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15th International Conference on Computer Science and education, ICCSE 2020

作者： Zhang, Rui Mestre, Pedro Serodio, Carlos Prada, Marilia Gao, Wenjuan Jilin University Key Lab of Symbolic Computation and Knowledge Engineering Chinese Ministry of Education College of Computer Science and Technology Changchun130012 China University of Minho Centro Algoritmi Guimaraes4800-058 Portugal Department of Social and Organizational Psychology Lisboa Portugal Library of Changchun University Changchun130022 China

ISBN: (纸本)9781728172675

In a increasingly globalized world, it is very important to help students to acquire the hard and soft skills needed to cope with the challenges they will face when working in such environments. However, even though students find this subject interesting and volunteer for it, it is hard, even impossible to teach college students such a distributed fashion of teamwork. In this paper, a case study is provided to illustrate the methodology and experiences of a joint course, distributed software development, of multiple universities from Asia, Europe, North and South Americas. The results show that student can get the spirit, effective communication for such web-based teamwork environment and achieve software project development. The recent developments because of the Covid-19 pandemic are the facts that now students have to cooperate remotely with each other not only across continents but also inside their own city or country. It has increased the awareness of the need for this kind of training and effectively improved the interactions between team elements in different countries and improved the students' feeling of belonging to their team. © 2020 IEEE.

关键词： Students

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Integrity constraint validation for ontology using modularization

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Journal of Information and computational Science 2014年第8期11卷 2705-2713页

作者： Cui, Xianji Ouyang, Dantong Ye, Yuxin Su, Jing College of Computer Science and Technology Jilin University Changchun 130012 China Key Lab. of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun 130012 China National Engineering Research Center of Geophysics Exploration Instruments Jilin University Changchun 130026 China

In the last years, integrity constraint validation over the ontology with expressive description logics is a prominent reasoning service in ontology engineering, as integrity constraints are added into the ontology to guarantee the legal states of ontology data. The integrity constraint validation can be converted into the conjunctive query answering, we thus check the satisfaction of integrity constraints in ontology knowledge base based on the tableau algorithm and further use the existing reasoning systems. With the increase of the ontology data set, however, these reasoning systems reveal the bad performance in real-life ontologies. In order to reduce the amount of information that must be taken into account, we apply the idea of modularization to break down the terminologies and assertions with respect to integrity constraints, and get smaller chunks for them. The empirical results are presented to demonstrate the high efficiency and an improvement of integrity constraint validation. Copyright © 2014 Binary Information Press.

关键词： Ontology

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Attention-based multi-level feature fusion for named entity recognition 29

Attention-based multi-level feature fusion for named entity ...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Yang, Zhiwei Chen, Hechang Zhang, Jiawei Ma, Jing Chang, Yi College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China IFM Lab Department of Computer Science Florida State University TallahasseeFL United States Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China International Center of Future Science Jilin University Changchun China

ISBN: (纸本)9780999241165

Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence lab.ling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Embeddings

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Heuristic black-box adversarial attacks on video recognition models

arXiv

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arXiv 2019年

作者： Wei, Zhipeng Chen, Jingjing Wei, Xingxing Jiang, Linxi Chua, Tat-Seng Zhou, Fengfeng Jiang, Yu-Gang Jilin University Fudan University Beihang University National University of Singapore Health Informatics Lab College of Computer Science and Technology Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin130012 China

We study the problem of attacking video recognition models in the black-box setting, where the model information is unknown and the adversary can only make queries to detect the predicted top-1 class and its probability. Compared with the black-box attack on images, attacking videos is more challenging as the computation cost for searching the adversarial perturbations on a video is much higher due to its high dimensionality. To overcome this challenge, we propose a heuristic black-box attack model that generates adversarial perturbations only on the selected frames and regions. More specifically, a heuristic-based algorithm is proposed to measure the importance of each frame in the video towards generating the adversarial examples. Based on the frames’ importance, the proposed algorithm heuristically searches a subset of frames where the generated adversarial example has strong adversarial attack ability while keeps the perturbations lower than the given bound. Besides, to further boost the attack efficiency, we propose to generate the perturbations only on the salient regions of the selected frames. In this way, the generated perturbations are sparse in both temporal and spatial domains. Experimental results of attacking two mainstream video recognition methods on the UCF-101 dataset and the HMDB-51 dataset demonstrate that the proposed heuristic black-box adversarial attack method can significantly reduce the computation cost and lead to more than 28% reduction in query numbers for the untargeted attack on both datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Heuristic methods

