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Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network
Journal of Imaging Article Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network Younes Ed-Doughmi 1,* , Najlae Idrissi 1 and Youssef Hbali 2 1 Department Computer Science, FST, University Sultan Moulay Sliman, 23000 Beni Mellal, Morocco; [email protected] 2 Computer Systems Engineering Laboratory Cadi Ayyad University, Faculty of Sciences Semlalia, 40000 Marrakech, Morocco; [email protected] * Correspondence: [email protected] Received: 6 January 2020; Accepted: 25 February 2020; Published: 4 March 2020 Abstract: In recent years, the rise of car accident fatalities has grown significantly around the world. Hence, road security has become a global concern and a challenging problem that needs to be solved. The deaths caused by road accidents are still increasing and currently viewed as a significant general medical issue. The most recent developments have made in advancing knowledge and scientific capacities of vehicles, enabling them to see and examine street situations to counteract mishaps and secure travelers. Therefore, the analysis of driver’s behaviors on the road has become one of the leading research subjects in recent years, particularly drowsiness, as it grants the most elevated factor of mishaps and is the primary source of death on roads. This paper presents a way to analyze and anticipate driver drowsiness by applying a Recurrent Neural Network over a sequence frame driver’s face. We used a dataset to shape and approve our model and implemented repetitive neural network architecture multi-layer model-based 3D Convolutional Networks to detect driver drowsiness. After a training session, we obtained a promising accuracy that approaches a 92% acceptance rate, which made it possible to develop a real-time driver monitoring system to reduce road accidents. -
Using Memristors in a Functional Spiking-Neuron Model of Activity-Silent Working Memory
Using memristors in a functional spiking-neuron model of activity-silent working memory Joppe Wik Boekestijn (s2754215) July 2, 2021 Internal Supervisor(s): Dr. Jelmer Borst (Artificial Intelligence, University of Groningen) Second Internal Supervisor: Thomas Tiotto (Artificial Intelligence, University of Groningen) Artificial Intelligence University of Groningen, The Netherlands Abstract In this paper, a spiking-neuron model of human working memory by Pals et al. (2020) is adapted to use Nb-doped SrTiO3 memristors in the underlying architecture. Memristors are promising devices in neuromorphic computing due to their ability to simulate synapses of ar- tificial neuron networks in an efficient manner. The spiking-neuron model introduced by Pals et al. (2020) learns by means of short-term synaptic plasticity (STSP). In this mechanism neu- rons are adapted to incorporate a calcium and resources property. Here a novel learning rule, mSTSP, is introduced, where the calcium property is effectively programmed on memristors. The model performs a delayed-response task. Investigating the neural activity and performance of the model with the STSP or mSTSP learning rules shows remarkable similarities, meaning that memristors can be successfully used in a spiking-neuron model that has shown functional human behaviour. Shortcomings of the Nb-doped SrTiO3 memristor prevent programming the entire spiking-neuron model on memristors. A promising new memristive device, the diffusive memristor, might prove to be ideal for realising the entire model on hardware directly. 1 Introduction In recent years it has become clear that computers are using an ever-increasing number of resources. Up until 2011 advancements in the semiconductor industry followed ‘Moore’s law’, which states that every second year the number of transistors doubles, for the same cost, in an integrated circuit (IC). -
Candidate for President (1 July 2018 – 30 June 2020) Jack Davidson
Candidate for President (1 July 2018 – 30 June 2020) Jack Davidson Professor of Computer Science University of Virginia Charlottesville, VA U.S.A. BIOGRAPHY Education and Employment • B.A.S. (Computer Science) Southern Methodist University, 1975; M.S. (Computer Science) Southern Methodist University, 1977; Ph.D. (Computer Science) University of Arizona, 1981. • University of Virginia, Professor, 1982–present. • President, Zephyr Software, 2001–present. • Princeton University, Visiting Professor, 1992–1993. • Microsoft Research, Visiting Researcher, 2000–2001. • Programmer Analyst III, University of Texas Health Science Center at Dallas, 1993–1997. ACM and SIG Activities • ACM member since 1975. • ACM Publications Board Co-Chair, 2010–present; ACM Publications Board member, 2007–2010. • ACM Student Chapter Excellence Award Judge, 2010–2017. • ACM Student Research