مطالب

The evaluation of students' writings and the allocation of scores are traditionally time-intensive and inherently subjective, often resulting in inconsistencies among human raters. Automated essay scoring systems were introduced to address these issues; however, their development has historically been resource-intensive, restricting their application to standardized tests such as TOEFL and IELTS. Consequently, these systems were not readily accessible to educators and learners. Recent advancements in Artificial Intelligence (AI) have expanded the potential of automated scoring systems, enabling them to analyze written texts and assign scores with increased efficiency and versatility. This study aimed to compare the efficacy of an AI-based scoring system, DeepAI, with human evaluators. A quantitative approach, grounded in Corder's (1974) Error Analysis framework, was used to analyze approximately 200 essays written by Persian-speaking EFL learners. Paired sample t-tests and Pearson correlation coefficients were employed to assess the congruence between errors identified and scores assigned by the two methods. The findings revealed a moderate correlation between human and AI scores, with AI diagnosing a greater number of errors than human raters. These results underscore the potential of AI in augmenting writing assessment practices while highlighting its pedagogical implications for language instructors and learners, particularly in evaluating the essays of EFL students.

An archaeological survey in 2004 in Mianab of Shushtar in Khuzestan, as well as excavations in 2005, led to the discovery of a Sasanian government hall and valuable stucco carvings. This article examines this hall and compares it with sites such as Damghan Hesar Hill and Hajiabad Lord’s House, as well as Chaltarkhan and columned halls of Bandian Dergaz Hill C. The result of this research shows that given the Sasanian columned halls that have so far been identified not older than the time of Bahram V (420-438 AD), the Dastova government hall probably dates back to the middle to the end of the Sasanian period.

Objective: This review explores the trustworthiness of multimodal artificial intelligence (AI) systems, specifically focusing on vision-language tasks. It addresses critical challenges related to fairness, transparency, and ethical implications in these systems, providing a comparative analysis of key tasks such as Visual Question Answering (VQA), image captioning, and visual dialogue. Background: Multimodal models, particularly vision-language models, enhance artificial intelligence (AI) capabilities by integrating visual and textual data, mimicking human learning processes. Despite significant advancements, the trustworthiness of these models remains a crucial concern, particularly as AI systems increasingly confront issues regarding fairness, transparency, and ethics. Methods: This review examines research conducted from 2017 to 2024, focusing on forenamed core vision-language tasks. It employs a comparative approach to analyze these tasks through the lens of trustworthiness, underlining fairness, explainability, and ethics. This study synthesizes findings from recent literature to identify trends, challenges, and state-of-the-art solutions. Results: Several key findings were highlighted. Transparency: The explainability of vision language tasks is important for user trust. Techniques, such as attention maps and gradient-based methods, have successfully addressed this issue. Fairness: Bias mitigation in VQA and visual dialogue systems is essential for ensuring unbiased outcomes across diverse demographic groups. Ethical Implications: Addressing biases in multilingual models and ensuring ethical data handling is critical for the responsible deployment of vision-language systems. Conclusion: This study underscores the importance of integrating fairness, transparency, and ethical considerations in developing vision-language models within a unified framework.

Background: With the rapid digital transformation across the African continent, ensuring the protection of personal data through effective regulatory frameworks is crucial. Key regulations, including Nigeria’s Data Protection Regulation (NDPR) and the African Union’s Convention on Cyber Security and Personal Data Protection, have been enacted to address growing concerns about data privacy and cybersecurity.Aims: This literature review critically examines the impact of data privacy regulations on cybersecurity practices in Nigeria and across Africa, focusing on empirical studies that highlight the interplay between regulation enforcement and cybersecurity outcomes.Methodology: This review synthesizes empirical studies that explore the effectiveness of these regulations in improving cybersecurity practices in both public and private sectors.Results: Empirical research from Nigeria indicates that while the NDPR has led to some positive changes in organizational data protection strategies, challenges in enforcement, resource allocation, and awareness continue to hinder its full impact. Studies reveal that small to medium enterprises (SMEs) face difficulties in complying with the regulations due to a lack of capacity and knowledge. Similarly, research across several African countries shows a significant gap in both the implementation of data privacy laws and the cybersecurity measures required to mitigate emerging threats, such as ransomware and data breaches. Furthermore, empirical evidence highlights that varying levels of regulatory enforcement across the continent result in inconsistent cybersecurity practices, leading to vulnerabilities in the digital infrastructure. The review also explores empirical findings on the socio-economic and political barriers that affect the successful enforcement of data privacy regulations, with particular focus on limited technical expertise, political instability, and insufficient resources for regulatory bodies. Additionally, studies suggest that there is a growing need for cross-border collaboration and capacity building to bridge the regulatory gaps and improve overall cybersecurity resilience.Conclusion: Empirical evidence underscores the need for stronger regulatory frameworks and greater cooperation across African nations to enhance the protection of personal data and fortify cybersecurity practices across the region. Recommendations for future policy development are provided, based on the insights gained from existing empirical studies.