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Pathological Brain Detection Based on Online Sequential Extreme Learning Machine

Pathological Brain Detection Based on Online Sequential Extr...

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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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Game Theoretic Approaches in Vehicular Networks: A Survey

arXiv

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arXiv 2020年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Anil, Carie Cao, Dongpu College of Computer Science and Technology Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Engineering VIT-AP AmaravatiAndhra Pradesh522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

—In the era of the Internet of Things (IoT), vehicles and other intelligent components in Intelligent Transportation System (ITS) are connected, forming the Vehicular Networks (VNs) that provide efficient and secure traffic, ubiquitous access to information, and various applications. However, as the number of connected nodes keeps increasing, it is challenging to satisfy various and large amounts of service requests with different Quality of Service (QoS) and security requirements in the highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources so that either an individual or a group objectives can be achieved. Game theory, a theoretical framework designed for strategic interactions among rational decision-makers who faced with scarce resources, can be used to model and analyze individual or group behaviors of communication entities in VNs. This paper primarily surveys the recent advantages of GT used in solving various challenges in VNs. As VNs and GT have been extensively investigate34d, this survey starts with a brief introduction of the basic concept and classification of GT used in VNs. Then, a comprehensive review of applications of GT in VNs is presented, which primarily covers the aspects of QoS and security. Moreover, with the development of fifth-generation (5G) wireless communication, recent contributions of GT to diverse emerging technologies of 5G integrated into VNs are surveyed in this paper. Finally, several key research challenges and possible solutions for applying GT in VNs are outlined. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

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 controls. The 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： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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Web-based Teamwork: Distributed Software Development Course under Covid-19

Web-based Teamwork: Distributed Software Development Course ...

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International Conference on Computer Science & education (ICCSE)

作者： Rui ZHANG Pedro Mestre Carlos Serodio Marília Prada Wenjuan GAO College of Computer Science and Technology Jilin University Key Lab of Symbolic Computation and Knowledge Engineering Chinese Ministry of Education Changchun China Centro Algoritmi University of Minho Guimaraes Portugal CITAB University of Tras-os-Montes and Alto Douro Vila Real Portugal Department of Social and Organizational Psychology Instituto Universitário de Lisboa (ISCTE-IUL) Lisboa Portugal Library of Changchun University Changchun China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

关键词： Software Software engineering Games Faces Teamwork Electronic mail Tools

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Biogeography-based optimization for cluster analysis

Biogeography-based optimization for cluster analysis

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International Conference on Computer, Communication and computational Sciences, ICCCCS 2016

作者： Wu, Xueyan Wang, Hainan Chen, Zhimin Lu, Zhihai Phillips, Preetha Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Nanjing Normal University NanjingJiangsu210023 China Key Laboratory of Statistical Information Technology & Data Mining State Statistics Bureau ChengduSichuan610225 China School of Computer and Information Engineering Henan Normal University XinxiangHenan453000 China School of Electronic Information Shanghai Dianji University Shanghai200240 China Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology GuilinGuangxi541004 China School of Natural Sciences and Mathematics Shepherd University ShepherdstownWV25443 United States West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Department of Electrical Engineering The City College of New York CUNY New YorkNY10031 United States State Key Lab of CAD & CG Zhejiang University HangzhouZhejiang310027 China Manchester Metropolitan University ManchesterM156BH United Kingdom

ISBN: (纸本)9789811037696

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Cluster analysis

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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