Reviewerly/exHarmony huggingface dataset:
Whispering salesperson: perceptual illusion of interpersonal distance and ventriloquism effect in service of virtual environment by use of whisper voice

The opportunities to receive and provide services on virtual environment (VE) continue to increase. In this study, we focused on the voice in VE, and investigated the effects of whisper voice, which is used in intimate relationships, on interpersonal distance between salesperson and customer and the effective range of ventriloquism effect. The experimental results showed two results when the salesperson used whisper voice. The interpersonal distance between them was significantly smaller than that of the normal voice. The effective range of the ventriloquism effect was significantly larger on the front side and significantly smaller on the backside compared to normal voice.
A Distributed Automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem

Automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem; (2) perform an in-depth analysis of our architecture in terms of accuracy and scalability; (3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language Processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets.
Dual-Tamper Detection and Self-Recovery Using Second-order Wavelet Transform for 1080p Images

This paper proposes a dual-tampering detection method for 1080p images, then further recovering its forged content with the recovery information, which is generated by the low-frequency of the second-order wavelet. First, the image is divided into non-overlapping blocks. To improve the quality of the recovered image, we employ a centrosymmetric mapping technique that ensures the security of recovery bits. MD5 is used to generate the first part authentication key based on the most significant bit-plane of the block. SHAI is used to generate the second part authentication key based on the recovery information. By embedding double authentication keys and recovery bits into the original image by replacing the least significant bit, we finally generate the watermarked image. When tampering detection is performed, the extracted authentication keys will be compared with the calculated authentication keys. When tampering detection is performed, the extracted authentication keys will be compared with the calculated authentication keys. If they were not equal, mark the block as tampered. Then use Centrosymmetric mapping to find the second order wavelet low-frequency coefficients for image self-recovery. After the inverse wavelet transform, we use the recovery data to fill into the tampered areas, thus completing the self-recovery of the image. Experimental results show that our scheme has higher invisibility and recovery quality.
Investigating HPC Job Resource Requests and Job Efficiency Reporting

High Performance Computing (HPC) systems are ever-evolving, increasing in complexity and heterogeneity, while providing high computing power to scientific and data analysis applications. They are attracting a broad range of users that execute applications from computational chemistry, physics, digital humanities, life sciences, and machine learning, to name a few. Not all users possess the knowledge and expertise to harness the full performance capabilities of complex HPC systems. These users tend to submit inaccurate resource requests for their jobs, especially regarding time limits. This leads to inefficient scheduling of jobs, increased wait times for other users, overall inefficient system utilization, and wasted computing resources and energy, ultimately slowing down scientific discovery. This situation motivates the analysis of the accuracy of resource requests and its reporting to users to raise awareness about job efficiency, improve future job submissions, and reduce job wait times. Existing analyses of job wait times often neglect the connection between the causes of job wait times that can range from user-given and Quality of Service (QoS) limits, to unavailable resources, and approaches of reducing wait times that can be reported to users in an understandable way. In this work, we analyze almost 350’000 jobs collected over 2 months on a local university HPC cluster. Our analysis placed an emphasis on time limit accuracy, QoS characteristics, and reasons for long wait times, as well as how to engage users to improve resource requests. This work shows the importance of analyzing reasons for wait times, approaches to reduce wait times, and motivates further research into supporting users to improve resource requests and system utilization, and to reduce avoidable resource waste.
System Architecture for Real-time Condition Monitoring and Anomaly Detection on Ships

Sea-based rescue organizations play a vital role in maritime civil security by resolving offshore emergencies, securing sea routes, and monitoring critical naval infrastructure. However, increasing marine traffic makes rescue operations more difficult, demanding better organization and orchestration of available resources. Therefore, ensuring efficient maintenance, availability and timely deployment of rescue cruisers is of great importance, particularly in rough sea conditions. To achieve this, we design an AI-based system for predictive maintenance and condition monitoring that enables real-time analyses of the ship’s sensory data and the detection of anomalies in the system behavior. In this paper, we detail our software and hardware architecture for these purposes, and we discuss the respective requirements regarding onboard and off-ship data analysis from various data sources (sensor data, camera data, weather data). We underpin our approach by emulating data flows derived from sensory data. Finally, we provide the first results in AI-based anomaly detection, allowing, for instance, early engine malfunctions identification before the actual failure occurs.
Software Development Process

