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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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Building extraction from panchromatic high-resolution remotely sensed imagery based on potential histogram and neighborhood Total variation

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Earth Science Informatics 2016年第4期9卷 497-509页

作者： Wenzao Shi Zhengyuan Mao Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Normal University Fuzhou China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou China

In order to extract buildings using only gray information, this article proposed an approach for recognizing and extracting buildings from panchromatic high-resolution remotely sensed imagery based on shadows and segmentation. First, shadows were detected by potential histogram function. Second, the value of neighborhood total variation for each pixel was calculated, and then binarization and annotation were implemented to generate lable regions whose centroids were used as the seeds of the region growing segmentation, candidate buildings were selected from the segmentation result with the constraint of aspect ratio and rectangularity. At last, shadows were processed with open, dilate and corrode operations respectively, buildings were extracted by computing the adjacency relationship of the processed shadows and candidate buildings, and the building boundaries were fitted with the minimum enclosing rectangle. For verifying the validity of the proposed method, eighteen representative sub-images were chosen from PLEIADES images covering Shenzhen, China. Experimental results show that the average precision and recall of the proposed method are 97.95 % and 79.40 % for the object-based evaluation, and are 98.75 % and 83.16 % for the area-based evaluation respectively, and it has more 10 % and 6 % increase in the overall performance for above two evaluation criterion comparing with two other similar methods.

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Towards structured semantic embedding of multimedia 15

Towards structured semantic embedding of multimedia

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7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015

作者： Li, Xirong Huo, Yujia Lan, Weiyu He, Xixi Liao, Shuai Key Lab of Data Engineering and Knowledge Engineering Renmin University of China 100872 China Multimedia Computing Lab School of Information Renmin University of China 100872 China

ISBN: (纸本)9781450335287

This abstract paper sketches our research towards Struc-tured Semantic Embedding of multimedia data. Though a tag may have multiple senses with completely different visual imagery, current semantic embedding methods represent the tag by a single vector regardless of its senses. We challenge this convention, arguing the importance of adding seman-tic structures into semantic embedding. In particular, we develop Hierarchical Semantic Embedding, a simple model that exploits the WordNet hierarchy to make the semantic embedding structured to some extent. We demonstrate the viability of structured semantic embedding for tag disam-biguation and zero-shot image tagging. © 2015 ACM.

关键词： Zero-shot learning

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Knowledge graph embedding translation based on constraints

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Journal of Information Hiding and Multimedia Signal Processing 2017年第5期8卷 1119-1131页

作者： Wu, Yunbing Pan, Jengshyang Lu, Ping Lin, Kaibiao Yu, Xiaoyan College of Mathematics and Computer Science Fuzhou University No.2. Xueyuan Road University Town Minhou Fuzhou CityFujian Province350116 China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology No.3. Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China Department of Business Xiamen University of Technology No.600 Ligong Road Jimei District XiamenFujian Province361024 China School of Computer and Information Engineering Xiamen University of Technology No.600 Ligong Road Jimei District XiamenFujian Province361024 China

Knowledge graph reasoning is discovering new entity relations by computing and inference from existing relations. However, most reasoning models of translation embedding-based knowledge graphs have not considered the semantic-type constraints of relations in the construction of corrupted triplets. Hence, the constructed corrupted triplets may not conform to the actual semantic information and may, thus, significantly affect the prediction accuracy of the model. Therefore, we propose a constraint-based embedding model in this paper. First, the model establishes the head and tail entity set for each relationship. Then, it ensures that both the replacing head and tail entities in the corrupted triplet belong to the respective entity set so that the corrupted triplets that do not conform to the responding semantic relations are excluded. To evaluate the proposed model, we conduct link prediction and triple classification on WordNet and Freebase databases. The experimental results show that our method remarkably improves the performance compared to several state-of-the-art baselines. © 2017, Ubiquitous International. All rights reserved.

