ali moeini

A data fabric architecture ensures that different types of data can be quickly and easily integrated, accessed, transformed, and managed. All users involved with data fabric technology, directly or indirectly, have access to appropriate information in real-time, from the correct location and in the proper manner. As data storage and access have become increasingly common, there are growing concerns about data security. In this paper, we present a new and more efficient data fabric architecture for providing secure communication between the data store and the customer, utilizing Shamir's Secret Sharing (SSS) and secure Attribute-Based Encryption (ABE). We call this architecture Data Fabric equipped with Attribute-Based Authentication and Access Control (DFAAA). It offers security features such as perfect forward secrecy, untraceability, anonymity, mutual authentication, and availability. We verify these features using both informal and formal techniques. The results demonstrate that our architecture not only provides greater security compared to other architecture.

Due to its power in modeling complex relations between entities, graph theory has been widely used in dealing with real-world problems. On the other hand, information retrieval has emerged as one of the major problems in the area of algorithms and computation. As graph-based information retrieval algorithms have shown to be efficient and effective, this paper aims to provide an analytical review of these algorithms and propose a categorization of them. Briefly speaking, graph-based information retrieval algorithms might be divided into three major classes: the first category includes those algorithms which use a graph representation of the corresponding dataset within the information retrieval process. The second category contains semantic retrieval algorithms which utilize the graph theory. The third category is associated with the application of the graph theory in the learning to rank problem. The set of reviewed research works is analyzed based on both the frequency as well as the publication time. As an interesting finding of this review is that the third category is a relatively hot research topic in which a limited number of recent research works are conducted.

With the start of the Coronavirus epidemic, remote education systems were widely accepted in all universities around the world. The University of Tehran, the top university in Iran, had an electronic education system since 2010, which provided services in this field to the academic community. With the spread of the pandemic, these services became widespread in all departments of the University of Tehran. Compared to traditional learning, E-Learning brings challenges for Users, the main challenge of which is Users’ Satisfaction. In this paper, by examining the developed model based on the TAM model and the factor analysis of this model, the level of satisfaction of student users with the elearning management system of the University of Tehran during the Corona era is carefully evaluated. The exploitation of this model with the analytical method of partial least squares in the construction of structural equations is based on the information collected from the questionnaire that has been provided to the users of the e-learning system of the University of Tehran. The results obtained from this study show that the Technology Acceptance Model (TAM) describes well the factors affecting the level of user satisfaction with the E-learning management system. The positive effect of the visual beauty and the quality of the information in this system has improved the technical quality of the service; as a result, the level of user satisfaction in using this system has increased.

Data governance frameworks have typically been discussed within the confines of an organization, with less focus on inter-organizational contexts. However, due to factors like the rise of electronic government applications and the growth of inter-organizational collaboration in competitive markets, data exchange among organizations is on the rise. This current research aims to address this gap by proposing a data governance framework for data exchange centers. Given the continuous growth of e-government applications and interactions, along with the significant role and value of data within them, implementing this framework can enhance the accuracy and efficiency of data and information exchange.

This research is qualitative and its method is grounded theory. Initially, the fundamentals of the research were examined. This involved reviewing both scientific and academic research as well as frameworks provided by technical and specialized associations. By exploring the basics and background of the research, foundational knowledge was acquired for subsequent steps. Subsequently, through conducting semi-structured interviews with experts and documenting them, over 400 phrases were collected, and categorized into 100 concepts. These were then analyzed using open coding (resulting in 44 subcategories), theoretical coding (leading to 15 categories), and the development of a theoretical framework. The data was sourced from national data exchange centers. Given the qualitative nature of the study, methods such as participant review, triangulation, and the Delphi technique were employed to ensure the validity and reliability of the findings.

The data governance framework in the data exchange center comprises 15 components, categorized into "main components of governance, "main components in the business of data exchange centers, and "supporting components in the business of data exchange centers. Some of these components include "roles, responsibilities, and rights, "standards and policies, "change management.