In this chapter, we will focus on requirements analysis and software design involving its structural and behavioural modeling and not get into the theoretical aspects of these topics. Verification and validation, which is one of the important phases of development, will be introduced in this chapter and will be discussed further in Chap. 5 as V&V is an essential part of software and system qualification.
GazeRayCursor: Facilitating Virtual Reality Target Selection by Blending Gaze and Controller Raycasting

Raycasting is a common method for target selection in virtual reality (VR). However, it results in selection ambiguity whenever a ray intersects multiple targets that are located at different depths. To resolve these ambiguities, we estimate object depth by projecting the closest intersection between the gaze and controller rays onto the controller ray. An evaluation of this method found that it significantly outperformed a previous eye convergence depth estimation technique. Based on these results, we developed GazeRayCursor, a novel selection technique that enhances Raycasting, by leveraging gaze for object depth estimation. In a second study, we compared two variations of GazeRayCursor with RayCursor, a recent technique developed for a similar purpose, in a dense target environment. The results indicated that GazeRayCursor decreased selection time by 45.0% and reduced manual depth adjustments by a factor of 10 in a dense target environment. Our findings showed that GazeRayCursor is an effective method for target disambiguation in VR selection without incurring extra effort.
Complying with Standards and Guides

The number of working standards is considerably large, and the system-level generic standards are further elaborated in the form of application-specific standards as well as standards specific to various software development phases. This chapter offers a holistic approach towards compliance with the standards accompanied by detailed discussions on their applicability at various stages of development.
MultiFacet: A Multi-Tasking Framework for Speech-to-Sign Language Generation

Sign language is a rich form of communication, uniquely conveying meaning through a combination of gestures, facial expressions, and body movements. Existing research in sign language generation has predominantly focused on text-to-sign pose generation, while speech-to-sign pose generation remains relatively underexplored. Speech-to-sign language generation models can facilitate effective communication between the deaf and hearing communities. In this paper, we propose an architecture that utilises prosodic information from speech audio and semantic context from text to generate sign pose sequences. In our approach, we adopt a multi-tasking strategy that involves an additional task of predicting Facial Action Units (FAUs). FAUs capture the intricate facial muscle movements that play a crucial role in conveying specific facial expressions during sign language generation. We train our models on an existing Indian Sign language dataset that contains sign language videos with audio and text translations. To evaluate our models, we report Dynamic Time Warping (DTW) and Probability of Correct Keypoints (PCK) scores. We find that combining prosody and text as input, along with incorporating facial action unit prediction as an additional task, outperforms previous models in both DTW and PCK scores. We also discuss the challenges and limitations of speech-to-sign pose generation models to encourage future research in this domain. We release our models, results and code to foster reproducibility and encourage future research1.
Expectations vs. Reality: The Impact of Adaptation Gap on Avatars in Social VR Platforms

Online VR Platforms allow users to use different kinds of avatar controls including motion-tracked and automated-gesturing avatars. The avatar control might change users’ experience and impact immersion when exposed to different avatars simultaneously due to the differences in the quality of behavior generation. In this paper, we propose to measure the difference in participants’ expectations and perceptions of both avatars in the form of Adaptation Gap scores, testing the assumption that differences in avatar behavior control type have an impact on user experience. Our results support the argument that avatar control type matters as well as the scenario in which the interaction takes place. We also show that the difference between user expectations and their perceptions is correlated with the believability of the avatars, which is known to have an important impact on the interaction.
Planning Locomotion Techniques for Virtual Reality Games

Locomotion is a fundamental component in many virtual reality (VR) games. However, few techniques have been designed with game’s demands in mind. In this paper, we propose two locomotion techniques for fast-paced VR games: Repeated Short-Range Teleports and Continuous Movement Pads. We conducted a user study with 27 participants using these techniques against Smooth Locomotion and Teleport in a game-like scenario. We found that Movement Pads can be a suitable alternative for games, with competitive performance on various criteria such as time, damage taken, usability, workload, and user preference. On the other hand, Repeated Short-Range Teleport displayed lower usability and higher mental workload.
Walking-in-Flat-Place on Non-flat Virtual Environment can be Sickening!

