Billions of distributed, heterogeneous, and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast, and offline inference on personal data. On-device ML is highly context dependent and sensitive to user, usage, hardware, and environment attributes. This sensitivity and the propensity toward bias in ML makes it important to study bias in on-device settings. Our study is one of the first investigations of bias in this emerging domain and lays important foundations for building fairer on-device ML. We apply a software engineering lens, investigating the propagation of bias through design choices in on-device ML workflows. We first identify reliability bias as a source of unfairness and propose a measure to quantify it. We then conduct empirical experiments for a keyword spotting task to show how complex and interacting technical design choices amplify and propagate reliability bias. Our results validate that design choices made during model training, like the sample rate and input feature type, and choices made to optimize models, like light-weight architectures, the pruning learning rate, and pruning sparsity, can result in disparate predictive performance across male and female groups. Based on our findings, we suggest low effort strategies for engineers to mitigate bias in on-device ML.

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Informing caregivers by providing them with contextual medical information can significantly improve the quality of patient care activities. However, information flow in hospitals is still tied to traditional manual or digitised lengthy patient record files that are often not accessible while caregivers are attending to patients. Leveraging the proliferation of pervasive awareness technologies (sensors, actuators and mobile displays), recent studies have explored this information presentation aspect borrowing theories from context-aware computing, i.e., presenting subtle information contextually to support the activity at hand. However, the understanding of the information space (i.e., what information should be presented) is still fairly abstruse, which inhibits the deployment of such real-time activity support systems. To this end, this paper first presents situated glyphs, a graphical entity to encode situation specific information, and then presents our findings from an in-situ qualitative study addressing the information space tailored to such glyphs. Applying technology probes using situated glyphs and different glyph display form factors, the study aimed at uncovering the information space pertained to both primary and secondary medical care. Our analysis has resulted in a large set of information types as well as given us deeper insight on the principles for designing future situated glyphs. We report our findings in this paper that we expect would provide a solid foundation for designing future assistive systems to support patient care activities.

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This note offers a reflection on the design space for a situated glyph - a single, adaptive and multivariate graphical unit that provides in-situ task information in demanding work environments. Rather than presenting a concrete solution, our objective is to map out the broad design space to foster further exploration. The analysis of this design space in the context of dynamic work environments covers i) information affinity - the type of information can be presented with situated glyphs, ii) representation density - the medium and fidelity of information presentation, iii) spatial distribution - distribution granularity and placement alternatives for situated glyphs, and finally iv) temporal distribution - the timing of information provision through glyphs. Our analysis has uncovered new problem spaces that are still unexplored and could motivate further work in the field.@en

The combination of the Internet and emerging technologies such as near-field communications, real-time localization, and embedded sensors lets us transform everyday objects into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of Things and enable novel computing applications. As a step toward design and architectural principles for smart objects, the authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity. In particular, they describe activity-, policy-, and process-aware smart objects and demonstrate how the respective architectural abstractions support increasingly complex application.

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Effectively assisting people in complex and highly dynamic work environment requires advances in high-level declarative activity models that can describe the flow of human work activities and their intended outcomes, as well as novel interaction approach for distributing and coordinating information across physical objects, time and space. This paper describes a novel modeling technique for pervasive systems based on high-level models of human activities, so-called situated flows and presents a corresponding flow driven distributed software framework. This framework provides the foundation for discovery, adaptation and execution of flows in real time matching the dynamics of real world activity and consequently drives the pervasive interaction using push interfaces to facilitate a synergic coordinated interaction experience in dynamic work environments.@en

Effectively guiding people in complex and highly dynamic work environment requires advances in high-level declarative activity models that can describe the flow of human work activities and their intended outcomes, as well as novel user interface models for distributing guidance information across time and space. This paper describes a new line of research aimed at developing a new programming and human interface approach for pervasive systems based on high-level models of human activities, so-called situated flows, and mobile projector interfaces for uncovering task information embedded in physical environments.@en

Effectively guiding people in complex and highly dynamic work environment requires advances in high-level declarative activity models that can describe the flow of human work activities and their intended outcomes, as well as novel user interface models for distributing guidance information across time and space. This paper describes a new line of research aimed at developing a new programming and human interface approach for pervasive systems based on high-level models of human activities, so-called situated flows, and mobile projector interfaces for uncovering task information embedded in physical environments.

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The importance of user innovation is widely accepted, but the development of the Internet of Things is primarily driven by large commercial players. Using an innovation perspective, this paper identifies how user innovation and market-based innovation can be combined in the Internet of Things (IoT). A survey of tools for user/developers in the IoT space uncovers a rich set of tools for creation of hardware, software and data but reveals poor support for distribution and sharing of such artifacts. To address this shortcoming we propose connected marketplaces as a way to provide users/developers with rich opportunities for sharing and trading of artifacts, and to enable effective user innovation in the IoT.@en

One shortcoming of self-describing smart objects augmented with digital resources is the limitation of output modalities due to their long established physical appearances. To overcome this drawback intangible representations e.g., sound, video projection etc. are usually coupled with the tangible representations of smart objects that enable access and interaction with their value added features. In this paper, we explore two mobile interaction techniques that associate such intangible representation to smart objects using a pico projector augmented camera phone. The first technique utilizes a Magic Lens metaphor applying mobile augmented reality (contextual information is overlaid while looking at a smart object through camera) to uncover and interact with smart objects. The second technique, Personal Projection follows similar mechanisms in discovery and interaction, except information is projected onto the nearest surface. We report the implementation of these two techniques and a comparative qualitative study with three prototype smart object applications. The findings give us deeper insights on the positive and negative aspects of these two techniques and open up a range of stimulating research issues that we discuss in the paper.@en