Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond the Bulging Binder: Family-Centered Design of a Digital Health Information Management System for Caregivers of Children Living with Health Complexity

Children Living with Health Complexity (CLHC), whose multiple, severe and chronic conditions differentiate them from those with “complicated” conditions, rely life-long on a vast and ever-shifting array of care providers. Their parent caregivers face a fragmented health care system, disconnected medical records, inter-stakeholder communication barriers and an impenetrable accumulation of documentation – from mundane to life-critical, and largely on paper. They must coordinate care while organizing, tracking and transmitting trends in many health parameters to myriad care providers. We engaged with parent caregivers of CLHC from 12 families through an iterative, 3-phase co-design process to understand their needs for a future digital management system. We share our deepened understanding of their information-centered challenges, a set of principles for how design best-practices need to shift when targeting this acutely high-needs group, and a medium-fidelity prototype user interface which from the ground-up prioritizes caregiver-centered data integration and humanization of the child and family, as well as integrated health record access.
Is this AI trained on Credible Data? The Effects of Labeling Quality and Performance Bias on User Trust

To promote data transparency, frameworks such as CrowdWorkSheets encourage documentation of annotation practices on the interfaces of AI systems, but we do not know how they affect user experience. Will the quality of labeling affect perceived credibility of training data? Does the source of annotation matter? Will a credible dataset persuade users to trust a system even if it shows racial biases in its predictions? To find out, we conducted a user study (N = 430) with a prototype of a classification system, using a 2 (labeling quality: high vs. low) × 4 (source: others-as-source vs. self-as-source cue vs. self-as-source voluntary action, vs. self-as-source forced action) × 3 (AI performance: none vs. biased vs. unbiased) experiment. We found that high-quality labeling leads to higher perceived training data credibility, which in turn enhances users’ trust in AI, but not when the system shows bias. Practical implications for explainable and ethical AI interfaces are discussed.
Throughput and Effective Parameters in Crossing

In pointing, throughput TP is used as a performance metric for the input device and operator. Based on the calculation of effective parameters (width We and amplitude Ae), TP should be independent of the speed-accuracy tradeoff. To examine the validity of TP and effective parameters for crossing actions, we conducted two experiments using two established crossing tasks. Our results demonstrate that applying effective parameters to Fitts’ law model improves the fit to the data for mixed biases in both tasks. Besides, we observed that effective parameters smoothed TPs across biases. However, unlike pointing, TP was observed to be unstable across IDs in one task, while was stable across IDs in the other task. Analyzing speed profiles showed that this was likely due to the fact that one of the tasks could be completed with a ballistic movement at low IDs, whereas this was impossible for the other task.
Mental Wellbeing at Work: Perspectives of Software Engineers

Software engineers exhibit higher burnout and suicide rates compared to many other information workers. Consequently, mental wellbeing is a growing concern to technology organizations. To better understand the challenges of supporting mental wellbeing in the context of the work of software engineering, we conducted 14 interviews with software engineers. We examine the different aspects of their lived experiences with mental wellbeing at work, their strategies for managing mental wellbeing, the challenges they face in using these strategies, and recommendations they have for mental wellbeing technologies. We contribute to the HCI literature by discussing how mental wellbeing should be considered within the context of work across individual, team, and organization levels, and highlight the need for integrating mental wellbeing into the technologies employees use at work.
An Expressivity-Complexity Tradeoff?: User-Defined Gestures from the Wheelchair Space are Mostly Deictic

We present empirical results about gesture expressivity and articulation complexity from an analysis of 231 gestures elicited from eleven wheelchair users, for which we employ a combination of McNeill’s gesture theory from psycholinguistics and a taxonomy used in Human-Computer Interaction for the analysis of gestures elicited from end users. We report that 53.7% of the gestures that we analyzed were deictic in nature, and 50.7% were performed toward the body. These findings suggest a potential tradeoff between the expressivity of iconic and metaphoric gestures, less represented in the gesture set analyzed in this work, for the low complexity of simple pointing movements performed from the wheelchair space. Our results complement findings of previous gesture elicitation studies conducted with users with motor and/or mobility impairments, and suggest future work opportunities for gesture input performed from the wheelchair space, including mixed-nature gestures that feature both low complexity and rich expressivity.
Tailoring a Persuasive Game to Promote Secure Smartphone Behaviour

