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DFTracker: detecting double-fetch bugs by multi-taint parallel tracking

Frontiers of Computer Science 2019年第2期13卷 247-263页

作者： Pengfei WANG Kai LU Gen LI Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China

A race condition is a common trigger for concurrency bugs. As a special case, a race condition can also occur across the kernel and user space causing a doublefetch bug, which is a field that has received little research attention. In our work, we first analyzed real-world doublefetch bug cases and extracted two specific patterns for doublefetch bugs. Based on these patter ns, we proposed an approach of multi-taint parallel tracking to detect double-fetch bugs. We also implemented a prototype called DFTracker (doublefetch bug tracker), and we evaluated it with our test suite. Our experiments demonstrated that it could effectively find all the double-fetch bugs in the test suite including eight realworld cases with no false negatives and minor false positives. In addition, we tested it on Linux kernel and found a new double-fetch bug. The execution overhead is approximately 2x for single-file cases and approximately 9x for the whole kernel test, which is acceptable. To the best of the authors1 knowledge, this work is the first to introduce multi-taint parallel tracking to double-fetch bug detection—an innovative method that is specific to double-fetch bug features—and has better path coverage as well as lower runtime overhead than the widely used dynamic approaches.

关键词： multi-taint parallel tracking double fetch race condition between kernel and user time of check to time of use real-world case analysis Clang Static Analyzer

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Analysis of single-event transient sensitivity in fully depleted silicon-on-insulator MOSFETs

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Nuclear Science and Techniques 2018年第4期29卷 108-113页

作者： Jing-Yan Xu Shu-Ming Chen Rui-Qiang Song Zhen-Yu Wu Jian-Jun Chen College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Based on 3 D-TCAD simulations, single-event transient(SET) effects and charge collection mechanisms in fully depleted silicon-on-insulator(FDSOI) transistors are investigated. This work presents a comparison between28-nm technology and 0.2-lm technology to analyze the impact of strike location on SET sensitivity in FDSOI devices. Simulation results show that the most SET-sensitive region in FDSOI transistors is the drain region near the gate. An in-depth analysis shows that the bipolar amplification effect in FDSOI devices is dependent on the strike locations. In addition, when the drain contact is moved toward the drain direction, the most sensitive region drifts toward the drain and collects more charge. This provides theoretical guidance for SET hardening.

关键词： Single-event transient Charge collection Bipolar amplification Fully depleted silicon-on-insulator

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Big data collaborative artificial intelligence and high-performance computing to drive physical design of fusion

Big data collaborative artificial intelligence and high-perf...

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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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Hybrid Routing: Towards Resilient Routing in Anonymous Communication Networks

Hybrid Routing: Towards Resilient Routing in Anonymous Commu...

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IEEE International Conference on Communications (ICC)

作者： Yusheng Xia Rongmao Chen Jinshu Su Chen Pan Han Su College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Anonymous communication networks (ACNs) are intended to protect the metadata during communication. As classic ACNs, onion mix-nets are famous for strong anonymity, in which the source defines a static path and wraps the message multi-times with the public keys of nodes on the path, through which the message is relayed to the destination. However, onion mix-nets lacks in resilience when the static on-path mixes fail. Mix failure easily results in message loss, communication failure, and even specific attacks. Therefore, it is desirable to achieve resilient routing in onion mix-nets, providing persistent routing capability even though node failure. The state-of-theart solutions mainly adopt mix groups and thus need to share secret keys among all the group members which may cause single point of failure. To address this problem, in this work we propose a hybrid routing approach, which embeds the onion mix-net with hop-by-hop routing to increase routing resilience. Furthermore, we propose the threshold hybrid routing to achieve better key management and avoid single point of failure. As for experimental evaluations, we conduct quantitative analysis of the resilience and realize a local T-hybrid routing prototype to test performance. The experimental results show that our proposed routing strategy increases routing resilience effectively, at the expense of acceptable latency.

关键词： Routing Resilience Public key Peer-to-peer computing Encryption

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Deep Learning for Social Network Information Cascade Analysis: a survey

Deep Learning for Social Network Information Cascade Analysi...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Liqun Gao Bin Zhou Yan Jia Hongkui Tu Ye Wang Chenguang Chen Haiyang Wang Hongwu Zhuang College of Computer National University of Defense Technology ChangSha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology ChangSha China

The following topics are dealt with: learning (artificial intelligence); text analysis; natural language processing; pattern classification; graph theory; data mining; security of data; social networking (online); fea...

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

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Clustering-preserving Network Flow Sketching

Clustering-preserving Network Flow Sketching

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yongquan Fu Dongsheng Li Siqi Shen Yiming Zhang Kai Chen Science and Technology Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology SING Lab Hong Kong University of Science and Technology

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Network monitoring is vital in modern clouds and data center networks that need diverse traffic statistics ranging from flow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash *** paper presents a clustering-preserving sketch method to be resilient to hash collisions. We provide an equivalence analysis of the sketch in terms of the K-means clustering. Based on the analysis result, we cluster similar network flows to the same bucket array to reduce the estimation variance and use the average to obtain unbiased estimation. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance under wide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over 10 3 to 10 5 times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%.

关键词： Monitoring Arrays Estimation error Adaptation models Approximation error Data centers

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A Heterogeneous Processor Design for CNN-Based AI Applications on IoT Devices 8

A Heterogeneous Processor Design for CNN-Based AI Applicatio...

