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IEEE International Workshop on Anti-counterfeiting, Security, Identification, ASID

作者： Meng Xu Lingfeng Wang Jian Ren Stefan Poslad State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China School of Electrical Engineering and Computer Science Queen Mary University of London London U.K

ISBN: (数字)9781728168791

ISBN: (纸本)9781728168807

More accurately estimating camera pose can be used to greatly improve localization in applications such as augmented reality, autonomous driving, and intelligent robots. Deep learning methods have achieved great progress to improve accuracy but still have limitations with respect to rotation, which results in angle regression errors. In this work, we combine a LSTM module with rotation classification loss to regress the camera pose. The algorithm uses a robust processing pipeline to supervise the pose estimation with dynamic, weighted, multi-losses in order to limit separate Euler angle (yaw, pitch, roll) losses, and common translation-quaternion losses. An empirical test on the 7Scenes benchmark dataset shows better results than when using common absolute pose regression methods.

关键词： 6G mobile communication Stairs Bayes methods

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EDOA: an efficient delay optimization approach for mixed-polarity Reed-Muller logic circuits under the unit delay model

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Frontiers of computer science 2019年第5期13卷 1102-1115页

作者： Zhenxue HE Limin XIAO Fei GU Li RUAN Zhisheng HUO Mingzhe LI Mingfa ZHU Longbing ZHANG Rui LIU Xiang WANG College of Information Science and Technology Hebei Agricultural University Baoding 071001 China State Key Laboratory of Software Development Environment Beihang University Beijing 100083 China School of Computer Science and Engineering Beihang University Beijing 100083 China School of Electronic and Information Engineering Beihang University Beijing 100083 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Science & Technology Resources Sharing Service Beijing 100083 China

Delay optimization has recently attracted signif-icant attention. However, few studies have focused on the delay optimization of mixed-polarity Reed-Muller (MPRM) logic circuits. In this paper, we propose an efficient delay op-timization approach (EDOA) for MPRM logic circuits under the unit delay model, which can derive an optimal MPRM logic circuit with minimum delay. First, the simplest MPRM expression with the fewest number of product terms is ob-tained using a novel Reed-Muller expression simplification approach (RMESA) considering don't-care terms. Second, a minimum delay decomposition approach based on a Huffman tree construction algorithm is utilized on the simplest MPRM expression. Experimental results on MCNC benchmark cir-cuits demonstrate that compared to the Berkeley SIS 1.2 and ABC, the EDOA can significantly reduce delay for most cir-cuits. Furthermore, for a few circuits, while reducing delay, the EDOA incurs an area penalty.

关键词： delay optimization mixed-polarity Reed-Muller logic circuits unit delay model don't-care terms,

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Topic Model Based Android Malware Detection 12th

Topic Model Based Android Malware Detection

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12th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2019

作者： Song, Yucai Chen, Yang Lang, Bo Liu, Hongyu Chen, Shaojie State Key Lab of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100191 China

ISBN: (纸本)9783030249069

Nowadays, the security risks brought by Android malwares are increasing. Machine learning is considered as a potential solution for promoting the performance of malware detection. For machine learning based Android malware detection, feature extraction plays a key role. Thinking the source codes of applications are comparable with text documents, we propose a new Android malware detection method based on the topic model which is an effective technique in text feature extraction. Our method regards the decompiled codes of an application as a text document, and the topic model is used to mine the potential topics in the codes which can reflect the semantic feature of the application. The experimental results demonstrate that, our approach performs better than the state-of-the-art methods. Also, our method mines the features in the application files automatically without manually design, and therefore overcomes the limitation in present methods which relies on experts’ prior knowledge. © Springer Nature Switzerland AG 2019.

关键词： Android malware

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A robust and generalized framework for adversarial graph embedding

arXiv

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arXiv 2021年

作者： Li, Jianxin Fu, Xingcheng Peng, Hao Wang, Senzhang Zhu, Shijie Sun, Qingyun Yu, Philip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China State Key Laboratory of Software Development Environment Beihang University Beijing100083 China School of Computer Science and Engineering Central South University Changsha410083 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States

Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high quality graph embedding vectors various types of graphs. However, most existing methods usually randomly select the negative samples from the original graph to enhance the training data without considering the noise. In addition, most of these methods only focus on the explicit graph structures and cannot fully capture complex semantics of edges such as various relationships or asymmetry. In order to address these issues, we propose a robust and generalized framework for adversarial graph embedding based on generative adversarial networks. Inspired by generative adversarial network, we propose a robust and generalized framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution, and enables the discriminator and generator to jointly learn each node's robust and generalized representation. Based on this framework, we propose three models to handle three types of graph data and derive the corresponding optimization algorithms, i.e., UG-AGE and DG-AGE for undirected and directed homogeneous graphs, respectively, and HIN-AGE for heterogeneous information networks. Extensive experiments show that our methods consistently and significantly outperform existing state-of-the-art methods across multiple graph mining tasks, including link prediction, node classification, and graph reconstruction. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

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System architecture for high-performance permissioned blockchains

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Frontiers of computer science 2019年第6期13卷 1151-1165页

作者： Libo FENG Hui ZHANG Wei-Tek TSAI Simeng SUN State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China Digital Society&Blockchain Laboratory School of Computer ScienceBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

Blockchain(BC),as an emerging distributed database technology with advanced security and reliability,has attracted much attention from experts who devoted to efinance,intellectual property protection,the internet of things(IoT)and so ***,the inefficient transaction processing speed,which hinders the BC’s widespread,has not been well tackled *** this paper,we propose a novel architecture,called Dual-Channel Parallel Broadcast model(DCPB),which could address such a problem to a greater extent by using three methods which are dual communication channels,parallel pipeline processing and block broadcast *** the dual-channel model,one channel processes transactions,and the other engages in the execution of *** parallel pipeline processing allows the system to operate *** block generation strategy improves the efficiency and speed of *** experiments have been applied to BeihangChain,a simplified prototype for BC system,illustrates that its transaction processing speed could be improved to 16K transaction per second which could well support many real-world scenarios such as BC-based energy trading system and Micro-film copyright trading system in CCTV.

关键词： blockchain concurrency performance dualchannel model parallel pipeline consensus algorithm

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A General Recurrent Tracking Framework without Real Data

A General Recurrent Tracking Framework without Real Data

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International Conference on computer Vision (ICCV)

作者： Shuai Wang Hao Sheng Yang Zhang Yubin Wu Zhang Xiong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing P.R.China Beihang Hangzhou Innovation Institute Yuhang Beihang University Xixi Octagon City Hangzhou P.R.China College of Information Science & Technology Beijing University of Chemical Technology Beijing P.R.China

ISBN: (纸本)9781665428132

Recent progress in multi-object tracking (MOT) has shown great significance of a robust scoring mechanism for potential tracks. However, the lack of available data in MOT makes it difficult to learn a general scoring mechanism. Multiple cues including appearance, motion and etc., are limitedly utilized in current manual scoring functions. In this paper, we propose a Multiple Nodes Tracking (MNT) framework that adapts to most trackers. Based on this framework, a Recurrent Tracking Unit (RTU) is designed to score potential tracks through long-term information. In addition, we present a method of generating simulated tracking data without real data to overcome the defect of limited available data in MOT. The experiments demonstrate that our simulated tracking data is effective for training RTU and achieves state-of-the-art performance on both MOT17 and MOT16 benchmarks. Meanwhile, RTU can be flexibly plugged into classic trackers such as DeepSORT and MHT, and makes remarkable improvements as well.

关键词： Training computer vision Tracking Manuals Benchmark testing

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Unbiased scene graph generation via rich and fair semantic extraction

arXiv

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arXiv 2020年

作者： Wen, Bin Luo, Jie Liu, Xianglong Huang, Lei State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University No. 37 Xueyuan Road Beijing100191 China Abu Dhabi United Arab Emirates

Extracting graph representation of visual scenes in image is a challenging task in computer vision. Although there has been encouraging progress of scene graph generation in the past decade, we surprisingly find that the performance of existing approaches is largely limited by the strong biases, which mainly stem from (1) unconsciously assuming relations with certain semantic properties such as symmetric and (2) imbalanced annotations over different relations. To alleviate the negative effects of these biases, we proposed a new and simple architecture named Rich and Fair semantic extraction network (RiFa for short), to not only capture rich semantic properties of the relations, but also fairly predict relations with different scale of annotations. Using pseudo-siamese networks, RiFa embeds the subject and object respectively to distinguish their semantic differences and meanwhile preserve their underlying semantic properties. Then, it further predicts subject-object relations based on both the visual and semantic features of entities under certain contextual area, and fairly ranks the relation predictions for those with a few annotations. Experiments on the popular Visual Genome dataset show that RiFa achieves state-of-the-art performance under several challenging settings of scene graph task. Especially, it performs significantly better on capturing different semantic properties of relations, and obtains the best overall per relation performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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All Your PLCs Belong to Me: ICS Ransomware Is Realistic

