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An Efficient Personalized Video Recommendation Algorithm Based on Mixed Mode

An Efficient Personalized Video Recommendation Algorithm Bas...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Wei Zhang Yi Wang Hao Chen Xia Wei National Engineering Research Center for ELearning Central China Normal University Wuhan China School of Computer Scientce of Information Engineering Hubei University Wuhan China

ISBN: (纸本)9781728152103

The collaborative filtering recommendation algorithm based on k-means clustering is more accurate in the case of large amount of users' ratings, and it is not suitable for the situations that users' ratings are relatively small. The video gene recommendation algorithm based on linear regression uses the opposite scenario. In order to solve the problem of general application of the single recommendation algorithm, this paper proposes a hybrid recommendation algorithm based on collaborative filtering and video gene. This algorithm first constructs user project matrix, calculates user similarity, and then cluster to get a recommendation list by k-means clustering. Then the genetic structure of the video is analysed, the gene preference is combined by style preference and regional preference, and the weight of the gene is determined by linear regression, at last, a recommendation list is obtained by selecting the object with high gene preference. Finally, the final recommendation results are obtained by weighting the recommended results in two recommendation lists. The experimental results show that the proposed algorithm has higher accuracy no matter when the number of users' ratings is large or small.

关键词： collaborative filtering genetics pattern clustering recommender systems regression analysis video signal processing collaborative filtering Video signal processing Recommender systems Cluster Analysis Videotapes Genetics regression analysis algorithms Linear Regression Preferences preference Genes

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An Adaptive Clustering Algorithm of Gaussian Curve Fitting the Histogram Based on K-Means

An Adaptive Clustering Algorithm of Gaussian Curve Fitting t...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Guangkun Ma Jinzhu Yang Xiaoxu Song Hong Zhao School of Computer Science and Engineering Northeastern University Shenyang China School of Software Shenyang University of Technology Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781728152103

The K-mean algorithm is simple and efficient in vascular segmentation. But in a K-means clustering algorithm, the number of clustering centers must be determined in advance and the random allocation of clustering centers seriously affects the efficiency of the algorithm. In order to solve this problem, an adaptive clustering algorithm based on K-means with the Gaussian curve fitting histogram is proposed, which is used to segment circular-like blood vessels. Firstly, the histogram outline is fitted into the Gaussian curve by the least square method and the maximum value of the curve is obtained. The number of maximal values is counted as the number of clustering centers and the maximum points are used as the central points of clustering. Also, the K-means algorithm is improved by combining the intensity and position information of pixels. The proposed method has been tested by 6 groups of medical images. The algorithm has the advantages of less iterations, fast convergence speed and getting more than 95% Dice Similarity Measure value. The algorithm can effectively solve the determination of multi-seed points and vascular segmentation in medical image segmentation.

关键词： blood vessels curve fitting Gaussian processes image segmentation medical image processing pattern clustering Gaussian distribution image segmentation Clustering algorithms Medical image processing Cluster Analysis Gaussian processes Curve fitting Blood Vessels histograms Least squares method

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Redis++: A high performance in-memory database based on segmented memory management and two-level hash index 16

Redis++: A high performance in-memory database based on segm...

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16th IEEE international symposium on Parallel and distributed Processing with applications, 17th IEEE international Conference on Ubiquitous computing and Communications, 8th IEEE international Conference on Big Data and Cloud computing, 11th IEEE international Conference on Social computing and Networking and 8th IEEE international Conference on Sustainable computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018

作者： Zhang, Peng Xing, Lichao Yang, Ninggou Tan, Guolin Liu, Qingyun Zhang, Chuang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences China

ISBN: (纸本)9781728111414

Redis is an open source in-memory data structure store, used as a database, cache and message broker. However, there are two problems that will degrade its performance. One is the memory fragmentation problem, another is cache miss problem. For the purpose, this paper presents a highperformance in-memory database Redis++. In the memory management mechanism, Redis++ will allocate and deallocate a fixedsize memory segment from the system. The data in each memory segment are stored continuously, and the memory segment is reclaimed based on the profit evaluation model. Secondly, a twolevel hash index structure is designed, the structure uses twolevel index to complete only one cache mapping per query. In addition, instruction-level parallelism is implemented using the single instruction multiple data instruction set, which speeds up the query efficiency of the secondary index. The experiments prove the effect of Redis++ on memory utilization, response latency and throughput. © 2018 IEEE.

关键词： Profitability

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Characterizing and Balancing the Workloads of Semi-Containerized Clouds

Characterizing and Balancing the Workloads of Semi-Container...

