• Complex
  • Title
  • Author
  • Keyword
  • Abstract
  • Scholars
Search
Sort by:
Default
  • Default
  • Title
  • Year
  • WOS Cited Count
  • Impact factor
  • Ascending
  • Descending
< Page ,Total 6 >
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)
Abstract&Keyword Cite

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

Cite:

Copy from the list or Export to your reference management。

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) .
Export to NoteExpress RIS BibTex
A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map EI Scopus CSSCI-E SSCI SCIE
期刊论文 | 2018 , 6 , 57562-57571 | IEEE Access
Abstract&Keyword Cite

Abstract :

Learning resource recommendation, such as curriculum video recommendation, is an effective way to reduce cognitive overload in online learning. The existing curriculum video recommendation systems are generally limited to one course, ignoring the knowledge correlation between courses. In this paper, we propose a two-stage cross-curriculum video recommendation algorithm that considers both the learners' implicit feedback and the knowledge association between course videos. First, we use collaborative filtering to generate a video seed set, which is based on the learner's implicit video feedback, such as video learning frequencies, video learning duration, and video pausing and dragging frequencies. Second, we construct a cross-curriculum video-associated knowledge map and use a random walk algorithm to measure the relevance of the course videos. The relevance is based on each video seed as a starting node and is extended to a video subgraph. Then, several cross-curricular video-oriented subgraphs are recommended for the learners. The experimental results indicate that our cross-curriculum video recommendation algorithm performs better than the traditional collaborative filtering-based recommendation algorithms in terms of accuracy, recall rate, and knowledge relevance. © 2013 IEEE.

Keyword :

Cognitive overload Implicit feedback Knowledge map Learning resource Online learning Random walk algorithms Recommendation algorithms Video feedback

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Zhu, Haiping , Liu, Yu , Tian, Feng et al. A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map [J]. | IEEE Access , 2018 , 6 : 57562-57571 .
MLA Zhu, Haiping et al. "A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map" . | IEEE Access 6 (2018) : 57562-57571 .
APA Zhu, Haiping , Liu, Yu , Tian, Feng , Ni, Yifu , Wu, Ke , Chen, Yan et al. A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map . | IEEE Access , 2018 , 6 , 57562-57571 .
Export to NoteExpress RIS BibTex
More Value, Less privacy, How to Evaluate the Privacy based on Trajectories Visualization & Analyzation CPCI-S
会议论文 | 2018 , 99-104 | 1st Annual International Conference on Sensor Networks and Signal Processing (SNSP)
Abstract&Keyword Cite

Abstract :

The trade-off among individual privacy, data utility and data feature of service has been a great concern when designing and evaluating privacy preserving schemes in trajectories publishing. The trajectories data is spatial and temporal correlated strongly. So, Privacy-preserving over them should take the human behaviors and their status into account. In this paper, we develop a novel method to investigate and analyze users' behaviors as well as the crowd density after abstracting users' ROIs. Finally, we evaluate the privacy stress via a well-designed indictor based on the trajectories visualization and analyzation. Experiments show that the method is capable of effectively finding both crowd living patterns and distribution, and the proposed indictor can quantize the mobility data utility precisely in grid.

Keyword :

ROI privacy evaluation Trajectory crowd density matrix privacy preserving POI heat map

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Dai, Huijun , Shen, Yi , Gui, Xiaolin . More Value, Less privacy, How to Evaluate the Privacy based on Trajectories Visualization & Analyzation [C] . 2018 : 99-104 .
MLA Dai, Huijun et al. "More Value, Less privacy, How to Evaluate the Privacy based on Trajectories Visualization & Analyzation" . (2018) : 99-104 .
APA Dai, Huijun , Shen, Yi , Gui, Xiaolin . More Value, Less privacy, How to Evaluate the Privacy based on Trajectories Visualization & Analyzation . (2018) : 99-104 .
Export to NoteExpress RIS BibTex
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)
Abstract&Keyword Cite

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.

Cite:

Copy from the list or Export to your reference management。

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 .
Export to NoteExpress RIS BibTex
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)
Abstract&Keyword Cite

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

Cite:

Copy from the list or Export to your reference management。

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 .
Export to NoteExpress RIS BibTex
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: 7 SCOPUS Cited Count: 9
Abstract&Keyword Cite

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

Cite:

Copy from the list or Export to your reference management。

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 .
Export to NoteExpress RIS BibTex
Exploring open information via event network EI AHCI SSCI SCIE Scopus
期刊论文 | 2018 , 24 (2) , 199-220 | NATURAL LANGUAGE ENGINEERING
WoS CC Cited Count: 1 SCOPUS Cited Count: 1
Abstract&Keyword Cite

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.

Cite:

Copy from the list or Export to your reference management。

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 .
Export to NoteExpress RIS BibTex
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: 20 SCOPUS Cited Count: 28
Abstract&Keyword Cite

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

Cite:

Copy from the list or Export to your reference management。

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 .
Export to NoteExpress RIS BibTex
Quality prediction of newly proposed questions in CQA by leveraging weakly supervised learning EI Scopus
会议论文 | 2017 , 10604 LNAI , 655-667 | 13th International Conference on Advanced Data Mining and Applications, ADMA 2017
Abstract&Keyword Cite

Abstract :

Community Question Answering (CQA) websites provide a platform to ask questions and share their knowledge. Good questions in CQA websites can improve user experiences and attract more users. To the best of our knowledge, a few researches have been studied on the question quality, especially the quality of newly proposed questions. In this work, we consider that a good question is popular and answerable in CQA websites. The community features of questions are extracted automatically and utilized to acquire massive good questions. The text features and asker features of good questions are utilized to train our weakly supervised model based on Convolutional Neural Network to recognize good newly proposed questions. We conduct extensive experiments on the publicly available dataset from StackExchange and our best result achieves F1-score at 91.5%, outperforming the baselines. © Springer International Publishing AG 2017.

Keyword :

Community question answering Convolutional neural network F1 scores Model-based OPC Quality prediction Text feature User experience Weakly supervised learning

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Zheng, Yuanhao , Wei, Bifan , Liu, Jun et al. Quality prediction of newly proposed questions in CQA by leveraging weakly supervised learning [C] . 2017 : 655-667 .
MLA Zheng, Yuanhao et al. "Quality prediction of newly proposed questions in CQA by leveraging weakly supervised learning" . (2017) : 655-667 .
APA Zheng, Yuanhao , Wei, Bifan , Liu, Jun , Wang, Meng , Chen, Weitong , Wu, Bei et al. Quality prediction of newly proposed questions in CQA by leveraging weakly supervised learning . (2017) : 655-667 .
Export to NoteExpress RIS BibTex
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
Abstract&Keyword Cite

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

Cite:

Copy from the list or Export to your reference management。

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 .
Export to NoteExpress RIS BibTex
10| 20| 50 per page
< Page ,Total 6 >

Export

Results:

Selected

to

Format:
FAQ| About| Online/Total:3511/65801872
Address:XI'AN JIAOTONG UNIVERSITY LIBRARY(No.28, Xianning West Road, Xi'an, Shaanxi Post Code:710049) Contact Us:029-82667865
Copyright:XI'AN JIAOTONG UNIVERSITY LIBRARY Technical Support:Beijing Aegean Software Co., Ltd.