Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Modelling of Gene Expression States in Breast Cancer and their Prediction from Routine Whole Slide Images

Identification of gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from (WSIs) using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinically and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining significant biological insights from routine pathology imaging directly. Graphical Abstract Highlights Data-driven discovery of co-expressing gene groups in breast caner Histological imaging based prediction of gene groups via deep learning Identification of phenotypic correlates of gene-expression in histological imaging Clinical and therapeutic impact of gene groups and their visual patterns identified
‘Specially For You’ – Examining the Barnum Effect’s Influence on the Perceived Quality of System Recommendations

The ‘Barnum effect’ is a psychological phenomenon under which people assign higher quality ratings to personality descriptions developed ‘specially for you’ than the same descriptions described as ‘generally true of people. ’ This effect suggests that recommender interfaces could elevate the perceived quality of recommendations simply by indicating that they are explicitly personalised. We therefore conducted a crowd-sourced experiment (n=492) that examined the perceived quality of personalised versus non-personalised movie recommendations for good and bad movies – importantly, the actual recommendations were identical, and were merely presented as being either personalised or not. Contrary to the Barnum effect, results showed numerically lower mean quality scores for personalised recommendations, but with no significant difference. Our findings suggest that Barnum-like effects of personalisation have at most a small influence on perceived quality, and that designers should not rely on this effect to improve user experience (despite online design guidance suggesting the opposite).
How Data Analysts Use a Visualization Grammar in Practice

Visualization grammars, often based on the Grammar of Graphics (GoG), have much potential for augmenting data analysis in a programming environment. However, we do not know how analysts conceptualize grammar abstractions, or how a visualization grammar works with data analysis in practice. Therefore, we qualitatively analyzed how experienced analysts (N = 6) from TidyTuesday, a social data project, wrangled and visualized data using GoG-based ggplot2 without given tasks in R Markdown. Though participants’ analysis and customization needs could mismatch with GoG component design, their analysis processes aligned with the goal of GoG to expedite visualization iteration. We also found a feedback loop and tight coupling between visualization and data transformation code, explaining both participants’ productivity and their errors. From these results, we discuss how future visualization grammars can become more practical for analysts and how visualization grammar and analysis tools can better integrate within a programming (i.e., computational notebook) environment.
Anonymous Online Support: Investigations of Identity and Heterogeneous Groups in Online Recovery Support

How does anonymity impact perceptions of the effectiveness of support in online recovery communities? Prior work has shown how the multifaceted concept of identity plays an important role in support seeking, but its effects with respect to support providers are still unclear. I ground my investigation of interpretations of 12-Step anonymity in online recovery to demonstrate the effects of different facets of anonymity and 12-Step values on perceptions of supportive interactions using a controlled online experiment. I present a novel transposition of person-centered support and demonstrate its utility outside of dyadic interactions. Finally, I discuss implications for the design of mixed online health communities and implications for future emotional support and anonymity research.
The Effect of Explanation Design on User Perception of Smart Home Lighting Systems: A Mixed-method Investigation

It has been shown that providing explanations about AI-based systems’ decisions can be an effective way to increase users’ trust and acceptance. The effect of explanation design in smart home systems on users’ acceptance and perceptions is however less known. We therefore explored the effect of different explanation designs on acceptance in the context of the Philips Hue smart home lighting system. We conducted interviews (N = 10) and an online experiment (N = 452) using three everyday smart home lighting scenarios with different explanation types. The results showed that although participants indicated a positive attitude towards explanations, receiving an explanation can potentially reduce the perceived control of the lighting system. Furthermore, participants preferred system-based explanations rather than user-based explanations. Our study also provides recommendations for the design of explanations in smart home systems.
Hexad-12: Developing and Validating a Short Version of the Gamification User Types Hexad Scale

The Hexad scale is a crucial tool for personalized gamification in user experience (UX) design. However, completing a 24-item questionnaire can increase dropout rates and screen fatigue within online surveys. When included in larger surveys, scale brevity makes a difference. To reduce the time required for the assessment process, we developed and validated a 12-item version of the Hexad scale. To create it, we carried out an exploratory factor analysis on an existing data set to identify appropriate items (n = 882). To validate the 12-item version, we conducted a confirmatory factor analysis on a new data set (n = 1, 101). Our results show that Hexad-12 outperforms the original Hexad scale regarding model fit, reliability, convergent, and discriminant validity. Therefore, Hexad-12 resolves issues found in studies using the original Hexad scale and provides a suitable and swift instrument for concisely assessing Hexad user types in tailored gamification design.
Visible Nuances: A Caption System to Visualize Paralinguistic Speech Cues for Deaf and Hard-of-Hearing Individuals

