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Design of intelligent community security system based on visual tracking and large data natural language processing technology

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Journal of Intelligent and Fuzzy Systems 2020年第6期38卷 7107-7117页

作者： Li, Daming Deng, Lianbing Cai, Zhiming Cai, Kaicheng Post-Doctoral Research Center of Zhuhai da Hengqin Science and Technology Development Co. Ltd. China City University of Macau China Huazhong University of Science and Technology China Zhuhai da Hengqin Science and Technology Development Co. Ltd. China Institute of Data Science City University of Macao China Automotive Engineering Research Institute of Guangzhou Automobile Group Co. Ltd. China

With the improvement of public security awareness, it is of great value to establish an intelligent crowd screening video early warning system for community security. The method is to use artificial intelligence and big data technology to analyze the facial features of past people through video data in video surveillance. In this paper, the authors analyze the design of intelligent community security system based on visual tracking and large data natural language processing technology. Through JMF technology and video acquisition card, the signal can be transmitted from the matrix machine to the server of the system. In the process of transmission, we also need to use multi-threading technology and double buffer technology to compress video, which can improve the compression efficiency and reduce the CPU usage time. The information module is mainly responsible for receiving and playing video. This module needs JAVA technology which can receive video. Once the client requests monitoring, the client can immediately send relevant operation instructions to the server, and the server will return the corresponding video stream in time. The simulation results show that the system can identify risks efficiently and accurately. © 2020 - IOS Press and the authors. All rights reserved.

关键词： security systems

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PDRM: A Probability Distribution Based Resource Management for Batch Workloads in Heterogeneous Cluster 16th

PDRM: A Probability Distribution Based Resource Management f...

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16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019

作者： Zhou, Jun Feng, Dan Wang, Fang School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Wuhan National Laboratory for Optoelectronics Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology Ministry of Education of China Wuhan China

ISBN: (纸本)9783030307080

Resource consumption prediction and dynamic resource provision based on historical consumption are common methods to improve cluster resource utilization, however they have to face the challenge of fluctuation in resource consumption for accurate prediction. We propose PDRM, an efficient resource management scheme based on resource consumption probability distribution for batch workloads to deal with this dilemma. Based on the common sense that the same type of tasks have similar resource consumption on the same node, we get the resource consumption probability distribution of each type of task to describe the fluctuations in its resource consumption. Based on the resource consumption distribution function, we can allocate resources precisely for tasks. Experimental results demonstrate that PDRM achieves good performance for various application in the heterogeneous cluster. PDRM can effectively improve resource utilization and reduce job completion time. © 2019, IFIP International Federation for Information Processing.

关键词： big data

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Unveiling the Multifaceted GRB 200613A: Prompt Emission Dynamics, Afterglow Evolution, and the Host Galaxy’s Properties

arXiv

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arXiv 2024年

作者： Fu, Shao-Yu Xu, Dong Lei, Wei-Hua de Ugarte Postigo, Antonio Kann, D. Alexander Thöne, Christina C. Fernández, José Feliciano Agüí Shuang-Xi, Yi Xie, Wei Zou, Yuan-Chuan Liu, Xing Jiang, Shuai-Qing Lu, Tian-Hua An, Jie Zhu, Zi-Pei Zheng, Jie Tang, Qing-Wen Zhao, Peng-Wei Xin, Li-Ping Wei, Jian-Yan Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China Department of Astronomy School of Physics Huazhong University of Science and Technology Wuhan430074 China Université de la Côte d’Azur Observatoire de la Côte d’Azur CNRS Artemis Nice06304 France Aix Marseille Univ. CNRS LAM Marseille France Hessian Research Cluster ELEMENTS Giersch Science Center Goethe University Frankfurt Max-von-Laue-Straße 12 Campus Riedberg Frankfurt am MainD-60438 Germany Astronomical Institute Czech Academy of Sciences Fričova Ondřejov298 Czech Republic Centro Astronómico Hispano en Andalucía Observatorio de Calar Alto Sierra de los Filabres Gérgal Almería04550 Spain School of Physics and Physical Engineering Qufu Normal University Qufu273165 China Department of Astronomy School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy Data Processing Guizhou Normal University Guiyang550001 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Department of Physics School of Science Nanchang University Nanchang330031 China

