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Large pre-trained transformer models using self-supervised learning have achieved state-of-the-art performances in various NLP tasks. However, for low-resource language like Nepali, pre-training of monolingual models remains a problem due to lack of training data and well-designed and balanced benchmark datasets. Furthermore, several multilingual pre-trained models such as mBERT and XLM-RoBERTa have been released, but their performance remains unknown for Nepali language. We compared Nepali monolingual pre-trained transformer models with multilingual models to determine their performance using a Nepali text classification dataset as a downstream task based on different number of classes and data sizes, taking machine learning (ML) and deep learning (DL) algorithms as baselines. Under-representation of Nepali language in mBERT resulted in overall poor performance, but, XLM-RoBERTa, which has a larger vocabulary size, produced state-of-the-art performance which is relatively similar to that of Nepali DistilBERT and DeBERTa, which outperformed all of the baseline algorithms. Bi-LSTM and SVM from the baselines also performed very well in variety of settings. Moreover, to assess the cross-language knowledge transfer for the cases when mono-lingual models are not available, we also evaluated HindiRoBERTa, a monolingual Indian language model on Nepali text dataset. This research mainly contributes to the Nepali NLP community by creation of news classification dataset with 20 classes, with over 200,000 articles and performance evaluation of various pre-trained monolingual Nepali transformers with multilingual transformers, DL and ML algorithms.

Mindfulness practices are well-known for their benefits to mental and physical well-being. Given the prevalence of smartphones, mindfulness applications have attracted growing global interest. However, the majority of existing applications use guided meditation that is not adaptable to each user's unique needs or pace. This article proposes a novel framework called Attention Regulation Framework (ARF) , which studies how more flexible and adaptable mindfulness applications could be designed, beyond guided meditation and toward self-regulated meditation. ARF proposes mindfulness interaction design guidelines and interfaces whereby practitioners naturally and constantly bring their attention back to the present moment and develop non-judgmental awareness. This is achieved by the performance of subtle movements, which are supported by non-intrusive detection-feedback mechanisms. We used two design cases to demonstrate ARF in static and kinetic meditation conditions. We conducted four user evaluation studies in unique situations where ARF is particularly effective, vis-à-vis mindfulness practice in busy environments and mindfulness interfaces that adapt to the pace of the user. The studies show that the design cases, compared with guided meditation applications, are more effective in improving attention, mindfulness, mood, well-being, and physical balance. Our work contributes to the development of self-regulated mindfulness technologies.

Debates regarding the nature and role of Human-Computer Interaction (HCI) have become increasingly common. This is because HCI lacks a clear philosophical foundation from which to derive a coherent vision and consistent aims and goals. This paper proposes a conceptual framework for ongoing discussion that can give more meaningful and pertinent direction to the future of HCI; we call the proposed approach Human-Engaged Computing (HEC). HEC is a focused yet adaptable philosophical approach which aims to establish “synergized interactions between engaged humans and engaging computers for high level wisdom which enhances our human survival probability and our full potential as humans”. In this paper, HEC is described through five perspectives—definition, components, principles, case studies and benefits. The paper concludes by suggesting future directions for HEC.

Flow Proneness - FP (i.e., dispositional tendency to experience flow) is subjectively different across individuals. An earlier study demonstrated that FP is correlated with the availability of dopamine receptors in the dorsal striatum including the caudate and the putamen. However, it remains unclear whether FP is associated with gray matter volume variations in the brain. The neuro-anatomical basis of FP has been investigated for the first time in the present study. We conducted a cross-sectional study with a high number of healthy employed Japanese adults (n = 680). We measured FP in different domains of everyday life (work, household maintenance, and leisure time) using a Japanese translation of a Swedish flow proneness questionnaire. We investigated gray matter volume using optimized voxel-based morphometry. As hypothesized, we found a significant region in the dorsal striatum. Our results indicate that an increase in gray matter volume in the right caudate is associated with an increase in overall FP in everyday life. However, the resulting correlation was relatively small (0.13). We discuss the potential reasons underlying these findings. Our findings might have further implications for flow research and well-being.

Thumb based interaction is becoming increasingly popular in mobile devices. However, the interaction still remains slow, ambiguous, and error-prone. This paper presents an exploratory user experiment results of one-thumb pointing and dragging task performance, based on three factors: mobile size, target size, and posture (sitting and walking positions). Beside obvious findings, we observed some surprising scenarios, such as the gripping style of most users was casual and did not follow any formal model or structure. We concluded our experiences into design implications with respect to mobile size, posture, and gripping styles.

