managing three types of risks to systems engineering and development: project development risk, technical risk, and obsolescence risk.}
}

Summary Risk is generally defined as the measure of the potential inability to achieve overall program objectives within defined cost, schedule, and technical constraints. What is missing from this definition are risks associated with failing to identify and capture evolving user, system, and technical requirements, which are often driven by evolving end-user or marketplace demands. Ignoring these evolving needs introduces risks, meaning you may be developing an obsolete product or one that only partially meets end-user needs. There are many different flavors of risk management, but each of these includes considerations for defining and assessing risk (including the likelihood it will occur and the potential impact if it does), determining how to decide which risks to mitigate and how to do so, and risk monitoring. This chapter focuses on managing three types of risks to systems engineering and development: project development risk, technical risk, and obsolescence risk.

@article{DBLP:journals/corr/abs-2305-10561,
  author       = {Chris Jenkins and
                  Shantanu Agarwal and
                  Joel Barry and
                  Steven Fincke and
                  Elizabeth Boschee},
  title        = {Massively Multi-Lingual Event Understanding: Extraction, Visualization,
                  and Search},
  journal      = {CoRR},
  volume       = {abs/2305.10561},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2305.10561},
  doi          = {10.48550/ARXIV.2305.10561},
  eprinttype    = {arXiv},
  eprint       = {2305.10561},
  timestamp    = {Thu, 25 May 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2305-10561.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2307-10245,
  author       = {Siyi Guo and
                  Zihao He and
                  Ashwin Rao and
                  Eugene Jang and
                  Yuanfeixue Nan and
                  Fred Morstatter and
                  P. Jeffrey Brantingham and
                  Kristina Lerman},
  title        = {Measuring Online Emotional Reactions to Offline Events},
  journal      = {CoRR},
  volume       = {abs/2307.10245},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2307.10245},
  doi          = {10.48550/ARXIV.2307.10245},
  eprinttype    = {arXiv},
  eprint       = {2307.10245},
  timestamp    = {Wed, 26 Jul 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2307-10245.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{ma2023mega,
  title={Mega: moving average equipped gated attention},
  author={Ma, Xuezhe and Zhou, Chunting and Kong, Xiang and He, Junxian and Gui, Liangke and Neubig, Graham and May, Jonathan and Zettlemoyer, Luke},
  booktitle={Proceedings of the 11th International Conference on Learning Representations (ICLR-2023)},
  year={2023}
}

@inproceedings{stripelis2023:metisfl,
	address = {New York, NY, USA},
	series = {{DistributedML} '23},
	title = {{MetisFL}: {An} {Embarrassingly} {Parallelized} {Controller} for {Scalable} \& {Efficient} {Federated} {Learning} {Workflows}},
	isbn = {9798400704475},
	shorttitle = {{MetisFL}},
	url = {https://doi.org/10.1145/3630048.3630186},
	doi = {10.1145/3630048.3630186},
	urldate = {2024-01-27},
	booktitle = {Proceedings of the 4th {International} {Workshop} on {Distributed} {Machine} {Learning}},
	publisher = {Association for Computing Machinery},
	author = {Stripelis, Dimitris and Anastasiou, Chrysovalantis and Toral, Patrick and Asghar, Armaghan and Ambite, Jos\'{e} Luis},
	month = dec,
	year = {2023},
	keywords = {data management, distributed machine learning, federated learning, machine learning systems},
	pages = {11--19},
}

@inproceedings{spie2023minimizefracture,
  title={Minimizing die fracture in three-dimensional IC advanced packaging wafer thinning process by inserting polyimide patterns},
  author={?Jaime Bravo, Philippe Morey-Chaisemartin, Lifu Chang, Eric Beisser, Frederic Brault, Joshua Zusman},
  booktitle={Proceedings of SPIE},
  number={1249511},
  doi={https://doi.org/10.1117/12.2657011},
  year={2023}
}

@inproceedings{10.1117/12.2657011,
author = {Jaime Bravo and Philippe Morey-Chaisemartin and Lifu Chang and Eric Beisser and Frederic Brault and Joshua Zusman},
title = {{Minimizing die fracture in three-dimensional IC advanced packaging wafer thinning process by inserting polyimide patterns}},
volume = {12495},
booktitle = {DTCO and Computational Patterning II},
editor = {Ryoung-Han Kim and Neal V. Lafferty},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {1249511},
keywords = {Advanced Packaging, 3D ICs, 3D Integration, Frame Generation, Thin Wafer, Fracture, Stress, Patterning},
year = {2023},
doi = {10.1117/12.2657011},
URL = {https://doi.org/10.1117/12.2657011}
}

@article{shaikh2023modeling,
  title={Modeling Cross-Cultural Pragmatic Inference with Codenames Duet},
  author={Shaikh, Omar and Ziems, Caleb and Held, William and Pariani, Aryan J and Morstatter, Fred and Yang, Diyi},
  journal={Association of Computational Linguistics},
  year={2023}
}

@inproceedings{tregubov2023mabs,
	title={Modeling cognitive workload in open-source communities via simulation},
	author={Tregubov, Alexey and Abramson, Jeremy and Hauser, Christophe and Hussain, Alefiya and Blythe, Jim},
	booktitle={AAMAS International Workshop on Multi-Agent-Based Simulation},
	url = {https://mabsworkshop.github.io/mabs2023/mabs2023/articles/tregubovEtAl2023.pdf},
	year={2023}
}

@incollection{bonjour2023multi,
  title={Multi-agent Game Domain: Monopoly},
  author={Bonjour, T and Haliem, M and Aggarwal, V and Kejriwal, M and Bhargava, B},
  booktitle={A Unifying Framework for Formal Theories of Novelty: Discussions, Guidelines, and Examples for Artificial Intelligence},
  pages={97--105},
  year={2023},
  publisher={Springer}
}

@conference{newman2023a,
  author    = {Harvey Newman and Justas Balcas and Raimondas Sirvinskas and Catalin Iordache and Preeti Bhat and Andres Moya and Sravya Uppalapati and Jin Chang and Azher Mughal and Dawn Boyd and Don S. Williams and Frank Wuerthwein and Tom deFanti and Larry Smarr and John Graham and Tom Hutton and Dima Mishin and Jonathan Guiang and Diego Davila and Igor Sfiligoi and Aashay Arora and Richard Yang and Ryan Yang and Jensen Zhang and Edmund Yeh and Yuanhao Wu and Volkan Mutlu and Yuezhou Liu and Susmit Shannigrahi and Sankalpa Timilsina and Lixia Zhang and Jason Cong and Michael Lo and Sichen Song and Oliver Gutsche and Phil Demar and Inder Monga and Chin Guok Tom Lehman and John MacAuley and Xi Yang and Justas Balcas and Mariam Kiran and Alex Sim and Garhan Attenbury and Andrew Melo and Edoardo Martelli and Carmen Misa and Jordi Ros-Giralt and Magnos Martinello and Moises R.N. Ribeiro and Christina Dominicini and Everson Borges and Rafael Guimaraes and Marcos Schwarz and Leandro Ciuffo and Frédéric LOUI and Sergio Novaes and Rogerio Iope and Antonio J F Francisco and Ney Lemke and Carlos Antonio Ruggiero and Jorge Marcos de Almeida and Bruno Baldim and Paulo Simionato and Alberto Santoro and Eduardo Revoredo and Sandro Rigo and Bijan Jabbari and Jerry Sobieski and Liang Zhang and Qiao Xiang and Chenyang Huang and Ridi Wen and Yuxin Wang and Jiwu shu and Louis Fox and Christopher Bruton and Sana Bellamine and Tony Nguyen and Celeste Anderson and Joe Mambretti and Jim Chen and Fei Yeh and Chris Wilkinson and Matt Zekauskas and Christian Todorov and Julio Ibarra and Jeronimo Bezerra and Vasilka Chergarova and Anthony Ulloa and Heidi Morgan and Scott Kohlert and Marc Lyonnais and Rod Wilson and Buseung Cho and Mazahir Hussain and Changjin Park and Thomas Tam and Alex Moura and Kevin Sale and Jason Roos and Bruno Hoeft and Carlyn-Ann Lee and Davide Pasavento and Chris Zane and Hans Trompert and Michal Hažlinský and Cole McKnight and Li-Chi Ku and Adrian Perrig and Jonghoon Kwon and François Wirz and David Hausheer and Marten Gartner and Kasandra Pillay and Brian Johnston and Johann Hugo and Sibelo Diamini and Duncan Greaves and Paul Sullivan and Ajay Makan and John Hay and Renier Pelser van Heerden and Thokozani Khwela and David Wilde and GNA-G AutoGOLE / SENSE WG Members},
  title     = {NRE-013: The Global Network Advancement Group: A Next Generation System for Data Intensive Sciences},
  journal = {Supercomputing Conference (SC23)},
    year = {2023},
  	month = {11/2023},
	pages = {Demonstration},
	publisher = {Supercomputing Conference 2023 (SC23)},
	type = {Conference Proceedings},
  address   = {Denver, CO},
  url       = {https://sc23.supercomputing.org/wp-content/uploads/2023/11/SC23-NRE-013-HarveyNewman-GNAGNextGenerationSystemforDataIntensiveSciences-1.pdf},
	editor = {Experience}
}

@conference{newman2023,
  author    = {Harvey Newman and Julio Ibarra and Kasandra Pillay and Thokozani Khwela and Renier Pelser van Heerden and Duncan Greaves and Ajay Makan and Paul Sullivan and Bryan Johnston and Marcos Felipe Schwarz and Sabelo Dlamini and Johann Hugo and John Hay and Jeronimo Bezerra and Anthony Ulloa and Heidi Morgan and Vasilka Chergarova},
  title     = {NRE-013: Turbocharging 100G Link: A High-Speed Data Filling Demo from South Africa to Denver!},
  journal = {Supercomputing Conference (SC23)},
    year = {2023},
  	month = {11/2023},
	pages = {Demonstration},
	publisher = {Supercomputing Conference 2023 (SC23)},
	type = {Conference Proceedings},
  address   = {Denver, CO},
  url       = {https://sc23.supercomputing.org/wp-content/uploads/2023/11/SC23-NRE-013-HarveyNewman-GNAGNextGenerationSystemforDataIntensiveSciences-1.pdf},
	editor = {Experience}
}

@conference{bezerra2023,
  author    = {Jeronimo Bezerra and Italo Valcy Da Silva Brito and Vinicius Arcanjo and Julio Ibarra and Renata Frez and Rogerio Motitsuki and Heidi Morgan and Vasilka Chergarova and Bruno Baldim and Mauro Cesar Bernardes and Joao Eduardo Ferreira and Harvey Newman},
  title     = {NRE-019: AmLight 2.0: Flexible Control, Deep Visibility, and Programmability @ Tbps!},
  journal = {Supercomputing Conference (SC23)},
    year = {2023},
  	month = {11/2023},
	pages = {Demonstration},
	publisher = {Supercomputing Conference 2023 (SC23)},
	type = {Conference Proceedings},
  address   = {Denver, CO},
  url       = {https://sc23.supercomputing.org/wp-content/uploads/2023/11/SC23-NRE-019-JeronimoBezarra-AmLight_2.0_Flexible_control_deep_visibility_and_programmability_at_Tbps.pdf}
}

@InProceedings{	  esquivel-morel-icnc-2023,
  Author	= {Esquivel Morel, Alicia and Calyam, Prasad and Qu, Chengyi
		  and Gafurov, Durbek and Wang, Cong and Thareja, Komal and
		  Mandal, Anirban and Lyons, Eric and Zink, Michael and
		  Papadimitriou, George and Deelman, Ewa},
  BookTitle	= {2023 International Conference on Computing, Networking and
		  Communications (ICNC)},
  Title		= {Network Services Management using Programmable Data Planes
		  for Visual Cloud Computing},
  Year		= {2023},
  Volume	= {},
  ISSN		= {},
  Pages		= {130-136},
  DOI		= {10.1109/ICNC57223.2023.10074183},
  Note		= {Funding Acknowledgments: NSF 1950873, 2018074}
}

@inproceedings{cornelio_neural_2023,
	title = {Neural {Network}-{Assisted} {Clustering} for {Improved} {Production} {Predictions} in {Unconventional} {Reservoirs}},
	url = {https://onepetro.org/SPEWRM/proceedings-abstract/23WRM/2-23WRM/519672},
	urldate = {2024-02-12},
	booktitle = {{SPE} {Western} {Regional} {Meeting}},
	publisher = {SPE},
	author = {Cornelio, Jodel and Mohd Razak, Syamil and Cho, Young and Liu, Hui-Hai and Vaidya, Ravimadhav and Jafarpour, Behnam},
	year = {2023},
	pages = {D021S004R001},
}

@article{sahoo2023:neurobridge,
	title = {{NeuroBridge} ontology: computable provenance metadata to give the long tail of neuroimaging data a {FAIR} chance for secondary use},
	volume = {17},
	issn = {1662-5196},
	shorttitle = {{NeuroBridge} ontology},
	url = {https://www.frontiersin.org/articles/10.3389/fninf.2023.1216443},
	urldate = {2024-01-27},
	doi = {10.3389/fninf.2023.1216443},
	pmid = {37554248},
	pmcid = {PMC10406126},
	pages = {1216443},
	journal = {Frontiers in Neuroinformatics},
	author = {Sahoo, Satya S. and Turner, Matthew D. and Wang, Lei and Ambite, Jose Luis and Appaji, Abhishek and Rajasekar, Arcot and Lander, Howard M. and Wang, Yue and Turner, Jessica A.},
	year = {2023},
}

@article{wang2023:neurobridge,
	title = {{NeuroBridge}: a prototype platform for discovery of the long-tail neuroimaging data},
	volume = {17},
	copyright = {cc by},
	issn = {1662-5196},
	shorttitle = {{NeuroBridge}},
	url = {https://www.frontiersin.org/articles/10.3389/fninf.2023.1215261},
	doi = {10.3389/fninf.2023.1215261},
	language = {eng},
	journal = {Frontiers in Neuroinformatics},
	author = {Wang, Lei and Ambite, José Luis and Appaji, Abhishek and Bijsterbosch, Janine and Dockes, Jerome and Herrick, Rick and Kogan, Alex and Lander, Howard and Marcus, Daniel and Moore, Stephen M and Poline, Jean-Baptiste and Rajasekar, Arcot and Sahoo, Satya S and Turner, Matthew D and Wang, Xiaochen and Wang, Yue and Turner, Jessica A},
	month = jan,
	year = {2023},
	pmid = {37720825},
	pmcid = {PMC10500076},
	keywords = {Addiction, Experimental design, Metadata, MRI, Ontology, Schizophrenia, Text-mining},
	pages = {1215261},
}

@ARTICLE{10.3389/fninf.2023.1144301,
AUTHOR={Kaiser, Md Abdullah-Al and Datta, Gourav and Wang, Zixu and Jacob, Ajey P. and Beerel, Peter A. and Jaiswal, Akhilesh R.},   
TITLE={Neuromorphic-P2M: processing-in-pixel-in-memory paradigm for neuromorphic image sensors},      
JOURNAL={Frontiers in Neuroinformatics},      
VOLUME={17},           
YEAR={2023},      
URL={https://www.frontiersin.org/articles/10.3389/fninf.2023.1144301},       
DOI={10.3389/fninf.2023.1144301},      
ISSN={1662-5196},   
ABSTRACT={Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor, in-sensor, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers; however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, for the first time, we propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating in-situ multi-bit multi-channel convolution inside the pixel array performing analog multiply and accumulate (MAC) operations that consume significantly less energy than their digital MAC alternative. To make this approach viable, we incorporate the circuit's non-ideality, leakage, and process variations into a novel hardware-algorithm co-design framework that leverages extensive HSpice simulations of our proposed circuit using the GF22nm FD-SOI technology node. We verified our framework on state-of-the-art neuromorphic vision sensor datasets and show that our solution consumes ~2× lower backend-processor energy while maintaining almost similar front-end (sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art while maintaining a high test accuracy of 88.36%.}
}

Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor, in-sensor, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers; however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, for the first time, we propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating in-situ multi-bit multi-channel convolution inside the pixel array performing analog multiply and accumulate (MAC) operations that consume significantly less energy than their digital MAC alternative. To make this approach viable, we incorporate the circuit's non-ideality, leakage, and process variations into a novel hardware-algorithm co-design framework that leverages extensive HSpice simulations of our proposed circuit using the GF22nm FD-SOI technology node. We verified our framework on state-of-the-art neuromorphic vision sensor datasets and show that our solution consumes  2× lower backend-processor energy while maintaining almost similar front-end (sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art while maintaining a high test accuracy of 88.36%.

@article{DBLP:journals/corr/abs-2303-04837,
  author       = {Rebecca Dorn and
                  Julie Jiang and
                  Jeremy Abramson and
                  Kristina Lerman},
  title        = {Non-Binary Gender Expression in Online Interactions},
  journal      = {CoRR},
  volume       = {abs/2303.04837},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2303.04837},
  doi          = {10.48550/ARXIV.2303.04837},
  eprinttype    = {arXiv},
  eprint       = {2303.04837},
  timestamp    = {Tue, 07 May 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2303-04837.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{10.1007/978-3-031-43129-6_3,
	title        = {Non-Binary Gender Expression in Online Interactions},
	author       = {Dorn, Rebecca and Mokhberian Negar and Jiang, Julie and Abramson, Jeremy and Morstatter, Fred and Lerman, Kristina},
	year         = 2023,
	month        = {September},
	booktitle    = {"16th International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation"},
	doi          = {}
}

@Article{Majidy2023,
author={Majidy, Shayan
and Braasch, William F.
and Lasek, Aleksander
and Upadhyaya, Twesh
and Kalev, Amir
and Yunger Halpern, Nicole},
title={Noncommuting conserved charges in quantum thermodynamics and beyond},
journal={Nature Reviews Physics},
year={2023},
month={Oct},
day={02},
issn={2522-5820},
doi={10.1038/s42254-023-00641-9},
url={https://doi.org/10.1038/s42254-023-00641-9}
}

@InProceedings{	  sahni-esciences-2023,
  Author	= {Sahni, Harshita and Carrillo-Cabada, Hector and Kots,
		  Ekaterina and Caino-Lores, Silvina and Marquez, Jack and
		  Deelman, Ewa and Cuendet, Michel and Weinstein, Harel and
		  Taufer, Michela and Estrada, Trilce},
  BookTitle	= {2023 IEEE 19th International Conference on e-Science
		  (e-Science)},
  Title		= {Online Boosted Gaussian Learners for In-Situ Detection and
		  Characterization of Protein Folding States in Molecular
		  Dynamics Simulations},
  Year		= {2023},
  Volume	= {},
  Number	= {},
  Pages		= {1-10},
  DOI		= {10.1109/e-Science58273.2023.10254895},
  Note		= {Funding Acknowledgments: NSF 1757207, 1741057, 1841758,
		  2138811 and 2223704}
}

@inproceedings{ye2023online,
  title={Online Networks of Support in Distressed Environments: Solidarity and Mobilization during the Russian Invasion of Ukraine},
  author={Ye, Jinyi and Jindal, Nikhil and Pierri, Francesco and Luceri, Luca},
   booktitle={AAAI ICWSM Workshop},
  year={2023}
}

@misc{bouleimen2023online,
  title={Online search is more likely to lead students to validate true news than to refute false ones},
  author={Bouleimen, Azza and Luceri, Luca and Cardoso, Felipe and Botturi, Luca and Hermida, Martin and Addimando, Loredana and Beretta, Chiara and Galloni, Marzia and Giordano, Silvia},
 Eprint = {arXiv:2303.13138},
Howpublished = {arXiv Preprint: https://arxiv.org/abs/2303.13138},
  year={2023}
}

@inproceedings{stan2022domain,
  title={Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distribution},
  author={Rostami, Mohammad and Galstyan, Aram},
  booktitle={AAAI},
  year={2023}
}

@article{tksk06,
author={Weizhao Jin and Erik Kline and Satish Thittamaranahalli and Lincoln Thurlow and Srivatsan Ravi},
title={P3V: Privacy-Preserving Path Validation System for Multi-Authority Sliced Networks},
journal={Cryptology ePrint Archive, Paper 2023/053},
year={2023}
}

@article{jin2023p3v,
  title={P3V: Privacy-Preserving Path Validation System for Multi-Authority Sliced Networks},
  author={Jin, Weizhao and Kline, Erik and Kumar, TK Satish and Thurlow, Lincoln and Ravi, Srivatsan},
  journal={Cryptology ePrint Archive},
  year={2023}
}

@inproceedings{wang2023pinto,
  title={PINTO: Faithful Language Reasoning Using Prompted-Generated Rationales},
  author={Wang, Peifeng and Chan, Aaron and Ilievski, Filip and Chen, Muhao and Ren, Xiang},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

@article{DBLP:journals/corr/abs-2305-18533,
  author       = {Ashwin Rao and
                  Siyi Guo and
                  Sze{-}Yuh Nina Wang and
                  Fred Morstatter and
                  Kristina Lerman},
  title        = {Pandemic Culture Wars: Partisan Asymmetries in the Moral Language
                  of {COVID-19} Discussions},
  journal      = {CoRR},
  volume       = {abs/2305.18533},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2305.18533},
  doi          = {10.48550/ARXIV.2305.18533},
  eprinttype    = {arXiv},
  eprint       = {2305.18533},
  timestamp    = {Wed, 07 Jun 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2305-18533.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/bigdataconf/RaoGWML23,
  author       = {Ashwin Rao and
                  Siyi Guo and
                  Sze{-}Yuh Nina Wang and
                  Fred Morstatter and
                  Kristina Lerman},
  editor       = {Jingrui He and
                  Themis Palpanas and
                  Xiaohua Hu and
                  Alfredo Cuzzocrea and
                  Dejing Dou and
                  Dominik Slezak and
                  Wei Wang and
                  Aleksandra Gruca and
                  Jerry Chun{-}Wei Lin and
                  Rakesh Agrawal},
  title        = {Pandemic Culture Wars: Partisan Differences in the Moral Language
                  of {COVID-19} Discussions},
  booktitle    = {{IEEE} International Conference on Big Data, BigData 2023, Sorrento,
                  Italy, December 15-18, 2023},
  pages        = {413--422},
  publisher    = {{IEEE}},
  year         = {2023},
  url          = {https://doi.org/10.1109/BigData59044.2023.10386084},
  doi          = {10.1109/BIGDATA59044.2023.10386084},
  timestamp    = {Tue, 20 Aug 2024 07:54:43 +0200},
  biburl       = {https://dblp.org/rec/conf/bigdataconf/RaoGWML23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{yang2023parameter,
  title={Parameter-Efficient Tuning with Special Token Adaptation},
  author={Yang, Xiaoocong and Huang, James Y and Zhou, Wenxuan and Chen, Muhao},
  booktitle={The 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL)},
  year={2023}
}

@incollection{JACOB2023369,
title = {Photonic-integrated circuit fabrication and test approaches},
editor = {Madeleine Glick and Ling Liao and Katharine Schmidtke},
booktitle = {Integrated Photonics for Data Communication Applications},
publisher = {Elsevier},
pages = {369-410},
year = {2023},
series = {Integrated Photonics Apps Specific Design &Manufacturing},
isbn = {978-0-323-91224-2},
doi = {https://doi.org/10.1016/B978-0-323-91224-2.00012-6},
url = {https://www.sciencedirect.com/science/article/pii/B9780323912242000126},
author = {Ajey P. Jacob and Sujith Chandran and Joris {Van Campenhout} and Marianna Pantouvaki and John Heck and Ken Giewont and Michal Rakowski and Yusheng Bian and Peter Debackere and Matthias Kuntz},
keywords = {Photonic-integrated circuit, high-volume manufacturing, monolithic photonic-integrated circuit, heterogeneous photonic-integrated circuit, hybrid photonic-integrated circuit, fully photonic-integrated circuit, InP III–V photonic-integrated circuit, photonics in-line and end-of-line testing},
abstract = {Photonic-integrated circuits (PICs) integrate photonics elements, such as light sources, passive and active components, along with electronics elements, such as drivers, amplifiers, and application-specific integrated circuits, on a homogeneous or a heterogeneous material platform for various applications, such as data centers, communications, LIDAR, sensors, and spectroscopy. These components are fabricated separately or assembled monolithically on the silicon line to form the PIC. This chapter discusses the various chip fabrication and testing schemes leading to high-volume manufacturing of the PIC, such as (1) hybrid, (2) heterogeneous, (3) monolithic, (4) fully monolithic on silicon, and (5) indium phosphide III–V material platforms.}
}

Photonic-integrated circuits (PICs) integrate photonics elements, such as light sources, passive and active components, along with electronics elements, such as drivers, amplifiers, and application-specific integrated circuits, on a homogeneous or a heterogeneous material platform for various applications, such as data centers, communications, LIDAR, sensors, and spectroscopy. These components are fabricated separately or assembled monolithically on the silicon line to form the PIC. This chapter discusses the various chip fabrication and testing schemes leading to high-volume manufacturing of the PIC, such as (1) hybrid, (2) heterogeneous, (3) monolithic, (4) fully monolithic on silicon, and (5) indium phosphide III–V material platforms.

