نشریه فناوری اطلاعات و ارتباطات ایران
سال سیزدهم شماره 49 (پاییز و زمستان 1400)

Nowadays, investing in information and communication technology (ICT) is inevitable, because it affects various aspects of human life, including the economy. Due to the rapid growth of population, increasing energy demand, and limited energy resources, one of the basic measures to achieve sustainable development in countries, is optimization and reform of energy consumption structures. Given that the home sector is one of the main sectors of energy consumption, one of the effective approaches in reducing and managing household energy expenditures is to use ICT capabilities. In this regard, in this study, the effect of ICT expansion on energy consumption of urban households in Iran using the Panel Data method and GLS model during the period 2008-2015 and in the form of provincial data has been analyzed. The results indicate that in some models, a significant reducing effect of ICT on energy expenditure was observed. However, in most of the estimated models, there is no significant reducing effect of ICT on household energy expenditure. It seems that the main reasons for this are the subsidy structure governing energy prices, the low share of energy in total household consumption expenditures, the lack of proper consumption culture.

The field of electronic content is one of the most influential pillars of digital transformation. For proper planning to develop or modify the process of social, economic and cultural influences in the digital transformation trend, it is necessary to provide a proper assessment of the position of electronic content and its status. Considering the various components of electronic content and also the areas affected by it, determining the status of native electronic content in the country requires having a suitable model to cover all dimensions and thus its evaluation. The purpose of this paper is to present an appropriate and local model to assess the electronic content in the country in terms of setting up programs and policies in this area, the effectiveness of the indices offered in the proposed model for different actors and stakeholders could be traceable based on the value chain. In this paper, with regard to the importance of explaining the basic concepts of electronic content, as well as review of upstream documents and existing policies, the focus group research method has been used to determine the model elements and indices of different parts of value chain. To evaluate the proposed model, we use field analytical methods and questionnaire patterns. The proposed model and its implementation procedure allow legislators and policy makers in the field of native electronic content to assess the status of electronic content in the country and improve it based on the different roles of actors and each of .the content areas

Since school has identified as one of the major agents in the socialization process, it has found remarkable position in the educational system of any country. Improving student learning is also a key factor to enhance the educational system quality in schools. As regular exercise has profoundly positive impact on learning, this paper mainly aims to provide an approach to enhance students' learning process through morning exercise based on artificial neural network (ANN) technique and intelligent water drop optimization algorithm. This study is a quantitative research, which is purposefully a descriptive-analytical and methodologically a practical study. To that end, ANN technique was used to classify and extract the results, as well as, intelligent water drop optimization algorithm was employed for feature selection. In ANN, eleven neurons were selected as the appropriate number of hidden layer neurons; a combination of two linear and sigmoidal activation functions were employed as interlayer transmission functions; a training function was applied to train the network; and a maximum 3000 duplicates was proposed for the training algorithm on dataset. The accuracy of the proposed method was 68%, which has improved by about 2.2% compared to the basic method, i.e., exercise has a positive effect on students' learning. The results showed a proper performance of the optimal classification on the dataset with homogeneous parameters as well as a better performance of the artificial neural networks than the novel methods. Accordingly, the proposed method can have an appropriate improvement in terms of output accuracy in strengthening the learning process.

the goal of this research is mainly presenting a framework for national data with the concentration on parameters respecting data governance in order to design an effective and comprehensive pattern for all spots interacting with national data. The author has adopted descriptive approach and mixed method for this research. In the first step, the articles regarding national data organization are extracted and subsequently accorded with the articles based on technology ecosystem design patterns, 10 key components are formed as main modules. Thereafter, for each module, indexes and sub-indexes are taken into account by considering articles and also taking advantages of interviews and Delphi method. by designing two questionnaires, strategy-management and technical-lawful oriented, total number of 22 indexes and 154 sub-indexes are collected. the research has the capacity of being a scientific reference for the national data vault. it is recommended that development of technical infrastructure and data governance patterns in national level accorded with indexes and sub-indexes counted in this research

Nowadays, online review of user’s sentiments and opinions on the Internet is an important part of the process of people deciding whether to choose a product or use the services provided. Despite the Internet platform and easy access to blogs related to opinions in the field of tourism and hotel industry, there are huge and rich sources of ideas in the form of text that people can use text mining methods to discover the opinions of. Due to the importance of user's sentiments and opinions in the industry, especially in the tourism and hotel industry, the topics of opinion research and analysis of emotions and exploration of texts written by users have been considered by those in charge. In this research, a new and combined method based on a common approach in sentiment analysis, the use of words to produce characteristics for classifying reviews is presented. Thus, the development of two methods of vocabulary construction, one using statistical methods and the other using genetic algorithm is presented. The above words are combined with the Vocabulary of public feeling and standard Liu Bing classification of prominent words to increase the accuracy of classification

In this paper, an automated method has been presented using a dual-path deep learning architecture model for the problem of soccer video analysis and it emphasizes the gate recognition as one of the most important elements of the goal event that is the most important soccer game event. The proposed architecture is considered as an extended form of the VGG 13-layer model in which a dual-path architectural model has been defined. For recognizing the gate in the first path using the proposed architectural model, the model is trained by the training dataset. But in the second path, the training dataset is first examined by a screening system and the best images containing features different from the features of the first path are selected. In another word, features of a network similar to the first path, but after passing through the screening system are generated in the second path. Afterwards, the feature vectors generated in two paths are combined to create a global feature vector, thus covering different spaces of the gate recognition problem. Different evaluations have been performed on the presented method. The evaluation results represent the improved accuracy of gate recognition using the proposed dual-path architectural model in comparison to the basic model. A comparison of proposed method with other existing outcomes also represents the improved accuracy of the proposed method in comparison to the published results.

