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International Conference on Dependable Systems and Networks (DSN)

作者： Jinyu Gu Zhichao Hua Yubin Xia Haibo Chen Binyu Zang Haibing Guan Jinming Li Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Institute of Parallel and Distributed Systems (IPADS) Shanghai Jiao Tong University Huawei Technologies Inc.

ISBN: (纸本)9781538605431

The recent commercial availability of Intel SGX (Software Guard eXtensions) provides a hardware-enabled building block for secure execution of software modules in an untrusted cloud. As an untrusted hypervisor/OS has no access to an enclave's running states, a VM (virtual machine) with enclaves running inside loses the capability of live migration, a key feature of VMs in the cloud. This paper presents the first study on the support for live migration of SGX-capable VMs. We identify the security properties that a secure enclave migration process should meet and propose a software-based solution. We leverage several techniques such as two-phase checkpointing and self-destroy to implement our design on a real SGX machine. Security analysis confirms the security of our proposed design and performance evaluation shows that it incurs negligible performance overhead. Besides, we give suggestions on the future hardware design for supporting transparent enclave migration.

关键词： Program processors Hardware Security Virtual machine monitors Cloud computing Context

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Density-Attentive Head Detector for Crowd Counting

Density-Attentive Head Detector for Crowd Counting

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International Conference on Data Science and Business Analytics (ICDSBA)

作者： Ziyu Zhao Xingyu Shen Long Lan Xiaoyao Yin Xiang Zhang Yonggang Che Zhigang Luo College of Computer National University of Defense Technology Changsha China Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728146447

ISBN: (纸本)9781728146454

In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which significantly restricts its applications. Detection-based method counts each head with their accurate locations but works only in middle or low-level density scenes. In this paper, a joint learning method, named Density-attentive Head Detector (DAHD) is developed to overcome their respective shortcomings via a multi-task training procedure. Specifically, to guarantee the sensitivity of the detector to small or partially occluded heads, we carefully equip the detector with a density map which learned from regression module. Moreover, a novel Dilated Feature Pyramid Network (DFPN) is introduced to our method to enlarge the receptive field of convolutional kernel, bringing confirmative additional benefits to identify small heads. Experiments on the popular ShanghaiTech and Mall datasets confirm the improved performance of DAHD compared with the current detection-based approaches, and a comparable performance to regression-based approaches in term of counting.

关键词： Detectors Feature extraction Object detection Faces Kernel Training Task analysis

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PTQ-SO: A Scale Optimization-based Approach for Post-training Quantization of Edge computing

PTQ-SO: A Scale Optimization-based Approach for Post-trainin...

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International Conference on Computer Supported Cooperative Work in Design

作者： Kangkang Liu Ningjiang Chen College of Computer and Electronic Information Guangxi University Nanning China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Nanning China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the increasing performance of deep convolutional neural networks, they have been widely used in many computer vision tasks. However, a huge convolutional neural network model requires a lot of memory and computing resources, which makes it difficult to meet the requirements of low latency and reliability of edge computing when the model is deployed locally on resource-limited devices in edge environments. Quantization is a kind of model compression technology, which can effectively reduce model size, calculation cost and inference delay, but the quantization noise will cause the accuracy of the quantization model to decrease. Aiming at the problem of precision loss caused by model quantization, this paper proposes a post-training quantization method based on scale optimization. By reducing the influence of redundant parameters in the model on the quantization parameters in the process of model quantization, the scale factor optimization is realized to reduce the quantization error and thus improve the accuracy of the quantized model, reduce the inference delay and improve the reliability of edge applications. The experimental results show that under different quantization strategies and different quantization bit widths, the proposed method can improve the accuracy of the quantized model, and the absolute accuracy of the optimal quantization model is improved by 1.36%. The improvement effect is obvious, which is conducive to the application of deep neural network in edge environment.

