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6th ACM Conference on Bioinformatics, Computational Biology, and Health informatics, BCB 2015

作者： Chen, Feng Mohammed, Noman Wang, Shuang He, Wenbo Cheng, Samuel Jiang, Xiaoqian School of Electrical and Computer Engineering University of Oklahoma TulsaOK74135 United States Department of Computer Science University of Manitoba WinnipegMBR3T 2N2 Canada Division of Biomedical Informatics UC San Diego San DiegoCA92093 United States School of Computer Science McGill University MontrealQCH3A 0G4 Canada

ISBN: (纸本)9781450338530

Data privacy is an important issue to address when multiple data owners are required to integrate and share sensitive information for data analysis. In this article, we study the privacy threats caused by distributed data sharing and present the first cloud-based data sharing framework to integrate horizontally partitioned data from multiple data owners. The cloud performs the anonymization in a top-down fashion. It proceeds from the most generalized values of attributes (serve as the root of the tree) and specializes them (i.e., generate less generalized values as siblings of the parent node) in every iteration. A candidate value is selected for specialization in each iteration based on its score. The score of each candidate is calculated securely using multiple cryptographic protocols to ensure security. Finally, the cloud adds noise to the integrated data and releases them in a differentially private manner. Experimental results on real-life data set demonstrate that the proposed algorithm retains data utility for supporting classification analysis and provide similar classification accuracy compared to that of the centralized data dissemination mechanism. Copyright 2015 ACM.

关键词： Iterative methods

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Analysis on connectivity performance for vehicular ad hoc networks subjected to user behavior

Analysis on connectivity performance for vehicular ad hoc ne...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Ruifeng Chen Zhengguo Sheng Zhangdui Zhong Minming Ni David G. Michelson Victor C.M. Leung State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Engineering and Informatics University of Sussex UK Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

The user behavior is one of the key factors to impact the communication performance of vehicular ad hoc networks. Generally, more active vehicles can enhance the connectivity performance. Nevertheless, an increasing number of active vehicles may bring about significant interference and thus negatively affect the connectivity. In this paper, a highway model is presented to study the effects of active vehicle ratio on connectivity performance in the presence of co-channel interference. A closed form expression of the inter-vehicle connectivity probability is also derived to evaluate the impacts of active ratio, traffic flow, and fading factors under Nakagami fading channel. Analytical results of inter-vehicle connectivity probability are shown to be consistent with the simulation results, and can be applied to estimate connectivity performance with interference in the network design for highway vehicular scenarios.

关键词： Interference Vehicles Fading Vehicular ad hoc networks Road transportation Nakagami distribution Random variables

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Latest Advances in Theories and Applications of Cognitive Robotics and Brain-Inspired Systems

Latest Advances in Theories and Applications of Cognitive Ro...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： YingXU Wang International Institute of Cognitive Informatics and Cognitive Computing(ICIC) Laboratory for Computational Intelligence Cognitive Systemsand Software Science Dept.of Electrical and Computer Engineering Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary

In the new era of intelligence revolution beyond information and computing revolutions,a brain-like intelligent system is emerging known as cognitive robots.A cognitive robot is an autonomous system that is capable of perception,learning,and inference mimicking the cognitive mechanisms of the brain by computational *** robotics is a discipline that studies theories,mathematical means,implementations,and engineering applications of cognitive *** theoretical foundations of cognitive robotics are underpinned in brain science,cognitive science,abstract intelligence,knowledge science,intelligence science,cognitive linguistics,and denotational *** keynote lecture presents the latest advances in theories and applications of cognitive robotics and brain-inspired *** theoretical framework of cognitive robotics and its denotational mathematical means such as concept algebra,semantic algebra,behavioral process algebra,and inference algebra are *** challenges in cognitive robotics are formally studied on what the necessary and sufficient intelligent behaviors of cognitive robots are,and what distinguish the intelligent capabilities of cognitive robots from their imperative *** structural and functional models of cognitive robots are explored at the intelligent layers of imperative,autonomic,and cognitive *** progress in cognitive robotics has led to the development of cognitive systems,nextgeneration computers,and cognitive machine learning engines,as well as the growth of contemporary knowledge-based industries.

