An Information-Driven Architecture for Cognitive Systems Research
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Cognitive Science 1
Cognitive Science 1 • Linguistics, Minor (https://e-catalogue.jhu.edu/arts-sciences/full- COGNITIVE SCIENCE time-residential-programs/degree-programs/cognitive-science/ linguistics-minor/) http://www.cogsci.jhu.edu For current course information and registration go to https://sis.jhu.edu/ Cognitive science is the study of the human mind and brain, focusing on classes/ how the mind represents and manipulates knowledge and how mental representations and processes are realized in the brain. Conceiving of the mind as an abstract computing device instantiated in the brain, Courses cognitive scientists endeavor to understand the mental computations AS.050.102. Language and Mind. 3 Credits. underlying cognitive functioning and how these computations are Introductory course dealing with theory, methods, and current research implemented by neural tissue. Cognitive science has emerged at the topics in the study of language as a component of the mind. What it is interface of several disciplines. Central among these are cognitive to "know" a language: components of linguistic knowledge (phonetics, psychology, linguistics, and portions of computer science and artificial phonology, morphology, syntax, semantics) and the course of language intelligence; other important components derive from work in the acquisition. How linguistic knowledge is put to use: language and the neurosciences, philosophy, and anthropology. This diverse ancestry brain and linguistic processing in various domains. has brought into cognitive science several different perspectives and Area: Natural Sciences, Social and Behavioral Sciences methodologies. Cognitive scientists endeavor to unite such varieties AS.050.105. Introduction to Cognitive Neuropsychology. 3 Credits. of perspectives around the central goal of characterizing the structure When the brain is damaged or fails to develop normally, even the most of human intellectual functioning. -
Reliability in Cognitive Neuroscience: a Meta-Meta-Analysis Books Written by William R
Reliability in Cognitive Neuroscience: A Meta-Meta-Analysis Books Written by William R. Uttal Real Time Computers: Techniques and Applications in the Psychological Sciences Generative Computer Assisted Instruction (with Miriam Rogers, Ramelle Hieronymus, and Timothy Pasich) Sensory Coding: Selected Readings (Editor) The Psychobiology of Sensory Coding Cellular Neurophysiology and Integration: An Interpretive Introduction. An Autocorrelation Theory of Form Detection The Psychobiology of Mind A Taxonomy of Visual Processes Visual Form Detection in 3-Dimensional Space Foundations of Psychobiology (with Daniel N. Robinson) The Detection of Nonplanar Surfaces in Visual Space The Perception of Dotted Forms On Seeing Forms The Swimmer: An Integrated Computational Model of a Perceptual-Motor System (with Gary Bradshaw, Sriram Dayanand, Robb Lovell, Thomas Shepherd, Ramakrishna Kakarala, Kurt Skifsted, and Greg Tupper) Toward A New Behaviorism: The Case against Perceptual Reductionism Computational Modeling of Vision: The Role of Combination (with Ramakrishna Kakarala, Sriram Dayanand, Thomas Shepherd, Jaggi Kalki, Charles Lunskis Jr., and Ning Liu) The War between Mentalism and Behaviorism: On the Accessibility of Mental Processes The New Phrenology: On the Localization of Cognitive Processes in the Brain A Behaviorist Looks at Form Recognition Psychomyths: Sources of Artifacts and Misrepresentations in Scientifi c Cognitive neuroscience Dualism: The Original Sin of Cognitivism Neural Theories of Mind: Why the Mind-Brain Problem May Never Be Solved Human Factors in the Courtroom: Mythology versus Science The Immeasurable Mind: The Real Science of Psychology Time, Space, and Number in Physics and Psychology Distributed Neural Systems: Beyond the New Phrenology Neuroscience in the Courtroom: What Every Lawyer Should Know about the Mind and the Brain Mind and Brain: A Critical Appraisal of Cognitive Neuroscience Reliability in Cognitive Neuroscience: A Meta-Meta-Analysis Reliability in Cognitive Neuroscience: A Meta-Meta-Analysis William R. -
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. -
The Place of Modeling in Cognitive Science