Debates regarding the nature and role of HCI research and practice have intensified in recent years, given the ever increasingly intertwined relations between humans and technologies. The framework of Human-Engaged Computing (HEC) was proposed and developed over a series of scholarly workshops to complement mainstream HCI models by leveraging synergy between humans and computers with its key notion of "engagement". Previous workshop meetings found "engagement" to be a constructive and extendable notion through which to investigate synergized human-computer relationships, but many aspects concerning the core concept remain underexplored. This SIG aims to tackle the notion of engagement considered through discussions of four thematic threads. It will bring together HCI practitioners and researchers from different disciplines including Humanities, Design, Positive Psychology, Communication and Media Studies, Neuroscience, Philosophy and Eastern Studies, to share and discuss relevant knowledge and insights and identify new research opportunities and future directions.

openaire: EC/H2020/637991/EU//COMPUTED Ability-based optimization is a computational approach for improving interface designs for users with sensorimotor and cognitive impairments. Designs are created by an optimizer, evaluated against task-specific cognitive models, and adapted to individual abilities. The approach does not necessitate extensive data collection and could be applied both automatically and manually by users, designers, or caretakers. As a first step, the authors present optimized touchscreen layouts for users with tremor and dyslexia that potentially improve text-entry speed and reduce error.

Full body gestures provide alternative input to video games that are more natural and intuitive. However, full-body game gestures designed by developers may not always be the most suitable gestures available. A key challenge in full-body game gestural interfaces lies in how to design gestures such that they accommodate the intensive, dynamic nature of video games, e.g., several gestures may need to be executed simultaneously using different body parts. This paper investigates suitable simultaneous full-body game gestures, with the aim of accommodating high interactivity during intense gameplay. Three user studies were conducted: first, to determine user preferences, a user-elicitation study was conducted where participants were asked to define gestures for common game actions/commands; second, to identify suitable and alternative body parts, participants were asked to rate the suitability of each body part (one and two hands, one and two legs, head, eyes, and torso) for common game actions/commands; third, to explore the consensus of suitable simultaneous gestures, we proposed a novel choice-based elicitation approach where participants were asked to mix and match gestures from a predefined list to produce their preferred simultaneous gestures. Our key findings include (i) user preferences of game gestures, (ii) a set of suitable and alternative body parts for common game actions/commands, (iii) a consensus set of simultaneous full-body game gestures that assist interaction in different interactive game situations, and (iv) generalized design guidelines for future full-body game interfaces. These results can assist designers and practitioners to develop more effective full-body game gestural interfaces or other highly interactive full-body gestural interfaces.

Part 5: Case Studies; International audience; Beside decreasing the abilities, individual difference prevails among older adults, as some individuals are completely healthy at the age 90 while some are not at even 60. In context of touchscreen interface design, it is critical to understand the design space as a function of abilities. In this work, we articulate a better understanding of the effects of ageing and examine their HCI task performing capabilities in terms of interfaces design. We design a text-entry interface in particular, as ageing users often achieve slow text entry performance, thus proving to be a bottleneck for technology use. Our developed text entry interface is tuned based on the parameter values for Elderly having finger tremor. We present initial study results showing the improvement of the accuracy of touch typing in smartphone over the baseline Qwerty keyboard. By carefully considering other sensorimotor abilities, we believe that the current smartphone text-entry interface designs will become more usable to the ageing populations.

Cognitive impairments decrease the quality of life of the elderly. Earlier studies show multitasking sedentary video games are an effective intervention. However, little work has studied multitasking motion video games which can be more directly beneficial for overall wellbeing. This project investigates the efficacy of multitasking motion video games for the cognitive enhancement of the elderly. As a response to this situation, we developed a custom-made game called Safari Move. Here we report the initial step towards our goal in which we studied whether or not elderly people enjoy playing our game. Two important game elements were studied - skill balancing methods and controller types. Our results demonstrate that our participants enjoyed playing our game, and that they prefer manual over dynamic difficulty adjustment, and Microsoft Kinect over Gamepad. Future work will use neuroimaging and cognitive assessment tools to investigate the effectiveness of Safari Move to enhance cognitive function.

