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International Conference on computing for Sustainable Global Development (INDIACom)

作者： Nour Mahmoud Bahbouh Abdulelah Alhussain Hassan Hakami Yaser Mohammed Aljohani Adnan Ahmed Abi Sen Ahmad Alkhodre Aeshah Alsughayyir Department of Information and Communication Sciences Granada University Granada Spain Saudi Electronic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Computing and Information Technology Islamic University Al-Madinah Saudi Arabia Faculty of Computer and Information Systems Islamic University Al-Madinah Saudi Arabia College of Computer Science and Engineering Taibah University Al-Madinah Saudi Arabia

Smart cities promise a lot of well-being to their users in all areas of life through millions of applications and services. Smart services rely heavily on collecting data and the preferences of users. But on the dark side, the users' information is posed to threat and penetration during transmission or stored far in the clouds. Depending on encryption only is not sufficient if the attacker has strong resources or if the attacker is the service provider (SP) itself. In addition, changing data before sending is not a practical solution in many systems because of the adverse impact on the quality of a main service. This research presented a new idea to address the issue of protection. The proposed method enhanced the privacy and security of users' data without affecting the accuracy of service. The core of the suggested solution relies on a knowledge base of services that are managed by experts. Also, the solution depends on fog nodes to measure the level of security and privacy of users' queries without delay. Moreover, the fog nodes manage contact with SPs. Finally, the proposed method divided the SP into two, one for user queries and the other for user data. The simulation and analytical discussion on a practical case in smart cities demonstrated the superiority of the proposed approach over previous methods in the level of protection by maintaining the quality of services, and the resistance to attacks.

关键词： Resistance Privacy Data privacy Machine learning algorithms Smart cities Knowledge based systems Quality of service

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Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

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arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Doe's effort to reduce truck aerodynamic drag - joint experiments and computations lead to smart design

Doe's effort to reduce truck aerodynamic drag - joint experi...

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34th AIAA Fluid Dynamics Conference and Exhibit 2004

作者： McCallen, Rose C. Salari, Kambiz Ortega, Jason M. DeChant, Larry J. Hassan, Basil Roy, Christopher J. Pointer, W. David Browand, Fred Hammache, Mustapha Hsu, Tsun-Ya Leonard, Anthony Rubel, Mike Chatalain, Philippe Englar, Robert Ross, James Satran, D. Heineck, James T. Walker, Stephen Yaste, D. Storms, B. Lawrence Livermore National Laboratory Livermore CA 94551 United States Sandia National Laboratories Albuquerque NM 87185-0825 United States Auburn University Auburn AL 36849 United States Argonne National Laboratory Argonne IL 60439 United States University of Southern California LosAngeles CA 90089-1191 United States Georgia Tech Research Institute Atlanta GA 30332 United States NASA Ames Research Center Moffet Field CA 94035 United States Center for Applied Scientific Computing P.O. Box 808 L-98 United States New Technologies Engineering Division P.O. Box 808 L-644 United States Aerosciences and Compressible Fluid Mechanics Dept. PO Box 5800 MS 0825 United States Aerospace Engineering Dept. 211 Aerospace Engineering Bldg United States Nuclear Engineering Division NE-208 United States Aerospace and Mechanical Engineering MS 1191 United States Engineering and Applied Science 1200 East California Blvd. MC 301-46 United States Graduate Aeronautical Laboratories 1200 East California Blvd. MC 205-45 United States Aerospace Transportation and Advanced Systems Code 0844 United States Systems Analysis Branch MS 260-1 United States Aeronautical Projects and Program Office MS 260-1 United States

ISBN: (纸本)9781624100314

At 70 miles per hour, overcoming aerodynamic drag represents about 65% of the total energy expenditure for a typical heavy truck vehicle. The goal of this US Department of Energy supported consortium is to establish a clear understanding of the drag producing flow phenomena. This is being accomplished through joint experiments and computations, leading to the 'smart' design of drag reducing devices. This paper will describe our objective and approach, provide an overview of our efforts and accomplishments, and discuss our future direction. © 2004 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Aerodynamic drag