关键词： Cognitive robotics cognitive informatics cognitive computing abstract intelligence computational intelligence denotational mathematics concept algebra semantic algebra brain science neuroinformatics cognitive machine learning cognitive knowledge base artificial intelligence and cognitive systems

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Author Correction: A portrait of the Higgs boson by the CMS experiment ten years after the discovery

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Nature 2023年第7985期623卷 E4页

作者： A. Tumasyan W. Adam J. W. Andrejkovic T. Bergauer S. Chatterjee K. Damanakis M. Dragicevic A. Escalante Del Valle P. S. Hussain M. Jeitler N. Krammer L. Lechner D. Liko I. Mikulec P. Paulitsch F. M. Pitters J. Schieck R. Schöfbeck D. Schwarz S. Templ W. Waltenberger C.-E. Wulz M. R. Darwish T. Janssen T. Kello H. Rejeb Sfar P. Van Mechelen E. S. Bols J. D’Hondt A. De Moor M. Delcourt H. El Faham S. Lowette S. Moortgat A. Morton D. Müller A. R. Sahasransu S. Tavernier W. Van Doninck D. Vannerom B. Clerbaux G. De Lentdecker L. Favart J. Jaramillo K. Lee M. Mahdavikhorrami I. Makarenko A. Malara S. Paredes L. Pétré N. Postiau E. Starling L. Thomas M. Vanden Bemden C. Vander Velde P. Vanlaer D. Dobur J. Knolle L. Lambrecht G. Mestdach M. Niedziela C. Rendón C. Roskas A. Samalan K. Skovpen M. Tytgat N. Van Den Bossche B. Vermassen L. Wezenbeek A. Benecke G. Bruno F. Bury C. Caputo P. David C. Delaere I. S. Donertas A. Giammanco K. Jaffel Sa. Jain V. Lemaitre K. Mondal J. Prisciandaro A. Taliercio T. T. Tran P. Vischia S. Wertz G. A. Alves E. Coelho C. Hensel A. Moraes P. Rebello Teles W. L. Aldá Júnior M. Alves Gallo Pereira M. Barroso Ferreira Filho H. Brandao Malbouisson W. Carvalho J. Chinellato E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao V. Dos Santos Sousa S. Fonseca De Souza J. Martins C. Mora Herrera K. Mota Amarilo L. Mundim H. Nogima A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel F. Torres Da Silva De Araujo A. Vilela Pereira C. A. Bernardes L. Calligaris E. M. Gregores P. G. Mercadante S. F. Novaes Sandra S. Padula T. R. Fernandez Perez Tomei A. Aleksandrov G. Antchev R. Hadjiiska P. Iaydjiev M. Misheva M. Rodozov M. Shopova G. Sultanov A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov E. Shumka T. Cheng T. Javaid M. Mittal L. Yuan M. Ahmad G. Bauer Z. Hu S. Lezki K. Yi G. M. Chen H. S. Chen M. Chen F. Iemmi C. H. Jiang A. Kapoor H. Liao Z.-A. Liu V. Milosevic F. Monti R. Sharma J. Tao J. Thomas-Wilsker J. Wang H. Zhang J. Zhao A. Agapitos Y. An Y. Ban C. Chen A. Levin C. Li Q. Li X Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Université Libre de Bruxelles Bruxelles Belgium Vrije Universiteit Brussel Brussel Belgium Korea University Seoul Korea Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil The University of the State of Amazonas Manaus Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China University of Chinese Academy of Sciences Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Department of Physics Nanjing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences. Beijing China University of Florida Gainesville Florida USA Massachusetts Institute of Technology Cambridge Massachusetts USA State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Northeastern University Boston Massachusetts USA Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) -Fudan University Shanghai China Rice University Houston Texas USA Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering

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Channel capacity under measurement-based model for cooperative vehicular ad hoc networks

Channel capacity under measurement-based model for cooperati...

