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International Conference on Network Protocols

作者： Yi Gao Wei Dong Chun Chen Jiajun Bu Gaoyang Guan Xuefeng Zhang Xue Liu Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University School of Computer Science McGill University

ISBN: (纸本)9781479912681

In wireless sensor networks, sensor nodes are usually self-organized, delivering data to a central sink in a multi-hop manner. Reconstructing the per-packet routing path enables fine-grained diagnostic analysis and performance optimizations of the network. The performances of existing path reconstruction approaches, however, degrade rapidly in large scale networks with lossy links. In this paper, we propose Pathfinder, a robust path reconstruction method against packet losses as well as routing dynamics. At the node side, Pathfinder exploits temporal correlation between a set of packet paths and efficiently compresses the path information using path difference. At the PC side, Pathfinder infers packet paths from the compressed information and employs intelligent path speculation to reconstruct the packet paths with high reconstruction ratio. We evaluate several variations of Pathfinder as well as two most related approaches using traces from a large scale deployment and extensive simulations. Results show that Pathfinder outperforms existing approaches, achieving both high reconstruction ratio and low transmission overhead.

关键词： Routing Vectors Containers Packet loss Encoding Wireless sensor networks

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Whom to Mention: Expand the Diffusion of Tweets by @ Recommendation on Micro-blogging Systems 13

Whom to Mention: Expand the Diffusion of Tweets by @ Recomme...

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International Conference on World Wide Web

作者： Beidou Wang Can Wang Jiajun Bu Chun Chen Wei Vivian Zhang Deng Cai Xiaofei He Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China Microsoft Corporation Redmond WA USA State Key Lab of CAD&CG College of Computer Science Zhejiang University China

ISBN: (纸本)9781450324441

Nowadays, micro-blogging systems like Twitter have become one of the most important ways for information sharing. In Twitter, a user posts a message (tweet) and the others can forward the message (retweet). Mention is a new feature in micro-blogging systems. By mentioning users in a tweet, they will receive notifications and their possible retweets may help to initiate large cascade diffusion of the tweet. To enhance a tweet's diffusion by finding the right persons to mention, we propose in this paper a novel recommendation scheme named as whom-to-mention. Specifically, we present an in-depth study of mention mechanism and propose a recommendation scheme to solve the essential question of whom to mention in a tweet. In this paper, whom-to-mention is formulated as a ranking problem and we try to address several new challenges which are not well studied in the traditional information retrieval tasks. By adopting features including user interest match, content-dependent user relationship and user influence, a machine learned ranking function is trained based on newly defined information diffusion based relevance. The extensive evaluation using data gathered from real users demonstrates the advantage of our proposed algorithm compared with the traditional recommendation methods.

关键词： Micro-blogging Systems Twitter Mention Recommendation Information Diffusion Information Retrieval

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Reprogramming over Low Power Link Layer in Wireless Sensor Networks

Reprogramming over Low Power Link Layer in Wireless Sensor N...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Yi Gao Chun Chen Xue Liu Jiajun Bu Wei Dong Xianghua Xu College of Computer Science Zhejiang Provincial Key Lab of Service Robot McGill University School of Computer Science Hangzhou Dianzi University School of Computer Science

Reprogramming over the air is important for maintaining a wireless sensor network. Traditional reprogramming approaches assume always-on link layers. Given the energy limitation of sensor nodes, always-on link layers are often not desired for most sensor network applications. In this paper, we propose ROLP, a novel reprogramming protocol built on a widely used low power link layer in wireless sensor networks. ROLP employs an efficient control packets self-suppression scheme for reliable data transmission. ROLP also employs an adaptively falling asleep scheme based on the neighbor information to reduce the energy consumption. We implement ROLP based on TinyOS and evaluate its performance in two different indoor networks. Compared with the standard reprogramming protocol in TinyOS, ROLP is able to reduce the radio-on time by 57.6% and 39.0% in the two networks. Since radio operations cost most of the energy, these reductions save significant amount of energy and prolong the lifetime of a wireless sensor network.

