Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Real-Life Test for Reciprocity in Autistic Adults: The Interactive Drawing Test

Impaired social-emotional reciprocity is a defining feature of Autism Spectrum Disorder (ASD). Deficits in reciprocity can be difficult to assess, particularly in adults with average or above average intelligence. The recently developed Interactive Drawing Test (IDT) measures reciprocity in children and adolescents with and without ASD based on spontaneous non-verbal interactions during the joint creation of a drawing. In this study, we examined if the IDT is able to differentiate between 19 normally intelligent adults with ASD and 18 without ASD. The IDT total reciprocity score, including the number of meaningful contributions to objects initially drawn by the examiner, was lower in the autistic adults compared to those without ASD. By assessing both the quantity and quality of spontaneous reciprocal behavior, the IDT was able to identify subtle differences in reciprocal behavior of adults with and without ASD with average intelligence. Even though a larger sample is required to determine its psychometric properties, the IDT appears a promising tool to enrich the diagnostic process of ASD in adults. Impaired social-emotional reciprocity is a defining feature of Autism Spectrum Disorder (ASD). The clinical field lacks sensitive tests for assessing impaired reciprocity. The recently developed Interactive Drawing Test (IDT) for reciprocity was tested in autistic and non-autistic adults. During the IDT, an examiner and participant make a joint drawing, taking turns, without specifying what they will draw. We aimed to investigated whether autistic adults showed less reciprocal behavior on the IDT compared to non-autistic participants. Autistic participants were less likely to jointly draw with the examiner, in particular when the examiner initiated a topic. The IDT revealed subtle but characteristic differences in reciprocal behavior related to ASD, suggesting it may be a promising diagnostic tool.
Factors Contributing to Persistent Frequent Attendance in Primary Care Among the Oldest Old: Longitudinal Evidence From the AgeCoDe-AgeQualiDe Study

Objective Since there is a lack of longitudinal studies in this area, our aim was to identify the determinants of persistent frequent attendance in primary care among the oldest old in Germany. Methods Longitudinal data (follow-up wave 7–9) were taken from the multicenter prospective cohort “Study on needs, health service use, costs, and health-related quality of life in a large sample of oldest-old primary care patients (85+)” (AgeQualiDe), covering primary care patients ≥ 85 years (FU7 n = 741, mean age 88.9 years (SD 2.9; 85–100)). Persistent frequent attenders of general practitioner (GP) services (the patients in the top decile of the number of GP consultations in two or more consecutive waves) were our main outcome of interest. Logistic random-effects models were used. Results Our analysis included 1,891 observations (766 individuals). Across three waves, we identified 56 persistent frequent attenders. Results of random-effects logistic regressions showed that the odds of being persistent frequent attender were higher for widowed individuals (OR = 4.57; 95% CI [1.07–19.45]). Moreover, a one-point increase in the frailty score and having one more chronic condition increased the odds of being a persistent frequent attender by 68% (OR =1.68; 95% CI [1.05–2.69]) and 23% (OR=1.23, 95% CI [1.05–1.44]), respectively. Conclusion Our study stressed the longitudinal association between frailty and widowhood as well as chronic diseases and persistent frequent attendance among the oldest old in Germany.
Chinese Primary School Students’ Peer Relationship and Chinese Language Scores: The Chain Mediation Effect of Parental Involvement and Sense of Autonomy

This study investigated the internal mechanism of the relationship between primary school students’ peer relationships and their performance in the Chinese language and literature. We constructed a chain mediation model, focused on the mediation effects of parental involvement and the sense of autonomy, on the correlation between peer relationships and performance in Chinese language scores. A questionnaire survey was conducted among 1,503 students in grades 4–6, and their parents, in three cities in Jiangsu Province. The result indicated the following: (1) there was a significant positive correlation between primary school students’ peer relationships, parental involvement, sense of autonomy, and the level of Chinese language scores; (2) parental involvement and the sense of autonomy, respectively, mediate the relationship between peer relationships and Chinese language scores; (3) primary school students’ parental involvement and sense of autonomy play a chain-like mediating role in the relationship between their peer relationships and Chinese language scores. The research results provide a reference for exploring the educational strategies of primary school students’ Chinese literacy.
Artificial Intelligence Advancements in the Cardiovascular Imaging of Coronary Atherosclerosis

