The approach of evaluating the final scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to financial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on different data structures more comprehensively. Moreover, not only the final MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identified using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min–max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the “Maximum normalization-based fuzzy integrated CODAS procedure” was proposed to decision-makers to measure the economic performance of the countries.

Posterior reversible encephalopathy syndrome (PRES) and reversible cerebral vasoconstriction syndrome (RCVS) may cause ischaemic stroke and intracranial haemorrhage. The aim of our study was to assess the frequency of the afore‐mentioned outcomes.

<p><strong>Introduction.</strong> Digital literacy includes things like being able to use information systems and supporting infrastructure. With the increasing use of technology in healthcare, it is important for healthcare staff to be digitally literate. The aim of the paper is to determine the attitudes of primary and secondary health care workers towards the use of computers in health care and to examine the influence of sociodemographic factors on the information literacy of health care workers.&nbsp;<br /><strong>Methods.</strong> The research was conducted according to the principle of a cross-sectional study. The research included 80 respondents, healthcare workers. Data analysis included methods of descriptive and inferential statistics. The data will be presented in the form of a table.<br /><strong>Results.</strong> The results showed that certain socio-demographic factors influenced the attitude of health workers towards the use of computers. The most significant factors were the level of education and previous IT education, but the time the respondents sopent working on the computer and whether they used the computer exclusively at work or at home also had an impact.<br /><strong>Conclusion. </strong>Healthcare workers showed a positive attitude towards the use of computers in healthcare. The most significant socio-demographic factors influencing knowledge of computer work are the level of education of the respondents and whether and where they received their education in information technology.</p>

A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS‐disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first‐degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.

Robot designers commonly conceptualize robot sociality as a collection of features and capabilities. In contrast, sociologists define sociality as continuously constructed through interpersonal interactions. Based on the latter perspective, we trace how robots are incorporated into emerging social interaction ritual chains by robot companies and their staff and by robot owners across diverse contexts: homes, cafes, robot stores, user-organized meetups, and company events for robot users. Our empirical findings from ethnographic field work in Japan relating to three robots – aibo, RoboHon, and LOVOT – show how companies create positive interactions between people and robots by incorporating familiar design patterns into robots, modeling successful interactions in person and online, and bringing owners together in events that establish common values of acceptance of social robots as artifacts to live with and nurture. Owners, for their part, develop interaction rituals that include robots in their daily activities, make interpersonal connections, and experience emotionally resonant interactions, around robots in public meetups and events. Through these emerging interaction ritual chains, companies and owners construct the notion of robots as social agents to live with as a meaningful component of their emotional experiences and broader social relationships. Our work suggests that social robot design should consider this broader framing of sociality and create affordances for establishing interaction ritual chains more explicitly.CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; Field studies; Empirical studies in interaction design; Interaction design theory, concepts and paradigms.

Though teens are a population with growing agency and use of smart technologies, their concerns surrounding privacy with AI and robots are under-represented in research. Using focus group discussions and a mixed methods analysis, we present teens’ comfort levels with robotic information collection and sharing during three hypothetical scenarios involving a child interacting with the Haru social robot in the home. We find participant concerns align with an access-based definition of privacy which prioritizes being in control of their information and of when the robot behaves autonomously. Responses also indicate that teens conceptualize Haru not just as an intelligent device, but also as a social entity. Their shifts in comfort and discussions reflect an engagement in social relationship management with robots in the home in cases where the robot mediates a user’s responsibilities and relationships with others.CCS CONCEPTS• Security and privacy → Social aspects of security and privacy; • Human-centered computing → User studies; • Social and professional topics → Adolescents.

Socially assistive robots can be used as therapeutic technologies to address depression symptoms. Through three sets of workshops with individuals living with depression and clinicians, we developed design guidelines for a personalized therapeutic robot for adults living with depression. Building on the design of Therabot™, workshop participants discussed various aspects of the robot’s design, sensors, behaviors, and a robot connected mobile phone app. Similarities among participants and workshops included a preference for a soft textured exterior and natural colors and sounds. There were also differences - clinicians wanted the robot to be able to call for aid, while participants with depression differed in their degree of comfort in sharing data collected by the robot with clinicians.CCS CONCEPTS• Human-centered computing → HCI design and evaluation methods; • Social and professional topics → User characteristics.

