Ratatoskr: An Open-Source Framework for In-Depth Power, Performance, and Area Analysis and Optimization in 3D NoCs

We introduce Ratatoskr , an open-source framework for in-depth power, performance, and area (PPA) analysis in Networks-on-Chips (NoCs) for 3D-integrated and heterogeneous System-on-Chips (SoCs). It covers all layers of abstraction by providing an NoC hardware implementation on Register Transfer Level (RTL), an NoC simulator on cycle-accurate level and an application model on transaction level. By this comprehensive approach, Ratatoskr can provide the following specific PPA analyses: Dynamic power of links can be measured within 2.4% accuracy of bit-level simulations while maintaining cycle-accurate simulation speed. Router power is determined from RTL-to-gate-level synthesis combined with cycle-accurate simulations. The performance of the whole NoC can be measured both via cycle-accurate and RTL simulations. The performance (i.e., timing) of individual routers and the NoC area are obtained from RTL synthesis results. Despite these manifold features, Ratatoskr offers easy two-step user interaction: (1) A single point-of-entry allows setting design parameters. (2) PPA reports are generated automatically. For both the input and the output, different levels of abstraction can be chosen for high-level rapid network analysis or low-level improvement of architectural details. The synthesizable NoC-RTL model shows improved total router power and area in comparison to a conventional standard router. As a forward-thinking and unique feature not found in other NoC PPA-measurement tools, Ratatoskr supports heterogeneous 3D integration that is one of the most promising integration paradigms for upcoming SoCs. Thereby, Ratatoskr lays the groundwork to design their communication architectures. The framework is publicly available at https://github.com/ratatoskr-project .

Evaluating system usability of mobile augmented reality application for teaching Karnaugh-Maps

The education system evolves and transforms towards interactive and immersive learning tools in this digital age. Augmented reality has also evolved as a ubiquitous, robust, and effective technology for providing innovative educational tools. In engineering education, many abstract concepts require technological intervention for conceptual understanding and better instructional content. While learning through the immersive tools, system usability has great importance in terms of effectiveness, efficiency, and satisfaction. Effectiveness refers to users' accuracy and completeness in achieving defined goals; efficiency relates to expended resources about the precision and completeness with which users achieve their objectives; satisfaction deals with a positive attitude towards using the product. If the system fails to provide good usability, it may cause adverse effects such as increasing stress, lacking necessary features, increasing the users' cognitive load, and negatively impacting the student's motivation. In this study, two mobile augmented reality (MAR) applications were developed as an instructional tool to teach the students about Karnaugh maps in the digital electronics course. The first application is a Keypad-based MAR application that uses a keypad matrix for user interaction and the second application is a Marker-based MAR application that uses multiple markers to solve K-Map for producing an optimum solution of the given problem. An experimental study was conducted to determine the student's opinion of the developed MAR applications. The study was designed to determine the system usability of the two MAR applications using the System Usability Score (SUS) and Handheld Augmented Reality Usability Score (HARUS) models. 90 engineering students participated in the study, and they were randomly divided into two different groups: keypad-based group and Marker-based group. The keypad-based group included 47 students who had hands-on experience with a keypad-based MAR application, whereas the marker-based group included 43 students who had hands-on experience with multiple marker-based MAR applications. The experimental outcomes indicated that the keypad-based MAR application has better SUS and HARUS scores than the marker-based MAR application which suggests that the keypad-based MAR application has provided better user interaction.

Virtual Reality (VR) Multi-User Lab for Immersive Teaching

As VR technology matures, it offers opportunities to provide state of the art learning experiences. The use of this technology in education is not new, however, it can be significantly improved. Situated (or contextual) learning is one of the key pillars of immersive learning. Multi-user interaction in virtual environments has always been one of the goals of VR and this has been represented by the tools developed for it. In this preliminary work, we aim to introduce Project Mobius, which is a collaborative project between Edify.ac and the University of Glasgow. We describe how our multi-user lab has been set up and potential future applications for teaching and learning.