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Improving dynamic collision frequencies: impacts on dynamic structure factors and stopping powers in warm dense matter

arXiv

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arXiv 2023年

作者： Hentschel, Thomas W. Kononov, Alina Olmstead, Alexandra Cangi, Attila Baczewski, Andrew D. Hansen, Stephanie B. School of Applied & Engineering Physics Cornell University IthacaNY United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Nanoscience and Microsystems Engineering Program University of New Mexico AlbuquerqueNM United States Center for Advanced Systems Understanding Helmholtz-Zentrum Dresden-Rossendorf Görlitz Germany Department of Physics and Astronomy University of New Mexico AlbuquerqueNM United States Pulsed Power Sciences Center Sandia National Laboratories AlbuquerqueNM United States

Simulations and diagnostics of high-energy-density plasmas and warm dense matter rely on models of material response properties, both static and dynamic (frequency-dependent). Here, we systematically investigate variations in dynamic electron-ion collision frequencies ν(ω) in warm dense matter using data from a self-consistent-field average-atom model. We show that including the full quantum density of states, strong collisions, and inelastic collisions lead to significant changes in ν(ω). These changes result in red shifts and broadening of the plasmon peak in the dynamic structure factor, an effect observable in x-ray Thomson scattering spectra, and modify stopping powers around the Bragg peak. These changes improve the agreement of computationally efficient average-atom models with first-principles time-dependent density functional theory in warm dense aluminum, carbon, and deuterium. © 2023, CC BY.

关键词： Quantum theory

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Real-time embedded system support for the BTeV level 1 muon trigger

Real-time embedded system support for the BTeV level 1 muon ...

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2003 IEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference

作者： Haney, Michael J. Beauregard, D. Iyer, R. Kalbarczyk, Z. Liu, Q. Wang, L. Haney, M. Selen, M. Mosse, D. Shigiltchoff, O. Chopade, R. Hovey, L. Jung, M. Messie, D. Oh, J. Stone, S. Bapty, T. Neema, S. Nordstrom, S. Shetty, S. Vashishtha, S. Yao, D. Sheldon, P. Vaandering, E. Butler, J. Gottschalk, E. Kowalkowski, J. Votava, M. Appel, J. University of Illinois Urbana IL 61801 United States Des./Validation Reliable N.S.R.G. University of Illinois High Energy Physics Group Department of Physics University of Illinois Department of Computer Science Coll. in High Sch. Educ. Program University of Pittsburgh Department of Electrical Engineering Syracuse University High Energy Physics Group Department of Physics Syracuse University Inst. for Software Intgd. Systems Vanderbilt University Department of Physics BTeV Group Vanderbilt University BTeV Collaboration Particle Physics Division Fermi Natl. Accelerator Laboratory Computing Division Fermi Natl. Accelerator Laboratory Fermilab Education Office Fermi Natl. Accelerator Laboratory

The Level 1 Muon Trigger subsystem for BTeV will be implemented using the same architectural building blocks as the BTeV Level 1 Pixel Trigger: pipelined field programmable gate arrays feeding a farm of dedicated processing elements. The muon trigger algorithm identifies candidate tracks, and is sensitive to the muon charge (sign);candidate dimuon events are identified by complementary charge track-pairs. To insure that the trigger is operating effectively, the trigger development team is actively collaborating in an independent multi-university research program for reliable, self-aware, fault adaptive behavior in real-time embedded systems (RTES). Key elements of the architecture, algorithm, performance, and engineered reliability are presented.

关键词： Embedded systems

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Exploring the effect of image enhancement techniques on COVID‐19 detection using chest X‐rays images

arXiv

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arXiv 2020年

作者： Rahman, Tawsifur Khandakar, Amith Qiblawey, Yazan Tahir, Anas Kiranyaz, Serkan Kashem, Saad Bin Abul Islam, Mohammad Tariqul Maadeed, Somaya Al Zughaier, Susu M. Khan, Muhammad Salman Chowdhury, Muhammad E.H. Department of Biomedical Physics & Technology University of Dhaka Dhaka 1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Robotics and Advanced Computing Qatar Armed Forces‐Academic Bridge Program Qatar Foundation Doha24404 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine Biomedical and Pharmaceutical Research Unit QU Health Qatar University Doha2713 Qatar University of Engineering and Technology Peshawar Pakistan

