Anhui Province Key Laboratory of Software in Computing and Communication

Hefei, China

Anhui Province Key Laboratory of Software in Computing and Communication

Hefei, China

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Cao X.-B.,Anhui University of Science and Technology | Cao X.-B.,Anhui Province Key Laboratory of Software in Computing and Communication | Du W.-B.,Anhui University of Science and Technology | Du W.-B.,Anhui Province Key Laboratory of Software in Computing and Communication | Rong Z.-H.,Donghua University
Physica A: Statistical Mechanics and its Applications | Year: 2010

The public goods game (PGG) is generally considered as a suitable paradigm to explain ubiquitous cooperative behavior. In this study, we investigated the evolutionary PGG on scale-free networks and studied the effect of individual heterogeneity by setting the cooperator x an investment value correlated to its degree as Ix = N {dot operator} kxβ / ∑j kjβ, where kx is the degree of x, j runs over all players and β is a tunable parameter. It is shown that the cooperation level is remarkably promoted by negative values of β whereas it is highly depressed by positive values of β. Moreover, the effect of environmental noise has also been investigated. Our result may sharpen the understanding of cooperation induced by the individual diversity. © 2009 Elsevier B.V. All rights reserved.


Shao C.,Hefei University of Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication | Xiao L.,Hefei University of Technology
ICCRD2011 - 2011 3rd International Conference on Computer Research and Development | Year: 2011

NURBS method is extensively used in the field of Geometric Modeling. Since the original NURBS method does not have time factor, it can not display the huge advantage in the dynamics field. In this article, inspired by the segmented nature of basis functions, we propose a time factor method which can be directly applied in reconstruction of missing data, with remarkable performance in matching the original data. This work does not only give an expression of chaotic time series, but also highlights a possible way for the challenge in reconstruction of missing data. © 2011 IEEE.


Shao C.,Microsoft | Shao C.,Anhui University of Science and Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication | Huang C.,Microsoft | Huang C.,Anhui University of Science and Technology
NSWCTC 2010 - The 2nd International Conference on Networks Security, Wireless Communications and Trusted Computing | Year: 2010

Reliability is one of the essential attributes of the dependable software, and an important factor for quantitatively characterizing software quality. Conventional methodology is Software Reliability Growth Model (SRGM), which specifies the form of a random process that describes the behavior of software failures with respect to time. In this paper, we propose that the behavior of software failures possesses determinacy as well as randomness. So we apply chaos theory to software reliability assessment. Based on chaos theory, we can estimate software reliability according to the objective law hidden in the data, unlike the SRGM which usually makes a number of assumptions. This approach avoids the subjectivity in the SRGM. Also, we analyze the actual software failures data sets with the add-weighted one-rank localregion method (AOLM) based on chaos theory and compare the results with the conventional stochastic SRGM JM (Jelinsky-Moranda) model and NHPP (nonhomogeneous poisson process) mode. Comparison results show that the proposed method fits better than the stochastic ones. © 2010 IEEE.


Yuan Y.,University of Science and Technology of China | Fanping Z.,University of Science and Technology of China | Fanping Z.,Anhui Province Key Laboratory of Software in Computing and Communication | Guanmiao Z.,University of Science and Technology of China | And 2 more authors.
Communications in Computer and Information Science | Year: 2011

Testing is a critical activity to find software errors. And choosing an effective test suite is the key problem in software testing area. Program invariant, as an attribute of program, can record the implementation state of test case very well and reveal the coverage of program data. In this paper, we integrate adaptive random testing and invariant technology, and present a new method which makes full use of the feedback information of program invariant and invalid case suite to generate the next case. Experiment results show that, compared with other similar methods, running for the same time, our method can achieve higher coverage and faster convergence speed. © 2011 Springer-Verlag.


He X.,Hefei University of Technology | He X.,Anhui Province Key Laboratory of Software in Computing and Communication | Shao C.,Hefei University of Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication | Xiong Y.,Hefei University of Technology
Journal of Computational Information Systems | Year: 2013

Among different time series data mining, time series classification is one of the most important aspects. How to design a suitable similarity measure of similarity is a burning issue for accurate time series classification. In this paper, we propose a new similarity measure based on feature exaction of the original time series. The new similarity measure is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted three sets of experiments, testing effectiveness on different time series datasets from a wide variety of application domains. Experimental evaluations show that the proposed similarity measure can tolerate the most distortions than other three typical similarity; besides, the new classifier is not only superior to other conventional classifier, but also more excellent in 1nn classifier than other similarity measure. © 2013 by Binary Information Press.


