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- surveyPublished By ACMPublished By ACM
A Comprehensive Survey on Rare Event Prediction
- Chathurangi Shyalika
Artificial Intelligence Institute, University of South Carolina, Columbia, United States
, - Ruwan Wickramarachchi
Artificial Intelligence Institute, University of South Carolina, Columbia, United States
, - Amit P. Sheth
Artificial Intelligence Institute, University of South Carolina, Columbia, United States
ACM Computing Surveys, Volume 57, Issue 3•March 2025, Article No.: 70, pp 1-39 • https://doi.org/10.1145/3699955Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it ...
- 1Citation
- 309
- Downloads
MetricsTotal Citations1Total Downloads309Last 12 Months309Last 6 weeks309- 1
Supplementary Material3699955.pdf
- Chathurangi Shyalika
- Article
Hi Model, generating “nice” instead of “good” is not as bad as generating “rice”! Towards Context and Semantic Infused Dialogue Generation Loss Function
- Abhisek Tiwari
https://ror.org/01ft5vz71Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India
, - Muhammed Sinan
https://ror.org/01ft5vz71Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India
, - Kaushik Roy
https://ror.org/02b6qw903Artificial Intelligence Institute, University of South Carolina, Columbia, USA
, - Amit Sheth
https://ror.org/02b6qw903Artificial Intelligence Institute, University of South Carolina, Columbia, USA
, - Sriparna Saha
https://ror.org/01ft5vz71Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India
, - Pushpak Bhattacharyya
https://ror.org/02qyf5152Indian Institute of Technology Bombay, Mumbai, India
Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track•September 2024, pp 342-360• https://doi.org/10.1007/978-3-031-70371-3_20AbstractOver the past two decades, dialogue modeling has made significant strides, moving from simple rule-based responses to personalized and persuasive response generation. However, despite these advancements, the objective functions and evaluation ...
- 0Citation
MetricsTotal Citations0
- Abhisek Tiwari
- research-articleOpen Access
Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety
- Manas Gaur
University of Maryland, Baltimore County Baltimore Maryland USA
, - Amit Sheth
AI Institute, University of South Carolina Columbia South Carolina USA
AbstractExplainability and Safety engender trust. These require a model to exhibit consistency and reliability. To achieve these, it is necessary to use and analyze data and knowledge with statistical and symbolic AI methods relevant to the AI ...
- 1Citation
MetricsTotal Citations1
- Manas Gaur
- extended-abstractPublished By ACMPublished By ACM
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models✱
- Aidin Shiri
University of Maryland Baltimore County, USA
, - Kaushik Roy
AI Institute, University of South Carolina, SC, USA, USA
, - Amit Sheth
AI Institute, University of South Carolina, SC, USA, USA
, - Manas Gaur
University of Maryland Baltimore County, USA
CODS-COMAD '24: Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)•January 2024, pp 592-594• https://doi.org/10.1145/3632410.3632494Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices. This necessitates the development of a Lifelong Learning (L3) framework that continuously adapts to a stream ...
- 1Citation
- 84
- Downloads
MetricsTotal Citations1Total Downloads84Last 12 Months84Last 6 weeks4
- Aidin Shiri
- research-articlePublished By ACMPublished By ACM
Tutorial: Neuro-symbolic AI for Mental Healthcare
- Kaushik Roy
Computer Science, Artificial Intelligence Institute, University of South Carolina, US
, - Usha Lokala
Artificial Intelligence Institute, University of South Carolina, US
, - Manas Gaur
University of Maryland, Baltimore County, US
, - Amit P Sheth
University of South Carolina, US
AIMLSystems '22: Proceedings of the Second International Conference on AI-ML Systems•October 2022, Article No.: 28, pp 1-3• https://doi.org/10.1145/3564121.3564817Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to ...
