Reviewerly/exHarmony huggingface dataset:
Preliminary Experiments in Hybrid Moving Images

We describe our preliminary experiments in hybrid moving images. A hybrid image is generated by synthesizing two different still images into one in such a way that the interpretation can change depending on the viewing distance of either of these two images. However, existing works on hybrid images only apply this technique to still images, and the application to moving images is still largely unexplored. Thus, investigating hybrid moving images has the potential to contribute to both research and artistic practices. We have developed software to create video clips of hybrid images and have performed a preliminary investigation into hybrid moving images. The results suggest that hybrid moving images still retain the characteristics of hybrid images, but the synthesis of a moving image and a still image may likely extend the range over which the synthesized moving image can still be perceived. On the other hand, the synthesis of two moving images seems to narrow the possible viewing distances for both synthesized images. Faster movement may also improve visibility.
The Art of Artificial Intelligent Generated Content for Mobile Photography

Mobile photography is the most commonly used way for people to record images of their lives and work. As AI technology advances, specifically in neural style transfer and large-scale multi-modal models, AIGC (Artificial Intelligent Generated Content) provides a new possibility for the evolution of mobile photography. Through analyzing the habits of ordinary people and professional photographers in taking and editing mobile photos, we investigated how AI can help to turn professionals' best practice into standard assistance for ordinary people, effectively improving the quality and efficiency. Through analyzing the visual elements and structure of AIGC, we found how to decompose and reconstruct a style in the aspects of form, color and detail such as line and texture. We found how to fuse AIGC and a photo by blending stylized images and the original photo in different ways for different themes. These specific methods can be summarized into corresponding replicable models and processes, rather than artistically inspired creations, making the automatic implementation possible.
Designing a Robot for Enhancing Attention of Office Workers with the Heavily Use of Screen

In the context of the epidemic and the Internet era, the computer screen has become a necessary device for people in the workplace. According to statistics, office workers face the computer for eight hours a day on average. Prolonged gaze at the screen has been shown to be one of the causes of reduced productivity, in addition to workplace loneliness, distraction, lack of motivation, and lack of work planning are the current state of low productivity. Existing work has shown that companion robotics has achieved remarkable success in helping humans and improving human well-being. In this paper, we design a work companion robot to improve worker productivity in the work office in three dimensions: mental, physical, and mental health. To support our study, we conducted a survey of daily computer office workers to understand where the problems lie and to gather information about the possible use of robots to support workers. Then, we proposed the concept of a companion robot to alert workers and suggest next actions by monitoring their pupil status, sight tracking, and blink frequency to determine their concentration and fatigue. To convert between pupil state and concentration level, we collected attention-related eye movement data under task state from a group of computer workers based on the Stroop paradigm and classified and quantified the concentration status using a random forest regression model. Finally, we designed the appearance of the robot, its internal structure, and its interaction. Future work will focus on experimentally verifying the effectiveness of this robot in improving workers’ attention and work isolation, and refining more features.
Impact of Synchronous and Asynchronous Learning Approach: A Comparative Study of Graphic Design Students in a Developing Country (Botswana) Verses a Developed Country (UK)

The move to synchronous and asynchronous learning occurred during the extraordinary disruption of the Covid-19 pandemic. This case study, conducted by a self-completion questionnaire, aimed to provide a dual-perspective and comparative picture of the situation by investigating the impact of this new learning approach on graphic design students in a developing country (Botswana) verses a developed country (UK). This was done by comparing how teaching and learning were affected, how education stakeholders responded to the educational disruption, and how the students felt about it within the University of Botswana (Botswana) and the University of Huddersfield (UK). Whilst research currently exists on teaching problems in developing and developed countries, they do not focus specifically on graphic design students in an internationally comparable way. A future benefit to this report, that would require further investigation and collaboration, is that academics who shift from one country to another could develop a framework to enable that shift to be seamless. This report presents data collected from two different universities in two different countries covering topics like previous experience with learning online, preferences with learning online, benefits, hindrances and how to address queries. Whilst most of the students demonstrated a positive attitude towards online classes during the pandemic, with both country’s liking the flexible and convenient way to study, only students from Botswana would be interested in staying online. This study further emphasizes that close monitoring and evaluation of the learning process is still required regardless of whether the programme has a synchronous and/or asynchronous learning approach.
Usability Study of a Pilot Database Interface for Consulting Open Educational Resources in the Context of the ENCORE Project

