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Historical Sensing data Mining in cognitive Radio Networks

Historical Sensing Data Mining in Cognitive Radio Networks

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8th international conference on Intelligent Systems and Knowledge Engineering (ISKE)

作者： Huang, Xin-Lin Xu, Yuan Liu, Erwu Tongji Univ Dept Informat & Commun Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9783642549243;9783642549236

cognitive radio networks (CRNs) is one of the hottest topics in wireless communications, where the cognitive radio (CR) is required to acquire the spectrum states of wireless channels intelligently and accurately. Hence, some works proposed to use artificial intelligence theory in CRNs to promote its intelligence. In order to obtain an accurate spectrum sensing results, CR samples wireless signal frequently. Furthermore, as we know, the wireless channel state may be the same in different time-slots. Hence, it is necessary to use the historical sensing data to reduce the uncertainty of current sensing data, and thus improve spectrum sensing performance. Meantime, the Hierarchical Dirichlet Process (HDP) was recently proposed in artificial intelligence field to realize automatically data clustering, which may be used to cluster the historical sensing data with different hidden channel states, and derive a consensus spectrum state within each cluster. In this paper, we first briefly review CRNs and HDP, respectively. Then, we discuss the possible implementation of HDP over CRN, especially in spectrum sensing problem.

关键词： cognitive radio networks Spectrum sensing Hierarchical dirichlet process Historical sensing data mining

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Why Aren't the Stars Aligned? An Analysis of Online Review Content and Star Ratings

Why Aren't the Stars Aligned? An Analysis of Online Review C...

引用

47th Annual Hawaii international conference on System Sciences

作者： Mudambi, Susan M. Schuff, David Zhang, Zhewei Temple Univ Philadelphia PA 19122 USA

ISBN: (纸本)9781479925049

Consumer-generated product evaluations posted on online retailer or third party web-sites have been shown to increase buyer trust and aid consumer decision making. These online reviews typically have two components: star ratings and review text. This can communicate a complex, conflicting message to consumers, as the text of a review carries more nuance than what can be communicated through a simple numerical score. Misalignment between the star rating and the text can lead to increased consumer cognitive processing costs, suboptimal purchase decisions, and lower overall utility of the review site. This study seeks to understand where this misalignment is mostly likely to occur. We find that star rating/review text misalignment occurs more often for (1) experience goods, and (2) goods that receive high star ratings. Misalignment is especially pronounced for experience goods with high star ratings.

关键词： Web sites consumer behaviour content management decision making purchasing retail data processing trusted computing buyer trust consumer cognitive processing costs consumer decision making consumer-generated product evaluations experience goods numerical score online retailer online review content review site review text misalignment star ratings suboptimal purchase decisions third party Web sites Cameras Classification algorithms Consumer behavior Decision making Educational institutions Guidelines Software internet retailers machine learning online reviews search and experience goods text mining

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Towards a social interaction-based cognition model: An analysis of Spatial data Infrastructure

Towards a social interaction-based cognition model: An analy...

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ieee international conference on cognitive Informatics

作者： Luis Reynoso Eduardo Grosclaude Laura Sánchez Mabel Álvarez Facultad de Informatica Universidad del Comahue Neuquen Universidad de la Patagonia San Juan Bosco Trelew Chubut

Distributed cognition is a psychological theory which assumes that knowledge lies not only within the individual but also in the individual's social and physical environment. Today, we tend to achieve cognitive results by means of a sequence of complex and subtly interwoven interactions, using technological devices, and with the help of other, more knowledgeable people. As dwellers of a new century, we are agents of a novel technological assembly where knowledge is socially produced, and nurtured by the various sources of many communities of practice (CoP). In this paper we propose a social interaction-based cognition model which applies distributed cognition and interoperability between different actors of CoP in a web environment. The core components of the model are Authenticity and Coupling, Assemblage and Shared Meanings. We also present the main findings of qualitative research in a case of study to Spatial data Infraestructure (SDI) and CoP around this technological assemblies.

关键词： Cognition Interoperability Communities Context Social network services Organizations Semantics

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Research on modeling and simulation of space information system based on agent

Research on modeling and simulation of space information sys...