- 1Citation
- 213
- Downloads
MetricsTotal Citations1Total Downloads213Last 12 Months141Last 6 weeks12
- Kaushik Roy
- tutorialOpen AccessPublished By ACMPublished By ACM
Tutorials at The Web Conference 2023
- Valeria Fionda
University of Calabria, Italy
, - Olaf Hartig
Linköping University, Sweden
, - Reyhaneh Abdolazimi
Syracuse University, USA
, - Sihem Amer-Yahia
CNRS, Univ. Grenoble Alpes, France
, - Hongzhi Chen
AWS Shanghai AI Lab, China
, - Xiao Chen
The Hong Kong Polytechnic University, Hong Kong
, - Peng Cui
Tsinghua University, China
, - Jeffrey Dalton
University of Glasgow, United Kingdom
, - Xin Luna Dong
Meta Reality Labs, USA
, - Lisette Espin-Noboa
Central European University & Complexity Science Hub Vienna, Austria
, - Wenqi Fan
The Hong Kong Polytechnic University, Hong Kong
, - Manuela Fritz
University of Passau, Germany
, - Quan Gan
AWS Shanghai AI Lab, China
, - Jingtong Gao
City University of Hong Kong, Hong Kong
, - Xiaojie Guo
IBM T. J. Watson Research Center, USA
, - Torsten Hahmann
University of Maine, USA
, - Jiawei Han
University of Illinois at Urbana-Champaign, USA
, - Soyeon Han
The University of Sydney, Australia
, - Estevam Hruschka
Megagon Labs, USA
, - Liang Hu
Tongji University, China
, - Jiaxin Huang
University of Illinois at Urbana-Champaign, USA
, - Utkarshani Jaimini
University of South Carolina, USA
, - Olivier Jeunen
ShareChat, United Kingdom
, - Yushan Jiang
University of Connecticut, USA
, - Fariba Karimi
Vienna University of Technology & Complexity Science Hub Vienna, Austria
, - George Karypis
AWS AI Research and Education, USA
, - Krishnaram Kenthapadi
Fiddler AI, USA
, - Himabindu Lakkaraju
Harvard University, USA
, - Hady W. Lauw
Singapore Management University, Singapore
, - Thai Le
The University of Mississippi, USA
, - Trung-Hoang Le
Singapore Management University, Singapore
, - Dongwon Lee
The Pennsylvania State University, USA
, - Geon Lee
KAIST, Republic of Korea
, - Liat Levontin
Technion, Israel
, - Cheng-Te Li
National Cheng Kung University, Taiwan
, - Haoyang Li
Tsinghua University, China
, - Ying Li
Netflix, USA
, - Jay Chiehen Liao
National Cheng Kung University, Taiwan
, - Qidong Liu
City University of Hong Kong, Hong Kong
, - Usha Lokala
University of South Carolina, USA
, - Ben London
Amazon, USA
, - Siqu Long
The University of Sydney, Australia
, - Hande Kücük Mcginty
Kansas State University, USA
, - Yu Meng
University of Illinois at Urbana-Champaign, USA
, - Seungwhan Moon
Meta Reality Labs, USA
, - Usman Naseem
The University of Sydney, Australia
, - Pradeep Natarajan
Amazon Alexa AI, USA
, - Behrooz Omidvar-Tehrani
AWS AI Labs, USA
, - Zijie Pan
University of Connecticut, USA
, - Devesh Parekh
Netflix, USA
, - Jian Pei
Duke University, USA
, - Tiago Peixoto
Central European University, Austria
, - Steven Pemberton
CWI, Netherlands
, - Josiah Poon
The University of Sydney, Australia
, - Filip Radlinski
Google, United Kingdom
, - Federico Rossetto
University of Glasgow, United Kingdom
, - Kaushik Roy
University of South Carolina, USA
, - Aghiles Salah
Rakuten Group, Inc., France
, - Mehrnoosh Sameki
Microsoft Azure AI, USA
, - Amit Sheth
University of South Carolina, USA
, - Cogan Shimizu
Wright State University, USA
, - Kijung Shin
KAIST, Republic of Korea
, - Dongjin Song
University of Connecticut, USA
, - Julia Stoyanovich
New York University, USA
, - Dacheng Tao
The University of Sydney, Australia
, - Johanne Trippas
RMIT University, Australia
, - Quoc Truong
Amazon, Canada
, - Yu-Che Tsai
National Taiwan University, Taiwan
, - Adaku Uchendu
The Pennsylvania State University, USA
, - Bram Van Den Akker
Booking.com, Netherlands
, - Lin Wang
The Hong Kong Polytechnic University, Hong Kong
, - Minjie Wang
AWS Shanghai AI Lab, China
, - Shoujin Wang
University of Technology Sydney, Australia
, - Xin Wang