We are often overwhelmed by everyday stressors. Mindfulness meditation can help slow things down and bring one's attention into the present moment. Given the prevalence of smartphones, mindfulness-based mobile applications (MBMAs) have received much attention. Current MBMAs mainly use the guided meditation method which may not be always effective, e.g., users may not be able to follow the pace of instructions and they need a private environment. This paper presents a framework for interactive MBMAs which allows users to self-regulate their attention according to their abilities and conditions. The framework is described by an Attention-Regulation Process and has two components: (1) Relaxation Response and (2) Attention Restoration Theory. The framework is validated by our experiment. It also informs future development for interactive meditation and has broad implications for designing mindfulness and well-being.

In our fast-paced society, stress and anxiety have become increasingly common. Meditation for relaxation has received much attention. Meditation apps exploit various senses, e.g., touch, audio and vision, but the relationship between human senses and interactive meditation is not well understood. This paper empirically evaluates the effects of single and combined human senses on interactive meditation. We found that the effectiveness of human senses can be defined by their respective roles in maintaining the balance between relaxation and focus. This work is the first to attempt to understand these relationships. The findings have broad implications for the field of multi-modal interaction and interactive meditation applications.

Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.

Skill balancing increases game engagement by providing users with suitable challenges. Two common methods of skill balancing are manual difficulty adjustment (MDA) and dynamic difficulty adjustment (DDA). However, little is known about the effect of skill balancing on elderly player experience (PX). We compared two types of skill balancing (MDA and DDA) across two controller types (GamePad and Microsoft Kinect). Our results demonstrate that MDA has higher impact on PX of the elderly. Also we found eagerness of the elderly to attempt playing higher challenges.

Engagement is a key reason for introducing gamification to learning and thus serves as an important measurement of its effectiveness. Based on a literature review and meta-synthesis, this paper proposes a comprehensive framework of engagement in gamification for learning. The framework sketches out the connections among gamification strategies, dimensions of engagement, and the ultimate learning outcome. It also elicits other task - and user - related factors that may potentially impact the effect of gamification on learner engagement. To verify and further strengthen the framework, we conducted a user study to demonstrate that: 1) different gamification strategies can trigger different facets of engagement; 2) the three dimensions of engagement have varying effects on skill acquisition and transfer; and 3) task nature and learner characteristics that were overlooked in previous studies can influence the engagement process. Our framework provides an in-depth understanding of the mechanism of gamification for learning, and can serve as a theoretical foundation for future research and design.

Mid-air gestures enable intuitive and natural interactions. However, few studies have investigated the use of mid-air gestures for blind people. TV interactions are one promising use of mid-air gestures for blind people, as "listening"' to TV is one of their most common activities. Thus, we investigated mid-air TV gestures for blind people through two studies. Study 1 used a user-elicitation approach where blind people were asked to define gestures given a set of commands. Then, we present a classification of gesture types and the frequency of body parts usage. Nevertheless, our participants had difficulty imagining gestures for some commands. Thus, we conducted Study 2 that used a choice-based elicitation approach where the participants selected their favorite gesture from a predefined list of choices. We found that providing choices help guide users to discover suitable gestures for unfamiliar commands. We discuss concrete design guidelines for mid-air TV gestures for blind people.

This SIG advances the study of mobile user interfaces for the aging population. The topic is timely, as the mobile device has become the most widely used computer terminal and at the same time the number of older people will soon exceed the number of children worldwide. However, most HCI research addresses younger adults and has had little impact on older adults. Some design trends, like the mantra “smaller is smarter”, contradict the needs of older users. Developments like this may diminish their ability to access information and participate in society. This can lead to further isolation (social and physical) of older adults and increased widening of the digital divide. This SIG aims to discuss mobile interfaces for older adults. The SIG has three goals: (i) to map the state-of-art, (ii) to build a community gathering experts from related areas, and (iii) to raise awareness within the SIGCHI community. The SIG will be open to all at CHI.

Human engagement is at the heart of every interactive technology. However, a concrete framework for synergizing the capabilities of humans and technologies to allow fully engaging interactions to happen is yet to be developed. We posit that such a framework should be grounded in a deeper understanding of human nature (e.g., mind-body relations), which in the field of HCI has primarily been built upon the Western philosophies. There are scattered, underexplored Eastern philosophies (e.g., Yijing, Zen) that may provide new lens and tools to analyze how humans interact with resources in their environments, including technological artefacts. Discussions of leveraging and possibly integrating Eastern and Western insights for human engagement studies will be an exciting and a radical forum for the HCI community.