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ieee international conference on Progress in Informatics and computing (PIC)

作者： Chao Guo Wei Xiong Equipment Academy Beijing China

ISBN: (纸本)9781479920327

Space information system (SIS) is a typical complex system, which challenges traditional modeling methods. Agent-based modeling and simulation (ABMS) can be an appropriate and effective approach to modeling and simulation of SIS. Firstly, the advantages and basic concepts of ABMS are introduced briefly. Secondly, the complexity of SIS is analyzed, and it is concluded that the complexity of SIS results from the diverse component entities and their relations. Thirdly, the entities involved in SIS are classified as three categories, and on the basis, the structure models of Agents and object are built, including reactive Agent, cognitive Agent and environment object. Finally, an implementation plan of SIS simulation is put forward, and SIS simulation system software with basic functions is developed. And the foundation of further researches on modeling and simulation of SIS is laid.

关键词： Load modeling complexity theory data models databases data visualization Satellites Sensors

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Reducing problem space using Bayesian classification on semantic logs for enhanced application monitoring and management

Reducing problem space using Bayesian classification on sema...

引用

ieee international conference on cognitive Informatics

作者： Omair Shafiq Reda Alhajj Jon G. Rokne Department of Computer Science University of Calgary Calgary Alberta Canada

Monitoring managing large scale applications has always been a crucial and complex task on which enormous efforts and research have been carried out towards making the process efficient, effective and automated. However, the process is still complex, lacks efficiency and effectiveness because execution workflow representation and logging (outcome from real-time execution) is rendered in a syntactic and unstructured manner. The information is quite limited and requires additional manual interpretation till date for effectively handling the process. Hence, it makes the monitoring and management process slow, cumbersome and hard. We propose our solution by semantically (highly structured, formalized and expressive) modeling of execution workflow and logs, and then using adapted Bayesian Classification based inference technique to process formalized logs to help for enhancing the process of monitoring and management by reducing the problem space. Our hybrid approach of partially using semantics to formalize log and workflow data, and adapting classification technique combines the best of both. Semantics help in providing high-level of precision, structure and expressivity to execution workflow and logs. Such kind of formalized data can be used in an effective manner to effectively interpret and process highly structured information from the generated logs during the execution by classification technique to reduce problem space during the process of monitoring and management of applications. This paper first presents a review of related approaches, then methodology towards the hybrid solution, design of our proposed solution and implementation, followed by evaluation of our proposed solution on real-life application scenario.

关键词： Monitoring Semantics Web services Context Bayes methods data mining

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A cognitive approach for texture analysis using neighbors-based binary patterns

A cognitive approach for texture analysis using neighbors-ba...

引用

ieee international conference on cognitive Informatics

作者： Izem Hamouchene Saliha Aouat Computer Science Department USTHB University Algiers Algeria

The human brain receives images from the natural world and understands scenes, places and events quickly, outperforming the most advanced artificial vision system. Most of surfaces are textured in real life. Thus, In this paper, a novel texture analysis method has been proposed. The texture can be seen as a visual representation of complex patterns that lead to cognitive understanding of the environment. Our method is inspired from the Local Binary Pattern (LBP) method. The proposed Neighbor based Binary Pattern (NBP) extracts the local pattern from the texture using an analysis window. Each neighbor of the central pixel is thresholded by the next neighbor and encoded (starting from the top-left neighbor and going clockwise). Thus, the central pixel describes the relative pertinent information between its neighboring pixels. The rotation invariant version of the NBP method extracts patterns which are robust against rotation. For this, the encoding process starts always from the higher neighbor. The encoding process is applied on whole the original image in order to obtain the RINBP image. A histogram is calculated from the RINBP image to describe the texture. The size of the obtained histogram was reduced while keeping the relevant information. In the experiments, the performance of the proposed feature is evaluated on thirteen textured images from Brodatz texture album. It is shown that the RINBP method outperforms the earlier versions of the rotation-invariant LBP and the classical NBP method. This is due to its ability to extract the relative and relevant information from the local neighborhood.

关键词： Feature extraction Histograms Robustness Encoding Visualization databases data mining

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An Extended UML Method for the Verification of Security Protocols

An Extended UML Method for the Verification of Security Prot...

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ieee international conference on Engineering of complex Computer Systems

作者： Gang Shen Xiaohong Li Ruitao Feng Guangquan Xu Jing Hu Zhiyong Feng School of Computer Science and Technology Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China

This paper presents a formal modeling method of security protocols based on the extended UML framework. In order to simplify the process and reduce the difficulty of security protocol modelling, extending mechanisms for the class diagram and sequence diagram of UML are presented, which provide an engineering specification for the security protocol formalizing. Therefore, for verifying the confidentiality and correspondence of security protocols by Prover if, a transformation from extended UML model to Prover if Spi calculus model is realized with matching rules and knowledge reasoning, and then the verifying results are analyzed through a regular expression. Finally, the handshake, NS public key and buyer-seller watermarking protocols are verified, the attack traces of unsatisfied security properties are exported, that show the validity and applicability of the approach provided by this paper.

