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International Conference on Awareness Science and Technology (iCAST)

作者： Fatma S. Abousaleh Neng-Hao Yu Kai-Lung Hua Wen-Huang Cheng Social Networks and Human Centered Computing Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Computer Science National Chengchi University Taipei Taiwan Department of Computer Science and Information Engineering Taiwan University of Science and Technology Taipei Taiwan

Image noise reduction, or denoising, became an attractive research domain in the recent years. There are several published approaches in the image denoising field and each one has its presumptions, advantages, and limitations. In this paper, we address the problem of eliminating or even decreasing the noise from medical images. Some schemes for image noise reduction are proposed. The schemes include DCT_OMP, DCT_BOMP, Log Gabor_BOMP, DCT_OCMP and Wavelet_OMP. In these schemes, the image content is represented as a sparse linear combination of a set of atoms that can be obtained from some trained dictionaries. In literature, several algorithms are proposed to build up these kinds of dictionaries. In our study, the K-SVD algorithm is utilized to obtain a dictionary that can effectively describe the image. Also, some greedy algorithms are used to perform the sparse-coding of the signal. The proposed schemes have been tested on two different medical images with different levels of an additive noise. The Log Gabor_BOMP scheme showed a significant outperformance, in both the de-noising results and computational time, on all the other schemes.

关键词： Matching pursuit algorithms Dictionaries Approximation algorithms Image denoising Noise reduction Discrete cosine transforms Algorithm design and analysis

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Influence Value: Quantifying Topic Influence on social Media 2

Influence Value: Quantifying Topic Influence on Social Media

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2nd IEEE International Conference on Multimedia Big Data (BigMM)

作者： Calderon, Fernando H. Chen, Yi-Shin Natl Tsing Hua Univ Inst Informat Syst & Applicat Hsinchu 30013 Taiwan Acad Sinica Taiwan Int Grad Program Social Networks & Human Centered Comp Taipei 115 Taiwan

ISBN: (纸本)9781509021796

social Media specifically Micro-blogging sites have become very rich data repositories. However the generated data is dynamic by nature, tied to temporal conditions and the subjectivity of its users. Everyday life experiences, discussions or events have a direct impact on the behaviors reflected in social networks. It is therefore of great importance to asses the impact these interactions are having over a social group. An alternative to answer this is determining how influential a topic is according to the behavior presented on a social network over time. It is then necessary to find and develop methods that can contribute towards this task. Having identified a topic in social media we can first classify it as time specific or long term, then posts relevant to the topic are collected and each one is assigned an emotion label. We then propose an Influence Value score which will be given to each topic based on its lifespan, emotion transition and reach. This lays ground to quantify how influential a topic is over a social group, specifically from events detected on twitter.

关键词： Emotion detection Influence social media

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Massive Machine Type Communication in Cellular System: A Distributed Queue Approach

Massive Machine Type Communication in Cellular System: A Dis...

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IEEE International Conference on Communications (ICC)

作者： Awuor, Fredrick Mzee Wang, Chih-Yu Acad Sinica Taiwan Int Grad Program Inst Informat Sci Social Networks & Human Centered Comp Program Taipei Taiwan Natl Chengchi Univ Dept Comp Sci Taipei Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan

ISBN: (纸本)9781479966646

Massive machine type communication (MTC) brings new challenges to next generation wide-area networks. Random Access Channel procedure (RACH) used in LTE-Advanced system for request delivery, for instance, is inefficient in handling massive number of concurrent access requests. The S-Aloha approach in RACH will lead to unsustainable delays and resource wastage due to collisions from concurrent access in massive MTC networks. To address this issue, we propose a distributed queue based MTC communication protocol that exploits multiple packet reception (MPR) capability of MTC devices to self-organize themselves into a logical queue in a distributed way such that the devices connect to BS depending on their positions in the queue. We derive the algorithm and present analysis of its performance in regard to delay and throughput. The algorithm behaves like S-Aloha when MTC traffic load is low and seamlessly and smoothly switches to a reservation based protocol during heavy traffic. From the simulation results, the proposed scheme exhibits better performance compared to conventional RACH procedure as it increases number of successful transmissions by 2.7 times and reduces both the average access delay and transmission failure probability by 20%.

关键词： Wide area networks

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Massive Machine Type Communication in Cellular System: A Distributed Queue Approach

Massive Machine Type Communication in Cellular System: A Dis...

