Reviewerly/exHarmony huggingface dataset:
Analysis and Design of Information Systems for Vehicle Driver Service Applications

This study aims to analyze people's needs for transportation services and design an information system in the form of an application display that provides private driver hire services so that they can travel safely when carrying out work, especially during this pandemic. In designing the appearance of this application, it is made using the Unified Modeling Language (UML) method with the diagrams used, namely Activity Diagrams, Use Case Diagrams, Use Case Descriptions, Domain Class Diagrams, and System Sequence Diagrams. The analysis was carried out by collecting data with quantitative methods and the results of the answers from the respondents regarding the application of ordering and hiring drivers and the problems experienced. The results of the analysis are the answer to the solution to the problem and the creation of an application design that can give customers the freedom to choose a private driver according to their wishes, choose rental times flexibly, and make it easier to travel far from city to city.
FingerButton: Enabling Controller-Free Transitions between Real and Virtual Environments

With the recent Virtual Reality (VR) Head-Mounted Displays (HMDs), users can seamlessly transition between the virtual and real worlds with techniques such as passthrough. These techniques leverage on-device cameras to capture the real world and show users a view of their physical surroundings while wearing the HMDs. However, they often require users to hold a controller or frequently tap on the HMD, limiting the potential for hands-free interaction and thereby hindering a truly immersive and natural VR experience. To address this limitation, we designed FingerButton, a finger-worn push button device that enables seamless transitions between real and virtual environments. We conducted two studies, where the first one explored a set of hand gestures for transitioning between two environments that are commonly used for “mode switching” within realities. In the second study, we compared FingerButton with the best two-hand gesture identified in the first study and other commercially available solutions (e.g., double tap) for a between-reality selection task. The results show that the physical finger button is faster and user-preferred than other techniques for the transition tasks. Overall, this research contributes to understanding and improving the interaction techniques for fluid switching between the real and virtual worlds, thereby enhancing VR user experiences.
Video Analysis of Behavioral Patterns During Prolonged Work in VR

VR has recently been promoted as a tool for knowledge workers and studies have shown that it has the potential to improve knowledge work. However, studies on its prolonged use have been scarce. A prior study compared working in VR for one week to working in a physical environment, focusing on performance measures and subjective feedback. However, a nuanced understanding and comparison of participants’ behavior in VR and the physical environment is still missing. To this end, we analyzed video material made available from this previously conducted experiment, carried out over a working week, and present our findings on comparing the behavior of participants while working in VR and in a physical environment.
Towards a Model for Space and Time in Transitional Collaboration

Transitional collaboration is a unique form of cross-reality collaboration within transitional interfaces. Despite being at different locations of the reality-virtuality continuum, users can closely collaborate and overcome traditional boundaries of space and time. Despite the renewed interest in transitional interfaces as a specific type of hybrid user interfaces, this form of collaboration remains relatively under-explored, primarily since suitable analytical tools and frameworks have been introduced only recently and traditional models from the CSCW community may not fully capture the true nature of transitional collaboration. Therefore, in this position paper, we present the current state of our work on formulating a new model that integrates classic CSCW models with more recent research findings from cross-reality and mixed-reality research on transitional interfaces. We present examples based on existing systems and theoretical literature to demonstrate its practical application, including the visualization of different types of transitional collaboration.
Sense of Convergence: Exploring the Artistic Potential of Cross-modal Sensory Transfer in Virtual Reality

The Sense of Convergence (SoC) project explores the artistic potential of cross-modal sensory transfer within a virtual reality context, leveraging insights from cognitive science to delve into the nature of genuine perception. In this paper, we propose a framework for cross-modal sensation parameters for assets and introduce our project showcase, a three-step experience sequence based on interaction design. To design the framework, we first conducted a preliminary psychophysical experiment that established the foundation for sensory alignment and examined the effective transfer of the smooth-rough sensation across visual, tactile, and auditory modalities. Subsequently, we designed a three-step VR experience that detaches the participant from the current reality, then gradually transfers two sensory modalities through convergence, and finally reintegrates them into reality. Feedback from participant interviews following our pilot showcase suggests that our approach successfully encourages questioning the absoluteness of reality and stimulates thought about sensory transfer and the relativity of perception. Through this work, we laid the groundwork for a VR artwork framework that investigates the concept of cognitive relativity via sensory substitution. We propose a novel approach that integrates cognitive sciences and virtual reality interaction, challenging the idea of an absolute reality as perceived through our senses.
ImmerView: Adaptive Multi-View Layout for Immersive Situated Visualizations

