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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Pan Liu Gongjie Liu Jie Liu Chencheng Liu Wenbin Xiong Zhangcun Tang Yinzhao Zhou Yonggang Wang Zhouming Yuan Tianfu Innovation Energy Establishment Chengdu China Beijng PARATERA Technology Corp. Ltd Beijing China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Central South University of Forestry and Technology Changsha China Nuclear and Radiation Safety Center MEE Beijing China University of Electronic Science and Technology of China Chengdu China

Controlled thermonuclear fusion has always been a dream pursued by mankind. However, the physical processes of controlled thermonuclear fusion are complex, requiring numerical simulations with high performance computing, and the amount of data generated by the physical processes on spatial, temporal and temperature scales is too large to be captured, managed, processed and collated in a reasonable time frame by mainstream software tools to achieve more aggressive fusion physical design. The data are too large to be captured, managed, processed, and collated into more aggressive targets for fusion physical design in a reasonable time by mainstream software tools. At the same time, the failure of fusion ignition can be caused by the distortion of various key physical quantities, and only by decomposing the process step by step and clarifying the changes of key physical quantities in the fusion physics process, can an effective mechanism be formed to prevent the distortion of key physical quantities from causing ignition failure in experimental physics. Big data in collaboration with artificial intelligence and high performance computing to drive the physical design of fusion is a novel avenue. By data acquisition with and pre-processing, this allows the creation of small sample libraries and deep learning. With supervised learning function convergence, incorporating solid/fluid computational methods, further network layering and cell expansion, the parameters of the physical model conforming to Lawson’s criterion will become experimental physical parameters, and we find the new approach in Data capacity, Model combination approach, Type of material, Calculation speed, Optimisation of design iteration times, etc. are superior to the traditional approach.

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MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Sy...

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Annual Conference on Privacy, Security and Trust, PST

作者： Panyu Wu Zhenfu Cao Jiachen Shen Xiaolei Dong Yihao Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

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不确定Skyline查询技术研究

不确定Skyline查询技术研究

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第29届中国数据库学术会议

作者： Wang Yijie 王意洁 Li Xiaoyong 李小勇 Yang Yongtao 杨永滔 Qi Yafei 祁亚斐 Wang Guangdong 王广东 National Key Laboratory for Parallel and Distributed Processing College of Computer National Unive 国防科学技术大学计算机学院并行与分布处理国家重点实验室长沙 410073

当前不确定数据广泛存在于诸如传感器网络、RFID网络、基于位置服务、移动对象管理网上购物和市场监控等各种实际应用中。不确定Skyline查询作为不确定数据管理的一个重要方面，由于其在决策制定、市场分析、环境监控和数据挖掘等方面... 详细信息

当前不确定数据广泛存在于诸如传感器网络、RFID网络、基于位置服务、移动对象管理网上购物和市场监控等各种实际应用中。不确定Skyline查询作为不确定数据管理的一个重要方面，由于其在决策制定、市场分析、环境监控和数据挖掘等方面的重要作用，近年来在数据库和网络计算领域受到广泛关注。首先，概述了各种不确定数据类型上的Skyline查询定义，包括离散、连续概率分布模型以及不完全数据上的Skyline查询定义;其次，分析了不确定Skyline查询的特点，并在此基础上综述了现有的各种不确定数据集上的集中式和分布式Skyline查询方法，重点分析了各种算法的原理和优缺点;再次，介绍了不确定数据流上的Skyline查询定义并综述了各种不确定数据流上的Skyline查询方法;最后，基于最新研究动态指出了未来不确定Skyline查询研究的趋势。

关键词：数据查询不确定数据分布式技术

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基于移动云计算的大规模上下文流处理框架研究

基于移动云计算的大规模上下文流处理框架研究

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第九届中国通信学会学术年会

作者： XIAO You 肖友 SHI Dian-xi 史殿习 National Key Laboratory for Parallel and Distributed Processing School of Computer ScienceNational 国防科技大学计算机学院并行与分布处理国家重点实验室长沙410073

随着移动云计算的兴起，有关于大规模移动设备上下文信息处理的研究成为了热点。目前比较流行的解决方案是采用基于MapReduce计算模型的Hadoop框架，它虽能解决大规模数据处理的吞吐量问题但是却不能很好的解决数据处理的时效问题。因... 详细信息

随着移动云计算的兴起，有关于大规模移动设备上下文信息处理的研究成为了热点。目前比较流行的解决方案是采用基于MapReduce计算模型的Hadoop框架，它虽能解决大规模数据处理的吞吐量问题但是却不能很好的解决数据处理的时效问题。因此，本文针对存在的问题，提出了移动云计算与流处理技术相结合的解决方案，并构建与实现了一个实时处理大规模上下文的流处理框架。最后通过此框架对本文的研究内容进行了验证，且以一个道路实况监测的实例验证了框架的可行性及有效性。

关键词：移动云计算上下文信息流处理框架可行性分析

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基于物理轨迹数据和社会网络的泛化行程推荐

基于物理轨迹数据和社会网络的泛化行程推荐

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第六届中国传感器网络学术会议(CWSN 2012)

作者： MENG Xiang-Xu 孟祥旭 WANG Xiao-Dong 王晓东 ZHOU Xing-Ming 周兴铭 National Key Laboratory of Parallel and Distributed Processing(College of Computer Science National 并行与分布处理国家重点实验室(国防科学技术大学计算机学院) 湖南长沙410073

人类活动行程的制定往往基于宽泛的最初意向，通过综合考虑各种约束条件加以优化而完成。当前，基于位置点名称查找的行程制定方法，不支持用户一次性提交多个具有时序关系的宽泛出行意向，更不能同时为多个地理位置点提供详细的最优驾... 详细信息

