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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Measuring productivity of software projects: A data envelopment analysis approach

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DECISION SCIENCES 1996年第1期27卷 57-80页

作者： Mahmood, MA Pettingell, KJ Shaskevich, AI Information and Decision Sciences University of Texas–El Paso El Paso TX 79968 E-mail: mmahmood@mail.utep.edu Information and Decision Sciences University of Texas–El Paso El Paso TX 79968 950 Camino Del Rex Las Cruces NM 88001 [?]1Mo Adam Mahmood is a professor of information systems and Ellis and Susan Mayfield Endowed Professor in Business Administration at the University of Texas at El Paso. He received his Ph.D. in management information system from Texas Tech University. His research interests include software engineering information technology in support of superior organizational shategic and ecxlIKlmic perfonnamethe strategic manager's perspectives on information technology and infamation technology for national and inkmatid competitiveness. Dr. Mahtnd has published in Decision SciencesMIS Quarterly Joumal of Management Information SystemsEuropean Joud of Information Systems INFOR-Canadian' Journal of Operation Research and Information Processing and Information and Management among others. He has also edited and published a book in the information technology investment and performance area. He is cmntly serving as editor of the Journal of End-User Computing. He is also serving as guest editor associate editor member of the editorial review board or referee for a number of information systems journals. In addition he has presented research papers served as both a reviewer and discussant chaired sessions and tracks and participated in program committees of several regional national and international conferences.

Current economic conditions are forcing information system departments to focus simultaneously on decreasing costs while increasing software productivity. Improving software productivity is becoming critical because software costs of large in-house software companies have been increasing rapidly. For many organizations, however, measuring software productivity has been a difficult task. Using Data Envelopment Analysis (DEA), this research study investigates the productivity of 78 commercial system projects. The results of this study have practical implications for software project managers undertaking software development. The results showed that the DEA technology can be successfully used to identify efficient and inefficient software projects. Furthermore, within the inefficient group, DEA can also identify factors that affect software productivity in a positive or negative manner, allowing managers to take corrective actions. Based on the findings of this study, the manuscript also provides some practical guidelines for managers to follow in software development.

关键词： Data Envelopment Analysis Information systems implementation systems development methodologies

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Journal of Information Science and engineering: Editorial notice

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Journal of Information Science and engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

No abstract available

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Clarifying Assumptions about Artificial Intelligence before Revolutionising Patent Law

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GRUR International 2022年第4期71卷 295-321页

作者： Kim, Daria Alber, Maximilian Kwok, Man Wai Mitrovic, Jelena Ramirez-Atencia, Cristian Perez, Jesus Alberto Rodriguez Zille, Heiner Max Planck Institute for Innovation and Competition Munich Germany Charite Institute of Pathology Berlin Germany Chinese University of Hong Kong Department of Physics (Formerly) Hong Kong BMBF Program for Women in AI Faculty of Computer Science and Mathematics Chair of Data Science University of Passau Germany Department of Computer Systems Universidad Politecnica de Madrid Spain School of Computing Science College of Science and Engineering University of Glasgow United Kingdom Faculty of Computer Science Otto von Guericke University Magdeburg Germany

This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of 'artificial ingenuity'. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where 'intelligent' systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited. © 2022 Published by OUP and CH Beck on behalf of GRUR e.V.

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Appointments for 2005-2006 term

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IEEE Transactions on Very Large Scale Integration (VLSI) systems 2005年第7期13卷 773-782页

作者： Ranganathan, Nagarajan Agrawal, Vishwani D. Chakradhar, Srimat T. Chakrabarty, Krishnendu Courtois, Bernard DeMara, Ronald Hu, Xiaobo Sharon Ismail, Yehea I. Jha, Niraj K. John, Lizy Kurian Ker, Ming-Dou Koren, Israel Liu, Bin-Da Marculescu, Diana Marculescu, Radu Narayanan, Vijaykrishnan Nassif, Sani R. Nowick, Steven M. Sapatnekar, Sachin S. Sherlekar, Sunil Sylvester, Dennis Vemuri, Ranga Pham, Michael Department of Computer Science and Engineering University of South Florida Tampa FL 33620 United States Department of Computer Science and Engineering Nanomaterials and Nanomanufacturing Research Center University of South Florida Tampa United States IEEE United States Systems United States Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ United States IETE India ACM NEC Laboratories America Inc. Princeton NJ United States Duke University Durham NC United States ACM SIGDA Sigma Xi United States Laboratory of Techniques of Informatics and Microelectronics for Computer Architectecture ASME IMAPS IEEE Computer Society University of Central Florida Orlando United States Computer Engineering Program SEECS Department of Computer Science and Engineering University of Notre Dame Notre Dame IN United States Northwestern University Evanston IL United States Department of Electrical Engineering Princeton University Princeton NJ United States Association for Computing Machinery Austin United States UT Austin Engineering Foundation United States Department of Electronics Engineering National Chiao-Tung University Taiwan Department of Electrical and Computer Engineering University of Massachusetts Amherst United States Department of Electrical Engineering Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States Electrical and Computer Engineering Faculty Carnegie Mellon University Pittsburgh PA United States Computer Science and Engineering Department Pennsylvania State University University Park United States IBM Austin Research Laboratory Austin TX United States Columbia University New York NY United States Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States Tata Consultancy Services Centre of Excellence for Embedded Systems India Department of Electrical Engineering IIT Bombay India Department of Electrical Engineering Uni

No abstract available

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Book reviews

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Minds and Machines 1996年第2期6卷 239-285页