Open educational resources (OER) are materials such as textbooks, lessons, and other teaching and learning tools that are freely accessible for use. OER are gaining popularity as a means for educators to give their students access to high-quality, economical educational materials. OER can encourage sharing information and resources throughout the educational community while also helping lower the cost of education for both students and teachers. In this context, the ENCORE project seeks, among other goals, to assist students and workers in acquiring the skills necessary to deal with economic, ecological, and technological challenges as well as to address the skills gap between the supply of educational institutions and the demand of employers and assist educators in staying abreast of the constantly changing landscape of skills. One of the first steps to reach the project's goals is to build a robust database that contains quality OERs linked to green, digital, and entrepreneurial (GDE) skills. A graphical interface has been developed to retrieve and display information about the OERs, and, in turn, to make these resources available for any stakeholder. However, due to the significant quantity of information, it is important to develop an interface that enhances user experience. This work presents a usability study of the ENCORE project's OER database interface carried out through a System Usability Scale (SUS) questionnaire, as well as future interface improvements based on the results.
Empowering Creativity Through Children’s Assistive Design Workshops with Orthorigami

The tactile nature of smart materials, coupled with a maker approach and fabrication technology, is considered to empower children in creative design. We describe how children’s creativity was fostered through designing customized assistive devices using Orthorigami, made out of Shape Memory Polymer (SMP) using folding techniques [24]. Three day participatory making workshops are conducted with children ages 9-12 with a set of activities including: idea brainstorming, sketching, sculpting, laser-cutting, and making e-textiles. The study outlines what types of fabrication and crafting technology make the hands-on nature of SMP accessible to children for design of DIY Assistive Technology (AT). It also outlines, how using SMP fosters creativity and collaboration, and enables children to feel confident in their design capabilities.
Expert perspectives on how educational technology may support autonomous learning for remote out-of-school children in low-income contexts

Across Sub-Saharan African, 98 million children are illiterate and innumerate and do not attend school. Educational technologies (EdTech) that promote autonomous learning may ameliorate this learning poverty. Yet, little is known if or how these technologies can be implemented effectively within communities to support out-of-school children to learn basic literacy and numeracy skills. To address this knowledge gap, we explored expert perspectives of the perceived impact and challenges of implementing a unique large-scale EdTech learning competition conducted by the XPRIZE Foundation in 172 remote villages in Tanzania with 2500 out-of-school children. A qualitative expert elicitation was conducted with 14 key informants of the competition, using semi-structured interviews administered online over a 7-month period. Reflexive thematic analysis was used to analyse the data. Four key themes were generated: ‘Technology as a novel concept’, ‘Children don't learn in a vacuum’, ‘Respecting the cultural context’ and ‘Accessibility problems in a mobile world’. Results demonstrated considerable community support throughout the competition, leading us to question the extent to which children can learn autonomously with EdTech alone. This study revealed communities are critical partners for the successful deployment of EdTech directly to communities in low-income settings, which has implications for organisations addressing the global learning crisis.
Detecting Stress in VR $$360^{\circ }$$ Immersive Experiences for Older Adults Through Eye Tracking and Psychophysiological Signals

This paper presents the architecture and a preliminary prototype of a system for detecting stress during immersive virtual reality (VR) simulations. The VR4AGE system is designed for professionals that wish to use virtual reality headsets to allow older adults with dementia to experience 360 videos with an increased safety. The system aims at using eye tracking and unobtrusive psychophysiological signal monitoring to detect cognitive activation and distress. Informing as soon as possible the healthcare professionals about the patient’s status would allow them to continue or stop the therapy, depending on the patient’s reactions. This could help assessing whether nursing home residents with dementia that are unable to speak and express their emotions react positively or negatively to the VR simulation.
Effects of Different Postures on User Experience in Virtual Reality

Virtual reality (VR) applications are normally developed around the assumed body posture that maximizes the immersive experience. Users can, however, assume less optimal postures for reasons such as tiredness or body restrictions. We believe that accommodating such situations would increase the appeal and spread of VR. We investigated the difference in VR user experience based on body posture by conducting a preliminary study and a user study. Our preliminary study with frequent VR users found that they adopt several postures and prefer to experience VR by sitting first, standing second, lying down third, then other postures. The most popular applications were VRChat, Beat Saber, then others. In our user study, the participants engaged in VR while adopting three different postures: standing upright, sitting on a chair, and lying down on a bean bag. Three VR applications were tested: Beat Saber, VRChat, and Youtube VR. The three postures yielded similar performance with Beat Saber. Lying down offered the slowest performance with VRChat. One reason for this is the difficulty of interacting with in-game objects in VRChat while lying down. Our research shows the importance of VR application design as the developers must take into consideration extra factors. We suggest several solutions, such as having an adjustable virtual ground to accommodate the user's height and posture in real life or designing a user interface such that users can interact with their VR environment no matter their posture.
Research on the Development of 3D Virtual Reality Fire Hazard Factor Identification Training System

