作者机构：
[Ma, Yuanyuan] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Ma, Yuanyuan] Anyang Normal Univ, Anyang, Peoples R China.
通讯机构：
[Jiang, Xingpeng] C;Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)
会议时间：
DEC 15-18, 2016
会议地点：
Shenzhen, PEOPLES R CHINA
会议主办单位：
[Ma, Yuanyuan] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.^[Hu, Xiaohua;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Ma, Yuanyuan] Anyang Normal Univ, Anyang, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
Human Microbiome;Laplacian Regularization;Multi-view Clustering;Symmetric Nonnegative Matrix Factorization
摘要：
In this paper, a novel and effective algorithm is proposed for noise reduction and contrast enhancement in low light images based on luminance map and haze removal model. The proposed method is divided into two steps: i) A combined denoising method using the improved guided filtering based on gradient information and median filtering is proposed to obtain the initial denoised image. ii)Considering that an inverted low light image presents quite similar to a haze image, the haze removal model is used to enhance the denoised low light image. The luminance component L is extracted to obtain the transmission map with the adaptive weight from the inverted denoised image which is applied to Lab color space. Then the classical quad-tree subdivision is utilized to estimate the atmospheric light, and then the de-hazed image is recovered by the haze removal model. At last, we can get the final enhanced image by inverting the de-hazed image back. The experimental results show that the proposed algorithm reduces the noise and enhances the contrast of the low light image more effectively and robustly than the conventional and the state-of-the-art algorithms.
作者：
Luo Changri;Zhang Xinhua*;He Tingting*（何婷婷）;Huang Baohua;Wu, Shaojing;...
期刊：
PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC),2017年:2286-2290
通讯作者：
Zhang Xinhua;He Tingting
作者机构：
[Luo Changri; Xie, Yaohui] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Sch Vocat & Continuing Educ, Wuhan, Hubei, Peoples R China.;[Zhang Xinhua] Wuhan Vocat Coll Software & Engn, Sch Comp Sci, Wuhan, Hubei, Peoples R China.;[He Tingting] Cent China Normal Univ, Acad Comp Sci, Wuhan, Hubei, Peoples R China.;[Huang Baohua] Cent China Normal Univ, Sch Vocat & Continuing Educ, Wuhan, Hubei, Peoples R China.;[Wu, Shaojing] Cent China Normal Univ, Sch Informat Management, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
通讯机构：
[Zhang Xinhua] W;[He Tingting] C;Wuhan Vocat Coll Software & Engn, Sch Comp Sci, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Acad Comp Sci, Wuhan, Hubei, Peoples R China.
会议名称：
3rd IEEE International Conference on Computer and Communications (ICCC)
会议时间：
DEC 13-16, 2017
会议地点：
Chengdu, PEOPLES R CHINA
会议主办单位：
[Luo Changri;Xie, Yaohui] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Sch Vocat & Continuing Educ, Wuhan, Hubei, Peoples R China.^[Zhang Xinhua] Wuhan Vocat Coll Software & Engn, Sch Comp Sci, Wuhan, Hubei, Peoples R China.^[He Tingting] Cent China Normal Univ, Acad Comp Sci, Wuhan, Hubei, Peoples R China.^[Huang Baohua] Cent China Normal Univ, Sch Vocat & Continuing Educ, Wuhan, Hubei, Peoples R China.^[Wu, Shaojing] Cent China Normal Univ, Sch Informat Management, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
关键词：
two-stage clustering;online learning;learning collaboration group
摘要：
Many studies have confirmed that the role of online learning collaboration group is very important. For large-scale online learning, how to effectively find the learning collaboration group is a difficult problem. The online learning forum is the main place for learners to learn and communicate, so it is the main venue for learning collaboration groups to implement collaborative learning. In the implementation process of learning collaboration, there are two characteristics between learning team members. First, there is interaction between them. Second, the contents of their discussion have high relevance. In this paper, the study uses these two important characteristics and carries out two-stage clustering algorithm based on the interaction structure and interactive contents of learners to find the potential learning collaboration groups in the large-scale online learning forum. The experimental results show that the method proposed in this paper is effective. This has practical significance for large-scale online learning support services.
作者：
Wu, Shangyu;Ye, Junming*;Wang, Zhifeng*;Luo, Daxiong;Zhao, Rong
作者机构：
[Ye, Junming; Wu, Shangyu; Luo, Daxiong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Zhao, Rong; Wang, Zhifeng] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Hubei, Peoples R China.
会议名称：
2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE)
会议时间：
SEP 17-18, 2017
会议地点：
Shenzhen, PEOPLES R CHINA
会议主办单位：
[Wu, Shangyu;Ye, Junming;Luo, Daxiong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Wang, Zhifeng;Zhao, Rong] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Hubei, Peoples R China.
