Reviewerly/exHarmony huggingface dataset:
An Approach for Visual Realism Complexity Classification of 3D Models in Virtual and Augmented Reality

3D models are at the core of every virtual and augmented reality application. They appear in varying degrees of visual realism complexity, depending on the intention of use and contextual as well as technical limitations. The research concerning the effects induced by different degrees of visual realism complexity of 3D models can be used to define in which fields and applications a high visual realism is needed. This paper proposes a classification of 3D models in extended reality applications, according to their visual realism complexity. To identify the aspects of realism classification this work considers how past studies defined the virtual realism complexity of 3D models and looks into approaches of diverse disciplines. After defining the visual realism complexity scale, the paper validates the method in a user study. The study was conducted with 24 participants that viewed 16 different 3D models in virtual reality and rated them with the proposed scale. The collected data shows a significant correlation between subjective realism and the proposed scale. Next to depicting visual realism, the scale also improves the explicitness of realism ratings. The classification enables scientists to specify the level of visual realism complexity of their used 3D models. At the same time, this opens up opportunities to conduct comparable research about visual realism complexity.
A VR Office Applying Modern Search and Filter Concepts for Knowledge Workers

Virtual, Augmented and Mixed Reality (VAMR) fields of applications are met with the demand for innovation within the field to build the bridge between the theory of its use and our daily life. However, much of the innovation stops at conceptualising the opportunities that virtual environments offer and instead drifts back to our familiar 2D screens. This work presents the design of interactive 3D tools for a VR-simulated office space to support tasks related to the workflow of a knowledge worker with the focus on one part of a knowledge worker’s workflow: the collection of knowledge. We propose two tools for a VR-Office environment primarily targeting the facilitation of manually acquiring and sorting research publications related to specific topics of interest. In the process of our work, we created a unique virtual office environment offering two prototype research tools Synonym Finder and Pyramid, which aim to support a knowledge worker’s tasks. Throughout our project, we conducted a qualitative user evaluation with seven participants. Overall, we achieved excellent results with the VR prototype and received insightful feedback to improve our application.
Measuring Behavioural Cybersecurity: An Overview of Options

As the field of cybersecurity is maturing, there is more attention to the behaviour of end-users in securing data and systems. Awareness campaigns are gaining popularity and behavioural change initiatives are deployed. However, many organisations do not know where to start when attempting to effectively measure the maturity of the behavioural component in their cybersecurity strategy. While some measures, such as knowledge, attitudes and skills are assessed, (objective) measurements of behaviour, and the interplay between these factors are less commonly measured. This paper discusses the importance of measuring behavioural cybersecurity and presents an overview of possible measurements and relevant factors. First, the paper outlines why measuring behavioural cybersecurity is vital in understanding behaviour related problems and coming up with evidence-based solutions. Then, the various measurement levels and current practices are discussed before turning to options regarding these levels. These include both self-reported as well as objective behavioural measurements in addition to attitudes, knowledge and skills. Lastly, some issues surrounding these measurements are discussed including spill-over effects and ethical considerations.
Cooperation Mode of 2D and 3D Interfaces on Destination Planning Tasks in the Location-Based AR Application

While Augmented Reality (AR) allows users to obtain augmented information about the real world, Location-Based AR (LBAR) promotes further integration of virtual content and architectural landscapes. How to support users to better access architectural or spatial information through the interface is one of the most important directions in LBAR. Previous studies have adopted different strategies to present information in the traditional user interface and AR space. However, few studies have focused on how interfaces in LBAR applications cooperate to better support destination planning tasks in the urban shopping area. We investigated the performance of 6 cooperation modes of the interfaces in LBAR through an empirical study and summarized the findings in terms of presentation cooperation and interaction cooperation. In the end, we propose two new cooperation modes as well as a series of design suggestions to inspire subsequent practice.
Visual Attention in Extended Reality and Implications for Aviation Safety