The use of computer-aided diagnosis in the reliable and fast detection of corona virus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. CXR images are sometimes of poor quality and so image enhancement techniques can help the machine learning models to extract valuable discriminating features from the image. Numerous works have been reported on COVID-19 detection from smaller set of original X-ray images. However, the effect of image enhancement in COVID-19 detection was not reported in the literature. This paper explores the effect of various popular image enhancement techniques and states the © 2020, CC BY.

关键词： Image enhancement

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K, T and D-like fuzzy Kripke models

K, T and D-like fuzzy Kripke models

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Benjamin Bedregal Regivan H. N. Santiago Mario Benevides Petrúcio Viana Group of Logic Language Information Theory and Applications-LoLITA Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil Program of Engineering of Systems and Computing-PESC Department of Computer Science Federal University of Rio de Janeiro Rio de Janeiro Rio de Janeiro Brazil Department of Analysis Fluminense Federal University (UFF) Niteroi Rio de Janeiro Brazil

The idea of a Kripke semantics endowed with possibility/plausibility information is not new; in fact there are different approaches for that; see: [6], [13], [16], [19]. This paper follows the approach found in [6], but whereas [6] provides a fixed interpretation for connectives into [0,1] here we provide a characterization of a fuzzy semantics for connectives in such a way that the resulting fuzzy frames: K, T and D, are described precisely by the set of statements which also describes, respectively, the usual modal systems K, T and D.

关键词： Semantics Fuzzy sets Fuzzy logic Bismuth Electronic mail Uncertainty Reliability

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Real-time organ motion tracking and fast image registration system for MRI-guided surgery

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systems and Computers in Japan 2006年第1期37卷 83-92页

作者： Tokuda, Junichi Hirano, Masaya Tsukamoto, Tetsuji Dohi, Takeyoshi Hata, Nobuhiko Graduate School of Information Science and Technology University of Tokyo Tokyo 113-8656 Japan GE Yokogawa Medical Systems Ltd. Hino 191-8503 Japan International Society for Computer-Aided Surgery Japan Society for Computer-Aided Surgery Japan Society for Magnetic Resonance in Medicine GE Yokogawa Medical Systems Ltd. Imaging Application Tech. Center MR Group R and D Laboratory Technology Center GE Yokogawa Medical Systems Ltd. Society for Magnetic Resonance in Medicine Graduate School of Information Science and Technology Society for Life Support Technology Japan Society for Organ Preservation Biology and Medicine Society for Precision Engineering Society for Medical Image Computing and Computer Assisted Intervention International Union for Physics and Engineering Sciences in Medicine JSMEBE Japan Society for Life Assistance Technology Brigham and Women's Hospital Harvard Medical School MR Therapy Program

The advantage of magnetic resonance imaging (MRI)-guided surgery, in which MR images taken during surgery are used to guide the surgery, has been recognized recently. However, there is a problem, due to long imaging time of MRI, that it is difficult to capture the respiratory organ motion. It is often a constraint that the patient must hold his or her breath, especially in the treatment of the abdominal region. Consequently, this paper proposes a method of eliminating this constraint in MRI-guided surgery by real-time monitoring of the movement of the organ involved in the surgery by means of the signal derived from MRI. Specifically, the navigator echo is extracted in imaging during surgery. The movement of the organ involved in surgery is detected on the basis of the change in the calculated projection profile, and the results are presented to the surgeon. In this study, the procedure is built into a 1.5-T clinical MRI system. The feasibility of the proposed system was examined under clinical conditions by evaluating its accuracy and delay in a phantom experiment, and also in an in vivo experiment on the liver of a volunteer. The results are reported in this paper. © 2005 Wiley Periodicals, Inc.

关键词： Motion estimation

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AAAI 2008 workshop reports

AAAI 2008 workshop reports

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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;Intelligent Techniques for Web Personalization and Recommender systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer Learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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