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作者： Zhang, Haijun Nie, Yani Cheng, Julian Leung, Victor C. M. Nallanathan, Arumugam Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an710071 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T1Z4 Canada College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China Beijing University of Posts and Telecommunications Beijing100876 China School of Engineering University of British Columbia KelownaBCV1V1V7 Canada Department of Informatics King's College LondonWC2R2LS United Kingdom

Cognitive radio enabled small cell network is an emerging technology to address the exponential increase of mobile traffic demand in next generation mobile communications. Recently, many technological issues, such as resource allocation and interference mitigation pertaining to cognitive small cell network have been studied, but most studies focus on maximizing spectral efficiency. Different from the existing works, we investigate the power control and sensing time optimization problem in a cognitive small cell network, where the cross-tier interference mitigation, imperfect hybrid spectrum sensing, and energy efficiency are considered. The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem. We solve the proposed problem in an asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed by considering imperfect hybrid spectrum sensing, cross-tier interference mitigation, minimum data rate requirement, and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in the cognitive small cell network. © 2016 IEEE.

关键词： Resource allocation

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RESeED: A secure regular-expression search tool for storage clouds

RESeED: A secure regular-expression search tool for storage ...

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作者： Salehi, Mohsen Amini Caldwell, Thomas Fernandez, Alejandro Mickiewicz, Emmanuel Rozier, Eric W. D. Zonouz, Saman Redberg, David HPCC lab. School of Computing and Informatics University of Louisiana at Lafayette LA70503 United States Microsoft Corp. BellevueWA98004 United States Citrix Systems Inc. Fort LauderdaleFL33309 United States SIGFOX Inc. Toulouse France Department of Computer Science Iowa State University AmesIA50011 United States Electrical and Computer Engineering Department Rutgers University New BrunswickNJ08854 United States Fortinet Inc. New York CityNY10119 United States

Lack of trust has become one of the main concerns of users who tend to utilize one or multiple Cloud providers. Trustworthy Cloud-based computing and data storage require secure and efficient solutions which allow clients to remotely store and process their data in the Cloud. User-side encryption is an established method to secure the user data on the Cloud. However, using encryption, we lose processing capabilities, such as searching, over the Cloud data. In this paper, we present RESeED, a tool that provides user-transparent and Cloud-agnostic regular-expression search functionality over encrypted data across multiple Clouds. Upon a client's intent to upload a new document to the Cloud, RESeED analyzes the document's content and updates its data structures accordingly. Then, it encrypts and transfers the document to the Cloud. RESeED provides the regular-expression search functionality over encrypted data by translating the search queries on-the-fly to finite automata and analyzing concise and secure representations of the data before asking the Cloud to download the encrypted documents. RESeED's parallel architecture enables efficient search over large-scale (and potentially big data scale) data-sets. We evaluate the performance of RESeED experimentally and demonstrate its scalability and correctness using real-world data-sets from *** and Internet engineering Task Force (IETF). Our results show that RESeED produces accurate query responses with a reasonable (6%) storage overhead. The results also demonstrate that for many search queries, RESeED performs faster in compare with the grep utility that functions on unencrypted data. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Cloud storage

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Scalable Nearest Neighbor Sparse Graph Approximation by Exploiting Graph Structure

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IEEE Transactions on Big Data 2016年第4期2卷 365-379页

作者： Shao, Ming Wu, Xindong Fu, Yun Department of Computer and Information Science University of Massachusetts Dartmouth DartmouthMA02747 United States School of Computing and Informatics University of Louisiana at Lafayette LafayetteLO70503 United States Department of Electrical and Computer Engineering College of the Computer and Information Science Northeastern University BostonMA02115 United States

We consider exploiting graph structure for sparse graph approximations. Graphs play increasingly important roles in learning problems: manifold learning, kernel learning, and spectral clustering. Specifically, in this paper, we concentrate on nearest neighbor sparse graphs which are widely adopted in learning problems due to its spatial efficiency. Nonetheless, we raise an even challenging problem: can we save more memory space while keep competitive performance for the sparse graph? To this end, first, we propose to partition the entire graph into intra-and inter-graphs by exploring the graph structure, and use both of them for graph approximation. Therefore, neighborhood similarities within each cluster, and between different clusters are well preserved. Second, we improve the space use of the entire approximation algorithm. Specially, a novel sparse inter-graph approximation algorithm is proposed, and corresponding approximation error bound is provided. Third, extensive experiments are conducted on 11 real-world datasets, ranging from small-to large-scales, demonstrating that when using less space, our approximate graph can provide comparable or even better performance, in terms of approximation error, and clustering accuracy. In large-scale test, we use less than 1/100 memory of comparable algorithms, but achieve very appealing results. © 2015 IEEE.