@article{mohd_razak_physics-guided_2023,
	title = {Physics-{Guided} {Deep} {Learning} for {Improved} {Production} {Forecasting} in {Unconventional} {Reservoirs}},
	url = {https://onepetro.org/SJ/article/doi/10.2118/214663-PA/519934},
	urldate = {2024-02-12},
	journal = {SPE Journal},
	author = {Mohd Razak, Syamil and Cornelio, Jodel and Cho, Young and Liu, Hui-Hai and Vaidya, Ravimadhav and Jafarpour, Behnam},
	year = {2023},
	pages = {1--23},
}

@inproceedings{priniski2023pipeline,
  title={Pipeline for modeling causal beliefs from natural language},
  author={Priniski, John and Verma, Ishaan and Morstatter, Fred},
  booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
  pages={436--443},
  year={2023}
}

@article{Ruprechter2023_wiki,
	abstract = {Wikipedia is an important source of general knowledge covering a wide range of topics. Moreover, for many people around the world, it also serves as an essential news source for major events such as elections or disasters. Although Wikipedia covers many such events, some events are underrepresented and lack attention, despite their newsworthiness predicted from news value theory. In this paper, we analyze 17 490 event articles in four Wikipedia language editions and examine how the economic status and geographic region of the event location affects the attention and coverage it receives. We find that major Wikipedia language editions have a skewed focus, with more attention given to events in the world's more economically developed countries and less attention to events in less affluent regions. However, other factors, such as the number of deaths in a disaster, are also associated with the attention an event receives. Overall, this work provides a nuanced understanding of attention and coverage on Wikipedia through event articles and adds new empirical analysis to news value theory.},
	author = {Ruprechter, Thorsten AND Burghardt, Keith AND Helic, Denis},
	doi = {10.1371/journal.pone.0289325},
	journal = {PLOS ONE},
	month = {11},
	number = {11},
	pages = {1-19},
	publisher = {Public Library of Science},
	title = {Poor attention: The wealth and regional gaps in event attention and coverage on Wikipedia},
	url = {https://doi.org/10.1371/journal.pone.0289325},
	volume = {18},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1371/journal.pone.0289325}}

Wikipedia is an important source of general knowledge covering a wide range of topics. Moreover, for many people around the world, it also serves as an essential news source for major events such as elections or disasters. Although Wikipedia covers many such events, some events are underrepresented and lack attention, despite their newsworthiness predicted from news value theory. In this paper, we analyze 17 490 event articles in four Wikipedia language editions and examine how the economic status and geographic region of the event location affects the attention and coverage it receives. We find that major Wikipedia language editions have a skewed focus, with more attention given to events in the world's more economically developed countries and less attention to events in less affluent regions. However, other factors, such as the number of deaths in a disaster, are also associated with the attention an event receives. Overall, this work provides a nuanced understanding of attention and coverage on Wikipedia through event articles and adds new empirical analysis to news value theory.

@article{horn2023foodmobility,
  title={Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations},
  author={Horn, Abigail L and Bell, Brooke M and Bulle Bueno, Bernardo Garc{\'\i}a and Bahrami, Mohsen and Bozkaya, Bur{\c{c}}in and Cui, Yan and Wilson, John P and Pentland, Alex and Moro, Esteban and de la Haye, Kayla},
  journal={NPJ Digital Medicine},
  volume={6},
  number={1},
  pages={208},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{ramanathan2023practical,
  title={Practical Intent-driven Routing Configuration Synthesis},
  author={Ramanathan, Sivaramakrishnan and Zhang, Ying and Gawish, Mohab and Mundada, Yogesh and Wang, Zhaodong and Yun, Sangki and Lippert, Eric and Taha, Walid and Yu, Minlan and Mirkovic, Jelena},
  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
  pages={629--644},
  year={2023}
}

@inproceedings{tiwari2023preface,
  title={Preface for the Second International Workshop on Knowledge Graph Generation from Text},
  author={ and others},
  booktitle={CEUR WORKSHOP PROCEEDINGS},
  volume={3447},
  pages={1--3},
  year={2023},
  organization={CEUR-WS}
}

@article{tksk02,
author={Yi Zheng and Hang Ma and Sven Koenig and Erik Kline and Satish Thittamaranahalli},
title={Priority-Based Search for the Virtual Network Embedding Problem},
journal={Proceedings of the Thirty-Third International Conference on Automated Planning and Scheduling (ICAPS-2023)},
year={2023}
}

@article{Zheng_Ma_Koenig_Kline_Kumar_2023, 
  title={Priority-Based Search for the Virtual Network Embedding Problem}, 
  volume={33}, url={https://ojs.aaai.org/index.php/ICAPS/article/view/27227}, 
  DOI={10.1609/icaps.v33i1.27227}, 
  number={1}, 
  journal={Proceedings of the International Conference on Automated Planning and Scheduling}, 
  author={Zheng, Yi and Ma, Hang and Koenig, Sven and Kline, Erik and Kumar, T. K. Satish}, 
  year={2023}, 
  month={Jul.}, 
  pages={472-480} 
}

@inproceeding{pierri2022propaganda,
  title={Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine},
  author={Pierri, Francesco and Luceri, Luca and Jindal, Nikhil and Ferrara, Emilio},
 booktitle={15th ACM Web Science Conference 2023},
  year={2023}
}

@unpublished{ahrabian:arxiv23,
    author = "Ahrabian, Kian and Du, Xinwei and Myloth, Richard Delwin and Ananthan, Arun Baalaaji Sankar and Pujara, Jay",
    sec = "preprint",
    title = "PubGraph: A Large Scale Scientific Temporal Knowledge Graph",
    url = "https://arxiv.org/pdf/2302.02231",
    year = "2023"
}

@INPROCEEDINGS{QuantPipe,
  author={Wang, Haonan and Imes, Connor and Kundu, Souvik and Beerel, Peter A. and Crago, Stephen P. and Paul Walters, John},
  booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  title={{QuantPipe}: Applying Adaptive Post-Training Quantization For Distributed Transformer Pipelines In Dynamic Edge Environments},
  year={2023},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/ICASSP49357.2023.10096632},
  ISIArea = {ML, CAS}
}

@article{shen2023quantifying,
  title={Quantifying COVID-19 policy impacts on subjective well-being during the early phase of the pandemic: A cross-sectional analysis of United States survey data from March to August 2020},
  author={Shen, Ke and Kejriwal, Mayank},
  journal={Plos one},
  volume={18},
  number={9},
  pages={e0291494},
  year={2023},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{kejriwal2023quantifying,
  title={Quantifying Gender Disparity in Pre-Modern English Literature using Natural Language Processing},
  author={Kejriwal, Mayank and Nagaraj, Akarsh},
  journal={Journal of Data Science},
  pages={1--20},
  year={2023},
  publisher={School of Statistics, Renmin University of China}
}

@article{shen2023quantifying,
  title={Quantifying confidence shifts in a BERT-based question answering system evaluated on perturbed instances},
  author={Shen, Ke and Kejriwal, Mayank},
  journal={Plos one},
  volume={18},
  number={12},
  pages={e0295925},
  year={2023},
  publisher={Public Library of Science San Francisco, CA USA}
}

@inproceedings{liu-etal-2023-recap,
    title = "{RECAP}: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation",
    author = "Liu, Shuai  and
      Cho, Hyundong  and
      Freedman, Marjorie  and
      Ma, Xuezhe  and
      May, Jonathan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.468",
    doi = "10.18653/v1/2023.acl-long.468",
    pages = "8404--8419",
    abstract = "Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge. In this work, we propose a new retrieval-enhanced approach for personalized response generation. Specifically, we design a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. Extensive experiments on a real-world dataset demonstrate the effectiveness of our model at generating more fluent and personalized responses. We quantitatively evaluate our model{'}s performance under a suite of human and automatic metrics and find it to be superior compared to state-of-the-art baselines on English Reddit conversations.",
}

Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge. In this work, we propose a new retrieval-enhanced approach for personalized response generation. Specifically, we design a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. Extensive experiments on a real-world dataset demonstrate the effectiveness of our model at generating more fluent and personalized responses. We quantitatively evaluate our model's performance under a suite of human and automatic metrics and find it to be superior compared to state-of-the-art baselines on English Reddit conversations.

@inproceedings{schmidt2023_3,
	author = {D. Soni and N. Neda and N. Zhang and Benedict Reynwar and B. Heyman and M. Moopan and A. Badawi and Y. Polyakov and Kellie Canidaand Anrew G. Schmidt and M. Pedram and D. Cousins and Matthew French and F. Franchetti and R. Karri and M. Maniatakos and B. Reagen},
	title = {RPU: A Ring Processing Unit with Applications to FHE},
	booktitle = {IEEE International Symposium on Performance Analysis of Systems and Software (ISPAS2023)},
	year = {2023}}

@conference {Soni2023,
    title = {RPU: The Ring Processor Unit},
    organization = {IEEE International Symposium on Performance Analysis of Systems and Software},
    year = {2023},
    month = {April},
    author = {Deepraj Soni, Negar Neda, Naifeng Zhang, Benedict Reynwar, Homer Gamil, Benjamin Heyman, Mohammed Nabeel, Ahmad Al Badawi, Yuriy Polyakov, Kellie Canida, Massoud Pedram, Michail Maniatakos, David Bruce Cousins, Franz Franchetti, Matthew French, Andrew Schmidt, Brandon Reagen},
    ISIArea = {CAS, MES}
    }

@article{DBLP:journals/corr/abs-2305-11316,
  author       = {Kristina Lerman and
                  Aryan Karnati and
                  Shuchan Zhou and
                  Siyi Chen and
                  Sudesh Kumar and
                  Zihao He and
                  Joanna Yau and
                  Abigail Horn},
  title        = {Radicalized by Thinness: Using a Model of Radicalization to Understand
                  Pro-Anorexia Communities on Twitter},
  journal      = {CoRR},
  volume       = {abs/2305.11316},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2305.11316},
  doi          = {10.48550/ARXIV.2305.11316},
  eprinttype    = {arXiv},
  eprint       = {2305.11316},
  timestamp    = {Thu, 25 May 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2305-11316.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{ciosici2023remember,
  title={Remember what you did so you know what to do next},
  author={Ciosici, Manuel R and Hedges, Alex and Kankanampati, Yash and Martin, Justin and Freedman, Marjorie and Weischedel, Ralph},
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
  pages={1550--1562},
  year={2023}
}

@inproceedings{brinkley2023reproducibility,
  title={Reproducibility as a Stepping Stone to Intelligent Assistants for Data Analysis: Recreating a Study of Physical Activity, Sleep, and Work Shift in Nurses},
  author={Brinkley, Detravious Jamari and Johnson, Emmanuel and Feng, Tiantian and Gil, Yolanda},
  booktitle={Companion Proceedings of the 28th International Conference on Intelligent User Interfaces},
  pages={246--250},
  year={2023}
}

@Article{	  afle-arxiv-2023,
  Author	= "Afle, Chaitanya and Miles, Patrick R. and Caino-Lores,
		  Silvina and Capano, Collin D. and Tews, Ingo and Vahi,
		  Karan and Deelman, Ewa and Taufer, Michela and Brown,
		  Duncan A.",
  Title		= "{Reproducing the results for NICER observation of PSR
		  J0030+0451}",
  EPrint	= "2304.01035",
  ArchivePrefix	= "arXiv",
  PrimaryClass	= "astro-ph.HE",
  reportnumber	= "LA-UR-22-29359",
  Month		= "4",
  Year		= "2023",
  Note		= {Funding Acknowledgments: NSF 2207264, 2041977, 2041901,
		  2028923, 2028930, 1841399, and 1941443; DOE
		  DE-AC52-06NA25396 }
}

@inproceedings{lagatta2023retrieving,
  title={Retrieving false claims on Twitter during the Russia-Ukraine conflict},
  author={La Gatta, Valerio and Wei, Chiyu and Luceri, Luca and Pierri, Francesco and Ferrara, Emilio},
 booktitle={Companion Proceedings of The 2023 World Wide Web Conference},
  year={2023}
}

@inproceedings{rao2023retweets,
  title={Retweets Distort Exposure to Polarized Information},
  author={Rao, Ashwin and Morstatter, Fred and Lerman, Kristina},
  booktitle={Proceeddings of ASONAM},
  year={2023}
}

@ARTICLE{10225481,
  author={Saad, Muhammad and Anwar, Afsah and Ravi, Srivatsan and Mohaisen, David},
  journal={IEEE/ACM Transactions on Networking}, 
  title={Revisiting Nakamoto Consensus in Asynchronous Networks: A Comprehensive Analysis of Bitcoin Safety and Chain Quality}, 
  year={2023},
  volume={},
  number={},
  pages={1-15},
  keywords={Bitcoin;Blockchains;Safety;Peer-to-peer computing;Propagation delay;Delays;Security;Nakamoto consensus;Bitcoin partitioning},
  doi={10.1109/TNET.2023.3302955}
  }
  
  

@InProceedings{	  lores-pasc-2023,
  Author	= {Caino-Lores, Silvina and Cuendet, Michel and Marquez, Jack
		  and Kots, Ekaterina and Estrada, Trilce and Deelman, Ewa
		  and Weinstein, Harel and Taufer, Michela},
  Title		= {Runtime Steering of Molecular Dynamics Simulations Through
		  In Situ Analysis and Annotation of Collective Variables},
  Year		= {2023},
  ISBN		= {9798400701900},
  Publisher	= {Association for Computing Machinery},
  Address	= {New York, NY, USA},
  URL		= {https://doi.org/10.1145/3592979.3593420},
  DOI		= {10.1145/3592979.3593420},
  BookTitle	= {Proceedings of the Platform for Advanced Scientific
		  Computing Conference},
  articleno	= {21},
  numpages	= {11},
  Keywords	= {molecular dynamics, scientific workflows, collective
		  variables, simulation ensembles, in situ analysis},
  Location	= {Davos, Switzerland},
  Series	= {PASC '23},
  Note		= {Funding Acknowledgments: NSF 1741057, 1841758, 2138811 and
		  2223704}
}

@misc{li2023sabaf,
  title={SABAF: Removing Strong Attribute Bias from Neural Networks with Adversarial Filtering},
  author={Li, Jiazhi and Khayatkhoei, Mahyar and Zhu, Jiageng and Xie, Hanchen and Hussein, Mohamed E and AbdAlmageed, Wael},
  Howpublished={arXiv preprint arXiv:2311.07141},
  year={2023},
  month={November},
  url_Paper={https://arxiv.org/pdf/2311.07141.pdf},
  url_Link={https://arxiv.org/abs/2311.07141},
  ISIArea = {ML, VISTA}
}

@proceedings{Schuler2023c,
	address = {{New York, NY, USA}},
	date-added = {2024-01-22 12:05:54 -0800},
	date-modified = {2024-01-22 12:05:54 -0800},
	doi = {10.1145/3603719},
	editor = {Schuler, Robert and Kesselman, Carl and Chard, Kyle and Bugacov, Alejandro},
	isbn = {9798400707469},
	publisher = {{Association for Computing Machinery}},
	title = {{{SSDBM}} '23: {{Proceedings}} of the 35th {{International Conference}} on {{Scientific}} and {{Statistical Database Management}}},
	url = {https://doi.org/10.1145/3603719},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1145/3603719}}

@inbook{doi:https://doi.org/10.1002/9781394203314.ch10,
    author = {Orosz, Michael and Duffy, Brian and Charlton, Craig and Saunders, Hector and Shih, Michael},
    publisher = {John Wiley & Sons, Ltd},
    isbn = {9781394203314},
    title = {Scaling Agile Principles to an Enterprise},
    booktitle = {Systems Engineering for the Digital Age},
    chapter = {10},
    pages = {201-218},
    doi = {https://doi.org/10.1002/9781394203314.ch10},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781394203314.ch10},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781394203314.ch10},
    year = {2023},
    keywords = {agile, scaled agile, DevSecOps, large-system acquisition, software acquisition},
    abstract = {Summary Scaling agile and DevSecOps to large enterprise systems such as automotive manufacturing or space-based communication systems offers many challenges. These systems are typically composed of multiple interconnected systems and subsystems, each developed and maintained by multiple vendors operating on different timelines and priorities. The challenge to the acquisition professional is how to manage the agile development process to ensure that all components are developed as a system of systems and not as independent and isolated entities. Multiple vendors, differing timelines, delays in releases, changing requirements, the availability of reliable supply chains, and various internal and external dependencies will need to be considered. This chapter discusses the unique challenges and offers recommended strategies in scaling agile and DevSecOps to large enterprise systems with a particular focus on software-based systems. Where appropriate, reference to hardware-only or hybrid hardware- and software-based systems will also be noted.}
}

Summary Scaling agile and DevSecOps to large enterprise systems such as automotive manufacturing or space-based communication systems offers many challenges. These systems are typically composed of multiple interconnected systems and subsystems, each developed and maintained by multiple vendors operating on different timelines and priorities. The challenge to the acquisition professional is how to manage the agile development process to ensure that all components are developed as a system of systems and not as independent and isolated entities. Multiple vendors, differing timelines, delays in releases, changing requirements, the availability of reliable supply chains, and various internal and external dependencies will need to be considered. This chapter discusses the unique challenges and offers recommended strategies in scaling agile and DevSecOps to large enterprise systems with a particular focus on software-based systems. Where appropriate, reference to hardware-only or hybrid hardware- and software-based systems will also be noted.

@article{DBLP:journals/tissec/LembkeRRE23,
  author    = {James Lembke and
               Srivatsan Ravi and
               Pierre{-}Louis Roman and
               Patrick Eugster},
  title     = {Secure and Reliable Network Updates},
  journal   = {{ACM} Trans. Priv. Secur.},
  volume    = {26},
  number    = {1},
  pages     = {8:1--8:41},
  year      = {2023},
  url       = {https://doi.org/10.1145/3556542},
  doi       = {10.1145/3556542},
  timestamp = {Mon, 05 Dec 2022 13:35:16 +0100},
  biburl    = {https://dblp.org/rec/journals/tissec/LembkeRRE23.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{zhu2023shadow,
  title={Shadow Datasets, New Challenging Datasets for Causal Representation Learning},
  author={Zhu, Jiageng and Xie, Hanchen and Wu, Jianhua and Li, Jiazhi and Khayatkhoei, Mahyar and Hussein, Mohamed E and AbdAlmageed, Wael},
  Howpublished={arXiv preprint arXiv:2308.05707},
  year={2023},
  month={August},
  url_Paper={https://arxiv.org/pdf/2308.05707.pdf},
  url_Link={https://arxiv.org/abs/2308.05707},
  ISIArea = {ML, VISTA}
}

@misc{jiang2023social,
  title={Social Approval and Network Homophily as Motivators of Online Toxicity},
  author={Jiang, Julie and Luceri, Luca and Walther, Joseph B and Ferrara, Emilio},
  Eprint = {arXiv:2310.07779},
  Howpublished = {arXiv Preprint: https://arxiv.org/abs/2310.07779},
  year={2023}
}

@article{burghardt2023socio,
  title={Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts},
  author={Burghardt, Keith and Rao, Ashwin and Guo, Siyi and He, Zihao and Chochlakis, Georgios and Sabyasachee, Baruah and Rojecki, Andrew and Narayanan, Shri and Lerman, Kristina},
  journal={arXiv preprint arXiv:2305.11867},
  year={2023}
}

@misc{kejriwal2023special,
  title={Special Issue on Artificial Intelligence and Complex Systems},
  author={Kejriwal, Mayank},
  journal={Applied Sciences},
  volume={13},
  number={20},
  pages={11153},
  year={2023},
  publisher={MDPI}
}

@article{DBLP:journals/iacr/HauserNSTRKV23,
  author       = {Christophe Hauser and
                  Shirin Nilizadeh and
                  Yan Shoshitaishvili and
                  Ni Trieu and
                  Srivatsan Ravi and
                  Christopher Kruegel and
                  Giovanni Vigna},
  title        = {Street Rep: {A} Privacy-Preserving Reputation Aggregation System},
  journal      = {{IACR} Cryptol. ePrint Arch.},
  pages        = {1346},
  year         = {2023},
  url          = {https://eprint.iacr.org/2023/1346},
  timestamp    = {Sat, 07 Oct 2023 17:43:39 +0200},
  biburl       = {https://dblp.org/rec/journals/iacr/HauserNSTRKV23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{https://doi.org/10.1002/inst.12438,
    author = {Dove, Rick and Lunney, Kerry and Orosz, Michael and Yokell, Mike},
    title = {Systems Engineering Agility in a Nutshell},
    journal = {INSIGHT},
    volume = {26},
    number = {2},
    pages = {11-14},
    doi = {https://doi.org/10.1002/inst.12438},
    url = {https://incose.onlinelibrary.wiley.com/doi/abs/10.1002/inst.12438},
    eprint = {https://incose.onlinelibrary.wiley.com/doi/pdf/10.1002/inst.12438},
    abstract = {ABSTRACT Systems engineering must necessarily have the agility to anticipate and effectively respond to an increasingly dynamic and uncertain environment. Agile systems engineering, agile software engineering, and agile any-kind-of engineering share common goals and leverage common agility-enabling strategies. This article succinctly describes eight strategic aspects with application discussions at the systems engineering level.},
    year = {2023}
}

ABSTRACT Systems engineering must necessarily have the agility to anticipate and effectively respond to an increasingly dynamic and uncertain environment. Agile systems engineering, agile software engineering, and agile any-kind-of engineering share common goals and leverage common agility-enabling strategies. This article succinctly describes eight strategic aspects with application discussions at the systems engineering level.

@article{santos2023tg,
  title={TG-CSR: A human-labeled dataset grounded in nine formal commonsense categories},
  author={Santos, Henrique and Mulvehill, Alice M and Shen, Ke and Kejriwal, Mayank and McGuinness, Deborah L},
  journal={Data in Brief},
  volume={51},
  pages={109666},
  year={2023},
  publisher={Elsevier}
}

@inproceedings{schmidt2023_2,
	author = {D. Cousins and Y. Polyakov and A. Badawi and Andrew G. Schmidt and Justinand Benedict Reynwar and Kellie Canida and Matthew French and A.Jaiswal and C. Mathew and H. Gamil and N. Neda and D. Soni and M. Maniatakos and B.Reagen},
	booktitle = {Government Microcircuit Applications \& Critical Techology Conference (GOMACTech)},
	title = {TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation},
	year = {2023}}

@conference {French2023a,
    title = {TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation},
    organization = {Government Microcircuit Applications and Critical Technology Conference (GOMACTech)},
    year = {2023},
    month = {March},
    author = {M. French, A. Schmidt, A. Jacob, B. Reynwar, K. Canida, A. Jaiswal, C. Mathew, H. Gamil, N. Neda, D. Soni, M. Maniatakos, B. Reagen, N. Zhang, F. Franchetti, P. Brinich, J. Johnson, P. Broderick, M. Franusich, B. Zhang, Z. Cheng, M. Pedram, Y. Ployakov, A. Al Badawi, D. Cousins},
    ISIArea = {CAS, MES}
    }

@inproceedings{lee:emnlp23a,
    author = "Lee, Dong-Ho and Ahrabian, Kian and Jin, Woojeong and Morstatter, Fred and Pujara, Jay",
    acceptrate = "23.3\%",
    arxiv_url = "https://arxiv.org/pdf/2305.10613",
    bib_url = "/pubs/bib/lee-emnlp23a.bib",
    booktitle = "Conference on Empirical Methods in Natural Language Processing",
    code_url = "https://github.com/usc-isi-i2/isi-tkg-icl",
    doi_url = "https://doi.org/10.18653/v1/2023.emnlp-main.36",
    pdf_url = "/pubs/2023/lee-emnlp23a/lee-emnlp23a.pdf",
    sec = "conf",
    title = "Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning",
    year = "2023"
}

@inproceedings{DBLP:conf/sss/CoccimiglioBR23,
  author       = {Gaetano Coccimiglio and
                  Trevor Brown and
                  Srivatsan Ravi},
  editor       = {Shlomi Dolev and
                  Baruch Schieber},
  title        = {The Fence Complexity of Persistent Sets},
  booktitle    = {Stabilization, Safety, and Security of Distributed Systems - 25th
                  International Symposium, {SSS} 2023, Jersey City, NJ, USA, October
                  2-4, 2023, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14310},
  pages        = {36--51},
  publisher    = {Springer},
  year         = {2023},
  url          = {https://doi.org/10.1007/978-3-031-44274-2\_3},
  doi          = {10.1007/978-3-031-44274-2\_3},
  timestamp    = {Sun, 08 Oct 2023 13:20:04 +0200},
  biburl       = {https://dblp.org/rec/conf/sss/CoccimiglioBR23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{10.1145/3618257.3624841,
author = {Ardi, Calvin and Calder, Matt},
title = {The Prevalence of Single Sign-On on the Web: Towards the Next Generation of Web Content Measurement},
year = {2023},
isbn = {9798400703829},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3618257.3624841},
doi = {10.1145/3618257.3624841},
abstract = {Much of the content and structure of the Web remains inaccessible to evaluate at scale because it is gated by user authentication. This limitation restricts researchers to examining only a superficial layer of a website: the landing page or public, search-indexable pages. Since it is infeasible to create individual accounts across thousands of webpages, we examine the prevalence of Single Sign-On (SSO) on the web to explore the feasibility of using a few accounts to authenticate to many sites. We find that 58\% of the top 10K websites with logins are accessible with popular 3rd-party SSO providers, such as Google, Facebook, and Apple, indicating that leveraging SSO offers a scalable solution to access a large volume of user-gated content.},
booktitle = {Proceedings of the 2023 ACM on Internet Measurement Conference},
pages = {124–130},
numpages = {7},
keywords = {web measurement, web authentication, top lists, single sign-on},
location = {Montreal QC, Canada},
series = {IMC '23}
}

Much of the content and structure of the Web remains inaccessible to evaluate at scale because it is gated by user authentication. This limitation restricts researchers to examining only a superficial layer of a website: the landing page or public, search-indexable pages. Since it is infeasible to create individual accounts across thousands of webpages, we examine the prevalence of Single Sign-On (SSO) on the web to explore the feasibility of using a few accounts to authenticate to many sites. We find that 58% of the top 10K websites with logins are accessible with popular 3rd-party SSO providers, such as Google, Facebook, and Apple, indicating that leveraging SSO offers a scalable solution to access a large volume of user-gated content.