会议论文集名称：
AER-Advances in Engineering Research
关键词：
education big data;online learning;learning analysis
摘要：
With the rapid development of Internet technology, big data has become more and more frequently mentioned. How to use the latest technology to dig valuable information from big data has become a hot topic. In this paper, online learning analysis platform based on education big data integrated the acquisition and analysis of students' data. It allowed teacher to teach online, students and students to learn online. At the meantime, it would gather students' data automatically and filter out effective data based on some requirements. Finally, it would analyze data using learning analysis technology and give teacher a feedback of the analysis results. This system would assist teachers more targeted teaching activities.
摘要：
It's necessary to acquire semantic knowledge in Natural Language Processing research. In this paper, we present an approach for acquiring Chinese semantic knowledge based on maximum entropy model. Semantic knowledge units are composed of central word and a group of feature words. Because the maximum entropy to extract features, we utilize it to calculate the semantic distance between the central word and feature words in large-scale network corpus. In the experiment, tests on a number of manual defined data sets show that the Spearman correlation coefficient has been increased 6.2%-20.9%.
期刊：
INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE),2017年190:632-644 ISSN：2475-8841
通讯作者：
Chen, Hao
作者机构：
[Cai, Xia; Liu, Wei; Ruan, Yunxing] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.;[Chen, Hao] Wuhan City Polytech, Sch Engn & Architecture, Wuhan 430064, Hubei, Peoples R China.
通讯机构：
[Chen, Hao] W;Wuhan City Polytech, Sch Engn & Architecture, Wuhan 430064, Hubei, Peoples R China.
会议名称：
INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE)
会议时间：
OCT 21-23, 2017
会议地点：
Shanghai, PEOPLES R CHINA
会议主办单位：
[Liu, Wei;Ruan, Yunxing;Cai, Xia] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.^[Chen, Hao] Wuhan City Polytech, Sch Engn & Architecture, Wuhan 430064, Hubei, Peoples R China.
会议论文集名称：
DEStech Transactions on Computer Science and Engineering
摘要：
Social image platforms allow their users sharing and searching their photos based on images' tags. These tags are provided by different users. Inevitably, the tags are spontaneously ambiguous, and personalized. So, learning the relevance between tags and images is playing an important role in tag-based retrieval systems. Choosing visual neighbors for seed images as voters is a widely used method for learning tag relevance. However, most existing methods of choosing visual neighbors for seed images are based on the global features of the whole images, ignoring the local features. In this paper we propose a pixel voting method to choose the visual neighbors for seed images. Experiment shows that this method is a more natural way to measure the similarity of images. Based the selected neighbors we learn the tag relevance, and the experiment on the MIR Flickr dataset shows that our algorithm is effective in tag de-noising and tag ranking.
作者机构：
[Li, Tao; Liu, Huayong] Cent China Normal Univ, Dept Comp Sci, Wuhan, Hubei, Peoples R China.
会议名称：
12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
会议时间：
AUG 13-15, 2016
会议地点：
Changsha, PEOPLES R CHINA
会议主办单位：
[Liu, Huayong;Li, Tao] Cent China Normal Univ, Dept Comp Sci, Wuhan, Hubei, Peoples R China.
关键词：
color histogram;equal-area of rectangular ring;adaptive threshold;shot detection
摘要：
An adaptive threshold shot detection algorithm based on improved block color features is proposed in this paper. This paper adopts an improved block color feature extraction method based on equal area of rectangular ring. Sub-block accumulative color histogram is extracted as color features and different weight for different rectangle rings is set in order to highlight the central part of frame. Then, adaptive threshold of detecting abrupt shot and gradual shot is calculated, and different detection modules is used according to the distance of the characteristics between frames. In the abrupt shots detection, several frames' frame difference and the edge shape features between adjacent frames are calculated to detect the flash. In the gradual shots detection, the discontinuous frame differences between the current frame and back frames are used to detect the boundary of the gradual shot. The experimental results show that this method has better effect to different types of video.
期刊：
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH,2016年08-12-September-2016:2706-2710 ISSN：2308-457X
通讯作者：
Yan, Rui
作者机构：
[Song, Yiping; Yan, Rui; Mou, Lili; Zhang, Ming] Peking Univ, Beijing, Peoples R China.;[Mou, Lili] Peking Univ, MoE, Key Lab High Confidence Software Technol, Beijing, Peoples R China.;[Yan, Rui] Baidu Inc, Nat Language Proc Dept, Beijing, Peoples R China.;[Hu, Xiaohua; Yan, Rui; Yi, Li; Zhu, Zinan] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
通讯机构：
[Yan, Rui] P;[Yan, Rui] B;[Yan, Rui] C;Peking Univ, Beijing, Peoples R China.;Baidu Inc, Nat Language Proc Dept, Beijing, Peoples R China.