The place of modeling in cognitive science James L. McClelland Department of Psychology and Center for Mind, Brain, and Computation Stanford University James L. McClelland Department of Psychology Stanford University Stanford, CA 94305 650-736-4278 (v) / 650-725-5699 (f) [email protected] Running Head: Modeling in cognitive science Keywords: Modeling frameworks, computer simulation, connectionist models, Bayesian approaches, dynamical systems, symbolic models of cognition, hybrid models, cognitive architectures Abstract I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. As explorations, simplification is essential – it is only through simplification that we can fully understand the implications of the ideas. This is not to say that simplification has no downsides; it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000-fold increase? 1. Introduction With the inauguration of a new journal for cognitive science, thirty years after the first meeting of the Cognitive Science Society, it seems essential to consider the role of computational modeling in our discipline. -
Author: Edwin Hutchins Title: Cognitive Ecology Affiliation: Department of Cognitive Science, University of California San Dieg
Author: Edwin Hutchins Title: Cognitive Ecology Affiliation: Department of Cognitive Science, University of California San Diego Tel: 858 534-1134 Fax: 858 822-2476 email: [email protected] Running Head: Cognitive Ecology Abstract: Cognitive ecology is the study of cognitive phenomena in context. In particular, it points to the web of mutual dependence among the elements of a cognitive ecosystem. At least three fields were taking a deeply ecological approach to cognition thirty years ago: Gibson’s ecological psychology, Bateson’s ecology of mind, and Soviet cultural-historical activity theory. The ideas developed in those projects have now found a place in modern views of embodied, situated, distributed cognition. As cognitive theory continues to shift from units of analysis defined by inherent properties of the elements to units defined in terms of dynamic patterns of correlation across elements, the study of cognitive ecosystems will become an increasingly important part of cognitive science. Keywords: units of analysis for cognition, ecological psychology, ecology of mind, activity theory, embodied cognition, situated cognition, distributed cognition, brain-body-world systems, human culture. Cognitive Ecology Choosing units of analysis for cognition Cognitive ecology is the study of cognitive phenomena in context. Elements of cognitive ecology have been present in various corners, but not the core, of cognitive science since the birth of the field. It is now being rediscovered as cognitive science shifts from viewing cognition as a logical process to seeing it as a biological phenomenon. Everything is connected to everything else. Fortunately, not all connectivity is equally dense. The non- uniformity of connectivity makes science possible. -
Live Distributed Objects
LIVE DISTRIBUTED OBJECTS A Dissertation Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Krzysztof Jan Ostrowski August 2008 c 2008 Krzysztof Jan Ostrowski ALL RIGHTS RESERVED LIVE DISTRIBUTED OBJECTS Krzysztof Jan Ostrowski, Ph.D. Cornell University 2008 Distributed multiparty protocols such as multicast, atomic commit, or gossip are currently underutilized, but we envision that they could be used pervasively, and that developers could work with such protocols similarly to how they work with CORBA/COM/.NET/Java objects. We have created a new programming model and a platform in which protocol instances are represented as objects of a new type called live distributed objects: strongly-typed building blocks that can be composed in a type-safe manner through a drag and drop interface. Unlike most prior object-oriented distributed protocol embeddings, our model appears to be flexible enough to accommodate most popular protocols, and to be applied uniformly to any part of a distributed system, to build not only front-end, but also back-end components, such as multicast channels, naming, or membership services. While the platform is not limited to applications based on multicast, it is replication- centric, and reliable multicast protocols are important building blocks that can be used to create a variety of scalable components, from shared documents to fault-tolerant storage or scalable role delegation. We propose a new multicast architecture compatible with the model and designed in accordance with object-oriented principles such as modu- larity and encapsulation. -
The Rising Field of Bioinformatics
