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Parallel k-dominant skyline queries over uncertain data streams with capability index 21

Parallel k-dominant skyline queries over uncertain data stre...

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21st IEEE International conference on high performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Li, Xiaoyong Liu, Jun Ren, Kaijun Li, Xiaoling Ren, Xiaoli Deng, Kefeng College of Meteorology and Oceanography National University of Defense Technology China College of Computer Science and Technology National University of Defense Technology China PLA 91550 China

ISBN: (纸本)9781728120584

The skyline query over uncertain data streams, as an important aspect of big data analysis, plays a significant role in various domains like financial data analysis, environmental monitoring, and wireless sensor network. However, with the diversity of user query requirements, the traditional skyline query is not practical enough and even cannot meet users' requirements. To address the problem that the number of uncertain skyline queries results is so numerous that cannot offer any practical insights effectively, we propose a dominance-capability based parallel uncertain k-dominant skyline queries method named PKDS in this paper. Firstly, the method defines the k-dominant skyline query problem over uncertain data streams. Secondly, PKDS maps the new arriving items to multiple compute nodes according to the streaming items mapping strategy based on sliding-window partitioning, in order to support the parallel processing for the k-dominant skyline queries over uncertain data streams efficiently. Specifically, an index structure based on the k-dominant capability of streaming items is developed to efficiently manage streaming items, which could greatly improve the k-dominance tests and further the efficiency of parallel k-dominant skyline queries over uncertain data streams. Extensive experimental results demonstrate that, PKDS method not only can reduce the results of skyline queries over high-dimensional streaming items to the scope that could give a better decision support, but also can greatly improve the query efficiency. © 2019 IEEE.

关键词： Wireless sensor networks

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Virtual reactors towards the exascale supercomputing era 2nd

Virtual reactors towards the exascale supercomputing era

引用

2nd International Workshop on high performance Computing for Advanced Modeling and Simulation in Nuclear Energy and Environmental Science, HPCMS 2018, and 1st International Workshop on HPC Supported Data Analytics for Edge Computing, HiDEC 2018 held at the 32nd ACM International conference on Supercomputing, ACM ICS 2018

作者： Wang, Xianmeng Hu, Changjun Yang, Wen Liu, Tiancai Wang, Zhaoshun Wang, Xuesong Yang, Hongwei Wu, Mingyu Wang, Jue Wang, An Zhou, Zhifeng School of Computer and Communication Engineering University of Science and Technology Beijing China Beijing Key Laboratory of Knowledge Engineering for Material Science Beijing China China Institute of Atomic Energy Beijing China Computer Network Information Center Chinese Academy of Science Beijing China

ISBN: (纸本)9789813299863

Virtual reactor is an interplanetary research field, which leverages the leading supercomputers to provide a profound understanding of existing reactors and design guidance for future reactors. Virtual reactor is at the cutting-edge of high performance computing and nuclear engineering research fields. The United States, European Union and China have put great effort into virtual reactor research. The research areas of virtual reactors include integrated platform, safety analysis, system analysis, core physics components, database, uncertainty quantification, verification and validation. Typically, core physics consist of thermal-hydraulics, mechanics, neutronics, fuel, and material performance software. Virtual reactor requires enormous computing and storage resource. The computing capabilities of Exascale supercomputers enables realistic models and high-resolution numerical methods come into play. Compared to traditional way, virtual reactor makes simulation more accurate (high-fidelity models are used, such as LES, multi-group transport theory), larger scaled (such as more atoms in molecular dynamics) and tighter coupled. Meanwhile, great challenges have been posed to models, numerical methods, algorithms and programing, especially considering the complicated modern architectures of supercomputers. The paper focuses on the research areas, computing and storage requirements, challenges of virtual reactors. © Springer Nature Singapore Pte Ltd 2019.