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international Conference on Parallel and distributed Systems (ICPADS)

作者： Shang Zhao Shuai Xue Quan Chen Minyi Guo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728125848

With the development of Cloud computing, many applications are deployed on the Clouds (either public Clouds or private Clouds) for the elastic resource management. A key challenge here is satisfying the Quality-of-Service requirement of online services that requires short response time while maximizing the resource utilization. While applications should be co-located to improve the resource utilization, the co-location results in performance interference between the co-located applications that hurts the QoS of latency-sensitive services. To understand this problem in depth, we characterize and analyze the real business-level application co-location scenario based on the trace of an actual Cloud product released by Alibaba. Our observation shows that the datacenter suffers from severe load imbalance that often results in QoS violation of the online services on the servers with heavy workload. To balance the workload, we propose a scheduling algorithm based on the resource reservation and utilization of containers. Based on the comparison between the experiment and the existing operation results, the proposed algorithm reduces the standard deviation of the load on servers from 5.27 to 1.43 (CPU) and 9.83 to 1.93 (Memory).

关键词： Alibaba trace Co-locations Workload balance Scheduling strategy Co-location scheduling policy Clouds Information Services Resource utilization Workload unbalanced load Servers Quality of service Reaction Time

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Research on New Energy Grid Connection and Electric Vehicle Charging Fault Resolution Method

Research on New Energy Grid Connection and Electric Vehicle ...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Menglong Xu Xiaohua Long Wenwen Zhou Research and Development Center PONOVO Power Co. Ltd Beijing China Research and Development Center Changjun Meixihu Middle School Changsha China

ISBN: (纸本)9781728152103

With the development of energy industry, the power generation and transportation industry have developed from traditional petrochemical energy to renewable energy (RE). Many wind farms and photovoltaic (PV) power plants have been set up. More and more electric vehicles (EV) and EV charging stations are connected to the power grid which has brought new challenges to the safe and stable operation of the power grid. The EV grid-connected charging is accompanied by various faults. The unstable RE also induced lots of new problems. In this paper an intelligent test platform was designed to realize the grid-connected RE station and EV charging station online diagnose and fault prediction. IoT, big data analysis technologies were used to process the collected various characteristic dimensions of multi-heterogeneous data. Based on Random Forest (RF) algorithm machine learning technology was used to realize the online diagnose and fault prediction.

关键词： Big Data electric vehicle charging electric vehicles Internet of Things learning (artificial intelligence) petrochemicals photovoltaic power systems power engineering computing power generation faults power grids wind power plants Electric vehicles Power grids big data Petrochemicals Power engineering computing photovoltaic power systems Wind power plants Internet of things Wind farms Transportation industry

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A Low Cost and Easy Implement Highway Accident Detection Model Based on Big Data

A Low Cost and Easy Implement Highway Accident Detection Mod...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Chuntao Song Yong Wang Shiyan Zhang Lexi Xu China United Network Communications Corporation Network Technology Research Institute Beijing P. R. China China United Network Communications Group Corporation Beijing P. R. China Heilongjiang Branch China Continent Property & Casualty Insurance Corporation Harbin P.R.China

ISBN: (纸本)9781728152103

With the rapid growth of motor vehicles and highway construction, car accident becomes a major problem. Fast detection of an accident can save human life, reduce rescue time and public resources. In this paper, we propose a fast and implementable model for highway accident detection based on big data method and mobile operators' call data. To avoid the cost of infrastructure construction, sensor installation and data collection barriers between different departments, this model only uses the mobile operators' measurement report (MR) data to build the model, without any additional installation of traffic sensors, cameras, or any other detection equipment. This model creates a fingerprint map for a road with direction and location labels. Then this model uses the logistic regression (LR) algorithm to determine a passing mobile vehicle's location based on the map and MR data, alarm with different degree raised after a decision rule include the vehicle's location, velocity and velocity reduction, as well as emergency alarm suggestion for the following vehicles.

关键词： Big Data mobile computing regression analysis road accidents road safety road traffic traffic engineering computing big data Road accidents Road safety Road traffic Mobile computing Highway accidents Mobile Operators engineering computing regression analysis decision rule DETECTION EQUIPMENT

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Finding Global Contrast Core Subgraphs in Large-Scale Genetic Association Study

Finding Global Contrast Core Subgraphs in Large-Scale Geneti...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Lei Qiu Yuan Li Jing Sun Jinsheng Liu Yuhai Zhao School of Information Science and Technology North China University of Technology Beijing China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781728152103

Genetic association study (GAS) is crucial to reveal the underlying principles of complex diseases. This is the first work that introduces contrast subgraph mining in two networks with significantly different edges or edge weights but the same vertices to solve GAS problem. It can not only detect the genetic loci highly associated with certain diseases, but discriminate between disease causing and disease preventing genetic loci, which captures more comprehensive and informative value for biologists. Inspired by the concept of r-core and minimum vertex weight, we propose to identify the novel global contrast r-core subgraphs r-GCCSs, which is more robust to outliers and redundancy. Further, we formulate the top-k r-GCCSs detection problem based on global contrast measure. In particular, (1) a linear time search algorithm is carefully developed to find the top-k r-GCCSs; (2) To further reduce the high computational cost, a linear space index is devised to support the top-k search. Comprehensive experiments on four large-scale real datasets demonstrate the efficiency and effectiveness of our approaches.

