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International Conference on Wireless communications, Networking and Mobile computing (WiCom)

作者： Fanping Zeng Qing Cao Liangliang Mao Zhide Chen Department of Computer Anhui Province Key Laboratory of Software in Computer and Communication University of Science and Technology Hefei Anhui China Department of Computer University of Science and Technology Hefei Anhui China

software testing is an effective way to find software errors. Generating a good test suite is the key. A program invariant is a property that is true at a particular program point or points. The property could reflect the program's execution over a test suite. Based on this point, we integrate the random test case generation technique and the invariant extraction technique, achieving automatic test case generation and selection. With the same invariants, compared with the traditional random test case generation technique, the experimental results show that the approach this paper describes can generate a smaller test suite.

关键词： software testing Electronic mail Automatic testing System testing Vehicle crash testing Data mining Costs Programming Automation Application software

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Research on Buffer Overflow Test Based on Invariant

Research on Buffer Overflow Test Based on Invariant

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International Conference on Computer and Information Technology (CIT)

作者： Fanping Zeng Minghui Chen Kaitao Yin Xufa Wang Department of Computer Anhui Province Key Laboratory of Software in Computer and Communication University of Science and Technology Hefei Anhui China Department of Computer University of Science and Technology Hefei Anhui China

Buffer overflow (BOF) is one of the major vulnerabilities that lead to non-secure software. Testing an implementation for BOF vulnerabilities is challenging as the underlying reasons of buffer overflow vary widely. This paper presents a novel method for BOF test for ANSI C language, which uses program instrumentation and mutation test technology to test the BOF vulnerabilities, on the basis of analyzing the invariants for BOF vulnerabilities. The implementation shows that it can check the attack of BOF vulnerabilities adequately and accurately, in the circumstances of no large losses in performance.

关键词： Buffer overflow Instruments software testing Genetic mutations Monitoring Probes software libraries Runtime Information technology Performance loss

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Scheduling Aircraft Landing Based on Clonal Selection Algorithm and Receding Horizon Control

Scheduling Aircraft Landing Based on Clonal Selection Algori...

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International Conference on Intelligent Transportation

作者： Xiaolan Jia Xianbin Cao Yuanping Guo Hong Qiao Jun Zhang Province Key Laboratory of Software Computing and Communication University of Science and Technology Hefei Anhui China University of Manchester Institute of Science and Technology UK Institute of Automation Chinese Academy and Sciences Beijing China School of Electronic and Information Engineering Beihang University Beijing China

The task of aircraft landing scheduling (ALS) is to give a landing sequence and landing times for a given set of aircrafts where many constraints must be satisfied. ALS is an NP-hard problem with large-scale and multi-constraint characteristics, thus it is hard to find optimal solution efficiently. In this paper, a hybrid algorithm of Clonal Selection Algorithm (CSA) and Receding Horizon Control (RHC) was proposed for ALS problem. In details, constrained CSA based on infeasibility degree (IFD) schedules aircrafts in current receding horizon, and then RHC repeats that optimization procedure using excellent gene segment spread (EGSS) until all aircrafts have landed. Comparative experiments show that the CSA-RHC hybrid algorithm is able to obtain an optimal landing sequence and landing times rapidly and effectively.

关键词： Aircraft Aerospace control Scheduling algorithm Air traffic control Processor scheduling Computational intelligence Sequences Intelligent transportation systems Linear programming Constraint optimization

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A new private-preserving algorithm based on SMART

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Journal of Computers (Taiwan) 2016年第4期27卷 171-185页

作者： Han, Bowen Zhang, Zhenjiang Uden, Lorna School of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Jiaotong University Beijing100044 China School of Software Engineering Key Laboratory of Communication and Information Systems Beijing Jiaotong University Beijing100044 China School of Computing FACS Staffordshire University The Octagon Beaconside StaffordSt180AD United Kingdom

Privacy is a critical issue in many WSN applications because wireless sensor networks (WSNs) are vulnerable to malicious attacks due to their characteristics. Existing hop by hop and shuffling based privacy preserving protocols does not provide an energy efficient, accurate and secure data aggregation because of the energy consuming decryption at the aggregator node. How to provide effective privacy-preserving algorithm during data aggregation is important. Our main aim is to provide an energy efficient and secure data aggregation scheme, which guarantees the privacy, authenticity and freshness of individual sensed data as well as the confidentiality, accuracy and integrity of aggregated data. This paper improved on the limitations of the SMART (Slice-Mix-AggRegaTe) algorithm, and proposes a new privacy protection algorithm for wireless sensor network (WSN). We have different operations between different nodes, bring in probabilistic segmentation, and also add the data coefficient as well as the positive and negative factors. In addition we also consider making exchange time random in the algorithm. Simulation results demonstrate that the proposed algorithm reduces the communication consumption and the data traffic, improves the precision of data aggregation and the degree of privacy-preserving to a certain extent. © 2016, Computer Society of the Republic of China. All rights reserved.

