HCI as a means to prosociality in the economy

HCI research often involves intervening in the economic lives of people, but researchers only rarely give explicit consideration to what actually constitutes prosociality in the economy. Much has been said previously regarding sustainability but this has largely focused on environmental rather than interpersonal relations. This paper provides an analysis of how prosocial HCI has been discussed and continues to be defined as a research field. Based on a corpus of published works, we describe a variety of genres of work relating to prosocial HCI. Key intellectual differences are explored, including the epistemological and ethical positions involved in designing for prosocial outcomes as well as how HCI researchers posit economic decision-making. Finally, emerging issues and opportunities for further debate and collaboration are discussed in turn

Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed

Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition. In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition

Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed

Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition. In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition

Design analysis: Understanding e-waste recycling by generation Y

This paper aims to understand e-waste recycling behavior of Generation Y. It presents a pilot study that explores this generation’s e-waste recycling practices, their attitudes towards ewaste recycling, and the barriers to e-waste recycling. The findings reveal the complexity of the actual e-waste recycling behavior, many participants in this study hold a positive attitude towards e-waste recycling, yet there is a shortage of convenient recycling options and e-waste recycling information. Based on the Motivation-Opportunity-Abilities model, this paper also uncovers the decision-making process involved in each recycling action. We use these findings to present a preliminary analysis of design implications to provoke design ideas and services that support ewaste recycling, and discuss our further research direction

Bots in Wikipedia: Unfolding their duties

The success of crowdsourcing systems such as Wikipedia relies on people participating in these systems. However, in this research we reveal to what extent human and machine intelligence is combined to carry out semi-automatic workflows of complex tasks. In Wikipedia, bots are used to realize such combination of human-machine intelligence. We provide an extensive overview on various edit types bots carry out in this regard through the analysis of 1,639 approved task requests. We classify existing tasks by an action-object-pair structure and reveal existing differences in their probability of occurrence depending on the investigated work context. In the context of community services, bots mainly create reports, whereas in the area of guidelines or policies bots are mostly responsible for adding templates to pages. Moreover, the analysis of existing bot tasks revealed insights that suggest general reasons, why Wikipedia’s editor community uses bots as well as approaches, how they organize machine tasks to provide a sustainable service. We conclude by discussing how these insights can prepare the foundation for further research

Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.Comment: 11.5 pages (excl. references), 6 figures, 1 tabl

Digitizing the chemical senses: possibilities & pitfalls

Many people are understandably excited by the suggestion that the chemical senses can be digitized; be it to deliver ambient fragrances (e.g., in virtual reality or health-related applications), or else to transmit flavour experiences via the internet. However, to date, progress in this area has been surprisingly slow. Furthermore, the majority of the attempts at successful commercialization have failed, often in the face of consumer ambivalence over the perceived benefits/utility. In this review, with the focus squarely on the domain of Human-Computer Interaction (HCI), we summarize the state-of-the-art in the area. We highlight the key possibilities and pitfalls as far as stimulating the so-called ‘lower’ senses of taste, smell, and the trigeminal system are concerned. Ultimately, we suggest that mixed reality solutions are currently the most plausible as far as delivering (or rather modulating) flavour experiences digitally is concerned. The key problems with digital fragrance delivery are related to attention and attribution. People often fail to detect fragrances when they are concentrating on something else; And even when they detect that their chemical senses have been stimulated, there is always a danger that they attribute their experience (e.g., pleasure) to one of the other senses – this is what we call ‘the fundamental attribution error’. We conclude with an outlook on digitizing the chemical senses and summarize a set of open-ended questions that the HCI community has to address in future explorations of smell and taste as interaction modalities

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

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