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6th Joint International Conference on Semantic Technology, JIST 2016

作者： Zhang, Xiang Lv, Yulian School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Data Engineering and Knowledge Services Nanjing University Nanjing China Southeast University Suzhou China

ISBN: (纸本)9783319501116

Semantic association represents group relationship among objects in linked data. Searching semantic associations is complicated, which involves the search of multiple objects and the search of their group relationships simultaneously. In this paper, we propose this kind of search as a multi-target search, and we compare it to traditional search tasks, which we classify as single-target search. A novel search model is introduced, and the notion of virtual document is used to extract linguistic information of semantic associations. Multi-target search is finally fulfilled by a PageRank-like ranking scheme and a top-K selection policy considering object affinity. Experiments show that our approach is effective in improving retrieval precision on semantic associations. © Springer International Publishing AG 2016.

关键词： Linked data

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An empirical investigation of the older adults' continuance intention towards SNSs

An empirical investigation of the older adults' continuance ...

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作者： Liu, Wei Jiangxi Key Laboratory of Data and Knowledge Engineering Institute of Information Resources Management School of Information Technology Jiangxi University of Finance and Economics Nanchang360013 China

Although Social Network Services (SNSs) continuance usage has recently emerged as an important issue in information systems adaption, the research into older adults' continuance intention towards SNS is still very low, and the acceptance-discontinuance anomaly phenomenon (i.e. users discontinue using e-learning after initially accepting it) is a common occurrence. This study joins the perceived risk and perceived enjoyment to our prior research model which integrated the Expectation-Confirmation Model (ECM), the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) to hypothesise a theoretical model to explain and predict the older adults' intentions to continue using SNSs. The hypothesised model is validated empirically using a sample collected from 320 older adults who had prior experience with SNSs and was tested against the proposed research model using structure equation modelling. The results demonstrate that satisfaction has the most significant effect on older adults' continuance intention, followed by perceived usefulness, perceived risk, subject norm and perceived behavioural control. The results of these findings for SNSs practitioners are discussed at the end of this work. © Copyright 2016 Inderscience Enterprises Ltd.

关键词： Social networking (online)

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OCBMLC: An overlapping clustering based multi-label classification algorithm

OCBMLC: An overlapping clustering based multi-label classifi...

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IEEE International Conference on Cloud Computing and Big data Analysis (ICCCBDA)

作者： Liwen Peng Yongguo Liu Huan Liao Peng Zhang Knowledge and Data Engineering Laboratory of Chinese Medicine University of Electronic Science and Technology of China Chengdu China College English Department Guangdong University of Foreign Studies Guangzhou China School of Science Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9781509045006

This paper explores the clustering based multi-label classification problem. We propose an overlapping clustering based multi-label classification method (OCBMLC). In the experiment we compare three multi-label classification algorithms that are based on clustering. In the experimental setting section, we use three clustering methods for datasets before multi-label classification. Two popular multi-label benchmark datasets are used in our experiments: emotions dataset and yeast dataset. We employ micro F1-measure and hamming loss as multi-label classification performance evaluation metrics. The experimental results demonstrate that various clustering algorithms generate different performance of the multi-label classification, and the overlapping clustering based multi-label classification algorithm may adapt to multi-label classification more.

关键词： Bioinformatics

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DPListCF: A Differentially Private Approach for Listwise Collaborative Filtering

DPListCF: A Differentially Private Approach for Listwise Col...

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IEEE Symposium on Computers and Communications

作者： Yuncheng Wu Juru Zeng Hong Chen Yao Wu Wenjuan Liang Hui Peng Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China

ISBN: (纸本)9781509006809

Recently, listwise ranking-oriented collaborative filtering (CF) algorithms have gained great success in recommender systems. However, the ranked preference list may compromise the privacy of individuals. A notable paradigm for offering strong privacy guarantee is differential privacy. In this paper, we propose DPListCF, a differentially private algorithm based on ListCF (a state-of-art listwise CF algorithm). The main idea of DPListCF is to make both of the similarity calculation phase and rank prediction phase of ListCF satisfy differential privacy, by using input perturbation method and output perturbation method in the two phases respectively. Extensive experiments using two real datasets evaluate the performance of DPListCF, and demonstrate that the proposed algorithm outperforms state-of-art approaches.