Captions help deaf and hard-of-hearing (DHH) individuals visually communicate voice information to better understand video content. In speech, the literal content and paralinguistic cues (e.g., pitch and nuance) work together to create real intention. However, current captions are limited in their capacity to deliver fine nuances because they cannot fully convey these paralinguistic cues. This paper proposes an audio-visualized caption system that automatically visualizes paralinguistic cues into various caption elements (thickness, height, font type and motion). A comparative study with 20 DHH participants demonstrates how our system supports DHH individuals to be better accessible to paralinguistic cues while watching videos. Particularly in the case of formal talks, they could accurately identify the speaker’s nuance more often compared to current captions, without any practice or training. Addressing some issues on legibility and familiarity, the proposed caption system has potentials to enrich DHH individuals’ video watching experience more as hearing people enjoy.
Semi-Automated Approach for Evaluating Severe Weather Risk Communication

Communicating risk to the public in the lead-up to and during severe weather events has the potential to reduce losses from such events. Globally, severe weather events are anticipated to increase due to climate change rendering effective risk communication an integral component of climate adaptation policies. The identification of best practices in risk communication has long been studied by risk communication professionals. However, few articles have attempted to quantify the compliance of severe weather risk messages with these best practices at scale or developed tools that can be used by this population. The current work makes two contributions toward this goal. First, we use string-matching to evaluate the degree to which risk communication issued in Canada complies with best practices and suggest ways to improve this messaging. Second, we suggest a writing support tool to be used by risk communication professionals to evaluate risk messages.
Take My Hand: Automated Hand-Based Spatial Guidance for the Visually Impaired

Tasks that involve locating objects and then moving hands to those specific locations, such as using touchscreens or grabbing objects on a desk, are challenging for the visually impaired. Over the years, audio guidance and haptic feedback have been a staple in hand navigation based assistive technologies. However, these methods require the user to interpret the generated directional cues and then manually perform the hand motions. In this paper, we present automated hand-based spatial guidance to bridge the gap between guidance and execution, allowing visually impaired users to move their hands between two points automatically, without any manual effort. We implement this concept through FingerRover, an on-finger miniature robot that carries the user’s finger to target points. We demonstrate the potential applications that can benefit from automated hand-based spatial guidance. Our user study shows the potential of our technique in improving the interaction capabilities of people with visual impairments.
Using Pseudo-Stiffness to Enrich the Haptic Experience in Virtual Reality

Providing users with a haptic sensation of the hardness and softness of objects in virtual reality is an open challenge. While physical props and haptic devices help, their haptic properties do not allow for dynamic adjustments. To overcome this limitation, we present a novel technique for changing the perceived stiffness of objects based on a visuo-haptic illusion. We achieved this by manipulating the hands’ Control-to-Display (C/D) ratio in virtual reality while pressing down on an object with fixed stiffness. In the first study (N=12), we determine the detection thresholds of the illusion. Our results show that we can exploit a C/D ratio from 0.7 to 3.5 without user detection. In the second study (N=12), we analyze the illusion’s impact on the perceived stiffness. Our results show that participants perceive the objects to be up to 28.1% softer and 8.9% stiffer, allowing for various haptic applications in virtual reality.
Impacts of the Strength and Conformity of Social Norms on Well-Being: A Mixed-Method Study Among Hybrid Workers in Japan

Previous studies have suggested that organizational social norms can positively affect employee well-being. However, such social norms have not been well developed during the post-COVID-19 transition to hybrid work, which combines office and remote work, and it is unclear how employees’ perceptions of social norms for hybrid work affect their well-being. In this study, we investigated the impact of social norms for hybrid work on the well-being of hybrid workers living in Japan through a mixed-method approach consisting of an online survey (n = 212) and semi-structured interviews (n = 20). The results indicate that hybrid workers who feel subject to strong social norms have lower well-being. Conversely, those who are more willing to conform to social norms have higher well-being. Given our findings, we discuss implications for the design of systems to help hybrid workers conform to organizational social norms and to improve their well-being.
Respecifying Phubbing: Video-Based Analysis of Smartphone Use in Co-Present Interactions