Multi-view visualizations play a crucial role in presenting and comparing information within immersive analytics. These views can be positioned at various locations in the immersive environment, with a preference for attaching information close to the actual data, a concept referred to as situated visualization. While existing research has primarily concentrated on the multi-view layout and situated information based on objects’ real-world positions, there has been limited exploration of users’ subjective preferences for multiple views. These preferences might be influenced by factors such as the user’s location, view direction, occlusion in the environment, and also correlate with visualization tasks. This paper introduces a method for developing a flexible layout for multi-view visualizations. This method takes into account the spatial arrangement of objects in relation to the user’s position, viewing angle, and the object of focus. With this data, an optimal layout is generated for the views, ensuring that information maintains an equal distance from the user. Consequently, this promotes easier and more user-friendly information comparison, thus enriching the immersive analytics experience.
A Standalone Augmented Reality Tool for X-ray-like Vision: Harnessing Body Tracking and Sensor Capabilities of HoloLens 2

Augmented Reality can enable "x-ray-like vision" for detailed spatial knowledge about patient anatomy, making it a useful tool for medical training. Prior applications have thus far relied on external cameras tethered to a powerful processing unit to track a patient’s body and overlay anatomy. This paper presents a first-of-its-kind standalone AR application that relies solely on the sensors of the Microsoft HoloLens 2 and performs all processing in real time in the cloud. We measured 3D body pose tracking alignment accuracy by measuring how closely 3D virtual anatomy renderings aligned with real anatomical landmarks. Across multiple landmarks and body poses, the median 3D body pose tracking alignment was 8.7 cm (IQR = 6.9 cm). User feedback underscored the application’s potential for teaching anatomy, visualizing trauma cases, and enhancing training simulations.
Identifying Virtual Reality Users Across Domain-Specific Tasks: A Systematic Investigation of Tracked Features for Assembly

Recently, there has been much interest in using virtual reality (VR) tracking data to authenticate or identify users. Most prior research has relied on task-specific characteristics but newer studies have begun investigating task-agnostic, domain-specific approaches. In this paper, we present one of the first systematic investigations of how different combinations of VR tracked devices (i.e., the headset, dominant hand controller, and non-dominant hand controller) and their spatial representations (i.e., position and/or rotation as Euler angles, quaternions, or 6D) affect identification accuracy for domain-specific approaches. We conducted a user study $( n =45)$ involving participants learning how to assemble two distinct full-scale constructions. Our results indicate that more tracked devices improve identification accuracies for the same assembly task, but only headset features afford the best accuracies across the domain-specific tasks. Our results also indicate that spatial features involving position and any rotation yield better accuracies than either alone.
Performance Impact of Immersion and Collaboration in Visual Data Analysis

Immersive Analytics is a recent field of study that focuses on utilizing emerging extended reality technologies to bring visual data analysis from the 2D screen to the real/virtual world. The effectiveness of Immersive Analytics, when compared to traditional systems, has been widely studied in this field’s corpus, usually concluding that the immersive solution is superior. However, when it comes to comparing collaborative to single-user immersive analytics, the literature is lacking in user studies. As such, we developed a comprehensive experimental study with the objective of quantifying and analysing the impact that both immersion and collaboration have on the visual data analysis process. A two-variable (immersion: desktop/virtual reality; number of users: solo/pair) full factorial study was conceived with a mixed design (within-subject for immersion and between subject for number of users). Each of the 24 solo and 24 pairs of participants solved five visual data analysis tasks in both a head-mounted display-based virtual world and a desktop computer environment. The results show that, in terms of task time to completion, there were no significant differences between desktop and virtual reality, or between the solo and pair conditions. However, it was possible to conclude that collaboration is more beneficial the more complex the task is in both desktop and virtual reality, and that for less complex tasks, collaboration can be a hindrance. System Usability Scale scores were significantly better in the virtual reality condition than the desktop one, especially when working in pairs. As for user preference, the virtual reality system was significantly more favoured both as a visual data analysis platform and a collaborative data analysis platform over the desktop system. All supplemental materials are available at https://osf.io/k94u5/.
How Do Users with Autism Perceive Certain VR Interaction Techniques? An Experience Evaluation through Brainwave Measurement

