C. Software/Software Engineering
S. Beiranvand; M.A. Z.Chahooki
Abstract
Software project management is one of the significant activates in the software development process. Software Development Effort Estimation (SDEE) is a challenging task in the software project management. SDEE is an old activity in computer industry from 1940s and has been reviewed several times. A SDEE ...
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Software project management is one of the significant activates in the software development process. Software Development Effort Estimation (SDEE) is a challenging task in the software project management. SDEE is an old activity in computer industry from 1940s and has been reviewed several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before software project contract. Due to the uncertain nature of development estimates and in order to increase the accuracy, researchers recently have focused on machine learning techniques. Choosing the most effective features to achieve higher accuracy in machine learning is crucial. In this paper, for narrowing the semantic gap in SDEE, a hierarchical method of filter and wrapper Feature Selection (FS) techniques and a fused measurement criteria are developed in a two-phase approach. In the first phase, two stage filter FS methods provide start sets for wrapper FS techniques. In the second phase, a fused criterion is proposed for measuring accuracy in wrapper FS techniques. Experimental results show the validity and efficiency of the proposed approach for SDEE over a variety of standard datasets.
C. Software/Software Engineering
D. Darabian; H. Marvi; M. Sharif Noughabi
Abstract
The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through ...
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The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to the noisy original speech signal. The pre-emphasized original speech segmented into overlapping time frames, then it is windowed by a modified hamming window .Higher order autocorrelation coefficients are extracted. The next step is to eliminate the lower order of the autocorrelation coefficients. The consequence pass from FFT block and then power spectrum of output is calculated. A Gaussian shape filter bank is applied to the results. Logarithm and two compensator blocks form which one is mean subtraction and the other one are root block applied to the results and DCT transformation is the last step. We use MLP neural network to evaluate the performance of proposed MFCC method and to classify the results. Some speech recognition experiments for various tasks indicate that the proposed algorithm is more robust than traditional ones in noisy condition.
C. Software/Software Engineering
H. Motameni
Abstract
To evaluate and predict component-based software security, a two-dimensional model of software security is proposed by Stochastic Petri Net in this paper. In this approach, the software security is modeled by graphical presentation ability of Petri nets, and the quantitative prediction is provided by ...
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To evaluate and predict component-based software security, a two-dimensional model of software security is proposed by Stochastic Petri Net in this paper. In this approach, the software security is modeled by graphical presentation ability of Petri nets, and the quantitative prediction is provided by the evaluation capability of Stochastic Petri Net and the computing power of Markov chain. Each vulnerable component is modeled by Stochastic Petri net and two parameters, Successfully Attack Probability (SAP) and Vulnerability Volume of each component to another component. The second parameter, as a second dimension of security evaluation, is a metric that is added to modeling to improve the accuracy of the result of system security prediction. An isomorphic Markov chain is obtained from a corresponding SPN model. The security prediction is calculated based on the probability distribution of the MC in the steady state. To identify and trace back to the critical points of system security, a sensitive analysis method is applied by derivation of the security prediction equation. It provides the possibility to investigate and compare different solutions with the target system in the designing phase.