Competition Grand Finals Judge, 2011–2017. • Associate Editor, ACM TOPLAS, 1994–2000. Associate Editor, ACM TACO, 2005–2016. Member of SIGARCH, SIGBED, SIGCAS, SIGCSE, and SIGPLAN • SIGPLAN Chair, 2005–2007. • SIGPLAN Executive Committee, 1999–2001, 2003–2005. • SGB Representative to ACM Council, 2008–2010. • SGB Executive Committee, 2006–2008. Awards and Honors • DARPA Cyber Grand Challenge Competition, 2nd Place, $1M prize (2016) • ACM Fellow (2008). • IEEE Computer Society Taylor L. Booth Education Award (2008). • UVA ACM Student Chapter Undergraduate Teaching Award (2000). • NCR Faculty Innovation Award (1994). Jack Davidson’s research interests include compilers, computer architecture, system software, embedded systems, computer security, and computer science education. He is co-author of two introductory textbooks: C++ Program Design: An Introduction to Object Oriented Programming and Java 5.0 Program Design: An Introduction to Programming and Object-oriented Design. Professionally, he has helped organize many conferences across several fields. -
David C. Parkes John A
David C. Parkes John A. Paulson School of Engineering and Applied Sciences, Harvard University, 33 Oxford Street, Cambridge, MA 02138, USA www.eecs.harvard.edu/~parkes December 2020 Citizenship: USA and UK Date of Birth: July 20, 1973 Education University of Oxford Oxford, U.K. Engineering and Computing Science, M.Eng (first class), 1995 University of Pennsylvania Philadelphia, PA Computer and Information Science, Ph.D., 2001 Advisor: Professor Lyle H. Ungar. Thesis: Iterative Combinatorial Auctions: Achieving Economic and Computational Efficiency Appointments George F. Colony Professor of Computer Science, 7/12-present Cambridge, MA Harvard University Co-Director, Data Science Initiative, 3/17-present Cambridge, MA Harvard University Area Dean for Computer Science, 7/13-6/17 Cambridge, MA Harvard University Harvard College Professor, 7/12-6/17 Cambridge, MA Harvard University Gordon McKay Professor of Computer Science, 7/08-6/12 Cambridge, MA Harvard University John L. Loeb Associate Professor of the Natural Sciences, 7/05-6/08 Cambridge, MA and Associate Professor of Computer Science Harvard University Assistant Professor of Computer Science, 7/01-6/05 Cambridge, MA Harvard University Lecturer of Operations and Information Management, Spring 2001 Philadelphia, PA The Wharton School, University of Pennsylvania Research Intern, Summer 2000 Hawthorne, NY IBM T.J.Watson Research Center Research Intern, Summer 1997 Palo Alto, CA Xerox Palo Alto Research Center 1 Other Appointments Member, 2019- Amsterdam, Netherlands Scientific Advisory Committee, CWI Member, 2019- Cambridge, MA Senior Common Room (SCR) of Lowell House Member, 2019- Berlin, Germany Scientific Advisory Board, Max Planck Inst. Human Dev. Co-chair, 9/17- Cambridge, MA FAS Data Science Masters Co-chair, 9/17- Cambridge, MA Harvard Business Analytics Certificate Program Co-director, 9/17- Cambridge, MA Laboratory for Innovation Science, Harvard University Affiliated Faculty, 4/14- Cambridge, MA Institute for Quantitative Social Science International Fellow, 4/14-12/18 Zurich, Switzerland Center Eng. -
Integer SDM and Modular Composite Representation Dissertation V35 Final
INTEGER SPARSE DISTRIBUTED MEMORY AND MODULAR COMPOSITE REPRESENTATION by Javier Snaider A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Major: Computer Science The University of Memphis August, 2012 Acknowledgements I would like to thank my dissertation chair, Dr. Stan Franklin, for his unconditional support and encouragement, and for having given me the opportunity to change my life. He guided my first steps in the academic world, allowing me to work with him and his team on elucidating the profound mystery of how the mind works. I would also like to thank the members of my committee, Dr. Vinhthuy Phan, Dr. King-Ip Lin, and Dr. Vasile Rus. Their comments and suggestions helped me to improve the content of this dissertation. I am thankful to Penti Kanerva, who introduced the seminal ideas of my research many years ago, and for his insights and suggestions in the early stages of this work. I am grateful to all my colleagues at the CCRG group at the University of Memphis, especially to Ryan McCall. Our meetings and discussions opened my mind to new ideas. I am greatly thankful to my friend and colleague Steve Strain for our discussions, and especially for his help editing this manuscript and patiently teaching me to write with clarity. Without his amazing job, this dissertation would hardly be intelligible. I will always be in debt to Dr. Andrew Olney for his generous support during my years in the University of Memphis, and for being my second advisor, guiding me academically and professionally in my career. -
The Cognitive Computing Era Is Here: Are You Ready?
