Reviewerly/exHarmony huggingface dataset:
Emotional Reaction and Mental Workload in Virtual and Non-virtual Environment: A Pilot Study

The advanced platform of virtual reality (VR) provides fascinating experiences by offering a beyond reality, perpetual and persistent multiuser environment. However, users express dissatisfaction towards VR due to ocular problems, headache, disorientation, balance distribution, nausea, and degraded performance during exposure. Therefore, there exists a necessity to measure people's emotional responses and cognitive workload in virtual and non-virtual environment. Thus, we plan to measure 30 individuals' emotional responses and mental workload while they perform cognitively demanding tasks in virtual and non-virtual 2D environments. Until now, we hired 8 participants to play Fruit Ninja 2 at two difficulty levels: easy and hard. An iPad was used to play the game in a non-virtual 2D environment, while a head mount device was used to project the game virtually. After each game, the participants filled out the Positive and Negative Affect Schedule (PANAS) and NASA Task Load Index (TLX) questionnaires. The Friedman test result suggested that there were no significant differences between the Positive Affect (PA) scores of the game in both virtual and non-virtual environments. However, marginally significant differences were observed between the NASA TLX scores, and higher scores were associated with the hard level in the virtual environment, while lower scores were found for the easy level in the non-virtual environment. This test result indicates that people express positiveness towards the virtual environment regardless of their high mental workload. This conclusion was drawn based on the findings of a pilot study, and to obtain a better understanding, we are still recruiting participants.
Multi-user VR Experience for Creating and Trading Non-fungible Tokens

Non-fungible tokens (NFTs) have shown a great potential in creating, identifying, and trading digital ownership, and can be used to build the economics in the future metaverse. It is important for the users to understand the basic concepts of NFTs before they engage in a virtual society or application that uses NFTs to represent digital assets. Traditional learning methods like reading articles are inefficient to support active learning of a new concept. In this work, we designed and developed a multi-user VR-based learning method that provides a learner-oriented experience for users to create their own digital assets, witness the process of minting the assets into NFTs, and trade the NFTs. We conduct a study with participants to understand the learning efficiency of the VR-based method in comparing to the traditional reading-based method. The evaluation result shows that the multi-user experience was more engaging than a solo reading-based experience, and the participants became more motivated to learn about NFT topics in the VR experience.
Reflecting on Collaboration in Participatory Design Facilitation

While democratic practices, balancing powers, mutual learning and tools and techniques are the principles of Participatory Design (PD), they are all influenced by the practice of PD “facilitation” and the role of the facilitator(s). Facilitation in PD has been usually studied as a single entity with a facilitator that faces dilemmas in her/his role in relation to PD participants and her/his own motives and values. In this paper, we contribute to PD facilitation practice literature by studying facilitation as collaboration work compounded by many entities. We conducted a reflective analysis of the facilitation practices in two PD projects that we have managed. We found that PD projects have a multitude of facilitators. These facilitators belong to four categories: user experts, domain experts, PD experts and assistant facilitators. In time and in different PD activities facilitators may join or leave the project and also shift between categories while their expertise expands. We define this as “the network of facilitators” and discuss how such a perspective can help to improve the facilitation practice in PD.
Comparing Sentiment Analysis and Emotion Analysis of Algorithms vs. People

Sentiment analysis and emotion analysis have seen continued growth within recent years. Due to this growth, it is important to question the methodology used in previous studies to validate new and novel approaches to these subjects. In this paper we show that the use of secondary sentiment is a valid replacement for primary source sentiment in cases where obtaining primary sentiment might not be available. In other words, state-of-the-art sentiment analysis is approximately as good as crowdsourcing sentiment analysis from humans. However, when applying the same concept with emotion analysis we find that state-of-the-art emotion analysis is far inferior to crowdsourcing emotion from humans.
A Model of Computational Creativity Based on Engram Cell Theory

Artificial Intelligence technology has made remarkable progress in machine learning, but it is still in its infancy in creative thinking or computational creativity. In 2018, Yang and Li proposed that the physiological basis for the formation of memories and concepts in the human brain is engram cells (interneuron), and creative thinking is the process of forming new engram cells to connect previously seemingly unrelated concepts. During this process, association and prediction play a key role. In this study, a computational model based on engram cell theory was coded in Python to mimic the process of creative thinking. The validity of the model was tested by simulating the phenomenon of language generation and summarizing the artificial food-set regularity in the plus maze. The results show that, given 29 initial words and certain grammatical rules, the language generation program generates 25,405 sentences after 130,000 calculations, and these generated sentences can be combined into various short paragraphs. After 50 times of training in the cross-maze puzzle solving program, the model can master 100% of the rules of artificial food settings. In conclusion, a computational model of creative thinking based on engram cell theory can creatively and automatically generate sentences and paragraphs and can learn and summarize laws to solve simple puzzles. We plan to further use this model to address complex real-world problems, such as the study of cancer therapeutic targets.
Analysis of Country and Regional User Password Characteristics in Dictionary Attacks