In recent years, numerous fire incidents in nursing homes and elderly welfare institutions in Taiwan have resulted in severe injuries and significant financial losses. To address this issue, effective emergency response training is crucial. With the advancement of virtual reality technology, this innovative approach has been adopted in various industries and fields, demonstrating a no table improvement in training efficacy. This project aims to address this issue by developing a virtual reality-based fire hazard identification training system for nursing homes and elderly welfare institutions in Kaohsiung, Taiwan, in accordance with the Fire Bureau of the Kaohsiung City Government. The system considers common fire hazards and deficiencies frequently observed in small nursing homes and elderly welfare institutions, and is developed through three stages: (1) analysis of potential fire hazard factors, (2) experience content and interface design, and (3) 3D virtual reality integration design. The application is intended for use in institution hazard recognition training to enhance safety awareness and knowledge.
One Size Does Not Fit All:

Office robots can be a solution to the shortage of skilled workers in certain areas. They perform tasks automatically and work around the clock. Examples of tasks performed by these robots include data processing, clerical work, and administrative tasks. We propose five types of robot users based on interviews after real-life use cases of an office robot. We investigate these types in an online study that shows relevant patterns associated with each type and first indications of type distribution. By using these individual robot user types, organizations can tailor robot implementation to their workforce and create ideal human-robot interactions in the workplace.
Design of an AR Application to Support Students with CVD in Learning Chemistry

Color Vision Deficiency (CVD) is a common visual impairment. Research shows that CVD affects studying and life by facing difficulties in color distinction. However, it often relies on people having CVD to self-adjust or find alternative tools when resolving the problem or providing general color distinguishing support without considering the need for reading and learning in a specific subject. Therefore, this research dives into using augmented reality(AR), particularly its synchronous integration feature, to develop color vision support in science learning and design a system to support CVD students in high school chemistry reading. This research first reviewed existing technological approaches to support color vision, and the national curriculum of chemistry in secondary education. An exemplary lesson plan with the selected topic of the universal indicator was developed accordingly to test the AR color vision support with a texture-based approach. 8 high school students joined the user testing to assess the interface and flow of the prototype.
COMPREHENSIVE STUDY OF DEEP LEARNING BASED TELUGU OCR

The aim of the project is to understand offline One of the most popular and difficult pattern recognition subjects is the use of optical character recognition (OCR) to read handwritten Telugu letters. This study suggests a three-stage OCR solution for Telugu documents that includes pre-processing, feature extraction, and classification. For the extraction of boundary edge pixel points during preprocessing, we used median filtering on the input characters as well as normalisation and skeletonization techniques. Each character is initially divided into three 3x3 grids during the feature extraction stage, and the associated centroid for each of the nine zones is assessed. This allows us to recognise characters in various styles. Following that, we drew the projection angel's horizontal and vertical symmetry to the character's closest pixel.
A Case Study in Organizational Adoption of User Personas: Assessing Perceptions, Awareness, and Hesitation to Adopt

In a prior study, our team created research-based personas through an iterative, mixed-methods approach; however, as a B2B SaaS (business-to-business, software as a service) company, we faced unique challenges not only when generating those personas but evangelizing them throughout our organization. In examining the existing literature on persona building, we notice a distinct gap in reflecting on the organizational buy-in of these personas after their development. Much of the research in this area emphasizes the “how” of persona building, without exploring their ultimate impact, utility, and effectiveness in an actual organization. Therefore, the current study will explore the aftermath of persona generation, assessing adoption within our organization. To this end, we implemented a survey to assess employee perceptions of our user personas a year after their development, both within and outside of the product team. Measures within the survey assessed persona perceptions [10], awareness, and perceived utility. Additional open ended questions asked employees about any hesitations they have regarding our user personas that might lead them to not adopt them, opportunities for educating the broader organization on the existence of and utility of these personas, and how employees are using personas to influence decisions and communicate today. Analysis of these open ended questions helped define future directions for our personas, and additional education to facilitate organizational adoption of personas and effective communication about our users.
Methodology and Design for Assessing the Normative Exercise Posture of Young Females in Media-Home Fitness with Motion Capture Technology