Tsinghua University, China
, - Ingmar Weber
Saarland University, Germany
, - Henry Weld
The University of Sydney, Australia
, - Lingfei Wu
Pinterest, USA
, - Da Xu
Walmart Labs, USA
, - Ethan Yifan Xu
Meta Reality Labs, USA
, - Shuyuan Xu
Rutgers University, USA
, - Bo Yang
LinkedIn, USA
, - Ke Yang
UMass Amherst, USA
, - Elad Yom-Tov
Microsoft, Israel
, - Jaemin Yoo
Carnegie Mellon University, USA
, - Zhou Yu
Columbia University, USA
, - Reza Zafarani
Syracuse University, USA
, - Hamed Zamani
University of Massachusetts Amherst, USA
, - Meike Zehlike
Zalando Research, Germany
, - Qi Zhang
University of Technology Sydney, Australia
, - Xikun Zhang
The University of Sydney, Australia
, - Yongfeng Zhang
Rutgers University, USA
, - Yu Zhang
University of Illinois at Urbana-Champaign, USA
, - Zheng Zhang
AWS Shanghai AI Lab, China
, - Liang Zhao
Emory University, USA
, - Xiangyu Zhao
City University of Hong Kong, Hong Kong
, - Wenwu Zhu
Tsinghua University, China
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023•April 2023, pp 648-658• https://doi.org/10.1145/3543873.3587713This paper summarizes the content of the 28 tutorials that have been given at The Web Conference 2023.
- 1Citation
- 1,295
- Downloads
MetricsTotal Citations1Total Downloads1,295Last 12 Months656Last 6 weeks97
- Valeria Fionda
- Article
Supporting Complex Thematic, Spatial and Temporal Queries over Semantic Web Data
- Matthew Perry
Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA
, - Amit P. Sheth
Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA
, - Farshad Hakimpour
LSDIS Lab, Department of Computer Science, University of Georgia, Athens, GA, USA
, - Prateek Jain
Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA
AbstractSpatial and temporal data are critical components in many applications. This is especially true in analytical domains such as national security and criminal investigation. Often, the analytical process requires uncovering and analyzing complex ...
- 0Citation
MetricsTotal Citations0
- Matthew Perry
- research-article
CLUE-AD: a context-based method for labeling unobserved entities in autonomous driving data
- Ruwan Wickramarachchi
AI Institute, University of South Carolina, Columbia, SC
, - Cory Henson
Bosch Center for Artificial Intelligence, Pittsburgh, PA
, - Amit Sheth
AI Institute, University of South Carolina, Columbia, SC
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence•February 2023, Article No.: 2020, pp 16491-16493• https://doi.org/10.1609/aaai.v37i13.27089Generating high-quality annotations for object detection and recognition is a challenging and important task, especially in relation to safety-critical applications such as autonomous driving (AD). Due to the difficulty of perception in challenging ...
- 0Citation
MetricsTotal Citations0
- Ruwan Wickramarachchi
- research-article
Demo alleviate: demonstrating artificial intelligence enabled virtual assistance for telehealth: the mental health case
- Kaushik Roy
Artificial Intelligence Institute, University of South Carolina Columbia, South Carolina
, - Vedant Khandelwal
Artificial Intelligence Institute, University of South Carolina Columbia, South Carolina
, - Raxit Goswami
Artificial Intelligence Institute, University of South Carolina Columbia, South Carolina
, - Nathan Dolbir
Artificial Intelligence Institute, University of South Carolina Columbia, South Carolina
, - Jinendra Malekar
Artificial Intelligence Institute, University of South Carolina Columbia, South Carolina
, - Amit Sheth
Artificial Intelligence Institute, University of South Carolina Columbia, South Carolina
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence•February 2023, Article No.: 2016, pp 16479-16481• https://doi.org/10.1609/aaai.v37i13.27085After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient ...