会议名称：
17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016)
会议时间：
SEP 08-12, 2016
会议地点：
San Francisco, CA
会议主办单位：
[Song, Yiping;Mou, Lili;Yan, Rui;Zhang, Ming] Peking Univ, Beijing, Peoples R China.^[Mou, Lili] Peking Univ, MoE, Key Lab High Confidence Software Technol, Beijing, Peoples R China.^[Yan, Rui] Baidu Inc, Nat Language Proc Dept, Beijing, Peoples R China.^[Yan, Rui;Yi, Li;Zhu, Zinan;Hu, Xiaohua] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
摘要：
Most of the existing information retrieval models assume that the terms of a text document are independent of each other. These retrieval models integrate three major variables to determine the degree of importance of a term for a document: within document term frequency, document length and the specificity of the term in the collection. Intuitively, the importance of a term for a document is not only dependent on the three aspects mentioned above, but also dependent on the degree of semantic coherence between the term and the document. In this paper, we propose a heuristic approach, in which the degree of semantic coherence of the query terms with a document is adopted to improve the information retrieval performance. Experimental results on standard TREC collections show the proposed models consistently outperform the state-of-the-art models.
摘要：
The fuzzy classification plays an important role to predict defect of software modules. In this paper, the fuzzy measure (FM) is used to improve the predict accuracy and capability by acquiring all possible interactions among metrics and apply Choquet integral (CI) for classifying in n dimensional space and automatic searching the least misclassification rate based on distance. To implement the model, we also need to determine the unknown parameters, and which is implemented using genetic algorithm (GA) on the training data. The proposed model is tested on the four NASA software projects. The results indicate that the predict performance of proposed model is better than other predict models. Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814740104_0064
作者机构：
[Jin, Cong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Jin, Shu-Wei] Ecole Normale Super, Dept Phys, 24 Rue Lhomond, F-75231 Paris 5, France.
会议名称：
International Conference on Internet of Things and Big Data (IoTBD)
会议时间：
APR 23-25, 2016
会议地点：
Rome, ITALY
会议主办单位：
[Jin, Cong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.^[Jin, Shu-Wei] Ecole Normale Super, Dept Phys, 24 Rue Lhomond, F-75231 Paris 5, France.
摘要：
Learning an effective semantic distance measure is very important for the practical application of image analysis and pattern recognition. Automatic image annotation (AIA) is a task of assigning one or more semantic concepts to a given image and a promising way to achieve more effective image retrieval and analysis. Due to the semantic gap between low-level visual features and high-level image semantic, the performances of some image distance metric learning (IDML) algorithms only using low-level visual features is not satisfactory. Since there is the diversity and complexity of large-scale image dataset, only using visual similarity to learn image distance is not enough. To solve this problem, in this paper, the semantic labels of the training image set participate into the image distance measure learning. The experimental results confirm that the proposed image semantic distance metric learning (ISDML) can improve the efficiency of large-scale AIA approach and achieve better annotation performance than the other state-of-the art AIA approaches.
期刊：
Lecture Notes in Computer Science,2016年10102:300-311 ISSN：0302-9743
通讯作者：
Zhou, Guangyou
作者机构：
[He, Tingting; Zeng, Zhao; Zhou, Guangyou; Xie, Zhiwen] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
通讯机构：
[Zhou, Guangyou] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
会议名称：
第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)
会议时间：
2016-12-02
会议地点：
昆明
会议主办单位：
Kunming Univ Sci & Technol
会议论文集名称：
第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)论文集
摘要：
This paper focuses on the task of knowledge-based question answering (KBQA). KBQA aims to match the questions with the structured semantics in knowledge base. In this paper, we propose a two-stage method. Firstly, we propose a topic entity extraction model (TEEM) to extract topic entities in questions, which does not rely on hand-crafted features or linguistic tools. We extract topic entities in questions with the TEEM and then search the knowledge triples which are related to the topic entities from the knowledge base as the candidate knowledge triples. Then, we apply Deep Structured Semantic Models based on convolutional neural network and bidirectional long short-term memory to match questions and predicates in the candidate knowledge triples. To obtain better training dataset, we use an iterative approach to retrieve the knowledge triples from the knowledge base. The evaluation result shows that our system achieves an \(\text {Average} F_1\) measure of 79.57% on test dataset.