Coronary artery disease is a leading cause of death worldwide. There has been a myriad of advancements in the field of cardiovascular imaging to aid in diagnosis, treatment, and prevention of coronary artery disease. The application of artificial intelligence in medicine, particularly in cardiovascular medicine has erupted in the past decade. This article serves to highlight the highest yield articles within cardiovascular imaging with an emphasis on coronary CT angiography methods for % stenosis evaluation and atherosclerosis quantification for the general cardiologist. The paper finally discusses the evolving paradigm of implementation of artificial intelligence in real world practice.
Bullying, Mental Health, and the Moderating Role of Supportive Adults: A Cross-National Analysis of Adolescents in 45 Countries

Objectives: Relationships with supportive adults during adolescence may be a protective factor that lowers the risks associated with bullying. The current study aimed to examine the moderating role of supportive adults in the associations between bullying involvement (in-person and cyber) and mental health problems (psychological symptoms and low life satisfaction). Methods: Data from 45 countries and regions taking part in the 2017/18 Health Behaviour in School-Aged Children study (N = 230,757) were used. Multivariable Poisson regression models were used to estimate relative risks of bullying on mental health. Effect estimates were compared across the number of supportive adults to examine a possible cumulative protective effect of relationships with supportive adults. Results: Bullying involvement was consistently associated with poor mental health across the 45 countries. Risk of mental health problems associated with bullying involvement was greatest among students reporting relationships with multiple supportive adults. This was true for all indicators of bullying involvement. Conclusion: Bullying remains a prevalent and harmful experience for youth worldwide. Merely having supportive adults is not sufficient in protecting youth from experiencing the mental health risks associated with bullying.
A CFD Study of the Aerodynamic Characteristics of Twardowsky and FOK Rockets

Abstract Stability and performance are crucial characteristics for aerospace vehicles. The ability to investigate the aerodynamics and performance of rockets gives an insight into their stability before flight and the potential for design and performance enhancements. For the past 13 years, the rocketry Division within the students’ space Association of Warsaw University of technology has been developing sounding rockets of different designs and mission profiles. Two rockets have been chosen for the CFD (Computational Fluid Dynamics) campaigns, FOK and Twardowsky. This paper describes the mathematical model of aerodynamic loads used by the Division for sounding rocket simulation, followed by CFD campaigns for the two rockets. The results of the CFD analysis are then used to calculate the rockets’ aerodynamic derivatives according to a previously defined mathematical model.
Agenda- and Activity-Based Triggers for Microlearning

The ubiquity of mobile devices has fueled the popularity of microlearning, namely informal self-directed learning during brief personal downtime. However, learner engagement is challenging to maintain, and microlearning habits are hard to establish. Scheduled reminders are ineffective as they do not match the users’ variable schedules and their intention or capacity to engage. In this paper, we propose a schedule-based and an activity-based trigger for microlearning. The first trigger is sensitive to the learners’ agenda and device status and includes a snooze mechanism. A four-week study (n=10) showed slightly lower response times when compared to triggers scheduled at a fixed time but did not improve learner engagement. The second trigger initiates audio-based microlearning when plugging in headphones. Thus, we minimize the access to personal data and capture a moment where learners engage with their device for a listening activity. In an exploratory user study (n=10), the plugin trigger achieved high compliance rates and was less likely to induce annoyance in users than lock screen notifications. We conclude that intelligent reminders with simple interaction options can contribute to learner engagement.
Study Cybersecurity of Cyber Physical System in the Virtual Environment: A Survey and New Direction

The Industry 4.0 is defined as connecting the physical system to the cyber world with the help of transformative technologies. As a result, the number of cyber-attacks have grown exponentially. Cybersecurity has been identified as one of main challenges in applying the transformative technologies of Industry 4.0. An industrial system within the context of Industry 4.0 generally, a cyber physical system (CPS), is incredibly complex and involves heterogeneous devices. How to detect the attacks and analyse the vulnerability of the system are actively explored and researched. However, because of the high cost and difficulty to access real industrial CPS, it is difficult for researchers to get opportunities to use real industrial systems in the research. For this reason, a variety of software tools and virtualization methods have been used to create virtual or hybrid systems for researchers. This paper study and review existing cybersecurity research on CPS in the virtual environment. Then authors point out four challenges in the state-of-the-art research based on the findings and discussion. From this, Digital Twin (DT), one of the main enabling technologies in Industry 4.0, has been identified to have the great potential in solving these challenges. DT sheds light on the real fusion of the cyber world and the physical world, and it has the potential to be used as a technology to help people build virtulized system with high-fidelity, hence greatly lower bar on the cybersecurity research of the cyber physical system and create other DT based cybersecurity applications.
How Do People Rank Multiple Mutant Agents?