关键词： biology computing data mining diseases genetic algorithms genetics search problems biology computing tabu search maladjustment Genetic Algorithm (GA) data mining Genetics Genetic Association Studies Genetic Loci

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Connection Degree Cost and Reward Based Algorithm in Cognitive Radio Networks

Connection Degree Cost and Reward Based Algorithm in Cogniti...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Yi Zhang Yao Wang Cen Wu Bin Zhao Jiamei Chen Jing Dong Department of Air Defense Forces Noncommissioned Officer Academy Institute of Army Artillery and Air Defense Forces Shenyang China College of Electrical and Information Engineering Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Spectrum management is a vital problem in cognitive radio networks. Unfortunately, most current spectrum management algorithms don't consider power control at the same time, which deviates from the original intention for cognitive radio (CR). Thus, a new spectrum management mechanism named Connection degree cost and Reward Based (CRB) algorithm for CR networks is presented in the paper. The new algorithm allocates available resources properly, while guaranteeing quality of service (QoS) for secondary users (SUs). And a new utility function is designed to characterize the system total utility more realistically in the network model. Numerical results validate the new algorithm gains better performance than the existing schemes both in the system total utility performance and in the system fairness performance.

关键词： cognitive radio quality of service radio spectrum management resource allocation Cognitive radio Radio spectrum management Quality of service Resource utilization Spectrum management Utility functions Radio networks algorithms management of resources Reward Power control

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D&D: A distributed and Disposable Approach to Privacy Preserving Data Analytics in User-Centric Healthcare

D&D: A Distributed and Disposable Approach to Privacy Preser...

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IEEE international Conference on Service-Oriented computing and applications (SOCA)

作者： Zheng Li Esteban J. Pino Department of Computer Science University of Concepción Concepción Chile Department of Electrical Engineering University of Concepción Concepción Chile

Benefiting from the modern information and communication technologies, user centricity has become a clear evolution trend in healthcare. Unfortunately, given the high sensitivity of health data and the uncertainty in user environments, user-centric healthcare systems inevitably suffer from more frequent privacy threats, not to mention that technologies and business of data exploitation have generally outpaced the current privacy regulations and laws. Although there exist well-defined privacy preserving mechanisms, such as Data Encryption, Data Perturbation, and De-identification, they have been considered inadequate to address the diverse privacy challenges in big healthcare data analytics. Our argument is that, before considering any sophisticated mechanism, practitioners should first try to imitate human memory's forgetting process as an intrinsic privacy preserving strategy in user-centric healthcare. Technically, we implement this strategy by changing traditional data analytics routines into a distributed and disposable manner, so as to naturally exclude the data owners' sensitive information. The technical implementation essentially acts as a concrete How-To solution to satisfying a fundamental principle of privacy law, i.e. data minimization. We have initially applied our work to a smart bed project for sleep quality analytics, and received positive feedback on the effectiveness of privacy preservation in suitable homecare scenarios.

关键词： Data privacy Medical services Privacy Big Data Data analysis distributed databases Memory

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A low cost method for determining the entire network path bandwidths of indirect high-performance computer networks 16

A low cost method for determining the entire network path ba...

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作者： Yan, Baicheng Zhou, Yi Xiao, Limin Yang, Zhang Wu, Hongyu Wei, Bing School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Applied Physics and Computational Mathematics Beijing100094 China

ISBN: (纸本)9781728111414

Bandwidth is an important performance metric for high-performance computer network (HINET). How to determine large-scale HINET path bandwidths quickly and precisely is a highly pivotal challenge for parallel application performance analysis and prediction, optimizing end-to-end data transport performance, network management, etc. However, it is hard to determine the entire network point-to-point bandwidths of HINET since it often costs amounts of measurement time. Existing path bandwidth measurement methods focus on improving the accuracy of single path bandwidth measurement, however, lack considerations for reducing the entire network path bandwidths measurement times. To address this issue, we present an efficient method to generate the full-scale point-to-point bandwidth measurement plan for rapidly obtaining each path bandwidth in HINET. For evaluating, a source routing Fat-tree simulator and comprehensive experiments were conducted for different application scenarios. Both experimental and analysis showed that our method can reduce the measurement times significantly. In particular, compared to the exhaustive method, the measurement times of our method are one in 185 for the entire path bandwidth fault and can save 95% measurement times in 1024 nodes network with synthetic traffic. © 2018 IEEE.

关键词： Bandwidth

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