The Cognitive Computing Era is Here: Are You Ready? By Peter Fingar. Based on excerpts from the new book Cognitive Computing: A Brief Guide for Game Changers www.mkpress.com/cc Artificial Intelligence is likely to change our civilization as much as, or more than, any technology thats come before, even writing. --Miles Brundage and Joanna Bryson, Future Tense The smart machine era will be the most disruptive in the history of IT. -- Gartner The Disruptive Era of Smart Machines is Upon Us. Without question, Cognitive Computing is a game-changer for businesses across every industry. --Accenture, Turning Cognitive Computing into Business Value, Today! The Cognitive Computing Era will change what it means to be a business as much or more than the introduction of modern Management by Taylor, Sloan and Drucker in the early 20th century. --Peter Fingar, Cognitive Computing: A Brief Guide for Game Changers The era of cognitive systems is dawning and building on today s computer programming era. All machines, for now, require programming, and by definition programming does not allow for alternate scenarios that have not been programmed. To allow alternating outcomes would require going up a level, creating a self-learning Artificial Intelligence (AI) system. Via biomimicry and neuroscience, Cognitive Computing does this, taking computing concepts to a whole new level. Once-futuristic capabilities are becoming mainstream. Let s take a peek at the three eras of computing. Fast forward to 2011 when IBM s Watson won Jeopardy! Google recently made a $500 million acquisition of DeepMind. Facebook recently hired NYU professor Yann LeCun, a respected pioneer in AI. -
Machine Learning to Improve Marine Science for the Sustainability of Living Ocean Resources Report from the 2019 Norway - U.S
NOAA Machine Learning to Improve Marine Science for the Sustainability of Living Ocean Resources Report from the 2019 Norway - U.S. Workshop NOAA Technical Memorandum NMFS-F/SPO-199 November 2019 Machine Learning to Improve Marine Science for the Sustainability of Living Ocean Resources Report from the 2019 Norway - U.S. Workshop Edited by William L. Michaels, Nils Olav Handegard, Ketil Malde, Hege Hammersland-White Contributors (alphabetical order): Vaneeda Allken, Ann Allen, Olav Brautaset, Jeremy Cook, Courtney S. Couch, Matt Dawkins, Sebastien de Halleux, Roger Fosse, Rafael Garcia, Ricard Prados Gutiérrez, Helge Hammersland, Hege Hammersland-White, William L. Michaels, Kim Halvorsen, Nils Olav Handegard, Deborah R. Hart, Kine Iversen, Andrew Jones, Kristoffer Løvall, Ketil Malde, Endre Moen, Benjamin Richards, Nichole Rossi, Arnt-Børre Salberg, , Annette Fagerhaug Stephansen, Ibrahim Umar, Håvard Vågstøl, Farron Wallace, Benjamin Woodward November 2019 NOAA Technical Memorandum NMFS-F/SPO-199 U.S. Department National Oceanic and National Marine Of Commerce Atmospheric Administration Fisheries Service Wilbur L. Ross, Jr. Neil A. Jacobs, PhD Christopher W. Oliver Secretary of Commerce Acting Under Secretary of Commerce Assistant Administrator for Oceans and Atmosphere for Fisheries and NOAA Administrator Recommended citation: Michaels, W. L., N. O. Handegard, K. Malde, and H. Hammersland-White (eds.). 2019. Machine learning to improve marine science for the sustainability of living ocean resources: Report from the 2019 Norway - U.S. Workshop. NOAA Tech. Memo. NMFS-F/SPO-199, 99 p. Available online at https://spo.nmfs.noaa.gov/tech-memos/ The National Marine Fisheries Service (NMFS, or NOAA Fisheries) does not approve, recommend, or endorse any proprietary product or proprietary material mentioned in the publication. -
Acm Council on Women Hails Innovator in Information Retrieval
