Relevant bibliographies by topics / Cognitive science engineering

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Cognitive science engineering'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Cognitive science engineering"

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Wang, Yingxu, Bernard Carlos Widrow, Bo Zhang, et al. "Perspectives on the Field of Cognitive Informatics and its Future Development." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 1 (2011): 1–17. http://dx.doi.org/10.4018/jcini.2011010101.

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The contemporary wonder of sciences and engineering has recently refocused on the beginning point of: how the brain processes internal and external information autonomously and cognitively rather than imperatively like conventional computers. Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of eight position statements presented in the plenary panel of IEEE ICCI’10 on Cognitive Informatics and Its Future Development contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.
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Raubal, Martin. "Cognitive Engineering for Geographic Information Science." Geography Compass 3, no. 3 (2009): 1087–104. http://dx.doi.org/10.1111/j.1749-8198.2009.00224.x.

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Wang, Yingxu, George Baciu, Yiyu Yao, et al. "Perspectives on Cognitive Informatics and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 1 (2010): 1–29. http://dx.doi.org/10.4018/jcini.2010010101.

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Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.
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Ross, Don. "Empiricism, sciences, and engineering: cognitive science as a zone of integration." Cognitive Processing 20, no. 2 (2019): 261–67. http://dx.doi.org/10.1007/s10339-019-00916-z.

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Blomberg, Olle. "Conceptions of Cognition for Cognitive Engineering." International Journal of Aviation Psychology 21, no. 1 (2011): 85–104. http://dx.doi.org/10.1080/10508414.2011.537561.

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Wang, Yingxu. "On the Mathematical Theories and Cognitive Foundations of Information." International Journal of Cognitive Informatics and Natural Intelligence 9, no. 3 (2015): 42–64. http://dx.doi.org/10.4018/ijcini.2015070103.

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A recent discovery in computer and software sciences is that information in general is a deterministic abstract quantity rather than a probability-based property of the nature. Information is a general form of abstract objects represented by symbolical, mathematical, communication, computing, and cognitive systems. Therefore, information science is one of the contemporary scientific disciplines collectively known as abstract sciences such as system, information, cybernetics, cognition, knowledge, and intelligence sciences. This paper presents the cognitive foundations, mathematical models, and formal properties of information towards an extended theory of information science. From this point of view, information is classified into the categories of classic, computational, and cognitive information in the contexts of communication, computation, and cognition, respectively. Based on the three generations of information theories, a coherent framework of contemporary information is introduced, which reveals the nature of information and the fundamental principles of information science and engineering.
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Woods, David D., Jennifer C. Watts, John M. Graham, Daniel L. Kidwell, and Philip J. Smith. "Teaching Cognitive Systems Engineering." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 4 (1996): 259–63. http://dx.doi.org/10.1177/154193129604000425.

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Our motivation for this paper is to stimulate discussions within the human factors community about teaching Cognitive Engineering at the undergraduate level. For the last fourteen years, the Cognitive Systems Engineering Laboratory at the Ohio State University has offered an undergraduate course in Cognitive Engineering (multiple offerings per year to Industrial Engineering, Industrial Design, Computer Science and Psychology students). In this paper, we will draw from our teaching experiences and describe our framework for teaching Cognitive Engineering.
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Woods, David D. "GUTs or no GUTs (Grand Unified Theories): Does/Can/Should Cognitive Engineering have G.U.T.s?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 3 (2002): 468–71. http://dx.doi.org/10.1177/154193120204600353.

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What are the GUTs of Cognitive Systems Engineering (CSE)? G.U.T. is an abbreviation for Grand Unified Theory. As Cognitive Science matured, Allen Newell proposed a unifying model of cognition expressed as a software architecture SOAR. Similarly, John Anderson developed ACTR also claiming it represented a unified theory of cognition in the form of a computer simulation. Both of these cognitive architectures are computer programs that claim to simulate or be the basis for creating simulations of how people perform and learn cognitive tasks. Taking the development of Cognitive Science as a possible analogy for the potential development of Cognitive Systems Engineering, this panel discussion provides a platform to stimulate a vigorous exchange of ideas about the foundation of and potential futures of CSE.
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Wang, Yingxu, Robert C. Berwick, Simon Haykin, et al. "Cognitive Informatics and Cognitive Computing in Year 10 and Beyond." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (2011): 1–21. http://dx.doi.org/10.4018/jcini.2011100101.

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Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. The latest advances in CI leads to the establishment of cognitive computing theories and methodologies, as well as the development of Cognitive Computers (CogC) that perceive, infer, and learn. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC’11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.
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REASON, JAMES. "Commentary Broadening the cognitive engineering horizons: more engineering, less cognition and no philosophy of science, please." Ergonomics 41, no. 2 (1998): 150–52. http://dx.doi.org/10.1080/001401398187161.

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Dissertations / Theses on the topic "Cognitive science engineering"

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Zayour, Iyad. "Reverse engineering: A cognitive approach, a case study and a tool." Thesis, University of Ottawa (Canada), 2002. http://hdl.handle.net/10393/6075.

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Software maintenance (SM) for large legacy systems is a very inefficient process; on average, 70% of software costs are spent on maintenance [Swanson 89]. The inefficiency of SM has been related to the difficulty comprehending software systems; therefore program comprehension is considered to be a key bottleneck of SM. Reverse engineering tools have been used to alleviate this bottleneck with lower than expected success. We present a cognitively based approach for reverse engineering tool development. We use ideas from cognitive psychology and other disciplines to formulate the approach. We also describe a case study in which we applied the approach in a telecommunication company. The case study resulted in the development of DynaSee, a reverse engineering tool which helps software engineers analyze dynamic program information. DynaSee reads routine call traces, and provides several processing and visualization features that make the use of traces much more useful for software maintenance and program comprehension. Next, we describe and evaluate the various features of DynaSee that compress, abstract and augment traces to make them comprehensible and useful for SM tasks. Finally, based on our experience in developing DynaSee, we generalize the aspects of our findings and techniques that are based on psychology by relating them to the mainstream psychological literature and to other disciplines where similar techniques have been used.
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Goodman, Katherine Ann. "The Transformative Experience in Engineering Education." Thesis, University of Colorado at Boulder, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3743651.

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<p> This research evaluates the usefulness of transformative experience (TE) in engineering education. With TE, students 1) apply ideas from coursework to everyday experiences without prompting <i>(motivated use);</i> 2) see everyday situations through the lens of course content <i>(expanded perception);</i> and 3) value course content in new ways because it enriches everyday affective experience <i>(affective value).</i> In a three-part study, we examine how engineering educators can promote student progress toward TE and reliably measure that progress.</p><p> For the first study, we select a mechanical engineering technical elective, Flow Visualization, that had evidence of promoting <i>expanded perception </i> of fluid physics. Through student surveys and interviews, we compare this elective to the required Fluid Mechanics course. We found student interest in fluids fell into four categories: complexity, application, ubiquity, and aesthetics. Fluid Mechanics promotes interest from application, while Flow Visualization promotes interest based in ubiquity and aesthetics. Coding for <i> expanded perception,</i> we found it associated with students&rsquo; engineering identity, rather than a specific course. In our second study, we replicate atypical teaching methods from Flow Visualization in a new design course: Aesthetics of Design. Coding of surveys and interviews reveals that open-ended assignments and supportive teams lead to increased ownership of projects, which fuels risk-taking, and produces increased confidence as an engineer.</p><p> The third study seeks to establish parallels between <i>expanded perception</i> and measurable perceptual expertise. Our visual expertise experiment uses fluid flow images with both novices and experts (students who had passed fluid mechanics). After training, subjects sort images into laminar and turbulent categories. The results demonstrate that novices learned to sort the flow stimuli in ways similar to subjects in prior perceptual expertise studies. In contrast, the experts&rsquo; significantly better results suggest they are accessing conceptual fluids knowledge to perform this new, visual task. The ability to map concepts onto visual information is likely a necessary step toward expanded perception.</p><p> Our findings suggest that open-ended aesthetic experiences with engineering content unexpectedly support engineering identity development, and that visual tasks could be developed to measure conceptual understanding, promoting expanded perception. Overall, we find TE a productive theoretical framework for engineering education research.</p>
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Dreany, Harry Hayes. "Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10688677.

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<p> This paper presents the integration of an intelligent decision support model (IDSM) with a cognitive architecture that controls an autonomous non-deterministic safety-critical system. The IDSM will integrate multi-criteria, decision-making tools via intelligent technologies such as expert systems, fuzzy logic, machine learning, and genetic algorithms. </p><p> Cognitive technology is currently simulated within safety-critical systems to highlight variables of interest, interface with intelligent technologies, and provide an environment that improves the system&rsquo;s cognitive performance. In this study, the IDSM is being applied to an actual safety-critical system, an unmanned surface vehicle (USV) with embedded artificial intelligence (AI) software. The USV&rsquo;s safety performance is being researched in a simulated and a real-world, maritime based environment. The objective is to build a dynamically changing model to evaluate a cognitive architecture&rsquo;s ability to ensure safe performance of an intelligent safety-critical system. The IDSM does this by finding a set of key safety performance parameters that can be critiqued via safety measurements, mechanisms, and methodologies. The uniqueness of this research lies in bounding the decision-making associated with the cognitive architecture&rsquo;s key safety parameters (KSPs). Other real-time applications (RTAs) that would benefit from advancing cognitive science associated with safety are unmanned platforms, transportation technologies, and service robotics. Results will provide cognitive science researchers with a reference for the safety engineering of artificially intelligent safety-critical systems. </p><p>
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Horine, Brent. "Bootstrapping Cognitive Radio Networks." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5306.

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Cognitive radio networks promise more efficient spectrum utilization by leveraging degrees of freedom and distributing data collection. The actual realization of these promises is challenged by distributed control, and incomplete, uncertain and possibly conflicting knowledge bases. We consider two problems in bootstrapping, evolving, and managing cognitive radio networks. The first is Link Rendezvous, or how separate radio nodes initially find each other in a spectrum band with many degrees of freedom, and little shared knowledge. The second is how radio nodes can negotiate for spectrum access with incomplete information. To address the first problem, we present our Frequency Parallel Blind Link Rendezvous algorithm. This approach, designed for recent generations of digital front-ends, implicitly shares vague information about spectrum occupancy early in the process, speeding the progress towards a solution. Furthermore, it operates in the frequency domain, facilitating a parallel channel rendezvous. Finally, it operates without a control channel and can rendezvous anywhere in the operating band. We present simulations and analysis on the false alarm rate for both a feature detector and a cross-correlation detector. We compare our results to the conventional frequency hopping sequence rendezvous techniques. To address the second problem, we model the network as a multi-agent system and negotiate by exchanging proposals, augmented with arguments. These arguments include information about priority status and the existence of other nodes. We show in a variety of network topologies that this process leads to solutions not otherwise apparent to individual nodes, and achieves superior network throughput, request satisfaction, and total number of connections, compared to our baselines. The agents independently formulate proposals based upon communication desires, evaluate these proposals based upon capacity constraints, create arguments in response to proposal rejections, and re-evaluate proposals based upon received arguments. We present our negotiation rules, messages, and protocol and demonstrate how they interoperate in a simulation environment.<br>Ph.D.<br>Doctorate<br>Electrical Engineering and Computer Science<br>Engineering and Computer Science<br>Electrical Engineering
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Alhanai, Tuka(Tuka Waddah Talib Ali Al Hanai). "Detecting cognitive impairment from spoken language." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122724.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 141-165).<br>Dementia comes second only to spinal cord injuries in terms of its debilitating effects; from memory-loss to physical disability. The standard approach to evaluate cognitive conditions are neuropsychological exams, which are conducted via in-person interviews to measure memory, thinking, language, and motor skills. Work is on-going to determine biomarkers of cognitive impairment, yet one modality that has been relatively less explored is speech. Speech has the advantage of being easy to record, and contains the majority of information transmitted during neuropsychological exams. To determine the viability of speech-based biomarkers, we utilize data from the Framingham Heart Study, that contains hour-long audio recordings of neuropsychological exams for over 5,000 individuals. The data is representative of a population and the real-world prevalence of cognitive conditions (3-4%). We first explore modeling cognitive impairment from a relatively small set of 92 subjects with complete information on audio, transcripts, and speaker turns. We loosen these constraints by modeling with only a fraction of audio (~2-3 minutes), of which the speaker segments are defined through text-based diarization. We next apply this diarization method to extract audio features from all 7,000+ recordings (most of which have no transcripts), to model cognitive impairment (AUC 0.83, spec. 78%, sens. 79%). Finally, we eliminate the need for feature-engineering by training a neural network to learn higher-order representations from filterbank features (AUC 0.85, spec. 81%, sens. 82%). Our speech models exhibit strong performance and are comparable to the baseline demographic model (AUC 0.85, spec. 93%, sens. 65%). Further analysis shows that our neural network model automatically learns to detect specific speech activity which clusters according to: pause followed by onset of speech, short burst of speech, speech activity in high-frequency spectral energy bands, and silence.<br>by Tuka Alhanai.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Hu, Hongzhan. "Exploring the concept of feedback with perspectives from psychology and cognitive science." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-107090.

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This study explores the concept of feedback from various perspectives in psychology and cognitive science. Specifically, the theories of ecological psychology, situated and Distributed Cognition, Cognitive Systems Engineering and Embodied cognition are investigated and compared. Cognitive Systems Engineering provides a model of feedback and related constructs, to understand human behavior in complex working environments. Earlier theories such as ecological psychology, considered feedback as direct perception. Situated cognition clearly inherits ideas from ecological psychology, whereas distributed cognition provides a deeper understanding of feedback through artifact use. Cognitive Systems Engineering provides a systematic view of feedback and control. This framework is a suitable perspective to understanding feedback in human-machine settings.
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McWhirter, Nathan Daniel. "Teaching Engineering Students About Cognitive Barriers During Design for Sustainable Infrastructure." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/81310.

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Sustainability is a complex socio-technical challenge that requires new ways of thinking. To help meet this challenge, I have created three case-based modules that teach engineering students how to apply sustainability principles and help them recognize potential cognitive traps, or barriers, that may prevent more consideration for sustainability during design. Each of my three case studies is built into a PowerPoint-guided module for undergraduate engineering classes, which may be taught in 1-3 class days. I have implemented each of the three modules in senior-level classes at Virginia Tech, assessed survey data, and scored student assignments. This work and the underlying literature background is reflected in three journal papers, one for each module. My case study modules, along with all associated teaching materials, are shared in the Center for Sustainable Engineering repository for other instructors to adapt and use. Each module includes a case study about an infrastructure project recognized and awarded by the Envision rating system, demonstrating a case of sustainability done well. Adaptable PowerPoint slides are used to teach about the Envision rating system and credits particularly relevant to the project. Active learning assignments allow students to apply the Envision framework and design criteria to complex and ill-structured problems related to the case study. Slides also cover the relation of three selected behavioral decision science concepts to each case study; these include cognitive biases and barriers which tend to inhibit sustainability outcomes, as well as some potential solutions to mitigate or overcome such barriers. Paired with the decision-making framework of Envision, awareness of these transdisciplinary concepts will allow students to more effectively manage the complex decisions found in real-world projects. Results were assessed through a variety of methods to determine the modules' level of effectiveness in accomplishing defined student learning outcomes. Pre-module and post-module student surveys were employed to measure several indicators: changes in self-assessed confidence levels, perceptions of sustainable design (characteristics and barriers), and accuracy of module concept definitions. Each of several active learning assignments was scored on a simple rubric. Concept maps were also tested as further type of assessment, and scored with both traditional and holistic methods. However, fully integrating the concept mapping approach is left to the future work of others. These modules are a significant contribution to engineering education, as they integrate diverse topics and disciplines into a unified and relevant teaching package. Over 350 students have already been reached through the three modules, and sharing the materials in a peer-reviewed repository allows for expansion, adaptation, and capacity building. Each module's content and pedagogy align with ABET accreditation requirements and ASCE's Body of Knowledge, making them relevant tools for equipping the future generation of engineers. Future development of similar case studies can build partnerships between academia and industry, as well as increase cross-disciplinary collaboration. These efforts will both improve undergraduate education and advance the profession.<br>Master of Science
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Zheng, Xijia Ph D. Massachusetts Institute of Technology. "Cognitive optical network architecture in dynamic environments." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/126997.

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Thesis: Ph. D. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 149-154).<br>Emerging network traffic requires a more agile network management and control system to deal with the dynamic network environments than today's networks use. The bursty and large data transactions introduced by new technological applications can cause both high costs and extreme congestion in networks. The prohibitive cost of massive over-provisioning will manifest as huge congestions during peak demand periods. The network management and control system must be able to sense the traffic changes and reconfigure in a timely manner (in tens of milliseconds instead of minutes or hours) to use network resources efficiently. We propose the use of cognitive techniques for fast and adaptive network management and control of future optical networks. The goal of this work is to provide timely network reconfigurations in response to dynamic traffic environments and prevent congestion from building up.<br>We make a simplified model of the expected traffic arrival rate changes as a multistate Markov process based on the characteristics of the dynamic, bursty, and high granularity traffic. The traffic is categorized into different network traffic environments by the length of the network coherence time, which is the time that the traffic is unvarying. The tunneled network architecture is adopted due to its supremacy in reducing the control complexity when the traffic volume is at least one wavelength. In the long coherence time regime where traffic changes very slowly, the traffic detection performances of two Bayesian estimators and a stopping-trial (sequential) estimator are examined, based on the transient behaviors of networks. The stopping trial estimator has the fastest response time to the changes of traffic arrival statistics. We propose a wavelength reconfiguration algorithm with continuous assessment where the system reconfigures whenever it deems necessary.<br>The reconfiguration can involve addition or subtraction of multiple wavelengths. Using the fastest detection and reconfiguration algorithm can reduce queueing delays during traffic surges without over-provisioning and thus can reduce network capital expenditure and prevent wasting resources on erroneous decisions when surges occur. For traffic with moderate coherence time (where traffic changes at a moderate rate) and the short coherence time (where traffic changes quickly), the stopping-trial estimator still responds to the traffic changes with a short detection time. As long as the inter-arrival times of traffic transactions are independent, the algorithm is still optimum. The algorithm provides no prejudice on the exact network traffic distribution, avoiding having to sense and estimate detailed arrival traffic statistics.<br>To deal with fast-changing traffic, we model the transient convergent behaviors of network traffic drift as a result of traffic transition rate changes and validate the feasibility and utility of the traffic prediction. In a simple example when the network traffic rate changes monotonically in a linear model, the sequential maximum likelihood estimator will capture the traffic trend with a small number of arrivals. The traffic trend prediction can help to provide fast reconfiguration, which is very important for maintaining quality of service during large traffic shifts. We further investigate the design of an efficient rerouting algorithm to maintain users' quality of service when the incremental traffic cannot be accommodated on the primary path. The algorithm includes the fast reconfiguration of wavelengths in the existing lit and spatially routed fibers, and the setting up and lighting of new fibers.<br>Rerouting is necessary to maintain users' quality of service when the queueing delay on the primary path (determined by shortest path routing) exceeds the requirement. Our algorithm triggers reconfiguration when a queueing delay threshold is crossed on the primary path. The triggering by a threshold on the queueing delay is used due to its simplicity, and it is directly measurable by the exact traffic transaction sizes and the queue size, which reflect both the current network traffic environment and the network configurations. A dynamic rerouting algorithm implemented with a shortest path algorithm is proposed to find the secondary paths for rerouting. We make the conjecture that it is desirable that the alternate paths for rerouting have small numbers of hops and are disjoint with other busy paths when the hops on the path are independent. In addition, the conjecture suggests that a good candidate network topology should have high edge-connectivity.<br>Wavelength reservation for rerouted traffic does not maximize wavelength utilization. We make the conjecture that traffic with different sizes should be broken up into multi-classes with dedicated partitioned resources and the queueing delay should be normalized by the transmission time for rerouting triggering to realize better network utilization.<br>by Xijia Zheng.<br>Ph. D. in Computer Science and Engineering<br>Ph.D.inComputerScienceandEngineering Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Rezaee, Arman. "Towards a cognitive network management and control system." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128329.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2020<br>Cataloged from PDF of thesis.<br>Includes bibliographical references (pages 155-159).<br>Future networks have to accommodate an increase of 3-4 orders of magnitude in data rates with heterogeneous session sizes and strict time deadline requirements. The dynamic nature of scheduling of large transactions and the need for rapid actions by the network management and control system, require timely and judicious collection of network state information. Within this context we will focus on the problem of shortest path routing, and identify pragmatic schemes that allow a central controller to collect relevant delay statistics from various links and nodes within the network. We present Significant Sampling as an adaptive monitoring technique to collect and disseminate network state information when it can be of significant value to the optimal operation of the network, and in particular when it can help in identifying the shortest routes.<br>We start by developing an analytical framework that can identify the optimal time for the collection of such information in a small but realistic setting, when the underlying delay model is a continuous-time diffusion process (e.g. Wiener process or Ornstein-Uhlenbeck process) and its parameters are known by the controller. We show that this technique balances the need for updated state information against the collection and dissemination costs and provides an algorithm that yields near optimum performance. We then extend the results by introducing a reinforcement learning framework that learns the aforementioned optimal policy from its own interactions with the network, and without any prior assumptions regarding the underlying delay model. In addition to achieving a performance comparable to the analytically derived policies, the deep reinforcement learning solution is more flexible and general and can accommodate a diverse set of network environments.<br>This is particularly important because it can provide good solutions for complex network environments where analytically tractable solutions are not feasible. We conclude our work by noting that sensible network controllers should continue to deliver a good performance between distinct instances of state collection and thus any meaningful solution should strive to meet application demands despite the unavoidable uncertainty about the instantaneous state of the network. To that end, we introduce a novel diversity routing scheme that can accommodate requirements regarding delay variations despite a controller's relative uncertainty about the instantaneous state of the network. More specifically we utilize mean-variance analysis as the basis for traffic distribution and route selection, and show that this technique can improve the users' quality of service by taking into account the correlated nature of delay across different paths.<br>We conclude this work by commenting on the potential application of this method to general transportation networks.<br>by Arman Rezaee.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Liang, Wei. "Cooperative communication for cognitive radio networks." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/382935/.

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A Cooperative Cognitive Radio (CCR) network, which integrates a conventional cooperative system and cognitive radios (CRs) into a holistic system, is a promising paradigm for the next generation mobile communication systems. The spectral efficiency, power efficiency, bandwidth reduction and system complexity in CCR networks are the fundamental parameters of our system design and optimization. In this thesis, we focus our attention on opportunistically exploiting the underutilized spectrum band in the CR network with the aid of cooperative protocols. Furthermore, we invoke channel coding schemes in our CCR system in order to improve the overall system throughput. In our CCR system, the overlay based cooperation scheme of Primary Users (PUs) and Cognitive Users (CUs) is considered, which has the potential of leading to a transmission power reduction and transmission rate improvement for both the PU and the CU. More explicitly, our cooperative protocol allows a group of CUs to serve as Relay Nodes (RNs) for relaying the signal of the PUs’ transmitters to the PUs’ intended destinations. To elaborate further, both one way relaying and two-way relaying schemes are used in our proposed system, so that the bandwidth requirement of the PUs is reduced. Alternatively, the freed bandwidth may be leased to a group of CUs for their secondary communications. Our numerical and simulation results show that the bandwidth reduction attained by the proposed two-way relaying based CR scheme may approach as much as 80% of the PU’s bandwidth. Moreover, an Adaptive Dynamic Network Coding (ADNC) scheme is also conceived for this overlay CCR system, which is designed for supporting communications between multiple PUs and a common Base Station (BS). More particularly, the near-instantaneously Adaptive Turbo Trellis Coded Modulation (ATTCM) is employed for appropriately adjusting both the modulation mode as well as the channel coding rate and the network coding rate, according to the near-instantaneous channel conditions. In order to facilitate the recovery of the source information at the BS, the CUs invoke the ADNC technique, which is assisted by our cooperative protocol operating by exchanging the CCR-based control information between the near-instantaneously ATTCM and network coding codec as well as between the CUs and the BS. Additionally, the network encoder may also be activated in its adaptive mode for supporting the CUs, depending on the Boolean value of the feedback flags generated based on the success/failure of the ATTCMdecoder and of the network decoder, which is evaluated and fed back by the BS. Quantitatively, it was found that the joint holistic design of our ATTCM-ADNC-CCR scheme is either capable of freeing up an approximately 40% of the PU’s bandwidth in comparison to its non-cooperative counterpart, or increasing the attainable throughput by as much as 2 bit/symbol. Furthermore, a Pragmatic Distributed Algorithm (PDA) is proposed for supporting the efficient spectral access of multiple PUs and CUs in CCR networks. The novelty of our PDA is that the Pus negotiate with the CUs concerning the specific amount of relaying and transmission time, and the CU will decide either to accept or to decline this offer. These CUs relay the signal received from the PUs to the PUs’ receiver, but only when both the PUs’ and the CUs’ minimum rate requirements are satisfied. Moreover, we show that the cooperative spectral access based on our PDA reaches an equilibrium, when it is repeated for a sufficiently long duration. These benefits are achieved, because the PUs are motivated to cooperate by the incentive of achieving a higher PU rate, whilst defecting from cooperation can be discouraged with the aid of a limited-duration punishment. Therefore, our proposed PDA outperforms the benchmark, despite its significantly lower overhead and lower complexity. Finally, we present the joint design of coding, modulation, user-cooperation and CR techniques, which may lead to significant mutual benefits for both the PUs and the CUs.
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Books on the topic "Cognitive science engineering"

1

Mark, Pejtersen Annelise, and Goodstein L. P, eds. Cognitive systems engineering. Wiley, 1994.

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A, Tarbell Mark, ed. The cognitive dynamics of computer science. John Wiley & Sons, 2006.

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Gyurky, Szabolcs Michael De. The Cognitive Dynamics of Computer Science. John Wiley & Sons, Ltd., 2006.

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Model based reasoning in science and engineering: Cognitive science, epistemology, logic. College Publications, 2006.

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Stanney, Kay M., and Kelly S. Hale. Advances in cognitive engineering and neuroergonomics. Taylor & Francis, 2012.

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Boy, Guy A., and David B. Kaber. Advances in cognitive ergonomics. Taylor & Francis, 2011.

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Cognitive function analysis. Ablex Pub. Corp., 1998.

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Ross, Deming, Ilin Roman, and SpringerLink (Online service), eds. Emotional Cognitive Neural Algorithms with Engineering Applications: Dynamic Logic: FromVague to Crisp. Springer-Verlag Berlin Heidelberg, 2011.

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Roco, Mihail C. Converging Technologies for Improving Human Performance: Nanotechnology, Biotechnology, Information Technology and Cognitive Science. Springer Netherlands, 2003.

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Hierarchical task analysis. Taylor & Francis, 2001.

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Book chapters on the topic "Cognitive science engineering"

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Bradley, Gordon H. "Cognitive Science View of Software Engineering." In Software Engineering Education. Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4720-3_4.

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McSherry, David. "An Interactive Learning Environment for Knowledge Engineering." In Artificial Intelligence and Cognitive Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45750-x_30.

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Cross, G., J. Blakley, and F. Shui. "Application of Expert Systems in Electrical Engineering." In AI and Cognitive Science ’90. Springer London, 1991. http://dx.doi.org/10.1007/978-1-4471-3542-5_6.

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Iswarya, N. D., M. A. Maluk Mohamed, and N. Vijaya. "Cognitive Science Based Scheduling In Grid Environment." In Progress in Systems Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08422-0_30.

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Gavrilova, Tatiana, Alexander Voinov, and Ekaterina Vasilyeva. "Visual knowledge engineering as a cognitive tool." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0100542.

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Maruyama, Yoshihiro. "Rationality, Cognitive Bias, and Artificial Intelligence: A Structural Perspective on Quantum Cognitive Science." In Engineering Psychology and Cognitive Ergonomics. Cognition and Design. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49183-3_14.

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Leyh, Werner, Maria Fava, Narumi Abe, et al. "Citizen Science Involving Collections of Standardized Community Data." In Advances in Neuroergonomics and Cognitive Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60642-2_39.

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Shit, Sahadeb, and Srijibendu Bagchi. "Cyclostationary feature detection based FRESH filter in cognitive radio network." In Computational Science and Engineering. CRC Press, 2016. http://dx.doi.org/10.1201/9781315375021-59.

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Kido, Hiroyuki, and Keishi Okamoto. "Balancing Between Cognitive and Semantic Acceptability of Arguments." In Knowledge Science, Engineering and Management. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63558-3_14.

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Morgan, Phillip L., Victoria Smy, Helen Seeby, and John Patrick. "A Novel Approach to Cognitive Engineering: Manipulating Access Cost." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39473-7_87.

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Conference papers on the topic "Cognitive science engineering"

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Adams, Ray. "Cognitive science meets computing science: The future of cognitive systems and cognitive engineering." In Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces (ITI). IEEE, 2009. http://dx.doi.org/10.1109/iti.2009.5196041.

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Wang, Yingxu. "On cognitive foundations of big data science and engineering." In 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2016. http://dx.doi.org/10.1109/icci-cc.2016.7862044.

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Jiang, Yanan, Yahui Liu, Jinsong Bao, Jie Li, Jie Zhang, and Yunhao Fang. "Human-in-Cognition Manufacturing-Loop (HCML): Framework and Technologies." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8399.

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Abstract The wave of intelligent manufacturing has swept the world, and intelligent manufacturing has realized the interconnection of the physical world and the information world. With the deep integration of intelligent manufacturing, cognitive computing and cognitive science, the manufacturing industry presents a new manufacturing model, which is called cognitive manufacturing. Cognitive manufacturing, as the evolution stage of intelligent manufacturing, endows industrial manufacturing system with perception and judgment ability. In addition, it enables the machine to realize self-adaptation, self-organization and self-decision-making based on cognition, thus completing accurate execution. But the manufacturing system cannot be entirely controlled by machines without the involvement of human. Based on the new generation of intelligent manufacturing for human-information-physical system and human-computer synthetic-intelligent system. This paper compares and extends the relevant technical models of intelligent manufacturing and cognitive manufacturing. Furthermore, it presents a framework that integrates the cognitive ability of human and machine, putting forward a cognitive manufacturing system architecture of human-machine collaborative cognition, called Human-in-Cognition Manufacturing-Loop (HCML). Additionally, the connotation, key implementation technology and future development trend of the system are discussed in details, and the role of artificial intelligence in the system are introduced.
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Sathyanarayana, Dileep, and Sundar Krishnamurty. "A Visualization-Based Approach to Engineering Kinematics Using Cognitive Models." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57717.