@inproceedings{salinas2023unequal,
  title={The Unequal Opportunities of Large Language Models: Examining Demographic Biases in Job Recommendations by ChatGPT and LLaMA},
  author={Salinas, Abel and Shah, Parth and Huang, Yuzhong and McCormack, Robert and Morstatter, Fred},
  booktitle={Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization},
  pages={1--15},
  year={2023}
}

@misc{akerman2023ebible,
      title={The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages},
      author={Vesa Akerman and David Baines and Damien Daspit and Ulf Hermjakob and Taeho Jang and Colin Leong and Michael Martin and Joel Mathew and Jonathan Robie and Marcus Schwarting},
      year={2023},
      eprint={2304.09919},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{gatta2023interconnected,
  title={The interconnected nature of online harm and moderation: investigating the cross-platform spread of harmful content between youtube and Twitter},
  author={Gatta, Valerio La and Luceri, Luca and Fabbri, Francesco and Ferrara, Emilio},
  booktitle={Proceedings of the 34th ACM conference on hypertext and social media},
  year={2023}
}

@article{marmarelis2023tighter,
  title={Tighter Prediction Intervals for Causal Outcomes Under Hidden Confounding},
  author={Marmarelis, Myrl G and Steeg, Greg Ver and Galstyan, Aram and Morstatter, Fred},
  journal={arXiv preprint arXiv:2306.09520},
  year={2023}
}

@article{tksk04,
author={Han Zhang and Oren Salzman and Ariel Felner and Satish Thittamaranahalli and Shawn Skyler and Carlos Hernandez and Sven Koenig},
title={Towards Effective Multi-Valued Heuristics for Bi-Objective Shortest-Path Algorithms via Differential Heuristics},
journal={Proceedings of the Sixteenth International Symposium on Combinatorial Search (SOCS-2023)},
year={2023}
}

@incollection{gil-chapter-23, 
  title = {Towards Reflection Competencies in Intelligent Systems for Science},
  author = {Yolanda Gil},
  year      = {2023},
  booktitle = {Artificial Intelligence for Science: A Deep Learning Revolution}, 
  editors = {Alok Choudhary and Geoffrey Fox and Tony Hey}, 
  publisher = {World Scientific, London, UK},
  ee = {https://doi.org/10.1142/13123},
    url = {https://knowledgecaptureanddiscovery.github.io/yolanda_gil_website/papers/Gil-Chapter-2023.pdf}
}

@misc{nogara2023toxic,
  title={Toxic Bias: Perspective API misreads German as more toxic},
  author={Nogara, Gianluca and Pierri, Francesco and Cresci, Stefano and Luceri, Luca and T{\"o}rnberg, Petter and Giordano, Silvia},
  Eprint = {arXiv:2312.12651},
  Howpublished = {arXiv Preprint: https://arxiv.org/abs/2312.12651},
  year={2023}
}

@inproceedings{nandi2023trainfors,
  title={TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization},
  author={Nandi, Soumyaroop and Natarajan, Prem and Abd-Almageed, Wael},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={403--414},
  year={2023}
}

@article{cornelio_transfer_2023,
	title = {Transfer {Learning} with {Prior} {Data}-{Driven} {Models} from {Multiple} {Unconventional} {Fields}},
	url = {https://onepetro.org/SJ/article-abstract/doi/10.2118/214312-PA/519406},
	urldate = {2024-02-12},
	journal = {SPE Journal},
	author = {Cornelio, Jodel and Mohd Razak, Syamil and Cho, Young and Liu, Hui-Hai and Vaidya, Ravimadhav and Jafarpour, Behnam},
	year = {2023},
	note = {Publisher: SPE},
	pages = {1--30},
}

@misc{hussein2023trojan,
      title={Trojan Model Detection Using Activation Optimization}, 
      author={Mohamed E. Hussein and Sudharshan Subramaniam Janakiraman and Wael AbdAlmageed},
      year={2023},
      eprint={2306.04877},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      howpublished={arXiv Preprint: http://arxiv.org/abs/2306.04877}
}

@article{doctor2023understanding,
  title={Understanding and Estimating Domain Complexity Across Domains},
  author={Doctor, Katarina and Kejriwal, Mayank and Holder, Lawrence and Kildebeck, Eric and Resmini, Emma and Pereyda, Christopher and Steininger, Robert J and Oliven{\c{c}}a, Daniel V},
  journal={arXiv preprint arXiv:2312.13487},
  year={2023}
}

@inbook{doi:https://doi.org/10.1002/9781394203314.ch31,
    author = {Orosz, Michael and Duffy, Brian and Charlton, Craig and Saunders, Hector and Thomas, Ellins},
    publisher = {John Wiley & Sons, Ltd},
    isbn = {9781394203314},
    title = {Unique Challenges in Mission Engineering and Technology Integration},
    booktitle = {Systems Engineering for the Digital Age},
    chapter = {31},
    pages = {665-681},
    doi = {https://doi.org/10.1002/9781394203314.ch31},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781394203314.ch31},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781394203314.ch31},
    year = {2023},
    keywords = {digital engineering, DevSecOps, model-based systems engineering, MBSE, large-system acquisition, software acquisition},
    abstract = {Summary Undertaking mission engineering and technology integration in large enterprise systems such as automotive manufacturing or space-based communication systems offer many challenges. These systems are typically composed of multiple interconnected systems and subsystems, each developed and maintained by multiple vendors operating on different timelines and priorities. Mission engineering refers to applying systems engineering processes and principles to the complete product lifecycle – requirements analysis, design, development, integration, testing, deployment, and sustainment of a complex systems of systems project. Such processes and principles include DevSecOps, digital engineering, model-based systems engineering (MBSE), Agile and other systems design and development processes. Technology integration refers to the processes and principles of inserting technology into both the engineering and development processes of a system's acquisition program. Examples of technology insertion include adding or expanding system requirements to meet changing market conditions or reacting to changing operating environments. This chapter discusses the unique challenges and offers recommended strategies in undertaking mission engineering and technology insertion in large enterprise systems with a particular focus on software-based systems. Where appropriate, reference to hardware-only or hybrid hardware and software-based systems will also be noted.}
}

Summary Undertaking mission engineering and technology integration in large enterprise systems such as automotive manufacturing or space-based communication systems offer many challenges. These systems are typically composed of multiple interconnected systems and subsystems, each developed and maintained by multiple vendors operating on different timelines and priorities. Mission engineering refers to applying systems engineering processes and principles to the complete product lifecycle – requirements analysis, design, development, integration, testing, deployment, and sustainment of a complex systems of systems project. Such processes and principles include DevSecOps, digital engineering, model-based systems engineering (MBSE), Agile and other systems design and development processes. Technology integration refers to the processes and principles of inserting technology into both the engineering and development processes of a system's acquisition program. Examples of technology insertion include adding or expanding system requirements to meet changing market conditions or reacting to changing operating environments. This chapter discusses the unique challenges and offers recommended strategies in undertaking mission engineering and technology insertion in large enterprise systems with a particular focus on software-based systems. Where appropriate, reference to hardware-only or hybrid hardware and software-based systems will also be noted.

@misc{tian2023unsupervised,
  title={Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies},
  author={Tian, Mulin and Khayatkhoei, Mahyar and Mathai, Joe and AbdAlmageed, Wael},
  Howpublished={arXiv preprint arXiv:2311.17088},
  year={2023},
  month={November},
  url_Paper={https://arxiv.org/pdf/2311.17088.pdf},
  url_Link={https://arxiv.org/abs/2311.17088},
  ISIArea = {ML, VISTA}
}

@inproceedings{zhou2023unveiling,
  title={Unveiling the Dynamics of Censorship, COVID-19 Regulations, and Protest: An Empirical Study of Chinese Subreddit r/china\_irl},
  author={Zhou, Siyi and Luceri, Luca and Ferrara, Emilio},
   booktitle={AAAI ICWSM Workshop},
  year={2023}
}

@misc{mathew2023user,
      title={User Study for Improving Tools for Bible Translation},
      author={Joel Mathew and Ulf Hermjakob},
      year={2023},
      eprint={2302.00778},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{DBLP:conf/icassp/ChochlakisMBBLN23a,
  author       = {Georgios Chochlakis and
                  Gireesh Mahajan and
                  Sabyasachee Baruah and
                  Keith Burghardt and
                  Kristina Lerman and
                  Shrikanth Narayanan},
  title        = {Using Emotion Embeddings to Transfer Knowledge between Emotions, Languages,
                  and Annotation Formats},
  booktitle    = {{IEEE} International Conference on Acoustics, Speech and Signal Processing
                  {ICASSP} 2023, Rhodes Island, Greece, June 4-10, 2023},
  pages        = {1--5},
  publisher    = {{IEEE}},
  year         = {2023},
  url          = {https://doi.org/10.1109/ICASSP49357.2023.10095597},
  doi          = {10.1109/ICASSP49357.2023.10095597},
  timestamp    = {Sun, 05 Nov 2023 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/icassp/ChochlakisMBBLN23a.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{TechPolicyPress2023,
	author = {S Giordano and F Menczer and N Just and F Saurwein and J Bryden and L Luceri},
	date-added = {2023-12-31 15:50:31 -0900},
	date-modified = {2023-12-31 15:56:35 -0900},
	howpublished = {Tech Policy Press},
	keywords = {abuse agents myown osome social},
	title = {Using Science To Guide Social Media Regulation},
	url = {https://www.techpolicy.press/using-science-to-guide-social-media-regulation/},
	year = {2023},
	bdsk-url-1 = {https://www.techpolicy.press/using-science-to-guide-social-media-regulation/}}

@article{shen2023using,
  title={Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy},
  author={Shen, Ke and Kejriwal, Mayank},
  journal={PLOS Global Public Health},
  volume={3},
  number={5},
  pages={e0001151},
  year={2023},
  publisher={Public Library of Science San Francisco, CA USA}
}

@Article{	  gesing-cise-2023,
  Author	= {Gesing, Sandra and Deelman, Ewa and Hildreth, Michael and
		  Makhija, Ramandeep and McDowell, Mary Ann and Meyers,
		  Natalie K. and Thain, Douglas},
  Journal	= {Computing in Science & Engineering},
  Title		= {VisDict: Improving Communication Via a Visual Dictionary
		  in a Science Gateway},
  Year		= {2023},
  Volume	= {25},
  Number	= {2},
  Pages		= {7-11},
  DOI		= {10.1109/MCSE.2023.3275711},
  Note		= {Funding Acknowledgments: NSF 2216851, 2100561, and 2100636
		  }
}

@InProceedings{zhang2023visual-crop,
  title={Visual Cropping Improves Zero-Shot Question Answering of Multimodal Large Language Models},
  author={Zhang, Jiarui and Khayatkhoei, Mahyar and Chhikara, Prateek and Ilievski, Filip},
  booktitle={Advances in Neural Information Processing Systems Workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models},
  year={2023},
  month={December},
  url_Paper={https://openreview.net/pdf?id=YrYcoV2dAk},
  url_Link={https://neurips.cc/virtual/2023/76680},
  ISIArea = {ML, VISTA, NLP}
}

@TechReport{Baltra23a,
	author = 	"Guillermo Baltra and John Heidemann",
	title = 	"What Is The {Internet}? Partial Connectivity
                  of the Internet Core",
	institution = 	"USC/Information Sciences Institute",
	year = 		2023,
	sortdate = 		"2023-03-20", 
	project = "ant, eieio, minceq",
	jsubject = "routing",
	notes = "released 2021-07-23, updated 2022-05-24, v3 update 2023-03-20",
	number =	"arXiv:2107.11439v3",
	month =		mar,
	jlocation =	"johnh: pafile",
        keywords =   "trinocular, outages, partial outages",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Baltra23a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Baltra23a.pdf",
	doi = "https://doi.org/10.48550/2107.11439v3",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "
``A collection of interconnected networks'' defines what the Internet is, but not what it is not. Events threaten Internet fragmentation: politics suggest countries or ISPs may secede or be de-peered, disputes between ISPs result in persistent unreachability between their customers, and architectural changes risk breaking the ``one'' Internet. Understanding such threats benefits from a testable definition of what the Internet is and is not, enabling discussion and quantification of partial connectivity. We provide a conceptual definition giving an idealized asymptote of connectivity. It implies peninsulas of persistent, partial connectivity, and islands when one or more computers are partitioned from the main Internet. We provide algorithms to measure, operationally, the number, size, and duration of peninsulas and islands. We apply these algorithms in rigorous measurement from two complementary measurement systems, one observing 5M networks from a few locations, and the other a few destinations from 10k locations. Results show that peninsulas (partial connectivity) are about as common as Internet outages, quantifying this long-observed problem. Root causes show that most peninsula events (45\%) are routing transients, but most peninsula-time (90\%) is from a few long-lived events (7\%). Our analysis helps interpret DNSmon, a system monitoring the DNS root, separating measurement error and persistent problems from underlying differences and operationally important transients. Finally, our definition confirms the international nature of the Internet: no single country can unilaterally claim to be ``the Internet'', but countries can choose to leave.
",
}

``A collection of interconnected networks'' defines what the Internet is, but not what it is not. Events threaten Internet fragmentation: politics suggest countries or ISPs may secede or be de-peered, disputes between ISPs result in persistent unreachability between their customers, and architectural changes risk breaking the ``one'' Internet. Understanding such threats benefits from a testable definition of what the Internet is and is not, enabling discussion and quantification of partial connectivity. We provide a conceptual definition giving an idealized asymptote of connectivity. It implies peninsulas of persistent, partial connectivity, and islands when one or more computers are partitioned from the main Internet. We provide algorithms to measure, operationally, the number, size, and duration of peninsulas and islands. We apply these algorithms in rigorous measurement from two complementary measurement systems, one observing 5M networks from a few locations, and the other a few destinations from 10k locations. Results show that peninsulas (partial connectivity) are about as common as Internet outages, quantifying this long-observed problem. Root causes show that most peninsula events (45%) are routing transients, but most peninsula-time (90%) is from a few long-lived events (7%). Our analysis helps interpret DNSmon, a system monitoring the DNS root, separating measurement error and persistent problems from underlying differences and operationally important transients. Finally, our definition confirms the international nature of the Internet: no single country can unilaterally claim to be ``the Internet'', but countries can choose to leave.

@inproceedings{jiang2022your,
  title={What are Your Pronouns? Examining Gender Pronoun Usage on Twitter},
  author={Jiang, Julie and Chen, Emily and Luceri, Luca and Muri{\'c}, Goran and Pierri, Francesco and Chang, Ho-Chun Herbert and Ferrara, Emilio},
  booktitle={AAAI ICWSM Workshop},
  year={2023}
}

@article{seo2023menu,
  title={What is On the Menu? Towards Predicting Nutritional Quality of Food Environments},
  author={Seo, DongHyeon and Horn, Abigail L and Abeliuk, Andr{\'e}s and Burghardt, Keith},
  journal={medRxiv},
  pages={2023--12},
  year={2023},
  publisher={Cold Spring Harbor Laboratory Press},
  url={https://www.medrxiv.org/content/10.1101/2023.12.08.23299691v1}
}

@inproceedings{felkner-etal-2023-winoqueer,
    title = "{W}ino{Q}ueer: A Community-in-the-Loop Benchmark for Anti-{LGBTQ}+ Bias in Large Language Models",
    author = "Felkner, Virginia  and
      Chang, Ho-Chun Herbert  and
      Jang, Eugene  and
      May, Jonathan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.507",
    doi = "10.18653/v1/2023.acl-long.507",
    pages = "9126--9140",
    abstract = "We present WinoQueer: a benchmark specifically designed to measure whether large language models (LLMs) encode biases that are harmful to the LGBTQ+ community. The benchmark is community-sourced, via application of a novel method that generates a bias benchmark from a community survey. We apply our benchmark to several popular LLMs and find that off-the-shelf models generally do exhibit considerable anti-queer bias. Finally, we show that LLM bias against a marginalized community can be somewhat mitigated by finetuning on data written about or by members of that community, and that social media text written by community members is more effective than news text written about the community by non-members. Our method for community-in-the-loop benchmark development provides a blueprint for future researchers to develop community-driven, harms-grounded LLM benchmarks for other marginalized communities.",
}

We present WinoQueer: a benchmark specifically designed to measure whether large language models (LLMs) encode biases that are harmful to the LGBTQ+ community. The benchmark is community-sourced, via application of a novel method that generates a bias benchmark from a community survey. We apply our benchmark to several popular LLMs and find that off-the-shelf models generally do exhibit considerable anti-queer bias. Finally, we show that LLM bias against a marginalized community can be somewhat mitigated by finetuning on data written about or by members of that community, and that social media text written by community members is more effective than news text written about the community by non-members. Our method for community-in-the-loop benchmark development provides a blueprint for future researchers to develop community-driven, harms-grounded LLM benchmarks for other marginalized communities.

@inproceedings{lee:acl23,
    author = "Lee, Dong-Ho and Kadakia, Akshen and Joshi, Brihi and Chan, Aaron and Liu, Ziyi and Narahari, Kiran and Shibuya, Takashi and Mitani, Ryosuke and Sekiya, Toshiyuki and Pujara, Jay and Ren, Xiang",
    acceptrate = "37\%",
    bib_url = "/pubs/bib/lee-acl23.bib",
    booktitle = "Association for Computational Linguistics",
    doi_url = "http://dx.doi.org/10.18653/v1/2023.acl-demo.25",
    pdf_url = "/pubs/2023/lee-acl23/lee-acl23.pdf",
    sec = "conf",
    title = "{XMD}: An End-to-End Framework for Interactive Explanation-Based Debugging of {NLP} Models",
    year = "2023"
}

@techreport{zhu_cfr_2023,
	type = {preprint},
	title = {cfr (v2023.9.14): a {Python} package for climate field reconstruction},
	shorttitle = {cfr (v2023.9.14)},
	url = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2098/},
	abstract = {Abstract. Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. The climate fields can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and timeconsuming, as they involve: (i) preprocessing of the proxy records, climate model simulations, and instrumental observations, (ii) application of one or more statistical methods, and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. It provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with an extensive documentation of the application interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database: applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the Graphical Expectation-Maximization (GraphEM) algorithm, respectively.},
	urldate = {2023-11-17},
	institution = {Climate and Earth system modeling},
	author = {Zhu, Feng and Emile-Geay, Julien and Hakim, Gregory J. and Guillot, Dominique and Khider, Deborah and Tardif, Robert and Perkins, Walter A.},
	month = sep,
	year = {2023},
	doi = {10.5194/egusphere-2023-2098},
}

Abstract. Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. The climate fields can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and timeconsuming, as they involve: (i) preprocessing of the proxy records, climate model simulations, and instrumental observations, (ii) application of one or more statistical methods, and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. It provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with an extensive documentation of the application interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database: applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the Graphical Expectation-Maximization (GraphEM) algorithm, respectively.

@inproceedings{lee:asilomar23,
    Author = "Lee, Ju-Hyung and Lee, Dong-Ho and Sheen, Eunsoo and Choi, Thomas and Pujara, Jay and Kim, Joongheon",
    booktitle = "Asilomar Conference on Signals, Systems, and Computers",
    sec = "conf",
    title = "seq2seq-SC: End-to-End Semantic Communication Systems with Pre-Trained Language Models",
    year = "2023"
}

@inproceedings{weinstein2022long,
  title={A Long Wave Infrared Communication Transmitter using Tunable Emissivity in Graphene},
  author={Weinstein, Haley and Habif, Jonathan and Cai, Zhi and Cronin, Steve},
  booktitle={Frontiers in Optics},
  pages={JW5A--48},
  year={2022},
  organization={Optica Publishing Group}
}

@inproceedings{GrapheneDeviceFIOLS,
author = {Haley Weinstein, Zhi Cai, Steve Cronin, and Jonathan L. Habif },
booktitle = {Frontiers in Optics $+$ Laser Science 2022 (FIO, LS)},
journal = {Frontiers in Optics $+$ Laser Science 2022 (FIO, LS)},
keywords = {Bit error rate; Chemical vapor deposition; Emissivity; Free space optics; Graphene; Long infrared},
pages = {JW5A.48},
publisher = {Optica Publishing Group},
title = {A Long Wave Infrared Communication Transmitter using Tunable Emissivity in Graphene},
year = {2022},
url = {https://opg.optica.org/abstract.cfm?URI=FiO-2022-JW5A.48},
doi = {10.1364/FIO.2022.JW5A.48},
abstract = {We have performed a laboratory-scale communications demonstration using a compound graphene transmitter device with tunable electro-thermo-optic properties that modulates radiation in the LWIR. OOK and PAM-4 data formats were demonstrated with low BER.},
}

We have performed a laboratory-scale communications demonstration using a compound graphene transmitter device with tunable electro-thermo-optic properties that modulates radiation in the LWIR. OOK and PAM-4 data formats were demonstrated with low BER.

@article{marmarelis2022metric,
  title={A Metric Space for Point Process Excitations},
  author={Marmarelis, Myrl G and Ver Steeg, Greg and Galstyan, Aram},
  journal={Journal of Artificial Intelligence Research},
  volume={73},
  pages={1323--1353},
  year={2022}
}

@inproceedings{gaul2022physics,
  title={A Physics based MTJ Compact Model for State-of-the-Art and Emerging STT-MRAM Failure Analysis and Yield Enhancement},
  author={Gaul, Nishtha S and Jaiswal, Akhilesh and Yoon, Hongsik and Lee, Taeyoung and Yamane, Kazutaka and Versaggi, Joe and Carter, Rick and Paul, Bipul C},
  booktitle={2022 IEEE International Memory Workshop (IMW)},
  pages={1--4},
  year={2022},
  organization={IEEE}
}

@article{DBLP:journals/corr/abs-2203-12184,
  author    = {Henrique Santos and
               Ke Shen and
               Alice M. Mulvehill and
               Yasaman Razeghi and
               Deborah L. McGuinness and
               Mayank Kejriwal},
  title     = {A Theoretically Grounded Benchmark for Evaluating Machine Commonsense},
  journal   = {CoRR},
  volume    = {abs/2203.12184},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2203.12184},
  doi       = {10.48550/arXiv.2203.12184},
  eprinttype = {arXiv},
  eprint    = {2203.12184},
  timestamp = {Tue, 29 Mar 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2203-12184.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{dibaji2022tutorial,
  title={A Tutorial on Security and Privacy Challenges in CPS},
  author={Dibaji, Seyed Mehran and Hussain, Alefiya and Ishii, Hideaki},
  journal={Security and Resilience of Control Systems},
  pages={121--146},
  year={2022},
  publisher={Springer, Cham}
}

@inproceedings{andreoletti2022framework,
  title={A framework for emotion-driven product design through virtual reality},
  author={Andreoletti, Davide and Paoliello, Marco and Luceri, Luca and Leidi, Tiziano and Peternier, Achille and Giordano, Silvia},
  booktitle={Information Technology for Management: Business and Social Issues: 16th Conference, ISM 2021, and FedCSIS-AIST 2021 Track, Held as Part of FedCSIS 2021, Virtual Event, September 2--5, 2021, Extended and Revised Selected Papers},
  pages={42--61},
  year={2022},
  organization={Springer}
}

@article{datta2022processing,
  title={A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications},
  author={Datta, Gourav and Kundu, Souvik and Yin, Zihan and Lakkireddy, Ravi Teja and Mathai, Joe and Jacob, Ajey P and Beerel, Peter A and Jaiswal, Akhilesh R},
  journal={Scientific Reports},
  volume={12},
  number={1},
  pages={14396},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@article{datta2022ace,
  title={ACE-SNN: Algorithm-hardware co-design of energy-efficient \& low-latency deep spiking neural networks for 3d image recognition},
  author={Datta, Gourav and Kundu, Souvik and Jaiswal, Akhilesh and Beerel, Peter A},
  journal={Frontiers in neuroscience},
  pages={400},
  year={2022},
  publisher={Frontiers}
}

@inproceedings{don58862022,
author={Rajat Tandon and Pithayuth Charnsethikul and Michalis Kallitsis and Jelena Mirkovic},
title={AMON-SENSS: Scalable and Accurate Detection of Volumetric DDoS Attacks at ISPs},
booktitle={Globecom},
year={2022}}

@InProceedings{	  do-pottier-ccgrid-2022,
  Title		= {Accelerating Scientific Workflows on HPC Platforms with In
		  Situ Processing},
  Author	= {Do, Tu Mai Anh and Pottier, Lo\"ic and Yildiz, Orcun and
		  Vahi, Karan and Krawczuk, Patrycja and Peterka, Tom and
		  Deelman, Ewa},
  BookTitle	= {2022 IEEE/ACM 22nd International Symposium on Cluster,
		  Cloud and Internet Computing (CCGrid)},
  DOI		= {10.1109/CCGrid54584.2022.00009},
  Pages		= {1--10},
  Year		= {2022},
  Organization	= {IEEE},
  Note		= {Funding Acknowledgments: NSF 1664162, DOE
		  DE-AC02-06CH11357, DE-AC02-05CH11231, DE-SC0012636 and
		  DE-SC0022328}
}