Contact: Virginia Gold 212-626-0505 [email protected] ACM COUNCIL ON WOMEN HAILS INNOVATOR IN INFORMATION RETRIEVAL Microsoft Research’s Dumais Changed the Way People Search for Information NEW YORK, April 8, 2014 – ACM-W (the Association for Computing Machinery’s Council on Women in Computing) today named Susan T. Dumais of Microsoft Research as the 2014-2015 Athena Lecturer. Dumais introduced novel algorithms and interfaces for interactive retrieval that have made it easier for people to find, use and make sense of information. Her research, at the intersection of human-computer interaction and information retrieval, has broad applications for understanding and improving searching and browsing from the Internet to the desktop. The Athena Lecturer award celebrates women researchers who have made fundamental contributions to computer science. It includes a $10,000 honorarium provided by Google Inc. “Dumais has helped us understand that the search is not the end goal,” said Mary Jane Irwin, who heads the ACM-W awards committee. “Her focus is on understanding when and why people search, and presenting results in context to help integrate those results into the larger search process. Her sustained contributions have shaped the thinking and direction of human-computer interaction and information retrieval, and influenced generations of student interns through collaborative projects with academic and industry partners.” Dumais’ initial research demonstrated that different people use different vocabulary to describe the same thing, and that this mismatch limits the success of traditional keyword-based information retrieval methods. To build search systems that avoided the vocabulary problem, she and her colleagues invented Latent Semantic Indexing (LSI). -
ACM JDIQ) October 31, 2006
PROPOSAL FOR A NEW ACM PUBLICATION: ACM Journal of Data and Information Quality (ACM JDIQ) October 31, 2006 Contacts for the proposed ACM Journal of Data and Information Quality (ACM JDIQ) Dr. Yang Lee Phone and email: MIS-EIC (617) 373-5052 Associate Professor (617) 739-9367 (fax) College of Business Administration [email protected] Northeastern University 214 Hayden Hall 360 Huntington Avenue Boston, MA 021115-5000 Dr. Stuart Madnick Phone and email: CS-EIC (617) 253-6671 J N Maguire Professor of Information Technology and (617) 253-3321 (fax) Professor of Engineering Systems [email protected] Massachusetts Institute of Technology Room E53-321 Cambridge, MA 02142 Dr. Elizabeth Pierce Phone and email: Managing Editor (501) 569-3488 Associate Professor (501) 569-7049 (fax) Department of Information Science [email protected] University of Arkansas at Little Rock ETAS Building, Room 258 2801 South University Avenue Little Rock, Arkansas 72204 1. JUSTIFICATION 1.1 Background for this Proposal Today’s organizations are increasingly investing in technology to collect, store, and process vast volumes of data. Even so, they often find themselves stymied in their efforts to translate this data into meaningful insights that can be used to improve business processes and to make better decisions. The reasons for this difficulty can often be traced to issues of quality. These involve both technical issues, such as that the data collected may be inconsistent, inaccurate, incomplete, or out-of-date as well as the fact that many organizations lack a cohesive strategy across the enterprise to ensure that the right stakeholders have the right information in the right format at the right place and time. -
Prepare. Protect. Prosper
Prepare. Protect. Prosper. Cybersecurity White Paper From Star Trek to Cognitive Computing: Machines that understand Security Second in a series of cybersecurity white papers from Chameleon Integrated Services In this issue: The rise of cognitive computing Data, Data, Everywhere – how to use it Cyber Defense in Depth Executive Summary ur first white-paper on cybersecurity, “Maskelyne and Morse Code: New Century, Same Threat” discussed the overwhelming nature of today’s cybersecurity threat, where O cyberwarfare is the new norm, and we gave some historical perspective on information attacks in general. We also discussed preventative measures based on layered security. In our second in the series we delve into the need for the “super analyst” and the role of Artificial Intelligence (AI) in Cyber Defense and build on the concept of “defense in depth”. Defense in depth, describes an ancient military strategy of multiple layers of defense, which is the concept of multiple redundant defense mechanisms to deal with an overwhelming adversary. The application of AI to cybersecurity is the next frontier and represents another layer of defense from cyber-attack available to us today. The original television series Star Trek, is a cultural icon that inspired significant elements of the evolution of technologies that we all take for granted today, from the original Motorola flip phone to tablet computers. Dr. Christopher Welty, a computer scientist and an original member of the IBM artificial intelligence group that was instrumental in bringing Watson to life was among those heavily influenced by the fictional technology he saw on Star Trek as a child (source startrek.com article)i. -
VLSI Technology: Impact and Promise
DOCUMENT RESUME ED 350 762 EC 301 569 AUTHOR Bayoumi, Magdy TITLE VLSI Technology: Impact and Promise. Identifying Emerging Issues and Trends in Technology for Special Education. INSTITUTION COSMOS Corp., Washington, DC. SPONS AGENCY Special Education Programs (ED/OSERS), Washington, DC. PUB DATE Jun 92 CONTRACT HS90008001 NOTE 45p.; For related documents, see EC 301 540, EC 301 567-574. PUB TYPE Reports Descriptive (141) Information Analyses (070) EDRS PRICE MF01/PCO2 Plus Postage. DESCRIPTORS *Computer Uses in Education; *Disabilities; Educational Media; Educational Trends; Elementary Secondary Education; History; Microcomputers; *Research and Development; Special Education; *Technological Advancement; Technology Transfer; Theory Practice Relationship; Trend Analysis IDENTIFIERS *Very Large Scale Integration Technology ABSTRACT As part of a 3-year study to identify emerging issues and trends in technology for special education, this paper addresses the implications of very large scale integrated (VLSI) technology. The first section reviews the development of educational technology, particularly microelectronics technology, from the 1950s to the present. The implications of very high computational power available at low cost are then examined for the fields of education, medicine, assistive devices for individuals with disabilities, home electronics, business, management information systems, music, defense applications, space programs, industrial applications, and super computing. The second section then briefly considers future applications of new technologies including gallium arsenide technology, fuzzy logic, and neural networks. A high level of technological integration is predicted for the 21st century based on the immense power of VLSI-based automation. An appendix outlines, through text and figures, the fundamentals of VLSI. (Contains 25 references and 5 figures.) (DB) ******AA..*******A*************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. -
Outline of Machine Learning
Outline of machine learning The following outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of computer science[1] (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[3] Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions. Contents What type of thing is machine learning? Branches of machine learning Subfields of machine learning Cross-disciplinary fields involving machine learning Applications of machine learning Machine learning hardware Machine learning tools Machine learning frameworks Machine learning libraries Machine learning algorithms Machine learning methods Dimensionality reduction Ensemble learning Meta learning Reinforcement learning Supervised learning Unsupervised learning Semi-supervised learning Deep learning Other machine learning methods and problems Machine learning research History of machine learning Machine learning projects Machine learning organizations Machine learning conferences and workshops Machine learning publications