They want to tell us: Attention-aware Design and Evaluation of Ambient Displays for Learning

This paper explores the interaction between users and ambient displays and the evaluation thereof in a learning context. A formative design study examined the user attention towards ambient displays as well as the influence of different display designs. Experimental prototypes were varied on two design dimensions, namely representational fidelity and notification level, and deployed on a university campus. For the evaluation a combined approach using quantitative attention data as well as qualitative assessment methods was used. The results show a high degree of user interest in the displays over time, but do not provide clear evidence that the design of the displays influences the user attention. Nevertheless, the combination of quantitative and qualitative measurement does provide a more holistic view on user attention. The gathered insights can inform future designs and developments of ambient displays also beyond the learning context

NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring

Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.Comment: To appear in the fifth International Conference on Future Energy Systems (ACM e-Energy), Cambridge, UK. 201

DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation

There is an undeniable communication barrier between deaf people and people with normal hearing ability. Although innovations in sign language translation technology aim to tear down this communication barrier, the majority of existing sign language translation systems are either intrusive or constrained by resolution or ambient lighting conditions. Moreover, these existing systems can only perform single-sign ASL translation rather than sentence-level translation, making them much less useful in daily-life communication scenarios. In this work, we fill this critical gap by presenting DeepASL, a transformative deep learning-based sign language translation technology that enables ubiquitous and non-intrusive American Sign Language (ASL) translation at both word and sentence levels. DeepASL uses infrared light as its sensing mechanism to non-intrusively capture the ASL signs. It incorporates a novel hierarchical bidirectional deep recurrent neural network (HB-RNN) and a probabilistic framework based on Connectionist Temporal Classification (CTC) for word-level and sentence-level ASL translation respectively. To evaluate its performance, we have collected 7,306 samples from 11 participants, covering 56 commonly used ASL words and 100 ASL sentences. DeepASL achieves an average 94.5% word-level translation accuracy and an average 8.2% word error rate on translating unseen ASL sentences. Given its promising performance, we believe DeepASL represents a significant step towards breaking the communication barrier between deaf people and hearing majority, and thus has the significant potential to fundamentally change deaf people's lives

Real-Time Non-Intrusive Assessment of Viewing Distance During Computer Use

Purpose: To develop and test the sensitivity of an ultrasound-based sensor to assess the viewing distance of visual display terminals operators in real-time conditions. Methods: A modified ultrasound sensor was attached to a computer display to assess viewing distance in real time. Sensor functionality was tested on a sample of 20 healthy participants while they conducted four 10-minute randomly presented typical computer tasks (a match-three puzzle game, a video documentary, a task requiring participants to complete a series of sentences, and a predefined internet search). Results: The ultrasound sensor offered good measurement repeatability. Game, text completion, and web search tasks were conducted at shorter viewing distances (54.4 cm [95% CI 51.3-57.5 cm], 54.5 cm [95% CI 51.1-58.0 cm], and 54.5 cm [95% CI 51.4-57.7 cm], respectively) than the video task (62.3 cm [95% CI 58.9-65.7 cm]). Statistically significant differences were found between the video task and the other three tasks (all p < 0.05). Range of viewing distances (from 22 to 27 cm) was similar for all tasks (F = 0.996; p = 0.413). Conclusions: Real-time assessment of the viewing distance of computer users with a non-intrusive ultrasonic device disclosed a task-dependent pattern. (C) 2016 American Academy of OptometryPostprint (author's final draft

Context Aware Adaptable Applications - A global approach

Actual applications (mostly component based) requirements cannot be expressed without a ubiquitous and mobile part for end-users as well as for M2M applications (Machine to Machine). Such an evolution implies context management in order to evaluate the consequences of the mobility and corresponding mechanisms to adapt or to be adapted to the new environment. Applications are then qualified as context aware applications. This first part of this paper presents an overview of context and its management by application adaptation. This part starts by a definition and proposes a model for the context. It also presents various techniques to adapt applications to the context: from self-adaptation to supervised approached. The second part is an overview of architectures for adaptable applications. It focuses on platforms based solutions and shows information flows between application, platform and context. Finally it makes a synthesis proposition with a platform for adaptable context-aware applications called Kalimucho. Then we present implementations tools for software components and a dataflow models in order to implement the Kalimucho platform