The degree to which passwords are robust to guessing has become one of the fundamental interests in password research. For example, a method has been proposed to calculate the robustness of password guessing as a password's strength and provide feedback. Measuring guessing robustness has been studied from several perspectives, but most studies are based on password datasets from US and European users. On the other hand, several studies have shown that the characteristics of passwords differ between countries and regions. However, there needs to be a more extensive analysis of guess-robustness due to differences in these data sets. In this study, a large password dataset was used to analyze the password characteristics of countries and regions from the perspective of guess-robustness. The results revealed differences in guess-robustness between countries and regions, as well as differences in guess-robustness given by the datasets used in the dictionary.
Task-Based Open Card Sorting: Towards a New Method to Produce Usable Information Architectures

Open card sorting is the most widely used HCI technique for designing user-centered Information Architectures (IAs). The method has a straightforward data collection process, but data analysis can be challenging. Open card sorting has been also criticized as an inherently content-centric technique that may lead to unusable IAs when users are attempting tasks. This paper proposes a new variant of open card sorting, the Task-Based Open Card Sorting (TB-OCS), which considers users' tasks and simplifies data analysis. The proposed method involves two phases. First, small groups of participants perform classic open card sorting. Then, each participant performs findability tasks using each IA produced by the rest participants of the same group and their first-click success is measured. Analysis of the collected data involves simply calculating the first-click success rate per participants' IA and selecting the one with the highest value. We have also developed a web-based software tool to facilitate the conduction of TB-OCS. A within-subjects user testing study found that open card sorting produced IAs that had significantly higher first-click success rates and perceived usability ratings compared to the IAs produced by TB-OCS. However, this may be due to parameters of the new method that require finetuning, thus further research is required.
Parents, Passwords, and Parenting: How Parents Think about Passwords and are Involved in Their Children’s Password Practices

Though much is known about how adults understand and use passwords, little research attention has been paid specifically to parents or, more importantly, to how parents are involved in their children's password practices. To better understand both the password practices of parents, as well as how parents are involved in their children's password practices, we conducted a survey study of 265 parents in the United States (US) with school-aged children (kindergarten through 12th grade, 5 to 18 years old). We asked parents about their general technology use, the kinds of technologies and password-protected accounts they have; how they make and maintain their passwords; and about how, if at all, they help their children create and maintain passwords. We found that parent password practices align with research surrounding adult password practices, and that parents, especially those of younger children, are very involved in the creation and maintenance of their children's passwords. With these findings, we conclude with both recommendations for future research, as well as a call for the cybersecurity community to better support parents' password understandings and practices so that parents can better support their children.
Detecting Scoreboard Updates to Increase the Accuracy of ML Automatic Extraction of Highlights in Badminton Games

In recent years, numerous studies have focused on the automatic detection of video highlights in different sports, including badminton. Auto-detection of highlights can save labor, money, and time when producing an abbreviated video of a game. It can shorten the viewing time of the audience by filtering out the less exciting scenes, thereby increasing the audience’s interest while watching the game. This study examined different methods of capturing highlights and verified whether scoreboard updates can be used to improve the performance of a YOLOv5s auto-detection model in capturing highlights in badminton games. The model was trained to label the scoreboard correctly with different scores and different designs. The scoreboard was detected as the region of interest. Then, the right half of the scoreboard was divided into upper and lower halves to detect the scores earned by each team. Then, OpenCV’s functions (grayscale, erode, eliminate noise, dilate, and find contours) were used to fine-tune the frame of the score. The area of interest (the number on the far right) was then compared to its immediate previous state, using the average hash algorithm to detect any differences between the two scoreboards. If there was a geometric change, the 10 s of videos immediately before the change that had been stored in the memory would be stored separately as a highlight segment. This study showed that this YOLO-trained model was more than 70% accurate in extracting highlight segments from all five videos that we tested.
Comparative Analysis of Manipulation Skills of Experts and Non-experts in Cell Culture Using VR Gloves

