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IEEE 9th International Conference on computer and Information Technology, CIT 2009

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

ISBN: (纸本)9780769538365

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. © 2009 IEEE.

关键词： Testing

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Precisely reconstruct porous media model based on synchrotron radiation slice images

Precisely reconstruct porous media model based on synchrotro...

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2009 International Conference on Computational Intelligence and software Engineering, CiSE 2009

作者： Song, Bo Dong, Lanfang Lu, Detang Anhui Province Key Lab of Software in Computing and Communication School of Computer Science and Technology USTC Hefei China Institute of Engineering and Science Software USTC Hefei China

ISBN: (纸本)9781424445073

In this paper a method for precisely reconstructing porous media three-dimensional model based on slice images is proposed. It first processes the original slice images, then uses 3D seed filling algorithm to find all pores based on the constructed spatial data, finally uses the modified marching cubes algorithm based on bounding box technology to render material skeletons and pores. Associating with the porous media flow specific application, a novel invalid pore removing method is proposed. The final reconstructed model shows that our method can precisely reconstruct the pore network and recover the pore shape. It also can identify the pores which meet spatial scale threshold conditions. Comparison of running time between the original marching cubes algorithm and the modified marching cubes algorithm is given. It shows that due to the bounding box technology the running time of modified marching cubes algorithm is shorten greatly when the pores are far away from each other. ©2009 IEEE.

关键词： Porous materials

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Cooperative spectrum sensing with feedback

Cooperative spectrum sensing with feedback

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5th International Conference on Wireless communications, Networking and Mobile Computing, WiCOM 2009

作者： Ping, Zhu Jinglong, Li Xufa, Wang Anhui Province Key Laboratory of Software in Computing and Communication School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9781424436934

One of the main objective of spectrum sensing is to detect spectrum opportunity quickly and accurately. Both of the two aspects can be improved by hardware upgrading and software optimization. In this paper, we concentrate on sensing accuracy, which, differs from previous solutions, will be enhanced by modification of network topology. We expand the traditional parallel fusion network (PFN) to parallel feedback fusion network (PFFN), in which local cognitive sensor makes its local decision based on previous global decision and its recent observation. We then derived optimal decision rules for PFFN. Moreover, based on PFFN, sensing history of local cognitive sensor is introduced and studied. Theoretical analysis and experimental results show that PFFN outperforms the PFN in smaller system error probability under the same SNR. We also proved that PFFN and PFFN with sensing history (PFFNSH) emphasize on different aspects when certain condition holds in real world applications. Finally, a general PFFN framework is proposed and discussed. ©2009 IEEE.

关键词： Feedback

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software Backdoor Analysis Based on Sensitive Flow Tracking and Concolic Execution

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Wuhan University Journal of Natural Sciences 2016年第5期21卷 421-427页

作者： XU Xin WANG Jiajie CHENG Shaoyin ZHANG Tao JIANG Fan School of Computer Science and Technology University of Science and Technology of China Hefei 230027 Anhui China China Information Technology Security Evaluation Center Beijing 100085 China Anhui Province Key Lab of Software in Computing and Communication Hefei 230026 Anhui China

In order to effectively detect and analyze the backdoors this paper introduces a method named Backdoor Analysis based on Sensitive flow tracking and Concolic Execution（BASEC）.BASEC uses sensitive flow tracking to effectively discover backdoor behaviors, such as stealing secret information and injecting evil data into system, with less false negatives. With concolic execution on predetermined path, the backdoor trigger condition can be extracted and analyzed to achieve high accuracy. BASEC has been implemented and experimented on several software backdoor samples widespread on the Internet, and over 90% of them can be detected. Compared with behavior-based and system-call-based detection methods, BASEC relies less on the historical sample collections, and is more effective in detecting software backdoors, especially those injected into software by modifying and recompiling source codes.

关键词： software backdoor detection data flow tracking concolic execution malware detection

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A Real-Time Multi-Stage Architecture for Pose Estimation of Zebrafish Head with Convolutional Neural Networks

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Journal of computer Science & Technology 2021年第2期36卷 434-444页

作者： Zhang-Jin Huang Xiang-Xiang He Fang-Jun Wang Qing Shen School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027China School of Data Science University of Science and Technology of ChinaHefei 230027China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027China

In order to conduct optical neurophysiology experiments on a freely swimming zebrafish,it is essential to quantify the zebrafish head to determine exact lighting *** efficiently quantify a zebrafish head's behaviors with limited resources,we propose a real-time multi-stage architecture based on convolutional neural networks for pose estimation of the zebrafish head on *** stage is implemented with a small neural ***,a light-weight object detector named Micro-YOLO is used to detect a coarse region of the zebrafish head in the first *** the second stage,a tiny bounding box refinement network is devised to produce a high-quality bounding box around the zebrafish ***,a small pose estimation network named tiny-hourglass is designed to detect keypoints in the zebrafish *** experimental results show that using Micro-YOLO combined with RegressNet to predict the zebrafish head region is not only more accurate but also much faster than Faster R-CNN which is the representative of two-stage *** with DeeplabCut,a state-of-the-art method to estimate poses for user-defined body parts,our multi-stage architecture can achieve a higher accuracy,and runs 19x faster than it on CPUs.

关键词： convolutional neural network pose estimation real-time zebrafish

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A clustering system for gene expression data based upon genetic programming and the HS-model

A clustering system for gene expression data based upon gene...