@inproceedings{tommasini2022accepted,
  title={Accepted Tutorials at The Web Conference 2022},
  author={Tommasini, Riccardo and Basu Roy, Senjuti and Wang, Xuan and Wang, Hongwei and Ji, Heng and Han, Jiawei and Nakov, Preslav and Da San Martino, Giovanni and Alam, Firoj and Schedl, Markus and others},
  booktitle={Companion Proceedings of the Web Conference 2022},
  pages={391--399},
  year={2022}
}

@article{dhinagar2022alzheimer,
	author = {Dhinagar, Nikhil J and Thomopoulos, Sophia I and Owens-Walton, Conor and Stripelis, Dimitris and Ambite, Jose Luis and {Ver Steeg}, Greg and Thompson, Paul M},
	date-added = {2023-01-17 12:42:02 -0800},
	date-modified = {2023-01-17 12:42:20 -0800},
	journal = {Alzheimer's \& Dementia},
	pages = {e066446},
	publisher = {Wiley Online Library},
	title = {Alzheimer's Disease Detection with a 3D Convolutional Neural Network using Gray Matter Maps from T1-weighted Brain MRI},
	volume = {18},
	year = {2022}}

@article{dhinagar2022,
	title = {Alzheimer’s {Disease} {Detection} with a {3D} {Convolutional} {Neural} {Network} using {Gray} {Matter} {Maps} from {T1}-weighted {Brain} {MRI}},
	volume = {18},
	issn = {1552-5279},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/alz.066446},
	doi = {10.1002/alz.066446},
	language = {en},
	number = {S5},
	urldate = {2023-01-23},
	journal = {Alzheimer's \& Dementia},
	author = {Dhinagar, Nikhil J and Thomopoulos, Sophia I and Owens-Walton, Conor and Stripelis, Dimitris and Ambite, Jos\'{e} Luis and Steeg, Greg Ver and Thompson, Paul M},
	year = {2022},
	pages = {e066446},
}

@inproceedings{bezerra2022amlight,
  title={AmLight Real Time In-Band Telemetry Within the NoviFlow Switches},
  author={Bezerra, Jeronimo A and Ibarra, Julio E and Torres, Arturo Quintana and Brito, Italo Valcy Da Silva and Chergarova, Vasilka and Morgan, Heidi and LeClerc, Marc and Ker, Bryan},
  booktitle={Future of Information and Communication Conference},
  pages={644--655},
  year={2022},
  organization={Springer}
}

@inproceedings{berhanu_ai_2022,
	title = {An {AI} {Approach} to {Integrating} {Climate}, {Hydrology}, and {Agriculture} {Models}},
	url = {http://www.isi.edu/~gil/papers/berhanu-etal-siaia-2022.pdf},
	booktitle = {Proceedings of the {First} {International} {Workshop} on {Social} {Impact} of {AI} for {Africa} ({SIAIA}), held at the 36th {Annual} {Conference} of the {Association} for the {Advancement} of {Artificial} {Intelligence} ({AAAI}-22)},
	author = {Berhanu, Belete and Bisrat, Ethiopia and Gil, Yolanda and Khider, Deborah and Osorio, Maximiliano and Ratnakar, Varun and Vargas, Hernan},
	year = {2022},
}

@article{zhang2022empirical,
  title={An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs},
  author={Zhang, Jiarui and Ilievski, Filip and Ma, Kaixin and Francis, Jonathan and Oltramari, Alessandro},
  journal={arXiv preprint arXiv:2205.10661},
  year={2022}
}

@InProceedings{	  osinski-works-2022,
  Author	= {Osinski, Tomasz and Rynge, Mats and Hong, James K. and
		  Vahi, Karan and Chu, Ruilin and Sul, Cesar and Deelman, Ewa
		  and Kim, Byoung-Do},
  BookTitle	= {2022 IEEE/ACM Workshop on Workflows in Support of
		  Large-Scale Science (WORKS)},
  Title		= {An automated Cryo-EM computational environment on the HPC
		  system using Pegasus WMS},
  Year		= {2022},
  Volume	= {},
  Number	= {},
  Pages		= {60-67},
  DOI		= {10.1109/WORKS56498.2022.00013},
  Note		= {Funding Acknowledgments: NSF 1664162}
}

@inproceedings{zhou2022improved,
  title={An improved baseline for sentence-level relation extraction},
  author={Zhou, Wenxuan and Chen, Muhao},
  booktitle={Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)},
  year={2022}
}

@article{DBLP:journals/corr/abs-2212-00339,
  author       = {Kai Chen and
                  Zihao He and
                  Rong{-}Ching Chang and
                  Jonathan May and
                  Kristina Lerman},
  title        = {Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit},
  journal      = {CoRR},
  volume       = {abs/2212.00339},
  year         = {2022},
  url          = {https://doi.org/10.48550/arXiv.2212.00339},
  doi          = {10.48550/ARXIV.2212.00339},
  eprinttype    = {arXiv},
  eprint       = {2212.00339},
  timestamp    = {Wed, 07 Dec 2022 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2212-00339.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{zhou2022answer,
  title={Answer Consolidation: Formulation and Benchmarking},
  author={Zhou, Wenxuan and Ning, Qiang and Elfardy, Heba and Small, Kevin and Chen, Muhao},
  journal={NAACL},
  year={2022}
}

@InProceedings{Rizvi22a,
        author =        "{A S M} Rizvi and Leandro Bertholdo and
 Jo{\~a}o Ceron and John Heidemann",
 title = "Anycast Agility: Network Playbooks to Fight {DDoS}",
        booktitle =     "Proceedings of the " # "31st" # " {USENIX} Security Symposium ",
        year =          2022,
	sortdate = 		"2022-08-10", 
	project = "ant, ddidd, paaddos, sabres",
	jsubject = "network_security",
        pages =      "4201--4218",
        month =      aug,
        publisher =  "{USENIX}",
        jlocation =   "johnh: pafile",
        keywords =   "anycast, ddos, routing",
        doi =        "to appear",
	blogurl = "https://ant.isi.edu/blog/?p=1829",
	url =	"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi22a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi22a.pdf",
	oldotherurl = "https://www.usenix.org/system/files/sec22fall_rizvi.pdf",
	otherurl = "https://www.usenix.org/conference/usenixsecurity22/presentation/rizvi",
	dataurl =	"https://zenodo.org/record/6473023",
	talkurl = "https://www.usenix.org/conference/usenixsecurity22/presentation/rizvi",
	abstract = "IP anycast is used for services such as DNS and Content Delivery
Networks (CDN) to provide the capacity to handle Distributed
Denial-of-Service (DDoS) attacks.  During a DDoS attack service
operators redistribute traffic between anycast sites to take advantage
of sites with unused or greater capacity.  Depending on site traffic
and attack size, operators may instead concentrate attackers in a few
sites to preserve operation in others.  Operators use these actions
during attacks, but how to do so has not been described systematically
or publicly.  This paper describes several methods to use BGP to shift
traffic when under DDoS, and shows that a \emph{response playbook} can
provide a menu of responses that are options during an attack.  To
choose an appropriate response from this playbook, we also describe a
new method to estimate true attack size, even though the operator's
view during the attack is incomplete.  Finally, operator choices are
constrained by distributed routing policies, and not all are helpful.
We explore how specific anycast deployment can constrain options in
this playbook, and are the first to measure how generally applicable
they are across multiple anycast networks.
",
}

IP anycast is used for services such as DNS and Content Delivery Networks (CDN) to provide the capacity to handle Distributed Denial-of-Service (DDoS) attacks. During a DDoS attack service operators redistribute traffic between anycast sites to take advantage of sites with unused or greater capacity. Depending on site traffic and attack size, operators may instead concentrate attackers in a few sites to preserve operation in others. Operators use these actions during attacks, but how to do so has not been described systematically or publicly. This paper describes several methods to use BGP to shift traffic when under DDoS, and shows that a \emphresponse playbook can provide a menu of responses that are options during an attack. To choose an appropriate response from this playbook, we also describe a new method to estimate true attack size, even though the operator's view during the attack is incomplete. Finally, operator choices are constrained by distributed routing policies, and not all are helpful. We explore how specific anycast deployment can constrain options in this playbook, and are the first to measure how generally applicable they are across multiple anycast networks.

@inproceedings{sankaran2022app2net,
  title={App2Net: Moving Application Functions to Network \& a Case Study on Low-latency Feedback},
  author={Sankaran, Ganesh C and Chung, Joaquin and Kettimuthu, Rajkumar},
  booktitle={2022 IEEE/ACM International Workshop on Innovating the Network for Data-Intensive Science (INDIS)},
  pages={1--8},
  year={2022},
  organization={IEEE}
}

@InProceedings{	  choudhary-escience-2022,
  Author	= {Choudhary, Khushi and Nersisyan, Nona and Lin, Edward and
		  Chandrasekaran, Shobana and Mayani, Rajiv and Pottier,
		  Loïc and Murillo, Angela P. and Virdone, Nicole K. and
		  Kee, Kerk and Deelman, Ewa},
  BookTitle	= {2022 IEEE 18th International Conference on e-Science
		  (e-Science)},
  Title		= {Application of Edge-to-Cloud Methods Toward Deep
		  Learning},
  Year		= {2022},
  Volume	= {},
  Number	= {},
  Pages		= {415-416},
  DOI		= {10.1109/eScience55777.2022.00065},
  Note		= {Funding Acknowledgments: NSF 2127548}
}

@inproceedings{vadayath2022arbiter,
  title={Arbiter: Bridging the Static and Dynamic Divide in Vulnerability Discovery on Binary Programs},
  author={Vadayath, Jayakrishna and Eckert, Moritz and Zeng, Kyle and Weideman, Nicolaas and Menon, Gokulkrishna Praveen and Fratantonio, Yanick and Balzarotti, Davide and Doup{\'e}, Adam and Bao, Tiffany and Wang, Ruoyu and others},
  booktitle={USENIX Security},
  year={2022}
}

@misc{jaiswal2022array,
  title={Array of integrated pixel and memory cells for deep in-sensor, in-memory computing},
  author={Jaiswal, Akhilesh and Jacob, Ajey Poovannummoottil},
  year={2022},
  month=oct # "~11",
  publisher={Google Patents},
  note={US Patent 11,468,146}
}

@article{chiarito2022artificial,
  title={Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold},
  author={Chiarito, Mauro and Luceri, Luca and Oliva, Angelo and Stefanini, Giulio and Condorelli, Gianluigi},
  journal={European Cardiology Review},
  volume={17},
  year={2022},
  publisher={Radcliffe Cardiology}
}

@inproceedings{sun2022assessing,
  title={Assessing Scientific Research Papers with Knowledge Graphs},
  author={Sun, Kexuan and Qiu, Zhiqiang and Salinas, Abel and Huang, Yuzhong and Lee, Dong-Ho and Benjamin, Daniel and Morstatter, Fred and Ren, Xiang and Lerman, Kristina and Pujara, Jay},
  booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={2467--2472},
  year={2022}
}

@article{Cheng-Attack,
	title = {Attack-Agnostic Adversarial Detection},
	url = {https://arxiv.org/pdf/2206.00489},
	journal = {NeurIPS workshop on  Trustworthy and Socially Responsible
Machine Learning},
	author = {J. Cheng and M. Hussein and J. Billa and W. AbdAlmageed},
	year = {2022}, 
 	keywords="conference"
}

@inproceedings{wang_augmenting_2022,
	title = {Augmenting {Knowledge} {Graphs} for {Better} {Link} {Prediction}},
	volume = {3},
	url = {https://www.ijcai.org/proceedings/2022/316},
	doi = {10.24963/ijcai.2022/316},
	abstract = {Electronic proceedings of IJCAI 2022},
	language = {en},
	urldate = {2023-02-18},
	author = {Wang, Jiang and Ilievski, Filip and Szekely, Pedro and Yao, Ke-Thia},
	month = jul,
	year = {2022},
	note = {ISSN: 1045-0823},
	pages = {2277--2283},
	file = {Full Text PDF:C\:\\Users\\ktyao\\Zotero\\storage\\BFVZ3WTJ\\Wang et al. - 2022 - Augmenting Knowledge Graphs for Better Link Predic.pdf:application/pdf},
}

Electronic proceedings of IJCAI 2022

@inproceedings{joshi-etal-2022-augmenting,
    title = "Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic {E}-commerce Stores",
    author = "Joshi, Ashutosh  and
      Vishwanath, Shankar  and
      Teo, Choon  and
      Petricek, Vaclav  and
      Vishwanathan, Vishy  and
      Bhagat, Rahul  and
      May, Jonathan",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
    month = jul,
    year = "2022",
    address = "Hybrid: Seattle, Washington + Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-industry.19",
    pages = "160--167",
    abstract = "Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amounts of customer behavior data (e.g. queries, clicks, purchases) for training. However, behavioral data is limited in low-traffic e-commerce stores, impacting performance of these systems. In this paper, we present a technique that augments behavioral training data via query reformulation. We use the Aggregated Label eXtreme Multi-label Classification (AL-XMC) system (Shen et al., 2020) as an example semantic matching model and show via crowd-sourced human judgments that, when the training data is augmented through query reformulations, the quality of AL-XMC improves over a baseline that does not use query reformulation. We also show in online A/B tests that our method significantly improves business metrics for the AL-XMC model.",
}

Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amounts of customer behavior data (e.g. queries, clicks, purchases) for training. However, behavioral data is limited in low-traffic e-commerce stores, impacting performance of these systems. In this paper, we present a technique that augments behavioral training data via query reformulation. We use the Aggregated Label eXtreme Multi-label Classification (AL-XMC) system (Shen et al., 2020) as an example semantic matching model and show via crowd-sourced human judgments that, when the training data is augmented through query reformulations, the quality of AL-XMC improves over a baseline that does not use query reformulation. We also show in online A/B tests that our method significantly improves business metrics for the AL-XMC model.

@inproceedings{wang2022autocps,
  title={AutoCPS: Control Software Dataset Generation for Semantic Reverse Engineering},
  author={Wang, Haoda and Hauser, Christophe and Luis Garcia, University of Southern California},
  booktitle={IEEE Security and Privacy Workshops},
  number={SafeThings 2022},
  year={2022}
}

@inproceedings{wang2022autocps,
  title={AutoCPS: Control Software Dataset Generation for Semantic Reverse Engineering},
  author={Wang, Haoda and Hauser, Christophe and Garcia, Luis},
  booktitle={2022 IEEE Security and Privacy Workshops (SPW)},
  pages={236--242},
  year={2022},
  organization={IEEE}
}

@inproceedings{shbita2022automated,
  title={Automated Generation of Control Concepts Annotation Rules Using Inductive Logic Programming},
  author={Shbita, Basel and Moitra, Abha},
  editor={Hanus, Michael and Igarashi, Atsushi},
  booktitle={Functional and Logic Programming},
  year={2022},
  publisher={Springer International Publishing},
  address={Cham},
  pages={171--185},
  urlLink={https://link.springer.com/chapter/10.1007/978-3-030-99461-7_10},
  urlPaper={http://usc-isi-i2.github.io/papers/shbita22-flops.pdf},
  urlSlides={http://usc-isi-i2.github.io/slides/shbita22-flops-slides.pdf}
}

@inproceedings{avramidis2022automating,
  title={Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning},
  author={Avramidis, Kleanthis and Rostami, Mohammad and Chang, Melinda and Narayanan, Shrikanth},
  booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
  pages={3973--3977},
  year={2022},
  organization={IEEE}
}

@InProceedings{	  tanaka-cloud2things-2022,
  Author	= {Tanaka, Ryan and Papadimitriou, George and Viswanath, Sai
		  Charan and Wang, Cong and Lyons, Eric and Thareja, Komal
		  and Qu, Chengyi and Esquivel, Alicia and Deelman, Ewa and
		  Mandal, Anirban and Calyam, Prasad and Zink, Michael},
  BookTitle	= {2022 22nd IEEE International Symposium on Cluster, Cloud
		  and Internet Computing (CCGrid)},
  DOI		= {10.1109/CCGrid54584.2022.00098},
  Number	= {},
  Pages		= {826-833},
  Title		= {Automating Edge-to-cloud Workflows for Science: Traversing
		  the Edge-to-cloud Continuum with Pegasus},
  Volume	= {},
  Year		= {2022},
  Note		= {Funding Acknowledgments: NSF 2018074, 1664162. Best Paper
		  Award.}
}

@inproceedings{yixue_zhao_avgust_FSE2022,
 author = {Yixue Zhao and Saghar Talebipour and Kesina Baral and Hyojae Park and Leon Yee and Safwat Ali Khan and Yuriy Brun and Nenad Medvidovic and Kevin Moran},
 title = {Avgust: Automating Usage-Based Test Generation from Videos of App Executions},
 booktitle = {Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
 series = {ESEC/FSE '22},
 year = {2022},
 location = {Singapore}
}

@inproceedings{sohn2022bending,
  title={Bending the Future: Autoregressive Modeling of Temporal Knowledge Graphs in Curvature-Variable Hyperbolic Spaces},
  author={Sohn, Jihoon and Ma, Mingyu Derek and Chen, Muhao},
  booktitle={4th Conference on Automated Knowledge Base Construction (AKBC)},
  year={2022}
}

@conference {Schmidt2022,
    title = {Bitstream Assurance Checking Engine for Undocumented Functionality},
    booktitle = {Government Microcircuit Applications and Critical Technology Conference (GOMACTech)},
    year = {2022},
    month = {March},
    author = {A. Schmidt, J. Wilford, B. Reynwar, T. Sung, and M. French}
    }

    

@inproceedings{schmidt2022_0,
	author = {Andrew G. Schmidt and Justin Wilford and Benedict Reynwar and Ting-Yuan Sung and Matthew French},
	booktitle = {Government Microcircuit Applications \& Critical Techology Conference (GOMACTech)},
	title = {Bitstream Assurance Checking Engine for Undocumented Functionality (BRACE)},
	year = {2022}}

@inproceedings{marmarelis2022bounding,
	author = {Marmarelis, Myrl G and {Ver Steeg}, Greg and Galstyan, Aram},
	booktitle = {NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact},
	date-added = {2023-01-17 12:50:07 -0800},
	date-modified = {2023-01-17 13:02:07 -0800},
	title = {Bounding the Effects of Continuous Treatments for Hidden Confounders},
	year = {2022}}

@InProceedings{	  murillo-pearc-2022,
  Author	= {Murillo, Angela Patricia and Deelman, Ewa and Nabrzyski,
		  Jarek and Pottier, Lo\"{\i}c},
  Title		= {Broadening Student Participation in Cyberinfrastructure
		  Research and Development},
  Year		= {2022},
  ISBN		= {9781450391610},
  Publisher	= {Association for Computing Machinery},
  Address	= {New York, NY, USA},
  URL		= {https://doi.org/10.1145/3491418.3535175},
  DOI		= {10.1145/3491418.3535175},
  BookTitle	= {Practice and Experience in Advanced Research Computing},
  articleno	= {82},
  numpages	= {2},
  Keywords	= {workforce development, student fellowships,
		  cyberinfrastructure, Major Facilities},
  Location	= {Boston, MA, USA},
  Series	= {PEARC '22},
  Note		= {Funding Acknowledgments: NSF 2127548, 1842042}
}

@article{shbita2022building,
  title={Building Spatio-Temporal Knowledge Graphs from Vectorized Topographic Historical Maps},
  author={Shbita, Basel and Knoblock, Craig A and Duan, Weiwei and Chiang, Yao-Yi and Uhl, Johannes H and Leyk, Stefan},
  journal={Semantic Web},
  number={Preprint},
  pages={1--23},
  year={2022},
  publisher={IOS Press},
  urlLink={https://content.iospress.com/articles/semantic-web/sw222918}
}

@inproceedings{yu-etal-2022-building,
    title = "Building an Event Extractor with Only a Few Examples",
    author = "Yu, Pengfei  and
      Zhang, Zixuan  and
      Voss, Clare  and
      May and Heng Ji, Jonathan",
    booktitle = "Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing",
    month = jul,
    year = "2022",
    address = "Hybrid",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.deeplo-1.11",
    pages = "102--109",
    abstract = "t",
}

t

@InProceedings{	  deelman-pearc-2022,
  Author	= {Hacker, Thomas and Smith, Preston and Brunson, Dana and
		  Arafune, Lisa and Cheatham, Thomas and Deelman, Ewa},
  Title		= {Building the Research Innovation Workforce: Challenges and
		  Recommendations from a Virtual Workshop to Advance the
		  Research Computing Community*},
  Year		= {2022},
  ISBN		= {9781450391610},
  Publisher	= {Association for Computing Machinery},
  Address	= {New York, NY, USA},
  URL		= {https://doi.org/10.1145/3491418.3530288},
  DOI		= {10.1145/3491418.3530288},
  BookTitle	= {Practice and Experience in Advanced Research Computing},
  articleno	= {26},
  numpages	= {7},
  Keywords	= {cyberinfrastructure, research computing, workforce},
  Location	= {Boston, MA, USA},
  Series	= {PEARC '22},
  Note		= {Funding Acknowledgments: NSF 2036534}
}

@article{Li-CAT,
	title = {{CAT}: Controllable Attribute Translation for Fair Facial Attribute Classification},
	url = {https://arxiv.org/abs/2209.06850},
	journal = {European Conference on Computer Vision Workshop on VISION WITH BIASED OR SCARCE DATA},
	author = {J. Li and W. AbdAlmageed},
	year = {2022}, 
 	keywords="conference"
}

@inproceedings{srinivasanclimb,
  title={CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks},
  author={Srinivasan, Tejas and Chang, Ting-Yun and Alva, Leticia Leonor Pinto and Chochlakis, Georgios and Rostami, Mohammad and Thomason, Jesse},
 year={2022},
  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}
}

@article{DBLP:journals/corr/abs-2210-07519,
  author    = {Zhisheng Tang and
               Mayank Kejriwal},
  title     = {Can Language Representation Models Think in Bets?},
  journal   = {CoRR},
  volume    = {abs/2210.07519},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2210.07519},
  doi       = {10.48550/arXiv.2210.07519},
  eprinttype = {arXiv},
  eprint    = {2210.07519},
  timestamp = {Tue, 18 Oct 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2210-07519.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2203-06491,
  author    = {Mayank Kejriwal and
               Ke Shen},
  title     = {Can Scale-free Network Growth with Triad Formation Capture Simplicial
               Complex Distributions in Real Communication Networks?},
  journal   = {CoRR},
  volume    = {abs/2203.06491},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2203.06491},
  doi       = {10.48550/arXiv.2203.06491},
  eprinttype = {arXiv},
  eprint    = {2203.06491},
  timestamp = {Wed, 16 Mar 2022 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2203-06491.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@Article{chen2022charting,
    author={Chen, Emily and Jiang, Julie and Chang, Ho-Chun Herbert and Muric, Goran and Ferrara, Emilio},
    title={Charting the Information and Misinformation Landscape to Characterize Misinfodemics on Social Media: {COVID}-19 Infodemiology Study at a Planetary Scale},
    journal={JMIR Infodemiology},
    year={2022},
    month={Feb},
    day={8},
    volume={2},
    number={1},
    pages={e32378},
    issn={2564-1891},
    doi={10.2196/32378},
}

@misc{https://doi.org/10.48550/arxiv.2210.05096,
  doi = {10.48550/ARXIV.2210.05096},
  url = {https://arxiv.org/abs/2210.05096},
  author = {Gowda, Thamme and Gheini, Mozhdeh and May, Jonathan},
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Checks and Strategies for Enabling Code-Switched Machine Translation},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}

@InProceedings{Rizvi22b,
        author =        "{A S M} Rizvi and John Heidemann",
        title =         "Chhoyhopper: A Moving Target Defense with {IPv6}",
        booktitle =     "Proceedings of the " # "{IEEE} Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb)",
        year =          2022,
	sortdate = 		"2022-04-24", 
	project = "ant, ddidd, paaddos, sabres",
	jsubject = "network_security",
        pages =      "to appear",
        month =      apr,
        address =    "San Diego, California, USA",
        publisher =  "IEEE",
        jlocation =   "johnh: pafile",
        keywords =   "chhoyhopper, moving target defense, ipv6, https, tls, ssh",
        doi =        "https://dx.doi.org/10.14722/madweb.2022.23004",
	blogurl = "https://ant.isi.edu/blog/?p=1845",
	url =	"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi22a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Rizvi22b.pdf",
	abstract = "Services on the public Internet are frequently scanned, then subject
to brute-force password attempts and Denial-of-Service (DoS) attacks.
We would like to run such services stealthily, where they are
available to friends but hidden from adversaries.  In this work, we
propose a discovery-resistant moving target defense named
``Chhoyhopper'' that utilizes the vast IPv6 address space to conceal
publicly available services.  The client meets the server at an IPv6
address that changes in a pattern based on a shared, pre-distributed
secret and the time of day.  By hopping over a /64 prefix, services
cannot be found by active scanners, and passively observed information
is useless after two minutes.  We demonstrate our system with the two
important applications---SSH and HTTPS, and make our system publicly
available.",
}

Services on the public Internet are frequently scanned, then subject to brute-force password attempts and Denial-of-Service (DoS) attacks. We would like to run such services stealthily, where they are available to friends but hidden from adversaries. In this work, we propose a discovery-resistant moving target defense named ``Chhoyhopper'' that utilizes the vast IPv6 address space to conceal publicly available services. The client meets the server at an IPv6 address that changes in a pattern based on a shared, pre-distributed secret and the time of day. By hopping over a /64 prefix, services cannot be found by active scanners, and passively observed information is useless after two minutes. We demonstrate our system with the two important applications—SSH and HTTPS, and make our system publicly available.

