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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...

引用

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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Designing insulation coordination for Ultra High Voltage AC system by analyzing over voltages using frequency dependent line modeling

Designing insulation coordination for Ultra High Voltage AC ...

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Nirma University International Conference on Engineering (NUiCONE)

作者： Hemangkumar Tailor Satishkumar Joshi Tsinghua University School of Software and TNLIST College of Computer Science Zhejiang Key Laboratory of Service Robot

The Insulation coordination is one of the important criteria for designing Ultra High Voltage system. The method to design insulation coordination of any Ultra High Voltage system needs stress analysis and its comparison with the design strength of the equipment. In this paper types of overvoltages in Ultra High Voltage system, process of insulation coordination, frequency dependent line modeling and simulation using Electromagnetic Transient Program (EMTP) & design of insulation coordination for 1200 KV AC transmission line have been presented. The technique to reduce stress have been suggested by using Surge Arrester (SA) is examined.

关键词： Surges Insulation Arresters Switches Stress Voltage control

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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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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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Unsupervised face-name association via commute distance

Unsupervised face-name association via commute distance

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20th ACM International Conference on Multimedia, MM 2012

作者： Bu, Jiajun Xu, Bin Wu, Chenxia Chen, Chun Zhu, Jianke Cai, Deng He, Xiaofei 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)9781450310895

Recently, the task of unsupervised face-name association has received a considerable interests in multimedia and information retrieval communities. It is quite different with the generic facial image annotation problem because of its unsupervised and ambiguous assignment properties. Specifically, the task of face-name association should obey the following three constraints: (1) a face can only be assigned to a name appearing in its associated caption or to null;(2) a name can be assigned to at most one face;and (3) a face can be assigned to at most one name. Many conventional methods have been proposed to tackle this task while suffering from some common problems, eg, many of them are computational expensive and hard to make the null assignment decision. In this paper, we design a novel framework named face-name association via commute distance (FACD), which judges face-name and face-null assignments under a unified framework via commute distance (CD) algorithm. Then, to further speed up the on-line processing, we propose a novel anchor-based commute distance (ACD) algorithm whose main idea is using the anchor point representation structure to accelerate the eigen-decomposition of the adjacency matrix of a graph. Systematic experiment results on a large scale and real world image-caption database with a total of 194,046 detected faces and 244,725 names show that our proposed approach outperforms many state-of-the-art methods in performance. Our framework is appropriate for a large scale and real-time system. © 2012 ACM.

关键词： Real time systems

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