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Complexity analysis and comparison of test paths based on DSE

Complexity analysis and comparison of test paths based on DS...

ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Jiawen Lu Lizhi Cai Yang Zhang Keyi Hong Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China School of Information Science & Engineering East China University of Science and Technology Shanghai China Cyber Security Department of Shanghai Public Security Bureau Shanghai China

Dynamic symbolic execution (DSE) is a well-known program test technology which can generate test cases automatically through collecting constraints of the paths represented by symbolic value. Automated test based on DSE targets at covering all paths and branches in a limited time. Although DSE has been investigated for a few years, several challenges still exist which hinder the actual usage. Path explosion is one of the greatest challenges to solve, which decreases the test performance. Analyzing complexity of the execution paths of different test programs, and investigating the factors associated with the test efficiency become very important in the software testing life cycle.

关键词： Concrete Gold

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Optimizing interconnections to maximize the spectral radius of interdependent networks

引用

Physical Review E 2017年第3期95卷 032308-032308页

作者： Huashan Chen Xiuyan Zhao Feng Liu Shouhuai Xu Wenlian Lu State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China School of Mathematical Sciences Fudan University Shanghai 200433 People's Republic of China Department of Computer Science University of Texas at San Antonio San Antonio Texas 78249 USA Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai 200433 China

The spectral radius (i.e., the largest eigenvalue) of the adjacency matrices of complex networks is an important quantity that governs the behavior of many dynamic processes on the networks, such as synchronization and epidemics. Studies in the literature focused on bounding this quantity. In this paper, we investigate how to maximize the spectral radius of interdependent networks by optimally linking k internetwork connections (or interconnections for short). We derive formulas for the estimation of the spectral radius of interdependent networks and employ these results to develop a suite of algorithms that are applicable to different parameter regimes. In particular, a simple algorithm is to link the k nodes with the largest k eigenvector centralities in one network to the node in the other network with a certain property related to both networks. We demonstrate the applicability of our algorithms via extensive simulations. We discuss the physical implications of the results, including how the optimal interconnections can more effectively decrease the threshold of epidemic spreading in the susceptible-infected-susceptible model and the threshold of synchronization of coupled Kuramoto oscillators.

关键词： Network optimization

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Implementation of switching circuit models as transfer functions

Implementation of switching circuit models as transfer funct...

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International Symposium on Circuits and Systems

作者： David Kebo Houngninou Mitchell A Thornton Department of Computer Science and Engineering SMU Dallas Texas USA Darwin Deason Institute for Cyber Security SMU Dallas Texas USA

ISBN: (纸本)9781479953424

A transfer function is a mathematical function relating the output or response of a system to the input or stimulus. It is a concise mathematical model representing the input/output behavior of a system and is widely used in many areas of engineering including system theory and signal analysis. Binary Decision Diagrams (BDDs) are a canonical representation of Boolean functions. We implement a framework to build transfer function models of digital switching functions using BDDs and demonstrate their application on simulation and implication.

关键词： Logic gates Transfer functions Binary decision diagrams Mathematical model Wires Integrated circuit modeling Boolean functions

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New efficient padding methods secure against padding oracle attacks 18th

New efficient padding methods secure against padding oracle ...

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18th International Conference on Information security and Cryptology, ICISC 2015

作者： Kang, HyungChul Park, Myungseo Moon, Dukjae Lee, Changhoon Kim, Jongsung Kim, Kimoon Kim, Juhyuk Hong, Seokhie Korea University Seoul Korea Republic of The Affiliated Insutitude of ETRI Seoul Korea Republic of Department of Computer Science and Engineering Seoul University of Science and Technology Seoul Korea Republic of Department of Mathematics Department of Financial Information Security Kookmin University Seoul Korea Republic of Korea Security and Internet Agency Seoul Korea Republic of

ISBN: (纸本)9783319308395

This paper proposes three new padding methods designed to withstand padding oracle attacks, which aim at recovering a plaintext without knowing the secret key by exploiting oracle’s characteristic of checking the padding during decryption. Of the ten existing padding methods, only two (ABYT-PAD and ABIT-PAD) can withstand padding oracle attacks. However, these methods are not efficient since they either use a random number generator or require MAC verification in applications. The three new padding methods proposed in this paper are secure against padding oracle attacks and more efficient compared to the two aforementioned padding methods. © Springer International Publishing Switzerland 2016.

关键词： Number theory

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An Iterative Co-Saliency Framework for RGBD Images

arXiv

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arXiv 2017年

作者： Cong, Runmin Lei, Jianjun Fu, Huazhu Lin, Weisi Huang, Qingming Cao, Xiaochun Hou, Chunping School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Computer Engineering Nanyang Technological University Singapore639798 Singapore Institute for Infocomm Research Ocular Imaging Department Agency for Science Technology and Research Singapore138632 Singapore School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China University of Chinese Academy of Sciences

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework. Copyright © 2017, The Authors. All rights reserved.