Based on the results of this research, "roles, responsibilities, and rights, "standards and policies, and "monitoring and auditing" are the main components of a data governance framework in data exchange centers. Additionally, "stakeholder collaboration" and "commercialization" also distinguish it from other data governance frameworks. Although this framework is developed for data exchange centers, it can also be used in similar inter-organizational contexts that require data exchange.

In the era of pervasive internet use and the dominance of social networks, researchers face significant challenges in Persian text mining, including the scarcity of adequate datasets in Persian and the inefficiency of existing language models. This paper specifically tackles these challenges, aiming to amplify the efficiency of language models tailored to the Persian language. Focusing on enhancing the effectiveness of sentiment analysis, our approach employs an aspect-based methodology utilizing the ParsBERT model, augmented with a relevant lexicon. The study centers on sentiment analysis of user opinions extracted from the Persian website 'Digikala.' The experimental results not only highlight the proposed method's superior semantic capabilities but also showcase its efficiency gains with an accuracy of 88.2% and an F1 score of 61.7. The importance of enhancing language models in this context lies in their pivotal role in extracting nuanced sentiments from user-generated content, ultimately advancing the field of sentiment analysis in Persian text mining by increasing efficiency and accuracy.

Graphene quantum dots (GQDs) are one of the most uprising nanomaterials that have been used in biomedical applications because of their interesting luminescent properties. This work presents a facile way to attach the LCysteine onto the surface of GQDs, which enhances the applicability of the final molecules in biomedical applications. Furthermore, the luminescent properties of synthesized GQDs and LCysteine-GQDs with different methods of prevalent spectroscopy. The obtained GQDs- LCysteine show a red shift in PL results with an increase in wt.% LCysteine compared to GQDs. According to the results, this platform has the potential to be used in many biological applications, such as bio-imaging and bio-labeling.

Online social networks (OSNs) such as Facebook, Twitter, Instagram, etc. have attracted many users all around the world. Based on the centrality concept, many methods are proposed in order to find influential users in an online social network. However, the performance of these methods is not always acceptable. In this paper, we proposed a new improvement on centrality measures called P-centrality measure in which the effects of node predecessors are considered. In an extended measure called EP-centrality, the effect of the preceding predecessors of node predecessors are also considered. We also defined a combination of two centrality measures called NodePower (NP) to improve the effectiveness of the proposed metrics. The performance of utilizing our proposed centrality metrics in comparison with the conventional centrality measures is evaluated by Susceptible-Infected-Recovered (SIR) model. The results show that the proposed metrics display better performance finding influential users than normal ones due to Kendall’s τ coefficient metric.

Software plays a critical role in controlling the behavior of mechanical and electrical systems, as well as facilitating interactions among their components in cyber-physical systems (CPS). The risks associated with CPS systems can lead to the loss of functionality, performance, and even jeopardize human lives. Therefore, ensuring the reliability of software through comprehensive testing is of paramount importance in detecting and preventing potential errors. To achieve this objective, various methods and tools are employed to assess both the static and dynamic behavior of object-oriented software systems. One commonly used tool in the Unified Modeling Language (UML) is the state chart diagram, which visually represents the dynamic behavior of an object-oriented system. These diagrams depict the transitions and actions that occur as an object changes its state, driven by various inputs. To validate the accuracy of UML state chart diagrams, this paper proposes a method utilizing Finite State Machines (FSM) and Transition Tables. By creating a Transition Table, the UML state chart diagram can be effectively validated. To evaluate the proposed method, a set of Test Cases has been generated and applied to a real case study, ensuring the accuracy and reliability of the UML state chart diagram.

 The algorithmic classification of infected and healthy individuals by gene expression has been a topic of interest to researchers in numerous domains, including cancer. Several studies have presented numerous solutions, such as neural networks and support vector machines (SVMs), to classify a diverse range of cancer cases. Such classifications have provided some degrees of accuracy, which highly depend on optimization approaches and suitable kernels.

 This study aimed at proposing a method to classify cancer-prone and healthy cases under breast cancer and colorectal cancer (CRC), using machine learning methods efficiently, increasing the accuracy of the classification process.