The use of smartphones has become an integral part of everyone's lives. Due to the ubiquitous nature and multiple functionalities of smartphones, the data handled by these devices are sensitive in nature. Despite the measures companies take to protect users’ data, research has shown that people do not take the necessary actions to stay safe from security and privacy threats. Persuasive games have been implemented across various domains to motivate people towards a positive behaviour change. Even though persuasive games could be effective, research has shown that the one-size-fits-all approach to designing persuasive games might not be as effective as the tailored versions of the game. This paper presents the design and evaluation of a persuasive game to improve user awareness about smartphone security and privacy tailored to the user's motivational orientation using Regulatory Focus Theory. From the results of our mixed-methods in-the-wild study of 102 people followed by a one-on-one interview of 25 people, it is evident that the tailored version of the persuasive game performed better than the non-tailored version of the game towards improving users’ secure smartphone behaviour. We contribute to the broader HCI community by offering design suggestions and the benefits of tailoring persuasive games.
Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case Study

Collecting data is one of the bottlenecks of Human-Computer Interaction (HCI) research. Motivated by this, we explore the potential of large language models (LLMs) in generating synthetic user research data. We use OpenAI’s GPT-3 model to generate open-ended questionnaire responses about experiencing video games as art, a topic not tractable with traditional computational user models. We test whether synthetic responses can be distinguished from real responses, analyze errors of synthetic data, and investigate content similarities between synthetic and real data. We conclude that GPT-3 can, in this context, yield believable accounts of HCI experiences. Given the low cost and high speed of LLM data generation, synthetic data should be useful in ideating and piloting new experiments, although any findings must obviously always be validated with real data. The results also raise concerns: if employed by malicious users of crowdsourcing services, LLMs may make crowdsourcing of self-report data fundamentally unreliable.
Unlocking the Tacit Knowledge of Data Work in Machine Learning

Creating datasets for ML is an inherently human endeavor, as the data’s heterogeneity mandates human intervention. However, most data workflows being one-time and hardly transferable leads to a lack of standardization and reusability. There has been a push to impose more structure on the data work process, but little is known about the implicit or "tacit" knowledge of data workers, i.e., "know-how"s that is difficult to transfer to others. Identifying and formalizing this knowledge can help data work improve, leading it from current "exploration" to more systematic "engineering. " We interviewed 19 ML practitioners in this study to find "why" they use "what" tacit knowledge. As a result, we identified the following themes: 1) data is context/situation dependent, 2) human workers are inseparable from data, and 3) models must be understood to build data. We finally discuss future systematic supports and research to convert what is implicit to explicit.
Something Borrowed: Exploring the Influence of AI-Generated Explanation Text on the Composition of Human Explanations

Recent advances in Human-AI interaction have highlighted the possibility of employing AI in collaborative decision-making contexts, particularly in cases where the decision is subjective, without one ground truth. In these contexts, researchers argue that AI could be used not just to provide a final decision recommendation, but to surface new perspectives, rationales, and insights. In this late-breaking work, we describe the initial findings from an empirical study investigating how complementary AI input influences humans’ rationale in ambiguous decision-making. We use subtle sexism as an example of this context, and GPT-3 to create explanation-like text. We find that participants change the language, level of detail, and even the argumentative stance of their explanations after seeing the AI explanation text. They often borrow language directly from this complementary text. We discuss the implications for collaborative decision-making and the next steps in this research agenda.
Do You Mind? User Perceptions of Machine Consciousness

The prospect of machine consciousness cultivates controversy across media, academia, and industry. Assessing whether non-experts perceive technologies as conscious, and exploring the consequences of this perception, are yet unaddressed challenges in Human Computer Interaction (HCI). To address them, we surveyed 100 people, exploring their conceptualisations of consciousness and if and how they perceive consciousness in currently available interactive technologies. We show that many people already perceive a degree of consciousness in GPT-3, a voice chat bot, and a robot vacuum cleaner. Within participant responses we identified dynamic tensions between denial and speculation, thinking and feeling, interaction and experience, control and independence, and rigidity and spontaneity. These tensions can inform future research into perceptions of machine consciousness and the challenges it represents for HCI. With both empirical and theoretical contributions, this paper emphasises the importance of HCI in an era of machine consciousness, real, perceived or denied.
Automation Confusion: A Grounded Theory of Non-Gamers’ Confusion in Partially Automated Action Games

Partial automation makes digital games simpler by performing game actions for players. It may simplify gameplay for non-gamers who have difficulty controlling and understanding games. However, the automation may make players confused about what they control and what the automation controls. To describe and explain non-gamers’ experiences of automation confusion, we analyzed gameplay, think-aloud, and interview data from ten non-gamer participants who played two partially automated games. Our results demonstrate how incorrect mental models, behaviours resulting from those models, and players’ attitudes towards the games led to different levels and types of confusion.
A novel sensor fault diagnosis approach for time-varying delay systems with non-linear uncertainty