It is well-known that employing the Walking-in-Place (WIP) interface can significantly reduce VR sickness in addition to promoting the sense of presence, immersion, and natural interaction. In this poster, we re-examine the conditions for which WIP will effectively reduce VR sickness. In particular, we investigate and compare the cases of applying WIP to navigating on flat terrain vs. up-and-down ramps with respect to the sickness reduction effect. We point out that naively designed WIP/navigation content has the possibility of actually worsening the VR sickness due to the sensory and reality mismatch between the flat real operating environment and the inclined virtual terrain.
Measuring and Comparing Collaborative Visualization Behaviors in Desktop and Augmented Reality Environments

Augmented reality (AR) provides a significant opportunity to improve collaboration between co-located team members jointly analyzing data visualizations, but existing rigorous studies are lacking. We present a novel method for qualitatively encoding the positions of co-located users collaborating with head-mounted displays (HMDs) to assist in reliably analyzing collaboration styles and behaviors. We then perform a user study on the collaborative behaviors of multiple, co-located synchronously collaborating users in AR to demonstrate this method in practice and contribute to the shortfall of such studies in the existing literature. Pairs of users performed analysis tasks on several data visualizations using both AR and traditional desktop displays. To provide a robust evaluation, we collected several types of data, including software logging of participant positioning, qualitative analysis of video recordings of participant sessions, and pre- and post-study questionnaires including the NASA TLX survey. Our results suggest that the independent viewports of AR headsets reduce the need to verbally communicate about navigating around the visualization and encourage face-to-face and non-verbal communication. Our novel positional encoding method also revealed the overlap of task and communication spaces vary based on the needs of the collaborators.
Earnormous: An educational VR game about how humans hear

We present a demo of Earnormous, a virtual reality game to teach about the human ear.
From Clocks to Pendulums: A Study on the Influence of External Moving Objects on Time Perception in Virtual Environments

This paper investigates the relationship between perceived object motion and the experience of time in virtual environments. We developed an application to measure how the motion properties of virtual objects and the degree of immersion and embodiment may affect the time experience. A first study (n = 145) was conducted remotely using an online video survey, while a second study (n = 60) was conducted under laboratory conditions in virtual reality (VR). Participants in both studies experienced seven different virtual objects in a randomized order and then answered questions about time experience. The VR study added an "embodiment" condition in which participants were either represented by a virtual full body or lacked any form of virtual body representation. In both studies, time was judged to pass faster when viewing oscillating motion in immersive and non-immersive settings and independently of the presence or absence of a virtual body. This trend was strongest when virtual pendulums were displayed. Both studies also found a significant inverse correlation between the passage of time and boredom. Our results support the development of applications that manipulate the perception of time in virtual environments for therapeutic use, for instance, for disorders such as depression, autism, and schizophrenia. Disturbances in the perception of time are known to be associated with these disorders.
Stochastic Configured Fuzzy System Modeling based on Information Granulation and Association Analysis

In this paper, a stochastic configured fuzzy system with semantic interpretability is proposed and used for classification. Firstly, transfer entropy is adopted to conduct causal analysis on modeling variables. To reduce computational complexity and achieve effective screening of data variables, the information granule is designed to preprocess the data. Secondly, a research is carried out on the fuzzy modeling method based on the idea of stochastic configuration. Besides, an improved fuzzy system evaluation index for model parameter learning and system structure optimization is proposed. The improved index introduces the axiomatic fuzzy set theory to evaluate the information metric of modeling results, which increases the semantic interpretability of the fuzzy system. Finally, the proposed method is applied in some medical data sets. The experimental results show that it offers better performance in solving the classification problem.
Distance Metric Learning-optimized Attention Mechanism for Visual Question Answering

Visual Question Answering (VQA) is a multimodal task that answers questions by exploring the content of the images. A comprehensive understanding of both the images and questions is critical for VQA. Although great success has been achieved in VQA recently, the problem of language bias hinders the application of VQA models in real-life situations. Language bias leads to the inability to generalize the VQA model to test sets with different distributions. Therefore, we propose a distance metric learning optimized attention mechanism (DMA), which selects and directly adopts the image information that is related to the question, performing feature selection and compression via a novel unsupervised metric learning scheme. Specifically, we only keep the image features with higher similarities with the question to answer the question. We adopt unsupervised metric learning to separate image features with high similarity to the question from those with low similarity to the question in the feature representation space. By doing this, the difficulty of VQA models understanding of images is reduced and language bias is mitigated. Extensive experiments have proved the effectiveness of our method for both Vanilla Transformer and Cross Transformer, with improved results on VQA v2 and VQA-CP datasets. Particularly, by building on top of model LMH+CSS, we achieve State-of-the-art performance on VQA-CP datasets.
Social Distance Monitoring Using Object Detection and Tracking Techniques In the Era of COVID-19

