A challenging issue in complex network analysis is overlapping community detection, which has attracted many studies. Label Propagation Algorithm (LPA) is one of the famous studies to detect communities. But it has some weaknesses such as using local information and randomly choosing the sequences of processing nodes. We introduce Evolutionary Label Propagation Algorithm (ELPA) to solve these problems and improve accuracy. ELPA uses an intelligent search instead of randomly processing nodes and fuses local and global perspectives. The proposed ELPA is compared with several state-of-the-art algorithms on synthetic and real-world networks with different sizes, densities, and complexities. The results indicate that ELPA provides better results on most of the test instances. Therefore, ELPA is an accurate and efficient algorithm for detecting overlapping communities.

Purpose This study aims to explore the impact of mobile learning on educating Iranian adult neo-literates within courses held by Iranian Literacy Movement Organization. Design/methodology/approach A concurrent mixed-methods design was used to investigate to what extent the adult neo-literates in Shiraz, Iran, were ready to use mobile phones in their courses. The qualitative section involved merging and summarizing basic themes into organizing themes. The themes were then integrated to create a single global theme. Findings The results showed that the participants were ready to embrace m-learning. Instrumentality was the most influential factor contributing to neo-literates’ readiness for m-learning. Findings also showed that from learners’ standpoint, mobile phones presented some unique features, enabling them to integrate elements from both distance and face-to-face courses. They also believed that mobile phones or other information and communications technology gadgets provided better opportunities for learning, although they fall short of fully replacing human instructors. Research limitations/implications The limitations of the study were the participants’ limited familiarity with the research procedure and the impossibility of holding joint gatherings at specific locations. Originality/value This applied study helps the literacy movement to take strong steps toward creating an educational environment that guarantees learning anytime and anywhere for its learners.

Social network analysis (SNA) has opened up different research areas to researchers, such as Community Detection and Influence Maximization. By modeling social networks as graphs, one can detect one’s communities or find the most Influential nodes for different applications. Despite extensive research in this area, existing methods have not yet fully met analysts’ needs and are still being improved. Researchers have recently begun to apply certain concepts of a research area in social network analysis to improve social network analysis methods in other areas. In this article, we claimed that applying Two-phase Influence Maximization can improve some community detection methods. To prove the claim, we made some changes in one of the current and efficient local community detection methods to improve the way of finding the initial nodes with the new approach to finding the most influential nodes. The results showed a significant improvement. Another problem was applying this method to dynamic networks, which could be time consuming. To solve this problem, proposed a new technique that allows us to find the initial nodes in each snapshot in a new way without carrying time consuming calculations. The experimental results showed that the novel approach and the new method outperformed the previous ones in both static and dynamic social networks.

Abstract Ransomware is one of the most challenging types of malware that uses cryptology to attack victims' computers. The attackers then demand ransom payments to recover encrypted resources. Ransomware is currently one of the most serious threats to individuals and organizations. Therefore, it is essential to detect them before they cause serious problems. Because of the obfuscation tactics used in Polymorphic and Metamorphic ransomware, it is difficult to detect them before they infect the system. Therefore, features from a program must be extracted in a way that is resistant to obfuscation techniques. The executable file header includes the fields that define the program structure. Extract this section of executable file does not require preprocessing or special resources. Note that changing the structure of the program changes, the header fields as well. The aims of ransomware and benign programs differ, resulting in discrepancies in parts of their headers. In recent paper, we propose a technique to detect ransomware utilizing executable file header bytes. According to the sequential character of header information, sequence processing algorithms are used to process it. Based on the alignment score of the important sections of the header and weighted vote technique, the proposed method determines the desired sample label. The results confirmed that this approach can detect up to 95% accuracy of ransomware, which is a significant improvement over previous methods.