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3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Liu, Guiquan Jiang, Xiufang Wen, Lingyun Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9780769540306

Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operations or predetermined parameters, which are difficult for gene data. Besides, gene data possess their own characteristics, such as large scale, high-dimension, and noise. Therefore, a systematic clustering algorithm should be proposed to effectively deal with gene data. In this paper, a novel genetic programming (GP) clustering system for gene data based on hierarchical statistical model (HS-model) is proposed. And an appropriate fitness function is also proposed in this system. This clustering system can largely eliminate the infection of data scale and dimension. The proposed GP clustering system is applied to cluster the whole intact yeast gene data without dimensionality reduction. The experimental results indicate that the algorithm is highly efficient and can effectively deal with missing values in gene dataset. © 2010 IEEE.

关键词： Cluster analysis

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Clustering time-evolving data using an efficient differential evolution 6th

Clustering time-evolving data using an efficient differentia...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Chen, Gang Luo, Wenjian Anhui Province key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui China

ISBN: (纸本)9783319204659

The previous evolutionary clustering methods for time-evolving data usually adopt the temporal smoothness framework, which controls the balance between temporal noise and true concept drift of clusters. They, however, have two major drawbacks: (1) assuming a fixed number of clusters over time;(2) the penalty term may reduce the accuracy of the clustering. In this paper, a Multimodal Evolutionary Clustering (MEC) based on Differential Evolution (DE) is presented to cope with these problems. With an existing chromosome representation of the ACDE, the MEC automatically determines the cluster number at each time step. Moreover, instead of adopting the temporal smoothness framework, we try to deal with the problem from view of the multimodal optimization. That is, the species-based DE (SDE) for multimodal optimization is adopted in the MEC. Thus the MEC is a hybrid of the ACDE and the SDE, and designed for time-evolving data clustering. Experimental evaluation demonstrates the MEC achieves good results. © Springer International Publishing Switzerland 2015.

关键词： Cluster analysis

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A fast algorithm for finding community structure based on community closeness

A fast algorithm for finding community structure based on co...

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3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Jiang, Xiufang Liu, Guiquan Lin, Zhiting Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9780769540306

Recently, the characterization of community structures in complex networks has received a considerable amount of attentions. Effective identification of these communities or clusters is a general problem in the field of data mining. In this paper we present a fast hierarchical agglomerative algorithm based on community closeness (FHACC) algorithm, for detecting community structure which is very efficient and faster than many other competing algorithms. FHACC tends to agglomerate such communities that share the most common vertices into larger ones. Its running time on a sparse network with n vertices and m edges is O(mk¯+mt), where k¯ denotes the mean vertex degree, and t is the iteration times of community agglomeration in FHACC algorithm. The algorithm was tested on several real-world networks and proved to be high efficient and effective in community finding. © 2010 IEEE.

关键词： Complex networks

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Three dimensional stratum modeling and a fast algorithm for arbitrary cutting based on half-edge structure

Three dimensional stratum modeling and a fast algorithm for ...

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2009 International Conference on Computational Intelligence and software Engineering, CiSE 2009

作者： Xi, Liu Dong, Lanfang Lu, Detang Hefei China Anhui Province Key Lab of Software in Computing and Communication School of Computer Science and Technology USTC Hefei China Institute of Engineering and Science Software USTC Hefei China

ISBN: (纸本)9781424445073

The three dimensional stratum modeling provides important basis of analysis and policy-making in areas such as Geophysics, Petroleum, Minerals, Urban construction and so on. In this paper, we firstly propose a general flow of three dimensional stratum modeling, then research stratum partition algorithm and reconstruct the surface of three dimensional stratum model (3DSM), finally program the module of the simulation and visualization of 3DSM, and propose a fast algorithm for arbitrary cutting based on half-edge structure. The result shows that the algorithm performs real-time, and the reconstructed model of it is perfect. The algorithm has general applicability in entity cutting, it can be used in 3DSM, medical treatment, city planning and other similar areas. ©2009 IEEE.

关键词： Three dimensional computer graphics

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A hybrid particle swarm optimization for high-dimensional dynamic optimization 11th

A hybrid particle swarm optimization for high-dimensional dy...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Luo, Wenjian Yang, Bin Bu, Chenyang Lin, Xin Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui230027 China

ISBN: (纸本)9783319687582

High-Dimensional Dynamic Optimization Problems (HDDOPs) commonly exist in real-world applications. In evolutionary computation field, most of existing benchmark problems, which could simulate HDDOPs, are non-separable. Thus, we give a novel benchmark problem, called high-dimensional moving peaks benchmark to simulate separable, partially separable, and non-separable problems. Moreover, a hybrid Particle Swarm Optimization algorithm based on Grouping, Clustering and Memory strategies, i.e. GCM-PSO, is proposed to solve HDDOPs. In GCM-PSO, a differential grouping method is used to decompose a HDDOP into a number of sub-problems based on variable interactions firstly. Then each sub-problem is solved by a species-based particle swarm optimization, where the nearest better clustering is adopted as the clustering method. In addition, a memory strategy is also adopted in GCM-PSO. Experimental results show that GCM-PSO performs better than the compared algorithms in most cases. © Springer International Publishing AG 2017.

关键词： Particle swarm optimization (PSO)

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