关键词： Open area test sites Privacy collaborative filtering Recommender systems Perturbation methods Arts CONTAINMENT FAILURE

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Opportunities and challenges faced by digital libraries in the big data era

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Journal of Library Science in China 2016年 5-16页

作者： SU Xinning School of Information Management Jiangsu Key Laboratory of Data Engineering and Knowledge Services Nanjing University

Construction of network-based academic resources is undergoing rapid development in this big data era, displaying a trend to replace digital libraries. This challenge calls for the construction of digital libraries to have big data related considerations, i.e., from resource construction aspect, we should expand the resource scope, aggrandize the breadth of resource integration, and increase the depth of the resource organization and processing;from technology application aspect, we should attach great importance to the semantic technology, emphasize the application of clustering technology, widely adopt data analysis technology, and elevate the retrieval technology level;from service aspect, we need to enrich services and product categories of digital libraries, and convert the passive, and general service mode to the proactive and personalized mode. The construction of next generation digital libraries must differ from the traditional way. Comprehensive service ideas should be established;comprehensive resource services should be provided;the conventional service mode and transmission form also need to be expanded;even the librarians of digital libraries are required to become data analysts.

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Security Analysis Model, System Architecture and Relational Model of Enterprise Cloud Services

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International Journal of Automation and computing 2016年第6期13卷 574-584页

作者： Dang-Dang Niu Lei Liu Xin Zhang Shuai Lu Zhuang Li College of Computer Science and Technology Jilin University Changchun 130012 China Data Center Consolidation (Beijing) Industrial and Commercial Bank of China Beijing 100096 China College of Mathematics Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun 130012 China

Cloud computing is one of the fastest growing and popular computer technologies, and there are more and more enterprise services based on the cloud computing. In order to save costs, more and more enterprises and their employees have hired the enterprise cloud services, and put much important information in the cloud gradually. Cloud service systems have become the main targets of malicious attacks. However, the cloud computing technologies are still not perfect, and the management and maintenance of enterprise cloud services are more complex compared to traditional network services of cloud computing. So, enterprise cloud services are more likely to encounter some security problems, and the influenced scale of these security problems is broad. But there are few researches on the security of enterprise cloud services. In this paper, we analyze the software as a service（Saa S） enterprise cloud services and introduce the research status of security problems in cloud computing environment. Combining with the analysis of the characteristics and application architecture of Saa S enterprise cloud services, we propose the security problems analysis model, the analysis system architecture and the relational model. Our researches can support further research of the automatic generation of solutions and guide the deployment of security policies of Saa S enterprise cloud services.

关键词： cloud deployment perfect maintenance likely users bases encounter relational costs

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Metric based on multi-order spaces for cross-modal retrieval

Metric based on multi-order spaces for cross-modal retrieval

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang Zhang Bingpeng Ma Guorong Li Qingming Huang Key Laboratory of Big Data Mining and Knowledge Management CAS China School of Computer and Control Engineering University of Chinese Academy of Sciences China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS China

ISBN: (纸本)9781509060689

This paper proposes a novel method for cross-modal retrieval. Different from vector (text)-to-vector (image) framework of the traditional cross-modal methods, we adopt a vector (text)-to-matrix (image) framework. We assume that compared with vectors, matrices can directly represent images and characterize the structure of feature space. Furthermore, we propose a Metric based on Multi-order spaces (MMs). Multi-order statistic features are used to represent images for enriching the semantic information, and metrics among the multi-spaces are jointly learned to measure the similarity between two different modalities. Specifically, there are three steps for MMs. First, we jointly use the bags of visual features (zero-order), mean (first-order) and covariance (second-order) to characterize each image. Second, considering that covariance matrices and vectors lie on a Riemannian manifold and an Euclidean space respectively, we embed multi-order spaces into their corresponding Hilbert spaces to reduce the heterogeneity among the original spaces. Finally, the similarity between two different modalities can be measured by learning multiple transformations from the different Hilbert spaces to a common subspace. The performance of the proposed method over the state-of-the-art has been demonstrated through the experiments on two public datasets.

关键词： Hilbert space Manifolds Correlation Covariance matrices Feature extraction Linear programming

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What and how affect influence the consequence of product trial 20

What and how affect influence the consequence of product tri...