- 0Citation
MetricsTotal Citations0
- Kaushik Roy
- research-article
A Semantic Web Approach to Fault Tolerant Autonomous Manufacturing
- Fadi El Kalach
University of South Carolina, Columbia, SC, USA
, - Ruwan Wickramarachchi
University of South Carolina, Columbia, SC, USA
, - Ramy Harik
University of South Carolina, Columbia, SC, USA
, - Amit Sheth
University of South Carolina, Columbia, SC, USA
, - Amit Sheth
IEEE Intelligent Systems, Volume 38, Issue 1•Jan.-Feb. 2023, pp 69-75 • https://doi.org/10.1109/MIS.2023.3235677The next phase of manufacturing is centered on making the switch from traditional automated to autonomous systems. Future factories are required to be agile, allowing for more customized production and resistance to disturbances. Such production lines ...
- 0Citation
MetricsTotal Citations0
- Fadi El Kalach
- research-article
Process Knowledge-Infused AI: Toward User-Level Explainability, Interpretability, and Safety
- Amit Sheth
University of South Carolina, Columbia, SC, USA
, - Manas Gaur
University of South Carolina, Columbia, SC, USA
, - Kaushik Roy
University of South Carolina, Columbia, SC, USA
, - Revathy Venkataraman
University of South Carolina, Columbia, SC, USA
, - Vedant Khandelwal
University of South Carolina, Columbia, SC, USA
, - Amit Sheth
IEEE Internet Computing, Volume 26, Issue 5•Sept.-Oct. 2022, pp 76-84 • https://doi.org/10.1109/MIC.2022.3182349AI has seen wide adoption for automating tasks in several domains. However, AI's use in high-value, sensitive, or safety-critical applications such as self-management for personalized health or personalized nutrition has been challenging. These require ...
- 5Citation
MetricsTotal Citations5
- Amit Sheth
- abstractPublished By ACMPublished By ACM
International Workshop on Knowledge Graphs: Open Knowledge Network
- Ying Ding
University of Texas at Austin, Austin, TX, USA
, - Amit Sheth
University of South Carolina, Columbia, SC, USA
, - Krzysztof W. Janowicz
University of California-Santa Barbara, Santa Barbara, CA, USA
, - Sergio Baranzini
University of California at San Francisco, San Francisco, CA, USA
, - Sharat Israni
University of California at San Francisco, San Francisco, CA, USA
, - Ilkay Altintas
San Diego Supercomputer Center, San Diego, CA, USA
, - Lilit Yeghiazarian
University of Cincinnati, Cincinnati, OH, USA
, - Ellie Young
Common Action, New York, NY, USA
, - Sam Klein
Harvard University, Cambridge, MA, USA
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2022, pp 4864-4865• https://doi.org/10.1145/3534678.3542899Knowledge networks/graphs provide a powerful approach for data discovery, integration, and reuse. The NSF's new Convergence Accelerator program, which focuses on transitioning research to practice and translational research, announced Track A on the Open ...
- 0Citation
- 149
- Downloads
MetricsTotal Citations0Total Downloads149Last 12 Months21Last 6 weeks2
- Ying Ding
- research-articleOpen AccessPublished By ACMPublished By ACM
Context-Enriched Learning Models for Aligning Biomedical Vocabularies at Scale in the UMLS Metathesaurus
- Vinh Nguyen
National Library of Medicine, USA
, - Hong Yung Yip
University of South Carolina, USA
, - Goonmeet Bajaj
The Ohio State University, USA
, - Thilini Wijesiriwardene
University of South Carolina, USA
, - Vishesh Javangula
George Washington University, USA
, - Srinivasan Parthasarathy
The Ohio State University, USA
, - Amit Sheth
University of South Carolina, USA
, - Olivier Bodenreider
National Library of Medicine, USA
WWW '22: Proceedings of the ACM Web Conference 2022•April 2022, pp 1037-1046• https://doi.org/10.1145/3485447.3511946The Unified Medical Language System (UMLS) Metathesaurus construction process mainly relies on lexical algorithms and manual expert curation for integrating over 200 biomedical vocabularies. A lexical-based learning model (LexLM) was developed to ...
