Decisional style, sleepiness, and online responsiveness.

As sleep problems can impair quality of work, an online questionnaire was used to examine relationships between sleepiness and decision making while obtaining unobtrusive indices of performance. Participants (N = 344) completed the Insomnia Severity Index, Epworth Sleepiness Scale, and the Melbourne Decision Making Questionnaire in a Qualtrics survey while reporting mobile phone use. Qualtrics recorded the time and the number of clicks required to complete each page of the survey. Multiple regression indicated that insomnia was associated with daytime sleepiness and Hypervigilance, and mobile phone use before bed. Participants with moderate sleepiness required a greater number of clicks to complete the questionnaire. Greater sleepiness was associated with longer times to complete these self-assessment tasks. Clinically significant sleepiness produces changes in performance that can be detected from online responsivity. As sleepy individuals can be appreciably and quantitatively slower in performing subjective self-assessment tasks, this argues for objective measures of sleepiness and automated interventions and the design of systems that allow better quality sleep.Practitioner summary: Work can require processing of electronic messages, but 24/7 accessibility increases workload, causes fatigue and potentially creates security risks. Although most studies use people's self-reports, this study monitors time and clicks required to complete self-assessment rating scales. Sleepiness affected online responsivity, decreasing online accuracy and increasing response times and hypervigilance.

A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods.

The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing how services and applications impact our daily lives. In traditional ML methods, data are collected and processed centrally. However, modern IoT networks face challenges in implementing this approach due to their vast amount of data and privacy concerns. To overcome these issues, federated learning (FL) has emerged as a solution. FL allows ML methods to achieve collaborative training by transferring model parameters instead of client data. One of the significant challenges of federated learning is that IoT devices as clients usually have different computation and communication capacities in a dynamic environment. At the same time, their network availability is unstable, and their data quality varies. To achieve high-quality federated learning and handle these challenges, designing the proper client selection process and methods are essential, which involves selecting suitable clients from the candidates. This study presents a comprehensive systematic literature review (SLR) that focuses on the challenges of client selection (CS) in the context of federated learning (FL). The objective of this SLR is to facilitate future research and development of CS methods in FL. Additionally, a detailed and in-depth overview of the CS process is provided, encompassing its abstract implementation and essential characteristics. This comprehensive presentation enables the application of CS in diverse domains. Furthermore, various CS methods are thoroughly categorized and explained based on their key characteristics and their ability to address specific challenges. This categorization offers valuable insights into the current state of the literature while also providing a roadmap for prospective investigations in this area of research.

Detecting Audio Adversarial Examples in Automatic Speech Recognition Systems Using Decision Boundary Patterns.

Automatic Speech Recognition (ASR) systems are ubiquitous in various commercial applications. These systems typically rely on machine learning techniques for transcribing voice commands into text for further processing. Despite their success in many applications, audio Adversarial Examples (AEs) have emerged as a major security threat to ASR systems. This is because audio AEs are able to fool ASR models into producing incorrect results. While researchers have investigated methods for defending against audio AEs, the intrinsic properties of AEs and benign audio are not well studied. The work in this paper shows that the machine learning decision boundary patterns around audio AEs and benign audio are fundamentally different. Using dimensionality-reduction techniques, this work shows that these different patterns can be visually distinguished in two-dimensional (2D) space. This in turn allows for the detection of audio AEs using anomal- detection methods.

Visualization and Cybersecurity in the Metaverse: A Survey.

The popularity of the Metaverse has rapidly increased in recent years. However, despite the attention, investment, and promise of the Metaverse, there are various cybersecurity issues that must be addressed before the Metaverse can truly be adopted in practice for serious applications. The realization of the Metaverse is envisioned by many as requiring the use of visualization technologies such as Virtual Reality (VR) and Augmented Reality (AR). This visual aspect of the Metaverse will undoubtedly give rise to emerging cybersecurity threats that have not received much attention. As such, the purpose of this survey is to investigate cybersecurity threats faced by the Metaverse in relation to visualization technologies. Furthermore, this paper discusses existing work and open research directions on the development of countermeasures against such threats. As the Metaverse is a multidisciplinary topic, the intention of this work is to provide a background of the field to aid researchers in related areas.

Self-reported gambling problems and digital traces.

The Diagnostic and Statistical Manual of Mental Disorder, Fifth Edition (DSM-5), lists concealment as one of the symptoms of a gambling disorder. However, some transactions are more likely to leave permanent records of gambling transactions (credit, consumer loyalty schemes) than others (cash, Internet cash, Internet cafes, prepaid phones). An online survey of 815 participants recruited through newspaper and online sites elicited consumer preferences for a variety of transactions and communication media. Hierarchical multiple regression accounted for age, gender, housing status, and involvement in gambling before considering relationships between consumer preferences and scores on the Problem Gambling Severity Index. Even after statistically allowing for the contributions of other variables, a greater risk of developing a gambling problem was associated with a preference for cash transactions, prepaid mobile phones, and Internet cafes. Problem gamblers may seek to reduce their digital trace.

Gambling involvement and increased risk of gambling problems.

The opportunity to gamble has undergone rapid expansion with technology allowing for access to gambling products 24 h a day. This increased online availability challenges governments' abilities to restrict access to gambling. Indeed, the ready access to multiple forms of gambling may potentially contribute to impaired control over urges for problem gamblers. The present study considered whether problem gamblers manifested a tendency to engage in multiple forms of gambling and identified forms of gambling which were more strongly related to problem gambling. In reanalyses of two surveys (Sample 1, N = 464, Sample 2, N = 1141), significant relationships accounting for between 11.3 and 13.5% of the variance were found between the numbers of forms of gambling accessed and degree of problem. Participation in online poker, playing cards and sports wagering were linked to problem gambling. Access to multiple forms of gambling may pose difficulties for the tracking and control of gambling.