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Chunlin L.,Wuhan University of Technology | Chunlin L.,State Key Laboratory for Novel Software Technology | Layuan L.,Wuhan University of Technology
Information Sciences | Year: 2014

This paper presents exploiting composition of mobile devices for maximizing user QoS under energy constraints in mobile grid. Mobile device service composition process includes two parts: mobile device service provisioning through device service market and mobile device resource allocation through device resource market. Interactions among mobile grid user agent, mobile device service agent and mobile device resource agent are mediated by means of market mechanisms. Mobile device service composition optimization maximizes the interests of mobile grid user agent, mobile device service agent and mobile device resource agent. Utility function is used to specify QoS requirement of mobile grid users and benefit of mobile device resource agents. The problem of services composition of mobile devices is formulated by utility optimization. The paper also presents a mobile grid services composition algorithm to maximize user QoS under energy constraint. In the simulation, the performance evaluation of proposed algorithm for services composition of mobile devices is conducted and compared with other related works. © 2014 Elsevier Inc. All rights reserved.


Yao Y.,State Key Laboratory for Novel Software Technology | Tong H.,City College of New York | Yan X.,University of California at Santa Barbara | Xu F.,State Key Laboratory for Novel Software Technology | Lu J.,State Key Laboratory for Novel Software Technology
WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web | Year: 2013

Trust inference, which is the mechanism to build new pair-wise trustworthiness relationship based on the existing ones, is a fundamental integral part in many real applications, e.g., e-commerce, social networks, peer-to-peer networks, etc. State-of-the-art trust inference approaches mainly employ the transitivity property of trust by propagating trust along connected users (a.k.a. trust propagation), but largely ignore other important properties, e.g., prior knowledge, multi-aspect, etc. In this paper, we propose a multi-aspect trust inference model by exploring an equally important property of trust, i.e., the multi-aspect property. The heart of our method is to view the problem as a recommendation problem, and hence opens the door to the rich methodologies in the field of collaborative filtering. The proposed multi-aspect model directly characterizes multiple latent factors for each trustor and trustee from the locally-generated trust relationships. Moreover, we extend this model to incorporate the prior knowledge as well as trust propagation to further improve inference accuracy. We conduct extensive experimental evaluations on real data sets, which demonstrate that our method achieves significant improvement over several existing benchmark approaches. Overall, the proposed method (MATRI) leads to 26.7% - 40.7% improvement over its best known competitors in prediction accuracy; and up to 7 orders of magnitude speedup with linear scalability. Copyright is held by the International World Wide Web Conference Committee (IW3C2).


Yang Y.,State Key Laboratory for Novel Software Technology | Yang L.,Microsoft | Wu G.,State Key Laboratory for Novel Software Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

In this paper, we present a query-dependent thumbnailing approach for web image search. Motivated by the fact that uniform downsampling cannot emphasize query objects while saliency-based methods may present incorrect foreground objects, we propose to employ common object discovery (COD) algorithms to mine the underlying canonical query objects from the result image collection and adopt the detected object regions of interest (ROIs) as a guide for image cropping. To make the employed COD approach more adaptive to our scenario, we enhance it by introducing text-based search rankings. We then decide for each image whether it should be cropped and determine the final cropping boundary by expanding the detected bounding box, so that the produced thumbnails are of proper appearances. The experimental results demonstrate that our method can outperform down-sampling and saliency-based methods on both object localization accuracy and general thumbnail quality. © Springer International Publishing Switzerland 2013.


Yang Y.,State Key Laboratory for Novel Software Technology | Yang Y.,Microsoft | Yang L.,Microsoft | Wu G.,State Key Laboratory for Novel Software Technology | Li S.,Microsoft
MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia | Year: 2012

Image search reranking has been an active research topic in recent years to boost the performance of the existing web image search engine which is mostly based on textual metadata of images. Various approaches have been proposed to rerank images for general queries and argue that, they may not necessarily be optimal for queries in specific domain, e.g., object queries, since the reranking algorithms are operated on whole images, instead of the relevant parts of images. In this paper, we propose a novel bag-of-objects retrieval model for image search reranking of object queries. Firstly, we employ a common object discovery algorithm to discover query-relevant objects from the search results returned by text-based image search engine. Then, the query and its result images are represented as a language model on the query relevant object vocabulary, based on which the ranking function can be derived. As the common object discovery is unreliable and may introduce noises, we propose to incorporate the attributes of the discovered objects, e.g., size, position, etc., into the ranking function through a linear model, and the weights on the object attributes can be learned. The experiments on two subsets of Web Queries dataset comprising object queries demonstrate that our approach can significantly outperform the existing reranking methods on object queries. © 2012 ACM.


