m. mosleh

With the extension of social networks, research on influence maximization (IM) in time-sensitive graphs has increased in recent years. IM is a problem to find a seed set with k nodes to maximize the information propagation range in the graph. Most of the research in this area consists of greedy, heuristic, meta-heuristic methods. However, most of these methods ignore the time-sensitivity to propagation delay and duration. The preceding time-sensitive centrality measures as a part of heuristic approaches take the propagation delay but only consider the nodes locally so that each graph node considers only the direct neighbors. Based on the above analysis, this article focuses on the time-sensitive IM problem. Here, a propagation value for each path in the graph is defined in terms of the probability of affecting through the edge and freshness amount of the edge. To solve the problem, we propose time-sensitive centrality measures that consider propagation value and both the direct and the indirect neighbors. Therefore, four measures of time-sensitive closeness centrality (TSCloseness), time-sensitive harmonic (TSHarmonic), time-sensitive decay centrality (TSDecay), and time-sensitive eccentricity centrality (TSEccentricity) were proposed. The experiments on five datasets demonstrate the efficiency and influence performance of the TSHarmonic measure on evaluation metrics.

Today, one of the most important challenges of information security and communication networks is the increasing number of malware and, consequently, finding suitable ways to protect systems against them. Knowing in time and finding ways to deal with the malicious effects of malware is one of the most important challenges for programmers and information security professionals. Is. Intelligent malware detection systems are able to model malicious behavior well. Extracting appropriate features and using efficient classifiers can improve the performance of such systems. In this paper, a new approach to malware detection is proposed using synergy of the features of the hardware counters and the optimization of the multilayer perceptron neural network classifier. The proposed system is able to identify healthy files from malware by extracting features with high discrimination and also using the neural network optimized by the dragonfly algorithm. In order to evaluate the proposed system, a data set including 168 healthy samples and 437 samples infected with malware is used. The results of the simulations show the higher performance of the proposed category compared to other categories, so that the proposed system has been able to detect the presence of malware-infected files with 86% accuracy.

The popularity of audio formats usually attracts the attention of intruders and criminals to use this medium as a cover for establishing their secret communications. The extensive use of this formats, along with  various modern techniques, designed for audio steganography, can cause the cyber spaces to be insecure environments. In order to deal with threats, some audio steganalysis techniques have been presented that statistically analyze various audio formats, such as music, MP3, and VoIP, efficiently. Among the presented approaches, combining the techniques of signal processing and machine learning has made possible the creation of steganalyzers that are highly accurate. However, since the statistical properties of audio files differ from purely speech ones, the current steganalysis methods cannot detect speech stego files,            accurately. Another issue is the large number of analysis dimensions which increase the implementation cost, significantly. As response to these issues, this paper proposes the percentage of equal adjacent  samples (PEAS) feature, as a one-dimensional feature for speech steganalysis. Using a classifier, based on the Gaussian membership function, on stego instances with 50% embedding ratio, the evaluation results for the designed steganalyzer, show a sensitivity of 99.82%. Additionally, it can efficiently estimate the length of a hidden message with the desirable accuracy. Also, the PEAS steganalysis was evaluated on a database, containing classic music instances, and the results show an 81.2% efficient performance.

Digital watermarking technology is presented as one of the best solutions for solving unauthorized copying, content identification and authentication of digital media. Digital watermarking can be applied in the fields of image, text, audio, and video contents. Audio watermarking has recently attracted the attention of researchers because the human auditory system is much more sensitive than his vision system. Therefore, insertion of the data into audio signals in a transparent way is much more difficult than other watermarking species. An efficient audio watermarking system should be able to improve reconciliation of the three measures of transparency, strength and capacitance. Improving the compromise between these three measures is a challenging problem, due to the fact that increasing the capacity of the input signal, causes distortions in the signal which in turn causes a reduction in transparency and robustness. In this paper, a new method is proposed for watermarking of audio signals that is able to improve the three criteria of transparency, robustness and capacity in an appropriate manner. In order to improve the resilience of the hidden  signal against signal processing attacks, the synergy of three efficient transforms in the field of signal processing namely, the graph-centric conversion (GBT), the discrete cosine transform (DCT) and the discrete wavelet transform (DWT), has been used. In addition, in order to maintain the transparency of the watermarked signal, the watermarked data in the high triangle matrix resulting from the LU decomposition of the approximation coefficients of the discrete wavelet are entered in an order which is based on the Fibonacci sequence. The procedure is based on the value of the watermarked bit, the non-zero values ​​of the upper triangular matrix are replaced by the nearest even or odd numbers in the Fibonacci sequence. The latent extraction operation is completely blind. The results of the evaluation of the   proposed method on audio files with Blue, Electronic, Classic and Jazz styles show that the proposed method, despite good resistance to various signal processing attacks, has an average signal rate of 45.13 dB and a placement rate of 625.75 bits per second.