@article{burghardt2022city,
  title={City definition affects long-term urban scaling analyses in the United States (1900-2015)},
  author={Burghardt, Keith and Uhl, Johannes H and Lerman, Kristina and Leyk, Stefan},
  journal={arXiv preprint arXiv:2209.10852},
  year={2022}
}

@misc{do_co-scheduling_2022,
	title = {Co-scheduling {Ensembles} of {In} {Situ} {Workflows}},
	url = {http://arxiv.org/abs/2208.09190},
	doi = {10.48550/arXiv.2208.09190},
	abstract = {Molecular dynamics (MD) simulations are widely used to study large-scale molecular systems. HPC systems are ideal platforms to run these studies, however, reaching the necessary simulation timescale to detect rare processes is challenging, even with modern supercomputers. To overcome the timescale limitation, the simulation of a long MD trajectory is replaced by multiple short-range simulations that are executed simultaneously in an ensemble of simulations. Analyses are usually co-scheduled with these simulations to efficiently process large volumes of data generated by the simulations at runtime, thanks to in situ techniques. Executing a workflow ensemble of simulations and their in situ analyses requires efficient co-scheduling strategies and sophisticated management of computational resources so that they are not slowing down each other. In this paper, we propose an efficient method to co-schedule simulations and in situ analyses such that the makespan of the workflow ensemble is minimized. We present a novel approach to allocate resources for a workflow ensemble under resource constraints by using a theoretical framework modeling the workflow ensemble's execution. We evaluate the proposed approach using an accurate simulator based on the WRENCH simulation framework on various workflow ensemble configurations. Results demonstrate the significance of co-scheduling simulations and in situ analyses that couple data together to benefit from data locality, in which inefficient scheduling decisions can lead up to a factor 30 slowdown in makespan.},
	urldate = {2022-08-29},
	publisher = {arXiv},
	author = {Do, Tu Mai Anh and Pottier, Loïc and da Silva, Rafael Ferreira and Suter, Frédéric and Caíno-Lores, Silvina and Taufer, Michela and Deelman, Ewa},
	month = aug,
	year = {2022},
	note = {arXiv:2208.09190 [cs]},
	keywords = {mentions sympy, parallel computing},
}

Molecular dynamics (MD) simulations are widely used to study large-scale molecular systems. HPC systems are ideal platforms to run these studies, however, reaching the necessary simulation timescale to detect rare processes is challenging, even with modern supercomputers. To overcome the timescale limitation, the simulation of a long MD trajectory is replaced by multiple short-range simulations that are executed simultaneously in an ensemble of simulations. Analyses are usually co-scheduled with these simulations to efficiently process large volumes of data generated by the simulations at runtime, thanks to in situ techniques. Executing a workflow ensemble of simulations and their in situ analyses requires efficient co-scheduling strategies and sophisticated management of computational resources so that they are not slowing down each other. In this paper, we propose an efficient method to co-schedule simulations and in situ analyses such that the makespan of the workflow ensemble is minimized. We present a novel approach to allocate resources for a workflow ensemble under resource constraints by using a theoretical framework modeling the workflow ensemble's execution. We evaluate the proposed approach using an accurate simulator based on the WRENCH simulation framework on various workflow ensemble configurations. Results demonstrate the significance of co-scheduling simulations and in situ analyses that couple data together to benefit from data locality, in which inefficient scheduling decisions can lead up to a factor 30 slowdown in makespan.

@inproceedings{ma2022coalescing,
  title={Coalescing Global and Local Information for Procedural Text Understanding},
  author={Ma, Kaixin and Ilievski, Filip and Francis, Jonathan and Nyberg, Eric and Oltramari, Alessandro},
  booktitle={COLING},
  year={2022}
}

@inproceedings{DBLP:conf/aips/ZhengRKKK22,
  author    = {Yi Zheng and
               Srivatsan Ravi and
               Erik Kline and
               Sven Koenig and
               T. K. Satish Kumar},
  editor    = {Akshat Kumar and
               Sylvie Thi{\'{e}}baux and
               Pradeep Varakantham and
               William Yeoh},
  title     = {Conflict-Based Search for the Virtual Network Embedding Problem},
  booktitle = {Proceedings of the Thirty-Second International Conference on Automated
               Planning and Scheduling, {ICAPS} 2022, Singapore (virtual), June 13-24,
               2022},
  pages     = {423--433},
  publisher = {{AAAI} Press},
  year      = {2022},
  url       = {https://ojs.aaai.org/index.php/ICAPS/article/view/19828},
  timestamp = {Wed, 20 Jul 2022 14:27:38 +0200},
  biburl    = {https://dblp.org/rec/conf/aips/ZhengRKKK22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@TechReport{Moura22b,
	  author = "G. Moura and W. Hardaker and J. Heidemann and M. Davids",
	  title = "Considerations for Large Authoritative {DNS} Server Operators",
	  institution =  "Internet Request For Comments",
	  year = 	       2022,
	sortdate = "2022-03-01",
	project = "ant, diiner, paaddos, classnet",
	jsubject = "network_observation",
	  type =	       "RFC",
	  number =       9199,
	  month =	       mar,
	note = "Informational",
	  jlocation =	"johnh: pafiles",
	  keywords =     "internet routing anycast, IP anycast, dns anycast,
                  rfc-9199, root dns anycast (but not explicitly)",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Moura22b.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Moura22b.pdf",
	  otherurl =	       "https://www.rfc-editor.org/rfc/rfc9199.txt",
	doi = "http://dx.doi.org/10.17487/RFC9199",
	abstract = "
   Recent research work has explored the deployment characteristics and
   configuration of the Domain Name System (DNS).  This document
   summarizes the conclusions from these research efforts and offers
   specific, tangible considerations or advice to authoritative DNS
   server operators.  Authoritative server operators may wish to follow
   these considerations to improve their DNS services. \newline
   It is possible that the results presented in this document could be
   applicable in a wider context than just the DNS protocol, as some of
   the results may generically apply to any stateless/short-duration
   anycasted service. \newline
   This document is not an IETF consensus document: it is published for
   informational purposes.
",
}

Recent research work has explored the deployment characteristics and configuration of the Domain Name System (DNS). This document summarizes the conclusions from these research efforts and offers specific, tangible considerations or advice to authoritative DNS server operators. Authoritative server operators may wish to follow these considerations to improve their DNS services. \newline It is possible that the results presented in this document could be applicable in a wider context than just the DNS protocol, as some of the results may generically apply to any stateless/short-duration anycasted service. \newline This document is not an IETF consensus document: it is published for informational purposes.

@Misc{wjh:cdnillicit,
title="Content Delivery Networks (CDNs) Overview and Illicit Material Considerations",
author="Wes Hardaker",
month=10,
year=2022,
URL="https://publicexchange.usc.edu/wp-content/uploads/2022/10/USC-Thorn-Report_CDNs-Overview-and-Illicit-Material-Considerations.pdf",
howpublished="\url{https://publicexchange.usc.edu/wp-content/uploads/2022/10/USC-Thorn-Report_CDNs-Overview-and-Illicit-Material-Considerations.pdf}"
}
                  

@article{wang2022contextualized,
  title={Contextualized Scene Imagination for Generative Commonsense Reasoning},
  author={Wang, PeiFeng and Zamora, Jonathan and Liu, Junfeng and Ilievski, Filip and Chen, Muhao and Ren, Xiang},
  journal={ICLR},
  year={2022}
}

@inproceedings{kudalippalliyalil2022coupled,
  title={Coupled Microdisk Resonators for Efficient Electro-Optic Quantum Frequency Conversion},
  author={Kudalippalliyalil, Ramesh and Chandran, Sujith and Jaiswal, Akhilesh and Jacob, Ajey P},
  booktitle={2022 Conference on Lasers and Electro-Optics (CLEO)},
  pages={1--2},
  year={2022},
  organization={IEEE}
}

@misc{https://doi.org/10.48550/arxiv.2205.11152,
  doi = {10.48550/ARXIV.2205.11152},
  url = {https://arxiv.org/abs/2205.11152},
  author = {M'hamdi, Meryem and Ren, Xiang and May, Jonathan},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Cross-lingual Lifelong Learning},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

@article{alam2022cryocim,
  title={CryoCiM: Cryogenic compute-in-memory based on the quantum anomalous Hall effect},
  author={Alam, Shamiul and Islam, Md Mazharul and Hossain, Md Shafayat and Jaiswal, Akhilesh and Aziz, Ahmedullah},
  journal={Applied Physics Letters},
  volume={120},
  number={14},
  pages={144102},
  year={2022},
  publisher={AIP Publishing LLC}
}

@article{Balenson2022cryptography,
  title={Cryptography Review of W3C Verifiable Credentials Data Model (VCDM) and Decentralized Identifiers (DIDs) Standards and Cryptography Implementation Recommendations (Revision 1)},
  author={Balenson, David and DeAngelis, David},
  year={2022}
}

@incollection{mookerjee-etal-gsa22, 
  title = {Cyberinfrastructure for collecting and integrating geology field data: Community priorities and research agenda},
  author = {Matty Mookerjee and Marjorie A. Chan and Yolanda Gil and Gurman Gill and Charles Goodwin 
and Terry L. Pavlis and Thomas F. Shipley and Taylor Swain and Basil Tikoff and Daniel Vieira},
  year      = {2022},
  booktitle = {Recent Advancements in Geoinformatics and Data Science: Geological Society of America Special Paper 558}, 
  editors = {Xiaogang Ma and Matty Mookerjee and Leslie Hsu and Denise Hills}, 
  publisher = {Geological Society of America},
  ee = {https://doi.org/10.1130/2022.2558(01)}
}

@inproceedings{ghazarian2022deam,
  title={DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations},
  author={Ghazarian, Sarik and Wen, Nuan and Galstyan, Aram and Peng, Nanyun},
  booktitle={ACL 2022},
  year={2022}
}

@inproceedings{hsu-etal-2022-degree,
    title = "{DEGREE}: A Data-Efficient Generation-Based Event Extraction Model",
    author = "Hsu, I-Hung  and
      Huang, Kuan-Hao  and
      Boschee, Elizabeth  and
      Miller, Scott  and
      Natarajan, Prem  and
      Chang, Kai-Wei  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.138",
    doi = "10.18653/v1/2022.naacl-main.138",
    pages = "1890--1908",
    abstract = "Event extraction requires high-quality expert human annotations, which are usually expensive. Therefore, learning a data-efficient event extraction model that can be trained with only a few labeled examples has become a crucial challenge. In this paper, we focus on low-resource end-to-end event extraction and propose DEGREE, a data-efficient model that formulates event extraction as a conditional generation problem. Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern. The final event predictions are then extracted from the generated sentence with a deterministic algorithm. DEGREE has three advantages to learn well with less training data. First, our designed prompts provide semantic guidance for DEGREE to leverage DEGREE and thus better capture the event arguments. Moreover, DEGREE is capable of using additional weakly-supervised information, such as the description of events encoded in the prompts. Finally, DEGREE learns triggers and arguments jointly in an end-to-end manner, which encourages the model to better utilize the shared knowledge and dependencies among them. Our experimental results demonstrate the strong performance of DEGREE for low-resource event extraction.",
}

Event extraction requires high-quality expert human annotations, which are usually expensive. Therefore, learning a data-efficient event extraction model that can be trained with only a few labeled examples has become a crucial challenge. In this paper, we focus on low-resource end-to-end event extraction and propose DEGREE, a data-efficient model that formulates event extraction as a conditional generation problem. Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern. The final event predictions are then extracted from the generated sentence with a deterministic algorithm. DEGREE has three advantages to learn well with less training data. First, our designed prompts provide semantic guidance for DEGREE to leverage DEGREE and thus better capture the event arguments. Moreover, DEGREE is capable of using additional weakly-supervised information, such as the description of events encoded in the prompts. Finally, DEGREE learns triggers and arguments jointly in an end-to-end manner, which encourages the model to better utilize the shared knowledge and dependencies among them. Our experimental results demonstrate the strong performance of DEGREE for low-resource event extraction.

@article{liu2022dangling,
  title={Dangling-Aware Entity Alignment with Mixed High-Order Proximities},
  author={Liu, Juncheng and Sun, Zequn and Hooi, Bryan and Wang, Yiwei and Liu, Dayiheng and Yang, Baosong and Xiao, Xiaokui and Chen, Muhao},
  journal={NAACL - Findings},
  year={2022}
}

@InProceedings{	  xin-icc-2022,
  Author	= {Xin, Yufeng and Fu, Shih-Wen and Mandal, Anirban and
		  Tanaka, Ryan and Rynge, Mats and Vahi, Karan and Deelman,
		  Ewa},
  BookTitle	= {ICC 2022 - IEEE International Conference on
		  Communications},
  Title		= {Data Integrity Error Localization in Networked Systems
		  with Missing Data},
  Year		= {2022},
  Volume	= {},
  Number	= {},
  Pages		= {341-346},
  DOI		= {10.1109/ICC45855.2022.9838996},
  Note		= {Funding Acknowledgments: NSF 1839900}
}

@inproceedings{DBLP:conf/wcnc/JinRK22,
  author    = {Weizhao Jin and
               Srivatsan Ravi and
               Erik Kline},
  title     = {Decentralized Privacy-Preserving Path Validation for Multi-Slicing-Authority
               5G Networks},
  booktitle = {{IEEE} Wireless Communications and Networking Conference, {WCNC} 2022,
               Austin, TX, USA, April 10-13, 2022},
  pages     = {31--36},
  publisher = {{IEEE}},
  year      = {2022},
  url       = {https://doi.org/10.1109/WCNC51071.2022.9771906},
  doi       = {10.1109/WCNC51071.2022.9771906},
  timestamp = {Tue, 24 May 2022 15:39:23 +0200},
  biburl    = {https://dblp.org/rec/conf/wcnc/JinRK22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tetci/BonjourHATLAKB22,
  author    = {Trevor Bonjour and
               Marina Haliem and
               Aala Oqab Alsalem and
               Shilpa Thomas and
               Hongyu Li and
               Vaneet Aggarwal and
               Mayank Kejriwal and
               Bharat K. Bhargava},
  title     = {Decision Making in Monopoly Using a Hybrid Deep Reinforcement Learning
               Approach},
  journal   = {{IEEE} Trans. Emerg. Top. Comput. Intell.},
  volume    = {6},
  number    = {6},
  pages     = {1335--1344},
  year      = {2022},
  url       = {https://doi.org/10.1109/TETCI.2022.3166555},
  doi       = {10.1109/TETCI.2022.3166555},
  timestamp = {Sun, 25 Dec 2022 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/tetci/BonjourHATLAKB22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{huynh2022deep,
  title={Deep Learning Radiographic Assessment of Pulmonary Edema: Training with Serum Biomarkers},
  author={Huynh, Justin and Masoudi, Samira and Noorbakhsh, Abraham and Mahmoodi, Amin and Kligerman, Seth and Yen, Andrew and Jacobs, Kathleen and Hahn, Lewis and Hasenstab, Kyle and Pazzani, Michael},
  journal={IEEE Access},
  year={2022},
  publisher={IEEE}
}

@inproceedings{huynh2022deep,
  title={Deep learning radiographic assessment of pulmonary edema: Training with serum biomarkers},
  author={Huynh, Justin and Masoudi, Samira and Noorbaksh, Abraham and Hasenstab, Kyle and Pazzani, Michael and Hsiao, Albert},
  booktitle={International Conference on Medical Imaging with Deep Learning},
  pages={590--604},
  year={2022},
  organization={PMLR}
}

@article{jagannathan2022demonstration,
  title={Demonstration of quantum-limited discrimination of multicopy pure versus mixed states},
  author={Jagannathan, Arunkumar and Grace, Michael and Brasher, Olivia and Shapiro, Jeffrey H and Guha, Saikat and Habif, Jonathan L},
  journal={Physical Review A},
  volume={105},
  number={3},
  pages={032446},
  year={2022},
  publisher={APS}
}

@article{DBLP:journals/natmi/KejriwalSMM22,
  author    = {Mayank Kejriwal and
               Henrique Santos and
               Alice M. Mulvehill and
               Deborah L. McGuinness},
  title     = {Designing a strong test for measuring true common-sense reasoning},
  journal   = {Nat. Mach. Intell.},
  volume    = {4},
  number    = {4},
  pages     = {318--322},
  year      = {2022},
  url       = {https://doi.org/10.1038/s42256-022-00478-4},
  doi       = {10.1038/s42256-022-00478-4},
  timestamp = {Sat, 10 Sep 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/natmi/KejriwalSMM22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{10.1007/978-3-031-26354-5_7,
  title = {Diabetic Foot Ulcer Segmentation Using Convolutional and Transformer-Based Models},
  booktitle = {Diabetic Foot Ulcers Grand Challenge},
  author = {Hassib, Mariam and Ali, Maram and Mohamed, Amina and Torki, Marwan and Hussein, Mohamed},
  editor = {Yap, Moi Hoon and Kendrick, Connah and Cassidy, Bill},
  year = {2022},
  copyright = {All rights reserved},
  isbn = {978-3-031-26354-5}
}

@article{https://doi.org/10.1111/head.14324,
	author = {Cowan, Robert P. and Rapoport, Alan M. and Blythe, Jim and Rothrock, John and Knievel, Kerry and Peretz, Addie M. and Ekpo, Elizabeth and Sanjanwala, Bharati M. and Woldeamanuel, Yohannes W.},
	title = {Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi-center study},
	journal = {Headache: The Journal of Head and Face Pain},
	volume = {62},
	number = {7},
	pages = {870-882},
	keywords = {artificial intelligence, diagnosis, diagnostic accuracy study, migraine, online engine, semi-structured interview},
	doi = {https://doi.org/10.1111/head.14324},
	url = {https://headachejournal.onlinelibrary.wiley.com/doi/abs/10.1111/head.14324},
	year={2022}
}

@InProceedings{Saluja22a,
        author =        "Tarang Saluja and Johnh Heidemann and Yuri Pradkin",
        title =         "Differences in Monitoring the {DNS} Root Over {IPv4} and {IPv6}",
        booktitle =     "Proceedings of the " # " National Symposium for NSF REU Research in Data Science, Systems, and Security ",
        year =          2022,
	sortdate = 	"2022-12-15", 
	project = "ant, eieio, reu, isireu",
	jsubject = "topology_modeling",
        pages =      "194--203",
        month =      dec,
        address =    "Portland, OR, USA",
        publisher =  "IEEE",
        jlocation =   "johnh: pafile",
        keywords =   "root server system, dnsmon, ripe atlas, ipv4, ipv6",
	doi = "https://doi.org/10.1109/BDCAT56447.2022.00036",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Saluja22a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Saluja22a.pdf",
	dataurl = "https://ant.isi.edu/ripe_atlas_islands/",
	blogurl = "https://ant.isi.edu/blog/?p=1937",
	abstract = "The Domain Name System (DNS) is an essential service for the Internet
which maps host names to IP addresses.  The DNS Root Sever System
operates the top of this namespace.  RIPE Atlas observes DNS from more
than 11k vantage points (VPs) around the world, reporting the
reliability of the DNS Root Server System in DNSmon.  DNSmon shows
that loss rates for queries to the DNS Root are nearly 10\% for IPv6,
much higher than the approximately 2\% loss seen for IPv4.  Although
IPv6 is ``new,'' as an operational protocol available to a third of
Internet users, it ought to be just as reliable as IPv4.  We examine
this difference at a finer granularity by investigating loss at
individual VPs.  We confirm that specific VPs are the source of this
difference and identify two root causes:  VP \emph{islands} with
routing problems at the edge which leave them unable to access IPv6
outside their LAN, and VP \emph{peninsulas} which indicate routing
problems in the core of the network.  These problems account for most
of the loss and nearly all of the difference between IPv4 and IPv6
query loss rates.  Islands account for most of the loss (half of IPv4
failures and 5/6ths of IPv6 failures), and we suggest these
measurement devices should be filtered out to get a more accurate
picture of loss rates.  Peninsulas account for the main differences
between root identifiers, suggesting routing disagreements root
operators need to address.  We believe that filtering out both of
these known problems provides a better measure of underlying network
anomalies and loss and will result in more actionable alerts.",
}

The Domain Name System (DNS) is an essential service for the Internet which maps host names to IP addresses. The DNS Root Sever System operates the top of this namespace. RIPE Atlas observes DNS from more than 11k vantage points (VPs) around the world, reporting the reliability of the DNS Root Server System in DNSmon. DNSmon shows that loss rates for queries to the DNS Root are nearly 10% for IPv6, much higher than the approximately 2% loss seen for IPv4. Although IPv6 is ``new,'' as an operational protocol available to a third of Internet users, it ought to be just as reliable as IPv4. We examine this difference at a finer granularity by investigating loss at individual VPs. We confirm that specific VPs are the source of this difference and identify two root causes: VP \emphislands with routing problems at the edge which leave them unable to access IPv6 outside their LAN, and VP \emphpeninsulas which indicate routing problems in the core of the network. These problems account for most of the loss and nearly all of the difference between IPv4 and IPv6 query loss rates. Islands account for most of the loss (half of IPv4 failures and 5/6ths of IPv6 failures), and we suggest these measurement devices should be filtered out to get a more accurate picture of loss rates. Peninsulas account for the main differences between root identifiers, suggesting routing disagreements root operators need to address. We believe that filtering out both of these known problems provides a better measure of underlying network anomalies and loss and will result in more actionable alerts.