Purpose The purpose of this study is to examine the effect of knowledge management (KM) on innovative performance with the mediating effect of unlearning (UNL) in Iran Water Resources Management Company. Design/methodology/approach In terms of data collection, this study has elements of library study and field study. To collect the data, a researcher-made questionnaire with three subscales of KM, UNL and innovative performance was used. The population of this study was 800 employees of Iran Water Resources Management Company, from whom a sample of 256 participants was selected through random sampling. Structural equation modeling was used to analyze the data and test the hypotheses through smart PLS software. Findings The results of data analysis showed that there is a significant relationship between KM and innovative performance, with UNL having a positive mediating effect. Research limitations/implications Recent research complements existing knowledge in the field of organizational KM. Practical implications This study provides a good view of the impact of using KM in improving innovative performance by applying the UNL process. Originality/value This study can be a beacon for decision-makers in the optimal use of KM in the organization. In the continuation of this study, managers can plan with special attention to KM processes, by removing barriers to organizational learning to achieve better innovative performance.

Team Formation Problems in Social Networks (TFP-SN) has become one of the most popular areas in Social Network Analysis (SNA). Researchers usually use a standard framework to solve these problems. A network of the experts is modeled by graphs; the nodes of which are representative of the experts and the edges between them represent the communications between them. After that, based on the costs of each expert and also the level of his relationship with other team members, the final team could be formed with the lowest cost. Therefore, they generally face with two objective functions, and both of which must be optimized in such problems. In previous studies, researchers have tried to provide a variety of objective functions for personal, communication, or both costs that lead them to a more efficient team at a lower cost. However, the current objectives are not able to take many other human considerations into account, resulting in sub-optimal teams. In this paper, we show how considering and formulating one of such human considerations can form teams with lower costs. More precisely, we first introduce a new objective function to calculate personal costs and then formulate one of the human considerations, which eventually results in removing experts with unusual salaries in the final team. The experimental results show that applying the new objective function, as well as the new consideration of human selection, can lead to superior results in reducing personal costs, communication costs and, therefore, the total cost of a team.

Significant advances in malware production methods in recent years and their use of advanced concealment mechanisms have made identifying such malware a major challenge in the field of computer system security. Recently generated malware has a high degree of self-protection mechanism, which makes it difficult to detect malware using traditional methods, if not impossible. Thus, there is a need to provide new malware detection methods. This research attempts to identify malware and help improve the security of computer systems by proposing a new machine learning based approach which use features extracted from image processing techniques. To this end, a new malware detection method based on image fusion of important sections of executable files is proposed. Employing a deep convolutional neural network to detect malware and focusing on the important sections of the file, the proposed method tries to convert the file into an RGB color image, fuse the resulting images, and extract the desired features. Using Transfer Matrix and RGB Mapping methods, the important sections of the file are first extracted to generate an RGB color image equivalent to the extracted sections. After this step, two color images are generated for each file, which are then combined using known image fusion techniques to obtain the final image. Then, AlexNet deep Convolutional Neural Network is used to classify malware and benign files.

This study analyzes the link between Mendeley indexes of scientific-citation networks and Scopus, taking into account the beneficial influence of researchers' actions in social networks on scientometric indices of works indexed in databases like Google scholar and WoS. In this basic/descriptive study, we use the Altmetrics approach to describe Iranian researchers’ activities in industrial engineering in scientific-citation networks. In this study, researchers whose activities are recorded with Iranian affiliation in scientific-citation networks have been briefly named Iranian researchers. The corpus of the study included the works of 160 Iranian researchers in the field of industrial engineering, indexed in the Scopus in the period 2000-2019. To test the likely correlation between the measures of social networks (SN) activities with scientometric ones, simple and multiple correlation tests were carried out by Excel and SPSS software. The correlation between the number of times a document was read, the number of citations, and the measures in the Mendeley, Scopus, We of Science (WoS), and Google Scholar (GS) was very high. However, the correlation between the number of readers in the Mendeley and co-authorship in Scopus was low. There was a strong correlation between the number of citations in Mendeley and that in other databases. The correlation between the authors' H-index in the Mendeley database and other databases is positive and significant, stronger in Scopus and WoS than Google Scholar. It was finally concluded that researchers’ activities in social networks attract more readers, increase the number of citations and thus increase the H-index score in databases. Therefore, they need to be more active in social networks to increase their H-index score and promote academic publications.https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.14.7

Overlapping community detection with low computation is one of the fundamental issues and challenges in large-scale complex network analysis. Detecting a community in a network means discovering a cluster of network nodes so that the density of edges between them is high. The existing methods use entire structure information or subgraphs with a fixed size to detect dense communities. Therefore, they are not efficient and accurate for large-scale networks. In this paper, the authors introduce an overlapping community detection algorithm that gradually improves density estimation by expanding the size of subgraphs and gathering information during the search process for finding communities. It is an efficient algorithm with low computational complexity for complex networks with one hundred thousand to several millions of nodes. Experimental results on synthetic and real-world networks with several hundred to four million nodes validate the performance assessment of the proposed method.