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8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019

作者： Liu, Zhiqiang Jiang, Jingfei Lei, Guoqing Chen, Kai Qin, Buyue Zhao, Xiaoqiang Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China College of System Engineering National University of Defense Technology Changsha China

This paper proposes a heterogeneous processor design for CNN-based AI applications on IoT devices. The heterogeneous processor contains an embedded RISC-V CPU that works as a general processor and an efficient CNN-accelerator that supports a variety of CNN models with a list of macro instructions. For demonstration, we implement a prototype on an FPGA platform with the RISC-V CPU working under 20 MHz and the CNN accelerator working under 100 MHz. As a case study, we run a CNN-based face detection and recognition application on this prototype. The prototype can process one image in 0.72 seconds and an ASIC implementation working under 400 MHz can process one image in less than 0.15 seconds by estimation, which can satisfy the needs for many IoT scenarios such as access control systems and check-in systems. © 2020 The Authors. Published by Elsevier B.V.

关键词： Internet of things

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Optimizing One by One Direct Convolution on ARMv8 Multi-core CPUs

Optimizing One by One Direct Convolution on ARMv8 Multi-core...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Qinglin Wang Dongsheng Li Songzhu Mei Siqi Shen Xiandong Huang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China School of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781728169811

ISBN: (纸本)9781728169828

Convolutional layers are ubiquitous in a variety of deep neural networks. Due to the lower computation complexity and the smaller number of parameters, convolutions with small filter sizes are often used, such as one by one convolution. Nevertheless, these small convolution operations are still time-consuming. A common approach to implementing convolutions is to transform them into matrix multiplications, known as GEMM-based convolutions. The approach maybe incurs additional memory overhead and calls matrix multiplication routines, which are not optimized for matrices generated by convolutions. In this paper, we present a new parallel one by one direct convolution implementation on ARMv8 multi-core CPUs, which doesn't incur any additional memory space requirement. Our implementation is verified on two ARMv8 CPUs, Phytium FT-1500A and FT-2000plus. In terms of performance and scalability, our implementation is better than GEMM-based implementations in all the tests on Phytium FT-1500A. On Phytium FT-2000plus, our approach gives much better performance and scalability than GEMM-based approaches in most cases.

关键词： Convolution Registers Layout Neural networks parallel processing Computer architecture Tensile stress

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Research on Integrated Detection of SQL Injection Behavior Based on Text Features and Traffic Features

Research on Integrated Detection of SQL Injection Behavior B...

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作者： Ming Li Bo Liu Guangsheng Xing Xiaodong Wang Zhihui Wang College of Intelligence Science and Technology National University of Defence Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology

With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens various Web application services and users' data security. There are both traditional detection methods and emerging methods based on deep learning technology with higher detection accuracy for the detection of SQL injection. However, they are all for detecting a single statement and cannot determine the stage of the attack. To further improve the effect of SQL injection detection, this paper proposes an integrated detection framework for SQL injection behavior based on both text features and traffic features. We propose a SQL-LSTM model based on deep learning technology as the detection model at the text features level. Meanwhile,the features of the data traffic are merged. By this integrated method, the detection effect of SQL injection is further improved.

关键词： SQL injection Integrated method SQL-LSTM Traffic features

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NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

arXiv

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arXiv 2024年

作者： Li, Xin Yuan, Kun Pei, Yajing Lu, Yiting Sun, Ming Zhou, Chao Chen, Zhibo Timofte, Radu Sun, Wei Wu, Haoning Zhang, Zicheng Jia, Jun Zhang, Zhichao Cao, Linhan Chen, Qiubo Min, Xiongkuo Lin, Weisi Zhai, Guangtao Sun, JianHui Wang, Tianyi Li, Lei Kong, Han Wang, Wenxuan Li, Bing Luo, Cheng Wang, Haiqiang Chen, Xiangguang Meng, Wenhui Pan, Xiang Shi, Huiying Zhu, Han Xu, Xiaozhong Sun, Lei Chen, Zhenzhong Liu, Shan Kong, Fangyuan Fan, Haotian Xu, Yifang Xu, Haoran Yang, Mengduo Zhou, Jie Li, Jiaze Wen, Shijie Xu, Mai Li, Da Yao, Shunyu Du, Jiazhi Zuo, Wangmeng Li, Zhibo He, Shuai Ming, Anlong Fu, Huiyuan Ma, Huadong Wu, Yong Xue, Fie Zhao, Guozhi Du, Lina Guo, Jie Zhang, Yu Zheng, Huimin Chen, Junhao Liu, Yue Zhou, Dulan Xu, Kele Xu, Qisheng Sun, Tao Ding, Zhixiang Hu, Yuhang University of Science and Technology of China China Kuaishou Technology China University of Würzburg Germany Shanghai Jiao Tong University China Nanyang Technological University Singapore Xiaohongshu China WeChat China Tencent China Wuhan University China ByteDance Shenzhen China Zhejiang University China Department of Electronic and Information Engineering Beihang University Beijing China Southern University of Science and Technology China Harbin Institute of Technology China Beijing University of Posts and Telecommunications China China Merchants Bank China Shandong Jianzhu University China Key Laboratory for Parallel and Distributed Processing Changsha China Institute of Automation CAS China University of Chinese Academy of Sciences China

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://***/lixinustc/KVQ-Challenge-CVPR-NTIRE2024. Copyright © 2024, The Authors. All rights reserved.

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