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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Baolei Cheng Jianxi Fan Dajin Wang Jiwen Yang Provincial Key Laboratory for Computer Information Processing Technology Soochow University China School of Computer Science and Technology Soochow University Suzhou China Department of Computer Science Montclair State University Upper Montclair NJ USA

ISBN: (纸本)9781467393010

Reliable broadcasting for a network can be obtained by using completely independent spanning trees(CISTs). Locally twisted cubes are popular networks which have been studied widely in the literature. In this paper, we study the problem of using CISTs to establish reliable broadcasting in locally twisted cubes. We first propose an algorithm, named LTQCIST, to construct two CISTs in locally twisted cubes, then exemplify the construction procedures to construct CISTs. Finally, we prove the correctness of Algorithm LTQCIST and simulate CISTs with JUNG.

关键词： Broadcasting Hypercubes Reliability computer science Conferences computer security

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Extracting more object usage scenarios for API protocol mining 27

Extracting more object usage scenarios for API protocol mini...

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27th International Conference on Software Engineering and Knowledge Engineering, SEKE 2015

作者： Chen, Deng Zhang, Yanduo Wang, Rongcun Qu, Binbin Ju, Jianping Wei, Wei Industrial Robot Engineering Center Wuhan Institute of Technology Wuhan China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Electronic Information Wuhan University Wuhan China

ISBN: (纸本)1891706373

Automatic protocol mining is a promising approach to infer precise and complete API protocols. However, the effect of the approach largely depends upon the quality of input object usage scenarios, in terms of noise and diversity. This paper aims to extract as many object usage scenarios as possible from object-oriented programs for automatic protocol mining. A large corpus of object usage scenarios can help with eliminating noise accurately and is likely to be diverse. Therefore, precise and complete protocols may be achieved. Given an object-oriented program p, generally, object usage scenarios that can be collected from a run of p is not more than the number of instances used in p. Relying on the inheritance relationship among classes, our technique can extract a maximum of n times more object usage scenarios from p, where n is the average inheritance depth of all object usage scenarios in p. In order to investigate the effect of our technique on mining protocols, we implement it in our previous prototype tool ISpecMiner and use the tool to mine protocols from several real-world applications. The experimental results show that our technique is promising to achieve complete and precise API protocols. In addition, protocols of classes that have not been used in programs can be also achieved, which is helpful for program documentation and understanding.

关键词： Object oriented programming

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HPOSN: A Novel Online Social Network Model Based on Hybrid P2P

HPOSN: A Novel Online Social Network Model Based on Hybrid P...

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International Conference on Cloud Computing and Big Data (CCBD)

作者： Jiwei Wang Fangai Liu Xu Li Haoran Liu Xiaohui Zhao School of Information Science & Engineering Shandong Provincial Key Laboratory for Distributed Computer Software Technology Jinan China

Currently, online social networks are experiencing explosive growth, and play important roles in all aspects of lives, such as daily communication, online study and online dating. People's everyday life can't stand away from online social networks. With the advances of online social networks and popularity of intelligent mobile devices, more and more service providers are marketing their own online social network services, which providing competent services and ample functionalities. Nonetheless, almost all the mainstream social networks are based on the centralized servers or distributed server clusters. The whole system functions on the basis of the servers, so users depend excessively on servers. This kind of system architecture has potential single-point or multi-point failures though it can offer superior services to users. Users can't log into the system to obtain social services when the servers run into faults, such as equipment failures, link failures or network attacks. More seriously, ifcentralized databases meet with troubles, physical damages or mistake operations, massive user data perhaps could not be used or even lost. This paper defines the problem as Local Service Fault Partition (LSFP) and proposes a novel social network model called HPOSN which applies thoughts of P2P to solve LSFP and optimizes the centralized social network services. The prototype of HPOSN optimizes the communication overhead to control the flooding problem of P2P according to social relationship. Several simulation experiments with two parameters have been conducted, including the Recovery Time and the Recovery Success Rate. Results indicate that HPOSN can solve LSFP problem in centralized social networks pragmatically. Users in the LSFP area can recovery the social services locally through selforganizing and self-managing when they lose the services from centralized server or distributed server clusters. So HPOSN can improve the stability of social network services and user e

关键词： Social network services Servers Stability analysis Peer-to-peer computing Prototypes IP networks Big data

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Local Structure Co-occurrence Pattern for Texture Image Retrieval

Local Structure Co-occurrence Pattern for Texture Image Retr...