人类活动行程的制定往往基于宽泛的最初意向，通过综合考虑各种约束条件加以优化而完成。当前，基于位置点名称查找的行程制定方法，不支持用户一次性提交多个具有时序关系的宽泛出行意向，更不能同时为多个地理位置点提供详细的最优驾车方案。基于位置社交网络信息和车辆历史轨迹数据，探索了支持用户多个模糊意向输入的泛化行程推荐框架，主要工作包括：(1)对泛化的行程推荐问题进行建模;(2)设计并实现了基于分类树的地理位置点(POI)查询策略和算法;(3)提出了基于Voronoi图的GPS轨迹分析模型，并实现了任意两个位置点间最优行驶路径计算方法;(4)联合社会网络和语义交通信息图，基于蚁群算法进行行程的推荐，并实现了原型系统。实验及问卷调查结果表明，推荐结果的用户满意度可达80％。

关键词：行程规划泛化模型优化设计物理轨迹数据社会网络

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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Profiling HPC Applications with Low Overhead and High Accuracy

Profiling HPC Applications with Low Overhead and High Accura...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jingyuan Zhao Xin Liu Yao Liu Penglong Jiao Jinshuo Liu Wei Xue School of Data Science & Engineering East China Normal University Shanghai China School of Cyber Science and Engineering Wuhan University Wuhan China National Research Centre of Parallel Computer Engineering and Technology Beijing China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Tsinghua University Beijing China

As the parallel scale of HPC applications represented by earth system models becomes larger and the computing cost becomes higher, the performance of HPC applications is increasingly critical. Profiling HPC applications accurately helps to model the applications and find the performance bottlenecks. However, due to the complexity of HPC applications, the diversity of programming languages, the differences of individual programming habits, and multiple architectures, accurate profiling becomes very tough. In this paper, we propose LPerf: a low-overhead and high-accuracy profiler for HPC applications. To reduce the profiling overhead and improve the profiling accuracy, we propose a preprocessing method which can automatically instrument with tunable granularity thus significantly reducing the run-time overhead of profiling, an aggregated caller-callee relationship which is used to locate relationship of functions efficiently, and a profiling-aware method which can precisely calculate running time of functions. The experimental results show that the error rate of profiling reaches 0.02%, and the overhead reaches 1.6%, in the earth system model named CAS-ESM. Compared with the baselines, the precision, accuracy, and overhead of LPerf have reached the state of the art.

关键词： Earth Analytical models Schedules Computer languages Costs Error analysis Computational modeling

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不确定数据流上的并行Skyline查询算法

不确定数据流上的并行Skyline查询算法

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第29届中国数据库学术会议

作者： WANG Guangdong 王广东 WANG Yijie 王意洁 LI Xiaoyong 李小勇 WANG Yuan 王媛 National Key Laboratory for Parallel and Distributed Processing College of Computer Science Nation 国防科技大学计算机学院并行与分布处理国家重点实验室长沙410073

不确定数据流上的Skyline查询技术逐步引起研究者的关注，传统的集中式流处理算法难以满足海量数据的查询需求，并且云计算所提供的海量计算资源和有效的存储管理模式，为研究并行Skyline查询技术提供了充足的条件。基于上述事实，提出... 详细信息

不确定数据流上的Skyline查询技术逐步引起研究者的关注，传统的集中式流处理算法难以满足海量数据的查询需求，并且云计算所提供的海量计算资源和有效的存储管理模式，为研究并行Skyline查询技术提供了充足的条件。基于上述事实，提出了一种不确定数据流上的并行Skyline查询算法(PSUDS)。该算法通过交叉划分滑动窗口的方式，将集中式流查询转化为并行处理，以并行执行的方式来解决集中式算法处理性能不足的问题。大量实验结果表明，该算法具有较好的并行可扩展性。

关键词：不确定数据流云计算并行处理查询算法性能测试

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MixedCache: Enabling Flow Directed Rule-Caching Scheme based on Heterogeneous Cache for OpenFlow

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Journal of Physics: Conference Series 2020年第1期1693卷

作者： Jialun Yang Yi Yang Jiahao Liu Rulin Liu Peng Qiao Baosheng Wang College of Computer Science and Technology National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China

OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups. Many increased flow tables are rapidly exhausting these hardware storage resources, which makes the switches have to balance high-speed search and massive storage. The rule-caching scheme is a popular method to solve this problem, which caches the most commonly used rules into hardware storage resources. The existing rule-caching schemes are based on single hardware storage resources, and they cannot flexibly adjust the caching strategy according to the traffic characteristics. Simultaneously, the deployed commodity switches face the problem of difficulty in changing the size of SRAM and TCAM. This paper innovatively proposes the MixedCache scheme, which makes full use of the hardware storage resources in the switch according to the skewed characteristics of network traffic. MixedCache stores the large flows in SRAM by exact match and stores the small flows in the TCAM by wildcard match. MixedCache does not need to change the size of the deployed switch hardware storage resources, but makes full use of existing resources. Compared with the rule-caching scheme based on the exact match, the cache hit rate can increase by up to 15.61%. Compared with the rule-caching scheme based on the wildcard match, the cache hit rate can increase by up to 29.69%.

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MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI Cardinality

MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI C...

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Annual Conference on Privacy, Security and Trust, PST

作者： Yihao Yang Xiaolei Dong Jiachen Shen Zhenfu Cao Yunbo Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Zongyang Hou Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan Print-Rite Technology Development Co. Ltd. Of Zhuhai China

Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 2 20 on server side and 2 16 on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.

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