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Exploring open information via event network EI AHCI SSCI SCIE Scopus
期刊论文 | 2018 , 24 (2) , 199-220 | NATURAL LANGUAGE ENGINEERING
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Abstract :

It is a challenging task to discover information from a large amount of data in an open domain.(1) In this paper, an event network framework is proposed to address this challenge. It is in fact an empirical construct for exploring open information, composed of three steps: document event detection, event network construction and event network analysis. First, documents are clustered into document events for reducing the impact of noisy and heterogeneous resources. Secondly, linguistic units (e.g., named entities or entity relations) are extracted from each document event and combined into an event network, which enables content-oriented retrieval. Then, in the final step, techniques such as social network or complex network can be applied to analyze the event network for exploring open information. In the implementation section, we provide examples of exploring open information via event network.

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GB/T 7714 Chen, Yanping , Zheng, Qinghua , Tian, Feng et al. Exploring open information via event network [J]. | NATURAL LANGUAGE ENGINEERING , 2018 , 24 (2) : 199-220 .
MLA Chen, Yanping et al. "Exploring open information via event network" . | NATURAL LANGUAGE ENGINEERING 24 . 2 (2018) : 199-220 .
APA Chen, Yanping , Zheng, Qinghua , Tian, Feng , Liu, Huan , Hao, Yazhou , Shah, Nazaraf . Exploring open information via event network . | NATURAL LANGUAGE ENGINEERING , 2018 , 24 (2) , 199-220 .
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A multi-constraint learning path recommendation algorithm based on knowledge map EI SSCI SCIE Scopus
期刊论文 | 2018 , 143 , 102-114 | KNOWLEDGE-BASED SYSTEMS
WoS CC Cited Count: 2 SCOPUS Cited Count: 2
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Abstract :

It is difficult for e-learners to make decisions on how to learn when they are facing with a large amount of learning resources, especially when they have to balance available limited learning time and multiple learning objectives in various learning scenarios. This research presented in this paper addresses this challenge by proposing a new multi-constraint learning path recommendation algorithm based on knowledge map. The main contributions of the paper are as follows. Firstly, two hypotheses on e-learners' different learning path preferences for four different learning scenarios (initial learning, usual review, pre-exam learning and pre-exam review) are verified through questionnaire-based statistical analysis. Secondly, according to learning behavior characteristics of four types of the learning scenarios, a multi constraint learning path recommendation model is proposed, in which the variables and their weighted coefficients considers different learning path preferences of the learners in different learning scenarios as well as learning resource organization and fragmented time. Thirdly, based on the proposed model and knowledge map, the design and implementation of a multi-constraint learning path recommendation algorithm is described. Finally, it is shown that the questionnaire results from over 110 e-learners verify the effectiveness of the proposed algorithm and show the similarity between the learners' self-organized learning paths and the recommended learning paths. (C) 2017 Elsevier B.V. All rights reserved.

Keyword :

E-learning Knowledge map Learning path recommendation Learning scenario

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GB/T 7714 Zhu, Haiping , Tian, Feng , Wu, Ke et al. A multi-constraint learning path recommendation algorithm based on knowledge map [J]. | KNOWLEDGE-BASED SYSTEMS , 2018 , 143 : 102-114 .
MLA Zhu, Haiping et al. "A multi-constraint learning path recommendation algorithm based on knowledge map" . | KNOWLEDGE-BASED SYSTEMS 143 (2018) : 102-114 .
APA Zhu, Haiping , Tian, Feng , Wu, Ke , Shah, Nazaraf , Chen, Yan , Ni, Yifu et al. A multi-constraint learning path recommendation algorithm based on knowledge map . | KNOWLEDGE-BASED SYSTEMS , 2018 , 143 , 102-114 .
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Deep Convolution Neural Networks for Twitter Sentiment Analysis EI SCIE Scopus
期刊论文 | 2018 , 6 , 23253-23260 | IEEE ACCESS
WoS CC Cited Count: 4 SCOPUS Cited Count: 7
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Abstract :

Twitter sentiment analysis technology provides the methods to survey public emotion about the events or products related to them. Most of the current researches are focusing on obtaining sentiment features by analyzing lexical and syntactic features. These features are expressed explicitly through sentiment words, emoticons, exclamation marks, and so on. In this paper, we introduce a word embeddings method obtained by unsupervised learning based on large twitter corpora, this method using latent contextual semantic relationships and co-occurrence statistical characteristics between words in tweets. These word embeddings are combined with n-grams features and word sentiment polarity score features to form a sentiment feature set of tweets. The feature set is integrated into a deep convolution neural network for training and predicting sentiment classification labels. We experimentally compare the performance of our model with the baseline model that is a word n-grams model on five Twitter data sets, the results indicate that our model performs better on the accuracy and F1-measure for twitter sentiment classification.