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2015 International Conference on Simulation, Modelling and Mathematical Statistics(SMMS 2015)

作者： Ke ZHANG Fan ZHANG Jia LU Ying-hua LU Jun KONG Ming ZHANG School of Computer Science and Information Technology Northeast Normal University Key Laboratory of Intelligent Information Processing Northeast Normal University

In this paper, an image feature descriptor called Local structure co-occurrence pattern(LSCP) is proposed. LSCP characterizes image structure via building local binary structure(LBS) based on local binary pattern(LBP), and is represented by co-occurrence matrix according to visual attention mechanism. Therefore, LSCP not only describes low-level visual features integrated with texture feature, color feature and shape feature, but also bridges high-level semantic comprehension. When applied to content-based texture image retrieval and compared with other existing methods on the benchmark datasets, MIT Vis Tex(40 textures), the usefulness, effectiveness and robustness of LSCP are obvious.

关键词： Local structure co-occurrence pattern Semantic comprehension Content-Based texture image retrieval

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Exploration of Image Search Results Quality Assessment

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IEEE Transactions on Big Data 2015年第3期1卷 95-108页

作者： Tian, Xinmei Lu, Yijuan Stender, Nate Yang, Linjun Tao, Dacheng CAS Key Laboratory of Technology Geo-Spatial Information Processing and Application Systems University of Science and Technology of China Hefei230027 China Department of Computer Science Texas State University San MarcosTX78666 United States Department of Computer Science University of Nebraska-Lincoln LincolnNE68588 United States Microsoft Corporation RedmondWA98052-7329 United States Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Image retrieval plays an increasingly important role in our daily lives. There are many factors which affect the quality of image search results, including chosen search algorithms, ranking functions, and indexing features. Applying different settings for these factors generates search result lists with varying levels of quality. However, no setting can always perform optimally for all queries. Therefore, given a set of search result lists generated by different settings, it is crucial to automatically determine which result list is the best in order to present it to users. This paper aims to solve this problem and makes four main innovations. First, a preference learning model is proposed to quantitatively study and formulate the best image search result list identification problem. Second, a set of valuable preference learning related features is proposed by exploring the visual characters of returned images. Third, a query-dependent preference learning model is further designed for building a more precise and query-specific model. Fourth, the proposed approach has been tested on a variety of applications including reranking ability assessment, optimal search engine selection, and synonymous query suggestion. Extensive experimental results on three image search datasets demonstrate the effectiveness and promising potential of the proposed method. © 2015 IEEE.

关键词： Image retrieval

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Portrait image segmentation based on improved grabcut algorithm 6th

Portrait image segmentation based on improved grabcut algori...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Li, Shuai Zheng, Xiaohui Chen, Xianjun Zhan, Yongsong Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology Guilin Guangxi541004 China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin Guangxi541004 China Information Engineering School Haikou College of Economics Haikou Hainan571127

ISBN: (纸本)9783319204680

Traditional computer portrait caricature system mainly take the method that exaggerate and deform real images directly, that lead the facial image background also been deformed when exaggerate facial image. If in pretreatment stage, we segmented the characters and background of the input image, and then do the subsequent processing, the problem may be solved. But for better portrait caricature effects, we need an excellent segmentation algorithm. So, we propose an improved Grabcut image segmentation algorithm and use it to extract the prospect character image for exaggeration and deformation. In practical application, we separate deform and exaggerate the foreground characters image with TPS method, then fuse it with the original or new background picture, get the final image. Application proves, the method solves the background deformation problem well, and improves the quality and rate of image segmentation, caricature synthesis effect reality and natural. © Springer International Publishing Switzerland 2015.

