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arXiv 2018年

作者： Chen, Ling-Jiao Gao, Jian CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China

A variety of rating-based recommendation methods have been extensively studied including the well-known collaborative filtering approaches and some network diffusion-based methods, however, social trust relations are not sufficiently considered when making recommendations. In this paper, we contribute to the literature by proposing a trust-based recommendation method, named CosRA+T, after integrating the information of trust relations into the resource-redistribution process. Specifically, a tunable parameter is used to scale the resources received by trusted users before the redistribution back to the objects. Interestingly, we find an optimal scaling parameter for the proposed CosRA+T method to achieve its best recommendation accuracy, and the optimal value seems to be universal under several evaluation metrics across different datasets. Moreover, results of extensive experiments on the two real-world rating datasets with trust relations, Epinions and FriendFeed, suggest that CosRA+T has a remarkable improvement in overall accuracy, diversity and novelty. Our work moves a step towards designing better recommendation algorithms by employing multiple resources of social network information. Copyright © 2018, The Authors. All rights reserved.

关键词： Complex networks

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Sequential monte carlo method for bayesian multiple testing of pairwise interactions among large number of neurons 14

Sequential monte carlo method for bayesian multiple testing ...

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14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Liu, Bin Vinci, Giuseppe Snyder, Adam C. Kass, Robert E. School of Computer Science Jiangsu Key Lab of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Statistics Rice University Houston United States Visual Neuroscience Lab University of Pittsburgh Pittsburgh United States Basis of Cognition Carnegie Mellon University Machine Learning Department Center for the Neural Department of Statistics Pittsburgh United States

ISBN: (纸本)9781538680971

The problem of multiple testing arises in many contexts, including testing for pairwise interaction among a large number of neurons. Recently a method was developed to control false positives when covariate information, such as distances between pairs of neurons, is available. This method, however, relies on computationally-intensive Markov Chain Monte Carlo (MCMC). Here we develop an alternative, based on Sequential Monte Carlo, which only requires one pass of the data. This scheme considers data items sequentially, with relevant probabilities being updated at each step. Simulation experiments demonstrate that the proposed algorithm delivers results as accurately as the previous MCMC method. We illustrate the method by using it to analyze neural recordings from extrastriate cortex in a macaque monkey. © 2018 IEEE.

关键词： Neurons

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Ship Detection Using the Surface Scattering Similarity and Scattering Power

Ship Detection Using the Surface Scattering Similarity and S...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tao Zhang Zhen Y ang Bo Mao Jian Y ang Yifang Ban Huilin Xiong Shanghai Key Lab. of Intelligent Sensing and Recognition Shanghai Jiao Tong University Shanghai China Jiangxi Science and Technology Normal University Nanchang China Jiangsu Key Laboratory of Grain Big Data Mining and Application Nanjing University of Finance & Economics Nanjing China Department of Electronic Engineering Tsinghua University Beijing China Division of Geoinformatics KTH Royal Institute of Technology Stockholm Sweden

Sea surface and ship have different backscattering mechanisms, in which surface scattering is predominant for sea surface in the low sea state case. Based on this fact, many ship detectors have been developed by suppressing the surface scattering resulted from sea surface. Actually, small ship may also have strong surface scattering sometimes. In such a case, the methods of avoiding using surface scattering features may easily miss the detection of small ships. To verify this point, in this paper, we first analyze the shortcomings of An's method which is based on surface scattering similarity and the power maximization synthesis detector (PMS), and then improve it for detecting small ships more effectively. In order to demonstrate the performance of the proposed method, AIRSAR L-Band Polarimetric SAR dataset is exploited. Comparing to other methods, the new method shows a better ship detection performance.

关键词： Marine vehicles Scattering Sea surface Covariance matrices Detectors Synthetic aperture radar Backscatter

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Day and Night Image Stitching and Rendering 4th

Day and Night Image Stitching and Rendering

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4th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2017

作者： Pan, Jeng-Shyang Liang, Lei Hung, Mao-Hsiung Zhuang, Yongjun Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Xueyuan Road University Town Fuzhou China Department of Computer Science and Technology Harbin Institute of Technology HIT Campus of University Town of Shenzhen Shenzhen China Qihan Research Qihan Technology Co. Ltd. Houhaibin Road Nanshan District Shenzhen China

ISBN: (纸本)9783319685267

We present a new method to day and night image stitching and rendering for exploring the space-time continuum within a two-dimensional still photograph. Our method is based on a two-scale decomposition of the input images. Homography matrix is calculated by matching feature points of detail layer pairs, which can avoid impact of illumination. Then detail layers and base layers are stitched together, respectively. We mapped the stitched base layer to a radiance map and then rendered it as time-lapse effect based on human vision system. Compared with previous method, our method is easier to implement and has a larger viewing angle. © 2018, Springer International Publishing AG.