Serena Hughes Case Study Spring 2019 The Rising Field of Bioinformatics Overview. Bioinformatics is an up-and-coming field within the intersection of biology and computer science. Biology and computer science were initially thought of separately, but with technological advances it soon became clear that there was an overlap where computer science algorithms could be used to further biological discoveries and biological systems could be used as a basis for computational models. The growth of research combining the two has led to the recent growth of the field of bioinformatics. Currently, bioinformatics is still relatively small, often housed as a specialization of biology, computer science, or other departments at universities. In 2009, only approximately 17% of bioinformatics programs were part of a bioinformatics department, with most programs being housed under biology or biomedical departments.[7] While it has been ten years since this data was collected, even in 2018 U.S. News’ ranking of bioinformatics programs included only 6 schools total.[11] Bioinformatics is a small and growing field, so the importance of the organization of bioinformatics is also growing. What is Being Organized? Bioinformatics has become increasingly interdisciplinary as it has grown, building on other scientific and mathematical fields. These subfields and the types of research they enable are the resources being organized. In organizing these resources, we will shed light on the structure of the field of bioinformatics as a whole. Why is it Being Organized? The term “bioinformatics” has been in use since the 1970s. Around that time, bioinformatics was “defined as the study of informatic processes in biotic systems”. -
A Lightweight, Customizable Tuple Space Supporting Context-Aware Applications
LighTS: A Lightweight, Customizable Tuple Space Supporting Context-Aware Applications Gian Pietro Picco, Davide Balzarotti and Paolo Costa Dip. di Elettronica e Informazione, Politecnico di Milano, Italy {picco, balzarot, costa}@elet.polimi.it ABSTRACT develop context-aware applications. It is interesting to look The tuple space model inspired by Linda has recently been briefly into the lessons learned from these experiences, in rediscovered by distributed middleware. Moreover, some re- that they motivate the contributions we put forth in this pa- searchers also applied it in the challenging scenarios involv- per. The work in [10] describes a simple location-aware ap- ing mobility and more specifically context-aware computing. plication supporting collaborative exploration of geographi- Context information can be stored in the tuple space, and cal areas, e.g., to coordinate the help in a disaster recovery queried like any other data. scenario. Users are equipped with portable computing de- Nevertheless, it turns out that conventional tuple space vices and a localization system (e.g., GPS), are freely mo- implementations fall short of expectations in this new do- bile, and are transiently connected through ad hoc wireless main. On one hand, many of the available systems provide links. The key functionality provided is the ability for a a wealth of features, which make the resulting implementa- user to request the displaying of the current location and/or tion unnecessarily bloated and incompatible with the tight trajectory of any other user, provided wireless connectivity resource constraints typical of this field. Moreover, the tra- is available towards her. The implementation exploits tuple spaces as repositories for context information—i.e., location ditional Linda matching semantics based on value equal- Lime ity are not appropriate for context-aware computing, where data. -
A Persistent Storage Model for Extreme Computing Shuangyang Yang Louisiana State University and Agricultural and Mechanical College
Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2014 A Persistent Storage Model for Extreme Computing Shuangyang Yang Louisiana State University and Agricultural and Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Computer Sciences Commons Recommended Citation Yang, Shuangyang, "A Persistent Storage Model for Extreme Computing" (2014). LSU Doctoral Dissertations. 2910. https://digitalcommons.lsu.edu/gradschool_dissertations/2910 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. A PERSISTENT STORAGE MODEL FOR EXTREME COMPUTING A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Computer Science by Shuangyang Yang B.S., Zhejiang University, 2006 M.S., University of Dayton, 2008 December 2014 Copyright © 2014 Shuangyang Yang All rights reserved ii Dedicated to my wife Miao Yu and our daughter Emily. iii Acknowledgments This dissertation would not be possible without several contributions. It is a great pleasure to thank Dr. Hartmut Kaiser @ Louisiana State University, Dr. Walter B. Ligon III @ Clemson University and Dr. Maciej Brodowicz @ Indiana University for their ongoing help and support. It is a pleasure also to thank Dr. Bijaya B. Karki, Dr. Konstantin Busch, Dr. Supratik Mukhopadhyay at Louisiana State University for being my committee members and Dr. -