关键词： Approximation algorithms

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Air pollution monitoring in lemesos using a wireless sensor network

Air pollution monitoring in lemesos using a wireless sensor ...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Theofylaktos Pieri Michalis P. Michaelides Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Lemesos Cyprus

ISBN: (纸本)9781509000593

Air pollution presents a serious threat to human health, especially in densely populated urban areas where the pollution levels continue to increase above the safe limits. In this paper, we describe the implementation and evaluation of a Wireless Sensor Network for monitoring the air pollution in a vibrant area of Lemesos, Cyprus. In particular, we describe the various parts of the system both in terms of hardware and software. We also detail the installation procedure with particular emphasis on the method used for calibrating the sensors. Finally, we offer some preliminary findings in comparing the sensor readings between the different nodes. The ultimate goal of this research is to provide accurate, in-situ, real-time information concerning the air pollution to the citizens and the relevant authorities for taking the necessary measures in improving the air quality.

关键词： Monitoring Air pollution Urban areas Intelligent sensors Wireless sensor networks Temperature sensors

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Technology Development of Salak (Salacca Zalacca) Chips With Vacuum Frying Machine Base On Expert System In Kramat-Bangkalan Regency

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Journal of Physics: Conference Series 2018年第1期953卷

作者： D F Rosida Happyanto Anggraeni Sugiarto Hapsari Department of Food Technology Faculty of Engineering University of Pembangunan Nasional 'Veteran' Jawa Timur Surabaya Indonesia Department of Electrical Politeknik Elektronika Negeri Surabaya Indonesia. Department of Informatics Technology Faculty of Computer Science University of Pembangunan Nasional 'Veteran' Jawa Timur Surabaya Indonesia Departement of Chemical Engineering Faculty of Engineering University of Pembangunan Nasional 'Veteran' Jawa Timur Surabaya Indonesia

Agropolitan Program is one form of regional development to improve agribusiness system and effort to improve the welfare of the community. One of the leading commodities in Bangkalan agroclimates is salak which is a potentially very large commodity to be developed. Salak commodities in Kramat Bangkalan Indonesia have developed varous salak produced such as dates of salak, syrup and dodol salak. Salak chips was the target of innovation from processed salak. The Production of salak chips using frying technology with vacuum system to obtain crunchy chips. To get the results need to be developed synergy technology to combine the process conditions and the right system in producing good quality salak chips. Bangkalan Regency is the potential to continue to develop products using a variety of salak to the processed form of vacuum frying machine based on expert system so that the resulting product would be great texture, aroma and taste. This will make the area of Bangkalan, Indonesia be more independent in producing and increasing revenue.

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A scientist's impact over time: The predictive power of clustering with peers 16

A scientist's impact over time: The predictive power of clus...

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20th International Database engineering and Applications Symposium, IDEAS 2016

作者： Gogoglou, Antonia Sidiropoulos, Antonis Katsaros, Dimitrios Manolopoulos, Yannis Department of Informatics Aristotle University of Thessaloniki Greece Department of Information Technology Alexander Technological Educational Institute of Thessaloniki Greece Department of Electrical and Computer Engineering University of Thessaly Greece

ISBN: (纸本)9781450341189

The identification of latent patterns in big scholarly data that concern the performance of researchers is a significant task because it can potentially impact scien-tific careers since they are based in funding and promo-tion. This article investigates the temporal evolution of a scientist's impact. Instead of taking a detailed, microscopic view that examines the citation curves of every scientist's article, the article develops a scalable, macroscopicmethodology that uses the articles' citation profiles to build a more abstract and high-level profile that characterizes a scientist. This profile is utilized to cluster scientists in a set of 'performance' clusters. To this end, established techniques such as Principal Com-ponent Analysis and Self-OrganizingMap clustering are employed as well as a set of proposed heuristics. The effectiveness of the proposed methodology is examined by comparing the resulting rankings with the outcomes of the peer-review procedures that resulted in the E. F. Codd and the Turing awards. The good match be-tween the outcomes of computerized and peer-review procedures provides solid evidence that the proposed techniques constitute a promising analysis method for big scholarly data. © ACM 2016.

