Reviewerly/exHarmony huggingface dataset:
Methods of Tailoring for Project Management

Project management offers a broad set of tools for the definition, planning, execution and completion of very different projects. There are various more or less generic standards for this toolkit of project management-related roles, processes and methods. These need to be tailored to the specific needs of projects and their respective contexts. This paper provides a broad overview of tailoring approaches in relevant standards and other literature. The systematic literature search revealed very different concepts such as process-oriented or structure-oriented approaches. To provide orientation, the different approaches are categorized and described by way of examples. The results have the potential to help practitioners select an appropriate tailoring approach and to further improve the overall tailoring process in more comprehensive tailoring systems.
Hype D-Live: XR Live Music System to Entertain Passengers for Anxiety Reduction in Autonomous Vehicles

Passengers in autonomous vehicles enjoy the comfort of being free from driving tasks, but they inevitably experience anxiety caused by autonomous vehicle stress (AVS). AVS encompasses vehicle behavior stress due to unpredictable acceleration, and external environmental stress due to potential collisions. Past research has explored approaches to improve passengers’ comfort through behavior control and information presentation. However, methods that utilize stressful vehicle behavior in Extended Reality (XR) entertainment to distract from AVS-related anxiety are limited. Hence, the goal of this study was to maximize passenger comfort in automated vehicles. To achieve this goal, we implemented an XR entertainment system that utilizes vehicle behavior and evaluated its effect on reducing anxiety. In this study, we proposed “Hype D-Live”, an XR live music system designed to reduce anxiety by providing multimodal visual, auditory, force, and vestibular stimuli using a hemispherical display and motion platform mounted on a vehicle. We developed system functions to adjust the force and vestibular senses according to the excitement level of the music and the direction of stressful acceleration and to reproduce moshing, a characteristic behavior at live music venues. However, we hypothesized that passengers might not fully enjoy the entertainment and could experience anxiety if the video content makes them aware of the external environment. Therefore, we conducted an experiment with a within-participant design, involving 24 participants (14 males and 10 females), comparing 3 types of video content for XR entertainment inside the autonomous vehicle: a real external environment, a virtual simulation of the external environment, and a virtual live music venue. The Wilcoxon signed rank test with the Bonferroni correction after the Friedman test revealed that, without the moshing function, the virtual live music venue video significantly enhanced enjoyment and reduced anxiety, compared to the real one.
Classification of Lung Adenocarcinoma Subtypes Based on ViT-DenseNet

Lung cancer is one of the most prevalent cancers with the highest mortality rate. And the classification of its adenocarcinoma subtypes is a challenge task for traditional CNN-based networks, due to such medical decision requires the comprehensively consideration of the complex spatial semantic information and the local texture information of adenocarcinoma tumors. Therefore, we propose an end-to-end dual stream network (ViT-DenseNet) to solve the problem of poor semantic representation of traditional CNNs with ViT’s strong global information modeling capabilities. Specifically, to cope with the negative fusion of the hybrid network, we propose a SRL method to improve the complementarity of the two feature extractors. Besides, the difference in inductive bias ability among sub-blocks is another critical factor incentive for negative fusion, we propose a MISM strategy to reduce the effect of such difference. Extensive experiments results demonstrate the effectiveness of SRL block and MISM strategy. And our approach also offers best average accuracy (85.88%) over other state-of-the-art models. In addition, by comparing visualization results, our proposed ViT-DenseNet dedicates more attention to the heterogeneous nuclei concerned by pathologists, thus offering stronger clinical interpretability.
AR.S.Space: An AR Casual Game for Social Engagement in Work Environments

In social situations, individuals often encounter communication challenges, particularly when adapting to new environments. While some studies have acknowledged the potential of AR social games to aid in effective socialization to some extent, little attention has been given to AR HMD-based games specifically designed to facilitate social interactions. In response, we propose AR.S.Space, an AR HMD-based social game that employs augmented reality features to engage users with virtual social agents through asynchronous communication. The game aims to mitigate the unease associated with initial social interactions and foster long-term connections. To assess its efficacy, a user study was conducted within a specific scenario (an office space), gathering quantitative data and qualitative feedback through questionnaires and interviews. The findings highlight the game’s potential to enhance socialization in small-scale environments. Moreover, the study offers valuable design guidelines for future research and the application of AR social games in similar settings.
Indigenous Technology Empowerment Model: A Community-based Design Framework