关键词： Iterative methods

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Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation

arXiv

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arXiv 2017年

作者： Cong, Runmin Lei, Jianjun Fu, Huazhu Huang, Qingming Cao, Xiaochun Hou, Chunping School of Electrical and Information Engineering Tianjin University Tianjin300072 China Ocular Imaging Department Institute for Infocomm Research Agency for Science Technology and Research Singapore School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100093 China

Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images. It is a newly emerging topic in computer vision community. Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency. First, the intra saliency map for each image is generated by the single image saliency model, while the inter saliency map is calculated based on the multi-constraint feature matching, which represents the constraint relationship among multiple images. Then, the optimization scheme, namely Cross Label Propagation (CLP), is used to refine the intra and inter saliency maps in a cross way. Finally, all the original and optimized saliency maps are integrated to generate the final co-saliency result. The proposed method introduces the depth information and multi-constraint feature matching to improve the performance of co-saliency detection. Moreover, the proposed method can effectively exploit any existing single image saliency model to work well in co-saliency scenarios. Experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed model. Copyright © 2017, The Authors. All rights reserved.

关键词： Image enhancement

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Modularity based community detection with deep learning 25

Modularity based community detection with deep learning

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Liang, Yang Cao, Xiaochun He, Dongxiao Chuan, Wang Xiao, Wang Weixiong, Zhang State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China School of Information Engineering Tianjin University of Commerce China School of Computer Science and Technology Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University China Department of Computer Science and Technology Tsinghua University China College of Math and Computer Science Institute for Systems Biology Jianghan University China Department of Computer Science and Engineering Washington University St. Louis United States

Identification of module or community structures is important for characterizing and understanding complex systems. While designed with different objectives, i.e., stochastic models for regeneration and modularity maximization models for discrimination, both these two types of model look for low-rank embedding to best represent and reconstruct network topology. However, the mapping through such embedding is linear, whereas real networks have various nonlinear features, making these models less effective in practice. Inspired by the strong representation power of deep neural networks, we propose a novel nonlinear reconstruction method by adopting deep neural networks for representation. We then extend the method to a semi-supervised community detection algorithm by incorporating pairwise constraints among graph nodes. Extensive experimental results on synthetic and real networks show that the new methods are effective, outperforming most state-of-the-art methods for community detection.

关键词： Deep neural networks

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Person re-identification by camera correlation aware feature augmentation

arXiv

引用

arXiv 2017年

作者： Chen, Ying-Cong Zhu, Xiatian Zheng, Wei-Shi Lai, Jian-Huang School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha410073 China Department of Computer Science and Engineering Chinese University of Hong Kong School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Key Laboratory of Machine Intelligence and Advanced Computing Sun Yatsen University Ministry of Education China Guangdong Province Key Laboratory of Information Security China

The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of

关键词： Cameras

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Distributed multi-robot coordination for dynamic perimeter surveillance in uncertain environments

Distributed multi-robot coordination for dynamic perimeter s...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Alexander Jahn Reza Javanmard Alitappeh David Saldaña Luciano C. A. Pimenta Andre G. Santos Mario F. M. Campos Graduate Program in Electrical Engineering (PPGEE) National Institute of Science and Technology for Cooperative Autonomous Systems Applied to Security and Environment Departamento de Informatica Universidade Federal de Vicosa Computer Science Department Universidade Federal de Minas Gerais Universidade Federal de Minas Gerais Belo Horizonte MG BR

In this work, multiple robots circulate around the boundary of a desired region in order to create a virtual fence. The aim of the this fence is to avoid internal or external agents crossing through the delimited area. In this paper, we propose a distributed technique that allows a team of robots to plan the deformation of the boundary shape in order to escort the safe region from one place to a goal. Our proposal is composed of two parts. First, we present a distributed planning method for the dynamic boundary. We model the resulting plan as a twice differentiable function. Second, we use the obtained function to guide the robot team, where every member uses only local information for the controller. The robots distribute themselves along the time-varying perimeter and patrol around it. We show in simulation how the robots behave in partially/totally unknown environments with static obstacles.

关键词： Shape Robot kinematics Trajectory Planning Robot sensing systems Dynamics

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Fast shortest-path queries on large-scale graphs

Fast shortest-path queries on large-scale graphs

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International Conference on Network Protocols

作者： Qiongwen Xu Xu Zhang Jin Zhao Xin Wang Tilman Wolf School of Computer Science Fudan University China Ministry of Education Engineering Research Center of Cyber Security Auditing and Monitoring China Department of Electrical and Computer Engineering University of Massachusetts Amherst USA

ISBN: (纸本)9781509032822

Shortest-path queries on weighted graphs are an essential operation in computer networks. The performance of such algorithms has become a critical challenge in emerging software-defined networks (SDN), since SDN controllers need to perform a shortest-path query for every flow. Unlike classic solutions (e.g., Dijkstra's algorithm), high-performance shortest-path query algorithms include two stages: preprocessing and query answering. Despite the improved query answering time, existing two-stage algorithms are still extremely time-consuming in preprocessing large-scale graphs. In this paper, we propose an efficient shortest-path query algorithm, called BBQ, which reduces the running time of both stages via tree decomposition. BBQ constructs a distance oracle in a bottom-top-bottom manner, which significantly reduces preprocessing time over existing algorithms. In addition, BBQ can answer batch queries in bulk by traversing the decomposed tree instead of executing separate queries. Our experimental results show that BBQ outperforms state-of-the-art approaches by orders of magnitude for the running time of the preprocessing stage. Meanwhile, BBQ also offers remarkable acceleration for answering batches of queries. As a result, SDN controllers that use BBQ can sustain 1.1-27.9 times higher connection request rates.

关键词： Phase locked loops Conferences Protocols Acceleration Runtime Time complexity

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