The concept of phubbing (generally defined as a practice of ignoring co-present others by focusing on one's mobile device) is now widely used in studies aiming to understand the effects of smartphone use on co-present interactions. However, most of these studies are quantitative in nature and fail to grasp the interactional context of smartphone use. Drawing on video recordings and utilizing multimodal interaction analysis, the present study examines phubbing in naturally occurring interactions among young adults. Contrary to most previous research, the analysis reveals that disengagement often precedes self-initiated smartphone use rather than follows it. The study identifies factors that affect whether phubbing is reciprocated and whether it is oriented to as problematic. As a result of the analysis, an alternative conceptualization of phubbing is offered. By reflecting on participants’ ways of managing phubbing and its consequences, we discuss design solutions for supporting them in this task.
"What else can I do?" Examining the Impact of Community Data on Adaptation and Quality of Reflection in an Educational Game

Adaptation, or ability and willingness to consider an alternative approach, is a critical component of learning through reflection, especially in educational games, where there are often multiple avenues to success. As a domain, educational games have shown increased interest in using retrospective visualizations to promote and support reflection. Such visualizations, which can facilitate comparison with peer data, may also have an impact on adaptation in educational games. This has, however, not been empirically examined within the domain. In this work, we examine how comparison with other players’ data influenced adaptation, a part of reflection, in the context of a game that teaches parallel programming. Our results indicate that comparison with peers does significantly impact willingness to try a different approach, but suggest that there may also be other ways. We discuss what these results mean for future use of retrospective visualizations in educational games and present opportunities for future work.
Motivation to Use Fitness Application for Improving Physical Activity Among Hispanic Users: The Pivotal Role of Interactivity and Relatedness

Is the current state of fitness applications effective at motivating and satisfying the needs of Hispanic users? With most mHealth research conducted with a predominantly white population, the answer to this question is lacking. In this study, we address this question through a survey study with Hispanic users of fitness applications (N= 211) and use the Motivational Technology Model (MTM) and Self-Determination Theory (SDT) as theoretical frameworks. We found that using interactivity features is essential to inspire more autonomous forms of motivation to use fitness applications. This is because interactivity helps satisfy users’ needs for relatedness. However, interactivity also decreased autonomy and competence suggesting the need to design fitness applications that increase relatedness without compromising autonomy. Implications for the design of fitness applications for the population at large and Hispanics, in particular, are discussed.
Towards User-Aware VR Learning Environments: Combining Brain-Computer Interfaces with Virtual Reality for Mental State Decoding

User-aware adaptive systems can greatly benefit from brain-computer interface (BCI) technologies. BCIs allow continuous monitoring of users’ mental states and tailoring of the system to their individual skills and needs. We conducted a feasibility study integrating a BCI using functional near-infrared spectroscopy (fNIRS) into a virtual reality (VR) environment for realistic industrial learning scenarios. Using a fNIRS-based BCI allowed us to a) identify learning progress of individuals based on their working memory load across multiple learning sessions and b) investigate the underlying brain patterns. Our results showed a non-linear relationship between task difficulty and brain responses in the prefrontal cortex (PFC). Finally, we were able to draw four major conclusions regarding architecture components and vital research perspectives, to progress towards a vision of user-aware adaptive system design.
Making Energy Matter: Soma Design for Ethical Relations in Energy Systems

There is a need to reframe our relationship to energy, particularly in Western energy contexts, where we have plentiful access and no meaningful barriers to use. This paper outlines a first-person engagement with energy systems and shows how somaesthetic design is one possible means to cultivate and design for new ways of ethical being with energy systems. Early autobiographical design work focused on designing for ’sustainability’ revealed a trajectory of fatalism and restriction. A turn towards enacting material relations, co-performed with others, opened into a more holistic relationship with energy. Reflecting on how this process unfolded, we argue that sustainability is not, in itself, a somaesthetic sensibility, and remains constrained within rational framings. We develop this argument to contribute to a new understanding of how we somatically relate to energy and how relational ethics in interaction design research and practice can encourage a felt sense for the materiality of energy.
Accessible Data Representation with Natural Sound