This paper addresses the lack of social and cultural inclusion in the design of systems and products based on emerging technologies. Through a review of existing design processes, and focusing on Virtual Reality (VR) and Indigenous Peoples as examples, we conclude that existing design approaches for such technologies are not socially and culturally inclusive, don’t consider users as a community with its protocols and knowledge, and are not suitable for marginalized groups such as Indigenous Peoples. Through a collaborative project aiming at a prototype VR gathering place for Indigenous communities, we investigate the needs and concerns of community members and propose a framework consisting of a new information system model and its design process. We refer to this framework as Indigenous Technology Empowerment Model (ITEM), which is implemented using a hierarchical community involvement approach and elements we call Participatory Action Unit (PAU). Our prototype development follows this framework, and a qualitative analysis of community participation shows the initial success of our approach in empowering the communities to play an active role in the design process and identify the important technical features to be worked on. Our research also identifies proper community training as an essential component of a community-based design process.
VR for Remote Monitoring of Automated Vehicles: Replications Across Follow-Up Evaluations

Although autonomous vehicles have shown remarkable scientific and industrial advancements, it is improbable that completely automated, SAE-level 5-compliant cars will become widely available in the near future. Instead, automated driving solutions are likely to rely on remote monitoring and teleoperation assistance for the foreseeable time. While Virtual Reality (VR) has been proposed as one potential interface for the teleoperation of remote vehicles, its benefits and challenges are not yet sufficiently understood. In this context, we discuss replicated conditions across two of our user studies [10].Study 1 (n=24) compared the physical interface with an interface optimized for monitoring, once displayed on a 56-inch screen and once in VR on HTC Vive Pro 2. Study 2 (n=24) compared this optimized interface displayed on the same 56-inch screen, HTC Vive Pro 2, and Meta Quest Pro.
Contrastive Alignment for Deep Multi-view Subspace Clustering

Deep multi-view subspace clustering can partition non-linear high-dimensional data by exploring a consensus self-expression among the samples and has attracted increasing attention. However, most existing methods learn the consensus self-expression by fusing view-specific self-expression, where they neglect that view-private meaningless information or noise may interfere the learning of consensus self-expression. In this paper, we propose a novel Contrastive Alignment for Deep Multi-view Subspace Clustering (CADMVSC) method. Specifically, the view-specific self-expression are obtained by self-expressive layer embedded in autoencoder network for each view. To guarantee the consistency among the views and reduce the interference of view-private information, we align the view-specific self-expression by contrastive learning. Moreover, all the aligned self-expression are fused adaptively to obtain the consensus self-expression by channel attention mechanism, which can fully explore the complementarity of views. Extensive experiments have shown the excellent clustering performance of our method over other state-of-the-art methods.
High Frequency Event-based Eye Tracking Towards Mental Health Diagnosis

Eye movement analysis is a key technique for understanding information processing in virtual reality (VR) and psychological therapy. Then the dynamics of the eye movement overtime is an important reference for mental health diagnosis. However, it is challenging for conventional CMOS/CCD cameras to capture the detailed change of fast moving eye balls. To bridge the gap, in this paper, we propose to use event cameras, which are novel sensors that record pixel-level brightness changes at high frequencies, to achieve over kilohertz frequency eye tracking. This enables us to collect rich and precise data on eye movements. To demonstrate its potential on providing high temporal resolution of eye movement for mental health diagnosis, we design a deep learning-based pipeline to accurately extract pupil region of human eyes recorded by event cameras and calculate a number of key parameters including pupil region, blink rate and pupil trail.
Designing Humanoid Avatars in Individualised Virtual Reality for Mental Health Applications

In both training and therapeutic Virtual Reality applications, avatars are commonly used to interact with participants. However, this literature reveals considerable heterogeneity in the quality of these Avatars owing to technological changes with respect to computer graphics and hardware. The VR community may assume that with the development of higher quality, and therefore ever more lifelike avatars, then this implies better avatar performance, which then yields superior human-avatar interactions. But is that true? There is some suggestive evidence that ‘lifelike’ high-quality avatars hinder VR therapeutic outcomes. It could, therefore, be possible that lesser-quality humanoid avatars are an affordable substitute with better interaction.
Cross-Reality Interaction and Collaboration in Museums, Education, and Rehabilitation

With Virtual Reality Head-Mounted Displays (VR HMDs) establishing themselves as a potent platform for collaborative tasks, their cross-reality capability and cross-domain applicability remain largely unexplored. This study intends to assess the effectiveness of cross-reality collaboration systems using a VR HMD and a desktop PC across three disparate sectors: museum visiting, chemical education, and assisted rehabilitation. The systems were designed to support social interactions and scenario-specific collaborative tasks. Evaluation of the systems showed above-average system usability and user experience. By probing into these varied environments, our study offers a comprehensive understanding of the applicability of such collaborative cross-reality systems in real scenarios, potentially fostering more immersive, efficient, and enriching multi-field applications of cross-reality technologies.
Research on GRU Based Emergency Material Delivery Prediction Algorithm