In this paper, a sensor fault diagnosis approach is presented for a class of time delay non-linear systems via the use of adaptive updating rules. The considered system is represented by a time-varying delay dynamical state space model, and is subjected to a non-linear vector, which represents the modelling uncertainty in the state equation. Firstly, a fault detector observer is constructed to detect the fault. Then, the method for choosing the threshold value is given. Furthermore, a fault diagnosis device is constructed to diagnose the fault. The Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the sensor fault. An adaptive diagnosis algorithm is developed to obtain information on the sensor fault. Finally, a simulated numerical example and a robotic example are included to demonstrate the use of the proposed approach, and experimental results show that the proposed adaptive diagnosis algorithm can track the fault signal and that the proposed method is valid.
Genome-wide characterization of major latex protein gene family in peanut and expression analyses under drought and waterlogging stress

Peanut is an important oilseed crop around the world which provides vegetable oil, protein and vitamins for humans. Major latex-like proteins (MLPs) play important roles in plant growth and development, as well as responses to biotic and abiotic stresses. However, their biological function in peanut is still unclear. In this study, a genome-wide identification of MLP genes in cultivated peanut and two diploid ancestor species was analyzed to determine their molecular evolutionary characteristics and the expression profile under drought and waterlogging stress conditions. Firstly, a total of 135 MLP genes were identified from the genome of tetraploid peanut (Arachis hypogaea) and two diploid species Arachis. duranensis and Arachis. ipaensis. Then, phylogenetic analysis revealed that MLP proteins were divided into five different evolutionary groups. These genes were distributed unevenly at the ends of chromosomes 3, 5, 7, 8, 9 and 10 in three Arachis species. The evolution of MLP gene family in peanut was conserved and led by tandem and segmental duplication. The prediction analysis of cis-acting elements showed that the promoter region of peanut MLP genes contained different proportions of transcription factors, plant hormones-responsive elements and so on. The expression pattern analysis showed that they were differentially expressed under waterlogging and drought stress. These results of this study provide a foundation for further research on the function of the important MLP genes in peanut.
Insight into the phytochemical profile and antimicrobial activities of Amomum subulatum and Amomum xanthioides: an in vitro and in silico study

Introduction Medicinal plants have been considered as potential source of therapeutics or as starting materials in drugs formulation. Methods The current study aims to shed light on the therapeutic potential of the Amomum subulatom and Amomum xanthioides Fruits by analyzing the phytochemical composition of their seeds and fruits using gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) techniques to determine the presence of bioactive components such as flavonoids, phenols, vitamins, steroids, and essential oils. Results and Discussion The protein content is usually higher than the total lipids in both species except the fruit of A. subulatum which contain more lipids than proteins. The total protein contents for A. subulatum were 235.03 ± 21.49 and 227.49 ± 25.82 mg/g dry weight while for A. xanthioides were 201.9 ± 37.79 and 294.99 ± 37.93 mg/g dry weight for seeds and fruit, respectively. The Carvacrol levels in A. subulatum is 20 times higher than that in A. xanthioides. Lower levels of α-Thujene, Phyllanderenes, Ascaridole, and Pinocarvone were also observed in both species. According to DPPH (2,2-diphenylpicrylhydrazyl) assay, seed the extract of A. subulatum exhibited the highest antioxidant activity (78.26±9.27 %) followed by the seed extract of A. xanthioides (68.21±2.56 %). Similarly, FRAP (Ferric Reducing Antioxidant Power) assay showed that the highest antioxidant activity was exhibited by the seed extract of the two species; 20.14±1.11 and 21.18±1.04 µmol trolox g−1 DW for A. subulatum and A. xanthioides, respectively. In terms of anti-lipid peroxidation, relatively higher values were obtained for the fruit extract of A. subulatum (6.08±0.35) and the seed extract of A. xanthioides (6.11±0.55). Ethanolic seed extracts of A. subulatum had the highest efficiency against four Gram-negative bacterial species which causes serious human diseases, namely Pseudomonas aeruginosa, Proteus vulgaris, Enterobacter aerogenes, and Salmonella typhimurium. In addition, P. aeruginosa was also inhibited by the fruit extract of both A. subulatum and A. xanthioides. For the seed extract of A. xanthioides, large inhibition zones were formed against P. vulgaris and the fungus Candida albicans. Finally, we have in silico explored the mode of action of these plants by performing detailed molecular modeling studies and showed that the antimicrobial activities of these plants could be attributed to the high binding affinity of their bioactive compounds to bind to the active sites of the sterol 14-alpha demethylase and the transcriptional regulator MvfR. Conclusion These findings demonstrate the two species extracts possess high biological activities and therapeutical values, which increases their potential value in a number of therapeutic applications.
High brightness red emitting polymer beads for immunoassays: Comparison between trifluoroacetylacetonates of Europium