关键词： Classification (of information)

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ACID encountering the CAP theorem: Two bank case studies 12

ACID encountering the CAP theorem: Two bank case studies

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12th Web Information System and Application Conference, WISA 2015

作者： Kong, Chao Gao, Ming Qian, Weining Zhou, Minqi Gong, Xueqing Zhang, Rong Zhou, Aoying Institute for Data Science and Engineering and Shanghai Key Lab for Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9781467393720

In the era of big data, we may adopt the distributed architecture for a transaction processing system due to some reasons, including distributed branches, heavy demand and operational expenditure, etc. In terms of the CAP Theorem, a transaction processing system associated with ACID properties is infeasible to work well in the distributed architecture. It is indispensable to address how to make a trade-off between availability and partition tolerance for a bank as it favors the consistency in the distributed system. In this research, we conduct two case studies to address the question using two transaction logs collected from a bank in China. We mainly analyze the table dependency and the table concurrency, and find that (1) it is arduous to partition the data in the database system associated with ACID properties, (2) in-memory architecture for updating transactions may be an alternative for building a transaction processing system. © 2015 IEEE.

关键词： Memory architecture

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Worldwide Soundscapes: A Synthesis of Passive Acoustic Monitoring Across Realms

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Global Ecology and Biogeography 2025年第5期34卷

作者： Kevin F. A. Darras Rodney A. Rountree Steven L. Van Wilgenburg Anna F. Cord Frederik Pitz Youfang Chen Lijun Dong Agnès Rocquencourt Camille Desjonquères Patrick Mauritz Diaz Tzu-Hao Lin Théophile Turco Louise Emmerson Tom Bradfer-Lawrence Amandine Gasc Sarah Marley Marcus Salton Laura Schillé Paul J. Wensveen Shih-Hung Wu Adriana C. Acero-Murcia Orlando Acevedo-Charry Matyáš Adam Jacopo Aguzzi Irmak Akoglu M. Clara P. Amorim Mina Anders Michel André Alexandre Antonelli Leandro Aparecido Do Nascimento Giulliana Appel Stephanie Archer Christos Astaras Andrey Atemasov Jamieson Atkinson Joël Attia Emanuel Baltag Luc Barbaro Fritjof Basan Carly Batist Julio Ernesto Baumgarten Just T. Bayle Sempere Kristen Bellisario Asaf Ben David Oded Berger-Tal Frédéric Bertucci Matthew G. Betts Iqbal S. Bhalla Thiago Bicudo Marta Bolgan Sara Bombaci Gerard Bota Martin Boullhesen Robert A. Briers Susannah Buchan Michal Budka Katie Burchard Giuseppa Buscaino Alice Calvente Marconi Campos-Cerqueira Maria Isabel Carvalho Gonçalves Maria Ceraulo Maite Cerezo-Araujo Gunnar Cerwén Adams A. Chaskda Maria Chistopolova Christopher W. Clark Kieran D. Cox Benjamin Cretois Chapin Czarnecki Luis P. da Silva Wigna da Silva Laurence H. De Clippele David de la Haye Ana Silvia de Oliveira Tissiani Devin de Zwaan M. Eugenia Degano Jessica Deichmann Joaquin del Rio Christian Devenish Ricardo Díaz-Delgado Pedro Diniz Dorgival Diógenes Oliveira-Júnior Thiago Dorigo Saskia Dröge Marina Duarte Adam Duarte Kerry Dunleavy Robert Dziak Simon Elise Hiroto Enari Haruka S. Enari Florence Erbs Britas Klemens Eriksson Pınar Ertör-Akyazi Nina C. Ferrari Luane Ferreira Abram B. Fleishman Paulo J. Fonseca Bárbara Freitas Nicholas R. Friedman Jérémy S. P. Froidevaux Svetlana Gogoleva Carolina Gonzaga José Miguel González Correa Eben Goodale Benjamin Gottesman Ingo Grass Jack Greenhalgh Jocelyn Gregoire Samuel Haché Jonas Hagge William Halliday Antonia Hammer Tara Hanf-Dressler Sylvain Haupert Samara Haver Becky Heath Daniel Hending Jose Hernandez-Blanco Dennis Higgs EFNO ECODIV INRAE Domaine des Barres Nogent-sur-Vernisson France Sustainable Agricultural Systems & Engineering Lab School of Engineering Westlake University Hangzhou Zhejiang China Chair of Computational Landscape Ecology Faculty of Environmental Sciences Dresden University of Technology Dresden Germany Biology Department University of Victoria Victoria British Columbia Canada The Fish Listener Waguoit Massachusetts USA Terrestrial Unit Prairie Region Canadian Wildlife Service Environment & Climate Change Canada Prairie & Northern Wildlife Research Centre Saskatoon Saskatchewan Canada Agro-ecological Modeling Group Faculty of Agriculture University of Bonn Bonn Germany Marine Mammal and Marine Bioacoustics Laboratory Department of Deep Sea Science Institute of Deep-sea Science and Engineering Chinese Academy of Sciences Sanya Hainan China Institut de Systématique Évolution Biodiversité Muséum National d’Histoire Naturelle Paris France Université Grenoble Alpes Université Savoie Mont Blanc CNRS LECA Grenoble France Climate Change and Biodiversity Hanns R. Neumann Stiftung Indonesia Muaradua Indonesia Biodiversity Research Center Academia Sinica Nankang Taipei Taiwan ENES Bioacoustics Research Laboratory University of Saint-Etienne CRNL CNRS UMR 5292 Inserm UMR_S 1028 Saint-Etienne France Australian Antarctic Division Science Branch Department of Climate Change Energy Environment and Water Channel Highway Kingston Tasmania Australia Biological and Environmental Sciences University of Stirling Stirling Scotland Centre for Conservation Science RSPB Edinburgh UK IMBE Aix Marseille Univ Avignon Univ CNRS IRD Aix-en-Provence France Scotland's Rural College Craibstone Estate Aberdeen UK BIOGECO INRAE University of Bordeaux Cestas France Westman Islands Research Centre University of Iceland Vestmannaeyjar Iceland Qigu Research Center Taiwan Biodiversity Research Institute Nantou County Taiwan Programa de Pós-graduação em Ecologia