关键词： Quantization (signal) Accuracy Computational modeling Computer network reliability Delays Reliability Convolutional neural networks

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A distributed Relation Detection Approach in the Internet of Things

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Mobile Information Systems 2017年第1期2017卷

作者： Zhu, Weiping Lu, Hongliang Cui, Xiaohui Cao, Jiannong International School of Software Wuhan University Wuhan China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong

In the Internet of Things, it is important to detect the various relations among objects for mining useful knowledge. Existing works on relation detection are based on centralized processing, which is not suitable for the Internet of Things owing to the unavailability of a server, one-point failure, computation bottleneck, and moving of objects. In this paper, we propose a distributed approach to detect relations among objects. We first build a system model for this problem that supports generic forms of relations and both physical time and logical time. Based on this, we design the distributed Relation Detection Approach (DRDA), which utilizes a distributed spanning tree to detect relations using in-network processing. DRDA can coordinate the distributed tree-building process of objects and automatically change the depth of the routing tree to a proper value. Optimization among multiple relation detection tasks is also considered. Extensive simulations were performed and the results show that the proposed approach outperforms existing approaches in terms of the energy consumption. © 2017 Weiping Zhu et al.

关键词： Internet of things

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Cooperative Spectrum Sharing in Relay-Trading Mode: A Fairness View

Cooperative Spectrum Sharing in Relay-Trading Mode: A Fairne...

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International Conference on Cyber-Enabled distributed computing and Knowledge Discovery, CyberC

作者： Lixia Liu Gang Hu Ming Xu Yuxing Peng National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China Department of Networks Engineering School of Computer National University of Defense Technology Changsha China

In the relay-trading mode of wireless cognitive radio networks the secondary user (SU) can achieve a promised spectrum access opportunity by relaying for the primary user (PU). How to utilize the exchanged resource efficiently and fairly is an interesting and practical problem. In this paper we proposed a cooperative spectrum sharing strategy (RT-CSS) for the relay-trading mode from the fairness view. The cooperative SUs are gathered in a cooperative sharing group (CSG), and contribution metric (CM) is proposed to measure each CSG member's contribution to CSG as well as benefit from CSG. The adjustment of CM can guarantee the fairness and efficiency of spectrum sharing. The numerical simulation shows that RT-CSS can achieve better performance than the sense-uncooperative mode.

关键词： Relays Cognitive radio Measurement Qualifications Wireless sensor networks Radio networks

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R-TAR: A Resilient Traffic Anomaly Recognition Mechanism for Backbone Network 10

R-TAR: A Resilient Traffic Anomaly Recognition Mechanism for...

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10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018

作者： Li, Xueyu Zhang, Xu Wang, Dongbin School of Network Education Beijing University of Posts and Telecommunications Beijing100876 China National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Ministry of Education and Trustworthy Distributed Computing and Service School of CyberSpace Security Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781538658369

The development of backbone networks leads to an increase in heterogeneous anomalies and cyberspace situation complexity, and traditional traffic monitoring cannot meet multiple-point concurrency design and resilience demand. Situational awareness from the military field can effectively deal with the anomalies with significant temporal-spatial characteristics. Considering the requirement of automatic control in practical application, the concept of resilient Cyber Physical System (R-CPS) is presented. Based on this concept, we propose resilient traffic anomaly recognition (R-TAR) which is a refined mechanism for spatiotemporal traffic anomaly assessment in backbone and develop the TAR algorithm to multi-HCRF (Hidden Conditional Random Fields). R-TAR provides a CPS-based feedback control loop integrating traffic sensing, decision making and information feedback. In view of the characteristics of backbone network traffic, we develop multi-HCRF to extract temporal and spatial situation from packet-granularity to flow-granularity for better performance, and obtain the overall-network-wide situation for feedback. The experimental evaluation establishes a proof-of-concept for the proposed model based on R-TAR, confirming the practicality and performance improvement of our approach on real datasets. © 2018 IEEE.