Background: The visual turn is one of the most important features of contemporary culture. Therefore, in climate change communication, images can play a crucial role in conveying environmental messages. Environmental advocates use the potential of visuals to deliver part of their messages to their audience. However, only a limited number of studies have focused on the visualization of climate change communication. Given the characteristics of social media, free from the constraints of mainstream media, they can serve as a suitable platform for visualizing climate change messages.Aims: Despite Iran's role in greenhouse gas emissions and the engagement of environmental activists, no research has been conducted on the role of images in conveying climate change messages on Iranian social media. Therefore, the main question of this research is: How do Iranian users on social media platform X frame climate change through images, and within what discourses?Methodology: To answer the question of the research, a combination of quantitative and qualitative visual discourse analysis was applied to 500 images in 2023.Findings: Studies on the visualization of climate change messages have predominantly focused on developed countries. The findings reveal that Iranian users have addressed climate change through four key discourses: scientific, political, ecological, and protest. Furthermore, the results show that visual discourses have been seen globally in Persian X and it has addressed the present tense and the framework of the consequences.Conclusion: In general, the public's attention to climate change in Iranian society reflects the dynamism of the environmental movement in Iran and the growth of environmental literacy among Iranian social media users.

Background: Space is a constructed concept: a cultural and ideological segmentation, and a translation of the sociopolitical structures of society. On social media platforms, the public becomes a composite of privates, where individuals’ multiple selves are (re)presented before a multiplicity of audiences within a third type of space, which we coin the “publivate” sphere. This sphere emerges from the combination and blurring of public and private spaces on social media platforms.Aims: This article aimed to shed light on issues related to cyber self-(re)presentation within this “publivate” space.Methodology: We utilized cross-level integration, synthesizing micro- and macro-level theories, and interdisciplinary integration, incorporating concepts and theories from diverse fields, including social media studies, sociology, psychology, and regulatory perspectives.Discussions: We discussed how the interplay among public, private, and “publivate” selves, along with the reciprocal influences of conformity, pluralistic ignorance, normalization, and normativity, have been transforming the traditional compartmentalization of spaces in Iranian social life. Additionally, we discussed the complications of trans-jurisdictional regulation of cyberspace and their implications for users, particularly Iranians.Conclusions: Given the dynamic nature of societies, norms, and cultures, effective governance of cyberspace and cultural practices should account for sociocultural dynamics.

Background: Social media platforms are increasingly used in healthcare to provide psychosocial support to cancer patients. Despite global enthusiasm, concerns about misinformation and emotional vulnerability persist. In Iran, cultural and regulatory contexts add complexity to this dynamic, necessitating a localized understanding of healthcare providers’ perspectives. Aims: This study aims to explore Iranian oncology nurses’ perceptions of the benefits and risks associated with hospitalized cancer patients' use of social media, and to identify factors influencing these perceptions. Methodology: A cross-sectional survey was conducted among 131 oncology nurses from various hospitals in Iran, primarily in Tehran. Participants completed a structured questionnaire assessing demographics, life satisfaction, personal social media use, and perceptions of social media's impact on patient morale. Data were analyzed using descriptive statistics, Pearson correlation, and path analysis. Results: Overall, nurses held a positive view of social media’s role in enhancing patient morale, with a mean perception score of 7.1/10. Younger nurses and those with junior professional ranks exhibited more favorable attitudes. Personal social media usage positively correlated with optimistic perceptions, while life satisfaction showed a weak but significant positive association. Path analysis indicated that daily social media usage and younger age were the strongest predictors of positive perceptions. Some nurses, however, expressed concerns about misinformation and patient vulnerability. Conclusions: The findings highlight the generally favorable yet nuanced attitudes of Iranian oncology nurses toward social media use by cancer patients. Efforts to enhance digital literacy among healthcare providers and culturally sensitive strategies are recommended to maximize social media’s psychosocial benefits while mitigating associated risks.

Background: Many efforts have been put into using serious games for education over the past two decades. Although there are different definitions for serious games, there is a relative agreement that the education of knowledge or skills through or by means of entertainment is at the heart of every serious game. However, educational capacities of serious games and learning strategies exploited in them require further studies.Aims: Considering the capacities of Fauconnier and Turner’s Blending theory, this paper aims to show that the learning strategies in serious games can have other dimensions.Methodology: This paper employs a conceptual and theoretical analysis methodology, drawing on Fauconnier and Turner’s Conceptual Blending Theory to examine the learning strategies embedded in serious games. Additionally, it utilizes a case study approach, analyzing the serious game FLIGBY to illustrate how structural and conceptual blending contribute to creative learning.Findings: The focus point of the paper as the source of the generation of different kinds of unconscious learning processes will be conceptual blending. In the end, the paper will also introduce the serious game FLIGBY from the perspective of Fauconnier and Turner’s blending theory at both structural and conceptual levels.Conclusions: By means of blending theory, it will be discussed that the elevation of serious games to a higher level is possible; a level that will not only reduce the tiredness of serious games but also pursue a more innovative learning, happening at two levels of structural and conceptual.

The Internet of Multimedia Things (IoMT) represents a significant advancement in the evolution of IoT technologies, focusing on the transmission and management of multimedia streams. As the volume of data continues to surge and the number of connected devices grows exponentially, internet traffic has reached unprecedented levels, resulting in challenges such as server overloads and deteriorating service quality. Traditional computer network architectures were not designed to accommodate this rapid increase in demand, leading to the necessity for innovative solutions. In response, Software-Defined Networks (SDNs) have emerged as a promising framework, offering enhanced management capabilities by decoupling the control layer from the data layer. This study explores the load balancing of servers within software-defined multimedia IoT networks. The Long Short-Term Memory (LSTM) prediction algorithm is employed to accurately estimate server loads and fuzzy systems are integrated to optimize load distribution across servers. The findings from the simulations indicate that the proposed approach enhances the optimization and management of IoT networks, resulting in improved service quality, reduced operational costs, and increased productivity.