Consequences such as increasing computational load, increasing time complexity, inability or inefficiency to implement some algorithms and processes, along with the increasing volume of information and data, bring about almost an end to classical calculations. In recent decades, quantum computing has emerged as one of the promising new technologies to overcome the limitations of classical computing, and a new  generation of computers and communication networks is being formed based on it. Digital signal  processing is one of the areas that has tried to use the capabilities of quantum computing. In this regard, there are a few methods for representing audio signals in quantum form, which are either unable to display accurately a digital signal in quantum form or can't be used for single-channel or multi-channel audio signals. Therefore, a comprehensive and precise method is needed. Accordingly, in this paper, a comprehensive quantum representation of digital signals (CQRDS) is proposed, which can accurately display each digital audio signal in the quantum form. Besides, it can be used to represent a variety of single-channel or multi-channel audio signals. The proposed representation uses three entangled qubit  sequences, including a 2's complement fractional sequence, to store the amplitude of the samples, and two quantum sequences in true form to store the channel information and the time information of the samples. Representation, preparation, retrieval, and compression of quantum audio signals are described by the  proposed method of this paper. Also, a precise comparison between the capabilities and features of the  proposed method with that of the existing methods is presented, indicating its comprehensiveness.

One of the most important principles in educational organizations and institutions is human capital and that talent management can help promote it. The main purpose of this study is to "present the talent management model of high school principals in Shiraz".

The present study is an applied in terms of purpose. This research is a descriptive survey in terms of data collection method. The instrument used in this study was a 39-item researcher-made questionnaire. In the pilot study, there were several tools and questionnaires in the field of talent management that these questionnaires examined only the three dimensions of attracting, retaining and promoting talent management. Therefore, in order to use more accurate tools, a researcher-made questionnaire has been developed. Due to the use of a researcher-made questionnaire from the heuristic factor analysis test through the validity of the construct has been confirmed. In order to compile the questionnaire, first by studying the existing empirical background and some resources related to talent management, the main components affecting talent management were identified and counted. In this research, exploratory factor analysis has been used to select the number of factors and special value. Eigenvalue is the amount of variance of all tests extracted by factors. According to this criterion, the minimum eigenvalue for factor selection is greater than one. The results of KMO and Bartlett tests were very suitable for factor analysis for research data. Because the value of KMO index is equal to 0.768 and the level of significance of this test is 0.000. Therefore, it can be said that the data have an acceptable appropriat eness for the factor analysis test. Finally, the reliability of the questionnaire was confirmed by Cronbach's alpha method and the total alpha was 0.899, which confirmed the high reliability of the questionnaire. The statistical population of this study includes all principals of secondary schools in Shiraz, which includes a total of 188 schools in four education districts of Shiraz, which were selected by the principals of these schools.

Structural equation test showed the order of path coefficient value from highest to lowest as following: (1) talent evaluation and discovery component; (2) the talent acquisition component and the third was related to the talent retention component, the fourth was the talent promotion, and finally, the fifth, to the development and training component. The results of Friedman rank test show that according to the degree of freedom (4) and Chi-square (856/139) and the level of significance (0.000), the five components studied have different means. The highest average is related to the talent absorption component (average 5) in the first rank and in the second rank the development and education component (average 3.84), talent retention in the third rank (average 2.41), the fourth rank in talent promotion (average 2.08 ) And is in the fifth and final rank of talent assessment and discovery (average 1.67).

The results of the research showed that several components such as development and education, evaluation and discovery of talent and talent promotion affect the talent management of principals in schools. It is necessary to apply the talent management model in a practical way in order to promote human capital in education.

In this paper, we present some efficient numerical algorithm for solving system of fuzzy polynomial equations based on Newton's method. The modified Adomian decomposition method is applied to construct the numerical algorithms. Some numerical illustrations are given to show the efficiency of algorithms.