@article{Do-op-Zhu,
	title = {{Do-Operation} Guided Causal Representation Learning with Reduced Supervision Strength},
	url = {https://arxiv.org/pdf/2206.01802},
	journal = {NeurIPS workshop on  Causal Machine Learning for Real-World Impact},
	author = {J. Zhu and H. Xie and W. AbdAlmageed},
	year = {2022}, 
 	keywords="conference"
}

@inproceedings{ilievski:kgswc22,
    author = "Ilievski, Filip and Pujara, Jay and Shenoy, Kartik",
    acceptrate = "39\%",
    bib_url = "/pubs/bib/ilievski-kgswc22.bib",
    booktitle = "Iberoamerican Knowledge Graphs and Semantic Web Conference",
    doi_url = "https://doi.org/10.1007/978-3-031-21422-6\_13",
    pdf_url = "/pubs/2022/ilievski-kgswc22/ilievski-kgswc22.pdf",
    sec = "conf",
    title = "Does Wikidata Support Analogical Reasoning?",
    year = "2022"
}

@inproceedings{xu2022does,
  title={Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing},
  author={Xu, Nan and Wang, Fei and Li, Bangzheng and Dong, Mingtao and Chen, Muhao},
  booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  year={2022}
}

@inproceedings{stan2022domain,
  title={Domain Adaptation for the Segmentation of Confidential Medical Images},
  author={Stan, Serban and Rostami, Mohammad},
  booktitle={British Machine Vision Conference},
  year={2022}
}

@inproceedings{blythe-tregubov2022,
	title={Dynamic graph reduction optimization technique for interdiction games},
	author={Blythe, James and Tregubov, Alexey},
	booktitle={AAMAS Workshop on Optimization and Learning in Multiagent Systems},
	url = {https://dash-agents.github.io/papers/Dynamic_graph_reduction_optimization_technique_for_interdiction_games.pdf},
	year={2022}
}

@article{habif2022electrically,
  title={Electrically modulated thermal emissivity in multi-layer graphene devices},
  author={Habif, Jonathan and Cronin, Stephen and Jagannathan, Arunkumar and Cai, Zhi and Weinstein, Haley},
  journal={Bulletin of the American Physical Society},
  year={2022},
  publisher={APS}
}

@inrpoceedings{GrapheneDevice2022MarchMeeting,
       author = {Haley Weinstein, Zhi Cai, Stephen Cronin, and Jonathan L. Habif},
        title = {Electrically modulated thermal emissivity in multi-layer graphene devices},
    booktitle = {APS March Meeting Abstracts},
         year = 2022,
       series = {APS Meeting Abstracts},
       volume = {2022},
        month = mar,
          eid = {T56.012},
        pages = {T56.012},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022APS..MART56012H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@inproceedings{razak_embedding_2022,
	title = {Embedding physical flow functions into deep learning predictive models for improved production forecasting},
	booktitle = {Unconventional {Resources} {Technology} {Conference}, 20–22 {June} 2022},
	publisher = {Unconventional Resources Technology Conference (URTeC)},
	author = {Razak, Syamil Mohd and Cornelio, Jodel and Cho, Young and Liu, Hui-Hai and Vaidya, Ravimadhav and Jafarpour, Behnam},
	year = {2022},
	pages = {2098--2117},
}

@article{guo2022emotion,
  title={Emotion Regulation and Dynamics of Moral Concerns During the Early COVID-19 Pandemic},
  author={Guo, Siyi and Burghardt, Keith and Rao, Ashwin and Lerman, Kristina},
  journal={arXiv preprint arXiv:2203.03608},
  year={2022}
}

@InProceedings{wang2022:AMIA,
  author = 	 {Xiaochen Wang and Yue Wang and Jos\'{e} Luis Ambite and Abhishek Appaji  and   Howard Lander and  Stephen M. Moore and  Arcot K. Rajasekar  and   Jessica A. Turner  and   Matthew D. Turner  and   Lei Wang  and Satya S. Sahoo},
  title = 	 {Enabling Scientific Reproducibility through FAIR Data Management: An Ontology-driven Deep Learning Approach in the NeuroBridge Project},
  booktitle = {American Medical Informatics Association {(AMIA)} 2022 Annual Symposium},
  year = 	 {2022},
  address = 	 {San Diego, California},
}

@inproceedings{zhang2022enriching,
  title={Enriching Wikidata with Linked Open Data},
  author={Zhang, Bohui and Ilievski, Filip and Szekely, Pedro},
  booktitle={Wikidata-22 workshop},
  year={2022}
}

@inproceedings{thawani:neurips22ws,
    Author = "Thawani, Avijit and Pujara, Jay and Kalyan, Ashwin",
    bib_url = "/pubs/bib/thawani-neurips22ws.bib",
    booktitle = "NeurIPS workshop on MathAI",
    pdf_url = "/pubs/2022/thawani-neurips22ws/thawani-neurips22ws.pdf",
    sec = "ws",
    title = "Estimating Numbers Without Regression",
    year = "2022"
}

@article{DBLP:journals/corr/abs-2208-07999,
  author    = {Tanmay Ghai and
               Yixiang Yao and
               Srivatsan Ravi and
               Pedro A. Szekely},
  title     = {Evaluating the Feasibility of a Provably Secure Privacy-Preserving
               Entity Resolution Adaptation of PPJoin using Homomorphic Encryption},
  journal   = {CoRR},
  volume    = {abs/2208.07999},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2208.07999},
  doi       = {10.48550/arXiv.2208.07999},
  eprinttype = {arXiv},
  eprint    = {2208.07999},
  timestamp = {Mon, 22 Aug 2022 15:19:07 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2208-07999.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{dhinagar2022evaluation,
	author = {Dhinagar, Nikhil J and Thomopoulos, Sophia I and Rajagopalan, Priya and Stripelis, Dimitris and Ambite, Jose Luis and Ver Steeg, Greg and Thompson, Paul M},
	booktitle = {International Symposium on Medical Information Processing and Analysis},
	date-added = {2023-01-17 12:58:15 -0800},
	date-modified = {2023-01-17 13:03:47 -0800},
	title = {Evaluation of Transfer Learning Methods for Detecting Alzheimer's Disease with Brain MRI},
	year = {2022}}

@InProceedings{dhinagar2022:SIPAIM,
  author = 	 {Nikhil Dhinagar and Sophia Thomopoulos and Priya Rajagopalan and Dimitris Stripelis and Jos\'{e} Luis Ambite and Greg Ver Steeg and Paul M Thompson},
  title = 	 {Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease with Brain {MRI}},
  booktitle = {18th International Symposium on Medical Information Processing and Analysis {(SIPAIM)}},
  year = 	 {2022},
  address = 	 {Valparaíso, Chile}
}

@inproceedings{song2022experimental,
  title={Experimental Demonstration of an Optical Half-Adder of Two 4-PSK, 10-Gbit/s Channels using Nonlinear Wave Mixing},
  author={Song, Hao and Zou, Kaiheng and Karapetyan, Narek and Minoofar, Amir and Zhou, Huibin and Su, Xinzhou and Almaiman, Ahmed and Habif, Jonathan L and Turd, Moshe and Willner, Alan E},
  booktitle={2022 IEEE Photonics Conference (IPC)},
  pages={1--2},
  year={2022},
  organization={IEEE}
}

@article{alishahi2022experimental,
  title={Experimental demonstration of remotely controlled tunable optical correlators of 10--50 Gbaud QPSK channels using linear and nonlinear components and laser-delivered powers},
  author={Alishahi, Fatemeh and Minoofar, Amir and Fallahpour, Ahmad and Zou, Kaiheng and Karapetyan, Narek and Zhou, Huibin and Habif, Jonathan L and Tur, Moshe and Willner, Alan E},
  journal={Optics Communications},
  volume={523},
  pages={128698},
  year={2022},
  publisher={Elsevier}
}

@article{article,
author = {Pazzani, Michael and Soltani, Severine and Kaufman, Robert and Qian, Samson and Hsiao, Albert},
year = {2022},
month = {06},
pages = {12280-12286},
title = {Expert-Informed, User-Centric Explanations for Machine Learning},
volume = {36},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
doi = {10.1609/aaai.v36i11.21491}
}

@inproceedings{DBLP:conf/iwmn/GarciaBRIHK22,
  author    = {Luis Garcia and
               Genevieve Bartlett and
               Srivatsan Ravi and
               Harun Ibrahim and
               Wes Hardaker and
               Erik Kline},
  title     = {Explaining Deep Learning Models for Per-packet Encrypted Network Traffic
               Classification},
  booktitle = {{IEEE} International Symposium on Measurements {\&} Networking,
               M{\&}N 2022, Padua, Italy, July 18-20, 2022},
  pages     = {1--6},
  publisher = {{IEEE}},
  year      = {2022},
  url       = {https://doi.org/10.1109/MN55117.2022.9887744},
  doi       = {10.1109/MN55117.2022.9887744},
  timestamp = {Wed, 12 Oct 2022 17:04:13 +0200},
  biburl    = {https://dblp.org/rec/conf/iwmn/GarciaBRIHK22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{albalak:emnlp22,
    author = "Albalak, Alon and Tuan, Yi-Lin and Jandaghi, Pegah and Pryor, Connor and Yoffe, Luke and Ramachandran, Deepak and Getoor, Lise and Pujara, Jay and Wang, William Yang",
    acceptrate = "17\%",
    bib_url = "/pubs/bib/albalak-emnlp22.bib",
    booktitle = "Conference on Empirical Methods in Natural Language Processing",
    doi_url = "http://dx.doi.org/10.18653/v1/2022.emnlp-main.751",
    pdf_url = "/pubs/2022/albalak-emnlp22/albalak-emnlp22.pdf",
    sec = "conf",
    title = "FETA: A benchmark for few-sample task transfer in open-domain dialogue",
    year = "2022"
}

@article{schuler_facebase_2022,
	title = {{FaceBase}: {A} {Community}-{Driven} {Hub} for {Data}-{Intensive} {Research}},
	issn = {0022-0345, 1544-0591},
	shorttitle = {{FaceBase}},
	url = {http://journals.sagepub.com/doi/10.1177/00220345221107905},
	doi = {10.1177/00220345221107905},
	abstract = {The FaceBase Consortium, funded by the National Institute of Dental and Craniofacial Research of the National Institutes of Health, was established in 2009 with the recognition that dental and craniofacial research are increasingly data-intensive disciplines. Data sharing is critical for the validation and reproducibility of results as well as to enable reuse of data. In service of these goals, data ought to be FAIR: Findable, Accessible, Interoperable, and Reusable. The FaceBase data repository and educational resources exemplify the FAIR principles and support a broad user community including researchers in craniofacial development, molecular genetics, and genomics. FaceBase demonstrates that a model in which researchers “self-curate” their data can be successful and scalable. We present the results of the first 2.5 y of FaceBase’s operations as an open community and summarize the data sets published during this period. We then describe a research highlight from work on the identification of regulatory networks and noncoding RNAs involved in cleft lip with/without cleft palate that both used and in turn contributed new findings to publicly available FaceBase resources. Collectively, FaceBase serves as a dynamic and continuously evolving resource to facilitate data-intensive research, enhance data reproducibility, and perform deep phenotyping across multiple species in dental and craniofacial research.},
	language = {en},
	urldate = {2022-08-04},
	journal = {Journal of Dental Research},
	author = {Schuler, R.E. and Bugacov, A. and Hacia, J.G. and Ho, T.V. and Iwata, J. and Pearlman, L. and Samuels, B.D. and Williams, C. and Zhao, Z. and Kesselman, C. and Chai, Y.},
	month = jul,
	year = {2022},
	pages = {002203452211079},
}

The FaceBase Consortium, funded by the National Institute of Dental and Craniofacial Research of the National Institutes of Health, was established in 2009 with the recognition that dental and craniofacial research are increasingly data-intensive disciplines. Data sharing is critical for the validation and reproducibility of results as well as to enable reuse of data. In service of these goals, data ought to be FAIR: Findable, Accessible, Interoperable, and Reusable. The FaceBase data repository and educational resources exemplify the FAIR principles and support a broad user community including researchers in craniofacial development, molecular genetics, and genomics. FaceBase demonstrates that a model in which researchers “self-curate” their data can be successful and scalable. We present the results of the first 2.5 y of FaceBase’s operations as an open community and summarize the data sets published during this period. We then describe a research highlight from work on the identification of regulatory networks and noncoding RNAs involved in cleft lip with/without cleft palate that both used and in turn contributed new findings to publicly available FaceBase resources. Collectively, FaceBase serves as a dynamic and continuously evolving resource to facilitate data-intensive research, enhance data reproducibility, and perform deep phenotyping across multiple species in dental and craniofacial research.

@inproceedings{failure,
	author = {Tigran Galstyan and Hrayr Harutyunyan and Hrant Khachatrian and Greg {Ver Steeg} and Aram Galstyan},
	booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
	date-added = {2022-03-04 11:39:18 -0800},
	date-modified = {2022-03-28 16:08:06 -0700},
	title = {Failure Modes of Domain Generalization Algorithms},
	year = {2022}}

@inproceedings{galstyan2022failure,
  title={Failure modes of domain generalization algorithms},
  author={Galstyan, Tigran and Harutyunyan, Hrayr and Khachatrian, Hrant and Steeg, Greg Ver and Galstyan, Aram},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19077--19086},
  year={2022}
}

@article{DBLP:journals/corr/abs-2201-12433,
  author       = {Yuhang Yao and
                  Carlee Joe{-}Wong},
  title        = {FedGCN: Convergence and Communication Tradeoffs in Federated Training
                  of Graph Convolutional Networks},
  journal      = {CoRR},
  volume       = {abs/2201.12433},
  year         = {2022},
  url          = {https://arxiv.org/abs/2201.12433},
  eprinttype    = {arXiv},
  eprint       = {2201.12433},
  timestamp    = {Wed, 02 Feb 2022 15:00:01 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2201-12433.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{stripelis2022:AAIC,
  author = 	 {Stripelis, Dimitris and Dhinagar, Nikhil J and Sanchez Romero, Rafael V. and Thomopoulos, Sophia I and Thompson, Paul M and Ambite, Jos\'{e} Luis},
  title = 	 {Federated Deep Learning for Detecting Alzheimer’s Disease in Multi-Cohort Brain MRI},
  booktitle = {Alzheimer's Association International Conference},
  year = 	 {2022},
  address = 	 {San Diego, California},
  note = 	 {Abstract + Presentation},
}

@inproceedings{stripelis2022:FL-NeurIPS,
title={Federated Progressive Sparsification (Purge-Merge-Tune)+},
author={Dimitris Stripelis and Umang Gupta and Greg Ver Steeg and Jose Luis Ambite},
booktitle={Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS 2022)},
year={2022},
address =  {New Orleans, Louisiana},
}

@inproceedings{harutyunyan2022formal,
  title={Formal limitations of sample-wise information-theoretic generalization bounds},
  author={Harutyunyan, Hrayr and Ver Steeg, Greg and Galstyan, Aram},
  booktitle={2022 IEEE Information Theory Workshop (ITW)},
  pages={440--445},
  year={2022},
  organization={IEEE}
}

@INPROCEEDINGS{harutyunyan2022formal,
  author={Harutyunyan, Hrayr and Steeg, Greg Ver and Galstyan, Aram},
  booktitle={2022 IEEE Information Theory Workshop (ITW)},
  title={Formal limitations of sample-wise information-theoretic generalization bounds},
  year={2022},
  volume={},
  number={},
  pages={440-445},
  doi={10.1109/ITW54588.2022.9965850}
}

@article{li2022functional,
	author = {Li, Qiang and {Ver Steeg}, Greg and Yu, Shujian and Malo, Jesus},
	date-added = {2023-01-17 12:41:01 -0800},
	date-modified = {2023-01-17 12:41:14 -0800},
	journal = {Entropy},
	number = {12},
	pages = {1725},
	publisher = {Multidisciplinary Digital Publishing Institute},
	title = {Functional Connectome of the Human Brain with Total Correlation},
	volume = {24},
	year = {2022}}

@INPROCEEDINGS{GOAL,
  author = {Pervaiz, Ahsan and Yang, Yao Hsiang and Duracz, Adam and Bartha, Ferenc and Sai, Ryuichi and Imes, Connor and Cartwright, Robert and Palem, Krishna and Lu, Shan and Hoffmann, Henry},
  title = {GOAL: Supporting General and Dynamic Adaptation in Computing Systems},
  year = {2022},
  isbn = {9781450399098},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3563835.3567655},
  doi = {10.1145/3563835.3567655},
  abstract = {Adaptive computing systems automatically monitor their behavior and dynamically adjust their own configuration parameters—or knobs—to ensure that user goals are met despite unpredictable external disturbances to the system. A major limitation of prior adaptation frameworks is that their internal adaptation logic is implemented for a specific, narrow set of goals and knobs, which impedes the development of complex adaptive systems that must meet different goals using different sets of knobs for different deployments, or even change goals during one deployment. To overcome this limitation we propose GOAL, an adaptation framework distinguished by its virtualized adaptation logic implemented independently of any specific goals or knobs. GOAL supports this logic with a programming interface allowing users to define and manipulate a wide range of goals and knobs within a running program. We demonstrate GOAL’s benefits by using it re-implement seven different adaptive systems from the literature, each of which has a different set of goals and knobs. We show GOAL’s general approach meets goals as well as prior approaches designed for specific goals and knobs. In dynamic scenarios where the goals and knobs are modified at runtime, GOAL achieves 93.7},
  booktitle = {Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software},
  pages = {16–32},
  numpages = {17},
  keywords = {domain-specific language, control theory, adaptive computing, resource allocation, energy},
  location = {Auckland, New Zealand},
  series = {Onward! 2022},
  ISIArea = {CAS, OTH}
}

Adaptive computing systems automatically monitor their behavior and dynamically adjust their own configuration parameters—or knobs—to ensure that user goals are met despite unpredictable external disturbances to the system. A major limitation of prior adaptation frameworks is that their internal adaptation logic is implemented for a specific, narrow set of goals and knobs, which impedes the development of complex adaptive systems that must meet different goals using different sets of knobs for different deployments, or even change goals during one deployment. To overcome this limitation we propose GOAL, an adaptation framework distinguished by its virtualized adaptation logic implemented independently of any specific goals or knobs. GOAL supports this logic with a programming interface allowing users to define and manipulate a wide range of goals and knobs within a running program. We demonstrate GOAL’s benefits by using it re-implement seven different adaptive systems from the literature, each of which has a different set of goals and knobs. We show GOAL’s general approach meets goals as well as prior approaches designed for specific goals and knobs. In dynamic scenarios where the goals and knobs are modified at runtime, GOAL achieves 93.7

@article{wang2022graphcache,
  title={GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction},
  author={Wang, Yiwei and Chen, Muhao and Zhou, Wenxuan and Cai, Yujun and Liang, Yuxuan and Hooi, Bryan},
  journal={NAACL - Findings},
  year={2022}
}

@article{cai2022gate,
  title={Gate-tunable modulation of the optical properties of multilayer graphene by the reversible intercalation of ionic liquid anions},
  author={Cai, Zhi and Aravind, Indu and Weinstein, Haley and Li, Ruoxi and Wu, Jiangbin and Wang, Han and Habif, Jonathan and Cronin, Stephen B},
  journal={Journal of Applied Physics},
  volume={132},
  number={9},
  pages={095102},
  year={2022},
  publisher={AIP Publishing LLC}
}

@article{10.1063/5.0093651,
    author = {Zhi Cai, Indu Aravind, Haley Weinstein, Ruoxi Li, Jiangbin Wu, Han Wang, Jonathan Habif, and Stephen B. Cronin},
    title = {Gate-tunable modulation of the optical properties of multilayer graphene by the reversible intercalation of ionic liquid anions},
    journal = {Journal of Applied Physics},
    volume = {132},
    number = {9},
    pages = {095102},
    year = {2022},
    month = {09},
    issn = {0021-8979},
    doi = {10.1063/5.0093651},
    url = {https://doi.org/10.1063/5.0093651},
    eprint = {https://pubs.aip.org/aip/jap/article-pdf/doi/10.1063/5.0093651/16512861/095102\_1\_online.pdf},
}

@article{lerman2022gendered,
  title={Gendered citation patterns among the scientific elite},
  author={Lerman, Kristina and Yu, Yulin and Morstatter, Fred and Pujara, Jay},
  journal={Proceedings of the National Academy of Sciences},
  volume={119},
  number={40},
  pages={e2206070119},
  year={2022},
  publisher={National Acad Sciences}
}

@inbook{oltramari2022generalizable,
  title={Generalizable Neuro-symbolic Systems for Commonsense Question Answering},
  author={Oltramari, Alessandro and Francis, Jonathan and Ilievski, Filip and Ma, Kaixin and Mirzaee, Roshanak},
  booktitle={Neuro-Symbolic Artificial Intelligence: The State of the Art},
  year={2022}
}

@inproceedings{10.1145/3546096.3546107,
author = {DeAngelis, David and Hussain, Alefiya and Kocoloski, Brian and Ardi, Calvin and Schwab, Stephen},
title = {Generating Representative Video Teleconferencing Traffic},
year = {2022},
isbn = {9781450396844},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3546096.3546107},
doi = {10.1145/3546096.3546107},
abstract = {Video teleconferencing (VTC) is a dominant network application, yet there is a dearth of tools to generate such traffic for systematic and reproducible experimentation. We present a framework to create representative video teleconferencing traffic and discuss our methodology for behavioral control of multiple bots to create human-like dialog coordination, including interactive talking and silence patterns. Our framework can be coupled with proprietary commercial VTC applications as well as deployed completely within a testbed environment to benchmark emerging networking technology and evaluate the next generation of traffic classification, quality of service (QoS) algorithms, and traffic engineering systems. Our traffic generators are open source and freely available at https://mergetb.org/projects/searchlight/.},
booktitle = {Proceedings of the 15th Workshop on Cyber Security Experimentation and Test},
pages = {100–104},
numpages = {5},
keywords = {cybersecurity testbeds, network traffic generation, VoIP, video teleconference},
location = {Virtual, CA, USA},
series = {CSET '22}
}

Video teleconferencing (VTC) is a dominant network application, yet there is a dearth of tools to generate such traffic for systematic and reproducible experimentation. We present a framework to create representative video teleconferencing traffic and discuss our methodology for behavioral control of multiple bots to create human-like dialog coordination, including interactive talking and silence patterns. Our framework can be coupled with proprietary commercial VTC applications as well as deployed completely within a testbed environment to benchmark emerging networking technology and evaluate the next generation of traffic classification, quality of service (QoS) algorithms, and traffic engineering systems. Our traffic generators are open source and freely available at https://mergetb.org/projects/searchlight/.

@inproceedings{lee_good_2022,
	address = {Dublin, Ireland},
	title = {Good {Examples} {Make} {A} {Faster} {Learner}: {Simple} {Demonstration}-based {Learning} for {Low}-resource {NER}},
	url = {https://aclanthology.org/2022.acl-long.192},
	doi = {10.18653/v1/2022.acl-long.192},
	booktitle = {Proceedings of the 60th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Lee, Dong-Ho and Kadakia, Akshen and Tan, Kangmin and Agarwal, Mahak and Feng, Xinyu and Shibuya, Takashi and Mitani, Ryosuke and Sekiya, Toshiyuki and Pujara, Jay and Ren, Xiang},
	month = may,
	year = {2022},
	pages = {2687--2700},
}

@inproceedings{man29832022,
author={Nicolaas Weideman and Haoda Wang and Tyler Kann and Spencer Zahabizadeh and Wei-Cheng Wu and Rajat Tandon and Jelena Mirkovic and Christophe Hauser},
title={Harm-DoS: Hash Algorithm Replacement for Mitigating Denial-of-Service Vulnerabilities in Binary Executables},
booktitle={RAID},
year={2022}}

@inproceedings{weideman2022harm,
  title={Harm-DoS: Hash Algorithm Replacement for Mitigating Denial-of-Service Vulnerabilities in Binary Executables},
  author={Weideman, Nicolaas and Wang, Haoda and Kann, Tyler and Zahabizadeh, Spencer and Wu, Wei-Cheng and Tandon, Rajat and Mikrovic, Jelena and Hauser, Christophe},
  booktitle={The 25th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2022)},
  year={2022}
}

@article{Weinstein:22,
author = {Haley A. Weinstein and Zhi Cai and Stephen B. Cronin and Jonathan L. Habif},
journal = {Opt. Lett.},
keywords = {Emissivity; Free space optics; Modulation techniques; Optical communications; Signal transmission; Systems design},
number = {23},
pages = {6225--6228},
publisher = {Optica Publishing Group},
title = {Harvesting Planck radiation for free-space optical communications in the long-wave infrared band},
volume = {47},
month = {Dec},
year = {2022},
url = {https://opg.optica.org/ol/abstract.cfm?URI=ol-47-23-6225},
doi = {10.1364/OL.476394},
abstract = {We demonstrate a free-space optical communication link with an optical transmitter that harvests naturally occurring Planck radiation from a warm body and modulates the emitted intensity. The transmitter exploits an electro-thermo-optic effect in a multilayer graphene device that electrically controls the surface emissivity of the device resulting in control of the intensity of the emitted Planck radiation. We design an amplitude-modulated optical communication scheme and provide a link budget for communications data rate and range based on our experimental electro-optic characterization of the transmitter. Finally, we present an experimental demonstration achieving error-free communications at 100 bits per second over laboratory scales.},
}

We demonstrate a free-space optical communication link with an optical transmitter that harvests naturally occurring Planck radiation from a warm body and modulates the emitted intensity. The transmitter exploits an electro-thermo-optic effect in a multilayer graphene device that electrically controls the surface emissivity of the device resulting in control of the intensity of the emitted Planck radiation. We design an amplitude-modulated optical communication scheme and provide a link budget for communications data rate and range based on our experimental electro-optic characterization of the transmitter. Finally, we present an experimental demonstration achieving error-free communications at 100 bits per second over laboratory scales.

@TechReport{Imana22a,
        author =        "Basileal Imana and Aleksandra Korolova and John Heidemann",
        title =         "Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest",
        institution =   "arXiv",
        year =          2022,
	sortdate = 	"2022-07-18", 
	project = "ant",
	jsubject = "network_observation",
        number =     "arXiv:2207.08773v2 [cs.CY]",
	note = "updated 2023-02-13",
        month =      jul,
        jlocation =   "johnh: pafile",
        keywords =   "linkedin, facebook, ad delivery algorithm, bias,
                  skew, discrimination, platform-supported auditing,
                  differential privacy",
        doi =     "https://doi.org/10.48550/arXiv.2207.08773",
        otherurl =        "https://arxiv.org/pdf/2207.08773v1.pdf",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Imana22a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Imana22a.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1889",
	abstract = "Social media platforms curate access to information and opportunities,
and so play a critical role in shaping public discourse today.  The
opaque nature of the algorithms these platforms use to curate content
raises societal questions.  Prior studies have used black-box methods
led by experts or collaborative audits driven by everyday users to
show that these algorithms can lead to biased or discriminatory
outcomes.  However, existing auditing methods face fundamental
limitations because they function independent of the platforms.
Concerns of potential harmful outcomes have prompted proposal of
legislation in both the U.S.~and the E.U.~to mandate a new form of
auditing where vetted external researchers get privileged access to
social media platforms.  Unfortunately, to date there have been no
concrete technical proposals to provide such auditing, because
auditing at scale risks disclosure of users' private data and
platforms' proprietary algorithms.  We propose a new method for
\emph{platform-supported auditing} that can meet the goals of the
proposed legislation.  The first contribution of our work is to
enumerate the challenges and the limitations of existing auditing
methods to implement these policies at scale.  Second, we suggest that
limited, privileged access to \emph{relevance estimators} is the key
to enabling generalizable platform-supported auditing of social media
platforms by external researchers.  Third, we show platform-supported
auditing need not risk user privacy nor disclosure of platforms'
business interests by proposing an auditing framework that protects
against these risks.  For a particular fairness metric, we show that
ensuring privacy imposes only a small constant factor increase
($6.34\times$ as an upper bound, and $4\times$ for typical parameters)
in the number of samples required for accurate auditing.  Our
technical contributions, combined with ongoing legal and policy
efforts, can enable public oversight into how social media platforms
affect individuals and society by moving past the
privacy-vs-transparency hurdle.
",
}

Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse today. The opaque nature of the algorithms these platforms use to curate content raises societal questions. Prior studies have used black-box methods led by experts or collaborative audits driven by everyday users to show that these algorithms can lead to biased or discriminatory outcomes. However, existing auditing methods face fundamental limitations because they function independent of the platforms. Concerns of potential harmful outcomes have prompted proposal of legislation in both the U.S. and the E.U. to mandate a new form of auditing where vetted external researchers get privileged access to social media platforms. Unfortunately, to date there have been no concrete technical proposals to provide such auditing, because auditing at scale risks disclosure of users' private data and platforms' proprietary algorithms. We propose a new method for \emphplatform-supported auditing that can meet the goals of the proposed legislation. The first contribution of our work is to enumerate the challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to \emphrelevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms' business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase ($6.34×$ as an upper bound, and $4×$ for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle.