We present our optical observations and multi-wavelength analysis of the GRB 200613A detected by Fermi satellite. Time-resolved spectral analysis of the prompt γ-ray emission was conducted utilizing the Bayesian block method to determine statistically optimal time bins. Based on the Bayesian Information Criterion (BIC), the data generally favor the Band+Blackbody (short as BB) model. We speculate that the main Band component comes from the Blandford-Znajek mechanism, while the additional BB component comes from the neutrino annihilation process. The BB component becomes significant for a low-spin, high-accretion rate black hole central engine, as evidenced by our model comparison with the data. The afterglow light curve exhibits typical power-law decay, and its behavior can be explained by the collision between the ejecta and constant interstellar medium (ISM). Model fitting yields the following parameters: EK,iso = (2.04+11.8-1.50) × 1053 erg, Γ0 = 354+578-217, p = 2.09+0.02-0.03, n18 = (2.04+9.71-1.87)×102 cm-3, θj = 24.0-5.54+6.50 degree, ϵe = 1.66+4.09-1.39)×10-1 and ϵB = (7.76+48.5-5.9)×10-6. In addition, we employed the public Python package Prospector perform a spectral energy distribution (SED) modeling of the host galaxy. The results suggest that the host galaxy is a massive galaxy (log(M∗/M☉) = 11.75-0.09+0.10) with moderate star formation rate (SFR = 22.58+13.63-7.22 M☉/yr). This SFR is consistent with the SFR of ∼ 34.2M☉ yr-1 derived from the [OII] emission line in the observed spectrum. © 2024, CC0.

关键词： Gamma rays

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Energy Saving Strategy of Power System Cluster Based on Container Virtualization

Energy Saving Strategy of Power System Cluster Based on Cont...

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IEEE Asia Power and Energy engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

关键词：

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An efficient design and implementation of deduplication on open-channel SSDs 21

An efficient design and implementation of deduplication on o...

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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： Hu, Qiqi Chen, Jianxi Feng, Dan Yang, Qing Liu, Bo Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China Hikstor Technology Co. LTD. Zhejiang China

ISBN: (纸本)9781728120584

Since the number of NAND flash write cycles reaches the upper limit, data cannot be reliably stored. Therefore, the lifespan of Solid State Disks (SSDs) is limited, making it impossible to become a reliable storage device in a write-intensive data center. Deduplication can effectively reduce write traffic and increase the lifespan of SSDs. However, the fingerprint calculation overhead in the traditional data deduplication scheme is too large, which will reduce the write performance of the SSDs. In this paper, we propose a new deduplication scheme D-pblk based on Open-Channel SSDs(OCSSDs), which can reduce fingerprint computing overhead with modern high performance CPUs. In D-pblk, we use the light-weight hash CRC32 to filter most of the non-duplicated data, and When CRC32 is first hit, we read the data from Open-Channel SSDs and calculate the heavy-weight hash SHA-1 for accurate comparison, which reducing fingerprint computing overhead and hardly affects the deduplication rate of workloads. And we use multi-thread to calculate fingerprints in parallel, further reducing fingerprint computing overhead. In addition, we use double ring buffers to solve the problem that the data size after deduplication may not match the size of a flash page. We use FEMU to simulate Open-Channel SSDs for evaluation. The experimental results show that D-pblk can achieve the deduplication rate ranging from 4.61% to 31.63% and the write latency is improved by a factor of up to 29%. © 2019 IEEE.

关键词： Program processors

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MalScan: Fast Market-Wide Mobile Malware Scanning by Social-Network Centrality Analysis