 This study presented an algorithm to diagnose individuals prone to breast cancer and CRC. The novelty of this algorithm lies in its suitable kernel and the feature extraction approach. By the application of this algorithm, this study first identified the genes closely associated with these types of cancers and, then, tried to find individuals susceptible to the concerned cancers using SVM. The present study highlighted the indirect gene expressions associated with these cancers, which might show health status complications for the patients. To this end, the algorithm consists of SVMs in conjunction with the k-fold method for validation.

 The results confirmed the superior performance of this approach, compared to the common neural networks. The algorithm’s identification accuracy values were 98.077% and 99.806% for breast cancer and CRC, respectively. The graphic representation of the cause-effect relationships was also provided to help researchers better understand the trend of cancer or other types of diseases.

 The feature extraction method highly affects the accuracy of the classification. In addition, relying on indirect disease-triggering genes’ expressions highlights a cause-effect relationship between genes and diseases. Such relationships can form Markov models in the clinical domain leading to treatment paths and prediction of patient outcomes.

During the COVID-19 pandemic, universities worldwide favored distance learning systems. Although e-learning provides numerous benefits, it also poses challenges for learners, so learners’ satisfaction is a significant concern to be assessed. The present study aimed to assess learners’ satisfaction with the University of Tehran's LMS during the pandemic. It applied a model based on the Technology Acceptance Model (TAM) and supplemented it with factor analysis. Research validity was assessed by using confirmatory factor analysis, while Cronbach's alpha was employed to measure the reliability of the research instrument. The study used the Partial Least Squares (PLS) analytical method to construct structural equations. In order to do so, data was collected from a comprehensive questionnaire distributed to 334 students at the University of Tehran, in which 143 participated. The findings reaffirmed the TAM's effectiveness in understanding the factors influencing learners’ satisfaction with the LMS. Consequently, these insights empower the technical team to enhance system functionality and infrastructure, aiming at improving service quality in the first semester of the academic year 2021. The Results showed that: 1) The TAM-based proposed scale has successfully explained factors predicting learners’ satisfaction. 2) Technical knowledge contributes to the perceived ease of use and influences perceived usefulness with a coefficient of 0.89, and 3) The significant relationships between technical knowledge and attitude were met. The paper's contribution is finding that learners’ technical knowledge significantly impacts their satisfaction, which helps the ICT team develop a user-friendly environment and increases the flexibility of templates. The study focused on creating training videos to improve learners’ technical knowledge. These results can change and improve the software development strategy for the next semester.

The growing trend of cyber threats against governments and communities, infrastructure and organizations continues at an unpredictable and rapid pace. Planning to defend against threats leading to cyber attacks is one of the things that has a special place in the macro security plans of governments. Appropriate measures in recognizing and analyzing behavior and encouraging attention to this particular phenomenon in cyberspace, which is a kind of violation of the principles of confidentiality, comprehensiveness and accessibility of information, have a significant effect on deterrence, confrontation and cyber resilience. . Based on the need to strengthen cyber threat identification measures, in this study with the aim of determining the priority and ranking of recurring threats announced by reputable global and international sources and with a simple weighting method focusing on the presence of these threats in the death chain and with Using conventional methods in graph theory, the results are presented in the form of rankings and illustrations in the form of graphs resulting from the use of Goff software. The results of this study indicate that it is important that "internal threats" are at the top of the threats that are in the death chain.

In order to facilitate processes and provide innovative services, Most of organizations pay special attention to using data center services to host new platforms, services and tools to manage and run their systems. In the field of data center cybersecurity, all aspects need to be considered to identify the causes and then solve the problems. Therefore, in this study, we seek to identify and provide management codes that consider all internal and external aspects of cyber security. In this research, using NIST cybersecurity framework and existing localized framework for Identify management codes of Comprehensive framework for data center cybersecurity by systematic literature review (SLR), 1831 documents was extracted by searching the Science direct, Scopus and ISC articles. After necessary examinations, 63 of them were identified as related documents. They were analyzed and coded using the Meta- synthesis method. Finally, the extracted features were classified into 5 main category and 23 sub categories and 108 codes, which were related to data center cyber security, i.e. Identification, protection, detection, response and retrieval.