关键词： Self organizing maps

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Checking critical software systems: A formal proposal 10

Checking critical software systems: A formal proposal

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10th International Conference on the Quality of Information and Communications Technology, QUATIC 2016

作者： Morales, Luis E. Mendoza Capel, Manuel I. Processes and Systems Department Simón Bolívar University Venezuela Faculty of Electrical and Computer Engineering Escuela Superior Politécnica del Litoral Ecuador Software Engineering Department University of Granada ETSI Informatics and Telecommunication Spain

ISBN: (纸本)9781509035816

As a contribution to the specification and verification of critical software systems, this article presents a formal proposal for compositional verification, which uses model checking technique and integrates a modelling infrastructure that propitiates the target system decomposition into separate components aimed at being individually specified and verified. Our goal with this proposal is to provide an infrastructure for developing and verifying critical software systems by fostering extensibility and modifiability of the software. In this way, validated components can be integrated into large computer programs readily. The compositional verification approach guarantees the correctness of the entire system during its execution. Also, is discussed a practical application of our proposal to a realistic industry project related to mobile phone communication. © 2016 IEEE.

关键词： Model checking

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Some highlights on fuzzy systems and data mining

Some highlights on fuzzy systems and data mining

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作者： Sun, Shilei Ionita, Silviu Volná, Eva Gavrilov, Andrey Liua, Feng International School of Software Wuhan University China Department of Electronics Computers and Electrical Engineering University of Pitesti Romania Department of Informatics and Computers University of Ostrava Czech Republic Department of Computer Engineering Novosibirsk State Technical University Russia

Newsday fuzzy systems are applied in many interdisciplinary fields, in which artificial intelligent tools are utilized to analyze different data in the corresponding fields. Under this background, the 2nd International Conference on Fuzzy Systems and Data Mining (FSDM 2016), organized by Macau University of Science and Technology, is scheduled to be held in Macau, December 11-14, 2016. This special issue contains some selected papers related to fuzzy systems, data mining and their applications, which provides some highlights on fuzzy systems and data mining during the past years in East Asia. © 2016 - IOS Press and the authors. All rights reserved.

关键词： Data mining

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Augmenting fMRI-generated brain connectivity with temporal information

Augmenting fMRI-generated brain connectivity with temporal i...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Fotios Tagkalakis Aimilia Papagiannaki Georgios Drakopoulos Vasileios Megalooikonomou Electrical and Computer Engineering Department University of Patras Hellas Computer Engineering and Informatics Department University of Patras Hellas

ISBN: (纸本)9781509034307

fMRI is an established neuroimaging technique providing a snapshot sequence of brain activity with very high spatial resolution. An efficient data mining algorithm is proposed for agumenting voxel activation detection, best performed spatially, with task-specific information, which is primarily temporal. Thus, the observation of evolution of brain region co-activation through fMRI is enhanced. Also, based on the AAL2 standard brain atlas, a neurophysiological interpretation of the findings during a motor task is offered, strengthening the validity of the proposed algorithm.

关键词： Brain Standards Algorithm design and analysis Time series analysis Hemodynamics Labeling Convolution

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Recursive Structure Similarity: A Novel Algorithm for Graph Clustering

Recursive Structure Similarity: A Novel Algorithm for Graph ...

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IEEE International Conference on Tools with Artificial Intelligence

作者： Han Hu Yixin Fang Rouming Jin Wei Xiong Xiaoning Qian Dejing Dou Hai Phan Dept. of Mathematical Sciences New Jersey Inst. of Technology Newark New Jersey USA Dept. of Computer Science Kent State University Kent Ohio USA Dept. of Electrical & Computer Engineering Texas A&M University College Station Texas USA Dept. of Computer and Information Science University of Oregon Eugene Oregon USA Dept. of Informatics New Jersey Inst. of Technology Newark New Jersey USA

A various number of graph clustering algorithms have been proposed and applied in real-world applications such as network analysis, bio-informatics, social computing, and etc. However, existing algorithms usually focus on optimizing specified quality measures at the global network level, without carefully considering the destruction of local structures which could be informative and significant in practice. In this paper, we propose a novel clustering algorithm for undirected graphs based on a new structure similarity measure which is computed in a recursive procedure. Our method can provide robust and high-quality clustering results, while preserving informative local structures in the original graph. Rigorous experiments conducted on a variety of benchmark and protein datasets show that our algorithm consistently outperforms existing algorithms.

关键词： Graph clustering Social network

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