MalScan: Fast Market-Wide Mobile Malware Scanning by Social-...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Yueming Wu Xiaodi Li Deqing Zou Wei Yang Xin Zhang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab National Engineering Research Center for Big Data Technology and System School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Texas at Dallas Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use either syntax-based features which can be evaded by transformation attacks or semantic-based features which are usually extracted by performing expensive program analysis. Therefor, in this paper, we propose a lightweight graph-based approach to perform Android malware detection. Instead of traditional heavyweight static analysis, we treat function call graphs of apps as social networks and perform social-network-based centrality analysis to represent the semantic features of the graphs. Our key insight is that centrality provides a succinct and fault-tolerant representation of graph semantics, especially for graphs with certain amount of inaccurate information (e.g., inaccurate call graphs). We implement a prototype system, MalScan, and evaluate it on datasets of 15,285 benign samples and 15,430 malicious samples. Experimental results show that MalScan is capable of detecting Android malware with up to 98% accuracy under one second which is more than 100 times faster than two state-of-the-art approaches, namely MaMaDroid and Drebin. We also demonstrate the feasibility of MalScan on market-wide malware scanning by performing a statistical study on over 3 million apps. Finally, in a corpus of dataset collected from Google-Play app market, MalScan is able to identify 18 zero-day malware including malware samples that can evade detection of existing tools.

关键词： Malware Social networking (online) Feature extraction Semantics Analysis of variance Static analysis Robustness

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Modeling Distributed Stream Processing Systems Under Heavy Workload

Modeling Distributed Stream Processing Systems Under Heavy W...

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International Conference on Cyberworlds

作者： Muhammad Mudassar Qureshi Hanhua Chen Hai Jin Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

big data applications play a significant role in diverse fields. Distributed Stream Processing Engines (DSPEs) are widely used to support real time applications efficiently. Partitioning algorithms are used to partition data streams into multiple nodes to process in parallel to gain efficient performance. Aggregation cost is an important factor when process stateful streaming applications using such partitioning algorithms because it plays an important role on performance when final result is being produced in stateful streaming applications. However, impact of aggregation cost in stream processing is not discussed comprehensively in existing literature. We use performance modeling to identify the importance of aggregation cost when workload is high. We implement performance model on a multi-node cluster to predict the same behavior as on single resource performance model. We demonstrate that stateful streaming applications need more resources as compare to stateless applications when workload is high and both stateful and stateless applications are running in the same DSPE. Experiments results show that a stateful streaming application needs more resources compared to a stateless streaming application when both applications are running on the same DSPE when the workload is high. Further experiment results show that the performance modeling may be helpful to predict maximum workload that can be process on a DSPE and increase in parallelism level is not guaranteed to increase the performance of streaming applications.

关键词： Partitioning algorithms data models Predictive models Heuristic algorithms big data applications Throughput

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Fast Triangle Counting on GPU

Fast Triangle Counting on GPU

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Chuangyi Gui Long Zheng Pengcheng Yao Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China

Triangle counting is one of the most basic graph applications to solve many real-world problems in a wide variety of domains. Exploring the massive parallelism of the Graphics Processing Unit (GPU) to accelerate the triangle counting is prevail. We identify that the stat-of-the-art GPU-based studies that focus on improving the load balancing still exhibit inherently a large number of random accesses in degrading the performance. In this paper, we design a prefetching scheme that buffers the neighbor list of the processed vertex in advance in the fast shared memory to avoid high latency of random global memory access. Also, we adopt the degree-based graph reordering technique and design a simple heuristic to evenly distribute the workload. Compared to the state-of-the-art HEPC Graph Challenge Champion in the last year, we advance to improve the performance of triangle counting by up to 5.9× speedup with> 109 TEPS on a single GPU for many large real graphs from graph challenge datasets.

关键词： Graphics processing units Prefetching Parallel processing Indexes Acceleration Optimization

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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

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A secure rational quantum state sharing protocol

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science China(Information sciences) 2018年第2期61卷 204-215页

作者： Zhao DOU Gang XU Xiu-Bo CHEN Xin LIU Yi-Xian YANG Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications School of Software Engineering Beijing University of Posts and Telecommunications Gui Zhou University Guizhou Provincial Key Laboratory of Public Big Data School of Computer Science Shaanxi Normal University School of Information Engineering Inner Mongolia University of Science and Technology

A novel rational protocol to share two arbitrary qubits among multiple parties is investigated in this paper. First, the protocol is presented, which is learned from Li et al.'s protocol. Second, the utility,security, correctness, fairness, Nash equilibrium, and Pareto optimality of our scheme are discussed in detail,where the utility, correctness, and fairness of rational quantum state sharing protocols are creatively given because the agent who recovers the state plays a different and more important role. Another important point is that assumptions about our protocol are more practical and suitable than existing protocols.

关键词： rational quantum state sharing Nash equilibrium secure correct

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