Numerous methods have been introduced to predict the reliability of software. In general, these methods can be divided into two main categories, namely parametric (e.g. software reliability growth models) and non-parametric (e.g. neural networks). Both approaches have been successfully implemented in software testing applications over the past four decades. Since most software reliability prediction data are available in the form of time series, deep recurrent network models (e.g. RNN, LSTM, NARX, and LSTM Encoder-Decoder networks) are considered as powerful tools to be employed in reliability-related problems. However, the problem of overfitting is a major concern when using deep neural networks for software reliability applications. To address this issue, we propose the use of dropout; therefore, this study utilizes a deep learning model based on LSTM Encoder-Decoder Dropout to predict the number of faults in software and assess software reliability. Experimental results show that the proposed model has better prediction performance compared with other RNN-based models.

Massive data volume, on the one hand, and the growing importance of the role of data in organizations, on the other hand, make data management inevitable. One of the important issues in data management is to determine the necessary roles and division of responsibilities and rights arising from it. The pattern of division of responsibilities in an organization depends on its structure, and the structure of an organization is a function of the three elements of complexity, formality, and focus. In coalition organizations, the division of responsibilities and powers between the central unit and its affiliated units is more complex and requires a clear and codified model for joint efforts and cooperation in various issues, including management. In this research, using the responsibility-sharing matrix, a model for cooperation and division of responsibilities related to data management in the main body and affiliated subdivisions of a coalition organization has been obtained and using the Delphi method to reach the consensus of experts and using, how to divide data management responsibilities in the Ministry of Science, Research and Technology of the Islamic Republic of Iran and its affiliated universities is explained.

Actor-Network theory (ANT), in social sense, is an approach to sociological analysis in the field of science and technology studies and focuses on understanding how things get to the situations in which they find themselves. In recent years, this theory has been considered by researchers as a methodological approach to analyze the use of technology in the workplace and describe the various dimensions of socio-technical systems. Despite numerous examples of research based on ANT methodology, no codified framework has been introduced for it and no examples of using this theory in the form of methodology have been found in the country. The present study tries to introduce a framework for data collection and analysis in the context of this theory, while explaining the methodology of this theory in an analytical-descriptive manner. The feasibility and validity of the proposed framework have been assessed in the form of a case study. The results of the research can be effective in the systematic application of ANT methodology, especially in the country.

 Semantic technologies (STs) have made machine reasoning possible by providing intelligent data management methods. This capability has created new forms of interaction between humans and STs, which is called "semantic interaction."  The increasing spread of this form of interaction in daily life reveals the need to identify the factors affecting it and introduce the requirements of a synergistic interaction, which in this study is interpreted as a model of symbiosis.

The main purpose of this study is to investigate what, why, and how human-ST symbiosis occurs in the form of a symbiosis model. Providing such a model could be valuable in developing active strategies in the face of intelligent technologies.

  The study introduces actor-networks of human symbiosis based on the actor-network methodology. Data was collected through in-depth interviews with eight managers, experts, and users in the Computer Research Centers of Islamic Sciences (CRCIS) and examined using the actor network method. All phases of data collection, implementations, coding and analysis were done under NVivo software.

In the human-ST symbiosis, beside human eleven other actors:  Semantic products, context, infrastructure, data, knowledge, social media, Web, scientific centers, organization, AI and ontology are identified. Their interaction establishes seven dynamic actors-networks of symbiosis: Product design and development, use, leadership and management, data, knowledge management, training and contextual conditions.

Semantic products alongside human beings are independent, autonomous, and self-aware actors who are able to go beyond mere mediation of change and govern social change in the Human-ST symbiosis. In such circumstances, man, as the creator and maintainer of the semantic product, in addition to strengthening the technical capabilities in the creation of the product, must entrust to the product the things that the product is able to do.