Faced with several AI-powered sequential decision-making systems, how might someone choose on which to rely? For example, imagine car buyer Blair shopping for a self-driving car, or developer Dillon trying to choose an appropriate ML model to use in their application. Their first choice might be infeasible (i.e., too expensive in money or execution time), so they may need to select their second or third choice. To address this question, this paper presents: 1) Explanation Resolution, a quantifiable direct measurement concept; 2) a new XAI empirical task to measure explanations: “the Ranking Task”; and 3) a new strategy for inducing controllable agent variations—Mutant Agent Generation. In support of those main contributions, it also presents 4) novel explanations for sequential decision-making agents; 5) an adaptation to the AAR/AI assessment process; and 6) a qualitative study around these devices with 10 participants to investigate how they performed the Ranking Task on our mutant agents, using our explanations, and structured by AAR/AI. From an XAI researcher perspective, just as mutation testing can be applied to any code, mutant agent generation can be applied to essentially any neural network for which one wants to evaluate an assessment process or explanation type. As to an XAI user’s perspective, the participants ranked the agents well overall, but showed the importance of high explanation resolution for close differences between agents. The participants also revealed the importance of supporting a wide diversity of explanation diets and agent “test selection” strategies.
Contextualization and Exploration of Local Feature Importance Explanations to Improve Understanding and Satisfaction of Non-Expert Users

The increasing usage of complex Machine Learning models for decision-making has raised interest in explainable artificial intelligence (XAI). In this work, we focus on the effects of providing accessible and useful explanations to non-expert users. More specifically, we propose generic XAI design principles for contextualizing and allowing the exploration of explanations based on local feature importance. To evaluate the effectiveness of these principles for improving users’ objective understanding and satisfaction, we conduct a controlled user study with 80 participants using 4 different versions of our XAI system, in the context of an insurance scenario. Our results show that the contextualization principles we propose significantly improve user’s satisfaction and is close to have a significant impact on user’s objective understanding. They also show that the exploration principles we propose improve user’s satisfaction. On the other hand, the interaction of these principles does not appear to bring improvement on both dimensions of users’ understanding.
Building Trust in Interactive Machine Learning via User Contributed Interpretable Rules

Machine learning technologies are increasingly being applied in many different domains in the real world. As autonomous machines and black-box algorithms begin making decisions previously entrusted to humans, great academic and public interest has been spurred to provide explanations that allow users to understand the decision-making process of the machine learning model. Besides explanations, Interactive Machine Learning (IML) seeks to leverage user feedback to iterate on an ML solution to correct errors and align decisions with those of the users. Despite the rise in explainable AI (XAI) and Interactive Machine Learning (IML) research, the links between interactivity, explanations, and trust have not been comprehensively studied in the machine learning literature. Thus, in this study, we develop and evaluate an explanation-driven interactive machine learning (XIML) system with the Tic-Tac-Toe game as a use case to understand how a XIML mechanism improves users’ satisfaction with the machine learning system. We explore different modalities to support user feedback through visual or rules-based corrections. Our online user study (n = 199) supports the hypothesis that allowing interactivity within this XIML system causes participants to be more satisfied with the system, while visual explanations play a less prominent (and somewhat unexpected) role. Finally, we leverage a user-centric evaluation framework to create a comprehensive structural model to clarify how subjective system aspects, which represent participants’ perceptions of the implemented interaction and visualization mechanisms, mediate the influence of these mechanisms on the system’s user experience.
Determination of Propagation Characteristics of Crucial Planar Wave Guide Structures by Finite Difference Method

Abstract Based on finite difference (FD) method, recently applied to investigate propagation characteristics of optical waveguides, we verify its applicability to predict the electric field and propagation parameter of planar waveguide for the entire range of normalized frequencies. As a test case, we crosscheck our FD result for symmetric step index planar waveguide and then predict the propagation characteristics of crucial planar waveguide structures like exponential index and Epstein layer model profile. Our findings show high degree of accuracy when compared with published results.
The ”Artificial” Colleague: Evaluation of Work Satisfaction in Collaboration with Non-human Coworkers