关键词： Image segmentation

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A MapReduce Optimization Method on Hadoop Cluster

A MapReduce Optimization Method on Hadoop Cluster

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International Conference on Industrial Informatics - Computing technology, Intelligent technology, Industrial information Integration

作者： Xiaodong Wu Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

ISBN: (纸本)9781467383134

The MapReduce parallel and distributed computing framework has been widely applied in both academia and industry. MapReduce applications are divided into two steps: Map and Reduce. Then, the input data is divided into splits, which can be concurrently processed, and the amount of the splits determines the number of map tasks. In this paper, we present a regression-based method to compute the number of Map tasks as well as Reduce tasks such that the performance of the MapReduce application can be improved. The regression analysis is used to predict the executing time of MapReduce applications. Experimental results show that the proposed optimization method can effectively reduce the execution time of the applications.

关键词： MapReduce Hadoop Optimization Polynomial Regression

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SCENE TEXT DETECTION USING SEQUENTIAL NONTEXT FILTERING

SCENE TEXT DETECTION USING SEQUENTIAL NONTEXT FILTERING

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IEEE International Conference on Image processing

作者： Qingqing Wang Yue Lu Ying Wen Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University

ISBN: (纸本)9781479983407

We present a scene text detection method based on sequential nontext filtering. Firstly, we start our work with multi-channel maximally stable extremal region (MSER) detection. Then nontext components are eliminated by a four-stage sequential nontext filtering strategy which consists of inner-channel MSER pruning, between-channel MSER pruning, unary feature-based nontext filtering, and binary feature-based nontext filtering. Finally, text components are grouped into words and false positives are eliminated. The proposed method achieves the state-of-the-art on the ICDAR2013 database when compared with some existing methods.

关键词： MSER Sequential nontext filtering Repeating components Unary features Binary features Filtration False-positives texts tree surgery Unary

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Learning Sentiment-inherent Word Embedding for Word-level and Sentence-Level Sentiment Analysis

Learning Sentiment-inherent Word Embedding for Word-level an...

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The International Conference on Asian Language processing (IALP)

作者： Zhihua Zhang Man Lan Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University

Vector-based word representations have made great progress on many Natural Language processing tasks. However,due to the lack of sentiment information, the traditional word vectors are insufficient to settle sentiment analysis tasks. In order to capture the sentiment information, we extended Continuous Skip-gram model(Skip-gram) and presented two sentiment word embedding models by integrating sentiment information into semantic word representations. Experimental results showed that the sentiment word embeddings learned by two models indeed capture sentiment and semantic information as well. Moreover,the proposed sentiment word embedding models outperform traditional word vectors on both Chinese and English corpora.

关键词： Learning Sentiment-inherent Word Embedding for Word-level and Sentence-Level Sentiment Analysis

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Protein model quality assessment by learning-to-rank

Protein model quality assessment by learning-to-rank

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IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

作者： Jing, Xiaoyang Dong, Qiwen Liu, Xuan Liu, Bin School of Computer Science Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China College of Engineering Shanghai Ocean University Shanghai201303 China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen Guangdong518055 China

ISBN: (纸本)9781467367981

Protein structures are essential to understand the function. The predicted models have a broad range of the accuracy. Reliable estimates of the model quality are critical in determining the usefulness of the model to address a specific problem. In this study, a novel method has been presented to rank the models by their relative qualities. The proposed method first extracts various features from the three dimensional structures of proteins and then the learning-to-rank algorithm is used to rank the models based on their similarities with the native structures. Furthermore, a quasi single-model method is presented, which uses the top five identified models as references and ranks the other models by the average similarity with the reference models. Benchmark test is performed on a newly developed, template-based decoy generators which covers all the main structure classes of proteins. The proposed learning-to-rank method achieves an average Pearson correlation coefficient of 0.94 and a AUC value of 0.97, which consistently outperform all other well-developed methods. The quasi single-model can further improves the performance and achieve nearly perfect results with both PCC and AUC value of 0.99. The results demonstrate that the proposed method is an effective methodology for model quality assessment and provides the state-of-the-art performance. © 2015 IEEE.

关键词： Proteins

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