关键词： Numerical methods

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A study on L1 data cache bypassing methods for high-performance GPUs 1

引用

19th International conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2018

作者： Do, Cong Thuan Moon, Min Goo Kim, Jong Myon Kim, Cheol Hong Department of Computer Science Korea University Seoul Korea Republic of School of Electronics and Computer Engineering Chonnam National University Gwangju Korea Republic of School of Electrical Engineering University of Ulsan Ulsan Korea Republic of

ISBN: (数字)9789811359071

ISBN: (纸本)9789811359064

Graphics Processing Units (GPUs) with massive parallel architecture have been widely used to boost performance of both graphics and general-purpose programs. GPGPUs become one of the most attractive platforms in exploiting plentiful thread-level parallelism. In recent GPUs, cache hierarchies have been employed to deal with applications with irregular memory access patterns. Unfortunately, GPU caches exhibit poor efficiency due to arising many performance challenges such as cache contention and resource congestion caused by large number of active threads in GPUs. Cache bypassing can be a solution to reduce the impact of cache contention and resource congestion. In this paper, we introduce a new cache bypassing technique that is able to make effective bypassing decisions. In particular, the proposed mechanism employs a small memory, which can be accessed before actual cache access, to record the tag information of the L1 data cache. By using this information, the mechanism can know the status of the L1 data cache and use it as a bypassing hint to make the cache bypassing decision close to optimal. Our experimental results based on a modern GPU platform reveal that our proposed cache bypassing technique achieves up to 10.4% of IPC improvement on average. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Graphics processing unit

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Joint Job offloading and resource allocation for distributed deep learning in edge computing 21

Joint Job offloading and resource allocation for distributed...

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作者： Wang, Haichuan Chen, Xi Xu, Hongli Liu, Jianchun Huang, Liusheng School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781728120584

Under the paradigm of Edge Computing, the enormous data generated at the network edge can be processed locally. Machine learning methods are often adopted to make full utilization of these data, among which the deep learning is a promising one. When considering the inherent distributed feature of these data, it is appeal to conduct distributed deep learning tasks directly at the edge. We focus on an edge computing system that conduct distributed deep learning tasks using gradient descent based approaches. To ensure the system's performance, there are at least two major challenges to cope with: how to offload the training jobs with multiple data source nodes and how to allocate the limited resources on each edge server among training jobs. In this paper, we jointly consider the two challenges, aiming to maximize the system throughput while ensuring the system's quality of service (QoS). We formulate the joint problem as an integer non-linear program and propose an efficient approximation algorithm based on reformulation and randomized rounding technique. Simulation results prove that the proposed algorithm can improve 56% of the system throughput and 53% of resource utilization when compared to the conventional baseline algorithms. © 2019 IEEE.

关键词： Edge computing

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Visual Evidence for Interpreting Diagnostic Decision of Deep Neural Network in computer-Aided Diagnosis

Visual Evidence for Interpreting Diagnostic Decision of Deep...

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conference on Medical Imaging - computer-Aided Diagnosis

作者： Kim, Seong Tae Lee, Jae-Hyeok Ro, Yong Man Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea

ISBN: (数字)9781510625488

ISBN: (纸本)9781510625488

Recent studies have reported that deep learning techniques could achieve high performance in medical image analysis such as computer-aided diagnosis (CADx). However, there is a limitation in interpreting the diagnostic decisions of deep learning due to the black-box nature. To increase confidence in the diagnostic decisions of deep learning, it is necessary to develop a deep neural network with the interpretable structure which could provide a reasonable explanation of diagnostic decisions. In this study, a novel deep neural network has been devised to provide visual evidence of the diagnostic decisions of CADx. The proposed deep network is designed to include a visual interpreter which could provide important areas as the visual evidence of the diagnostic decision in the deep neural network. Based on the observation that the radiologists usually make a diagnostic decision based on the lesion characteristics (the margin and the shape of masses), the visual interpreter provides visual evidence related with the margin and the shape, respectively. To verify the effectiveness of the proposed method, experiments were conducted on mammogram datasets. Experimental results show that the proposed method could provide more important areas as the visual evidence compared with the conventional visualization method. These results imply that the proposed visual interpretation method could be a promising approach to overcome the current limitation of the deep learning for CADx.