Automated Aircraft Dent Inspection via a Modified Fourier Transform Profilometry Algorithm

The search for dents is a consistent part of the aircraft inspection workload. The engineer is required to find, measure, and report each dent over the aircraft skin. This process is not only hazardous, but also extremely subject to human factors and environmental conditions. This study discusses the feasibility of automated dent scanning via a single-shot triangular stereo Fourier transform algorithm, designed to be compatible with the use of an unmanned aerial vehicle. The original algorithm is modified introducing two main contributions. First, the automatic estimation of the pass-band filter removes the user interaction in the phase filtering process. Secondly, the employment of a virtual reference plane reduces unwrapping errors, leading to improved accuracy independently of the chosen unwrapping algorithm. Static experiments reached a mean absolute error of ∼0.1 mm at a distance of 60 cm, while dynamic experiments showed ∼0.3 mm at a distance of 120 cm. On average, the mean absolute error decreased by ∼34%, proving the validity of the proposed single-shot 3D reconstruction algorithm and suggesting its applicability for future automated dent inspections.

ETHOS: a multi-label hate speech detection dataset

Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary to comply with the corresponding legislation and maintain a high level of service quality. A robust and reliable system for detecting and preventing the uploading of relevant content will have a significant impact on our digitally interconnected society. Several aspects of our daily lives are undeniably linked to our social profiles, making us vulnerable to abusive behaviours. As a result, the lack of accurate hate speech detection mechanisms would severely degrade the overall user experience, although its erroneous operation would pose many ethical concerns. In this paper, we present ‘ETHOS’ (multi-labEl haTe speecH detectiOn dataSet), a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined. Our key assumption is that, even gaining a small amount of labelled data from such a time-consuming process, we can guarantee hate speech occurrences in the examined material.

Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future

Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

The Usage of Emoji in Tourism-Related Instagram Posts: Suggestions from a Marketing Perspective

The relevance of emoji in social media marketing has attracted tremendous interest from academics and marketing professionals alike ever since emoji became a fixed component in user-to-user and business-to-user communication on online platforms such as Instagram and Facebook. Using a quantitative research approach in the form of a self-administered online survey in an experimental setting, the purpose of this study is to investigate what type of emoji positively impacts consumer behaviour, purchase intention, and user interaction in tourism-related Instagram posts. This research is novel in that it bridges the usage of emoji in the context of social media and tourism. The findings support tourism managers in the practical use of emoji for social media marketing campaigns on Instagram and show that (positive) face emoji evoke more positive emotions than non-face emoji.

How Is the Administrator's Experience in Managing Health Facebook Groups?

This decade, patients with chronic conditions take advantage of technological resources to share experiences, look for help, and get together in groups. Social media has become a productive spot of health information for users. This chapter clarifies the administrators' user experience of social media health groups within the context of the Inflammatory Bowel Disease (IBD). Semi-structured interviews with 7 Portuguese and Brazilian managers of Facebook groups were carried out. The authors propose a model of participation in online communities that take into account the variables of Presence–Interconnection between users in daily lives; Relationship–Aspects of experience where users meet; Reputation–Positioning of users related to the status; Groups–Relationship issues between members; Conversations – Extension of the user interaction; Sharing–Represents the exchange, distribution, and reception and Education–The information transmitted. Findings reinforced that administrators' groups use social media to lead patients´ empowerment.

Voice Versus Keyboard and Mouse for Text Creation on Arabic User Interfaces

Voice User Interfaces (VUIs) are increasingly popular owing to improvements in automatic speech recognition. However, the understanding of user interaction with VUIs, particularly Arabic VUIs, remains limited. Hence, this research compared user performance, learnability, and satisfaction when using voice and keyboard-and-mouse input modalities for text creation on Arabic user interfaces. A Voice-enabled Email Interface (VEI) and a Traditional Email Interface (TEI) were developed. Forty participants attempted pre-prepared and self-generated message creation tasks using voice on the VEI, and the keyboard-and-mouse modal on the TEI. The results showed that participants were faster (by 1.76 to 2.67 minutes) in pre-prepared message creation using voice than using the keyboard and mouse. Participants were also faster (by 1.72 to 2.49 minutes) in self-generated message creation using voice than using the keyboard and mouse. Although the learning curves were more efficient with the VEI, more participants were satisfied with the TEI. With the VEI, participants reported problems, such as misrecognitions and misspellings, but were satisfied about the visibility of possible executable commands and about the overall accuracy of voice recognition.