This research aims to investigate how individuals with autism perceive various virtual reality (VR) interaction techniques by integrating brainwave measurement into the experience evaluation method. The study will involve recruiting high-functioning individuals diagnosed with autism to participate in VR sessions that incorporate various interaction techniques. Electroencephalogram (EEG) signals will be collected to gain insight on users’ brain responses, specifically focusing on alpha and theta frequency bands. Additionally, both quantitative and qualitative data, including observer notes and participant feedback, will be gathered. The study will employ rigorous statistical analysis to identify correlations and patterns between interaction techniques, brain responses, and user experience. The findings from this study are expected to provide valuable insights into the preferences and experiences of individuals with autism, thereby informing the design of more tailored and effective VR interventions. This paper provides an overview of the research design and aims to stimulate further discussion in the field.
Gino .Aiki: Mixed Reality-based Physical Motor Skill Training in Aikido

We introduce a Mixed Reality (MR) technique to facilitate the learning of physical motor skills by visualizing metaphors illustrating the use of the body in motion with an MR headset. A user follows the visualized virtual objects designed from metaphors by moving their body: through following these actions, the user’s body naturally learns the physical motor skills. We focused on the Japanese martial art of Aikido as an exemplar target domain that includes physical motor skills that are difficult for non-experts to learn. We created an MR application to visualize metaphors for use in practicing Aikido. We held an Aikido mastery workshop targeting individuals engaged in physically demanding work in order to verify the effects in a simplistic manner.
How Does Explainability Look in Hybrid User Interfaces?

The recent growth of Artificial Intelligence (AI) based systems considerably widespread their use in many application areas and our daily lives [28] . For instance, AI models are nowadays imbued into web search engines, self-autonomous vehicles, recommendation systems, games, and healthcare [24] . Accordingly, the demand for eXplainable AI (XAI) has risen [20] , [21] to help users cope with the growing complexity and opaqueness of the emerging generation of AI models [20] , [21] . By allowing users to perceive and make sense of the behaviors and outcomes of such models, XAI enables users of diverse levels of expertise to trust, manage, design, inspect, and develop AI models [13] .
Hybrid User Interfaces for Multiple Views: why designer intuition is not enough

How to arrange multiple views (MVs) in immersive environments to present information is a common problem addressed by human-computer interaction (HCI) and user experience (UX) studies. Speciﬁcally, for hybrid user interfaces (UIs) that use multiple interactive devices in an immersive environment, the MV layouts are often based on expert opinion instead of empirically validated guidelines. Thus this paper makes the case that design guidelines for multi-view layouts in hybrid user interfaces are needed as existing guidelines for MV layouts in Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MX) environments often are focused on a single interactive device instead of hybrid user interfaces. Moreover, often current guidelines tend to lack empirical support and are not cohesive. Our work further argues that such guidelines need to be based on empirical evidence. In addition, it summarizes existing related work on MVs for hybrid user interfaces and discusses the basics for running controlled experiments and establishing evidence-based design guidelines for the layout of MVs in hybrid UIs.
Gaze-It: Intelligent Multimodal AR Cross-Reality Control System

AR HMDs are considered auspicious devices and may substitute mobile phones as the next generation of intelligent interactive devices. Nowadays, the Internet of Things (IoT) has entered our lives. However, in smart home, the common control method now is to use apps on the smartphone. Hence, in this work, we design an AR HMD based control system for cross-reality intelligent IoT devices. We focus on the real-life living room scene, and try to compactly integrate the remote control on many smart electric appliance (such as desk lamps, air conditioners, and water bottles) via AR HMD and natural multi-modal interaction including gazing, gesture, voice and typing. This AR HMD based remote controller is called Gaze-It. It enables to automatically recognize the user’s current state and adaptively receive multimodal input. Gaze-It can coexist with the current smart home ecosystem dominated by voice control, bring benefits to human intelligent life concurrently, and contribute to the development and popularization of AR HMDs.
Quantum Software Business: A literature study of business aspects of quantum computing

The short history of the computing discipline has been characterised by constant technological evolution and drastic changes caused by it. Yet, in the near future, the next significant technological leap is expected to take place. The recent development in the field of quantum computing and in the building of quantum computers have been remarkable. However, the foreseeable new technology will also provide changes to business and society surrounding it. While there is an increasing interest towards the quantum computing technology, its societal impacts as well as quantum software engineering, a less work has been done to understand the business aspects of quantum computers and software. This study summarises the academic discussion in the topic via an analysis of collected articles (n=19). A systematic literature study was used in the primary data collection. The results of the primary study analysis shows focus Quantum-Computing-as-a-Service (QCaaS) delivery model and concerns regarding the development of the field. This study argues that for enabling sustainable development of the quantum computing industry, a more focus should be put on searching a commercially viable killer app of quantum computing.
Modeling Gaze Behavior for Real-Time Estimation of Visual Attention and Expertise Level in Augmented Reality