Keyword :

sentiment analysis Twitter convolution neural network word embeddings

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GB/T 7714 Zhao Jianqiang , Gui Xiaolin , Zhang Xuejun . Deep Convolution Neural Networks for Twitter Sentiment Analysis [J]. | IEEE ACCESS , 2018 , 6 : 23253-23260 .
MLA Zhao Jianqiang et al. "Deep Convolution Neural Networks for Twitter Sentiment Analysis" . | IEEE ACCESS 6 (2018) : 23253-23260 .
APA Zhao Jianqiang , Gui Xiaolin , Zhang Xuejun . Deep Convolution Neural Networks for Twitter Sentiment Analysis . | IEEE ACCESS , 2018 , 6 , 23253-23260 .
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GHCC: Grouping-Based and Hierarchical Collaborative Caching for Mobile Edge Computing EI CPCI-S Scopus
会议论文 | 2018 | 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)
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Abstract :

Mobile edge computing (MEC) has emerged as a promising technique to address the challenge arising from the exponentially increasing data traffic. It not only supports mobile users to offload computations but also caches and delivers popular contents to mobile users. In this paper, we aim at designing novel content caching strategies in MEC networks to reduce access latency and improve energy efficiency. First, the distributed content delivery network based on MECs is developed to support users' requests locally. Moreover, based on users' distribution characteristics and MECs' location, a grouping-based and hierarchical collaborative caching strategy is proposed. Simulation results prove that our caching strategy is more efficient than alternative benchmark strategies in terms of average access latency, total energy consumption and content diversity.

Keyword :

grouping-based and hierarchical collaborative caching Mobile edge computing distributed content delivery

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GB/T 7714 Ren, Dewang , Gui, Xiaolin , Lu, Wei et al. GHCC: Grouping-Based and Hierarchical Collaborative Caching for Mobile Edge Computing [C] . 2018 .
MLA Ren, Dewang et al. "GHCC: Grouping-Based and Hierarchical Collaborative Caching for Mobile Edge Computing" . (2018) .
APA Ren, Dewang , Gui, Xiaolin , Lu, Wei , An, Jian , Dai, Huijun , Liang, Xin . GHCC: Grouping-Based and Hierarchical Collaborative Caching for Mobile Edge Computing . (2018) .
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OpenHI - An open source framework for annotating histopathological image CPCI-S
会议论文 | 2018 , 1076-1082 | IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Abstract :

Histopathological images carry informative cellular visual phenotypes and have been digitalized in huge amount in medical institutes. However, the lack of software for annotating the specialized images has been a hurdle of fully exploiting the images for educating and researching, and enabling intelligent systems for automatic diagnosis or phenotype-genotype association study. This paper proposes an open-source web framework, OpenHI, for the whole-slide image annotation. The proposed framework could be utilized for simultaneous collaborative or crowd-sourcing annotation with standardized semantic enrichment at a pixel-level precision. Meanwhile, our accurate virtual magnification indicator provides annotators a crucial reference for deciding the grading of each region. In testing, the framework can responsively annotate the acquired whole-slide images from TCGA project and provide efficient annotation which is precise and semantically meaningful. OpenHI is an open-source framework thus it can be extended to support the annotation of whole-slide images from different source with different oncological types. It is publicly available at https://gitlab.com/BioAI/OpenHI/. The framework may facilitate the creation of large-scale precisely annotated histopathological image datasets.