All Your PLCs Belong to Me: ICS Ransomware Is Realistic

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yipeng Zhang Zhonghao Sun Liqun Yang Zhoujun Li Qiang Zeng Yueying He Xiaoming Zhang State Key Lab of Software Development Environment Beihang University Beijing China CNCERT/CC Beijing China Department of Computer Science and Engineering University of South Carolina City United States

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Ransomware is a new business model for cybercrime which mainly targets individual users and machines. Many events have shown how profitable the technique can be. Industrial control systems (ICS) are becoming the next domain. More and more researchers and attackers have become the focus on this field and presented some ICS ransomware. But existing ICS ransomware is theoretically feasible and has a limited effect on real ICS. In this work, we present ICS-BROCK, a full-fledged ICS ransomware that can compromise a real-world. To demonstrate the capability of ICS-BROCK, we use SIEMENS S7-300 PLC, one of the most widely used devices in ICSs, to build a real water treatment environment. The results empirically demonstrate the feasibility of launching ICS ransomware attacks in a practical setting. In the end, we give some suggestions on ICS ransomware to aid in future study and defenses.

关键词： Integrated circuits Protocols Weapons Stability analysis Safety Ransomware Security

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Spatiotemporal Attacks for Embodied Agents 1

引用

16th European Conference on computer Vision, ECCV 2020

作者： Liu, Aishan Huang, Tairan Liu, Xianglong Xu, Yitao Ma, Yuqing Chen, Xinyun Maybank, Stephen J. Tao, Dacheng State Key Laboratory of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China UC Berkeley London United States Birkbeck University of London London United Kingdom UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney Sydney Australia

ISBN: (数字)9783030585204

ISBN: (纸本)9783030585198

Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness. Existing work on adversarial attacks have mainly focused on static scenes;however, it remains unclear whether such attacks are effective against embodied agents, which could navigate and interact with a dynamic environment. In this work, we take the first step to study adversarial attacks for embodied agents. In particular, we generate spatiotemporal perturbations to form 3D adversarial examples, which exploit the interaction history in both the temporal and spatial dimensions. Regarding the temporal dimension, since agents make predictions based on historical observations, we develop a trajectory attention module to explore scene view contributions, which further help localize 3D objects appeared with highest stimuli. By conciliating with clues from the temporal dimension, along the spatial dimension, we adversarially perturb the physical properties (e.g., texture and 3D shape) of the contextual objects that appeared in the most important scene views. Extensive experiments on the EQA-v1 dataset for several embodied tasks in both the white-box and black-box settings have been conducted, which demonstrate that our perturbations have strong attack and generalization abilities (Our code can be found at https://***/liuaishan/SpatiotemporalAttack). © 2020, Springer Nature Switzerland AG.

关键词： Textures

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A comparative study of large-scale cluster workload traces via multiview analysis 21

A comparative study of large-scale cluster workload traces v...

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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： Ruan, Li Xu, Xiangrong Xiao, Limin Yuan, Feng Li, Yin Dai, Dong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Institute of Software Application Technology Guangzhou AND Chinese Academy of Sciences Guangzhou China Department of Computer Science University of North Carolina Charlotte CharlotteNC United States

ISBN: (纸本)9781728120584

Understanding the characteristics of workloads of the large-scale Clusters is the key to diagnose the system bottlenecks, making optimal configuration decisions, improving the system throughput and resource usage. Due to the diversity and multiview of the workload traces, featuring the good designs and bottlenecks by analyzing the workloads under realworld scenarios becomes increasingly challenging. This paper introduces a multiview based trace comparative analysis method by comparatively characterizing how the architecture, jobs, tasks, machines, and resources usage were managed among different platforms. A case study is performed which verified the effectiveness of our method by comparatively analyzing two most representative big traces: Google trace and Alibaba 2018 trace. Quantitative findings, together with the performance bottleneck inferences and suggestions are also presented. To the best of our knowledge, we are the first to perform such comparative empirical study on these two traces using a multiview based approach. Our multifaceted analyses and new findings not only reveal insights that we believe are useful for system designers, IT practitioners and users, but also can promote more researches on big trace data comparative analysis in large-scale clusters. © 2019 IEEE.

关键词： Trace analysis

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