Ultra-Large-Scale Sites
Ultra-large-scale Sites <working title> – Scalability, Availability and Performance in Social Media Sites (picture from social network visualization?) Walter Kriha With a forword by << >> Walter Kriha, Scalability and Availability Aspects…, V.1.9.1 page 1 03/12/2010 Copyright <<ISBN Number, Copyright, open access>> ©2010 Walter Kriha This selection and arrangement of content is licensed under the Creative Commons Attribution License: http://creativecommons.org/licenses/by/3.0/ online: www.kriha.de/krihaorg/ ... <a rel="license" href="http://creativecommons.org/licenses/by/3.0/de/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by/3.0/de/88x31.png" /></a><br /><span xmlns:dc="http://purl.org/dc/elements/1.1/" href="http://purl.org/dc/dcmitype/Text" property="dc:title" rel="dc:type"> Building Scalable Social Media Sites</span> by <a xmlns:cc="http://creativecommons.org/ns#" href="wwww.kriha.de/krihaorg/books/ultra.pdf" property="cc:attributionName" rel="cc:attributionURL">Walter Kriha</a> is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/3.0/de/">Creative Commons Attribution 3.0 Germany License</a>.<br />Permissions beyond the scope of this license may be available at <a xmlns:cc="http://creativecommons.org/ns#" href="www.kriha.org" rel="cc:morePermissions">www.kriha.org</a>. Walter Kriha, Scalability and Availability Aspects…, V.1.9.1 page 2 03/12/2010 Acknowledgements <<master course, Todd Hoff/highscalability.com..>> Walter Kriha, Scalability and Availability Aspects…, V.1.9.1 page 3 03/12/2010 ToDo’s - The role of ultra fast networks (Infiniband) on distributed algorithms and behaviour with respect to failure models - more on group behaviour from Clay Shirky etc. -
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). -
Sharing of Semantically Enhanced Information for the Adaptive Execution of Business Processes
NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS DEPARTMENT OF INFORMATICS AND TELECOMUNICATIONS PROGRAM OF POSTGRADUATE STUDIES DIPLOMA THESIS Sharing of Semantically Enhanced Information for the Adaptive Execution of Business Processes Pigi D. Kouki Supervisor: Aphrodite Tsalgatidou, Assistant Professor NKUA Technical Support: Georgios Athanasopoulos, PhD Candidate NKUA ATHENS MAY 2011 DIPLOMA THESIS Sharing of Semantically Enhanced Information for the Adaptive Execution of Business Processes Pigi D. Kouki Registration Number: Μ 984 SUPERVISOR: Aphrodite Tsalgatidou, Assistant Professor NKUA TECHNICAL SUPPORT: Georgios Athanasopoulos, PhD Candidate NKUA MAY 2011 ABSTRACT Motivated from the Context Aware Computing, and more particularly from the Data-Driven Process Adaptation approach, we propose the Semantic Context Space (SCS) Engine which aims to facilitate the provision of adaptable business processes. The SCS Engine provides a space which stores semantically annotated data and it is open to other processes, systems, and external sources for information exchange. The specified implementation is inspired from the Semantic TupleSpace and uses the JavaSpace Service of the Jini Framework (changed to Apache River lately) as an underlying basis. The SCS Engine supplies an interface where a client can execute the following operations: (i) write: which inserts in the space available information along with its respective meta-information, (ii) read: which retrieves from the space information which meets specific meta-information constrains, and (iii) take: which retrieves and simultaneously deletes from the space information which meets specific meta-information constrains. In terms of this thesis the available types of meta-information are based on ontologies described in RDFS or WSML. The applicability of the SCS Engine implementation in the context of data-driven process adaptation has been ensured by an experimental evaluation of the provided operations.