关键词： Rendering (computer graphics)

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Comments on Yu et al’s Shared data Integrity Verification Protocol 4th

Comments on Yu et al’s Shared Data Integrity Verification P...

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4th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2017

作者： Wu, Tsu-Yang Lin, Yueshan Wang, King-Hang Chen, Chien-Ming Pan, Jeng-Shyang Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China School of Computer Science and Technology Harbin Institute of Technology - Shenzhen Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)9783319685267

Recently, Yu et al. proposed a secure shared data integrity verification protocol called SDVIP 2 to ensure the integrity of outsourced file in the cloud. Unfortunately, we exploit the vulnerability of their protocol in this paper. We demonstrate an active adversary can modify the outsourced file such that the auditor is unable to detect in the auditing process. At the end of this paper, we also provide two suggestions to fix the proposed attack. © 2018, Springer International Publishing AG.

关键词： Cloud storage

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Errata to “On-Edge Multi-Task Transfer Learning: Model and Practice With data-Driven Task Allocation”

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IEEE Transactions on Parallel and Distributed Systems 2020年第11期31卷 2569-2569页

作者： Qiong Chen Zimu Zheng Chuang Hu Dan Wang Fangming Liu National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan China Edge Cloud Innovation Lab Technical Innovation Department Cloud BU Huawei Technologies Company Ltd. Shenzhen China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong

Presents corrections to author information for the above named paper.

关键词： Task analysis Resource management Technological innovation Computational modeling big data Service computing Computer science

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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A Novel Acceleration Method for DGTD Algorithm on Sunway TaihuLight

A Novel Acceleration Method for DGTD Algorithm on Sunway Tai...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Geng Chen Lei Zhao Wenhua Yu Hu Ren Haohuan Fu National Supercomputing Center in Wuxi China Jiangsu Key Lab of Education Big Data Science and Engineering Jiangsu Normal University China National Supercomputing Center in Wuxi Tsinghua University China

In this paper, a novel acceleration method for dis-continuous Galerkin time domain (DGTD) algorithm on Sunway platform is proposed with a multi-level speedup technique. In the proposed method, the message passing interface (MPI) is used to connect each core-group (CG) and the registor level massaging passing interface (RL-MPI) is used to connect the slave core on each computing processing element (CPE) cluster, which efficiently overcomes the bottleneck caused by discrete memory accessing. Numerical results show that the performance of DGTD on SW26010 CPU can be dramatically improved by using RL-MPI.

关键词： Acceleration Registers Time-domain analysis Electronic mail Method of moments Computer architecture Transforms

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25th IEEE International Conference on Network Protocols, ICNP 2017

作者： Chen, Hanhua Zhang, Fan Jin, Hai Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781509065011

Real-world stream data with skewed distribution raises unique challenges to distributed stream processing systems. Existing stream workload partitioning schemes usually use a 'one size fits all' design, which leverage either a shuffle grouping or a key grouping strategy for partitioning the stream workloads among multiple processing units, leading to notable problems of unsatisfied system throughput and processing latency. In this paper, we show that the key grouping based schemes result in serious load imbalance and low computation efficiency in the presence of data skewness while the shuffle grouping schemes are not scalable in terms of memory space. We argue that the key to efficient stream scheduling is the popularity of the stream data. We propose and implement a differentiated distributed stream processing system, call DStream, which assigns the popular keys using shuffle grouping while assigns unpopular ones using key grouping. We design a novel efficient and light-weighted probabilistic counting scheme for identifying the current hot keys in dynamic real-time streams. Two factors contribute to the power of this design: 1) the probabilistic counting scheme is extremely computation and memory efficient, so that it can be well integrated in processing instances in the system;2) the scheme can adapt to the popularity changes in the dynamic stream processing environment. We implement the DStream system on top of Apache Storm. Experiment results using large-scale traces from real-world systems show that DStream achieves a 2.3× improvement in terms of processing throughput and reduces the processing latency by 64% compared to state-of-the-art designs. © 2017 IEEE.

关键词： Higher order statistics

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Load balancing for ultra-dense networks: A deep reinforcement learning based approach

arXiv

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arXiv 2019年

作者： Xu, Yue Xu, Wenjun Wang, Zhi Lin, Jiaru Cui, Shuguang Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

—In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intra-cluster load balancing afterwards. Second, for the intra-cluster load balancing, this paper proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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