Visual attention is essential to select and process relevant visual information while filtering out irrelevant stimuli. Thus, the visual attentional function is critical for aviation workers to perform workplace tasks effectively and safely. Emerging extended reality technology, including virtual and augmented reality, is increasingly used in aviation. This technology may heavily impact the user’s visual attentional processing due to its innovative methods of presenting visual information to the users, as well as its technical limitations. We reviewed various extended reality applications in aviation, which may involve differential demands and challenges for aviation workers’ attentional performance. Then, we presented a multifaceted construct of attention involving distinct functions such as alerting, orienting, executive, and spatial attention. Prior research suggests that alerting, orienting, and executive attention may have unique impacts on how effectively users can process virtual cues presented via extended reality and resolve conflict in the combination of virtual and physical visual stimuli. Cluttering and decluttering in extended reality may significantly impact executive attentional performance. Furthermore, head-mounted display (HMD) extended reality may change the spatial distribution of visual attention due to its restricted field of view and use of a cluttered focal visual field. Future research is warranted to investigate whether this innovative extended reality technology could present significant, possibly permanent, changes to the user’s various attentional functions.
Offensive Play Recognition of Basketball Video Footage Using ActionFormer

This paper will aim to conduct 3 experiments to determine the best-performing action recognition approach on the SpaceJam dataset. The 3 experiments are Temporal Segment Network (TSN), Inflated 3D-CNN (I3D) and Pose-estimation (Pose-C3D). TSN and I3D yielded similar results with TSN scoring 54.88% for the mean accuracy, 94.33% for top-5 accuracy and 54.88 top-1 accuracy. And I3D scored 53.07% mean accuracy, 91.65% for top-5 accuracy and 53.07 mean accuracy. When Pose-C3d is run for 240 epochs it achieves better results with a top 1 accuracy and mean-class accuracy of 63.15% and a top-5 accuracy of 95.51. These results indicate that the models can distinguish between similar actions such as running and walking in basketball with relative success.
Iterative Design of an Immersive Analytics Environment Based on Frame of Reference

Immersive analytics is an active research area that explores human-centric approaches to data exploration and analysis based on spatial arrangement and visualization of data elements in immersive 3D environments. The proliferation of extended reality (XR) technologies amplifies the need for design guidelines to help create coherent user experiences in XR environments. One of the important challenges is the accurate placement and identification of various visual elements. We explore the iterative design of the placement of visual elements and interaction with them in an immersive analytics environment based on frames of reference. The choice of a frame of reference can impact usability, ease of interaction, degree of immersion, and task performance, among other things. Our first design iteration explores the impact of visualization scale for two frames of reference: egocentric and exocentric. In response to the findings from the first study and based on performance measures such as degree of immersion, rank order, and subjective user feedback, tethered frame of reference is added to the second iteration. The second design iteration uses the collected user feedback to explore and compare the advantages and limitations of these three frames of reference. The findings are used to inform design guidelines.
Research on User Experience Design Based on Affinity Diagram Assisting User Modeling – Taking Music Software as an Example

User experience design always takes user research as the center. Designers guide the design by establishing a relatively correct user model. As a qualitative research method, user interviews are often used in user research. This paper introduces affinity graph method into how to deal with the data of consulting method, and helps to build user behavior patterns and psychological patterns in the numerous consulting data, so as to establish user models. In the specific practice, this paper takes a popular music software in China as the specific research object, and improves one module of the software by understanding the user’s experience, behavior characteristics, needs, and expectations. By recruiting real users, using affinity graph to sort out interview data, and comparing users with similar line trends on the user behavior variable graph, this paper finds that the line trends of 7 interviewees can be divided into three categories, and then three typical users are discovered. Through above, this paper develops the output of design models.
Learnability Assessment of Speech-Based Intelligent Personal Assistants by Older Adults

Older adults often have problems and difficulties with adopting technologies but the popularity of technology usage among older adults had increased significantly in recent years. Separately, Speech-Based Intelligent Personal Assistants (sIPA) or Voice Assistants (VA) have shown formidable promise in the successful aging and well-being of older adults. An in-depth learnability assessment of sIPAs’ for the elderly will provide practitioners in this domain to formulate new research outputs and studies to counter learnability issues incurred by older adults. The aim of this paper is to investigate the sIPA learnability issues from an older adult’s perspective. The research provides two separate studies to assess the learnability issues that older adults face while adopting sIPA. With prior Institutional Review Board’s (IRB) approval, Study 1 demonstrates Usability Scale Studies by utilizing System Usability Scale (SUS) and reported quantitative SUS value with comprehensive discussion. Study 2 provides an Interview Study with older adults where the goal was to investigate how older adults learn to use and adopt sIPAs’ in their lives through a qualitative approach. Both Study 1 and Study 2 concluded older adults have learnability issues regarding the adoption of sIPAs’ in their daily life. The finding concluded an average SUS score of 62.5. With a minimum SUS value being 35 and a maximum SUS value of 92.5 thus reaffirming that while some did find using sIPA easy, several others faced severe difficulties. The qualitative study concluded that older adults usually take aid from peers, family members, and the internet to tackle learnability issues thus suggesting their eagerness to learn and accept new technology for daily use. A comprehensive discussion was incorporated to validate and contrast preceding studies with our findings followed by pointing out the limitations of our work and future scopes of research which includes extended and prolonged longitudinal studies or through incorporating a larger sample size of a different demographic of a similar age group.
Automatic Identification of Daily Life Activities and Prediction of Falling Accidents Caused by Behavior