We explore the integration of a visual and thermal camera to approximate physiological changes as markers of cognitive load and child's engagement with a robot. The aim of our data pipeline is to enable non-invasive engagement tracking for a desktop social robot developed by Honda Research Institute named Haru. From utilizing these two cameras we can recognize engagement during child-robot interactions (CRI) using changes in nose-tip temperature. We tested our algorithm on data collected while a child participant interacted with Haru during a passive activity as well as an active activity. Then, we did a preliminary modeling of engagement with Hidden Markov models. This paper describes our experimental setup, our data collection, multi-modal pipeline, and some preliminary results from modeling the data.

One of the questions human-robot interaction (HRI) research needs to address prior to in-home robot deployment is when optimal moments for everyday interaction might occur. These can vary based on robot users' existing routines and personal preferences. As part of a larger project to design a conversational robot that can assist older adults in recognizing and maintaining their ikigai (sense of meaning and purpose in life), we explored the question "when might be good times for the robot to engage older adults in activities?". 11 older adults who were familiar with our prototype robot from prior participation in our research took part in a two week-long "diary study" to identify their habits and preferred times of engagement with the robot. The diary was performed by sending text messages to the older adults twice daily, asking what they were doing at the moment and whether this was a suitable time for interacting with the robot. The findings of the study allowed us to determine optimal times for interaction with the robot' - commonly before and after lunch and before sleep. Insights from this approach contribute to designing robots that can be integrated into the daily lives of older adults.

This work introduces a survey questionnaire about adult perceptions of privacy, attitudes, and comfort with robots in different spaces in the home. Additionally, in the survey, adult comfort was considered with the preconception that children would share information with robots and other third parties. As for the structure of the survey, it includes likert-style questions, multiple choice, and open responses for qualitative explanations of participant comfort in different situations. In this paper, we give more details about the survey, preliminary qualitative results, and suggestions for further use. We hope this work brings light to the importance of studying privacy concerns in the home with all family stakeholders.

As robots become increasingly active around human environments, they must navigate both physical and social realms, necessitating awareness of their surroundings and inhabitants, including the potential collection of sensitive data. To be accepted in human spaces, they need to be trusted to handle personal information adequately, not only adhering to security and data protection standards, but also aligning with contextual norms, individual expectations, and domain-specific requirements. Challenges intensify when robots engage with multiple humans across varied contexts over extended periods. Drawing from psychology, sociology, ethics, and law, and the experience of participants, we seek to outline dimensions and prerequisites for privacy-awareness in HRI. In this workshop we want to discuss methodologies, user interfaces, and personalizing options, and AI reasoning to design privacy-aware robot behavior in the human-robot interaction community.

Participatory robot design projects with older adults often use multiple sessions to encourage design feedback and active participation from users. Prior projects have, however, not analyzed the learning outcomes for older adults across co-design sessions and how they support constructive design feedback and meaningful participation. To bridge this gap, we examined the learning outcomes within a "longitudinal panel." This panel comprised seven co-design sessions with 11 older adults of varying cognitive abilities over six months, aimed at designing a robot to guide a photograph-based conversational activity. Using Nelson and Stolterman’s framework of the hierarchy of design-learning, we demonstrate how older adult panelists achieved multiple design-learning outcomes – capacity, confidence, capability, competence, courage, and connection – which allowed them to provide actionable design suggestions. We provide guidelines for conducting longitudinal panels that can enhance user design-learning and participation in robot design.CCS CONCEPTS• Human-centered computing → Participatory design; User centered design; • Computer systems organization → Robotics; • General and reference → Design.

Social robots are being studied for a wide variety of user populations, such as older adults, but programming these social robots typically requires deep technical knowledge. In this study, we developed a no-code end-user robot programming interface, with the goal of our interface being to empower individuals with no programming background to easily create social robot interactions with older adults using natural language. We evaluated five individuals with connections to adults older than 65 without robot programming experience. They were tasked with designing a simple conversation with the robot. We recorded their experiences using a survey and found that participants successfully used the interface to make the robot communicate with older adults. Overall, the participants found the interface easy to use and enjoyed the process. Thus, we provide recommendations on how to improve no-code end-user robot programming interfaces further.