Aim The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean). Location Worldwide, 12,343 sites, all ecosystem types. Time Period 1991 to present. Major Taxa Studied All soniferous taxa. Methods We synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms. Results Terrestrial sampling is spatially denser (46 sites per million square kilometre—Mkm 2 ) than aquatic sampling (0.3 and 1.8 sites/Mkm 2 in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds. Main Conclusions PAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio-temporal replication in freshwater habitats. Overall, this

关键词： ARU automated sound recorder biodiversity conservation biology ecoacoustics IUCN GET realm Passive Acoustic Monitoring phenology soundscape ecology

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IABC robotic evolutionary model for the foreign exchange rate prediction in Central America trading agreement events

IABC robotic evolutionary model for the foreign exchange rat...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Pei-Wei Tsai Jing Zhang Yao He Jui-Fang Chang Li-Hui Yang Wein-Duo Yang College of Information Science and Engineering Fujian Provincial Key Lab. of Big Data Mining & Apps. Fujian University of Technology Fuzhou China Department of International Business National Kaohsiung University of Applied Science Kaohsiung Taiwan

ISBN: (纸本)9781509042418

Taiwanese economy is extremely export-oriented. However, Taiwan also starts to actively look for opportunities for participation, because of the slowdown of multinational liberalizations, and as a result of the advance of the regional economic integration trend. As an example, Taiwan vigorously participates in FTAs with countries in Asia and Central America. International economic conditions are significantly important to the international trade and the volume of export/import volume, which is affected by the foreign exchange rate. If contemporary researchers could take full advantage of the exchange rate forecasting, Taiwan could maximize its trade surplus, thus boosting the economic growth. Conventional foreign exchange rate forecasting is usually provided by analyzing many financial indices or with the time-series method. Our goal is to produce the foreign exchange rate forecasting result by the robotic way with an evolutionary computing method called Interactive Artificial Bee Colony algorithm. Based on the event study methodology, the selected agreements include four FTA that are ECFA, BIA, ASTEP and ANZTEC, and the observation period setting is 70 days of pre-event period and 70 days of post-event period. This paper uses time series model (GARCH, EGARCH) and Interactive artificial bee colony (IABC) to establish the exchange rate predicting models. In addition, we adopt Mean Absolutely Percentage Error (MAPE) to compare the accuracy of exchange rate prediction. There are many exchange rate predicting models and the most frequently one to conduct maybe the time series model. This research reveals that even the IABC is relatively new it is the model has the best predictive ability among all the models.