In the present research, we developed an application that measures acceleration by a smartphone located at the waist, along with a deep learning system that automatically detects six behavioral activities (walking, running, standing, walking downstairs, walking upstairs, and falling). Specifically, we use a human activity database provided by the University of California–Irvine (UCI) to create a deep learning model with the UCI data. Next, transfer learning was applied to the learning model for the measurement data obtained by the acceleration acquisition application developed in this research. Using Sony's Neural Network Console development environment as a deep learning tool, in recognition experiments, our proposed method for automatic identification of activities and prediction of falling accidents caused by behavior achieved a high accuracy rate of almost 95%.
Capability Maturity Models for Targeted Cyber Security Training

There will be more and more connected devices in use around the world with the evolution of technologies like Internet of Things. Furthermore, with the emergence of remote working, humans and critical systems in organizations are increasingly connected through the internet. At the same time, there is an increased rate of cyber-attacks. These are reasons why cyber security is critically important to contemporary society as these factors make most organizations even more susceptible to cyber-attacks. Such cyber-attacks are often initiated through the lack of awareness of humans in organizations. Therefore, humans are regarded as the weakest link in cyber security. Effective and efficient cyber security training plays a crucial role to strengthen this weakest link. However, a factor that makes cyber security training not efficient in practice is the one-size-fits-all approach, instead of considering the current cyber security competence level of different personnel in the organization while choosing cyber security training. In this study, we mainly utilised Capability Maturity Models (CMMs) to tackle the above-mentioned challenge especially based on their applications in domains like e-learning. However, typical maturity levels used in such CMMs are not directly suitable for our application. Therefore, we relied on different levels of Bloom's taxonomy that are suitable for our application in determining the individual cyber security competence level. Finally, we illustrate the proposed framework using different examples involving specific group of personnel in an organization (i.e., Information and Communication Technology users). The main goal of this approach is to support the organization in strategically choosing targeted training activities for different personnel.
What Makes First Steps Users Rave About Virtual Reality? An Explorative Qualitative Study of Consumers’ First VR Experience

First experiences can make or break a new product. If consumers love it, they rave about the product and encourage others to try it. If they don’t, they move on and discourage others from trying. Considering the slow consumer adoption of VR, we explored user attitudes, expectations, and behaviors during their first VR experience using qualitative methods. At first, we interviewed ten participants and studied their feelings about VR before, during, and after the Oculus First Steps tutorial. Despite initial concerns, participants formed a positive attitude toward VR. Six participants reacted enthusiastically to First Step’s robot, which prompted us to conduct an online investigation of consumer discussions. Thematic analysis of users’ posts and responses to the First Step’s robot on Facebook, Reddit, and YouTube revealed strong positive emotions, which impacted users’ attitudes and increased their likelihood of recommending VR to others. Implications for content developers and future studies is discussed.
Business Continuity Planning (BCP) for Election Systems

Governments are embracing IT at an ever-increasing pace, incorporating technology into their critical infrastructure, and bringing cybersecurity to the forefront as policymakers balance system availability and convenience with privacy and security. However, achieving this balance can be challenging when planning for unforeseen circumstances during an election. In the United States, although there is guidance at the national level, plans to deliver election services during a cyber or physical attack or disaster are left up to each state and local jurisdiction. This paper covers a brief history of disaster recovery and business continuity planning. We then examine election systems as a critical system in the United States and discuss the status of each state's ability to respond to and plan for election system emergencies or failures during an election. Finally, we recommend promoting a more unified approach to business continuity planning (BCP) for election systems.
MOSI APP - A Motion Cueing Application to Mitigate Car Sickness While Performing Non-driving Task