关键词： Cyber Physical System

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Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

arXiv

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arXiv 2022年

作者： Xu, Hongzuo Wang, Yijie Jian, Songlei Liao, Qing Wang, Yongjun Pang, Guansong Beijing100091 China The National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Hunan410073 China The College of Computer National University of Defense Technology Hunan410073 China Guangdong518055 China The School of Computing and Information Systems Singapore Management University Singapore178902 Singapore

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration. Specifically, our approach adaptively penalizes uncertain predictions to restrain irregular samples in anomaly contamination during optimization, while simultaneously encouraging confident predictions on regular samples to ensure effective normality learning. This largely alleviates the negative impact of anomaly contamination. Our approach also creates native anomaly examples via perturbation to simulate time series abnormal behaviors. Through discriminating these dummy anomalies, our one-class learning is further calibrated to form a more precise normality boundary. Extensive experiments on ten real-world datasets show that our model achieves substantial improvement over sixteen state-of-the-art contenders. © 2022, CC BY.

关键词： Contamination

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Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

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International Conference on Computer Vision (ICCV)

作者： Cheng Xu Zhaoqun Li Qiang Qiu Biao Leng Jingfei Jiang School of Computer Science & Engineering Beihang University Duke University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Shape Three-dimensional displays Correlation Two dimensional displays Feature extraction Solid modeling Computational modeling

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An Efficient Approximation for Weapon-Target Assignment

An Efficient Approximation for Weapon-Target Assignment

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ISECS International Colloquium on computing, Communication, Control, and Management, CCCM

作者： Lina Chen ChuanJun Ren Su Deng C4ISR Technology National Defense Science and Technology Key Laboratory National University of Defense Technology Changsha Hunan China National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha Hunan China

The weapon-target assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research. The WTA problem can be formulated as a nonlinear integer programming problem and is known to be NP-complete. We present firstly a technique founded on the use weapon cell, which enables one-to-many mapping about weapon-target to become one-to-one mapping. Next, we introduce a framework of solving the WTA problem. Based on this framework, an approximation algorithm that is grounded on the rules about weapon cells and targets is proposed. Finally, the simulative results show that the proposed algorithm indeed is very efficient.

关键词： Weapons Heuristic algorithms Algorithm design and analysis Resource management Artificial neural networks Genetic algorithms Operations research

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Enabling Tissue-Scale Cardiac Simulations Using Heterogeneous computing on Tianhe-2

Enabling Tissue-Scale Cardiac Simulations Using Heterogeneou...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Johannes Langguth Qiang Lan Namit Gaur Xing Cai Mei Wen Chun-Yuan Zhang Simula Research Laboratory Lysaker Norway College of Computer National University of Defense Technology Changsha China National Key Laboratory of Parallel and Distributed Processing Changsha China Department of Informatics University of Oslo Oslo Norway

ISBN: (纸本)9781509053827

We develop a simulator for 3D tissue of the human cardiac ventricle with a physiologically realistic cell model and deploy it on the supercomputer Tianhe-2. In order to attain the full performance of the heterogeneous CPU-Xeon Phi design, we use carefully optimized codes for both devices and combine them to obtain suitable load balancing. Using a large number of nodes, we are able to perform tissue-scale simulations of the electrical activity and calcium handling in millions of cells, at a level of detail that tracks the states of trillions of ryanodine receptors. We can thus simulate arrythmogenic spiral waves and other complex arrhythmogenic patterns which arise from calcium handling deficiencies in human cardiac ventricle tissue. Due to extensive code tuning and parallelization via OpenMP, MPI, and SCIF/COI, large scale simulations of 10 heartbeats can be performed in a matter of hours. Test results indicate excellent scalability, thus paving the way for detailed whole-heart simulations in future generations of leadership class supercomputers.

关键词： Calcium Computational modeling Mathematical model Performance evaluation Hardware Instruction sets Supercomputers

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