@inproceedings{kudalippalliyalil2022heterogeneously,
  title={Heterogeneously Integrated Quantum Chip Interposer Packaging},
  author={Kudalippalliyalil, Ramesh and Chandran, Sujith and Jaiswal, Akhilesh and Wang, Kang L and Jacob, Ajey P},
  booktitle={2022 IEEE 72nd Electronic Components and Technology Conference (ECTC)},
  pages={1869--1874},
  year={2022},
  organization={IEEE}
}

@patent{cho_high-speed_nodate,
	title = {High-{Speed} {Compact} {Look}-{Up} {Table} with {Input} {Select} and {Registers}},
	nationality = {The United States},
	author = {Cho, Young and Widjaja, Yuniarto},
	year = {2022},
}

@inproceedings{don62742022,
author={Rajat Tandon and Pithayuth Charnsethikul and Ishank Arora and Dhiraj Murthy and Jelena Mirkovic},
title={I know what you did on Venmo: Discovering privacy leaks in mobile social payments},
booktitle={Privacy Enhancing Technologies Symposium (PETS)},
year={2022}}
    

@misc{rfc9318,
    series =    {Request for Comments},
    number =    9318,
    howpublished =  {RFC 9318},
    publisher = {RFC Editor},
    doi =       {10.17487/RFC9318},
    url =       {https://www.rfc-editor.org/info/rfc9318},
        author =    {Wes Hardaker and Omer Shapira},
    title =     {{IAB Workshop Report: Measuring Network Quality for End-Users}},
    pagetotal = 31,
    year =      2022,
    month =     oct,
    abstract =  {The Measuring Network Quality for End-Users workshop was held virtually by the Internet Architecture Board (IAB) on September 14-16, 2021. This report summarizes the workshop, the topics discussed, and some preliminary conclusions drawn at the end of the workshop. Note that this document is a report on the proceedings of the workshop. The views and positions documented in this report are those of the workshop participants and do not necessarily reflect IAB views and positions.},
}
                  

The Measuring Network Quality for End-Users workshop was held virtually by the Internet Architecture Board (IAB) on September 14-16, 2021. This report summarizes the workshop, the topics discussed, and some preliminary conclusions drawn at the end of the workshop. Note that this document is a report on the proceedings of the workshop. The views and positions documented in this report are those of the workshop participants and do not necessarily reflect IAB views and positions.

@inproceedings{klein2022identifying,
  title={Identifying Surprising Facts in Wikidata},
  author={Klein, Nicholas and Ilievski, Filip and Freedman, Hayden and Szekely, Pedro},
  booktitle={Wikidata-22 workshop},
  year={2022}
}

@article{hossain2022identifying,
  title={Identifying geopolitical event precursors using attention-based LSTMs},
  author={Hossain, KSM Tozammel and Harutyunyan, Hrayr and Ning, Yue and Kennedy, Brendan and Ramakrishnan, Naren and Galstyan, Aram},
  journal={Frontiers in Artificial Intelligence},
  volume={5},
  year={2022},
  publisher={Frontiers Media SA}
}
%2021

@misc{jaiswal2022image,
  title={Image sensor incorporating an array of optically switchable magnetic tunnel junctions},
  author={Jaiswal, Akhilesh R and Jacob, Ajey Poovannummoottil and Bian, Yusheng and Pritchard, David C},
  year={2022},
  month=jan # "~18",
  publisher={Google Patents},
  note={US Patent 11,226,231}
}

@inproceedings{yao_improved_2022,
	title = {Improved {Operational} {Carbon} {Footprint} {Reduction} by {Using} a {Data}-{Driven} {Method} in {Chemical} {Treatment} {Candidate} {Selection}},
	url = {https://onepetro.org/SPEATCE/proceedings-abstract/22ATCE/3-22ATCE/509047},
	doi = {10.2118/209999-MS},
	abstract = {Abstract. This paper presents a physics based and data driven machine learning approach for chemical treatment candidate well selection in fields producing heavy oil. In heavy oil fields, cyclic steam is often used to not only to stimulate the formation around a wellbore but also to open the casing perforations or liner slots plugged by asphaltene deposits. These asphaltene and paraffin build ups can partially or fully block the passages to fluid production. Such cyclic steam jobs are done when there is significant decline in production as measured with usually a threshold over 60 days of operation. But generating steam and using it in cyclic steam jobs is costly and contribute to the carbon footprint. An alternative solution is to employ chemical treatment of wells to clean up blockages that may occur in formations (Brownlee and Zern, 1997). When there is true blockage due to deposits by heavier oil components, cleaning the perforations or liner slots is expected to improve flow and increase the production. However, historical data show that not all chemical treatment jobs yield the same level of success. The problem addressed in this paper is using a data-driven predictive modeling approach to make recommendations for candidate well selection using chemical treatment based on past performance of chemically treated wells. This paper presents the development of a fully customized machine learning tool that has been designed as a decision support tool for a particular task for non-data scientist reservoir and production engineers. The architecture of this tool includes independent modules that can be re-trained in the future once additional production data becomes available from the fields. We attribute the success of this research to the fact that there was active collaboration among domain experts. Critical physical properties were included in this study, including chemistry studies of chemicals and limited field trials in validating the developed model. The resulting machine learning model can be used by field engineers for planning chemical well treatments given the continuously updated past performance data.},
	language = {en},
	urldate = {2023-02-18},
	publisher = {OnePetro},
	author = {Yao, Ke-Thia and Raghavenda, Cauligi S. and Ashayeri, Cyrus and Trivedi, Dhruvil and Patel, Tirth and Lal, Manish and Row, Richard and Aksehirli, Attila and Ershaghi, Iraj},
	month = sep,
	year = {2022},
	file = {Full Text PDF:C\:\\Users\\ktyao\\Zotero\\storage\\QJJGVHKB\\Yao et al. - 2022 - Improved Operational Carbon Footprint Reduction by.pdf:application/pdf},
}

Abstract. This paper presents a physics based and data driven machine learning approach for chemical treatment candidate well selection in fields producing heavy oil. In heavy oil fields, cyclic steam is often used to not only to stimulate the formation around a wellbore but also to open the casing perforations or liner slots plugged by asphaltene deposits. These asphaltene and paraffin build ups can partially or fully block the passages to fluid production. Such cyclic steam jobs are done when there is significant decline in production as measured with usually a threshold over 60 days of operation. But generating steam and using it in cyclic steam jobs is costly and contribute to the carbon footprint. An alternative solution is to employ chemical treatment of wells to clean up blockages that may occur in formations (Brownlee and Zern, 1997). When there is true blockage due to deposits by heavier oil components, cleaning the perforations or liner slots is expected to improve flow and increase the production. However, historical data show that not all chemical treatment jobs yield the same level of success. The problem addressed in this paper is using a data-driven predictive modeling approach to make recommendations for candidate well selection using chemical treatment based on past performance of chemically treated wells. This paper presents the development of a fully customized machine learning tool that has been designed as a decision support tool for a particular task for non-data scientist reservoir and production engineers. The architecture of this tool includes independent modules that can be re-trained in the future once additional production data becomes available from the fields. We attribute the success of this research to the fact that there was active collaboration among domain experts. Critical physical properties were included in this study, including chemistry studies of chemicals and limited field trials in validating the developed model. The resulting machine learning model can be used by field engineers for planning chemical well treatments given the continuously updated past performance data.

@inproceedings{inproceedings,
author = {Ahamed, Aadil and Alipour, Kamran and Kumar, Sateesh and Soltani, Severine and Pazzani, Michael},
year = {2022},
month = {10},
pages = {1-16},
title = {Improving Explanations of Image Classification with Ensembles of Learners},
doi = {10.5121/csit.2022.121801}
}

@article{article,
author = {Pazzani, Michael and Soltani, Severine and Kumar, Sateesh and Alipour, Kamran and Ahamed, Aadil},
year = {2022},
month = {11},
pages = {51-72},
title = {Improving Explanations of Image Classifiers: Ensembles and Multitask Learning},
volume = {13},
journal = {International Journal of Artificial Intelligence & Applications},
doi = {10.5121/ijaia.2022.13604}
}

@inproceedings{annealed_mi,
	author = {Rob Brekelmans and Sicong Huang and Marzyeh Ghassemi and Greg {Ver Steeg} and Roger Baker Grosse and Alireza Makhzani},
	booktitle = {International Conference on Learning Representations (ICLR)},
	date-added = {2022-03-04 11:41:56 -0800},
	date-modified = {2022-03-04 11:42:35 -0800},
	title = {Improving Mutual Information Estimation with Annealed and Energy-Based Bounds},
	year = {2022}}

@inproceedings{yang-ma-2022-improving,
    title = "Improving Stability of Fine-Tuning Pretrained Language Models via Component-Wise Gradient Norm Clipping",
    author = "Yang, Chenghao  and
      Ma, Xuezhe",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    pages = "4854--4859",
}

@inproceedings{ardi2022improving,
  title={Improving fidelity in video streaming experimentation on testbeds with a CDN},
  author={Ardi, Calvin and Hussain, Alefiya and Collins, Michael and Schwab, Stephen},
  booktitle={Proceedings of the 2nd International Workshop on Design, Deployment, and Evaluation of Network-Assisted Video Streaming},
  pages={1--7},
  year={2022}
}

@misc{karuppiah2022band,
  title={In-band-telemetry-based path MTU size determination system},
  author={Karuppiah, Kannan and Sankaran, Ganesh Chennimalai},
  year={2022},
  month=aug # "~2",
  note={US Patent US11405325B2}
}

@inproceedings{rostami2022increasing,
  title={Increasing Model Generalizability for Unsupervised Visual Domain Adaptation},
  author={Rostami, Mohammad},
  booktitle={Conference on Lifelong Learning Agents},
  pages={281--293},
  year={2022},
  organization={PMLR}
}

@article{abram2022inferring,
  title={Inferring topological transitions in pattern-forming processes with self-supervised learning},
  author={Abram, Marcin and Burghardt, Keith and Ver Steeg, Greg and Galstyan, Aram and Dingreville, Remi},
  journal={npj Computational Materials},
  volume={8},
  number={1},
  pages={205},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@article{he2022infusing,
  title={Infusing Knowledge from Wikipedia to Enhance Stance Detection},
  author={He, Zihao and Mokhberian, Negar and Lerman, Kristina},
  journal={arXiv preprint arXiv:2204.03839},
  year={2022}
}

@inproceedings{idres2022integrated,
  title={Integrated All-Optical Controlled Amplitude Modulator Using Laser Delivered Signals},
  author={Idres, Samer and Fallahpour, Ahmad and Willner, Alan E and Habif, Jonathan and Hashemi, Hossein},
  booktitle={CLEO: Applications and Technology},
  pages={JTh3A--48},
  year={2022},
  organization={Optica Publishing Group}
}

@TechReport{Enayet22b,
        author =        "Asma Enayet and John Heidemann",
        title =         "Internet Outage Detection using Passive
                  Analysis (poster abstract and poster)",
        institution =   "USC/Information Sciences Institute",
        year =          "2022",
	month = sep,
	sortdate = 		"2022-09-28", 
	project = "ant, eieio",
	jsubject = "routing",
        pages =      "to appear",
        address =    "Nice, France",
        publisher =  "ACM",
        jlocation =   "johnh: pafile",
        keywords =   "outage detection, passive data, b-root, ipv6",
        number =     "arxiv:2209.13767v3",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Enayet22b.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Enayet22b.pdf",
        doi =        "https://doi.org/10.48550/arXiv.2209.13767",
	abstract = "Outages from natural disasters, political events, software or hardware issues, and human error place a huge cost on e-commerce (\$66k per minute at Amazon). While several existing systems detect Internet outages, these systems are often too inflexible, with fixed parameters across the whole internet with CUSUM-like change detection. We instead propose a system using passive data, to cover both IPv4 and IPv6, customizing parameters for each block to optimize the performance of our Bayesian inference model. Our poster describes our three contributions: First, we show how customizing parameters allows us often to detect outages that are at both fine timescales (5 minutes) and fine spatial resolutions (/24 IPv4 and /48 IPv6 blocks). Our second contribution is to show that, by tuning parameters differently for different blocks, we can scale back temporal precision to cover more challenging blocks. Finally, we show our approach extends to IPv6 and provides the first reports of IPv6 outages."
}

Outages from natural disasters, political events, software or hardware issues, and human error place a huge cost on e-commerce ($66k per minute at Amazon). While several existing systems detect Internet outages, these systems are often too inflexible, with fixed parameters across the whole internet with CUSUM-like change detection. We instead propose a system using passive data, to cover both IPv4 and IPv6, customizing parameters for each block to optimize the performance of our Bayesian inference model. Our poster describes our three contributions: First, we show how customizing parameters allows us often to detect outages that are at both fine timescales (5 minutes) and fine spatial resolutions (/24 IPv4 and /48 IPv6 blocks). Our second contribution is to show that, by tuning parameters differently for different blocks, we can scale back temporal precision to cover more challenging blocks. Finally, we show our approach extends to IPv6 and provides the first reports of IPv6 outages.

@InProceedings{Enayet22a,
        author =        "Asma Enayet and John Heidemann",
        title =         "Internet Outage Detection using Passive
                  Analysis (poster abstract)",
        booktitle =     "Proceedings of the " # "ACM Internet Measurement Conference",
        year =          2022,
	sortdate = 		"2022-10-25", 
	project = "ant, eieio",
	jsubject = "routing",
	month = oct,
        pages =      "772--773",
        address =    "Nice, France",
        publisher =  "ACM",
        jlocation =   "johnh: pafile",
        keywords =   "outage detection, passive data, b-root, ipv6",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Enayet22a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Enayet22a.pdf",
        doi =        "https://doi.org/10.1145/3517745.3563032",
	abstract = "Outages from natural disasters, political events, software or hardware issues, and human error place a huge cost on e-commerce (\$66k per minute at Amazon). While several existing systems detect Internet outages, these systems are often too inflexible, with fixed parameters across the whole internet with CUSUM-like change detection. We instead propose a system using passive data, to cover both IPv4 and IPv6, customizing parameters for each block to optimize the performance of our Bayesian inference model. Our poster describes our three contributions: First, we show how customizing parameters allows us often to detect outages that are at both fine timescales (5 minutes) and fine spatial resolutions (/24 IPv4 and /48 IPv6 blocks). Our second contribution is to show that, by tuning parameters differently for different blocks, we can scale back temporal precision to cover more challenging blocks. Finally, we show our approach extends to IPv6 and provides the first reports of IPv6 outages."
}

Outages from natural disasters, political events, software or hardware issues, and human error place a huge cost on e-commerce ($66k per minute at Amazon). While several existing systems detect Internet outages, these systems are often too inflexible, with fixed parameters across the whole internet with CUSUM-like change detection. We instead propose a system using passive data, to cover both IPv4 and IPv6, customizing parameters for each block to optimize the performance of our Bayesian inference model. Our poster describes our three contributions: First, we show how customizing parameters allows us often to detect outages that are at both fine timescales (5 minutes) and fine spatial resolutions (/24 IPv4 and /48 IPv6 blocks). Our second contribution is to show that, by tuning parameters differently for different blocks, we can scale back temporal precision to cover more challenging blocks. Finally, we show our approach extends to IPv6 and provides the first reports of IPv6 outages.

@MISC{10945-70351,
	year = {2022},
	note = {Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research Symposium},
	author = {Orosz, Michael and Duffy, Brian and Charlton, Craig and Spear, Lieutenant Colonel},
	url = {https://hdl.handle.net/10945/70351},
	title = {Introducing Agile/DevSecOps into the Space Acquisition Environment},
	abstract = {The University of Southern California (USC) and its Information Sciences Institute (USC-ISI) is undertaking research into improving the space-based systems acquisition process through the adoption of agile and DevSecOps methodologies. The USC-ISI team is currently undertaking research and systems engineering analysis to explore the mission engineering methods, analysis, metrics and training needed to transition from a traditional DoDI 5000.02 waterfall development environment to an agile/DevSecOps space systems acquisition environment. Over the past several years, the project team has been embedded at the U.S. Space Force’s Space Systems Command, Production Corps (SSC/PC), developing performance measuring tools, collecting performance metrics and providing subject matter expertise on three projects – a traditional waterfall project, a hybrid parallel waterfall and agile development project and an on-going long-term highly agile development effort that is subject to traditional waterfall acquisition reporting requirements. This paper summarizes initial research results and lessons learned along with a discussion on next steps.}
}

The University of Southern California (USC) and its Information Sciences Institute (USC-ISI) is undertaking research into improving the space-based systems acquisition process through the adoption of agile and DevSecOps methodologies. The USC-ISI team is currently undertaking research and systems engineering analysis to explore the mission engineering methods, analysis, metrics and training needed to transition from a traditional DoDI 5000.02 waterfall development environment to an agile/DevSecOps space systems acquisition environment. Over the past several years, the project team has been embedded at the U.S. Space Force’s Space Systems Command, Production Corps (SSC/PC), developing performance measuring tools, collecting performance metrics and providing subject matter expertise on three projects – a traditional waterfall project, a hybrid parallel waterfall and agile development project and an on-going long-term highly agile development effort that is subject to traditional waterfall acquisition reporting requirements. This paper summarizes initial research results and lessons learned along with a discussion on next steps.

@misc{https://doi.org/10.48550/arxiv.2206.11083,
  doi = {10.48550/ARXIV.2206.11083},
  url = {https://arxiv.org/abs/2206.11083},
  author = {Sun, Jiao and Swayamdipta, Swabha and May, Jonathan and Ma, Xuezhe},
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Investigating the Benefits of Free-Form Rationales},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

@inproceedings{cho-etal-2022-know,
    title = "Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics",
    author = "Cho, Hyundong  and
      Sankar, Chinnadhurai  and
      Lin, Christopher  and
      Sadagopan, Kaushik  and
      Shayandeh, Shahin  and
      Celikyilmaz, Asli  and
      May, Jonathan  and
      Beirami, Ahmad",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.391",
    pages = "5345--5359",
    abstract = "Recent works that revealed the vulnerability of dialogue state tracking (DST) models to distributional shifts have made holistic comparisons on robustness and qualitative analyses increasingly important for understanding their relative performance. We present our findings from standardized and comprehensive DST diagnoses, which have previously been sparse and uncoordinated, using our toolkit, CheckDST, a collection of robustness tests and failure mode analytics. We discover that different classes of DST models have clear strengths and weaknesses, where generation models are more promising for handling language variety while span-based classification models are more robust to unseen entities. Prompted by this discovery, we also compare checkpoints from the same model and find that the standard practice of selecting checkpoints using validation loss/accuracy is prone to overfitting and each model class has distinct patterns of failure. Lastly, we demonstrate how our diagnoses motivate a pre-finetuning procedure with non-dialogue data that offers comprehensive improvements to generation models by alleviating the impact of distributional shifts through transfer learning.",
}

Recent works that revealed the vulnerability of dialogue state tracking (DST) models to distributional shifts have made holistic comparisons on robustness and qualitative analyses increasingly important for understanding their relative performance. We present our findings from standardized and comprehensive DST diagnoses, which have previously been sparse and uncoordinated, using our toolkit, CheckDST, a collection of robustness tests and failure mode analytics. We discover that different classes of DST models have clear strengths and weaknesses, where generation models are more promising for handling language variety while span-based classification models are more robust to unseen entities. Prompted by this discovery, we also compare checkpoints from the same model and find that the standard practice of selecting checkpoints using validation loss/accuracy is prone to overfitting and each model class has distinct patterns of failure. Lastly, we demonstrate how our diagnoses motivate a pre-finetuning procedure with non-dialogue data that offers comprehensive improvements to generation models by alleviating the impact of distributional shifts through transfer learning.

@misc{https://doi.org/10.48550/arxiv.2205.12453,
  doi = {10.48550/ARXIV.2205.12453},
  url = {https://arxiv.org/abs/2205.12453},
  author = {Gheini, Mozhdeh and Ma, Xuezhe and May, Jonathan},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-tuning},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

@article{DBLP:journals/tbd/KejriwalS22,
  author    = {Mayank Kejriwal and
               Pedro A. Szekely},
  title     = {Knowledge Graphs for Social Good: An Entity-Centric Search Engine
               for the Human Trafficking Domain},
  journal   = {{IEEE} Trans. Big Data},
  volume    = {8},
  number    = {3},
  pages     = {592--606},
  year      = {2022},
  url       = {https://doi.org/10.1109/TBDATA.2017.2763164},
  doi       = {10.1109/TBDATA.2017.2763164},
  timestamp = {Thu, 02 Jun 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/tbd/KejriwalS22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/information/Kejriwal22,
  author    = {Mayank Kejriwal},
  title     = {Knowledge Graphs: {A} Practical Review of the Research Landscape},
  journal   = {Inf.},
  volume    = {13},
  number    = {4},
  pages     = {161},
  year      = {2022},
  url       = {https://doi.org/10.3390/info13040161},
  doi       = {10.3390/info13040161},
  timestamp = {Tue, 16 Aug 2022 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/information/Kejriwal22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/aaai/FinckeAMB22,
  author       = {Steven Fincke and
                  Shantanu Agarwal and
                  Scott Miller and
                  Elizabeth Boschee},
  title        = {Language Model Priming for Cross-Lingual Event Extraction},
  booktitle    = {Thirty-Sixth {AAAI} Conference on Artificial Intelligence, {AAAI}
                  2022, Thirty-Fourth Conference on Innovative Applications of Artificial
                  Intelligence, {IAAI} 2022, The Twelveth Symposium on Educational Advances
                  in Artificial Intelligence, {EAAI} 2022 Virtual Event, February 22
                  - March 1, 2022},
  pages        = {10627--10635},
  publisher    = {{AAAI} Press},
  year         = {2022},
  url          = {https://doi.org/10.1609/aaai.v36i10.21307},
  doi          = {10.1609/AAAI.V36I10.21307},
  timestamp    = {Mon, 04 Sep 2023 12:29:24 +0200},
  biburl       = {https://dblp.org/rec/conf/aaai/FinckeAMB22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{muric2022large,
  title={Large-scale agent-based simulations of online social networks},
  author={Muri{\'c}, Goran and Tregubov, Alexey and Blythe, Jim and Abeliuk, Andr{\'e}s and Choudhary, Divya and Lerman, Kristina and Ferrara, Emilio},
  journal={Autonomous Agents and Multi-Agent Systems},
  volume={36},
  number={2},
  pages={1--21},
  year={2022},
  publisher={Springer US}
}

@article{Song-Learning,
	title = {Learning Robust Representations Of Generative Models Using Set-Based Artificial Fingerprints},
	url = {https://arxiv.org/pdf/2206.02067.pdf},
	author = {H. Song and W. AbdAlmageed},
	year = {2022}, 
 	keywords="arxiv"
}

@inproceedings{he2022learning,
  title={Learning fairer interventions},
  author={He, Yuzi and Burghardt, Keith and Guo, Siyi and Lerman, Kristina},
  booktitle={Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society},
  pages={317--323},
  year={2022}
}

@inproceedings{jin_leveraging_2022,
	address = {Dublin, Ireland},
	title = {Leveraging {Visual} {Knowledge} in {Language} {Tasks}: {An} {Empirical} {Study} on {Intermediate} {Pre}-training for {Cross}-{Modal} {Knowledge} {Transfer}},
	url = {https://aclanthology.org/2022.acl-long.196},
	doi = {10.18653/v1/2022.acl-long.196},
	booktitle = {Proceedings of the 60th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Jin, Woojeong and Lee, Dong-Ho and Zhu, Chenguang and Pujara, Jay and Ren, Xiang},
	month = may,
	year = {2022},
	pages = {2750--2762},
}

@article{he2022leveraging,
  title={Leveraging change point detection to discover natural experiments in data},
  author={He, Yuzi and Burghardt, Keith A and Lerman, Kristina},
  journal={EPJ Data Science},
  volume={11},
  number={1},
  pages={49},
  year={2022},
  publisher={Springer Berlin Heidelberg}
}

@article{Sabir-MONET,
	title = {{MONet}: Multi-scale Overlap Network for Duplication Detection in Biomedical Images},
	url = {https://arxiv.org/abs/2207.09107?context=cs},
	journal = {IEEE International Conference on Image Processing},
	author = {E. Sabir and S. Nandi and W. AbdAlmageed and P. Natarajan},
	year = {2022}, 
 	keywords="conference"
}

@inproceedings{chami2022taxonomy,
  author={Ines Chami AND Sami Abu-El-Haija AND Bryan Perozzi AND Christopher Re AND Kevin Murphy},
  title={Machine Learning on Graphs: A Model and Comprehensive Taxonomy},
  booktitle={Journal of Machine Learning Research},
  year={2022},
}

@misc{https://doi.org/10.48550/arxiv.2205.12514,
  doi = {10.48550/ARXIV.2205.12514},
  url = {https://arxiv.org/abs/2205.12514},
  author = {Bremerman, Jacob and Ren, Xiang and May, Jonathan},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Machine Translation Robustness to Natural Asemantic Variation},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

@article{Charbonneau2022,
	abstract = {The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs' Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.},
	author = {Charbonneau, Amanda L and Brady, Arthur and Czajkowski, Karl and Aluvathingal, Jain and Canchi, Saranya and Carter, Robert and Chard, Kyle and Clarke, Daniel J B and Crabtree, Jonathan and Creasy, Heather H and D'Arcy, Mike and Felix, Victor and Giglio, Michelle and Gingrich, Alicia and Harris, Rayna M and Hodges, Theresa K and Ifeonu, Olukemi and Jeon, Minji and Kropiwnicki, Eryk and Lim, Marisa C W and Liming, R Lee and Lumian, Jessica and Mahurkar, Anup A and Mandal, Meisha and Munro, James B and Nadendla, Suvarna and Richter, Rudyard and Romano, Cia and {Rocca-Serra}, Philippe and Schor, Michael and Schuler, Robert E and Tangmunarunkit, Hongsuda and Waldrop, Alex and Williams, Cris and Word, Karen and Sansone, Susanna-Assunta and Ma'ayan, Avi and Wagner, Rick and Foster, Ian and Kesselman, Carl and Brown, C Titus and White, Owen},
	date-added = {2024-01-22 12:05:54 -0800},
	date-modified = {2024-01-22 12:05:54 -0800},
	doi = {10.1093/gigascience/giac105},
	eprint = {https://academic.oup.com/gigascience/article-pdf/doi/10.1093/gigascience/giac105/48360815/giac105.pdf},
	file = {/Users/schuler/Zotero/storage/8LZZUHCX/Charbonneau et al. - 2022 - Making Common Fund data more findable catalyzing .pdf},
	issn = {2047-217X},
	journal = {GigaScience},
	month = nov,
	pages = {giac105},
	title = {Making {{Common Fund}} Data More Findable: Catalyzing a Data Ecosystem},
	url = {https://doi.org/10.1093/gigascience/giac105},
	volume = {11},
	year = {2022},
	bdsk-url-1 = {https://doi.org/10.1093/gigascience/giac105}}

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs' Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.