Augmented reality (AR) technologies have recently gained substantial attention within the industry due to their potential applications in on-the-job training and assistance across diverse industrial settings. However, personalizing AR instructions and feedback interventions that cater to individual user needs and skill levels remains a relatively less explored area of research. This paper aims to bridge this gap by utilizing eye tracking data coupled with computer vision to examine the gaze and pupil behaviors of individuals with various levels of expertise performing AR-guided procedural tasks. The main goal is to investigate the relationship between eye tracking data, visual attention, and expertise by exploring four research questions associated with (1) differences in fixation and saccade duration between novices and experts, (2) variations in visual attention allocation to action-relevant areas of interest (AOI) between novices and experts, (3) the influence of expertise on scanpath and transitions between AOIs, and (4) the correlation between pupil size variations and fixation/saccade behaviors. The findings of a study on humans that focused on two procedural tasks are reported. The study uses synchronized gaze, pupillometry, and egocentric videos to analyze gaze interactions with AOIs and background stimuli based on object detection models. This research advances our understanding of the relationship between gaze behaviors, visual attention, and expertise, thus offering new insights into enabling adaptive and personalized interventions in AR. These insights are specifically relevant to AR use cases centered on training or on-the-job assistance.
An Improved SIOU-YoloV5 Algorithm Applied to Human Flow Detection

At present, object detection technology in computer vision is widely used to monitor an area and obtain the information of the scene. This study is aimed at stampede accidents, using this technology to detect the size of the human flow. In this paper, based on the basic framework of YOLO, in order to obtain more detection accuracy and stability in the identification of human traffic, a variety of LOSS functions are improved to improve the detection performance of the same scene. Then, the results obtained after these improvements were evaluated, and compared with the original YOLOV5, it can be concluded that the performance of various improved methods in the human flow detection is better, the target detection is more accurate, and the recognition accuracy is improved. The results show that the improvement effect of SIOU is the best, with a 1% increase in each indicator. Therefore, SIOU is more suitable for human flow detection.
Short Text Sentiment Analysis via Adversarial Variational Bayes Classifier

With the rapid proliferation of social platforms, users are afforded the ability to disseminate personal opinions and daily sentiments across online social forums. These data full of sentiment information bear significant relevance to multiple applications. However, the extraction of text sentiment relies heavily on text representation methods, and conventional methods are inadequate for short text segments that are commonly encountered in social media. In light of this, we propose an adversarial variational Bayes classifier for short text sentiment classification. To improve the delicate and unstable training process of the discriminator, we employ spectrum normalization, while an additional classifier is integrated to guide the optimization of the inference model, thereby facilitating end-to-end learning from topic modeling to sentiment classification. For better short text representation, we propose three stages of attention weights to achieve the fusion of topic features and pre-trained word vector features. Qualitative and quantitative experiments have proven that the classifier effectively guides the generation of more discriminative topic features, which are helpful for downstream classification tasks. Empirical results on three real short text datasets demonstrate that our improved model outperforms several baseline methods on short text sentiment classification tasks.
Extending the World-in-Miniature Metaphor to Access Situated Information in a Pervasive Augmented Reality Environment

Recently, the concept of Pervasive Augmented Reality (PAR) was introduced. It is defined as a continuous use of Augmented Reality (AR), where the information displayed comes from applications simultaneously opened by the user or from situated and embedded information placed by various sources. This can lead to potential information clutter. Additionally, PAR environments apply physical constraints over users due to the nature of their displayed information, forcing users to walk to get to situated or embedded information in the environment if they want more details. This work explores the use of the World-in-Miniature (WiM) metaphor inside a PAR context and extends this technique to better fit the constraints implied by such a context: 1) search and filter information of use wherever it might be, 2) access potentially distant and/or occluded information without any required physical movement, and 3) keep context of distant and situated information. Our contributions are an extension to the WiM metaphor and the evaluation of a fully working prototype through the User Experience Questionnaire (UEQ) with results coming from 26 participants.
Fantastic Hybrid User Interfaces and How to Define Them

Hybrid User Interfaces (HUIs) describe a combination of several devices into one system. Such a combination promises to alleviate the disadvantages of one device by adding features of another. The term HUIs was first coined by Feiner and Shamash [10] in 1991. Since then, many research projects have focused on exploring possible systems within this interface category, showing the importance and interest of this term. However, the broadness of the follow-up research projects can be seen as "wild growth", which caused the term HUI to become unspecific and fragmented - as also the call for submission of this workshop states [14] . Additionally, a set of related terms have been used since then, which partially overlap with HUI, like cross-device interfaces or augmented displays.