International Journal Information and Communication Technology Research
Volume:17 Issue: 1, Winter 2025

An efficient mapping can reduce the negative effects of tile scattering in the input programs and consider the running order of the tasks and their communication based on the criteria of reducing energy consumption and communication cost. The HHMap algorithm considers both homogeneous and heterogeneous 3D NoC architectures. This algorithm achieves optimized mapping in the limited heuristic states by implementing sorting functions among the graph tasks and clustering these tasks in the order of their input and output communication volume. By considering the priority of task clusters allocated to the tile clusters and the dependency of each task on the other tasks, this proposed algorithm avoids scattering the allocated tiles on the whole network during the mapping process. It significantly reduces energy consumption and communication cost. The proposed HHMap algorithm for mapping frequently used graphs has been evaluated, and its experimental results show good efficiency compared with recent mapping methods.

This paper investigates the physical layer security of a wireless network assisted by a Reconfigurable Intelligent Surface (RIS) in the presence of full-duplex active eavesdropping. In this scenario, the RIS cooperates with the Base Station (BS) to transfer information to the intended user while an active attacker attempts to intercept the information through a wiretap channel. In addition, the attacker sends jamming signals to obstruct with the legitimate user’s signal reception and increase the eavesdropping rate. Our objective is to maximize the secrecy rate by jointly optimizing the active and passive beamformers at the BS and RIS, respectively. To solve the resulting non-convex optimization problem, we propose a solution that decomposes it into two disjoint beamforming design sub-problems solved iteratively using Alternating Optimization (AO) techniques. Numerical analysis is conducted to evaluate the effects of varying the number of active attacking antennas and elements of the RIS on the secrecy performance of the considered systems under the presence of jamming signals sent by the attacker. The results demonstrate the importance of considering the impact of jamming signals on physical layer security in RIS-aided wireless networks. Overall, our work contributes to the growing body of literature on RIS-aided wireless networks and highlights the need to address the effects of jamming and active eavesdropping signals in such systems.

According to today's statistics, more than half a billion vehicles are moving in the world and inspection and monitoring is one of the basic needs of any traffic system. All cars have an identification number or the same license plate as their primary ID, which today is one of the most suitable vehicle authentication tools. In this paper, the high capacity of deep neural networks in learning license plate identifiers is used. The proposed model of this paper has two stages of highlighting the license plate and reading the ID. In this regard, for highlighting, the combination of YOLO and XGBOOST network is used in encoder-coder network. The proposed model is evaluated on the FZU Cars dataset and based on the results of the experiments, the proposed model has a higher accuracy than the basic methods.

Text summarization is the process of condensing a source text while retaining its key points, tailored to a specific audience or task. The research extractive summarization, where each news article was segmented into individual sentences. Each sentence underwent processing through the ParsBERT algorithm. Subsequently, an attention layer combined the sentence weights with the Bidirectional GRU algorithm's output to extract summarized sentences for labeling. The dataset comprised over 175,000 articles sourced from reputable Persian news agencies (ISNA-TASNIM), covering various topics such as science, politics, and sports. Evaluation of the summarization techniques was conducted using Rouge metrics. The results of the investigation revealed precision values of 0.7923 (Rouge-1), 0.7613 (Rouge-2), and 0.8582 (Rouge-L). The study also evaluated the effectiveness of Gated Recurrent Unit (GRU) algorithms in extractive summarization by integrating its architecture with the attention network. The results demonstrated an improvement in news text summarization compared to other deep learning hybrid algorithms.

A maturity model, is a tool that can be applied to assess the “As-is” situation regarding specific dimensions. Inherent to the growing business-related data creation and storage in many enterprises, data analytics plays a challenging key role in business success. The more an organization is data dependent, the more the data analysis maturity model is crucial. In this paper, we review data analysis maturity models and propose an easy-to-use data analysis maturity model with tools to assess maturity level. As a case study, the proposed model has been successfully applied to a financial/banking enterprise, in which data analysis is crucial and can gain deeper insights into business operations, improve decision-making processes, manage risks more effectively, and ultimately drive better financial performance. The proposed model is beneficial to be used for any enterprise, especially data-driven ones whose data analysis enhances their competency.

In response to the critical need for protecting critical infrastructure and managing cyber crises, this article introduces an operational framework for establishing national-level Cyber Situational Awareness (Cyber SA). The Information Sharing and Alerting System (ISAS), as the central authority, integrates the cyber situational awareness postures of Information Sharing and Analysis Centers (ISACs) across infrastructures, forming a unified international Cyber SA posture. Our framework offers a quantifiable and coherent metric for national-level Cyber SA, based on cybersecurity risk, determined by the impact of cyber threats on sector-specific macro missions and their interdependencies. Application to cyberattack scenarios demonstrates the framework's accuracy in reflecting situational dynamics and assessing the relative significance of different sectors and ISACs. In summary, our framework simplifies national Cyber SA measurement, enhances cyber crisis management and decision-making, and systematically addresses interdependencies among critical infrastructures.