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Ad-Hoc and Sensor networks Symposium

作者： Fredrick Mzee Awuor Chih-Yu Wang Taiwan International Graduate Program Social Networks and Human-Centered Computing Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica

ISBN: (纸本)9781479966653

关键词： Concurrent Access Queue Minesite Technical Committees cellular system S-ALOHA

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A novel comparative deep learning framework for facial age estimation

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2016年第1期2016卷 1页

作者： Abousaleh, Fatma S. Lim, Tekoing Cheng, Wen-Huang Yu, Neng-Hao Hossain, M. Anwar Alhamid, Mohammed F. Acad Sinica IIS Taiwan Int Grad Program Social Networks & Human Centered Comp Program Acad Rd Taipei 11529 Taiwan Acad Sinica Res Ctr Informat Technol Innovat CITI Acad Rd Taipei 11529 Taiwan Natl Chengchi Univ Dept Comp Sci ZhiNan Rd Taipei 11605 Taiwan King Saud Univ Dept Software Engn Coll Comp & Informat Sci Riyadh 11362 Saudi Arabia

Developing automatic facial age estimation algorithms that are comparable or even superior to the human ability in age estimation becomes an attractive yet challenging topic emerging in recent years. The conventional methods estimate one person's age directly from the given facial image. In contrast, motivated by human cognitive processes, we proposed a comparative deep learning framework, called Comparative Region Convolutional Neural Network (CRCNN), by first comparing the input face with reference faces of known age to generate a set of hints (comparative relations, i.e., the input face is younger or older than each reference). Then, an estimation stage aggregates all the hints to estimate the person's age. Our approach has several advantages: first, the age estimation task is split into several comparative stages, which is simpler than directly computing the person's age;secondly, in addition to the input face itself, side information (comparative relations) can be explicitly involved to benefit the estimation task;finally, few incorrect comparisons will not influence much the accuracy of the result, making this approach more robust than the conventional approach. To the best of our knowledge, the proposed approach is the first comparative deep learning framework for facial age estimation. Furthermore, we proposed to incorporate the Method of Auxiliary Coordinates (MAC) for training, which reduces the ill-conditioning problem of the deep network and affords an efficient and distributed optimization. In comparison to the best results from the state-of-the-art methods, the CRCNN showed a significant outperformance on all the benchmarks, with a relative improvement of 13.24% (on FG-NET), 23.20% (on MORPH), and 4.74% (IoG).

关键词： Deep learning Facial age estimation Region convolutional neural network Comparative framework

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Multilayer CPW-Fed Patch Antenna on New AMC Ground Plane for 60 GHz Millimeter-Wave Communications

Multilayer CPW-Fed Patch Antenna on New AMC Ground Plane for...

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IEEE Vehicular Technology Conference

作者： Imad Ali Ronald Y. Chang Jenny Yi-Chun Liu Taiwan International Graduate Program in Social Networks and Human-Centered Computing Institute of Information Science Academia Sinica Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering National Tsing Hua University

ISBN: (纸本)9781509016990

This paper presents a 60 GHz CPW-fed patch antenna on multilayer LTCC- and LCP-based materials with new and compact AMC. The proposed antenna structure consists of top three layers of antenna and bottom three layers of AMC ground plane. First, a new and compact three-layer AMC is designed on both LTCC- and LCP-based substrates. The designed AMC is more compact and provides 37.5% higher in-phase bandwidth as compared to a state-of-the-art design. Then, a three-layer CPW-fed patch antenna is designed and optimized on LTCC- and LCP-based substrates on top of the three-layer AMC ground plane of the same material. We perform a gain-bandwidth comparative study of antennas on multilayer LTCC- and LCP-based substrates with AMC/PEC ground planes for 60 GHz. We observe that antennas on LCP-based substrate outperform those on LTCC-based substrate in terms of input impedance, bandwidth, and gains on both AMC and PEC ground planes, while antennas on LTCC-based substrate have smaller antenna and AMC dimensions.

关键词： AMCN gene antecedent moisture condition (amc) Patch antennas ANTENNA Ground plane Planing antennae impedance Substrates plane

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Influence Value: Quantifying Topic Influence on social Media

Influence Value: Quantifying Topic Influence on Social Media

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Fernando H. Calderon Yi-Shin Chen Taiwan International Graduate Program Social Networks and Human-Centered Computing Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781509021802

关键词： Media social groups Twitter Wheels Measurement Big data

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Distributed keyword vector representation for document categorization

Distributed keyword vector representation for document categ...

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Yu-Lun Hsieh Shih-Hung Liu Yung-Chun Chang Wen-Lian Hsu Department of Computer Science National Chengchi University Taiwan Social Networks and Human-Centered Computing Program Academia Sinica Taiwan Institute of Information Science Academia Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Departmem of Information Management National Taiwan University Taiwan

ISBN: (纸本)9781467396073

In the age of information explosion, efficiently categorizing the topic of a document can assist our organization and comprehension of the vast amount of text. In this paper, we propose a novel approach, named DKV, for document categorization using distributed real-valued vector representation of keywords learned from neural networks. Such a representation can project rich context information (or embedding) into the vector space, and subsequently be used to infer similarity measures among words, sentences, and even documents. Using a Chinese news corpus containing over 100,000 articles and five topics, we provide a comprehensive performance evaluation to demonstrate that by exploiting the keyword embeddings, DKV paired with support vector machines can effectively categorize a document into the predefined topics. Results demonstrate that our method can achieve the best performances compared to several other approaches.

关键词： Computational modeling Manuals

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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