One of the key pillars of any operating system is its proper software performance. Software failure can have dangerous effects and consequences and can lead to adverse and undesirable events in the design or use phases. The goal of this study is to identify and evaluate the most significant software risks based on the FMEA indices with respect to reduce the risk level by means of experts’ opinions. To this end, TOPSIS as one of the most applicable methods of prioritizing and ordering the significance of events has been used. Since uncertainty in the data is inevitable, the entropy principle has been applied with the help of fuzzy theory to overcome this problem to weigh the specified indices.The applicability and effectiveness of the proposed approach is validated through a real case study risk analysis of an Air/Space software system. The results show that the proposed approach is valid and can provide valuable and effective information in assisting risk management decision making of our software system that is in the early stages of software life cycle. After obtaining the events and assessing their risk using the existing method, finally, suggestions are given to reduce the risk of the event with a higher risk rating.

Despite the increasing development of research in the service supply chain, the IT service supply chain measurements have not  grown proportionally. In this paper, based on a  survey the key indicators in the IT service supply chain and ranking them, a new hybrid method for measuring the supply chain performance in IT service providers is presented.  IT services are examined in three subcategories: Customer Relationship Management (CRM), Supplier Relationship Management (SRM) and Mutual Trust. In this method, comparative vectors in the high dimension space using the AHP method are developed from relationships between effective IT service supply chain factors and the Kernel LS-SVM method is presented for outranking. The Kernel LS-SVM method allows the presentation of mean surface and provides a hyperplane for outranking. The result shows that knowledge and skills, management information system, security service management system, ability to communicate effectively with the customer, ability to establish effective relationships with suppliers, performance of provided services and customer response time, criteria are the highest importance among 112 examined indicators and More attention to them caused an significant increase in quality of measuring  the performance of companies.

The research is intended to extract repetitive themes in the field of semantic-rich learning and to express the basic opportunities and challenges therein. The method applied was to review the articles published in the WOS database, during the years 2000 to 2020 by using the paradigm funnel technique; moreover the Nvivo software was used for document analysis and theme extraction. In the study, it was found that establishing access to appropriate educational content, proper analysis and representation of knowledge, human capabilities enhancement, personalization of learning, and improving the quality of assessment, are the most important positive effects of using STs in learning; Also, in this study, nine themes and seven major challenges in the field of semantic-rich learning were identified. personalization and adaptation, and the development of various ontologies, are the most cited themes; and access to learning content and concerns about the design and development of learning systems are the most important challenges facing semantic-rich learning environments. We believe that in order to overcome the enumerated challenges, the combination of STs with other emerging cognitive and communication technologies, such as IoT, is necessary and could be the subject of future research in this field.

Scientific management in organizations requires careful design of the appropriate policy model that can best drive the organization to its goals. Therefore, designing a policy model to make decisions with minimum error and intelligent use of data is inevitable. Lack of a smart or systematic model or lack of a system in place, lack of timely and appropriate decision making, lack of evidence-based policymaking processes, departures from governance tasks and addressing or drowning in operational and ongoing work. There are reasons why organizations, especially the Ministry of Science, Research and Technology, need to design an appropriate model for this purpose. The purpose of the present study is to design an appropriate policy Framework in the Ministry of Science, Research and Technology in a macro and detailed manner. In fact, this research seeks to answer the research question, "What is the appropriate Framework of policymaking process at the Ministry of Science, Research and Technology?" Is. For this purpose, first, to design a macro Framework, using an exploratory research approach, and using qualitative research method and Grounded theory strategy, the necessary data were collected and then analyzed by Grounded Theory, macro policy Framework Designed at the Ministry of Science, Research and Technology.Then, the detailed policy model in the Ministry of Science, Research and Technology was analyzed and designed using the Structured Analysis Method (SSADM). The quality of the designed patterns was evaluated and validated by scientific methods and Confirmed. finally the six components were identified and explained

Business development in multiple industries and markets results in platform competition. Emerging approach requires adopting new managerial perspectives and defining appropriate business models. Reliability and performance of a platform besides on the capability of the platform owner, depends on the capabilities of partners and their bilateral and multilateral relations. So, relying on the principals of linear business modeling to design platform business models does not seem effective. This study aimed to fill the existing research gap in the field of platform business model and provide a comprehensive framework for developing business models tailored to the needs and specifications of the platforms. So, literature systematic review and meta-synthesis method is used to identify the components of the platform business model. Extracting research studies (2008-2019) from valid scientific databases and refinements based on specific criteria, 32 selected research articles has been analyzed. Consequently, a framework is defined to design platform business model in two levels, the first including six dimensions and the second 22 components. Proposed model is based on cooperation and value co-creation in, which is the platform core competency.