作者： Abbott, Barbara Herskovits, Annette Peterson, Philip L. Mele, Alfred R. Cole, David J. Crevier, Daniel Pelletier, Francis Jeffry Berkeley, Istvan S. N. Kitts, Brendan J. Brown, Mike Paliouras, George Department of Linguistics and Languages Michigan State University East Lansing USA Department of Philosophy Michigan State University East Lansing USA Program in Applied Linguistics Boston University Boston USA Department of Philosophy Syracuse University Syracuse USA Department of Philosophy Davidson College Davidson USA Department of Philosophy University of Minnesota Duluth USA Department of Electrical Engineering École de Technologie Supérieure Université du Québec Montréal Canada Department of Philosophy University of Alberta Edmonton Canada Department of Computing Science University of Alberta Edmonton Canada Biological Computation Project University of Alberta Edmonton Canada Center for Complex Systems Brandeis University Waltham USA Department of Computer Science University of Manchester Manchester U.K.

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Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

arXiv

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arXiv 2024年

作者： Mao, Lingchao Wang, Hairong Hu, Leland S. Tran, Nhan L. Canoll, Peter D. Swanson, Kristin R. Li, Jing H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Mathematical NeuroOncology Lab Precision Neurotherapeutics Innovation Program Mayo Clinic Arizona 5777 East Mayo Blvd Support Services Building Suite 2-700 PhoenixAZ85054 United States Department of Radiology Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Neurosurgery Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States Department of Cancer Biology Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States Department of Pathology and Cell Biology Columbia University Medical Center 630 West 168th Street New YorkNY10032 United States

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine *** Codes 92B99 Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge representation

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Assessing Engraftment Following Fecal Microbiota Transplant

arXiv

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arXiv 2024年

作者： Herman, Chloe Barker, Bridget M. Bartelli, Thais F. Chandra, Vidhi Krajmalnik-Brown, Rosa Jewell, Mary Li, Le Liao, Chen McAllister, Florencia Nirmalkar, Khemlal Xavier, Joao B. Caporaso, J. Gregory Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Department of Clinical Cancer Prevention University of Texas MD Anderson Cancer Center HoustonTX United States Biodesign Center for Health Through Microbiomes Arizona State University TempeAZ United States School of Sustainable Engineering and the Built Environment Arizona State University TempeAZ United States Independent author Salt Lake CityUT United States Program for Computational and Systems Biology Memorial Sloan-Kettering Cancer Center New YorkNY United States Department of Gastrointestinal Medical Oncology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Immunology The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Biological Sciences Northern Arizona University FlagstaffAZ United States

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn’t respond because the engrafted microbiome didn’t produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn’t engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment using methods such as alpha diversity comparisons, beta diversity comparisons, and microbiome source tracking. 2) Donated Microbiome Indicator Features tracks donated microbiome features (e.g., amplicon sequence variants or species of interest) as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified (e.g., from FMT or disease-relevant literature). 3) Temporal Stability examines how resistant post-FMT recipient’s microbiomes are to reverting back to their baseline microbiome. Individually, these criteria each highlight a critical aspect of microbiome engraftment;investigated together, however, they provide a clearer assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria,

关键词： Diseases

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PARALLEL AND DISTRIBUTED TLM COMPUTATION WITH SIGNAL-PROCESSING FOR ELECTROMAGNETIC-FIELD MODELING

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 1995年第3-4期8卷 169-185页

作者： SO, PPM ESWARAPPA, C HOEFER, WJR NSERC/MPR Teltech Research Chair in RF Engineering Department of Electrical and Computer Engineering University of Victoria Victoria British Columbia V8W 3P6 Canada Poman So received his B.Sc. degree in computer science and physics from the University of Toronto Toronto Onatario Canada in 1985. He obtained his B.A. Sc. and M.A.Sc. degrees in electrical engineering (summa cum laude) from the University of Ottawa Ottawa Ontario Canada in 1987 and 1989 respectively. Mr So was a research engineer in the University of Ottawa from January 1989 to October 1991 his research interests included CAD techniques of microwave circuits and numerical methods for electromagnetic wave modelling. He specialized in the development of electromagnetic engineering CAD software and successfully implemented a number of electromagnetic wave simulators based on the two-dimensional and three-dimensional transmission-line matrix methods. From August 1990 to February 1991 he accompanied Professor W. J. R. Hoefer to Rome Italy and Sophia Antiopolas France. During that time he implemented a parallel version of the 3D-TLM simulator for the CM-2 Connection Machine using C-Star and X Windows Library. He is a research engineer and a Ph.D. student at the University of Victoria Victoria British Columbia Canada. He ported the 2D- and 3D-TLM simulators to DECmpp 1200 massively parallel computer using MPL C and C++. He also combined OSA90/hope a commercially available microwave CAD program with a 2D-TLM simulation module for geometry optimization using the Datapipe technique of OSA90/hope. Recently he has developed a distributed client-server computing technique for the TLM method this technique could accelerate the TLM simulation by more than an order of magnitude. Channabasappa Eswarappa received the M.Tech. degree in electrical engineering from the Indian Institute of Technology Kanpur India in 1983 and Ph. D. degree from the University of Ottawa Canada in 1990. He worked as an Assistant Executive Engineer and later as

This paper describes the implementation of transmission-line matrix (TLM) method algorithms on a massively parallel computer (DECmpp 12000), the technique of distributed computing in the UNIX environment, and the combination of TLM analysis with Prony's method as well as with autoregressive moving average (ARMA) digital signal processing for electromagnetic field modelling. By combining these advanced computation techniques, typical electromagnetic field modelling of microwave structures by TLM analysis can be accelerated by a few orders of magnitude.

关键词： Mathematical models

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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