The advance of “artificial intelligence”-(AI)-based technologies has the potential to transform work tremendously. Work is a major part of life and, thus, its meaningfulness or lack thereof will impact overall well-being. Previous research investigated human-AI collaboration at work mostly with a focus on performance. However, little attention is given to how collaboration with AI influences the meaningfulness of work and job satisfaction. In this paper, we present an online experiment to compare the perception of meaningfulness and relationship to the collaborator across different task distributions and collaborators (human/AI). Our results show that working with a human is more motivating and meaningful compared to working with an AI independent of the task. Moreover, the AI is more often viewed as a subordinate, while the human is perceived as a teammate. These results provide preliminary implications for the design of collaboration with AI that consider job satisfaction.
Predictive risk modelling in mental health issues using machine learning on graphs

Mental health is an essential part of overall health and wellbeing. Alcohol abuse, dementia, depression, drug abuse and psychoses are common mental health issues that have been found to have to impact on an individual's physical health. This study proposed a risk prediction model based on administrative data that assesses the risk of mental health issues using a machine learning on graphs technique. For this purpose, a patient network is built from two cohorts (i.e., patients with mental health issues and patients without mental health issues) using the bipartite graph projection technique. We then use graph neural network (GNN) models and test various edge types (i.e., simple weighting, Jaccard index and overlap weighting) to predict the risk of mental health issues. The GNN models show outstanding performance compared to baseline models, and the accuracy ranged from 85.08% to 90.59%. Moreover, the pattern for the two cohorts can be demonstrated by visualising the last hidden layers of GNN models using the principal component analysis technique, confirming the discriminative strength of the GNN models. The proposed risk prediction framework could help medical practitioners and other stakeholders build health management strategies for people at high risk of acquiring mental health issues.
How do Conversational Agents Transform Qualitative Interviews? Exploration and Support of Researchers’ Needs in Interviews at Scale

In recent years, conversational agents (CAs) have been receiving more attention as tools for collecting data through qualitative interviews. The problem is we know little about how CAs affect both the interviewees and interviewers. This PhD project is dedicated to studying how to evaluate CA-mediated interviews and their effects on participants (both interviewees and interviewers). The findings of this project will allow us to support the interview practitioners with the tools for interview analytics and interview data analysis. It will be especially helpful in the large-scale settings which CA-mediated interviews enable. This proposal describes State-of-the-art on the topic and presents the motivation of a study with key research questions to answer.
Emotion Recognition in Conversations Using Brain and Physiological Signals

Emotions are complicated psycho-physiological processes that are related to numerous external and internal changes in the body. They play an essential role in human-human interaction and can be important for human-machine interfaces. Automatically recognizing emotions in conversation could be applied in many application domains like health-care, education, social interactions, entertainment, and more. Facial expressions, speech, and body gestures are primary cues that have been widely used for recognizing emotions in conversation. However, these cues can be ineffective as they cannot reveal underlying emotions when people involuntarily or deliberately conceal their emotions. Researchers have shown that analyzing brain activity and physiological signals can lead to more reliable emotion recognition since they generally cannot be controlled. However, these body responses in emotional situations have been rarely explored in interactive tasks like conversations. This paper explores and discusses the performance and challenges of using brain activity and other physiological signals in recognizing emotions in a face-to-face conversation. We present an experimental setup for stimulating spontaneous emotions using a face-to-face conversation and creating a dataset of the brain and physiological activity. We then describe our analysis strategies for recognizing emotions using Electroencephalography (EEG), Photoplethysmography (PPG), and Galvanic Skin Response (GSR) signals in subject-dependent and subject-independent approaches. Finally, we describe new directions for future research in conversational emotion recognition and the limitations and challenges of our approach.
Language Models Can Generate Human-Like Self-Reports of Emotion

Computational interaction and user modeling is presently limited in the domain of emotions. We investigate a potential new approach to computational modeling of emotional response behavior, by using modern neural language models to generate synthetic self-report data, and evaluating the human-likeness of the results. More specifically, we generate responses to the PANAS questionnaire with four different variants of the recent GPT-3 model. Based on both data visualizations and multiple quantitative metrics, the human-likeness of the responses increases with model size, with the largest Davinci model variant generating the most human-like data.