The COVID-19 pandemic has transformed learning environments, challenging educators to understand students' behaviors during the online mode of learning, in particular emotions associated with students' attention during virtual classrooms. As learning transitions between physical and virtual spaces, the ability to interpret student attention and engagement has become complex. In response to this challenge, our research investigates the use of GPT-4o, a multimodal large language model, for identifying student emotions by analyzing images in diverse learning settings. The study involved analyzing online classroom images featuring 149 faces, utilizing three distinct approaches: a computer vision model (YOLO), the multimodal LLM (GPT-4o), and a human-annotated baseline. The analysis systematically categorized facial expressions into eight emotional categories: Happy, Sad, Angry, Neutral, Contempt, Disgust, Fear, and Surprise. The findings indicate that multimodal LLMs can effectively detect student emotions, achieving an average accuracy of 93.8%, which aligns with the human baseline accuracy of 97.0%. In contrast, YOLO models maintained an average accuracy of 81.9%, performing well for basic emotions but struggling with subtle expressions. This research contributes to enhancing educational practices by providing valuable insights regarding the application of multimodal LLMs to assist educators in comprehending student emotions within both physical and digital classroom settings.

Heritage sites are central to the cultural identity and historical narrative of a community. The Al Qattara Oasis, located in the United Arab Emirates (UAE), illustrates this function well. This study addresses the pressing need to preserve and maintain built heritage by employing modern technological solutions for infrastructure upkeep. Specifically, it focuses on crack detection, a critical aspect of ensuring the structural integrity of heritage buildings. Utilizing data collected from various structures across the UAE, obtained through handheld cameras, the effectiveness of Vision Transformers (ViTs) and Swin Transformers in identifying cracks within Al Qattara is assessed. The research thoroughly evaluates models, with particular attention to different input patch sizes. Through systematic experimentation and analysis over 100 epochs, ViT models with a patch size 16 exhibit significant promise in crack detection. Notably, the ViT-16 model achieves good performance metrics, including training and validation accuracies of 85% and (82%) respectively, and correctly classifies 791 out of 957 cracks and 772 out of 903 non-cracks. In contrast, Swin Transformers show lower validation accuracies (70-74%) and higher misclassification rates. The outcomes underscore the potential of ViT models in enhancing infrastructure maintenance efforts within heritage sites like Qattara Oasis. By combining advanced technology with a profound respect for historical preservation, this study aims to contribute to the sustainable conservation and protection of cultural heritage, ensuring that future generations can continue to appreciate the enduring legacy of sites such as Qattara Oasis.

The ongoing expansion of the digital landscape has led to a growing convergence between the fields of artificial intelligence (AI) and network security. This has necessitated the need for universities to incorporate AI into their network security curriculum. Although traditional network security courses are considered crucial, they lack the agility to address constantly evolving threats. AI offers a transformative solution to such difficulties with its predictive analytics, real-time intrusion detection, and adaptive learning capabilities. This study highlights the importance of incorporating AI into network security curricula at the undergraduate level. A modification to the curriculum is proposed, wherein AI themes are integrated into network security courses and labs. The proposed curricula include understanding theoretical AI concepts and designing AI -augmented hands-on laboratories. The pedagogy emphasizes the tools, and frame-works that facilitate the construction of AI models for intrusion detection, mal ware analysis, and network analytics. This plays a significant importance in providing a simulated environment for students to engage with AI tools and methods to address authentic cyber threats.

Fog computing is a technology that brings computing, storage, and networking services closer to devices and systems, aiming to improve speed, efficiency, and data processing capabilities for various applications. The growing importance of fog and edge computing technologies means we need new and better ways to teach students about these areas. This paper offers a detailed guide on how to use xFogSim, an extended version of the Omnet++ network simulator, for teaching fog and edge computing. We give students a clear path to follow, starting with simple network designs and moving to more complex ones, helping them understand how federated learning works. We tested xFogSim with a group of students and found that it really helps them grasp fog and edge computing ideas better than traditional teaching methods. xFogSim also gives practical information about important performance metrics, helping bridge the gap between what students learn in class and what they need to know in the real world. This paper shows that using xFogSim in classrooms gives students a strong base in distributed computing systems, getting them ready for future tech challenges.

The realm of academic security education is primarily focused on defensive strategies. However, there's a growing acceptance of offensive techniques, initially crafted by hackers. Several educators in the field of information security believe that incorporating offensive strategies into the curriculum creates more adept security professionals than focusing solely on defensive methods. Students in information security courses must engage in offensive and defensive tactics to effectively handle malicious activities and devise suitable security measures. This paper presents a case study on executing an in-depth, practical cybersecurity laboratory exercise centered on a prevalent network attack, the DNS spoofing attack, which is vital for network security training. The primary educational goal of this hands-on lab exercise is to equip students with the skills to conduct a DNS spoofing attack within a controlled, virtual network environment using Kali Linux. The introduction of this offensive cybersecurity lab exercise resulted in enhanced student performance; however, it also raised significant ethical issues. Consequently, the paper outlines several measures that academic institutions should consider to mitigate the risks associated with teaching offensive strategies in information security education programs.