This research explores the impact of video quality on viewer experience (VX) in the digital age. Videos are ubiquitous in our lives, yet our understanding of how quality variations affect satisfaction and engagement remains limited. By introducing a one-to-many relationship between Quality of Service (QoS) and Quality of Experience (QoE), the study aims to provide practical and deeper insights for content creators and streaming platforms that contemporary subjective metrics cannot provide. It introduces the concept of VX, a novel extension of the QoE, to better capture the complexities of human response to multimedia content. The research combines qualitative and quantitative methods, utilizing established quality assessment frameworks like SSIMplus. Through a combination of statistical and thematic explorations, we provide the basis of a novel framework that has real-world implications for enhancing user satisfaction and the overall quality of video-based content in an increasingly digital world.

This article introduces the global-local image perceptual score (GLIPS), an image metric designed to assess the photorealistic image quality of AI-generated images with a high degree of alignment to human visual perception. Traditional metrics such as Fréchet inception distance (FID) and kernel inception distance scores do not align closely with human evaluations. The proposed metric incorporates advanced transformer-based attention mechanisms to assess local similarity and maximum mean discrepancy to evaluate global distributional similarity. To evaluate the performance of GLIPS, we conducted a human study on photorealistic image quality. Comprehensive tests across various generative models demonstrate that GLIPS consistently outperforms existing metrics like FID, structural similarity index measure, and multiscale structural similarity index measure in terms of correlation with human scores. In addition, we introduce the interpolative binning scale, a refined scaling method that enhances the interpretability of metric scores by aligning them more closely with human evaluative standards. The proposed metric and scaling approach not only provide more reliable assessments of AI-generated images but also suggest pathways for future enhancements in image generation technologies.

Phishing, a prevalent cybercrime tactic for decades, remains a significant threat in today's digital world. By leveraging clever social engineering elements and modern technology, cybercrime targets many individuals, businesses, and organizations to exploit trust and security. These cyber-attackers are often disguised in many trustworthy forms to appear as legitimate sources. By cleverly using psychological elements like urgency, fear, social proof, and other manipulative strategies, phishers can lure individuals into revealing sensitive and personalized information. Building on this pervasive issue within modern technology, this paper will aim to analyze the effectiveness of 15 Large Language Models (LLMs) in detecting phishing attempts, specifically focusing on a randomized set of “419 Scam” emails. The objective is to determine which LLMs can accurately detect phishing emails by analyzing a text file containing email metadata based on predefined criteria. The experiment concluded that the following models, ChatGPT 3.5, GPT-3.5-Turbo-Instruct, and ChatGPT, were the most effective in detecting phishing emails.

In this article, we used a computational cognitive architecture called queuing network–adaptive control of thought rational–situation awareness (QN–ACTR–SA) to model and simulate the brake perception response time (BPRT) to visual roadway hazards. The model incorporates an integrated driver model to simulate human driving behavior and uses a dynamic visual sampling model to simulate how drivers allocate their attention. We validated the model by comparing its results to empirical data from human participants who encountered on-road and roadside hazards in a simulated driving environment. The results showed that BPRT was shorter for on-road hazards compared to roadside hazards and that the overall model fitness had a mean absolute percentage error of 9.4% and a root mean squared error of 0.13 s. The modeling results demonstrated that QN–ACTR–SA could effectively simulate BPRT to both on-road and roadside hazards and capture the difference between the two contrasting conditions.

Given the widespread popularity of Autonomous Vehicles (AVs), researchers have been exploring the ethical implications of AVs. Researchers believe that empirical experiments can provide insights into human characterization of ethically sound machine behavior. Previous research indicates that humans generally endorse utilitarian AVs, however, this paper explores an alternative account on the discourse of ethical decision-making in AVs. We refrain from favoring consequentialism or non-consequential ethical theories, and argue that human moral decision-making is pragmatic, or in other words, ethically and rationally bounded. We hold the perspective that our moral preferences shift based on various externalities and biases. To further this concept, we conduct two Amazon Mechanical Turk studies to investigate factors, such as, the `degree of harm', and `level of affection', which influence people's moral decision-making. Our experimental findings seem to suggest that human moral judgements cannot be wholly deontological or utilitarian. We discovered that as the degree of harm decreased, people became less utilitarian (more deontological), and as the level of affection increased, people became less utilitarian (more deontological). These findings offer evidence on the ethical variations in human decision-making processes and refutes the view that aim to advocate application of a specific moral framework based on empirical evidence. The findings also offer useful insights for policymakers to explore the overall public perception on the ethical implications of AV.

This research explores the design, implementation, and evaluation of a prototype augmented reality application that assists operators in performing procedural tasks in control room settings. Our prototype uses a tablet display to supplement an operator’s natural view of existing control panel elements with sequences of interactive visual and attention guiding cues. An experiment, conducted using a nuclear power plant simulator, examined university students completing both standard and emergency operating procedures. The augmented reality condition was compared against two other conditions – a paper-based procedure condition using paper manuals and a computer-based procedure condition using digital procedures presented on a desktop display. The results demonstrated that the augmented reality -based procedure system had benefits in terms of reduced mental workload in comparison to the other two conditions. Regarding task completion time, accuracy, and situation awareness, the augmented reality condition had no significant difference when compared against the computer-based procedure condition but performed better than the paper-based procedure condition. It was also found that the augmented reality condition resulted in fewer intra-team inquiry communication exchanges in comparison to both paper-based and computer-based conditions. The augmented reality condition, however, yielded poorer memory retention score when assessed against the other two conditions.

Navigation systems have been widely used in outdoor environments, but indoor navigation systems are still in early development stages. In this paper, we introduced an augmented-reality-based indoor navigation application to assist people navigate in indoor environments. The application can be implemented on electronic devices such as a smartphone or a head-mounted device. In particular, we examined Google Glass as a wearable head-mounted device in comparison with handheld navigation aids including a smartphone and a paper map. We conducted both a technical assessment study and a human factors study. The technical assessment established the feasibility and reliability of the system. The human factors study evaluated human-machine system performance measures including perceived accuracy, navigation time, subjective comfort, subjective workload, and route memory retention. The results showed that the wearable device was perceived to be more accurate, but other performance and workload results indicated that the wearable device was not significantly different from the handheld smartphone. We also found that both digital navigation aids were better than the paper map in terms of shorter navigation time and lower workload, but digital navigation aids resulted in worse route retention. These results could provide empirical evidence supporting future designs of indoor navigation systems. Implications and future research were also discussed.

This research comprehensively illustrates the design, implementation and evaluation of a novel marker less environment tracking technology for an augmented reality based indoor navigation application, adapted to efficiently operate on a proprietary head-mounted display. Although the display device used, Google Glass, had certain pitfalls such as short battery life, slow processing speed, and lower quality visual display but the tracking technology was able to complement these limitations by rendering a very efficient, precise, and intuitive navigation experience. The performance assessments, conducted on the basis of efficiency and accuracy, substantiated the utility of the device for everyday navigation scenarios, whereas a later conducted subjective evaluation of handheld and wearable devices also corroborated the wearable as the preferred device for indoor navigation.