- 1Citation
- 972
- Downloads
MetricsTotal Citations1Total Downloads972Last 12 Months165Last 6 weeks21
- Vinh Nguyen
- Article
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits
- Kaushik Roy
Artificial Intelligence Institute, University of South Carolina, Columbia, USA
, - Qi Zhang
Artificial Intelligence Institute, University of South Carolina, Columbia, USA
, - Manas Gaur
Artificial Intelligence Institute, University of South Carolina, Columbia, USA
, - Amit Sheth
Artificial Intelligence Institute, University of South Carolina, Columbia, USA
Machine Learning and Knowledge Discovery in Databases. Research Track•September 2021, pp 35-50• https://doi.org/10.1007/978-3-030-86486-6_3AbstractContextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc. However, most of the algorithms use flat feature vectors to represent context whereas, in the real ...
- 1Citation
MetricsTotal Citations1
- Kaushik Roy
- extended-abstractPublished By ACMPublished By ACM
Designing Children’s New Learning Partner: Collaborative Artificial Intelligence for Learning to Solve the Rubik’s Cube
- Forest Agostinelli
AI Institute University of South Carolina, United States
, - Mihir Mavalankar
Indendent Researcher, United States
, - Vedant Khandelwal
AI Institute, University of South Carolina, United States
, - Hengtao Tang
Department of Education, University of South Carolina, United States
, - Dezhi Wu
University of South Carilona, United States
, - Barnett Berry
University of South Carolina, United States
, - Biplav Srivastava
AI Institute, University of South Carolina, United States
, - Amit Sheth
AI Institute, University of South Carolina, United States
, - Matthew Irvin
Department of Education University of South Carolina, United States
IDC '21: Proceedings of the 20th Annual ACM Interaction Design and Children Conference•June 2021, pp 610-614• https://doi.org/10.1145/3459990.3465175Developing the problem solving skills of children is a challenging problem that is crucial for the future of our society. Given that artificial intelligence (AI) has been used to solve problems across a wide variety of domains, AI offers unique ...
- 7Citation
- 321
- Downloads
MetricsTotal Citations7Total Downloads321Last 12 Months88Last 6 weeks8
- Forest Agostinelli
- tutorialPublic AccessPublished By ACMPublished By ACM
Knowledge-infused Deep Learning
- Manas Gaur
University of South Carolina, Columbia, SC, USA
, - Ugur Kursuncu
University of South Carolina, Columbia, SC, USA
, - Amit Sheth
University of South Carolina, Columbia, SC, USA
, - Ruwan Wickramarachchi
University of South Carolina, Columbia, SC, USA
, - Shweta Yadav
University of South Carolina, Columbia, SC, USA
HT '20: Proceedings of the 31st ACM Conference on Hypertext and Social Media•July 2020, pp 309-310• https://doi.org/10.1145/3372923.3404862Deep Learning has shown remarkable success during the last decade for essential tasks in computer vision and natural language processing. Yet, challenges remain in the development and deployment of artificial intelligence (AI) models in real-world cases,...
- 9Citation
- 1,288
- Downloads
MetricsTotal Citations9Total Downloads1,288Last 12 Months270Last 6 weeks28
- Manas Gaur
- introductionfreePublished By ACMPublished By ACM
Emoji Understanding and Applications in Social Media: Lay of the Land and Special Issue Introduction
- Sanjaya Wijeratne
Holler Technologies, Inc., CA, USA
, - Horacio Saggion
Universitat Pompeu Fabra, Barcelona, Spain
, - Emre Kiciman
Microsoft Research AI, WA, USA
, - Amit P. Sheth
Artificial Intelligence Institute, University of South Carolina, SC, USA
ACM Transactions on Social Computing, Volume 3, Issue 2•June 2020, Article No.: 6, pp 1-5 • https://doi.org/10.1145/3386120- 3Citation
- 2,150
- Downloads
MetricsTotal Citations3Total Downloads2,150Last 12 Months417Last 6 weeks52
- Sanjaya Wijeratne
- research-articlePublished By ACMPublished By ACM
eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection
- Ramnath Kumar
Birla Institute of Technology and Science Hyderabad, India
, - Shweta Yadav
Wright State University Dayton, Ohio, USA
, - Raminta Daniulaityte
Wright State University Dayton, Ohio, USA
, - Francois Lamy
Mahidol University Thailand
, - Krishnaprasad Thirunarayan
Wright State University Dayton, Ohio, USA
, - Usha Lokala
Wright State University Dayton, Ohio, USA
, - Amit Sheth
University of South Carolina Columbia, South Carolina, USA
WWW '20: Proceedings of The Web Conference 2020•April 2020, pp 1955-1965• https://doi.org/10.1145/3366423.3380263Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances ...