关键词： Receivers Protocols Data communication Wireless sensor networks Reliability Educational institutions Mathematical model

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D2: Anomaly Detection and Diagnosis in Networked Embedded Systems by Program Profiling and Symptom Mining

D2: Anomaly Detection and Diagnosis in Networked Embedded Sy...

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Real-Time Systems Symposium (RTSS)

作者： Wei Dong Chun Chen Jiajun Bu Xue Liu Yunhao Liu Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University China School of Computer Science McGill University Canada MOE Key Lab for Information System Security Tsinghua University China

Detecting and diagnosing anomalies in networked embedded systems like sensor networks is a very difficult task, due to the variable workloads and severe resource constraints. We notice that most node-level debugging tools can provide detailed program information inside the node but fail to detect when and where a problem occurs in the network. On the other hand, most network-level diagnosis tools can effectively detect a problem from the network but fail to narrow down the problem within the node because they lack detailed program information. To close the gap, we propose D2, a new anomaly detection and diagnosis method by combining program profiling and symptom mining. D2 employs binary instrumentation to perform lightweight function count profiling. Based on the statistics, D2 uses PCA (Principal Component Analysis) based approach for automatically detecting network anomalies. Compared to previous methods, D2 is able to point programmers closer to the most likely causes by a novel approach combining statistical tests and program call graph analysis. We implement our method based on TinyOS 2.1.1 and evaluate its effectiveness by case studies in the development of a working sensor network. Results show that our method is effective for detecting and diagnosing problems in real-world sensor network systems, and at the same time, incurs an acceptable overhead.

关键词： Instruments Principal component analysis Software Radiation detectors Educational institutions Debugging Random access memory

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Shape sparse correspondence for nearly-isometric meshes based on Green's function representation

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of computer-Aided Design and computer Graphics 2013年第5期25卷 714-723页

作者： Wang, Jinrong Feng, Jieqing Miao, Yongwei State Key Laboratory of CAD and CG Zhejiang University Hangzhou 310058 China Hangzhou Institute of Service Engineering Hangzhou Normal University Hangzhou 310036 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023 China

Based on global point signature and Green's function representation, a coarse-to-fine sparse correspondence algorithm for nearly-isometric meshes is proposed in this paper. To address the problem of undetermined signs of the corresponding basis vectors, the anchor points are extracted from the source mesh and the nearly-isometric destination mesh respectively based on the Morse theory and a modified hierarchically clustering approach. Their correspondences are then established by minimizing their GPS distances, which can be solved via a combinational searching strategy. Second, to solve the inconsistent problem of high dimensional GPS coordinates caused by different resolutions of meshes, a new Green's function presentation of point is proposed based on the anchor point pairs obtained. As a result, the sparse correspondence of nearly-isometric meshes is established incrementally. Experimental results demonstrate the proposed algorithm exhibits better computational efficiency and correspondence accuracy than other shape correspondence algorithms. It can be potentially applied to rigid or non-rigid mesh alignment, 3D morphing, shape matching, etc.

关键词： Computational efficiency

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Pose estimation with motionlet LLC coding

Pose estimation with motionlet LLC coding

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13th Pacific-Rim Conference on Multimedia, PCM 2012

作者： Sun, Li Song, Mingli Bu, Jiajun Chen, Chun Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China

ISBN: (纸本)9783642347771

3D human pose estimation is a challenging but important research topic with abundant applications. As for discriminative human pose estimation, the main goal is to learn a nonlinear mapping from image descriptors to 3D human pose configurations, which is difficult due to the high-dimensionality of human pose space and the multimodality of the distribution. To address these problems, we propose a novel motionlet LLC coding on a discriminative framework. A motionlet consists of training examples covering a local area in terms of image space, pose space and time stream. We first group most informative and helpful training examples into motionlets, then perform LLC Coding to learn the nonlinear mapping and get candidate poses, and finally choose the most appropriate pose as the result estimate. To further eliminate ambiguities and improve robustness, we extend our framework to incorporate multiviews. We conduct qualitative evaluation on our Taichi data set and quantitative evaluation on HumanEva data set, which show that our approach has gained the-state-of-the-art performance and significant improvement against previous approaches. © 2012 Springer-Verlag.

关键词： Mapping

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Colorization for gray scale facial image by locality-constrained linear coding

Colorization for gray scale facial image by locality-constra...