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Antennas and Propagation Society International Symposium

作者： Ruifeng Chen Zhengguo Sheng Minming Ni Zhangdui Zhong David G. Michelson State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Engineering and Informatics University of Sussex UK Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

Infrastructure-based cooperation can significantly boost the communication performance of vehicular ad hoc networks, especially when direct transmission of inter-vehicle communication can hardly achieve the stringent requirement of safety information dissemination. In this paper, an analytical model is presented to demonstrate the performance gain of channel capacity for infrastructure-based cooperative transmission. Furthermore, we study the capacity performance for both direct transmission and cooperative communication, under generic path loss model and the measurement-based dual-slope piecewise-linear model, respectively. The analytical results can be used for vehicular networks design and roadside infrastructure deployment in highway scenarios.

关键词： Channel capacity Loss measurement Vehicles Mathematical model Cooperative communication Gain Vehicular ad hoc networks

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Neutron-Gamma Classification by Evolutionary Fuzzy Rules and Support Vector Machines

Neutron-Gamma Classification by Evolutionary Fuzzy Rules and...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Pavel Krömer Zdenek Matej Petr Musilek Václav Prenosil Frantiek Cvachovec Department of Computer Science VSB - Technical University of Ostrava Ostrava Czech Republic Faculty of Informatics Masaryk University Brno Czech Republic Department of Electrical and Computer Engineering University of Alberta Alberta Canada

ISBN: (纸本)9781479986989

Accurate and fast methods for neutron-gamma discrimination play an essential role in the development of digital scintillation detectors. Digital detectors allow the use of state-of-the-art data analysis, mining, and classification methods in place of traditional approaches based on analog technology such as the pulse rise-time and charge-comparison methods. This work compares the ability of evolutionary fuzzy rules and support vector machines to perform accurate neutron-gamma classification. The accuracy and performance of both investigated methods are evaluated on two real-world data sets.

关键词： Support vector machines Neutrons Photonics Gamma-rays Information retrieval Genetic programming Training

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Statistical modeling of antennas via a generalized stochastic-FDTD method

Statistical modeling of antennas via a generalized stochasti...

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European Conference on Antennas and Propagation, EuCAP

作者： A. N. Papadimopoulos G. G. Pyrialakos A. X. Lalas T. T. Zygiridis N. V. Kantartzis C. S. Antonopoulos T. F. Eibert T. D. Tsiboukis Dept. of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Aristotle University of Thessaloniki Thessaloniki GR Dept. of Informatics & Telecommunications Engineering University of Western Macedonia Kozani Greece Dept. of Electrical Engineering & Information Technology Technische Universität München Munich Germany

The consistent analysis of complex antenna setups with statistically varying media attributes is performed in this paper through a 3-D curvilinear stochastic finite-difference time-domain technique. The new method generalizes the concept of covariant/contravariant metrics for curved structures and derives the mean value and standard deviation of field components in a single run. Also, for the acceleration of the resulting simulations, all codes are developed in terms of graphics processor units and parallel programming. The profits of the proposed algorithm over multiple-realization Monte-Carlo schemes are certified via comparisons from circular- and trapezoidal-toothed log-periodic antennas with various realistic uncertainties in their materials.

关键词： Antennas Time-domain analysis Finite difference methods Graphics processing units Standards Stochastic processes Media

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Iterative Classification for Sanitizing Large-Scale Datasets

Iterative Classification for Sanitizing Large-Scale Datasets

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IEEE International Conference on Data Mining (ICDM)

作者： Bo Li Yevgeniy Vorobeychik Muqun Li Bradley Malin Vanderbilt University Law School Nashville TN US Electrical Engineering and Computer Science Vanderbilt University Nashville TN Biomedical Informatics Vanderbilt University Nashville TN