The pandemic has altered some females' perspectives on fitness, home settings have become a more popular exercise venue than gyms, particularly among young females. In this study, through a survey of 160 young females, it was found that most prefer to engage in low to moderate intensity exercises during home fitness. It is noteworthy that 86% of young females expressed that playing media content during exercise would distract their attention, leading to difficulty in maintaining proper form throughout the workout. This study aims to optimize the home exercise experience for young females and reduce the risk of physical injury caused by improper exercise posture resulting from excessive immersion in media playback content. During testing, six young female participants were recorded using the AI motion capture tool "Plask" while performing an experimental task (rotating hula hoops) with and without media playback, measuring their conscious state and body posture. The experimental results demonstrate that the use of the AI motion capture tool can effectively obtain the movement parameters and limb data of participants from different perspectives. This helps health experts better analyze participants' exercise perception behavior and identify issues in young females' home exercise routines. Based on the evaluation results, a set of exercise reminder programs has been designed in this study to assist young females in maintaining exercise regularity through periodic reminders. Through usability testing, it has been demonstrated that this design can effectively enhance the positive effects of home fitness for young females.
Chatbot to Facilitate Opinion Formation in Web Search

In this study, we proposed a Chatbot that prompts users who generate opinions while conducting web searches on a particular topic with questions to encourage a deeper understanding of the topic. The proposed Chatbot(called Questioning Bot) is embedded in a web search engine. The Questioning Bot suggests a question to promote reflection on a particular aspect when it determines that a user is searching the web for a specific aspect among the various aspects required necessary for forming an opinion. The system suggests questions about aspects where web search is lacking and questions to promote a deeper understanding of the web-searched aspects where more research on opinion formation is necessary. Based on a deeper understanding of this theme, the system supports opinion formation by suggesting questions according to the web search status.
The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks

Artificial intelligence (AI) outperforms humans in plentiful domains. Despite security and ethical concerns, AI is expected to provide crucial improvements on both personal and societal levels. However, algorithm aversion is known to reduce the effectiveness of human-AI interaction and diminish the potential benefits of AI. In this paper, we built upon the Dual System Theory and investigate the effect of the AI response time on algorithm aversion for slow-thinking and fast-thinking tasks. To answer our research question, we conducted a 2 $$\,\times \,$$ 2 incentivized laboratory experiment with 116 students in an advice-taking setting. We manipulated the length of the AI response time (short vs. long) and the task type (fast-thinking vs. slow-thinking). Additional to these treatments, we varied the domain of the task. Our results demonstrate that long response times are associated with lower algorithm aversion, both when subjects think fast and slow. Moreover, when subjects were thinking fast, we found significant differences in algorithm aversion between the task domains.
Mixed data clustering based on a number of similar features

Finding the degree of similarity measurement is one of the challenges of mixed data clustering. In this article, it has been tried to design a more efficient method by innovating in three important parts of clustering. In the part of the general method, for assigning data objects to the cluster, in addition to the distance, attention is paid to the “number of similar features”. Compared to assigning each object to a cluster, in cases where the distances are equal or close, the cluster center with the highest number of features similar to the given objects will be appropriate. This method is more accurate than the Hamming distance. To determine the cluster centers, instead of random selection, a more suitable object is identified with a distance-based method. In accuracy in three datasets, the proposed algorithm has performed at least two percent better than the other algorithms.
Presumptive Reasoning in a Paraconsistent Setting

We explore presumptive reasoning in the paraconsistent case.Specifically, we provide semantics for non-trivial reasoning with presumptive arguments with contradictory assumptions or conclusions.We adapt the case models proposed by Verheij [25,26] and define the paraconsistent analogues of the three types of validity defined therein: coherent, presumptively valid, and conclusive ones.To formalise the reasoning, we define case models that use BD , an expansion of the Belnap-Dunn logic with the Baaz Delta operator.We also show how to recover presumptive reasoning in the original, classical context from our paraconsistent version of case models.Finally, we construct a two-layered logic over BD and biG (an expansion of Gödel logic with a coimplication or ) and obtain a faithful translation of presumptive arguments into formulas.