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13th Pacific-Rim Conference on Multimedia, PCM 2012

作者： Liang, Yang Song, Mingli Bu, Jiajun Chen, Chun Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China

ISBN: (纸本)9783642347771

Colorization for gray scale facial image is an important technique in various practical applications. However, the methods that have been proposed are essentially semi-automatic. In this paper, we present a new probabilistic framework based on Maximum A Posteriori (MAP) estimation to automatically transform the given gray scale facial image to corresponding color one. Firstly, the input image is divided into several patches and non-parametric Markov random field (MRF) is employed to formulate the global energy. Secondly, Locality-constrained Linear Coding (LLC) is employed to learn the color distribution for each patch. At the same time, the simulated annealing algorithm is employed to iteratively update the patches chosen by LLC to optimize the MRF by decreasing global energy cost. The experimental results demonstrate that the proposed framework is effective to colorize the gray scale facial images to corresponding color ones. © 2012 Springer-Verlag.

关键词： Simulated annealing

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Relational co-clustering via manifold ensemble learning 12

Relational co-clustering via manifold ensemble learning

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21st ACM International Conference on Information and Knowledge Management, CIKM 2012

作者： Li, Ping Bu, Jiajun Chen, Chun He, Zhanying Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou 310027 China

ISBN: (纸本)9781450311564

Co-clustering targets on grouping the samples and features simultaneously. It takes advantage of the duality between the samples and features. In many real-world applications, the data points or features usually reside on a submanifold of the ambient Euclidean space, but it is nontrivial to estimate the intrinsic manifolds in a principled way. In this study, we focus on improving the co-clustering performance via manifold ensemble learning, which aims to maximally approximate the intrinsic manifolds of both the sample and feature spaces. To achieve this, we develop a novel co-clustering algorithm called Relational Multi-manifold Co-clustering (RMC) based on symmetric nonnegative matrix tri-factorization, which decomposes the relational data matrix into three matrices. This method considers the inter-type relationship revealed by the relational data matrix and the intra-type information reflected by the affinity matrices. Specifically, we assume the intrinsic manifold of the sample or feature space lies in a convex hull of a group of pre-defined candidate manifolds. We hope to learn an appropriate convex combination of them to approach the desired intrinsic manifold. To optimize the objective, the multiplicative rules are utilized to update the factorized matrices and the entropic mirror descent algorithm is exploited to automatically learn the manifold coefficients. Experimental results demonstrate the superiority of the proposed algorithm. © 2012 ACM.

关键词： Matrix algebra

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A bregman divergence optimization framework for ranking on data manifold and its new extensions 26

A bregman divergence optimization framework for ranking on d...

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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： Xu, Bin Bu, Jiajun Chen, Chun Cai, Deng Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China State Key Lab of CADandCG College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9781577355687

Recently, graph-based ranking algorithms have received considerable interests in machine learning, computer vision and information retrieval communities. Ranking on data manifold (or manifold ranking, MR) is one of the representative approaches. One of the limitations of manifold ranking is its high computational complexity (O(n3), where n is the number of samples in database). In this paper, we cast the manifold ranking into a Bregman divergence optimization framework under which we transform the original MR to an equivalent optimal kernel matrix learning problem. With this new formulation, two effective and efficient extensions are proposed to enhance the ranking performance. Extensive experimental results on two real world image databases show the effectiveness of the proposed approach. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Graphic methods

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An exploration of improving collaborative recommender systems via user-item subgroups 12

An exploration of improving collaborative recommender system...

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21st Annual Conference on World Wide Web, WWW'12

作者： Xu, Bin Bu, Jiajun Chen, Chun Cai, Deng Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China State Key Lab. of CAD and CG College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9781450312295

Collaborative filtering (CF) is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. However we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more natural to make preference predictions for a user via the correlated subgroups than the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we propose an unified framework to extend the traditional CF algorithms by utilizing the subgroups information for improving their top-N recommendation performance. Our approach can be seen as an extension of traditional clustering CF models. Systematic experiments on three real world data sets have demonstrated the effectiveness of our proposed approach.

关键词： Collaborative filtering

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