- 9Citation
- 625
- Downloads
MetricsTotal Citations9Total Downloads625Last 12 Months27Last 6 weeks6
- Ramnath Kumar
- research-articlePublic AccessPublished By ACMPublished By ACM
Predicting public opinion on drug legalization: social media analysis and consumption trends
- Farahnaz Golrooy Motlagh
Wright State University
, - Saeedeh Shekarpour
University of Dayton
, - Amit Sheth
Wright State University
, - Krishnaprasad Thirunarayan
Wright State University
, - Michael L. Raymer
Wright State University
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining•August 2019, pp 952-961• https://doi.org/10.1145/3341161.3344380In this paper, we focus on the collection and analysis of relevant Twitter data on a state-by-state basis for (i) measuring public opinion on marijuana legalization by mining sentiment in Twitter data and (ii) determining the usage trends for six ...
- 1Citation
- 639
- Downloads
MetricsTotal Citations1Total Downloads639Last 12 Months96Last 6 weeks21
- Farahnaz Golrooy Motlagh
- research-articlePublic AccessPublished By ACMPublished By ACM
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate
- Ugur Kursuncu
Wright State University, Dayton, OH, USA
, - Manas Gaur
Wright state university, Dayton, OH, USA
, - Carlos Castillo
Universitat Pompeu Fabra, Barcelona, Spain
, - Amanuel Alambo
Wright State University, Dayton, OH, USA
, - Krishnaprasad Thirunarayan
Wright State University, Dayton, OH, USA
, - Valerie Shalin
Wright State University, Dayton, OH, USA
, - Dilshod Achilov
University of Massachusetts, Dartmouth, MA, USA
, - I. Budak Arpinar
The University of Georgia, Athens, GA, USA
, - Amit Sheth
Wright State Umniversity, Dayton, OH, USA
Proceedings of the ACM on Human-Computer Interaction, Volume 3, Issue CSCW•November 2019, Article No.: 151, pp 1-22 • https://doi.org/10.1145/3359253Terror attacks have been linked in part to online extremist content. Online conversations are cloaked in religious ambiguity, with deceptive intentions, often twisted from mainstream meaning to serve a malevolent ideology. Although tens of thousands of ...
- 30Citation
- 2,067
- Downloads
MetricsTotal Citations30Total Downloads2,067Last 12 Months475Last 6 weeks93
- Ugur Kursuncu
Author Profile Pages
- Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples: - History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
- Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.
Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
- Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
- Future Direction:
The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.
A direct search interface for Author Profiles will be built.
An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.
It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.
Bibliometrics
The ACM DL is a comprehensive repository of publications from the entire field of computing.
It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.
- Average citations per article = The total Citation Count divided by the total Publication Count.
- Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
- Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
- Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
- Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
- Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
- Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
- Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
- Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)
ACM Author-Izer Service
Summary Description
ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.
Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.
ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.
To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.
How ACM Author-Izer Works
Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.
The Service can be applied to all the articles you have ever published with ACM.
Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.
For authors who do not have a free ACM Web Account:
- Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.
For authors who have an ACM web account, but have not edited their ACM Author Profile page:
- Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.
For authors who have an account and have already edited their Profile Page:
- Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"
ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.
Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.
FAQ
- Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
- Q. What articles are eligible for ACM Author-Izer?
- A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.
- Q. Are there any restrictions on authors to use this service?
- A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.
- Q. What are the requirements to use this service?
- A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.
- Q. What is an ACM Author Profile Page?
- A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.
- Q. How do I find my Author Profile page and take ownership?
- A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours
- Q. Why does my photo not appear?
- A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters
- Q. What if I cannot find the Add Personal Information function on my author page?
- A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.
- Q. What happens if an author changes the location of his bibliography or moves to a new institution?
- A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.
- Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
- A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.
However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.
- Q. What happens if the author’s bibliography lives on a page with several aliases?
- A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.
- Q. Why should authors use ACM Author-Izer?
- A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.
- Q. Does ACM Author-Izer provide benefits for authors?
- A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.
Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.
- Q. How does ACM Author-Izer benefit the computing community?
- A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.
In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.
- Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
- A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.
- Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
- A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner