Relevant bibliographies by topics / Cognitive Computing

Contents

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

Academic literature on the topic 'Cognitive Computing'

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Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Cognitive Computing"

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Wang, Yingxu, Victor Raskin, Julia Rayz, George Baciu, Aladdin Ayesh, Fumio Mizoguchi, Shusaku Tsumoto, Dilip Patel, and Newton Howard. "Cognitive Computing." International Journal of Software Science and Computational Intelligence 10, no. 1 (January 2018): 1–14. http://dx.doi.org/10.4018/ijssci.2018010101.

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Cognitive Computing (CC) is a contemporary field of studies on intelligent computing methodologies and brain-inspired mechanisms of cognitive systems, cognitive machine learning and cognitive robotics. The IEEE conference ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This article reports the plenary panel (Part II) in IEEE ICCI*CC'17 at Oxford University. The summary is contributed by distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of cognitive computing.
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Modha, Dharmendra S., Rajagopal Ananthanarayanan, Steven K. Esser, Anthony Ndirango, Anthony J. Sherbondy, and Raghavendra Singh. "Cognitive computing." Communications of the ACM 54, no. 8 (August 2011): 62–71. http://dx.doi.org/10.1145/1978542.1978559.

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Pagel, Peter, Edy Portmann, and Karin Vey. "Cognitive Computing." Informatik-Spektrum 41, no. 1 (February 2018): 1–4. http://dx.doi.org/10.1007/s00287-018-1091-4.

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D’Onofrio, Sara, Edy Portmann, Michel Franzelli, and Christoph Bürki. "Cognitive Computing." Informatik-Spektrum 41, no. 2 (March 7, 2018): 113–22. http://dx.doi.org/10.1007/s00287-018-1095-0.

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Sridharan, Mohan, Gerald Tesauro, and James Hendler. "Cognitive Computing." IEEE Intelligent Systems 32, no. 4 (2017): 3–4. http://dx.doi.org/10.1109/mis.2017.3121554.

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Demirkan, Haluk, Seth Earley, and Robert R. Harmon. "Cognitive Computing." IT Professional 19, no. 4 (2017): 16–20. http://dx.doi.org/10.1109/mitp.2017.3051332.

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Wang, Yingxu, George Baciu, Yiyu Yao, Witold Kinsner, Keith Chan, Bo Zhang, Stuart Hameroff, et al. "Perspectives on Cognitive Informatics and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 1 (January 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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Farrell, Robert G., Jonathan Lenchner, Jeffrey O. Kephjart, Alan M. Webb, MIchael J. Muller, Thomas D. Erikson, David O. Melville, et al. "Symbiotic Cognitive Computing." AI Magazine 37, no. 3 (October 7, 2016): 81–93. http://dx.doi.org/10.1609/aimag.v37i3.2628.

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IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing. We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.
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Wang, Yingxu. "On Cognitive Computing." International Journal of Software Science and Computational Intelligence 1, no. 3 (July 2009): 1–15. http://dx.doi.org/10.4018/jssci.2009070101.

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Pagel, Peter, Edy Portmann, and Karin Vey. "Cognitive Computing – Teil 2." Informatik-Spektrum 41, no. 2 (April 2018): 81–84. http://dx.doi.org/10.1007/s00287-018-1101-6.

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Dissertations / Theses on the topic "Cognitive Computing"

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Püschel, Georg, and Frank J. Furrer. "Cognitive Computing: Collected Papers." Technische Universität Dresden, 2015. https://tud.qucosa.de/id/qucosa%3A28990.

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Cognitive Computing' has initiated a new era in computer science. Cognitive computers are not rigidly programmed computers anymore, but they learn from their interactions with humans, from the environment and from information. They are thus able to perform amazing tasks on their own, such as driving a car in dense traffic, piloting an aircraft in difficult conditions, taking complex financial investment decisions, analysing medical-imaging data, and assist medical doctors in diagnosis and therapy. Cognitive computing is based on artificial intelligence, image processing, pattern recognition, robotics, adaptive software, networks and other modern computer science areas, but also includes sensors and actuators to interact with the physical world. Cognitive computers – also called 'intelligent machines' – are emulating the human cognitive, mental and intellectual capabilities. They aim to do for human mental power (the ability to use our brain in understanding and influencing our physical and information environment) what the steam engine and combustion motor did for muscle power. We can expect a massive impact of cognitive computing on life and work. Many modern complex infrastructures, such as the electricity distribution grid, railway networks, the road traffic structure, information analysis (big data), the health care system, and many more will rely on intelligent decisions taken by cognitive computers. A drawback of cognitive computers will be a shift in employment opportunities: A raising number of tasks will be taken over by intelligent machines, thus erasing entire job categories (such as cashiers, mail clerks, call and customer assistance centres, taxi and bus drivers, pilots, grid operators, air traffic controllers, …). A possibly dangerous risk of cognitive computing is the threat by “super intelligent machines” to mankind. As soon as they are sufficiently intelligent, deeply networked and have access to the physical world they may endanger many areas of human supremacy, even possibly eliminate humans. Cognitive computing technology is based on new software architectures – the “cognitive computing architectures”. Cognitive architectures enable the development of systems that exhibit intelligent behaviour.:Introduction 5 1. Applying the Subsumption Architecture to the Genesis Story Understanding System – A Notion and Nexus of Cognition Hypotheses (Felix Mai) 9 2. Benefits and Drawbacks of Hardware Architectures Developed Specifically for Cognitive Computing (Philipp Schröppe)l 19 3. Language Workbench Technology For Cognitive Systems (Tobias Nett) 29 4. Networked Brain-based Architectures for more Efficient Learning (Tyler Butler) 41 5. Developing Better Pharmaceuticals – Using the Virtual Physiological Human (Ben Blau) 51 6. Management of existential Risks of Applications leveraged through Cognitive Computing (Robert Richter) 61
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Peniak, Martin. "GPU computing for cognitive robotics." Thesis, University of Plymouth, 2014. http://hdl.handle.net/10026.1/3052.

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This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.
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Riera, Villanueva Marc. "Low-power accelerators for cognitive computing." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669828.

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Deep Neural Networks (DNNs) have achieved tremendous success for cognitive applications, and are especially efficient in classification and decision making problems such as speech recognition or machine translation. Mobile and embedded devices increasingly rely on DNNs to understand the world. Smartphones, smartwatches and cars perform discriminative tasks, such as face or object recognition, on a daily basis. Despite the increasing popularity of DNNs, running them on mobile and embedded systems comes with several main challenges: delivering high accuracy and performance with a small memory and energy budget. Modern DNN models consist of billions of parameters requiring huge computational and memory resources and, hence, they cannot be directly deployed on low-power systems with limited resources. The objective of this thesis is to address these issues and propose novel solutions in order to design highly efficient custom accelerators for DNN-based cognitive computing systems. In first place, we focus on optimizing the inference of DNNs for sequence processing applications. We perform an analysis of the input similarity between consecutive DNN executions. Then, based on the high degree of input similarity, we propose DISC, a hardware accelerator implementing a Differential Input Similarity Computation technique to reuse the computations of the previous execution, instead of computing the entire DNN. We observe that, on average, more than 60% of the inputs of any neural network layer tested exhibit negligible changes with respect to the previous execution. Avoiding the memory accesses and computations for these inputs results in 63% energy savings on average. In second place, we propose to further optimize the inference of FC-based DNNs. We first analyze the number of unique weights per input neuron of several DNNs. Exploiting common optimizations, such as linear quantization, we observe a very small number of unique weights per input for several FC layers of modern DNNs. Then, to improve the energy-efficiency of FC computation, we present CREW, a hardware accelerator that implements a Computation Reuse and an Efficient Weight Storage mechanism to exploit the large number of repeated weights in FC layers. CREW greatly reduces the number of multiplications and provides significant savings in model memory footprint and memory bandwidth usage. We evaluate CREW on a diverse set of modern DNNs. On average, CREW provides 2.61x speedup and 2.42x energy savings over a TPU-like accelerator. In third place, we propose a mechanism to optimize the inference of RNNs. RNN cells perform element-wise multiplications across the activations of different gates, sigmoid and tanh being the common activation functions. We perform an analysis of the activation function values, and show that a significant fraction are saturated towards zero or one in popular RNNs. Then, we propose CGPA to dynamically prune activations from RNNs at a coarse granularity. CGPA avoids the evaluation of entire neurons whenever the outputs of peer neurons are saturated. CGPA significantly reduces the amount of computations and memory accesses while avoiding sparsity by a large extent, and can be easily implemented on top of conventional accelerators such as TPU with negligible area overhead, resulting in 12% speedup and 12% energy savings on average for a set of widely used RNNs. Finally, in the last contribution of this thesis we focus on static DNN pruning methodologies. DNN pruning reduces memory footprint and computational work by removing connections and/or neurons that are ineffectual. However, we show that prior pruning schemes require an extremely time-consuming iterative process that requires retraining the DNN many times to tune the pruning parameters. Then, we propose a DNN pruning scheme based on Principal Component Analysis and relative importance of each neuron's connection that automatically finds the optimized DNN in one shot.
Les xarxes neuronals profundes (DNN) han aconseguit un èxit enorme en aplicacions cognitives, i són especialment eficients en problemes de classificació i presa de decisions com ara reconeixement de veu o traducció automàtica. Els dispositius mòbils depenen cada cop més de les DNNs per entendre el món. Els telèfons i rellotges intel·ligents, o fins i tot els cotxes, realitzen diàriament tasques discriminatòries com ara el reconeixement de rostres o objectes. Malgrat la popularitat creixent de les DNNs, el seu funcionament en sistemes mòbils presenta diversos reptes: proporcionar una alta precisió i rendiment amb un petit pressupost de memòria i energia. Les DNNs modernes consisteixen en milions de paràmetres que requereixen recursos computacionals i de memòria enormes i, per tant, no es poden utilitzar directament en sistemes de baixa potència amb recursos limitats. L'objectiu d'aquesta tesi és abordar aquests problemes i proposar noves solucions per tal de dissenyar acceleradors eficients per a sistemes de computació cognitiva basats en DNNs. En primer lloc, ens centrem en optimitzar la inferència de les DNNs per a aplicacions de processament de seqüències. Realitzem una anàlisi de la similitud de les entrades entre execucions consecutives de les DNNs. A continuació, proposem DISC, un accelerador que implementa una tècnica de càlcul diferencial, basat en l'alt grau de semblança de les entrades, per reutilitzar els càlculs de l'execució anterior, en lloc de computar tota la xarxa. Observem que, de mitjana, més del 60% de les entrades de qualsevol capa de les DNNs utilitzades presenten canvis menors respecte a l'execució anterior. Evitar els accessos de memòria i càlculs d'aquestes entrades comporta un estalvi d'energia del 63% de mitjana. En segon lloc, proposem optimitzar la inferència de les DNNs basades en capes FC. Primer analitzem el nombre de pesos únics per neurona d'entrada en diverses xarxes. Aprofitant optimitzacions comunes com la quantització lineal, observem un nombre molt reduït de pesos únics per entrada en diverses capes FC de DNNs modernes. A continuació, per millorar l'eficiència energètica del càlcul de les capes FC, presentem CREW, un accelerador que implementa un eficient mecanisme de reutilització de càlculs i emmagatzematge dels pesos. CREW redueix el nombre de multiplicacions i proporciona estalvis importants en l'ús de la memòria. Avaluem CREW en un conjunt divers de DNNs modernes. CREW proporciona, de mitjana, una millora en rendiment de 2,61x i un estalvi d'energia de 2,42x. En tercer lloc, proposem un mecanisme per optimitzar la inferència de les RNNs. Les cel·les de les xarxes recurrents realitzen multiplicacions element a element de les activacions de diferents comportes, sigmoides i tanh sent les funcions habituals d'activació. Realitzem una anàlisi dels valors de les funcions d'activació i mostrem que una fracció significativa està saturada cap a zero o un en un conjunto d'RNNs populars. A continuació, proposem CGPA per podar dinàmicament les activacions de les RNNs a una granularitat gruixuda. CGPA evita l'avaluació de neurones senceres cada vegada que les sortides de neurones parelles estan saturades. CGPA redueix significativament la quantitat de càlculs i accessos a la memòria, aconseguint en mitjana un 12% de millora en el rendiment i estalvi d'energia. Finalment, en l'última contribució d'aquesta tesi ens centrem en metodologies de poda estàtica de les DNNs. La poda redueix la petjada de memòria i el treball computacional mitjançant l'eliminació de connexions o neurones redundants. Tanmateix, mostrem que els esquemes de poda previs fan servir un procés iteratiu molt llarg que requereix l'entrenament de les DNNs moltes vegades per ajustar els paràmetres de poda. A continuació, proposem un esquema de poda basat en l'anàlisi de components principals i la importància relativa de les connexions de cada neurona que optimitza automàticament el DNN optimitzat en un sol tret sense necessitat de sintonitzar manualment múltiples paràmetres
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Kazilas, Panagiotis. "Augmenting MPI Programming Process with Cognitive Computing." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-88913.

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Cognitive Computing is a new and quickly advancing technology. In thelast decade Cognitive Computing has been used to assist researchers in theirendeavors in many different scientific fields such as Health & medicine,Education, Marketing, Psychology and Financial Services. On the otherhand, Parallel programming is a more complex concept than sequentialprogramming. The additional complexity of Parallel Programming isintroduced by its nature that requires implementations of more complexalgorithms and it introduces additional concepts to the developers, namelythe communication between the processes (Distributed memory systems)that execute the parallel program and their synchronization (Share memorysystems). As a result of this additional complexity, a lot of novice developersare reserved in their attempts to implement parallel programs. The objectiveof this research project was to investigate whether we can assist parallelprogramming process through cognitive computing solutions. In order toachieve our objective, the MPI Assistant, a Q&A system has been developedand a case study has been carried out to determine our application’s efficiencyin our attempt to assist parallel programming developers. The case studyshowed that our MPI Assistant system indeed helped developers reduce thetime they spend to develop their solutions, but not improve the quality ofthe program or its efficiency as these improvements require features that areout of this research project’s scope. However, the case study had limitednumber of participants, which may affect our results’ reliability. As a nextstep in our attempt to determine if cognitive computing technologies are ableto assist developers in their parallel programming development, we movedto investigate if cognitive solutions can extract better and more completeresponses compared to our manually-created responses that we created forthe MPI Assistant. We have experimented with 2 different approaches to theproblem. An approach where we manually created responses for the MPIAssistant, and an approach where we investigated if cognitive solutions canautomatically extract better and complete responses. We compared the qualityof the latter automatic responses with the quality of the former which weremanually created.
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McArthur, Robert James. "Computing with meaning by operationalising socio-cognitive semantics." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16571/1/Robert_McArthur_Thesis.pdf.

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This thesis is motivated by the desire to provide technological solutions to enhance human awareness in information processing tasks. The need is pressing. Paradoxically, as information piles up people become less and less aware due to perceived scarce cognitive resources. As a consequence, specialisations become ever more specialised, projects and individuals in organisations become ever more insular. Technology can enhance awareness by informing the individual about what is happening outside their speciality. Systems which can assist people in these ways need to make sense of human communication. The computer system must know about what it is that it is processing; it must follow a socio-cognitive framework and reason with it. It must compute with meanings not symbolic surface structures. The hypothesis of the thesis is that knowledge potentially useful for enhancing awareness can be derived from interactions between people using computational models based on socio-cognitive semantics. The goals are whether an appreciable approximation of conceptual spaces can be realised through semantic spaces, and whether such semantic spaces can develop representations of meaning which have the potential to enhance the awareness of users? The two thesis questions are how well the socio-cognitive framework of G¨ardenfors could be brought into operational reality, and if a bridge can be made, then what practical issues can be involved? The theory of conceptual spaces of Peter G¨ardenfors is combined with methods from cognitive science for creating geometric spaces to represent meaning. Hyperspace Analogue to Language and Latent Semantic Analysis are used as exemplars of the cognitive science algorithms. The algorithms are modified by a variety of syntactic processing schemes to overcome a paucity of data and hence lack of expressivity in representations of meaning: part-of-speech tagging, index expressions and anaphora resolution are effected and incorporated into the semantic space. The practical element of the thesis consists of five case studies. These are developed in two parts: studies describing how meaning changes and evolves in semantic spaces, and studies describing semantic space applications featuring knowledge discovery. These studies are in a variety of domains with a variety of data: online communities of interest using a mailing list, a health-based mailing list, organisational blogs, "hallway chatter", and organisational email. The data is real world utterances that provide the situational factors that cognitive systems need to answer queries and provide context. The amounts of data are significantly less than previously used by semantic space methods, hence the need for syntactic assistance. The particular problems examined in the case studies are corporate expertise management, social network discovery, tracking ebbs and flows of topics, and noticing the change in a person's sense-of-self over time. These are significantly different to those usually examined using semantic spaces. The key differentiator of this work stems from its focus on the geometrically-based computational realisation of meaning. This thesis takes semantic spaces out of the closet and into real-world information technology applications, with a roadtest in real life.
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McArthur, Robert James. "Computing with meaning by operationalising socio-cognitive semantics." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16571/.

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This thesis is motivated by the desire to provide technological solutions to enhance human awareness in information processing tasks. The need is pressing. Paradoxically, as information piles up people become less and less aware due to perceived scarce cognitive resources. As a consequence, specialisations become ever more specialised, projects and individuals in organisations become ever more insular. Technology can enhance awareness by informing the individual about what is happening outside their speciality. Systems which can assist people in these ways need to make sense of human communication. The computer system must know about what it is that it is processing; it must follow a socio-cognitive framework and reason with it. It must compute with meanings not symbolic surface structures. The hypothesis of the thesis is that knowledge potentially useful for enhancing awareness can be derived from interactions between people using computational models based on socio-cognitive semantics. The goals are whether an appreciable approximation of conceptual spaces can be realised through semantic spaces, and whether such semantic spaces can develop representations of meaning which have the potential to enhance the awareness of users? The two thesis questions are how well the socio-cognitive framework of G¨ardenfors could be brought into operational reality, and if a bridge can be made, then what practical issues can be involved? The theory of conceptual spaces of Peter G¨ardenfors is combined with methods from cognitive science for creating geometric spaces to represent meaning. Hyperspace Analogue to Language and Latent Semantic Analysis are used as exemplars of the cognitive science algorithms. The algorithms are modified by a variety of syntactic processing schemes to overcome a paucity of data and hence lack of expressivity in representations of meaning: part-of-speech tagging, index expressions and anaphora resolution are effected and incorporated into the semantic space. The practical element of the thesis consists of five case studies. These are developed in two parts: studies describing how meaning changes and evolves in semantic spaces, and studies describing semantic space applications featuring knowledge discovery. These studies are in a variety of domains with a variety of data: online communities of interest using a mailing list, a health-based mailing list, organisational blogs, "hallway chatter", and organisational email. The data is real world utterances that provide the situational factors that cognitive systems need to answer queries and provide context. The amounts of data are significantly less than previously used by semantic space methods, hence the need for syntactic assistance. The particular problems examined in the case studies are corporate expertise management, social network discovery, tracking ebbs and flows of topics, and noticing the change in a person's sense-of-self over time. These are significantly different to those usually examined using semantic spaces. The key differentiator of this work stems from its focus on the geometrically-based computational realisation of meaning. This thesis takes semantic spaces out of the closet and into real-world information technology applications, with a roadtest in real life.
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Marojevic, Vuk. "Computing resource management in software-defined and cognitive radios." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/78033.

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Our research aims at contributing to the evolution of modern wireless communications and to the development of software-defined radio (SDR) and cognitive radio, in particular. It promotes a general resource management framework that facilitates the integration of computing and radio resource management. This dissertation discusses the need for computing resource management in software-defined and cognitive radios and introduces an SDR computing resource management framework with cognitive capabilities. The hard real-time computing requirements of software-defined digital signal processing chains (SDR applications), the associated radio propagation and quality of service (QoS) implications, and heterogeneous multiprocessor platforms with limited computing resources (SDR platforms) define the context of these studies. We examine heterogeneous computing techniques, multiprocessor mapping and scheduling in particular, and elaborate a flexible framework for the dynamic allocation and reallocation of computing resources for wireless communications. The framework should facilitate partial reconfigurations of SDR platforms, dynamic switches between radio access technologies (RATs), and service and QoS level adjustments as a function of the environmental conditions. It, therefore, assumes the facilities of the platform and hardware abstraction layer operating environment (P-HAL-OE). We suggest a modular framework, distinguishing between the computing system modeling and the computing resource management. Our modeling proposal is based on two computing resource management techniques, which facilitate managing the strict timing constraints of real-time systems. It is scalable and can account for many different hardware architectures and computing resource types. This work focuses on processing and interprocessor bandwidth resources and processing and data flow requirements. Our computing resource management approach consists of a general-purpose mapping algorithm and a cost function. The independence between the algorithm and the cost function facilitates implementing many different computing resource management policies. We introduce a dynamic programming based algorithm, the tw-mapping, where w controls the decision window. We present a general and parametric cost function, which guides the mapping process under the given resource constraints. An instance of it facilitates finding a mapping that meets all processing and data flow requirements of SDR applications with the available processing and bandwidth resources of SDR platforms. Several SDR reconfiguration scenarios and analyses based on simulations demonstrate the suitability and potentials of our framework for a flexible computing resource management. We extend our SDR computing resource management concepts to the cognitive radio context. The two primary objectives of cognitive radio are highly reliable communications whenever and wherever needed and the efficient use of the radio spectrum. We formulate a third objective as the efficient use of computing resources. We analyze the cognitive capabilities of our framework─the cognitive radio’s interface to SDR platforms─and indicate the potentials of our cognitive computing resource management proposal. The cognitive computing resource management needs to be coordinated with the radio resource management. We, therefore, introduce the joint resource management concept for cognitive radios. We present three cognitive cycles and discuss several interrelations between the radio, computing, and application resources, where application resources refer to the available SDR and user applications. Our approach potentiates flexibility and facilitates radio against computing resource tradeoffs. It promotes cognition at all layers of the wireless system for a cooperative or integrated resource management that may increase the performance and efficiency of wireless communications.
El objetivo de las investigaciones que se están llevando a cabo dentro del grupo de investigación es contribuir a la evolución de las radiocomunicaciones modernas y, en particular, al desarrollo de los conceptos software radio (SDR) y cognitive radio. El planteamiento general es el de extender la flexibilidad global del sistema de comunicaciones planteando la definición y desarrollo de un entorno en el que pudiesen explorarse las relaciones entre la computación y las prestaciones del sistema de comunicaciones móviles facilitando la integración de los recursos de computación con los recursos radio. Dentro de este marco, la presente tesis plantea la discusión de la necesidad de la gestión de los recursos de computación en entornos SDR y cognitive radio y define un entorno de operación que asume las características especificas del concepto SDR a la vez que incorpora capacidades cognitivas en la gestión de los recursos de computación de las plataformas que den soporte a las nuevas generaciones de sistemas móviles. Los estrictos requerimientos de procesado en tiempo real de las cadenas de procesado digital de la señal definidas por software (aplicaciones SDR), las implicaciones asociadas con la propagación radio y el concepto de calidad de servicio (QoS) y plataformas heterogéneas de múltiples procesadores con recursos de computo limitados (plataformas SDR) definen el contexto de estos estudios. Se examinan técnicas de cómputo de propósito general para definir un entorno de operación que fuese capaz de asignar de forma flexible y dinámica los recursos de cómputo necesarios para facilitar las radiocomunicaciones a los niveles de QoS deseados. Ello debería facilitar los cambios dinámicos de una tecnología de acceso radio a otra, permitiendo el ajuste del tipo de servicio o calidad de servicio en función de las preferencias de los usuarios y las condiciones del entorno. Dicho entorno de operación asume las potencialidades del platform and hardware abstraction layer operating environment (P-HAL-OE). La estructura del entorno de operación se define de forma modular y consiste en un modelado genérico y flexible de las plataformas de computación SDR y en una gestión de recursos de computación abierta y capaz de ajustarse a diferentes objetivos y políticas. En el trabajo se exponen dos técnicas de gestión que pretenden asegurar la consecución estricta de los límites temporales típicos de los sistemas en tiempo real. En cuanto al modelado, este es escalable y capaz de capturar un amplio abanico de arquitecturas hardware y recursos de computación. En el presente trabajo nos centramos en los recursos y requerimientos del procesado y transferencia de datos. Se introduce un algoritmo de mapeo genérico e independiente de la función de coste. La independencia entre el algoritmo y la función de coste facilita la implementación de diferentes políticas de gestión de recursos computacionales. El tw-mapping es un algoritmo basado en dynamic programming, donde w controla la ventana de decisión. Se presenta una función de coste genérica y parametrizable que permite guiar el proceso de gestión de los recursos. Una instancia de ella facilita encontrar una solución al proceso de asignación de recursos que cumpla todos los requerimientos de procesado y trasferencia de datos de las aplicaciones SDR con los recursos disponibles de las plataformas SDR. Diferentes escenarios y varios análisis basados en simulaciones demuestran la adecuación del entorno de trabajo definido y desarrollado, así como sus potencialidades para una gestión flexible de los recursos de cómputo. Se extienden los conceptos mencionados previamente para entornos cognitive radio. Los principales objetivos del concepto cognitive radio son la disponibilidad de comunicaciones altamente robustas en cualquier lugar y momento en que sean necesarias y el uso eficiente del espectro. Como tercer objetivo formulamos el uso eficiente de los recursos de cómputo. Analizamos las capacidades cognitivas de nuestro entorno de operación─la interfaz del sistema cognitive radio a las plataformas SDR─y resaltamos las potencialidades de nuestra propuesta de gestión cognitiva de los recursos computacionales. Dicha gestión cognitiva de los recursos computacionales plantea una integración con la gestión de los recursos radio. Para ello introducimos el concepto de gestión de recursos conjunta para entornos cognitive radio. Se presentan tres ciclos cognitivos y se discuten algunas interrelaciones entre los recursos radio, de cómputo y de aplicación, donde los recursos de aplicación se refieren a las aplicaciones SDR y de usuario disponibles. Nuestra propuesta de gestión de recursos conjunta potencia la flexibilidad y facilita los intercambios entre recursos radio y de computación
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Zhang, Chi. "Apply on Instance of IBM Watson Cognitive Computing System." Thesis, KTH, Industriell ekologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-203999.

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Smart Cities concern a variety of domains such as information, data, energy, transport, health, etc. The ‘Information Age’, which shifts from the Industrial Revolution to information computerisation, accesses to large volumes of data explored by sophisticated computer based analytics. ICT solutions interconnect businesses and customers through the cloud while driving the global economy and development of Smart Cities. This MSc thesis aims to investigate connections between Smart Cities and cloud-based Cognitive Computing, then demonstrate with instances how the combination of Watson cognitive system and Pepper humanoid robot can enhance living experience. The investigation is based on literature review in the area of Smart Cities and ICT focusing on Internet of Things, Cloud Computing, and Cognitive Computing, observation of services on Bluemix, and interview with consultants and engineers of IBM. The services of Watson cognitive computing system enable Pepper to process unstructured information and interact with humans. The results also contain use cases of the functionality of Watson-powered Pepper, which could be further implemented for public services.
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Goettel, Colby. "A Cognitive Approach to Predicting Academic Success in Computing." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/6732.

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This research examines the possible correlations between a computing student's learning preference and their academic success, as well as their overall satisfaction with their major. CS and IT seniors at BYU were surveyed about their learning preferences and satisfaction with their major. The research found that IT students who are more reflective in their learning preference tend to have higher grades in their major. Additionally, it found that student age and their parents' education level were significant players in their academic success. However, there were no correlations found between major satisfaction and academic performance.
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Green, Alison Julia Katherine. "Statistical computing : individual differences in the acquisition of a cognitive skill." Thesis, University of Aberdeen, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277291.

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The rate at which individuals acquire new cognitive skills may vary quite substantially, some acquiring a new skill more rapidly and efficiently than others. It has been shown through the analysis of think aloud protocols that learning performance on a map learning task, for instance, is associated with the use of certain learning procedures. In the domain of mathematical problem solving, it has also been shown that performance is associated with strategic as opposed to tactical decision making. Previous research on learning and problem solving has tended to focus on tactical processes, ignoring the role of strategic processes in learning and problem solving. There is clearly a need to examine the role of strategic processes in learning and to determine whether they might be an important source of individual differences in learning performance. A related question concerns teaching thinking skills. If it is possible to determine those learning procedures that differentiate good from poor learners, is it then possible to teach the effective procedures to a group of novice students in order to enhance the rate of skill acquisition? Results from the experiments reported here show that novices differ, and that learning performance is related to the use of certain learning procedures, as revealed by subjects' think aloud protocols. A follow-up study showed that novices taught to use the procedures differentiating good from poor learners performed at a higher level than two control groups of novices. A coding scheme was developed to explicitly examine learning at macroscopic and microscopic levels, and to contrast tactical with strategic processes. Discriminant function analysis was used to examine differences between good and poor learners. It was shown that good learners more frequently use executive processes in learning episodes. A study of the same subjects learning to use statistical packages on a microcomputer corroborate these findings. Thus, results extend those obtained from the first study. A study of the knowledge structures possessed by novices was complicated by differences in levels of statistical knowledge. Multidimensional scaling techniques revealed differences between novices with three statistical courses behind them, but not among those with only two statistical courses behind them. Among those novices with three statistical courses behind them, faster learners' knowledge structures more closely resembled those of experienced users of statistical packages than did those of slower learners.
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Books on the topic "Cognitive Computing"

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Haun, Matthias. Cognitive Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9.

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Portmann, Edy, and Sara D'Onofrio, eds. Cognitive Computing. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-27941-7.

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Mallick, Pradeep Kumar, Prasant Kumar Pattnaik, Amiya Ranjan Panda, and Valentina Emilia Balas, eds. Cognitive Computing in Human Cognition. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48118-6.

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Masood, Adnan, and Adnan Hashmi. Cognitive Computing Recipes. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4106-6.

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Xu, Ruifeng, Cheng Cai, and Liang-Jie Zhang, eds. Cognitive Computing – ICCC 2021. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96419-1.

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Xiao, Jing, Zhi-Hong Mao, Toyotaro Suzumura, and Liang-Jie Zhang, eds. Cognitive Computing – ICCC 2018. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94307-7.

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Xu, Ruifeng, Jianzong Wang, and Liang-Jie Zhang, eds. Cognitive Computing – ICCC 2019. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23407-2.

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Yang, Yujiu, Lei Yu, and Liang-Jie Zhang, eds. Cognitive Computing – ICCC 2020. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59585-2.

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Yang, Yujiu, Xiaohui Wang, and Liang-Jie Zhang, eds. Cognitive Computing – ICCC 2022. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23585-6.

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Wang, Yingxu, Du Zhang, and Witold Kinsner, eds. Advances in Cognitive Informatics and Cognitive Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16083-7.