@inproceedings{Schuler2022c,
	address = {{Salt Lake City, Utah, USA}},
	author = {Schuler, Robert E. and Kesselman, Carl},
	booktitle = {2022 {{IEEE}} 18th {{International Conference}} on E-{{Science}} (e-{{Science}})},
	date-added = {2022-05-18 10:40:56 -0700},
	date-modified = {2022-05-31 14:30:50 -0700},
	doi = {10.1109/eScience55777.2022.00035},
	file = {/Users/schuler/Zotero/storage/UJ3VD8P5/Schuler and Kesselman - 2022 - Managing Database-Application Co-Evolution in a Sc.pdf},
	keywords = {under review},
	month = oct,
	pages = {214--224},
	publisher = {{IEEE}},
	title = {Managing {{Database-Application Co-Evolution}} in a {{Scientific Data Ecosystem}}},
	year = {2022},
	bdsk-url-1 = {https://doi.org/10.1109/eScience55777.2022.00035}}

@misc{https://doi.org/10.48550/arxiv.2209.10655,
  doi = {10.48550/ARXIV.2209.10655},
  url = {https://arxiv.org/abs/2209.10655},
  author = {Ma, Xuezhe and Zhou, Chunting and Kong, Xiang and He, Junxian and Gui, Liangke and Neubig, Graham and May, Jonathan and Zettlemoyer, Luke},
  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Mega: Moving Average Equipped Gated Attention},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{orosz2022merging,
  title={Merging Agile/DevSecOps into the US DoD Space Acquisition Environment—A Multiple Case Study},
  author={Orosz, Michael and Spear, Grant and Duffy, Brian and Charlton, Craig},
  journal={INSIGHT},
  volume={25},
  number={4},
  pages={96--99},
  year={2022}
}

@InProceedings{	  hamed-ickg-2022,
  Author	= {Hamed, Ahmed Abdeen and Jonczyk, Jakub and Alam, Mohammad
		  Zaiyan and Deelman, Ewa and Lee, Byung Suk},
  BookTitle	= {2022 IEEE International Conference on Knowledge Graph
		  (ICKG)},
  Title		= {Mining Literature-Based Knowledge Graph for Predicting
		  Combination Therapeutics: A COVID-19 Use Case},
  Year		= {2022},
  Volume	= {},
  Number	= {},
  Pages		= {79-86},
  DOI		= {10.1109/ICKG55886.2022.00018}
}

@inproceedings{gupta-etal-2022-mitigating,
    title = "Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal",
    author = "Gupta, Umang  and
      Dhamala, Jwala  and
      Kumar, Varun  and
      Verma, Apurv  and
      Pruksachatkun, Yada  and
      Krishna, Satyapriya  and
      Gupta, Rahul  and
      Chang, Kai-Wei  and
      Ver Steeg, Greg  and
      Galstyan, Aram",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-acl.55",
    doi = "10.18653/v1/2022.findings-acl.55",
    pages = "658--678",
    abstract = "Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However, these models can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. Therefore, knowledge distillation without any fairness constraints may preserve or exaggerate the teacher model{'}s biases onto the distilled model. To this end, we present a novel approach to mitigate gender disparity in text generation by learning a fair model during knowledge distillation. We propose two modifications to the base knowledge distillation based on counterfactual role reversal{---}modifying teacher probabilities and augmenting the training set. We evaluate gender polarity across professions in open-ended text generated from the resulting distilled and finetuned GPT{--}2 models and demonstrate a substantial reduction in gender disparity with only a minor compromise in utility. Finally, we observe that language models that reduce gender polarity in language generation do not improve embedding fairness or downstream classification fairness.",
}

Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However, these models can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. Therefore, knowledge distillation without any fairness constraints may preserve or exaggerate the teacher model's biases onto the distilled model. To this end, we present a novel approach to mitigate gender disparity in text generation by learning a fair model during knowledge distillation. We propose two modifications to the base knowledge distillation based on counterfactual role reversal—modifying teacher probabilities and augmenting the training set. We evaluate gender polarity across professions in open-ended text generated from the resulting distilled and finetuned GPT–2 models and demonstrate a substantial reduction in gender disparity with only a minor compromise in utility. Finally, we observe that language models that reduce gender polarity in language generation do not improve embedding fairness or downstream classification fairness.

@inproceedings{Ngo2022,
abstract = {Safety-critical cyber-physical and industrial control systems (CPS/ICS) such as electrical power grids and water treatment plants are prone to external attacks and internal mistakes or threats. Still, research in this area does not involve high-fidelity human models in their CPS state estimation models. We provide a starting point to modeling the expansiveness of human behavior in CPS by proposing Cyber-Human Interaction Modeling for Cyber-Physical Systems (CHIMPS), a novel decision-making-based process to explicitly integrate human models in the design and implementation of CPS. We present a use case of our process focusing on safety-critical ICS that simulates a human ICS operator maintaining a water treatment plant.},
author = {Ngo, Steven and DeAngelis, Dave and Garcia, Luis},
booktitle = {2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)},
doi = {10.1109/MASS56207.2022.00106},
file = {:home/dave/Documents/References/Ngo, DeAngelis, Garcia - 2022 - Modeling Human-Cyber Interactions in Safety-Critical Cyber-PhysicalIndustrial Control Systems.pdf:pdf},
isbn = {978-1-6654-7180-0},
keywords = {cyber-physical systems,human-computer interaction,industrial control systems,system state estimation},
month = {oct},
pages = {716--717},
publisher = {IEEE},
title = {Modeling Human-Cyber Interactions in Safety-Critical Cyber-Physical/Industrial Control Systems},
url = {https://ieeexplore.ieee.org/document/9973673/},
year = {2022}
}

Safety-critical cyber-physical and industrial control systems (CPS/ICS) such as electrical power grids and water treatment plants are prone to external attacks and internal mistakes or threats. Still, research in this area does not involve high-fidelity human models in their CPS state estimation models. We provide a starting point to modeling the expansiveness of human behavior in CPS by proposing Cyber-Human Interaction Modeling for Cyber-Physical Systems (CHIMPS), a novel decision-making-based process to explicitly integrate human models in the design and implementation of CPS. We present a use case of our process focusing on safety-critical ICS that simulates a human ICS operator maintaining a water treatment plant.

@inproceedings{zhang2022modeling,
  title={Modeling the Energy Efficiency of GEMM using Optical Random Access Memory},
  author={Zhang, Bingyi and Jaiswal, Akhilesh and Mathew, Clynn and Lakkireddy, Ravi Teja and Jacob, Ajey P and Wijeratne, Sasindu and Prasanna, Viktor},
  booktitle={2022 IEEE High Performance Extreme Computing Conference (HPEC)},
  pages={1--7},
  year={2022},
  organization={IEEE}
}

@InProceedings{	  cranganore-escience-2022,
  Author	= {Cranganore, Sandeep Suresh and De Maio, Vincenzo and
		  Brandic, Ivona and Do, Tu Mai Anh and Deelman, Ewa},
  BookTitle	= {2022 IEEE 18th International Conference on e-Science
		  (e-Science)},
  Title		= {Molecular Dynamics Workflow Decomposition for Hybrid
		  Classic/Quantum Systems},
  Year		= {2022},
  Volume	= {},
  Number	= {},
  Pages		= {346-356},
  DOI		= {10.1109/eScience55777.2022.00048},
  Note		= {Funding Acknowledgments: NSF 1664162 and 1741040}
}

@inproceedings{thakur2022multimodal,
  title={Multimodal and Explainable Internet Meme Classification},
  author={Thakur, Abhinav Kumar and Ilievski, Filip and Sandlin, H{\^o}ng-{\^A}n and Mermoud, Alain and Sourati, Zhivar and Luceri, Luca and Tommasini, Riccardo},
  booktitle={AAAI Workshop on Artificial Intelligence for Social Good},
  year={2022}
} 

@article{Devine2022a,
  title = {{{MusMorph}}, a Database of Standardized Mouse Morphology Data for Morphometric Meta-Analyses},
  author = {Devine, Jay and {Vidal-Garc{\'i}a}, Marta and Liu, Wei and Neves, Amanda and Lo Vercio, Lucas D. and Green, Rebecca M. and Richbourg, Heather A. and Marchini, Marta and Unger, Colton M. and Nickle, Audrey C. and Radford, Bethany and Young, Nathan M. and Gonzalez, Paula N. and Schuler, Robert E. and Bugacov, Alejandro and Rolian, Campbell and Percival, Christopher J. and Williams, Trevor and Niswander, Lee and Calof, Anne L. and Lander, Arthur D. and Visel, Axel and Jirik, Frank R. and Cheverud, James M. and Klein, Ophir D. and Birnbaum, Ramon Y. and Merrill, Amy E. and Ackermann, Rebecca R. and Graf, Daniel and Hemberger, Myriam and Dean, Wendy and Forkert, Nils D. and Murray, Stephen A. and Westerberg, Henrik and Marcucio, Ralph S. and Hallgr{\'i}msson, Benedikt},
  year = {2022},
  journal = {Scientific Data},
  volume = {9},
  number = {1},
  pages = {230},
  doi = {10.1038/s41597-022-01338-x},
  abstract = {Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, https://doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).},
  date-added = {2022-05-31 14:28:50 -0700},
  date-modified = {2022-05-31 14:32:01 -0700},
  isbn = {2052-4463},
  file = {/Users/schuler/Zotero/storage/RWI8NBM9/Devine et al. - 2022 - MusMorph, a database of standardized mouse morphol.pdf}
}

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, https://doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).

@article{Devine2022a,
	abstract = {Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, https://doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).},
	author = {Devine, Jay and {Vidal-Garc{\'\i}a}, Marta and Liu, Wei and Neves, Amanda and Lo Vercio, Lucas D. and Green, Rebecca M. and Richbourg, Heather A. and Marchini, Marta and Unger, Colton M. and Nickle, Audrey C. and Radford, Bethany and Young, Nathan M. and Gonzalez, Paula N. and Schuler, Robert E. and Bugacov, Alejandro and Rolian, Campbell and Percival, Christopher J. and Williams, Trevor and Niswander, Lee and Calof, Anne L. and Lander, Arthur D. and Visel, Axel and Jirik, Frank R. and Cheverud, James M. and Klein, Ophir D. and Birnbaum, Ramon Y. and Merrill, Amy E. and Ackermann, Rebecca R. and Graf, Daniel and Hemberger, Myriam and Dean, Wendy and Forkert, Nils D. and Murray, Stephen A. and Westerberg, Henrik and Marcucio, Ralph S. and Hallgr{\'\i}msson, Benedikt},
	date-added = {2022-05-31 14:28:50 -0700},
	date-modified = {2022-05-31 14:32:01 -0700},
	doi = {10.1038/s41597-022-01338-x},
	file = {/Users/schuler/Zotero/storage/RWI8NBM9/Devine et al. - 2022 - MusMorph, a database of standardized mouse morphol.pdf},
	isbn = {2052-4463},
	journal = {Scientific Data},
	number = {1},
	pages = {230},
	title = {{{MusMorph}}, a Database of Standardized Mouse Morphology Data for Morphometric Meta-Analyses},
	volume = {9},
	year = {2022},
	bdsk-url-1 = {https://doi.org/10.1038/s41597-022-01338-x}}

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, https://doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).

@article{arakelyanns3,
  title={NS3: Neuro-Symbolic Semantic Code Search},
  author={Arakelyan, Shushan and Hakhverdyan, Anna and Allamanis, Miltiadis and Hauser, Christophe and Garcia, Luis and Ren, Xiang},
  journal={Advances in Neural Information Processing Systems},
  year={2022}
}

@patent{cornelio_neural_nodate,
	title = {Neural {Network} {Methods}},
	nationality = {The United States},
	author = {Cornelio, Jodel and Razak, Syamil and Cho, Young and Jafarpour, Behnam},
	year = {2022},
}

@inproceedings{chen2022ie-naacl,
  title={New Frontiers of Information Extraction},
  author={Chen, Muhao and Huang, Lifu and Li, Manling and Zhou, Ben and Ji, Heng and Roth, Dan},
  booktitle={NAACL},
  year={2022}
}

@inproceedings{spangher-etal-2022-newsedits,
    title = "{N}ews{E}dits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge",
    author = "Spangher, Alexander  and
      Ren, Xiang  and
      May, Jonathan  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.10",
    pages = "127--157",
    abstract = "News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspaper sources based in three countries, spanning 15 years of coverage (2006-2021).We define article-level edit actions: Addition, Deletion, Edit and Refactor, and develop a high-accuracy extraction algorithm to identify these actions. To underscore the factual nature of many edit actions, we conduct analyses showing that added and deleted sentences are more likely to contain updating events, main content and quotes than unchanged sentences. Finally, to explore whether edit actions are predictable, we introduce three novel tasks aimed at predicting actions performed during version updates. We show that these tasks are possible for expert humans but are challenging for large NLP models. We hope this can spur research in narrative framing and help provide predictive tools for journalists chasing breaking news.",
}



    

News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspaper sources based in three countries, spanning 15 years of coverage (2006-2021).We define article-level edit actions: Addition, Deletion, Edit and Refactor, and develop a high-accuracy extraction algorithm to identify these actions. To underscore the factual nature of many edit actions, we conduct analyses showing that added and deleted sentences are more likely to contain updating events, main content and quotes than unchanged sentences. Finally, to explore whether edit actions are predictable, we introduce three novel tasks aimed at predicting actions performed during version updates. We show that these tasks are possible for expert humans but are challenging for large NLP models. We hope this can spur research in narrative framing and help provide predictive tools for journalists chasing breaking news.

@inproceedings{mokhberian2022noise,
  title={Noise Audits Improve Moral Foundation Classification},
  author={Mokhberian, Negar and Hopp, Frederic R and Harandizadeh, Bahareh and Morstatter, Fred and Lerman, Kristina},
  booktitle={Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  year={2022}
}

@misc{jaiswal2022non,
  title={Non-volatile transistor embedded static random access memory (SRAM) cell},
  author={Jaiswal, Akhilesh R and Paul, Bipul C and Soss, Steven R},
  year={2022},
  month=oct # "~18",
  publisher={Google Patents},
  note={US Patent 11,475,941}
}

@InProceedings{	  novel-fair-works-2022,
  Author	= {Abhinit, Ishan and Adams, Emily K. and Alam, Khairul and
		  Chase, Brian and Deelman, Ewa and Gorenstein, Lev and
		  Hudson, Stephen and Islam, Tanzima and Larson, Jeffrey and
		  Lentner, Geoffrey and Mandal, Anirban and Navarro,
		  John-Luke and Nicolae, Bogdan and Pouchard, Line and Ross,
		  Rob and Roy, Banani and Rynge, Mats and Serebrenik,
		  Alexander and Vahi, Karan and Wild, Stefan and Xin, Yufeng
		  and da Silva, Rafael Ferreira and Filgueira, Rosa},
  BookTitle	= {2022 IEEE/ACM Workshop on Workflows in Support of
		  Large-Scale Science (WORKS)},
  Title		= {Novel Proposals for FAIR, Automated, Recommendable, and
		  Robust Workflows},
  Year		= {2022},
  Volume	= {},
  Number	= {},
  Pages		= {84-92},
  DOI		= {10.1109/WORKS56498.2022.00016},
  Note		= {Funding Acknowledgments: NSF 1839900 and DOE
		  DE-AC02-06CH11357, DE-AC05-00OR22725}
}

@InProceedings{Moura22a,
        author =        "Giovane C. M. Moura and John Heidemann and
 Wes Hardaker and Pithayuth Charnsethikul and Jeroen
 Bulten and Jo{\~a}o M. Ceron and Cristian Hesselman",
 title = "Old but Gold: Prospecting {TCP} to Engineer and Live  Monitor {DNS} Anycast",
        booktitle =     "Proceedings of the " # " Passive and Active Measurement Workshop",
	project = "ant, paaddos, ddidd",
	jsubject = "network_security",
        year =          2022,
	sortdate = 	"2022-03-28",
        pages =      "to appear",
        month =      mar,
	note = "best paper award",
        address =    "virtual",
        publisher =  "Springer",
        jlocation =   "johnh: pafile",
	keywords = 	"anycast, dns, tcp, latency, root, .nl-tld, monitoring",
        doi =        "10.1007/978-3-030-98785-5_12",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Moura22a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Moura22a.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1854",
	abstract = "DNS latency is a concern for many service operators:  CDNs exist to
reduce service latency to end-users but must rely on global DNS for
reachability and load-balancing.  Today, DNS latency is monitored by
active probing from distributed platforms like RIPE Atlas, with
Verfploeter, or with commercial services.  While Atlas coverage is
wide, its 10k sites see only a fraction of the Internet.  In this
paper we show that passive observation of TCP handshakes can measure
\emph{live DNS latency, continuously, providing good coverage of
current clients of the service}.  Estimating RTT from TCP is an old
idea, but its application to DNS has not previously been studied
carefully.  We show that there is sufficient TCP DNS traffic today to
provide good operational coverage (particularly of IPv6), and very
good temporal coverage (better than existing approaches), enabling
near-real time evaluation of DNS latency from \emph{real clients}.  We
also show that DNS servers can optionally solicit TCP to broaden
coverage.  We quantify coverage and show that estimates of DNS latency
from TCP is consistent with UDP latency.  Our approach finds
previously unknown, real problems:  \emph{DNS polarization} is a new
problem where a hypergiant sends global traffic to one anycast site
rather than taking advantage of the global anycast deployment.
Correcting polarization in Google DNS cut its latency from 100ms to
10ms; and from Microsoft Azure cut latency from 90ms to 20ms.
We also show other instances of routing problems that add 100--200ms
latency.  Finally, \emph{real-time} use of our approach for a European
country-level domain has helped detect and correct a BGP routing
misconfiguration that detoured European traffic to Australia.  We have
integrated our approach into several open source tools:  Entrada, our
open source data warehouse for DNS, a monitoring tool (ANTS), which
has been operational for the last 2 years on a country-level top-level
domain, and a DNS anonymization tool in use at a root server since
March 2021.",
}

DNS latency is a concern for many service operators: CDNs exist to reduce service latency to end-users but must rely on global DNS for reachability and load-balancing. Today, DNS latency is monitored by active probing from distributed platforms like RIPE Atlas, with Verfploeter, or with commercial services. While Atlas coverage is wide, its 10k sites see only a fraction of the Internet. In this paper we show that passive observation of TCP handshakes can measure \emphlive DNS latency, continuously, providing good coverage of current clients of the service. Estimating RTT from TCP is an old idea, but its application to DNS has not previously been studied carefully. We show that there is sufficient TCP DNS traffic today to provide good operational coverage (particularly of IPv6), and very good temporal coverage (better than existing approaches), enabling near-real time evaluation of DNS latency from \emphreal clients. We also show that DNS servers can optionally solicit TCP to broaden coverage. We quantify coverage and show that estimates of DNS latency from TCP is consistent with UDP latency. Our approach finds previously unknown, real problems: \emphDNS polarization is a new problem where a hypergiant sends global traffic to one anycast site rather than taking advantage of the global anycast deployment. Correcting polarization in Google DNS cut its latency from 100ms to 10ms; and from Microsoft Azure cut latency from 90ms to 20ms. We also show other instances of routing problems that add 100–200ms latency. Finally, \emphreal-time use of our approach for a European country-level domain has helped detect and correct a BGP routing misconfiguration that detoured European traffic to Australia. We have integrated our approach into several open source tools: Entrada, our open source data warehouse for DNS, a monitoring tool (ANTS), which has been operational for the last 2 years on a country-level top-level domain, and a DNS anonymization tool in use at a root server since March 2021.

@article{DBLP:journals/corr/abs-2211-13117,
  author    = {Mayank Kejriwal and
               Yuesheng Luo},
  title     = {On the Empirical Association between Trade Network Complexity and
               Global Gross Domestic Product},
  journal   = {CoRR},
  volume    = {abs/2211.13117},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2211.13117},
  doi       = {10.48550/arXiv.2211.13117},
  eprinttype = {arXiv},
  eprint    = {2211.13117},
  timestamp = {Wed, 30 Nov 2022 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2211-13117.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{chawla-etal-2022-opponent,
    title = "Opponent Modeling in Negotiation Dialogues by Related Data Adaptation",
    author = "Chawla, Kushal  and
      Lucas, Gale  and
      May, Jonathan  and
      Gratch, Jonathan",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-naacl.50",
    pages = "661--674",
    abstract = "Opponent modeling is the task of inferring another party{'}s mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent{'}s preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.",
}

Opponent modeling is the task of inferring another party's mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent's preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.

@misc{jaiswal2022optical,
  title={Optical neuro-mimetic devices},
  author={Jaiswal, Akhilesh R and Jacob, Ajey Poovannummoottil and Bian, Yusheng and Rakowski, Michal},
  year={2022},
  month=dec # "~27",
  publisher={Google Patents},
  note={US Patent 11,537,866}
}

@article{Datta,
	title = {{P2M-DeTrack}: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking},
	url = {https://arxiv.org/pdf/2205.14285},
	author = {Gourav Datta and Souvik Kundu and Zihan Yin and Joe Mathai and Zeyu Liu and Zixu Wang and Mulin Tian and Shunlin Lu and Ravi T Lakkireddy and Andrew Schmidt and Wael Abd-Almageed and Ajey P Jacob and Akhilesh R Jaiswal and Peter A Beerel},
	year = {2022}, 
 	keywords="arxiv"
}

@inproceedings{schmidt2022_5,
	author = {G. Datta, S. Kundu, Z. Yin, J. Mathai, Z. Liu, Z. Wang, M. Tian, S. Lu, R. T. Lakkireddy, Andrew G. Schmidt, W. Abd-Almageed, A. P. Jacob, A. R Jaiswal, P. A. Beerel},
	title = {P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking},
	booktitle = {IFIP IEEE 30th International Conference on Very Large Scale Integration (VLSI SoC)},
	year = {2022}}

@article{wang2022pinto,
  title={PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales},
  author={Wang, Peifeng and Chan, Aaron and Ilievski, Filip and Chen, Muhao and Ren, Xiang},
  journal={TSRML2022 workshop},
  year={2022}
}

@inproceedings{qasemi-etal-2022-pinks,
    title = "PInKS: Preconditioned Commonsense Inference with Minimal Supervision",
    author = "Qasemi, Ehsan and Khanna, Piyush and Ning, Qiang and Chen, Muhao",
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/aacl-22-ingestion/2022.aacl-main.26/",
    pages = "qasemi-etal-2022-pinks",
    abstract = "Reasoning with preconditions such as “glass can be used for drinking water unless the glass is shattered” remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and the model’s lack of support for such reasoning. We present PInKS , Preconditioned Commonsense Inference with WeaK Supervision, an improved model for reasoning with preconditions through minimum supervision. We show, empirically and theoretically, that PInKS improves the results on benchmarks focused on reasoning with the preconditions of commonsense knowledge (up to 40% Macro-F1 scores). We further investigate PInKS through PAC-Bayesian informativeness analysis, precision measures, and ablation study.",
    ISIArea = {NLP}
}

Reasoning with preconditions such as “glass can be used for drinking water unless the glass is shattered” remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and the model’s lack of support for such reasoning. We present PInKS , Preconditioned Commonsense Inference with WeaK Supervision, an improved model for reasoning with preconditions through minimum supervision. We show, empirically and theoretically, that PInKS improves the results on benchmarks focused on reasoning with the preconditions of commonsense knowledge (up to 40% Macro-F1 scores). We further investigate PInKS through PAC-Bayesian informativeness analysis, precision measures, and ablation study.