The COVID-19 has been posing threats to peoples health around the world due to its simple transmission route, strong mutation ability, and high infection. Currently, the superb vaccine breakthrough ability of mutant strains puts people's lives and health further at risk. Many measures are taken to fight against the epidemic. Keeping a safe social distance to prevent the spread of COVID-19 in public places. In this paper, we propose a method to monitor social distancing from a bird's-eye view. It mainly includes three parts. Firstly, an objector based on YOLOv5m is trained to detect human beings. It shows good performance compared with other models such as Faster R-CNN and YOLOv3. Secondly, we propose to use SORT algorithm to track each human by assigning it an ID. It also ensures the accuracy of multiple-object tracking. More importantly, it makes for tracking those who violate social distance. Thirdly, the Euclidean distance between detected people is used to judge whether human beings keep social distance or not. It is implemented by computing the pairwise distances of the detected bounding box centroids. Experiments conducted on birds view dataset show that our method can monitor social distance well. Hopefully the proposed method and dataset can provide some help in the fighting against COVID-19.
Enhancing VR Based Serious Games and Simulations Design: Bayesian Knowledge Tracing and Pattern-Based Approaches

This paper explores how Bayesian Knowledge Tracing (BKT) can be integrated with a pattern-based approach to enhance the development of virtual reality (VR) based serious games and simulations. These technologies allow for the prediction of user progress and the utilization of Artificial Intelligence (AI) methods to tailor difficulty levels based on individual needs. By combining BKT, pattern-based mechanics, and affective feedback, comprehensive data on user interactions, skills, and emotional states can be collected. This data enables the estimation of learners’ knowledge levels and the prediction of their progress.
Exploring Augmented Reality for Situated Analytics with Many Movable Physical Referents

Situated analytics (SitA) uses visualization in the context of physical referents, typically by using augmented reality (AR). We want to pave the way toward studying SitA in more suitable and realistic settings. Toward this goal, we contribute a testbed to evaluate SitA based on a scenario in which participants play the role of a museum curator and need to organize an exhibition of music artifacts. We conducted two experiments: First, we evaluated an AR headset interface and the testbed itself in an exploratory manner. Second, we compared the AR headset to a tablet interface. We summarize the lessons learned as guidance for designing and evaluating SitA.
Versatile Mixed-method Locomotion under Free-hand and Controller-based Virtual Reality Interfaces

Locomotion systems that allow the user to interact with large virtual spaces require precise input, competing with the same inputs available for performing a task in the virtual world. Despite extensive research on hand tracking input modalities, there is a lack of a widely adopted mechanism that offers general-purpose, high-precision locomotion across various applications. This research aims to address this gap by proposing a design that combines teleportation with a grab-pull locomotion scheme to bridge the divide between long-distance and high-precision locomotion in both a tracked-controller and free-hand environment. The implementation details for both tracked controller and tracked hand environments are presented and evaluated through a user study. The study findings indicate that each locomotion mechanism holds value for different tasks, with grab-pull providing more benefit in scenarios where smaller, more precise positioning is required. As found in prior research, controller tracking was found to be faster than hand tracking, but all participants were able to successfully use the locomotion system with both interfaces.
Intuitive User Interfaces for Real-Time Magnification in Augmented Reality

Various reasons exist why humans desire to magnify portions of our visually perceived surroundings, e.g., because they are too far away or too small to see with the naked eye. Different technologies are used to facilitate magnification, from telescopes to microscopes using monocular or binocular designs. In particular, modern digital cameras capable of optical and/or digital zoom are very flexible as their high-resolution imagery can be presented to users in real-time with displays and interfaces allowing control over the magnification. In this paper, we present a novel design space of intuitive augmented reality (AR) magnifications where an AR head-mounted display is used for the presentation of real-time magnified camera imagery. We present a user study evaluating and comparing different visual presentation methods and AR interaction techniques. Our results show different advantages for unimanual, bimanual, and situated AR magnification window interfaces, near versus far vergence distances for the image presentation, and five different user interfaces for specifying the scaling factor of the imagery.