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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yanhong Liu Hui Lv Changjun Zhou Zhixiang Yin Xianwen Fang Zhonglong Zheng Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China School of Mathematics and Big Data Anhui University of Science and Technology Huaian China College of Mathematics Zhejiang Normal University Jinhua China Department of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781538693810;9781538693803

In this paper, a nonlinear system with time delay mode for DNA strand displacement based on toehold exchange firstly is proposed. The boundedness of all the state variables of the DNA strand displacement system is proved. After that, local stability analysis is performed around the positive equilibrium point to obtain a necessary and sufficient condition for local stability, which depend on the coefficients of the characteristic equation corresponding to linearization of the system. In addition, the existence of the Hopf bifurcation is analyzed, which shows that the time delay parameter will guarantee that the system is stable within a certain range. However, Hopf bifurcation and chaos occur when the time delay parameter increases continuously and even exceeds the critical value. At last, numerical simulations have verified the correctness and validity of the DNA strand displacement studies.

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Comments on cut-set bounds on network function computation

Comments on cut-set bounds on network function computation

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作者： Huang, Cupjin Tan, Zihan Yang, Shenghao Guang, Xuan Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Computer Science University of Chicago ChicagoIL60637 United States School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China School of Mathematical Sciences and LPMC Nankai University Tianjin China Institute of Network Coding Chinese University of Hong Kong Hong Kong Hong Kong

A function computation problem over a directed acyclic network has been considered in the literature, where a sink node is required to compute a target function correctly with the inputs arbitrarily generated at multiple source nodes. The network links are error free but capacity limited, and the intermediate nodes perform network coding. The computing rate of a network code is the average number of times that the target function is computed for one use of the network, i.e., each link in the network is used at most once. In the existing papers, two cut-set bounds were proposed on the computing rate. However, we in this paper show that these bounds are not valid for general network function computation problems. We analyze the reason of the invalidity and propose a general cut-set bound by using a new equivalence relation associated with the inputs of the target function. Moreover, some results in the existing papers were proved by applying the invalid upper bound. We also justify the validity of these results. © 1963-2012 IEEE.

关键词： Network coding

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A relation prediction method based on PU learning

A relation prediction method based on PU learning

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Gao-Jing Peng Ke-Jia Chen Shijun Xue Bin Liu Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu China

This paper studies relation prediction in heterogeneous information networks under PU learning context. One of the challenges of this problem is the imbalance of data number between the positive set P (the set of node pairs with the target relation) and the unlabeled set U (the set of node pairs without the target relation). We propose a K-means and voting mechanism based technique SemiPUclus to extract the reliable negative set RN from U under a new relation prediction framework PURP. The experimental results show that PURP achieves better performance than comparative methods in DBLP co-authorship network data.

关键词： Reliability Artificial neural networks Clustering algorithms Algorithm design and analysis Prediction methods Sun

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ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

arXiv

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous Information. All rights reserved.

关键词： Semantics

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Acoustic and Kinematic Examination of Dysarthria in Cantonese Patients of Parkinson’s Disease

Acoustic and Kinematic Examination of Dysarthria in Cantones...

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International Symposium on Chinese Spoken Language Processing

作者： Yue Sun Manwa L. Ng Chongyuan Lian Lan Wang Feng Yang Nan Yan College of Big Data and Information Engineering Guizhou University Guiyang China Speech Science Laboratory University of Hong Kong Hong Kong China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Speech Therapy Shenzhen Children’s Hospital Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Instimtes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781538656280;9781538656273

Hypokinetic dysarthria is one of the core symptoms of Parkinson's disease, characterized by reduced loudness, slurred speech and distorted consonant productions. Dopaminergic medication for Parkinson's disease (PD) has been proven to be effective in treating limb and gross motor movement problems. However, the literature sees contradictory findings regarding dopaminergic effects on speech problems associated with PD. Previous perceptual and acoustic studies of PD hypokinetic dysarthria mostly involved heterogeneous population and variations of speech tasks which complicated data interpretation. Also, the lack of kinematic data limited our understanding of the details of articulation errors associated with PD, as well as the medication effects. Electromagnetic articulography (EMA) enabled the examination of PD articulatory patterns with high degree of accuracy and safety. The aim of the present study was to address these inconsistencies by providing an integrative description of basic kinematic and acoustic parameters of speech production about the dopaminergic effect on early PD speech by using EMA. The results revealed a significant improvement of articulatory function on dopaminergic effects for PD speech, evidenced by increased vowel space, the velocity and accelerated velocity initiation and coordination of articulation during bilabial or alveolar productions.

关键词： Kinematics Acoustics Auditory system Sun Parameter extraction Schedules Lips

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Itrace: An implicit trust inference method for trust-aware collaborative filtering

arXiv

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arXiv 2017年

作者： He, Xu Liu, Bin Chen, Ke-Jia School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithmrecommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and may lead to misleading recommendations in practice. A natural countermeasure is to design a trust-aware CF algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improvedCF algorithmtermed iTrace is proposed, which employs both the explicit and the predicted implicit trust to provide recommendations. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error. Copyright © 2017, The Authors. All rights reserved.

关键词： Collaborative filtering

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Linked data Crowdsourcing Quality Assessment based on Domain Professionalism

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Lu Yang Li Huang Zhenzhen Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

With the rapid development of Internet technology, crowdsourcing, as a flexible, effective and low-cost problem-solving method, has begun to receive more and more attention. The use of crowdsourcing to evaluate the quality of linked data has also become a research hotspot. This paper proposes the concept of Domain Specialization Test (DST), which uses domain professional testing tasks DSTs to evaluate the professionalism of workers, and combines the idea of Mini-batch Gradient Descent (MBGD) to improve the EM algorithm, and the MBEM algorithm is proposed to achieve efficient and accurate evaluation of task results. The experimental results show that the proposed method can screen out the appropriate workers for the linked data crowdsourcing task and improve the accuracy and iteration efficiency of the results.

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Emergency Event Matching using Hierarchical Blocking Method

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Chang Wen Yu Liu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

With the extensive application of the knowledge base (KB), how to complete it is a hot topic on Semantic Web. However, many problems go with the big data, and the event matching is one of these problems, which is finding out the entities referring to the same things in the real world and also the key point in the extending process. To enrich the emergency knowledge base (E-SKB) we constructed before, we need to filter out the news from several web pages and find the same news to avoid data redundancy. In this paper, we proposed a hierarchy blocking method to reduce the times of comparisons and narrow down the scope by extracting the news properties as the blocking keys. The method transforms the event matching problem into a clustering problem. Experimental results show that the proposed method is superior to the existing text clustering algorithm with high precision and less comparison times.

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On link formation in heterogeneous information networks: A view based on multi-Label learning 9

On link formation in heterogeneous information networks: A v...

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9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

作者： Chen, Ke-Jia Xue, Shijun Li, Yun Liu, Bin Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China Center for the Neural Basis of Cognition Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781450349932

This paper studies the problem of relationship prediction in heterogeneous information networks. Our goal is not only to predict links/relationships more accurately but also to provide more viable paths to facilitate the formation of new links/relationships. A relationship prediction method based on multi-label learning named ML3P is proposed. In ML3P, each meta-path between nodes is regarded as a type of relationship and is given a label. Under the framework of multi-label learning, any potential relationship including the target relationship can be predicted. The results of comparative experiments in DBLP and Twitter datasets show that ML3P better uses heterogeneous information in supervised learning process and thus achieves better performance. Moreover, our method can output the correlation between relationships. © 2017 Association for Computing Machinery.

关键词： Forecasting

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