Japan's pharmaceutical industry currently occupies the top position globally, but violations of Good Manufacturing Practice (GMP) standards during the manufacture of pharmaceuticals continue to occur. Among efforts to develop GMP education, the use of VR in technical education has attracted considerable. However, there is a problem that there are no established guidelines for teaching the specific techniques and points of caution of experts to non-experts with regard to hand movements. Therefore, this study conducted a comparative analysis of experts' and non-experts' hand movements in cell culture, a type of aseptic manipulation, using VR gloves. In this study, the absolute coordinates of the wrists, relative coordinates of each finger with respect to the wrist, and hand angles of experts and non-experts were obtained. Furthermore, a comparative analysis of hand movements was conducted using the data obtained to examine and critique the differences. The results revealed that the experts were able to perform the cell culture process in a shorter period, and differed from the non-experts with respect to the speed of sucking up and sweeping out the culture medium, the way they held the cell culture flask, and the way they held the lid of the container. The experts paid particular attention to the speed at which the medium was spat out. It is hoped that the characteristics of experts obtained in this study can be incorporated into education and training to enable efficient technical education.
Analysis of Information Quality and Data Security in the KPU (General Elections Commission) SIDALIH (Voter Data Information System) Application

The purpose of this study was to determine the quality of services provided and data security in the use of the KPU's SIDALIH (Voter Data Information System) application. This research uses qualitative methods by analyzing online news media. The news is collected using the NCapture feature and then the Nvivo 12 plus software is used to manage and analyze the data. The results show that the accurate indicator in the SIDALIH application service has the highest percentage of 33.33%, then the relevant indicator with a percentage of 29.17%, the completeness indicator with a percentage of 22.92%, and the timeliness indicator with a percentage of 14.58%. Infrastructure indicators have a percentage of 53.33% and management indicators have a percentage of 46.67%. The SIDALIH application can provide accurate information, but the delivery of information on the SIDALIH application takes time to be accessed. Security infrastructure needs to be improved so that it is not easy to be hacked and it is necessary to establish guidelines, rules, and task forces that play a role in tackling information technology disasters or data leaks. The limitation of this research is that the data obtained is only from online news media, so it still needs to be explored further regarding its truth and legitimacy. The findings in this study can be used as a reference for improving the quality of information and guaranteeing the security of user data in the future.
Analysis of Annotation Quality of Human Activities Using Knowledge Graphs

Human activity data from cameras and sensors have potential applications in diverse domains. However, annotation quality varies, and label inconsistency remains a challenge. Annotators' different interpretations also cause other issues. In this paper, we proposed an approach for analyzing the annotation quality for videos of human activities focusing on annotation issues. Specifically, we annotated the same videos using different strategies: (1) fine temporal granularity using primitive action ontology vocabularies and (2) coarse temporal granularity focusing on meaningful action sequences. We then constructed knowledge graphs based on the annotated data and analyzed the relationship between annotation types and noise label tendencies. The results of this study could potentially support the construction of high-quality annotated datasets necessary for understanding human activities in various scenes, from daily life to service operations.
Motion as a Determinant of Presence in Immersive Virtual Reality

Presence, defined as the "sense of being there", is suggested to be the main facilitator of success in immersive virtual reality (IVR) interventions. While system factors (e.g., refresh rate, resolution, degrees of freedom) have been at the forefront of IVR and presence research, less attention is paid to how individual behaviors in an IVR experience may affect the degree of presence achievable. These differences in behavior may influence users' experience, independent of the IVR hardware or software. With that, this study investigated if a user's motion in an IVR experience, specifically user-initiated motion (UIM), could predict their self-reported presence scores. Eighty younger adults (22 male, 57 female, 1 non-binary) completed a 10-min IVR experience in Vesper Peak on the HTC Vive where they were asked to explore the environment. Motion data were collected via the pyopenvr package in which telemetry data of the headset were recorded. UIM was then calculated by first calculating the distance between two of these data points and then averaging the distance across each pair of points for the entire dataset. Presence was collected via the IGroup Presence Questionnaire (Cronbach's α = 0.87). 5 participants' data were removed due to corruption. A regression model was used to analyze UIM and presence scores. The overall model was significant (R2 = 0.147, RSE = 5.561, F(1,73) = 12.61, p < 0.001), demonstrating that UIM could significantly predict presence. These results suggest that a user's behavior may influence feelings of presence.
A Hospital MR Navigation Design for Elderly People’s Medical Needs