In order to solve the problem of the influence of the large-scale flow mode of all kinds of emergency materials on the epidemic situation, this paper first analyzes the changes of the epidemic situation before and after putting in the vegetable package, and uses the time series clustering method to cluster the daily epidemic situation. It is determined that the vegetable package can control the epidemic situation to a certain extent. Then, the GRU prediction algorithm is established, and the epidemic data of unput vegetable bags in other regions at home and abroad are used to train GRU to predict the epidemic situation of unput vegetable bags in Changchun, and compared with the actual data, the accuracy of the algorithm reaches 97.8%. The results showed that the predicted data were significantly higher than the real data, both confirmed cases and asymptomatic infections. on the 32nd-40th day, the predicted confirmed cases were 2.7 times higher than the actual data. On the 23rd-81st day, the predicted asymptomatic data was 1.2 times higher than the actual data, which further verified that the vegetable package could effectively inhibit the epidemic.
StrEx: Towards a Modulator of Stressful Experience in Virtual Reality Games

In this work, we explored the advantages of dynamic game balancing using players’ affective state introducing StrEx, a plugin designed to modulate the stress level induced by a video game in an unobtrusive way. The proposed plugin is also intended to validate results obtained by [1]. Our system collects motion behavioral data and updates a model of the player’s stress level to guide the transitions of a Finite State Machine which regulates the stress level induced by the game through the generation of game content. We designed and developed a virtual reality horror-survival game, with the aim of testing the system functioning. Our approach shows promising potential to create more immersive and engaging video games exploiting affective-based adaptation.
The UUXR-Framework: A Draft Classification for Using Extended Reality in Usability and User Experience Research

Conducting human-centered evaluations in extended reality (XR) environments is a growing trend in user research and usability engineering. However, there has been little to no systematic investigation of the emerging methods in this field published to date. The motivation behind our work is to explore and classify strategies and methods for utilizing XR technologies in the context of usability and user experience (UUX) activities. This paper proposes a draft classification framework for the use of XR technologies in UUX activities, combining an informal exploration of relevant literature with established UUX methods. Within this framework, we propose 12 dimensions that we consider potentially relevant for determining whether and how the use of XR technologies can benefit product development and user research. To evaluate the structure and phrasing of our proposed dimensions, we conducted an initial evaluation with UUX professionals (N = 11). We believe that our dimensions form an early-stage foundation for future guidelines aimed at UUX researchers. The framework serves as a tool for assessing different levels of virtualization in UUX work and facilitating knowledge transfer between academia and industry.
Influence of Cross-Modal Correspondence between Auditory and Visual Stimuli on Vection Perception in Virtual Reality

Vection is an optical illusion that enhances the sense of self-translation or self-rotation, augmenting a sense of moving or rotating without any physical movement. This sense is closely related to visually induced motion sickness (VIMS), a critical issue of current virtual reality (VR) applications. In order to find solutions for VIMS, lots of research is being conducted to explore vection. Since future VR content utilizes multiple senses to provide an immersive and comfortable experience, it is essential to explore the effect of multisensory stimuli on vection perception for detailed understanding. However, the effect of multisensory stimuli is not explored as much as uni-modal stimuli, and there could be various factors such as congruency, location, and combination methods. Therefore, in this paper, we experimented (n=32) with five intensity levels of visual stimuli and three congruence statuses of audio stimuli in VR. The result showed an interaction effect in vection detection time between congruent audio and no audio conditions. Also, it showed that the incongruent audio condition has augmented a stronger sense of vection than no audio condition. Based on the result, we propose the effect of multisensory stimuli and its rotation direction congruency on vection perception for future exploration.
Tangible Avatar : Enhancing Presence and Embodiment During Seated Virtual Experiences with a Prop-Based Controller

We investigate using a prop to control human-like avatars in virtual environments while remaining seated. We believe that manipulating a tangible interface, capable of rendering physical sensations and reproducing the movements of an avatar, could lead to a greater virtual experience (presence) and strengthen the relationship between users and the avatar (embodiment) compared to other established controllers. We present a controller based on an instrumented artist doll that users can manipulate to move the avatar in virtual environments. We evaluated the influence of such a controller on the sense of presence and the sense of embodiment in 3 perspectives (third-person perspective on a screen, immersive third-person perspective, and immersive first-person perspective in a head-mounted display). We compared the controller with gamepad controllers to control the movements of an avatar in a kick-in-a-ball game as illustration. The results showed that the prop-based controller can increase the sense of presence and fun in all three perspectives. It also enhances the sense of embodiment in the immersive perspectives. It could therefore enhance the user experience in various simulations involving human-like avatars.
Visual ScanPath Transformer: Guiding Computers to See the World