Efficiently luminescing spherical polymer particles (beads) in the nanoscale regime of up to approximately 250 nm have become very valuable tools in bioanalytical assays. Eu3+- complexes imbedded in polymethacrylate and polystyrene in particular proved to be extraordinarily useful in sensitive immunochemical and multi-analyte assays, and histo- and cytochemistry. Their obvious advantages derive from both, the possibility to realize very high ratios of emitter complexes to target molecules, and the intrinsically long decay times of the Eu3+-complexes, which allows an almost complete discrimination against bothersome autofluorescence via time-gated measuring techniques; the narrow line emission in conjunction with large apparent Stokes shifts are additional benefits with regard to spectral separation of excitation and emission with optical filters. Last but not least, a reasonable strategy to couple the beads to the analytes is mandatory. We have thus screened a variety of complexes and ancillary ligands; the four most promising candidates evaluated and compared to each other were β-diketonates (trifluoroacetylacetonates, R-CO-CH-CO-CF3, R = - thienyl, -phenyl, -naphthyl and -phenanthryl); highest solubilities in polystyrene were obtained with trioctylphosphine co-ligands. All beads had overall quantum yields in excess of 80% as dried powders and lifetimes well beyond 600 µs. Core-shell particles were devised for the conjugation to model proteins (Avidine, Neutravidine). Their applicability was tested in biotinylated titer plates using time gated measurements and a Lateral Flow Assay as practical examples.
The generation, activation, and polarization of monocyte-derived macrophages in human malignancies

Macrophages are immune cells that originate from embryogenesis or from the differentiation of monocytes. They can adopt numerous phenotypes depending on their origin, tissue distribution and in response to different stimuli and tissue environment. Thus, in vivo, macrophages are endowed with a continuum of phenotypes that are rarely strictly pro-inflammatory or anti-inflammatory and exhibit a broad expression profile that sweeps over the whole polarization spectrum. Schematically, three main macrophage subpopulations coexist in human tissues: naïve macrophages also called M0, pro-inflammatory macrophages referred as M1 macrophages, and anti-inflammatory macrophages also known as M2 macrophages. Naïve macrophages display phagocytic functions, recognize pathogenic agents, and rapidly undergo polarization towards pro or anti-inflammatory macrophages to acquire their full panel of functions. Pro-inflammatory macrophages are widely involved in inflammatory response, during which they exert anti-microbial and anti-tumoral functions. By contrast, anti-inflammatory macrophages are implicated in the resolution of inflammation, the phagocytosis of cell debris and tissue reparation following injuries. Macrophages also play important deleterious or beneficial roles in the initiation and progression of different pathophysiological settings including solid and hematopoietic cancers. A better understanding of the molecular mechanisms involved in the generation, activation and polarization of macrophages is a prerequisite for the development of new therapeutic strategies to modulate macrophages functions in pathological situations.
The neuroprotective effect of ascorbic acid against imidacloprid-induced neurotoxicity and the role of HO-1 in mice

Imidacloprid (IMI) is not only a neurotoxic agricultural pesticide but also a possible food contaminant. The aims of this study were to (1) explore the relationship between recurrent IMI administration and neuronal toxicity in mice and (2) evaluate the potential neuroprotective effect of ascorbic acid (AA), a substance with significant free radical scavenger and having property to block the inflammatory pathways. Mice were categorized as naïve controls (administered vehicles for 28 days); the IMI-treatment animal group (administered po 45-mg/kg body weight of IMI per day for 28 days); and the IMI + AA treatment animal group (administered the same IMI dose + 200 mg/kg of AA orally for 28 days). On day 28, memory losses were assessed using the Y-maze and novel target identification behavioral tests. Mice were sacrificed 24 h after the final IMI treatments, as well as hippocampus tissues, were utilized to determine histological assessments, oxidative stress biomarkers, and Heme oxygenase-1 (HO-1) and nuclear factor erythroid 2-related factor 2 (Nrf2) gene expression levels. The findings demonstrated that IMI-treated mice had substantial impairment of spatial and non-spatial memory functions, as well as reduced antioxidant enzyme and acetylcholinesterase activity. The AA neuroprotective action was achieved through the suppression of the HO-1 expression as well as the stimulation of Nrf2 expression in hippocampal tissues. In summary, recurrent IMI exposure causes oxidative stress and neurotoxicity in mice, and the administration of AA significantly reduces the IMI toxicity possibly by the activation of the HO-1/Nrf2 pathway.