Towards a norm-driven design of context-aware e-health applications

In this paper, we explore the usefulness of elaborating process models with norms, especially focusing on the Norm Analysis Method (NAM) as an elaboration tool that can be combined with a process modeling tool, such as Petri Net (PN). The PN-NAM combination has been particularly considered in the paper in relation to a challenge that concerns the design of context-aware applications, namely the challenge of specifying and elaborating complex behaviors that may include alternative (context-driven) processes (we assume that a user context space can be defined and that each context state within this space corresponds to an alternative application service behavior). Hence, the main contribution of our paper comprises an adaptability-driven methodological and modeling support to the design of context-aware applications; modeling guidelines are proposed, considered together with corresponding modeling tools (in particular PN and NAM), and partially illustrated by means of an e-Health-related example. Given the multi-disciplinary nature of the e-Health domain, it is expected that the current research will be useful for it. In particular, e-Health system developers might benefit from the relevant methodological and modeling support, proposed in the paper

Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization

Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify PCA against outliers. A least-trimmed squares estimator of a low-rank bilinear factor analysis model is shown closely related to that obtained from an $\ell_0$-(pseudo)norm-regularized criterion encouraging sparsity in a matrix explicitly modeling the outliers. This connection suggests robust PCA schemes based on convex relaxation, which lead naturally to a family of robust estimators encompassing Huber's optimal M-class as a special case. Outliers are identified by tuning a regularization parameter, which amounts to controlling sparsity of the outlier matrix along the whole robustification path of (group) least-absolute shrinkage and selection operator (Lasso) solutions. Beyond its neat ties to robust statistics, the developed outlier-aware PCA framework is versatile to accommodate novel and scalable algorithms to: i) track the low-rank signal subspace robustly, as new data are acquired in real time; and ii) determine principal components robustly in (possibly) infinite-dimensional feature spaces. Synthetic and real data tests corroborate the effectiveness of the proposed robust PCA schemes, when used to identify aberrant responses in personality assessment surveys, as well as unveil communities in social networks, and intruders from video surveillance data.Comment: 30 pages, submitted to IEEE Transactions on Signal Processin

Intangible trust requirements - how to fill the requirements trust "gap"?

Previous research efforts have been expended in terms of the capture and subsequent instantiation of "soft" trust requirements that relate to HCI usability concerns or in relation to "hard" tangible security requirements that primarily relate to security a ssurance and security protocols. Little direct focus has been paid to managing intangible trust related requirements per se. This 'gap' is perhaps most evident in the public B2C (Business to Consumer) E- Systems we all use on a daily basis. Some speculative suggestions are made as to how to fill the 'gap'. Visual card sorting is suggested as a suitable evaluative tool; whilst deontic logic trust norms and UML extended notation are the suggested (methodologically invariant) means by which software development teams can perhaps more fully capture hence visualize intangible trust requirements

Competing or aiming to be average?: Normification as a means of engaging digital volunteers

Engagement, motivation and active contribution by digital volunteers are key requirements for crowdsourcing and citizen science projects. Many systems use competitive elements, for example point scoring and leaderboards, to achieve these ends. However, while competition may motivate some people, it can have a neutral or demotivating effect on others. In this paper we explore theories of personal and social norms and investigate normification as an alternative approach to engagement, to be used alongside or instead of competitive strategies. We provide a systematic review of existing crowdsourcing and citizen science literature and categorise the ways that theories of norms have been incorporated to date. We then present qualitative interview data from a pro-environmental crowdsourcing study, Close the Door, which reveals normalising attitudes in certain participants. We assess how this links with competitive behaviour and participant performance. Based on our findings and analysis of norm theories, we consider the implications for designers wishing to use normification as an engagement strategy in crowdsourcing and citizen science systems

Competition and norms: a self-defeating combination?

This paper investigates the effects of information feedback mechanisms on electricity and heating usage at a student hall of residence in London. In a randomised control trial, we formulate different treatments such as feedback information and norms, as well as prize competition among subjects. We show that information and norms lead to a sharp – more than 20% - reduction in overall energy consumption. Because participants do not pay for their energy consumption this response cannot be driven by cost saving incentives. Interestingly, when combining feedback and norms with a prize competition for achieving low energy consumption, the reduction effect – while present initially – disappears in the long run. This could suggest that external rewards reduce and even destroy intrinsic motivation to change behaviour

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial

Right here, right now: situated interventions to change consumer habits

Consumer behavior-change interventions have traditionally encouraged consumers to form conscious intentions, but in the past decade it has been shown that while these interventions have a medium-to-large effect in changing intentions, they have a much smaller effect in changing behavior. Consumers often do not act in accordance with their conscious intentions because situational cues in the immediate environment automatically elicit learned, habitual behaviors. It has therefore been suggested that researchers refocus their efforts on developing interventions that target unconscious, unintentional influences on behavior, such as cue-behavior (“habit”) associations. To develop effective consumer behavior-change interventions, however, we argue that it is first important to understand how consumer experiences are represented in memory, in order to successfully target the situational cues that most strongly predict engagement in habitual behavior. In this article, we present a situated cognition perspective of habits and discuss how the situated cognition perspective extends our understanding of how consumer experiences are represented in memory, and the processes through which these situational representations can be retrieved in order to elicit habitual consumer behaviors. Based on the principles of situated cognition, we then discuss five ways that interventions could change consumer habits by targeting situational cues in the consumer environment and suggest how existing interventions utilizing these behavior-change strategies could be improved by integrating the principles of the situated cognition approach