Zhou N.,State Key Laboratory for Novel Software Technology | Zhou N.,Nanjing University | Xie J.-Y.,State Key Laboratory for Novel Software Technology | Xie J.-Y.,Nanjing University
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2011

Current solutions on web services selection based on preferences mainly consider single user's preferences, moreover, most of them are quantitative methods. However, many users propose different preferences on QoS attributes of web services in real applications. On the other hand, in many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. This paper focuses on QoS-based qualitative service selection according to multi users' preferences. We propose an algorithm based on CP-net. The algorithm can deal with Web service selection in terms of multi users' preferences. Experimental results indicate that this method can obtain an optimal outcome which closely satisfies most users' preferences and show the performance of the method.


Rong G.,State Key Laboratory for Novel Software Technology | Rong G.,Nanjing University | Shao D.,State Key Laboratory for Novel Software Technology | Shao D.,Nanjing University
Proceedings - 2012 25th IEEE Conference on Software Engineering Education and Training, CSEE and T 2012 | Year: 2012

The importance of delivering software process courses to software engineering students has been more and more recognized in China in recent years. However, students usually cannot fully appreciate the value of software process courses by only learning methodology and principle in the classroom. Therefore, a process-specific project course was designed to fill the gap between the software process theoretical and experiential knowledge. But to design the course also has many challenges, such as: to provide enough guideline for students; to monitor every process task; to gather and use the process data, especially considering the large class size. We designed a summer school 6-weeks project course in Nanjing University based on TSP (Team Software Process) methodology. To support the course, we developed a supporting tool, the Advance Process Improvement Solution (APIS), which can record and use historical data, support teamwork, and provide process data to both students and teachers in real time. This paper describes the methodology, course organization, supporting tool, and evaluation in details. Based on our two years' experience, this course plays a key role for SE students to better understand software process. © 2012 IEEE.


Rong G.,State Key Laboratory for Novel Software Technology | Rong G.,Nanjing University | Zhang H.,State Key Laboratory for Novel Software Technology | Zhang H.,Nanjing University | And 2 more authors.
2014 IEEE 27th Conference on Software Engineering Education and Training, CSEE and T 2014 - Proceedings | Year: 2014

In order to enhance the understanding of important concepts and strengthen the awareness of software process, we designed a special project-practicing course in Nanjing University as an attempt to solve typical issues in these courses (e.g., focusing on aspects of software process, participation, limited time in a regular semester, etc.). The course is composed of 6-hour lecture and 32-hour bidding game. Preliminary results indicated several advantages with this new education approach on process-specific practicing course, which we already reported on CSEE&T2013. Since this course has been delivered to students from school (less experiences) and industry (more experiences), we noticed students' different performances on this course. In this paper, we collected course results from six classes, based on a comprehensive analysis from 8 different aspects; we try to understand where 'EXPERIENCE' impacts students' difference performance and benefit from the understanding to improve our education on software engineering. © 2014 IEEE.


Xu J.,State Key Laboratory for Novel Software Technology | Yao Y.,State Key Laboratory for Novel Software Technology | Tong H.,Arizona State University | Tao X.,State Key Laboratory for Novel Software Technology | Lu J.,State Key Laboratory for Novel Software Technology
IJCAI International Joint Conference on Artificial Intelligence | Year: 2015

Recommender system has becomean indispensable component in many e-commerce sites. One major challenge that largely remains open is the cold-start problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RAPARE) to break this ice barrier. The center-piece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RAPARE strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.


Yao Y.,State Key Laboratory for Novel Software Technology | Tong H.,Arizona State University | Xu F.,State Key Laboratory for Novel Software Technology | Lu J.,State Key Laboratory for Novel Software Technology
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | Year: 2014

Community Question Answering (CQA) sites have become valuable platforms to create, share, and seek a massive volume of human knowledge. How can we spot an insightful question that would inspire massive further discussions in CQA sites? How can we detect a valuable answer that benefits many users? The long-term impact (e.g., the size of the population a post benefits) of a question/answer post is the key quantity to answer these questions. In this paper, we aim to predict the long-term impact of questions/answers shortly after they are posted in the CQA sites. In particular, we propose a family of algorithms for the prediction problem by modeling three key aspects, i.e., non-linearity, question/answer coupling, and dynamics. We analyze our algorithms in terms of optimality, correctness, and complexity. We conduct extensive experimental evaluations on two real CQA data sets to demonstrate the effectiveness and efficiency of our algorithms. © 2014 ACM.


Zhu R.,Jiaxing University | Zhu R.,State Key Laboratory for Novel Software Technology | Guo B.,Jiaxing University
IASP 10 - 2010 International Conference on Image Analysis and Signal Processing | Year: 2010

Rapid development of various resources available on the Internet, the network just acts as a double-edged sword, which spreads useful and harmful information to the users simultaneously. In this paper, a novel Altering model for the erotic images embedded in web pages is proposed. It not only detects network eroticism, but also screens the web pages containing erotic images. For achieving a real-time processing in a dynamic network environment, the model is implemented by a BHO technique and its every part is designed on a cost-effectiveness analysis. The recognition accuracy of erotic images is improved significantly by Introducing color correction into an image Alter and adopting window mechanism into a text detector. Experimental results show that the proposed method obtains an excellent performance in terms of both efficiency and accuracy. ©2010 IEEE.

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