关键词： Exchange rates Predictive models Forecasting Mathematical model Time series analysis

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Contourlet and nearest feature line based feature extraction approach for one prototype sample problem

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Journal of Information Hiding and Multimedia Signal Processing 2016年第5期7卷 1052-1059页

作者： Pan, Jeng-Shyang Yan, Lijun Chu, Shu-Chuan Roddick, John F. Fujian University of International Studies and Trade Fujian Province China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology China Shenzhen Institute of Information Technology Shenzhen China School of Computer Science Engineering and Mathematics Flinders University Australia

In this paper, a novel algorithm for face recognition with one sample per person is proposed. The proposed algorithm is based on contourlet transformation. For simple prototype sample problem, many discriminant learning methods can not work. Because for most discriminant learning methods, the within class scatter of the prototype samples are very important. However, simple prototype sample problem does not have within class scatter. To enhance the representative capability of the prototype samples set, some new samples are generated using contourlet transformation. Multiple prototype samples for each class are constructed through the decomposition and reconstruction of original training images by contourlet transformation. Thus neighborhood discriminant nearest feature line analysis can be performed on the new database. The experimental results demonstrate the efficiency of the proposed algorithm. © 2016, Ubiquitous International. All Rights Reserved.

关键词： Image classification

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Speech emotion recognition with acoustic and lexical features 40

Speech emotion recognition with acoustic and lexical feature...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Jin, Qin Li, Chengxin Chen, Shizhe Wu, Huimin Computer Science Department School of Information Renmin University of China Beijing China Key Lab of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing China

ISBN: (纸本)9781467369978

In this paper we explore one of the key aspects in building an emotion recognition system: generating suitable feature representations. We generate feature representations from both acoustic and lexical levels. At the acoustic level, we first extract low-level features such as intensity, F0, jitter, shimmer and spectral contours etc. We then generate different acoustic feature representations based on these low-level features, including statistics over these features, a new representation derived from a set of low-level acoustic codewords, and a new representation from Gaussian Supervectors. At the lexical level, we propose a new feature representation named emotion vector (eVector). We also use the traditional Bag-of-Words (BoW) feature. We apply these feature representations for emotion recognition and compare their performance on the USC-IEMOCAP database. We also combine these different feature representations via early fusion and late fusion. Our experimental results show that late fusion of both acoustic and lexical features achieves four-class emotion recognition accuracy of 69.2%. © 2015 IEEE.

关键词： Acoustic features Emotion lexicon Emotion recognition Lexical features Support vector machine

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Promoting Business Analytics and Quantitative Management of Technology: Preface for ITQM 2016

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Procedia Computer Science 2016年 91卷 1-8页

作者： Lee, Heeseok Shi, Yong Lee, Jongwon Cordova, Felisa Dzitac, Ioan Kou, Gang Li, Jaiping College of Business Korea Advanced Institute Science and Technology Dongdaemum-gu Seoul130-722 Korea Republic of Research Center on Fictitiotus Economy and Data Science Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Business Administration Hoseo University Asan-si Chungcheongnam-do336-795 Korea Republic of Faculty of Engineering and Business University Finis Terrae Pedro de Valdivia 1509. Providencia Santiago Chile Universitatea Agora Agora University Piata Tineretului 8 Oradea410526 Romania School of Business Administration Southwestern University of Finance and Economics Chengdu Sichuan61130 China Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China

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