Scientific management in organizations requires the design of a proper planning framework that can intelligently achieve the goals of the organization. Designing a planning paradigm to make decisions with minimal error and using smart data is inevitable. The purpose of the present study is to design an appropriate and effective planning framework in the Ministry of Science, Research and Technology. For this purpose, by using exploratory research approach and by using qualitative research method and Grounded theory strategy and by purposeful sampling and interviewing 13 experts, planning framework in Ministry of Science, Research and Technology It was designed for business intelligence. Based on the encodings performed, the first 193 initial codes of research were identified. Then 2 concepts (main category) and 27 categories (subcategory) were identified. Then, by selective ranking and cumulative classification of the categories, 4 main components of the framework were identified and the relationships between them were identified and their user levels determined. Finally, the designed framework was evaluated and validated using scientific methods and its quality was confirmed and suggestions for future research were presented.

One of the most popular frameworks for big data processing is Apache Hadoop MapReduce. The default Hadoop scheduler uses queue system. However, it does not consider any specific priority for the jobs required for MapReduce programming model. In this paper, a new dynamic score is developed to improve the performance of the default Hadoop MapReduce scheduler. This dynamic priority score is computed based on effective factors such as job runtime estimation, input data size, waiting time, and length or bustle of the waiting queue. The implementation of the proposed scheduling method, based on this dynamic score, not only improves CPU and memory performance, but also reduced waiting time and average turnaround time by approximately $45%$ and $40%$ respectively, compared to the default Hadoop scheduler.

Community detection is one of the important topics in complex network study. There are many algorithms for community detection, some of which are based on finding maximal cliques in the network. In this paper, we improve Streaming Community Detection Algorithm (SCoDA) and Order Statistics Local Optimization Method (OSLOM). After finding maximal cliques and generating the corresponding graphs, the latter are used as input to SCoDA and OSLOM algorithms. Non-overlap and overlap synthetic graphs and real graphs data are used in our experiments.  As evaluation criteria F1score and NMI scores functions are utilized. It is shown that the improved version of SCoDA has better results in comparison to the original SCoDA algorithm, and the improved OSLOM algorithm has better performance in comparison with the original OSLOM algorithm.

The purpose of the present study is to identify priority indicators -based on the qualitative analysis of the results of the research carried out- and also to provide a framework for measuring the performance of the information security service supply chain.

The methodology of this research is essentially descriptive and qualitative and has been carried out in two stages. In the first phase, 133 papers were reviewed, after evaluation, 28 articles were approved. Then, by using the CASP method, finally, 15 articles on the measurement of the performance of the service chain received the minimum score for conducting a qualitative analysis of the content. By reviewing these articles, a conceptual framework for measuring the performance of the service chain was presented in the form of a "logic model". This model is a tool that illustrates the logic of doing things in the four components of inputs, processes, outputs and outcomes. After verifying the reliability of the results, in the second stage, in order to map the proposed framework into the field of information security services, by referring to ten experts in this field and receiving their views in the three rounds of the Fuzzy Delphi, validation and final modeling of the research was designed.

The evaluation of the performance of the information security service supply chain for 19 indicators and 35 effective sub-indicators, under the logic model, were agreed with the coefficient of coordination of Kendal of 0.742 between experts and specialists.

The review of experts' opinions showed that the sub-indicators of knowledge and skills, the provision of new and up-to-dated security technologies and the confidentiality and security of information make the most important and rate of personnel turnover sub-indicator, assessing the least important in the performance evaluation of the information security services supply chain.