关键词： Aggregates

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A Deep Concept-aware Model for predicting and explaining restaurant future status

A Deep Concept-aware Model for predicting and explaining res...

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IEEE International Conference on Web Services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms Data models Task analysis Business

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Smoothness in noise reduction of chaotic time series

Smoothness in noise reduction of chaotic time series

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2011 International Conference on Computers, communications, Control and Automation

作者： Chenxi Shao Jiangong Ji Jingyuan Shao Lipeng Xiao Department of Computer Science and Technology University of Science and Technology of China Department of Modern Physics and Nonlinear Science Center University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Information center Jianghuai Automobile Co.Ltd.

Noise is an omnipresent phenomenon. It obscures the real behavior of dynamical system. Lots of methods are proposed to remove the noise contaminating time series. However, almost all the methods consider the noise reduction in the phase space and often sharp points are kept. Different with these methods, this paper proposes a method directly on the time series itself, considering the gauss noise feature and the smoothness of the real data, uses curve-fitting way to eliminate the sharp points. The numeral results verify the effectiveness of our method.

关键词： smoothness,noise reduction,curve-fitting,Local-Geometric-Projection

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Airborne moving vehicle detection for video surveillance of urban traffic

Airborne moving vehicle detection for video surveillance of ...

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IEEE Symposium on Intelligent Vehicle

作者： Renjun Lin Xianbin Cao Yanwu Xu Changxia Wu Hong Qiao Key Laboratory of Software in Computing and Communication University of Science and Technology Hefei China University of Manchester Institute of Science and Technology UK Institute of Automation Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424435036

Urban traffic surveillance, which is designed to improve traffic management, is an important part of intelligent traffic system (ITS). In particular, airborne moving vehicle detection has become a new but hot research area since its wide view and low cost. However, airborne urban traffic surveillance is impacted by many difficulties such as camera vibration, vehicle congestion, background variance, serious thermal noise etc. Therefore, image subtraction and thermal image processing have low detection rate, while the optical flow method cannot meet the real-time application. In this paper, we propose a coarse-to-fine method, which can be divided into two stages of pre-processing and classification inspection. In pre-processing stage, the candidates regions of moving vehicle are obtained by employing Road Detection, Removal of Non-vehicle Regions and Moving Regions Extraction. The speed of this stage is fast but there is still relatively high false-positive-rate. In classification inspection stage, a well-trained cascade classifier, which refines the candidate regions, is designed to maintain a higher detection rate and a lower false alarm rate. Experimental results demonstrate that compared with representative algorithms, our method reach better performance in detection rate and false-positive-rate, while meeting the needs of real-time application.

关键词： Vehicle detection Video surveillance Optical noise Inspection Intelligent systems Costs Cameras Vehicles Background noise Image processing

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Container-based data-intensive application scheduling in hybrid cloud-edge collaborative environment

Container-based data-intensive application scheduling in hyb...

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作者： Tang, Bing Luo, Jincheng Zhang, Jiaming School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangdong Guangzhou China School of Information Engineering Wuhan University of Technology Hubei Wuhan China

Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. It can bring convenience to system deployment, operation, and maintenance. This article considers the implementation of AI-based IoT applications based on Docker and container technology in a cloud-edge collaborative environment. Three typical data-intensive applications, preprocessing, training, and inference in machine learning, are deployed using Docker containers. For these three types of tasks, this article proposes a container-based data-intensive application scheduling framework. Using an attribute-based data-driven method, a new scheduling approach considering multiple weighting factors is proposed, which is called DICS-OPT. The cloud nodes and edge nodes are scored and sorted uniformly. The container is scheduled to the node with the highest score, and then the task data is transmitted to the node to execute the task. NSGA-III-based genetic algorithm has been proposed to search for the optimal weighting factors. This article introduces the implementation of the prototype system and builds a cloud-edge collaborative testbed consisting of Raspberry Pis and PCs. The performance evaluation results indicates that the proposed scheduling approach outperforms existing approaches. Compared with existing approaches, DICS-OPT improves the average edge resource utilization by 10.05% to 69.04%, and saves the cloud resource cost by 16.02% to 36.68%. © 2023 John Wiley & Sons Ltd.

关键词： Edge computing

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A key frame extraction algorithm oriented for video copy detection based on PCC and the histogram of video frames

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Journal of Computational Information Systems 2014年第16期10卷 6999-7006页

作者： Peng, Fei Qian, Qin Long, Min College of Computer Science and Electronics Engineering Hunan University Changsha China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China College of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

Aim to improve the accuracy and the robustness of the video copy detection, a novel key frame extraction algorithm is proposed. The shot segmentation is accomplished by evaluating the Pearsons correlation coefficients (PCC) between adjacent frames, while the key frames are extracted by combining the boundary-based and visual content-based methods. Experimental results show that the proposed key frame extraction algorithm is effective, and it is robust against some content preserving video processing attacks for some existed video hash methods. It provides a novel effective method for key-frames extraction in the video copy detection. 1553-9105/Copyright © 2014 Binary Information Press

关键词： Extraction

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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