关键词： Unified modeling language Protocols Cryptography Cognition Calculus data models

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Non-rigid structure from motion via sparse representation

Non-rigid structure from motion via sparse representation

引用

ieee international conference on Multimedia and Expo (ICME)

作者： Kun Li Jingyu Yang Jianmin Jiang Tianjin Key Lab. of Cognitive Computing and Application Tianjin University Tianjin China School of Electronic Information Engineering Tianjin University Tianjin China Shenzhen University Shenzhen Guangdong CN

This paper proposes a new approach for non-rigid structure from motion with occlusion, based on sparse representation. We introduce sparse transform to the joint estimation of 3D shapes and motions. 3D shape trajectory space is fit by wavelet basis to achieve better modeling of complex motion. We address the occlusion problem based on the latest developments on sparse representation: matrix completion, which can recover the observation matrix that has high percentages of missing data and can also reduce the noises and outliers in the known elements. Experimental results on datasets without and with occlusion show that our method can better estimate the 3D shapes and motions, compared with state-of-the-art algorithms.

关键词： Shape Three-dimensional displays Trajectory Cameras Sparse matrices Noise Transforms

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Policy search for learning robot control using sparse data

Policy search for learning robot control using sparse data

引用

ieee international conference on Robotics and Automation (ICRA)

作者： B. Bischoff D. Nguyen-Tuong H. van Hoof A. McHutchon C. E. Rasmussen A. Knoll J. Peters M.P. Deisenroth Cognitive Systems Bosch Corporate Research Germany Intelligent Autonomous Systems TU Darmstadt Germany Computational and Biological Learning Lab Univ. of Cambridge UK Technische Universitat Munchen Munchen Bayern DE Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computing Imperial College London UK

In many complex robot applications, such as grasping and manipulation, it is difficult to program desired task solutions beforehand, as robots are within an uncertain and dynamic environment. In such cases, learning tasks from experience can be a useful alternative. To obtain a sound learning and generalization performance, machine learning, especially, reinforcement learning, usually requires sufficient data. However, in cases where only little data is available for learning, due to system constraints and practical issues, reinforcement learning can act suboptimally. In this paper, we investigate how model-based reinforcement learning, in particular the probabilistic inference for learning control method (Pilco), can be tailored to cope with the case of sparse data to speed up learning. The basic idea is to include further prior knowledge into the learning process. As Pilco is built on the probabilistic Gaussian processes framework, additional system knowledge can be incorporated by defining appropriate prior distributions, e.g. a linear mean Gaussian prior. The resulting Pilco formulation remains in closed form and analytically tractable. The proposed approach is evaluated in simulation as well as on a physical robot, the Festo Robotino XT. For the robot evaluation, we employ the approach for learning an object pick-up task. The results show that by including prior knowledge, policy learning can be sped up in presence of sparse data.

关键词： Robots Valves Grasping data models Pneumatic systems Computational modeling Heuristic algorithms

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SALT: Sensing enAbled Localization and Tracking for Geolocation database in TV White Space

SALT: Sensing enAbled Localization and Tracking for Geolocat...

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9th international Wireless Communications and Mobile computing conference (IWCMC)

作者： Chen, Li Zhang, Tengyi Tsang, Danny H. K. Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781467324809

Localization is the enabling technology of geolocation database assisted cognitive Radio Networks (CRN) in TV White Space (TVWS), as the location is required in the implementation of geolocation database approach by regulators such as FCC and Ofcom. This paper proposes a novel Sensing enAbled Localization and Tracking (SALT) system, which locates Secondary Users (SUs) and tracks their movements. In particular, the system localize SUs based on their measurement of Primary User (PU) signals in the TV spectrum. SALT also uses the SU movement information to dynamically manage the available spectrum with an optimal spectrum allocation algorithm based on the location estimation, so as to maximize the uplink throughput of the entire network. The performance of the proposed localization and tracking algorithm is verified with field measurement data from complex environment through extensive experimentation.

关键词： cognitive Radio Location Estimation Geolocation database TV White Space

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