ISBN: (纸本)9781467395052

Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose identities or other sensitive information. Much of the data now collected exhibits weak structure (e.g., natural language text) and machine learning approaches have been developed to identify and remove sensitive entities in such data. Learning-based approaches are never perfect and relying upon them to sanitize data can leak sensitive information as a consequence. However, a small amount of risk is permissible in practice, and, thus, our goal is to balance the value of data published and the risk of an adversary discovering leaked sensitive information. We model data sanitization as a game between 1) a publisher who chooses a set of classifiers to apply to data and publishes only instances predicted to be non-sensitive and 2) an attacker who combines machine learning and manual inspection to uncover leaked sensitive entities (e.g., personal names). We introduce an iterative greedy algorithm for the publisher that provably executes no more than a linear number of iterations, and ensures a low utility for a resource-limited adversary. Moreover, using several real world natural language corpora, we illustrate that our greedy algorithm leaves virtually no automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93% of the original data, and completes after at most 5 iterations.

关键词： Yttrium Data models Inspection Publishing Predictive models Natural languages Manuals

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Dynamic sparse state estimation using ℓ1-ℓ1 minimization: Adaptive-rate measurement bounds, algorithms and applications

Dynamic sparse state estimation using ℓ1-ℓ1 minimization: ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： João Mota Nikos Deligiannis Aswin C. Sankaranarayanan Volkan Cevher Miguel Rodrigues Electronic and Electrical Engineering Dept. University College London UK Department of Electronics and Informatics Vrije Universiteit Brussel Brussels Belgium Electrical and Computer Engineering Dept. Carnegie Mellon University USA Laboratory for Information and Inference Systems (LIONS) EPFL Switzerland

We propose a recursive algorithm for estimating time-varying signals from a few linear measurements. The signals are assumed sparse, with unknown support, and are described by a dynamical model. In each iteration, the algorithm solves an ℓ 1 -ℓ 1 minimization problem and estimates the number of measurements that it has to take at the next iteration. These estimates are computed based on recent theoretical results for ℓ 1 -ℓ 1 minimization. We also provide sufficient conditions for perfect signal reconstruction at each time instant as a function of an algorithm parameter. The algorithm exhibits high performance in compressive tracking on a real video sequence, as shown in our experimental results.

关键词： Minimization Compressed sensing Kalman filters Heuristic algorithms Noise measurement Standards Indexes

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Mobility-aware QoS assurance in software-defined radio access networks: An analytical study

Mobility-aware QoS assurance in software-defined radio acces...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Vassilios G. Vassilakis Ioannis D. Moscholios Andreas Bontozoglou Michael D. Logothetis Computer Laboratory University of Cambridge Cambridge U.K. Dept. of Informatics & Telecommunications University of Peloponnese Tripolis Greece School of Computer Science and Electronic Engineering University of Essex Colchester U.K. Dept. of Electrical & Computer Engineering University of Patras Patras Greece

Software-defined networking (SDN) has gained a tremendous attention in the recent years, both in academia and industry. This revolutionary networking paradigm is an attempt to bring the advances in computer science and software engineering into the information and communications technology (ICT) domain. The aim of these efforts is to pave the way for completely programmable networks and control-data plane separation. Recent studies on feasibility and applicability of SDN concepts in cellular networks show very promising results and this trend will most likely continue in near future. In this work, we study the benefits of SDN on the radio resource management (RRM) of future-generation cellular networks. Our considered cellular network architecture is in line with the recently proposed Long-Term Evolution (LTE) Release 12 concepts, such as control-data plane split, heterogeneous networks (HetNets) environment, and network densification through deployment of small cells. In particular, the aim of our RRM scheme is to enable the macro base station (BS) to efficiently allocate radio resources for small cell BSs in order to assure quality-of-service (QoS) of moving users/vehicles during handoffs. We develop an approximate, but very time- and space-efficient algorithm for radio resource allocation within a HetNet. Experiments on commodity hardware show algorithm running times in the order of a few seconds, thus making it suitable even in cases of fast moving users/vehicles. We also confirm a good accuracy of our proposed algorithm by means of computer simulations.

关键词： Probability Radio access networks Quality of service Resource management Accuracy Bit rate Long Term Evolution

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