Yu S.-P.,Hefei University of Technology | Yu S.-P.,Anhui Province Key Laboratory of Software in Computing and Communication | Cao X.-B.,Hefei University of Technology | Cao X.-B.,Anhui Province Key Laboratory of Software in Computing and Communication | And 2 more authors.
Applied Soft Computing Journal | Year: 2011

The Aircraft Landing Scheduling (ALS) problem has been a complex and challenging problem in air traffic control for a long time. In practice, it can be formulated as a constrained optimization problem that needs to be solved in real-time. Although quite a few optimization techniques, e.g., linear programming-based approaches and evolutionary algorithms, have been shown to be good solver of ALS problems with small number of aircrafts, their relatively high computational cost prohibits their applications in the real world. In this paper, we propose a cellular automata optimization (CAO) approach to the ALS problem. The CAO approach solves the ALS problem in two major steps. First, a good aircraft landing sequence is obtained by simulating the aircraft landing process using a CA model. Then, the exact landing time of each aircraft is determined by a simple yet effective local search procedure. Experimental study on 13 data sets in the OR-Library was conducted to compare the CAO approach and several popular approaches in the literature. It was observed that the CAO method managed to attain high quality solutions on most of the test problem. More importantly, the computational time (in CPU seconds) of CAO method is extremely short. In most cases, satisfactory solutions can be obtained by the CAO approach within 4 s, which perfectly fulfills the requirement of the real-world air traffic control system. © 2011 Elsevier B.V.


Shao C.,Hefei University of Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication | Liu Q.,Hefei University of Technology | Wang T.,Hefei University of Technology | And 2 more authors.
Chaos | Year: 2013

Time series is widely exploited to study the innate character of the complex chaotic system. Existing chaotic models are weak in modeling accuracy because of adopting either error minimization strategy or an acceptable error to end the modeling process. Instead, interpolation can be very useful for solving differential equations with a small modeling error, but it is also very difficult to deal with arbitrary-dimensional series. In this paper, geometric theory is considered to reduce the modeling error, and a high-precision framework called Series-NonUniform Rational B-Spline (S-NURBS) model is developed to deal with arbitrary-dimensional series. The capability of the interpolation framework is proved in the validation part. Besides, we verify its reliability by interpolating Musa dataset. The main improvement of the proposed framework is that we are able to reduce the interpolation error by properly adjusting weights series step by step if more information is given. Meanwhile, these experiments also demonstrate that studying the physical system from a geometric perspective is feasible. © 2013 AIP Publishing LLC.


He X.,Hefei University of Technology | He X.,Anhui Province Key Laboratory of Software in Computing and Communication | Shao C.,Hefei University of Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication
Proceedings - 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, IEEE CCIS 2012 | Year: 2012

In order to improve the performance of time series classification, we introduce a new approach of time series classification. The first step of the approach is to design a feature exaction model based on Trend and Surprise Abstraction tree (TSA-tree). The second step of the approach is to combine the exacted global feature and 1 nearest neighbor to classify time series. The proposed approach is compared with a number of known classifiers by experiments in artificial and real-world data sets. The experimental results show it can reduce the error rates of time series classification, so it is highly competitive with previous approaches. © 2012 IEEE.


Li T.,Hefei University of Technology | Li T.,Anhui Province Key Laboratory of Software in Computing and Communication | Cao X.,Hefei University of Technology | Cao X.,Anhui Province Key Laboratory of Software in Computing and Communication | And 2 more authors.
Proceedings of the World Congress on Intelligent Control and Automation (WCICA) | Year: 2010

Vision based cyclist detection is a new application in the field of intelligent transportation. Compared with pedestrian detection, this new problem is more challenging because various appearence and motion of bicycles increase the diversity of the detection objects; therefore existing pedestrian detection approaches can hardly get good overall performance because cyclist detection requires more information represented by more effective features to enable detection. For general object detection and pedestrian detection, histogram of oriented gradient (HOG) features achieved great success; however it have two major drawbacks: time-consuming caused by dense/overlap sampling and only local information is retained. In this paper, we proposed a more effective feature extraction method (i.e., HOG-LP) to overcome the drawbacks of general HOG feature extraction for crossing cyclist detection. On one hand, an improved light/non-overlap sampling method is proposed to speed up HOG feature extraction; on the other hand, pyramid sampling is utilized to extract additional global features in different scale spaces in order to retain more information for high classification accuray. With efficient feature extraction, a linear SVM classifier is used to further increase the detection speed. The experimental results tested on urban traffic videos show the effectiveness of the proposed method on crossing cyclist detection. © 2010 IEEE.


He X.,Anhui University of Science and Technology | Shao C.,Anhui University of Science and Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication | Xiong Y.,Anhui University of Science and Technology
Neurocomputing | Year: 2014

Due to the characteristics of noise and volatility, two similar time series always appear in diverse kinds of distortions, which usually are considered as the combinations of the following basic transformations: noise, amplitude shift, amplitude scaling, temporal scaling, and linear drift. In this paper, a novel similarity measure (SIMshape) invariant to these basic distortions and any combinations of them is proposed. It is parameter-free and easy to implement. Specifically, a multi-scale shape approximation for time series based on Discrete Haar Wavelet Transform, key point extraction and symbolization is presented first; then, based on this proposed representation and a scale-weight factor, a robust similarity measure is proposed. The novelty of SIMshape lies in two aspects as follows: (a) symbolizing key points sequence extracted from approximate wavelet coefficients; (b) adding the scale-weight factor and shape similarity in the similarity criterion. To show the effectiveness and efficiency, SIMshape is compared with other popular methods Euclidean Distance (ED), LB_keogh, Complexity Invariant Distance (CID), and ASEAL (Approximate Shape Exchange ALgorithm) using two indices: the number of kinds of distortions and the degree of distortion. Obtained results show that compared with ED, CID, LB_keogh, and ASEAL, SIMshape has better robustness in synthetic data, and shows better performance in real time series classification. © 2013 Elsevier B.V.

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