摘要：
In order to reduce Chinese text similarity calculation complexity and improve text clustering accuracy, this paper proposes a new text similarity calculation algorithm based on DF_LDA. First, we use DF method to realize feature extraction; then, we use LDA method to construct text topic model; finally, we use DF_LDA model obtained to calculate text similarity. Due to considering the text semantic and word frequency information, the new method can improve text clustering precision. In addition, DF_LDA method reduces text feature vector dimensions twice; it can efficiently save text similarity calculating time, and increases text clustering speed. Our experiments on TanCorp-12-Txt and FuDanCorp datasets demonstrate that the proposed method can reduce modeling time efficiently, and improves text clustering accuracy effectively.
作者机构：
[Hu, Po; Hu, Xiaohua; Yan, Rui; He, Tingting] Cent China Normal Univ, Comp Sch, Wuhan 430079, Hubei, Peoples R China.;[Yan, Rui] Baidu Inc, Nat Language Proc Dept, Beijing 100085, Peoples R China.;[Li, Cheng-Te] Acad Sinica, Taipei 11529, Taiwan.;[Hsieh, Hsun-Ping] Air Force Inst Technol, Kaohsiung 82047, Taiwan.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议名称：
25th International Conference on World Wide Web (WWW)
会议时间：
MAY 11-15, 2016
会议地点：
Montreal, CANADA
会议主办单位：
[Yan, Rui;Hu, Po;Hu, Xiaohua;He, Tingting] Cent China Normal Univ, Comp Sch, Wuhan 430079, Hubei, Peoples R China.^[Yan, Rui] Baidu Inc, Nat Language Proc Dept, Beijing 100085, Peoples R China.^[Li, Cheng-Te] Acad Sinica, Taipei 11529, Taiwan.^[Hsieh, Hsun-Ping] Air Force Inst Technol, Kaohsiung 82047, Taiwan.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
关键词：
Language model smoothing;bi-directional influence propagation;social networks
摘要：
In recent years, online social networks are among the most popular websites with high PV (Page View) all over the world, as they have renewed the way for information discovery and distribution. Millions of users have registered on these websites and hence generate formidable amount of user-generated contents every day. The social networks become "giants", likely eligible to carry on any research tasks. However, we have pointed out that these giants still suffer from their "Achilles Heel", i.e., extreme sparsity [34, 32]. Compared with the extremely large data over the whole collection, individual posting documents such as microblogs seem to be too sparse to make a difference under various research scenarios, while actually these postings are different. In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation. To further our previously proposed work to tackle the sparsity issue, we extend the socialized language model smoothing with bi-directional influence learned from propagation. Intuitively, it is insufficient not to distinguish the influence propagated between information source and target without directions. Hence, we formulate a bi-directional socialized factor graph model, which utilizes both the textual correlations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and social interactions. These factors are modeled as attributes and dependencies among documents and their corresponding users, and then are distinguished on the direction level. We propose an effective learning algorithm to learn the proposed factor graph model with directions. Finally we propagate term counts to smooth documents based on the estimated influence. We run experiments on two instinctive datasets of Twitter and Weibo. The results validate the effectiveness of the proposed model. By incorporating direction information into the socialized language model smoothing, our approach obtains improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of perplexity, nDCG and MAP measurements.
期刊：
2016 FIRST IEEE INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND THE INTERNET (ICCCI 2016),2016年:114-117
通讯作者：
Zhao, Fuzhe
作者机构：
[Zhao, Fuzhe; Huang, Yuqing; Wang, Tingying] Cent China Normal Univ, Sch Comp, Wuhan, Hubei Province, Peoples R China.
通讯机构：
[Zhao, Fuzhe] C;Cent China Normal Univ, Sch Comp, Wuhan, Hubei Province, Peoples R China.
会议名称：
1st IEEE International Conference on Computer Communication and the Internet (ICCCI)
会议时间：
OCT 13-15, 2016
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Zhao, Fuzhe;Wang, Tingying;Huang, Yuqing] Cent China Normal Univ, Sch Comp, Wuhan, Hubei Province, Peoples R China.
关键词：
smith controller;active queue;self-correcting;TS fuzzy model;congestion control
摘要：
In order to improve the quality of service (QOS) in network transmission, a fuzzy logic active algorithm based on innovative Smith controller is proposed in this paper. This algorithm adopts an improved fuzzy self-tuning principle to make the length of routing queue near to the reference value at a relatively stable level by adjusting the packet loss probability on TCP protocol and changing the active queue of the routing device by the TS fuzzy model. Simulation results show that the algorithm improves the network adaptability and stability compared with classic RED.