We propose to exploit the scanpath prediction technology to simulate human visual system to automatically generate gaze scanpaths for VR/AR applications, to alleviate the equipment and computational cost in foveated rendering. Specifically, we propose a novel deep learning-based scanpath prediction model called Visual ScanPath Transformer (VSPT), to predict human gaze scanpaths in both free viewing and task-driven viewing situations, based on which the VR/AR systems can execute foveated rendering rapidly and cheaply. The proposed VSPT first extracts highly task-related image features from the visual scene, and then explores the global dependency relationships among all the image regions to generate each image region a global feature. Next, VSPT simulates the human visual working memory to consider all the previous fixations’ influences when predicting each fixation. Experimental findings confirm that our model exhibits adherence to classical visual principles during saccadic decision-making, surpassing the current state-of-the-art performance in free-viewing and task-driven (goal-driven and question-driven) visual scenarios.
Are You Aroused Enough to See the Difference? The Role of Physiological Arousal in Perceiving Realism of Virtual Scene

Simulation realism is a crucial factor for task performance in virtual reality. The issue is that it does not simply result from a simulation's graphical quality and is often connected to users' expectations and the capacity of the cognitive system. It is assumed that perceived realism may be affected by physiological arousal which is the basis of emotional reactions and of attention prioritizing. The main aim of the presented study is to verify whether perceptual characteristics of a virtual scene – complexity and vividness of virtual objects – affect perceived realism. The secondary aim is to test whether realism assessment changes because of arousal. An experimental study was conducted with 100 participants in total. Four experimental groups differ in terms of the complexity of a virtual scene (i.e., number of objects in the field of view) and vividness of virtual objects (i.e., figure-ground contrast). Participants were asked to perform a task, that was taking on the role of a marshaller and positioning the plane on the airport apron in the virtual reality simulation. During the task, electrodermal activity was measured. After the virtual session, participants assessed perceived realism. Results indicate that the complexity and vividness of virtual scene do not affect perceived realism directly. Physiological arousal (i.e., skin conductance level) is a moderator of the relationship between the vividness of the virtual scene and perceived realism. A high level of arousal increases realism assessment in vivid simulations.
Secure Authentication in an IoT-Based Healthcare Environment with Strong Anonymity and Unclonable Device

The Internet of Things is the backbone of cutting-edge intelligent applications such as smart transportation, intelligent healthcare systems, Smart cities, etc. On another side, IoT opens enormous challenges for academia and researcher to resolve threats and security risks. For instance, intelligent healthcare system, where the patient uses a body sensor, the body sensor collects data from the patient's body, such as blood pressure using Sphygmomanometer, heartbeat using Electrocardiogram (ECG), and muscle signaling activity using Electromyography (EMG). The body sensor sends data to the server. The doctors, by the device in their hand, can check the patient's condition remotely based on data from the server. The doctor also can communicate with body sensors in real time with the help of the server after the establishment session key. In addition, the patients may be state officials or artists who must hide their identity by reasoning security. This article proposed a new authentication protocol with strong anonymity that can hide the actual identity and achieves non-traceability and unlinkability; hence our protocol is suitable for IoT-based healthcare systems. We conduct solid reasoning as an informal analysis and use the RoR model as the formal analysis to confirm that our protocol meets security competencies.
The dark web privacy dilemma: linguistic diversity, talkativeness, and user engagement on the cryptomarket forums

Abstract The users of the Dark Web require a secure and highly anonymous environment to exchange information while protecting their online privacy, which presents a privacy dilemma. This paper examines the digital footprints of user behavior on the three most popular cryptomarket forums on the Dark Web, namely Silk Road 1, Silk Road 2, and Agora. The results indicate that users who engage in more conversations and employ a wider range of vocabulary are more likely to discontinue their participation on the forum. Intriguingly, no significant relationship is found between network characteristics and user engagement. These findings emphasize that the risk of exposure within anonymous communities primarily stems from the potency of information rather than social connections, which sheds light on the privacy dilemma inherent in the Dark Web and provides deeper insights into the online user behavior surrounding anonymity-granting technologies on the Internet.