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Visualization and spatial reasoning are integral to developing an understanding of contemporary sciences. They form the basis for understanding a wide variety of topics across science, mathematics and engineering, including molecular structures, topologies, motion and forces, and manufacturing processes. Within engineering, it can be argued that challenging and time consuming topics such as kinematics can be better taught by faculty, and hence better understood and appreciated by students, by advancing our understanding of human visualization and spatial reasoning and using this knowledge to develop computer-based visualization instruction in ways that maximize their effectiveness. The achievement of such a goal will require importing proven extant theories from other fields such as psychology, education, engineering and computer science. This paper presents the results of one such effort for teaching engineering kinematics. The motivation for this work can be found in cognitive science literature, where motion comprehension has been identified and studied as a mental task. Accordingly, a major task in doing this work involved the study of cognitive models of motion comprehension, and identifying key stages present in them. Mapping such key stages in motion comprehension on to kinematics domain, this paper presents the framework for the visual comprehension based pedagogical approach to kinematics. A web-based gear-trains tutor has been developed to demonstrate this concept. Results from the tests on a controlled population of engineering students are presented and the efficacy of a visual comprehension based approach as an instructional tool is discussed.
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MacDonald, Erin, and Jinjuan She. "Seven Cognitive Concepts for Successful Sustainable Design." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70676.

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For the past forty years, social science researchers have studied how to encourage pro-environmental behaviors such as the adoption of recycling programs, water conservation strategies, and purchase of sustainable products. This article presents a synthesis of these research findings as they relate to the design of sustainable products and technologies. Research from environmental psychology, consumer studies, economics, decision sciences, public policy, and behavioral psychology are organized into cognitive concepts that are crucial to the successful purchase and use of sustainable products. The cognitive concepts reviewed are: responsibility, complex decision-making skills, decision heuristics, the altruism-sacrifice link, trust, cognitive dissonance/guilt, and motivation. Product examples are provided to highlight the role of these cognitive concepts in sustainable design. Design recommendations and relevant design methods are discussed. The recommendations require dynamic and on-going coordination between designers, manufacturers, marketers, and government policy-makers to achieve positive changes in individuals’ behaviors. The success of sustainable products depends on the success of this coordination.
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Ali, Ruba, Jolly Bhadra, Nitha Siby, and Noora Al-Thani. "From Sports To Science: Designing Sports Products to Experience Science and Engineering." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0268.

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Sports can have a substantial impact on fostering cognitive and non-cognitive skills in youth leading to higher productivity. Its potential to integrate within diverse academic subjects makes it an ideal choice to attract high school students to grasp the emphasis of STEM fields and careers. In the midst of gradual educational reformations in Qatar, a novel sports driven STEM program was launched to derive the competencies in the secondary students and enhance their STEM literacy and aspirations. Sports, being an intrinsic motivator favored by the Qatari students, instigating active participation and inspiration, is integrated to the innovative learning approach, thereby acknowledging the relevance of science to real world applications. The 248 participants from 15 secondary schools actively engaged in the program comprising sports product based scientific workshops and an engineering design challenge, bridging the gap between science and sports. Results implicating the active involvement of the students, manifesting the quintessential 21st century skills in engineering products, were drawn out from mixed methods. Quantitative statistical analysis of pre-post surveys, review of sports products and the substantiating observations of the facilitators successfully validate the application of diverse dispositions in the program. Student attitudes towards STEM fields and careers apparently augmented by virtue of the program outcomes is also interpreted from the analysis.
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Wang, Zhen-dong, Hui-qiang Wang, Guang-sheng Feng, Bing-yang Li, and Xiao-ming Chen. "Cognitive Networks and Its Layered Cognitive Architecture." In 2010 Fifth International Conference on Internet Computing for Science and Engineering (ICICSE). IEEE, 2010. http://dx.doi.org/10.1109/icicse.2010.46.

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Haidry, Shifa-e.-Zehra, Katrina Falkner, and Claudia Szabo. "Identifying Domain-Specific Cognitive Strategies for Software Engineering." In ITiCSE '17: Innovation and Technology in Computer Science Education. ACM, 2017. http://dx.doi.org/10.1145/3059009.3059032.

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Qian, Peng’an. "The Application of Engineering Cognitive Museum in Engineering Training Teaching." In Proceedings of the 2018 4th International Conference on Social Science and Higher Education (ICSSHE 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/icsshe-18.2018.86.

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Wang, Rui, Tieyan Wu, and Wei Liu. "Control frame of cognition in cognitive system with Fuzzy-Neural network." In 2011 International Conference on System Science and Engineering (ICSSE). IEEE, 2011. http://dx.doi.org/10.1109/icsse.2011.5961967.

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