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20th Pacific Asia Conference on Information Systems, PACIS 2016

作者： Sun, Kai Zuo, Meiyun Kong, Dong Key Laboratory of Data Engineering and Knowledge Engineering School of Business Renmin University of China Beijing China Research Institute of Smart Aged Care School of Information National Academy of Development and Strategy Renmin University of China Beijing China

ISBN: (纸本)9789860491029

IT product trial is an important method to promote consumers' attitude toward IT product. Affect is one of the most essential factors and components in determining consumers' attitude. However, previous studies pay more attention to the affective response to trial, which measure the holistic feeling about product trial experience. Affect in product trial is complex and multipartite that different parts may have different influence on consumers' attitude. Therefore, we build the Affective Response Framework to divide consumers' affect into affective response to product, affective response to behaviour, and affective response to environment. And then we analyse the relationship between three components of affective response and consumers' attitude. In order to test the hypothesis, we launch a field study of new IT product trial (an electroencephalogram product) among 205 college students. Results reveal that affective response to product and behaviour have positive influence on attitude toward product;affective response to behaviour and environment have positive influence on attitude toward trial.

关键词： Students

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Inverse extreme learning machine for learning with label proportions

Inverse extreme learning machine for learning with label pro...

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IEEE International Conference on Big data

作者： Limeng Cui Jiawei Zhang Zhensong Chen Yong Shi Philip S. Yu School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Computer Science Florida State University FL USA Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China Department of Computer Science University of Illinois at Chicago IL USA

In large-scale learning problem, the scalability of learning algorithms is usually the key factor affecting the algorithm practical performance, which is determined by both the time complexity of the learning algorithms and the amount of supervision information (i.e., labeled data). Learning with label proportions (LLP) is a new kind of machine learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, LLP can estimate a classifier from groups of weakly labeled data, where only the positive/negative class proportions of each group are known. Due to its weak requirements for the input data, LLP presents a variety of real-world applications in almost all the fields involving anonymous data, like computer vision, fraud detection and spam filtering. However, even through the required labeled data is of a very small amount, LLP still suffers from the long execution time a lot due to the high time complexity of the learning algorithm itself. In this paper, we propose a very fast learning method based on inversing output scaling process and extreme learning machine, namely Inverse Extreme Learning Machine (IELM), to address the above issues. IELM can speed up the training process by order of magnitudes for large datasets, while achieving highly competitive classification accuracy with the existing methods at the same time. Extensive experiments demonstrate the significant speedup of the proposed method. We also demonstrate the feasibility of IELM with a case study in real-world setting: modeling image attributes based on ImageNet Object Attributes dataset.

关键词： Training Calibration Support vector machines Supervised learning Visualization Optimization

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Strategy intervention for the evolution of fairness

arXiv

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arXiv 2017年

作者： Zhang, Yanling Fu, Feng Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Mathematics Dartmouth College HanoverNH United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth College HanoverNH United States

Masses of experiments have shown individual preference for fairness which seems irrational. The reason behind it remains a focus for research. The effect of spite (individuals are only concerned with their own relative standing) on the evolution of fairness has attracted increasing attention from experiments, but only has been implicitly studied in one evolutionary model. The model did not involve high-offer rejections, which have been found in the form of non-monotonic rejections (rejecting offers that are too high or too low) in experiments. Here, we introduce a high offer and a non-monotonic rejection in structured populations of finite size, and use strategy intervention to explicitly study how spite influences the evolution of fairness: five strategies are in sequence added into the competition of a fair strategy and a selfish strategy. We find that spite promotes fairness, altruism inhibits fairness, and the non-monotonic rejection can cause fairness to overcome selfishness, which cannot happen without high-offer rejections. Particularly for the group-structured population with seven discrete strategies, we analytically study the effect of population size, mutation, and migration on fairness, selfishness, altruism, and spite. A larger population size cannot change the dominance of fairness, but it promotes altruism and inhibits selfishness and spite. Intermediate mutation maximizes selfishness and fairness, and minimizes spite;intermediate mutation maximizes altruism for intermediate migration and minimizes altruism otherwise. The existence of migration inhibits selfishness and fairness, and promotes altruism;sufficient migration promotes spite. Our study may provide important insights into the evolutionary origin of fairness. Copyright © 2017, The Authors. All rights reserved.

关键词： Population statistics

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