Sonification translates data into non-speech audio. Such auditory representations can make data visualization accessible to people who are blind or have low vision (BLV). This paper presents a sonification method for translating common data visualization into a blend of natural sounds. We hypothesize that people’s familiarity with sounds drawn from nature, such as birds singing in a forest, and their ability to listen to these sounds in parallel, will enable BLV users to perceive multiple data points being sonified at the same time. Informed by an extensive literature review and a preliminary study with 5 BLV participants, we designed an accessible data representation tool, Susurrus, that combines our sonification method with other accessibility features, such as keyboard interaction and text-to-speech feedback. Finally, we conducted a user study with 12 BLV participants and report the potential and application of natural sounds for sonification compared to existing sonification tools.
Exploring key categories of social perception and moral responsibility of AI-based agents at work: Findings from a case study in an industrial setting

The increasing adoption of smart and interactive technologies, based on Artificial Intelligence (AI), in business and society transforms humans’ perception of these technologies towards social actors. However, since there are also reservations of humans to interact with artificial agents, it is fundamental to explore the social perception of this new technology and the implications thereof. Social perception governs social behavior and subsequently affects interactions with AI-agents. Moral responsibility plays a pivotal role in functioning societies and should thus be taken into account when implementing AI. This qualitative study examines workers in an industrial setting, who perform a decision-making task together with an AI-agent possessing no visual human-like characteristics. Based on the model of Responsibility in Hybrid Societies we show that workers assign capacities of social perception and moral responsibility to their AI-based counterpart. We present first evidence that theoretically derived models of social perception and moral responsibility are applicable in industrial settings.
Changes in Research Ethics, Openness, and Transparency in Empirical Studies between CHI 2017 and CHI 2022

In recent years, various initiatives from within and outside the HCI field have encouraged researchers to improve research ethics, openness, and transparency in their empirical research. We quantify how the CHI literature might have changed in these three aspects by analyzing samples of 118 CHI 2017 and 127 CHI 2022 papers—randomly drawn and stratified across conference sessions. We operationalized research ethics, openness, and transparency into 45 criteria and manually annotated the sampled papers. The results show that the CHI 2022 sample was better in 18 criteria, but in the rest of the criteria, it has no improvement. The most noticeable improvements were related to research transparency (10 out of 17 criteria). We also explored the possibility of assisting the verification process by developing a proof-of-concept screening system. We tested this tool with eight criteria. Six of them achieved high accuracy and F1 score. We discuss the implications for future research practices and education. This paper and all supplementary materials are freely available at https://doi.org/10.17605/osf.io/n25d6.
QWriter: Technology-Enhanced Alphabet Acquisition based on Reinforcement Learning

In Kazakhstan, the ongoing Cyrillic-to-Latin alphabet shift raises challenges for early literacy development and acquisition in the Kazakh language. This paper proposes the QWriter system to help young children learn the Latin-based Kazakh alphabet and its handwriting. The system consists of a humanoid robot NAO, a tablet with a stylus, and a Reinforcement Learning (RL) agent that learns a child’s mistakes and progress to maximize alphabet learning in the shortest period of time by adapting the order of practice words according to the child’s mistakes. To evaluate the effectiveness of the QWriter system, we conducted a between-subject design experiment with 59 Kazakh children aged 6-8 years old and compared their learning performance with a human tutor and the CoWriting Kazakh robot system. The results did not support our assumption, we found that the proposed system received significantly higher likability scores than the baseline human tutor.
Modeling Temporal Target Selection: A Perspective from Its Spatial Correspondence

Temporal target selection requires users to wait and trigger the selection input within a bounded time window, with a selection cursor that is expected to be delayed. This task conceptualizes, for example, a variety of game scenarios such as determining the timing of shooting a projectile towards a moving object. In this work, we explore models that predict “when” users typically perform a selection (i.e., user selection distribution) and their selection error rates in such tasks. We hypothesize that users react to temporal factors including “distance”, “width”, and “delay” as how they treat the corresponding variables in spatial target selection. The derived models are evaluated in a controlled experiment and an MTurk-based online study. Our research contributes new knowledge on user behavior in temporal target selection tasks and its potential connection with its spatial correspondence. Our models and conclusions can benefit both users and designers of relevant interactive applications.