It is common for passengers to perform non-driving tasks such as reading or gaming on smart phones when riding, which may easily lead to car sickness. The underlying cause is a possible sensory conflict between the signals received by the passenger's visual processing system and those received by the vestibular system. To reduce the conflict and mitigate car sickness, we propose the MOSI APP, a smart phone application to present the vehicle motion as a visual cue. Four arrows on phone screen are used for cueing, while the direction and color-filling indicate the vehicle motion direction and magnitude, respectively. When using the smart phone for non-driving tasks, passengers can observe and understand motion cues to gain situation awareness through the MOSI APP. A total of 30 participants of passenger were recruited for a series of driving simulator experiments, and reports of their sickness level, non-driving task performances and subjective evaluation on our application were collected and analyzed. Results show that: 1) for all 26 participants with valid data, comparing to the case without motion cues, the MOSI APP could contribute to motion sickness mitigation (though with no significance, $$p=0.157$$ ), and it could delay the aggravation of moderate or severe sickness level (by 8 to 10 min in our experiment setting); 2) for the 15 participants who were more susceptible to motion sickness in the experiments, the APP could significantly ( $$p = 0.047$$ ) alleviate their sickness level, while their non-driving task performances were not notably affected. This study may provide useful insights in how to mitigate car sickness, especially in the era of ubiquitous computing and connectivity on smart devices.
Visual Attention Analytics for Individual Perception Differences and Task Load-Induced Inattentional Blindness

Inattentional blindness (IB) is a detection failure of unexpected stimuli due to the concentration on main tasks. Scholars have applied load theory to provide a potential theoretical explanation for IB from the perspective of cognitive resources and task load, while the underlying visual mechanism is still unknown. In addition, apart from task load, individual perception differences may greatly affect IB frequency. Hence, the study aims to reveal the underlying visual mechanism of individual perception differences and task load-induced IB. Specifically, eye movement patterns across IBs and non-IBs, the respective impacts of individual perception differences and task load on eye movements, and their interaction effects are studied. 40 participants conducted a series of hybrid perceptual load experiments, including perceptual level tests and an IB-induced video viewing task. Our results show that IBs and non-IBs have significant differences in six eye movement metrics, such as saccades magnitude, blink frequency, and pupil diameter. Moreover, perception differences and task load have significant effects on three and four eye movement metrics respectively. Besides, first view time demonstrates the interaction effects between individual perception levels and task load. This research not only provides new evidence for the impacts of individual perception differences and task load on IB, but also elaborates on the changing rules of eye-tracking metrics. The visual attention analyses in this study provide a theoretical basis for the objective identification of IB, promoting the process of human-computer interaction.
Text Entry on Smartwatches Using Continuous Gesture Recognition and Word Dictionary

This work proposes the development of a method for text entry in smartwatches using continuous gesture recognition, Naïve Bayes classifier and a word dictionary. We performed an evaluation with experts to validate the proposed method. To perform text entry, a user inserts characters through simple gestures, based on geometric shapes, thus, a character is drawn by a user using a set of proposed gestures. We use Naïve Bayes classifier to identify the character that user is entering without the user having to draw it completely. Finally, we use a trie as a dictionary of words to predict words that can be written, considering characters already inserted. We also used the relative probability of word usage in the prediction process. The evaluation with experts showed that it is possible to insert phrases in smartwatches using the proposed method and that the words were inserted correctly.
Who Would You Rather Ask for Help? A Comparison Between Two Chatbot Personalities in Information Management

Chatbots are widely used in different areas, ranging from healthcare applications, social support, task assistance to customer service. Within this study, we investigated two different chatbot personalities in the field of information management (IM). In IM the motives for using a chatbot are mostly aimed at efficiency and productivity and less at social interaction. With the evaluation of the alpha version of the chatbot Liibot of the IT company avato we examined whether chatbots meet with a positive response from users in the field of IM and what influence different personalities have on the usage. The previous chatbot, which has a more socially-oriented personality, was compared with a more task-oriented personality, which we assumed to be more advantageous in IM. The evaluation was conducted online in telepresence as a between-subjects design and included 2 groups with a total number of 80 participants. The results show that even small changes in the linguistic answering style of the chatbot lead to a significantly different assessment of social competence and personality. No higher values for information and system quality could be found within the task-oriented chatbot version. It even showed worse service quality than the social-oriented chatbot, whose social behaviors also provided significantly higher enjoyment during the interaction. The level of task complexity makes a significantly difference for the willingness to use a chatbot. In summary, a task-oriented chatbot is not rated significantly better in the IT domain and in some aspects rated even worse, which does not confirm the assumption of preferring a task-oriented chatbot personality.