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GB/T 7714 Puttapirat, Pargorn , Zhang, Haichuan , Lian, Yuchen et al. OpenHI - An open source framework for annotating histopathological image [C] . 2018 : 1076-1082 .
MLA Puttapirat, Pargorn et al. "OpenHI - An open source framework for annotating histopathological image" . (2018) : 1076-1082 .
APA Puttapirat, Pargorn , Zhang, Haichuan , Lian, Yuchen , Wang, Chunbao , Zhang, Xingrong , Yao, Lixia et al. OpenHI - An open source framework for annotating histopathological image . (2018) : 1076-1082 .
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AutoCOT - AutoEncoder Based Cooperative Training For Sparse Recommendation CPCI-S
会议论文 | 2018 , 257-262 | IEEE 15th International Conference on E-Business Engineering (ICEBE)
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Abstract :

Currently, data sparseness problem caused by large amount of data has resulted in low recommendation quality of traditional recommendation algorithms. Aiming at this problem, this paper proposes an auto-encoder recommendation algorithm based on cooperative training (AutoCOT) that combines the auto-encoder framework with cooperative training (COT) model, which can not only better learn the non-linear relationship of data but alleviate the data sparseness problem, especially in large amount of user and item data. The experiments show that, on Movielen datasets, AutoCOT performs better in coverage, precision and recall rate when compares with the traditional collaborative filtering algorithms and pure auto-encoder recommendation algorithms.

Keyword :

Data sparseness Auto-encoder COT Collaborative filtering Recommendation

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GB/T 7714 Bai, Rong , Zhu, Haiping , Ni, Yifu et al. AutoCOT - AutoEncoder Based Cooperative Training For Sparse Recommendation [C] . 2018 : 257-262 .
MLA Bai, Rong et al. "AutoCOT - AutoEncoder Based Cooperative Training For Sparse Recommendation" . (2018) : 257-262 .
APA Bai, Rong , Zhu, Haiping , Ni, Yifu , Chen, Yan , Zheng, Qinghua . AutoCOT - AutoEncoder Based Cooperative Training For Sparse Recommendation . (2018) : 257-262 .
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A Bi-Target Based Mobile Relay Selection Algorithm for MCNs EI SCIE Scopus
期刊论文 | 2017 , 11 (11) , 5282-5300 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | IF: 0.611
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Abstract :

Multi-hop cellular networks (MCNs) reduce the transmit power and improve the system performance. Recently, several research studies have been conducted on MCNs. The mobile relay selection scheme is a rising issue in the design of MCNs that achieves these advantages. The conventional opportunistic relaying (OR) is performed on the single factor for maximum signal-to-interference-plus-noise ratio (SINR). In this paper, a comprehensive OR scheme based on Bi-Target is proposed to improve the system throughput and reduce the relay handover by constraining the amount of required bandwidth and SINR. Moreover, the proposed algorithm captures the variability and the mobility that makes it more suitable for dynamic real scenarios. Numerical and simulation results show the superiority of the proposed algorithm in both enhancing the overall performance and reducing the handover.

Keyword :

Mobile relay relay selection algorithm multi-objective optimization MCNs

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GB/T 7714 Dai, Huijun , Gui, Xiaolin , Dai, Zhaosheng et al. A Bi-Target Based Mobile Relay Selection Algorithm for MCNs [J]. | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS , 2017 , 11 (11) : 5282-5300 .
MLA Dai, Huijun et al. "A Bi-Target Based Mobile Relay Selection Algorithm for MCNs" . | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 11 . 11 (2017) : 5282-5300 .
APA Dai, Huijun , Gui, Xiaolin , Dai, Zhaosheng , Ren, Dewang , Gu, Yingjie . A Bi-Target Based Mobile Relay Selection Algorithm for MCNs . | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS , 2017 , 11 (11) , 5282-5300 .
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Mitigating Cloud Co-Resident Attacks via Grouping-based Virtual Machine Placement Strategy EI CPCI-S Scopus
会议论文 | 2017 , 1-8 | 36th IEEE International Performance, Computing, and Communications Conference (IPCCC)
WoS CC Cited Count: 2
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Abstract :

Security is one of the biggest concerns for the further adoption of Clouds. However, Cloud providers usually assign VMs leased by different customers upon the same physical server. Albeit maximizing resource efficiency, this cross-domain sharing poses a serious threat to customers' privacy concerns. A malicious VM could break or bypass the isolation mechanism and execute certain cross-VM attacks, such as side channel attacks or memory Dos attacks, etc. However, most of previous solutions are either attack-specific or unsuitable for immediate deployment, making the mitigation techniques for co-resident attacks still an important and worth-studying problem in cloud security. In this paper, we propose a novel grouping-based VM placement strategy to provide a secure optimization for existing VM placement policies. The theoretical analysis and simulation results show that our strategy decreases enormously the probability of co-residence while incurring only a slight loss on resource efficiency. The results also demonstrate that our strategy is significantly more effective in terms of both co-location resistance and resources efficiency, compared with the CLR policy.