Peer assessment in an oral presentation can motivate and give more sense of responsibility to students. In recent years, various methods have been proposed to evaluate peers. In this paper, a novel peer online assessment method is proposed for oral presentation using perceptual computing. The output of the proposed system can be a numerical score for the overall assessment of a student in the presentation, which allows comparison and ranking of student performance. Furthermore, a linguistic evaluation that describes the student's performance is obtained from the system. A case study has been conducted to show the effectiveness of the proposed method; then the results are analyzed and reviewed.

Objective: This research identified dimensions and Indicators of personal knowledge management and presented an Interpretative Structural Model of Indicators affecting personal knowledge management in the Regional Water Company of Fars.Methodology: This study is practical in terms of type and purpose and descriptive-analytical in terms of data collection method, which has been done with a quantitative approach. Data collection tools are checklists and factor matrices. The statistical population of this study includes all employees of Regional Water Company of Fars in 1400 (350 people). That by Using of purposive sampling method, 25 knowledge management experts were selected as the statistical sample of the research.Findings: According to Penetration power model and the degree of dependence identified among the Indicators studied in this study, Indicators of "scientific activities to achieve solutions to specific issues or problems in the Scientific field ", "Scientific activities for inventing new laboratory methods", "Patent or scientific discovery based on scientific activities", "Storage and classification of important and required files and folders", "Book Publishing (Compilation)", and "Publication of a scientific research article" are the most effective indicators of personal knowledge management among the employees of Regional Water Company of Fars.Originality: Identifying the Penetration power model and the degree of dependence of indicators of tacit knowledge management causes organizations to identify their strengths and weaknesses to improve the flow of knowledge in inside and outside of their organization. This research is conducted for the first time in Regional Water Company of Fars.

Nowadays, while increasing variety of online social networks (OSNs), content management has become harder. The paper suggests social media pages’ managers to publish their contents, including the composition of image, video or text, only once on a OSN and then contents will simply transfer to other OSNs with an add-on software without having to re-use the internet traffic. Suggested method has more high performance than similar ones on any OSN. Also, the proposed technique makes it possible to transmit content between OSNs belonged to different companies. In this study, due to the availability of suitable facilities in both OSNs of Telegram and Instagram, an intermediary software, was implemented under the Android operating system and this action was carried out for the transmission media from the Instagram OSN to the Telegram channels or groups. This study contains three innovations than related researches. The first one is transmission content with the least usage of Internet traffic between OSNs which are not supported by one company. The next innovation is a caching software system to reduce processes of extracting contents from source OSN’s network. The third item is transmission delay time decreased sharply. As a result, the trial version has served users and managers of pages and channels, including the official Telegram channel of a TV program named "90" and a news feed named "CANNews" (the analytical aviation industry news feed). Overall, this method not only has 98.81% correct action, but also contents are fitted into the post format of the destination OSN.

Communities in social networks are groups of individuals who are connected with specific goals. Discovering information on the structure, members and types of changes of communities have always been of great interest. Despite the extensive global researches conducted on these, discovery has not been confirmed yet and researchers try to find methods and improve estimated techniques by using Data Mining tools, Graph Mining tools and artificial intelligence techniques. This paper proposes a novel two-phase approach based on global and local information to detect communities in social network. It explores the global information in the first phase and then exploits the local information in the second phase to discover communities more accurately. It also proposes a novel algorithm which exploits the local information and mines deeply for the second phase. Experimental results show that the proposed method has better performance and achieves more accurate results compared with the previous ones.