As China has a large population with a large elderly group, in first-tier cities with a concentrated population, medical resources do not match the population. In particular, the average daily outpatient reception is large in tertiary hospitals. Taking a hospital in Beijing as an example, the average daily outpatient reception is about 10,400 person-times. The complex medical environment and various procedures also lead to patients need to constantly look for the next target department for a long time. Even if there are a few volunteers in the lobby to guide, the medical treatment process is still a challenge for elderly patients. The guidance of hospital departments is generally based on conventional text signs and some hospitals also try to use the indoor positioning and navigation system of smart phones to give instructions, but the difficulties of unaccompanied elderly people in smart phone applications are not taken into account. Mixed Reality (MR) holds the promise of presenting navigational instructions directly into the patient's field of vision, creating a humanoid sign of a virtual guide in the patient's vision, thus achieving the same effect as a human guide. From the beginning of entering the hospital, the virtual guide sign will automatically plan the medical route for the patient, which can save the time for the elderly to ask the medical staff for directions and understand the guide sign and the medical treatment process; moreover, the sense of companionship generated by the virtual humanoid sign based on Mixed Reality (MR) can alleviate the anxiety of the elderly patients when they seek medical treatment to some extent. Although there are still many problems to be discussed in the specific application and practical operation of the technology, the virtual humanoid guide sign with the nature of companion can still become an important design direction of the future hospital navigation system.
Virtual Construction Simulation: Evaluating Impact of Immersion and Interactivity on Novice Designers

Typical curricula in architecture include history and theory, design studios, structures, and building construction courses. While studio classes mainly use active forms of learning, building construction courses often rely on passive techniques . Active forms of teaching with greater student interaction with learning content have shown better learning outcomes and more inclusive toward those on a different learning spectrum. Thus, creating meaningful hands-on experiences for students to learn building construction is crucial. Drawing on theories related to media psychology, human-computer interaction, and architectural education, this study aims to understand the impact of immersion and interactivity on the learning experience and knowledge gained by building construction students. The study was designed as a full-factorial, 2 (high vs. low immersion) × 2 (high vs. low interactivity) experiment. Participants were randomly assigned to one of four groups and learned how to assemble a bus shelter in virtual reality. The learning experience was measured through questionnaires that assessed their spatial presence, attention, engagement, and knowledge gain to increase accuracy. Their knowledge gain was measured through a questionnaire that included cued and free recall tasks, labeling of building components, identifying correct connections between components, and a detailed drawing task. The study’s results provide insights into the relative contributions of various technology affordances and how they impact learning experiences and outcomes. The authors believe that understanding these nuances will allow for the customization of technology affordances to achieve desired learning outcomes, thus making virtual reality a more effective tool for teaching building construction.
High-Quality Synthetic Character Image Extraction via Distortion Recognition

Digital avatars have become indispensable in the digital age. In Japan, virtual characters using illustration-style avatars have gained popularity and is generating large economic impact. However, the creation of such avatars is a time-consuming and costly process that requires a great deal of expertise. To support avatar creation, research of automatic generation of character design and textures for 3D models have emerged. However, deep learning-based generative models sometimes synthesize corrupted outputs. Methods to detect collapsed outputs from a generative model has not been explored, and users of the generator needs to manually exclude such outputs. In this paper, we propose a method to extract high-quality images from a set of synthetic illustrations, generated by a deep learning model, based on the degree of distortion of the images. As it is difficult to prepare real-world distorted images to train a distortion recognition model, we propose a simple procedure to create pseudo-distorted images. Experimental results showed superior results of the proposed method in distinguishing between human-drawn images and generated images, compared to baseline methods. Furthermore, we sorted the generated images using the confidence level of the trained distortion detection model, and qualitatively confirmed that the proposed method produces results closer to human perception.
Inductive Model Using Abstract Meaning Representation for Text Classification via Graph Neural Networks

Text classification is a fundamental task in NLP. Recently, graph neural networks (GNN) have been applied to this field. GNN-based methods sufficiently capture text structures well and improve the performance of text classification. However, previous work could not accurately capture non-consecutive and long-distance semantics in individual documents. To address this issue, we propose in this study an ensemble model comprising two aspects: a model for capturing non-consecutive and long-distance semantics and another model for capturing local word sequence semantics. To capture each of the semantics, we use abstract meaning representation (AMR) graphs for the relations between entities and another set of graphs based on the fixed-size sliding window for capturing local word sequence semantics. Furthermore, we propose a learning method that considers the edge features for AMR graphs. Extensive experiments on benchmark datasets are conducted, and the results illustrate the effectiveness of our proposed methods and AMR graphs.