关键词： Visualization Neural networks Diagnostics computer aided diagnosis and therapy computer programming Mammography Medical imaging Breast Information visualization

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DDLB: A dynamic and distributed load balancing strategy 21

DDLB: A dynamic and distributed load balancing strategy

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作者： Liu, Bingqi Chang, Jiulong Xiao, Limin Qin, Guangjun Wei, Bing Huo, Zhisheng State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Smart City College Beijing Union University Beijing100101 China

ISBN: (纸本)9781728120584

In view of the problem of load imbalance for distributed file systems, the current solutions either adopt centralized algorithm and implement load balancing by increasing the number of files as the replica, or lack considerations for network transmission and network bandwidth. Therefore, the current solutions are relatively inefficient in distributed file system. To solve this problem, this paper proposes DDLB, a dynamic and distributed load balancing algorithm which is completely based on distributed load balancing architecture. DDLB can not only monitor the IO load on the data server dynamically and real time, but also use the load prediction model, which based on weighted mean, to reduce the impact of network delay and collection delay on load decision-making. In order to minimize the frequency of remote load collection and decrease network bandwidth occupation, DDLB adopts the remote distributed load collection method based on threshold and the cooling collection mechanism. Moreover, dynamic replica management mechanism of DDLB takes data blocks instead of files as the basic unit for replica replication, and reduces the IO load of the data server by adding replicas of hotspot data blocks. For proving the effectiveness of DDLB, this paper conducts a comprehensive evaluation in the HDFS cluster environment. The results show that compared with the traditional load balancing algorithm, DDLB can effectively balance the IO load between data servers in the distributed file system and improve the data access performance of IO-intensive applications. © 2019 IEEE.

关键词： Decision making

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Two-layer Residual Feature Fusion for Object Detection 8th

Two-layer Residual Feature Fusion for Object Detection

引用

8th International conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Choi, Jaeseok Lee, Kyoungmin Jeong, Jisoo Kwak, Nojun Seoul Natl Univ Seoul South Korea SK Holdings Seoul South Korea

ISBN: (纸本)9789897583513

Recently, a lot of single stage detectors using multi-scale features have been actively proposed. They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances. However, the feature maps in the lower layers close to the input which are responsible for detecting small objects in a single stage detector have a problem of insufficient representation power because they are too shallow. There is also a structural contradiction that the feature maps not only have to deliver low-level information to next layers but also have to contain high-level abstraction for prediction. In this paper, we propose a method to enrich the representation power of feature maps using a new feature fusion method which makes use of the information from the consecutive layer. It also adopts a unified prediction module which has an enhanced generalization performance. The proposed method enables more precise prediction, which achieved higher or compatible score than other competitors such as SSD and DSSD on PASCAL VOC and MS COCO. In addition, it maintains the advantage of fast computation of a single stage detector, which requires much less computation than other detectors with similar performance.

关键词： Object Detection computer Vision Machine Learning Neural Network Deep Learning

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Scale Pyramid Network for Crowd Counting 19

Scale Pyramid Network for Crowd Counting

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19th IEEE Winter conference on Applications of computer Vision (WACV)

作者： Chen, Xinya Bin, Yanrui Sang, Nong Gao, Changxin Huazhong Univ Sci & Technol Key Lab Minist Educ Image Proc & Intelligent Control Sch Automat Wuhan Hubei Peoples R China

