1,720 research outputs found
New Frontiers of Quantified Self 3: Exploring Understudied Categories of Users
Quantified Self (QS) field needs to start thinking of how situated needs may affect the use of self-tracking technologies. In this workshop we will focus on the idiosyncrasies of specific categories of users
Balancing smartness and privacy for the Ambient Intelligence
Ambient Intelligence (AmI) will introduce large privacy risks. Stored context histories are vulnerable for unauthorized disclosure, thus unlimited storing of privacy-sensitive context data is not desirable from the privacy viewpoint. However, high quality and quantity of data enable smartness for the AmI, while less and coarse data benefit privacy. This raises a very important problem to the AmI, that is, how to balance the smartness and privacy requirements in an ambient world. In this article, we propose to give to donors the control over the life cycle of their context data, so that users themselves can balance their needs and wishes in terms of smartness and privacy
On the Feature Discovery for App Usage Prediction in Smartphones
With the increasing number of mobile Apps developed, they are now closely
integrated into daily life. In this paper, we develop a framework to predict
mobile Apps that are most likely to be used regarding the current device status
of a smartphone. Such an Apps usage prediction framework is a crucial
prerequisite for fast App launching, intelligent user experience, and power
management of smartphones. By analyzing real App usage log data, we discover
two kinds of features: The Explicit Feature (EF) from sensing readings of
built-in sensors, and the Implicit Feature (IF) from App usage relations. The
IF feature is derived by constructing the proposed App Usage Graph (abbreviated
as AUG) that models App usage transitions. In light of AUG, we are able to
discover usage relations among Apps. Since users may have different usage
behaviors on their smartphones, we further propose one personalized feature
selection algorithm. We explore minimum description length (MDL) from the
training data and select those features which need less length to describe the
training data. The personalized feature selection can successfully reduce the
log size and the prediction time. Finally, we adopt the kNN classification
model to predict Apps usage. Note that through the features selected by the
proposed personalized feature selection algorithm, we only need to keep these
features, which in turn reduces the prediction time and avoids the curse of
dimensionality when using the kNN classifier. We conduct a comprehensive
experimental study based on a real mobile App usage dataset. The results
demonstrate the effectiveness of the proposed framework and show the predictive
capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape
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Sensory semantic user interfaces (SenSUI)
Rapid evolution of the World Wide Web with its underlying sources of data, knowledge, services and applications continually attempts to support a variety of users, with different backgrounds, requirements and capabilities. In such an environment, it is highly unlikely that a single user interface will prevail and be able to fulfill the requirements of each user adequately. Adaptive user interfaces are able to adapt information and application functionalities to the user context. In contrast, pervasive computing and sensor networks open new opportunities for context aware platforms, one that is able to improve user interface adaptation reacting to environmental and user sensors. Semantic web technologies and ontologies are able to capture sensor data and provide contextual information about the user, their actions, required applications and environment. This paper investigates the viability of an approach where semantic web technologies are used to maximize the efficacy of interface adaptation through the use of available ontology
Exploring sustainability research in computing:where we are and where we go next
This paper develops a holistic framework of questions mo- tivating sustainability research in computing in order to en- able new opportunities for critique. Analysis of systemat- ically selected corpora of computing publications demon- strates that several of these question areas are well covered, while others are ripe for further exploration. It also pro- vides insight into which of these questions tend to be ad- dressed by different communities within sustainable com- puting. The framework itself reveals discursive similarities between other existing environmental discourses, enabling reflection and participation with the broader sustainability debate. It is argued that the current computing discourse on sustainability is reformist and premised in a Triple Bottom Line construction of sustainability, and a radical, Quadruple Bottom Line alternative is explored as a new vista for com- puting research
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