Keyword :

co-resident attacks cloud computing virtual machine placement strategy group selection strategy grouping co-location resistance

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GB/T 7714 Liang, Xin , Gui, Xiaolin , An, Jian et al. Mitigating Cloud Co-Resident Attacks via Grouping-based Virtual Machine Placement Strategy [C] . 2017 : 1-8 .
MLA Liang, Xin et al. "Mitigating Cloud Co-Resident Attacks via Grouping-based Virtual Machine Placement Strategy" . (2017) : 1-8 .
APA Liang, Xin , Gui, Xiaolin , An, Jian , Ren, Dewang . Mitigating Cloud Co-Resident Attacks via Grouping-based Virtual Machine Placement Strategy . (2017) : 1-8 .
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Adaptive semi-supervised learning with discriminative least squares regression EI Scopus
会议论文 | 2017 , 0 , 2421-2427 | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
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Abstract :

Semi-supervised learning plays a significant role in multi-class classification, where a small number of labeled data are more deterministic while substantial unlabeled data might cause large uncertainties and potential threats. In this paper, we distinguish the label fitting of labeled and unlabeled training data through a probabilistic vector with an adaptive parameter, which always ensures the significant importance of labeled data and characterizes the contribution of unlabeled instance according to its uncertainty. Instead of using traditional least squares regression (LSR) for classification, we develop a new discriminative LSR by equipping each label with an adjustment vector. This strategy avoids incorrect penalization on samples that are far away from the boundary and simultaneously facilitates multi-class classification by enlarging the geometrical distance of instances belonging to different classes. An efficient alternative algorithm is exploited to solve the proposed model with closed form solution for each updating rule. We also analyze the convergence and complexity of the proposed algorithm theoretically. Experimental results on several benchmark datasets demonstrate the effectiveness and superiority of the proposed model for multi-class classification tasks.

Keyword :

Adaptive parameters Alternative algorithms Closed form solutions Geometrical distances Least squares regression Multi-class classification Probabilistic vector Semi- supervised learning

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GB/T 7714 Luo, Minnan , Zhang, Lingling , Nie, Feiping et al. Adaptive semi-supervised learning with discriminative least squares regression [C] . 2017 : 2421-2427 .
MLA Luo, Minnan et al. "Adaptive semi-supervised learning with discriminative least squares regression" . (2017) : 2421-2427 .
APA Luo, Minnan , Zhang, Lingling , Nie, Feiping , Chang, Xiaojun , Qian, Buyue , Zheng, Qinghua . Adaptive semi-supervised learning with discriminative least squares regression . (2017) : 2421-2427 .
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Mining suspicious tax evasion groups in a corporate governance network EI Scopus
会议论文 | 2017 , 10393 LNCS , 465-475 | 17th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2017
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Abstract :

There is a new tendency for corporations to evade tax via Interest Affiliated Transactions (IAT) that are controlled by a potential “Guanxi” between the corporations’ controllers. At the same time, the taxation data is a classic kind of big data. These issues challenge the effectiveness of traditional data mining-based tax evasion detection methods. To address this problem, we first coin a definition of controller interlock, which characterizes the interlocking relationship between corporations’ controllers. Next, we present a colored and weighted network-based model for characterizing economic behaviors, controller interlock and other relationships, and IATs between corporations, and generate a heterogeneous information network-corporate governance network. Then, we further propose a novel Graph-based Suspicious Groups of Interlock based tax evasion Identification method, named GSG2I, which mainly consists of two steps: controller interlock pattern recognition and suspicious group identification. Experimental tests based on a real-world 7-year period tax data of one province in China, demonstrate that the GSG2I method can greatly improve the efficiency of tax evasion detection. © Springer International Publishing AG 2017.

Keyword :

Corporate governance Detection methods Experimental test Group identification Heterogeneous information Identification method Tax evasions Weighted networks

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GB/T 7714 Wei, Wenda , Yan, Zheng , Ruan, Jianfei et al. Mining suspicious tax evasion groups in a corporate governance network [C] . 2017 : 465-475 .
MLA Wei, Wenda et al. "Mining suspicious tax evasion groups in a corporate governance network" . (2017) : 465-475 .
APA Wei, Wenda , Yan, Zheng , Ruan, Jianfei , Zheng, Qinghua , Dong, Bo . Mining suspicious tax evasion groups in a corporate governance network . (2017) : 465-475 .
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