With the development of computer networks, packet-based data transmission has found its way to Cyber-Physical Systems (CPS) and especially, networked control systems (NCS). NCSs are distributed industrial processes in which sensors and actuators exchange information between the physical plant and the controller via a network. Any loss of data or packet in the network links affects the performance of the physical system and its stability. This loss could be due to natural congestions in network or a result of intentional Denial of Service (DoS) attacks. In this paper, we analytically study the stability of NCSs with the possibility of data loss in the feed-forward link by modelling the system as a switching one. When data are lost (or replaced with a jammed or bogus invalid signal/packet) in the forward link, the physical system will not receive the control input sent from the controller. In this study, NCS is regarded as a stochastic switching system by using a two-position Markov jump model. In State 1, the control signal/packet passes through and gets to the system, while in State 2, the signal or packet is lost. We analyze the stability of system in State 2 by considering the situation as an open-loop control scenario with zero input. The proposed stochastic switching system is studied in both continuous and discrete-time spaces to see under what conditions it satisfies Lyapunov stability. The stability conditions are obtained according to random dwell times of the system in each state. Finally, the model is simulated on a DC motor as the plant. The results confirm the correctness of the obtained stability conditions.

Along with the development of information and communication technology in all areas, information technology in the management and administration of countries has also developed and concepts such as e-government and e-governance have emerged. Although e-government emphasizes providing services to stakeholders by internet, e-governance emphasizes the need to change the way the government communicates democratically with its citizens. Identifying the components of e-government evaluation In addition to identifying the important aspects for the development of e-government in the executive apparatus, it is a tool to know the status of progress of the executive apparatus and identify strengths and areas for improvement in the development of e-government. The main purpose of this study is to study the criteria and identify the components of evaluating the progress of government executive bodies in the development of e-government under the title of e-government evaluation framework. Accordingly, the initial criteria have been identified based on a literature study. Then, with the help of expert opinions, the criteria are reviewed and defined. With the help of a questionnaire, the importance of each criterion in the development of e-government in the country's executive apparatus has been questioned through random sampling. After measuring the importance of the criteria from the perspective of experts, effective criteria have been categorized using exploratory factor analysis. The results of factor analysis show 27 criteria identified in six components; e-governance quality, e-governance leadership and motivation, productivity and facilitation, support, e-services management, and e-legislation are summarized. Another output of this study is the coefficient of the importance of each of the criteria in measuring the identified factor.

Recommender systems on social networks and websites have been developed to reduce the production and processing of queries. The purpose of these systems is to recommend users various items such as books, music, and friends. Users' recommendation on social networks and instant messengers is useful for users to find friends and for marketers to find new customers. On social networks such as Facebook, finding target users for marketing is an integrated feature, but in instant messengers such as Telegram and WhatsApp, it is not possible to find the target community. In this paper, by using graph and modeling the intergroup behavior of users and also defining features related to groups, a method for recommending Telegram users has been presented. The proposed method consists of 8 steps and each step can be considered a separate method for user recommendation. The data used in this paper is a real data set including more than 900,000 supergroups and 120 million Telegram users crawled by the Idekav system. Evaluation of the proposed method on high-quality groups showed an average reduction in error by 0.0812 in RMSE and 0.128 in MAE.

The individual differences of Information Technology users influence on the selection and maintenance of passwords. To fill this gap, this paper, studies the relationships between gender, personality, education level and field of study in one direction and password security in another direction. The method was descriptive and correlational. A sample selected by Convenience sampling, answered the NEO Five-Factor Model, biographical and password security behavior questionnaires. The data of 529 accepted questionnaires were analyzed using Pearson, T-Test, anova and regression the results showed that male users select stronger passwords compared to female users. The users in mathematical science, computer science, and also accounting, breached password security more often in comparison with users in other majors. Neuroticism has positive relationship, Openness-to-Experience and Agreeableness has negative relationships and Conscientiousness has a dual relationship with password security breach. These findings contribute to cybersecurity, especially in Iran, by considering individual differences in security behaviors and perceptions.

Present study aimed at presenting a framework for commercial banks’ preparedness indicators in use of blockchain technology. To this end, the meta-synthesis research method was employed. Among from 306 resources found over meta-synthesis steps, a number of 38 papers included our acceptance criteria. The four organizational, technical, individual and environmental factors were identified as indicators of commercial banks’ preparedness to use Blockchain technology. Organizational indicators included infrastructures development, managerial skills, human resources development, company or organization size, financial resources, receptive organizational culture, organizational policies, and senior management support. The technical indicators included complexity, technological security, access to technology, use of technology and specialized manpower. In addition, individual indicators included attitudes toward use, ease of use, perceived usefulness, and understanding of Blockchain technology. Finally, environmental indicators included rules and regulations, technology maturity, information exchange, compatibility, market dynamics, and governmental supports.

The entrepreneurial process has always been associated with uncertainty and risk-taking, so it can be said that the risk-taking of this process is considered an accepted part of it.Applying information and communication technology (ICT) is very effective in reducing this uncertainty. The purpose of this study is to present a value co-creation model in the field of ICT so that the basic value creation activities in the ICT ecosystem and the actors associated are determined with each activity. For this purpose, using the subject literature and meta-synthesis were identified activities and actors of the entrepreneurial ecosystem and then the main activities were selected using a survey of ICT experts. Based on the results of this analysis, a conceptual model was designed to explain ICT-based entrepreneurial activities consisting of 12 hypotheses. This model was tested using structural equation modeling in the ICT Park (Sajjad Park), which led to the identification of 10 paths with the highest factor load factor.Then, the final structures of the model were extracted by confirmatory factor analysis method including 11 stages and 48 activities. finally, the relationship between the actors and the activities was drawn in the form of a pie chart according to the meta-analysis.