ISBN: (纸本)9781728119755

Crowd counting is a concerned yet challenging task in computer vision. The difficulty is particularly pronounced by scale variations in crowd images. Most state-of-art approaches tackle the multi-scale problem by adopting multicolumn CNN architectures where different columns are designed with different filter sizes to adapt to variable pedestrian/object sizes. However, the structure is bloated and inefficient, and it is infeasible to adopt multiple deep columns due to the huge resource cost. We instead propose a Scale Pyramid Network (SPN) which adopts a shared single deep column structure and extracts multi-scale information in high layers by Scale Pyramid Module. In Scale Pyramid Module, we specifically employ different rates of dilated convolutions in parallel instead of traditional convolutions with different sizes. Compared to other methods of coping with scale issues, our single column structure with Scale Pyramid Module can get more accurate estimation with simpler structure and less complexity of training. And our Scale Pyramid Module can be easily applied to a deep network. Experimental results on four datasets show that our method achieves state-of-the-art performance. On Shanghai-Tech Part A dataset which is challenging for its highly congested scenes and scale variation, we achieve 9.5% lower MAE and 13.5% lower MSE than the previous state-of-the-art method. We also extend our model on TRANCOS vehicle counting dataset and significantly achieve 5.9% lower GAME(0), 10% lower GAME(1), 24.5% lower GAME(2), 38.7% lower GAME(3) than the previous state-of-the-art method. The experimental results prove the robustness of our model for crowd counting, especially with scale variations.

关键词： Feature extraction computer architecture Periodic structures Training Task analysis Estimation computer vision

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Partitioning large-scale property graph for efficient distributed query processing 21

Partitioning large-scale property graph for efficient distri...

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作者： Pang, Haohan Gan, Peng Yuan, Pingpeng Jin, Hai Hua, Qiangsheng National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Clusters and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

Recently, many communities have published their domain knowledge in the form of graphs. A significant trend is that the relations between pairs of nodes in the graph have become so complex that these data can be treated as property graphs. Complex properties imply semantic constraints which can distinguish elements of property graph into different semantic-related aspects. If the property graph can be partitioned according to these aspects, cross-partition communication would be reduced when evaluating SPARQL queries, for the reason that a query can often be evaluated in a single aspect. Conventional graph partition methods, however, pay little attention to semantic constraints in property graphs. To solve this problem, we propose a new property graph partition method for query optimization. First, we propose the property semantic reachable path as basic partition element, with which to capture the semantic-related aspects in a property graph. Second, to combine paths into partitions, we develop a space-efficient algorithm which merges vertices rather than entire paths. Extensive experiments over representative property graph data confirm the effectiveness of our semantic awareness approach. The performance of our approach is comparable with leading competitors, in terms of load balance, data redundancy, and network overhead. © 2019 IEEE.

关键词： Semantics

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Incremental Spatial Clustering for Spatial Big Crowd Data in Evolving Disaster Scenario 16

Incremental Spatial Clustering for Spatial Big Crowd Data in...

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16th IEEE Annual Consumer Communications and Networking conference, CCNC 2019

作者： Wu, Yilang Pal, Amitangshu Wang, Junbo Kant, Krishna School of Computer Science and Engineering University of Aizu Aizuwakamatsu Japan Computer and Information Sciences Temple University Philadelphia United States

ISBN: (纸本)9781538655535

Spatial clustering of the events scattered over a geographical region has many important applications, including the assessment of needs of the people affected by a disaster. In this paper we consider spatial clustering of social media data (e.g., tweets) generated by smart phones in the disaster region. Our goal in this context is to find high density areas within the affected area with abundance of messages concerning specific needs that we call simply as 'situations'. Unfortunately, a direct spatial clustering is not only unstable or unreliable in the presence of mobility or changing conditions but also fails to recognize the fact that the 'situation' expressed by a tweet remains valid for some time beyond the time of its emission. We address this by associating a decay function with each information content and define an incremental spatial clustering algorithm (ISCA) based on the decay model. We study the performance of incremental clustering as a function of decay rate to provide insights into how it can be chosen appropriately for different situations. © 2019 IEEE.

关键词： Data Analytics

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