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Book chapters on the topic "Cognitive Computing"

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Kumar, Rohit. "Cognitive Computing." In Machine Learning and Cognition in Enterprises, 99–127. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3069-5_7.

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Haun, Matthias. "Prolog als Motivation." In Cognitive Computing, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9_1.

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Haun, Matthias. "Grundlagen." In Cognitive Computing, 11–128. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9_2.

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Haun, Matthias. "Vorgehensmodell: Brainware Engineering." In Cognitive Computing, 129–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9_3.

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Haun, Matthias. "Konzeptionalisierung: Naturanaloge Modelle." In Cognitive Computing, 165–242. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9_4.

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Haun, Matthias. "Implementierung: Kognitive Techniken." In Cognitive Computing, 243–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9_5.

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Haun, Matthias. "Validierung: Kognitive Anwendungen." In Cognitive Computing, 295–391. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9_6.

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Haun, Matthias. "Epilog als Ausblick und Motivation." In Cognitive Computing, 393–416. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9_7.

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Haun, Matthias. "Open Source." In Cognitive Computing, 417–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9_8.

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Haun, Matthias. "Musterlösungen." In Cognitive Computing, 445–520. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44075-9_9.

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Conference papers on the topic "Cognitive Computing"

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Ciftcioglu, Ozer, and Michael S. Bittermann. "Generic cognitive computing for cognition." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7256942.

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Sheu, Phillip. "Semantic computing and cognitive computing/informatics." In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2017. http://dx.doi.org/10.1109/icci-cc.2017.8109801.

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Wang, Guoyin, and Qinghua Zhang. "Granular Computing based cognitive computing." In 2009 8th IEEE International Conference on Cognitive Informatics (ICCI). IEEE, 2009. http://dx.doi.org/10.1109/coginf.2009.5250774.

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Nahamoo, David. "Cognitive computing journey." In the first workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2567634.2567646.

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Fiorini, Rodolfo A. "From computing with numbers to computing with numeric words." In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2017. http://dx.doi.org/10.1109/icci-cc.2017.8109734.

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Varghese, Dany, and Viju Shankar. "Cognitive computing simulator-COMPASS." In 2014 International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2014. http://dx.doi.org/10.1109/ic3i.2014.7019798.

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Nazir, Sajid, Shushma Patel, and Dilip Patel. "Autonomic computing meets SCADA security." In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2017. http://dx.doi.org/10.1109/icci-cc.2017.8109795.

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Frost, Jesse, Mostafa W. Numan, Michael Liebelt, and Braden J. Phillips. "A new computer for cognitive computing." In 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2015. http://dx.doi.org/10.1109/icci-cc.2015.7259363.

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Ali, Abbas Raza. "Cognitive Computing to Optimize IT Services." In 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2018. http://dx.doi.org/10.1109/icci-cc.2018.8482078.

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Wang, Yingxu. "A semantic algebra for cognitive linguistics and cognitive computing." In 2013 12th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2013. http://dx.doi.org/10.1109/icci-cc.2013.6622221.

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Reports on the topic "Cognitive Computing"

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Debenedictis, Erik, Fredrick Rothganger, James Bradley Aimone, Matthew Marinella, Brian Robert Evans, Christina E. Warrender, and Patrick Mickel. Cognitive Computing for Security. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1234812.

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Karlin, I., and A. Bertsch. Livermore Computing Integrates Advanced Cognitive Simulation Resource. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1643766.

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Jefferson, Brian. Reviewing Information Technology, Surveillance, and Race in the US. Just Tech, Social Science Research Council, May 2022. http://dx.doi.org/10.35650/jt.3033.d.2022.

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The past decade has been marked by a growing awareness of the potential harms of personal computing. This recent development was spurred by a surge of news reports, films, and studies on the unforeseen side effects of constantly using networked devices. As a result, the public has become increasingly aware of the cognitive, ideological, and psychological effects associated with the constant use of personal computing devices. Alongside these revelations, a growing chorus of activists, journalists, organizers, and scholars have turned attention to surveillance technology-related matters of a different kind—those related to the carceral state and border patrol. These efforts have sparked a shift in the public consciousness, from individual experiences of technology users to how technology is used to maintain social divisions. These studies show how the explosion of network devices not only changes society but also maintains longstanding divisions between social groups. This field review highlights key concepts and discussions on information technology, surveillance, carceral governance, and border patrol. Specifically, it explores the evolution of information communication technology and racial surveillance from the late nineteenth century until the present. The review concludes by exploring avenues for bringing these conversations into a transnational dialogue on surveillance, technology, and social inequality moving forward.
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