Accounting for Chance Agreement in Gesture Elicitation Studies

The level of agreement among participants is a key aspect of gesture elicitation studies, and it is typically quantified by means of agreement rates (AR). We show that this measure is problematic, as it does not account for chance agreement. The problem of chance agreement has been extensively discussed in a range of scientific fields in the context of inter-rater reliability studies. We review chance-corrected agreement coefficients that are routinely used in inter-reliability studies and show how to apply them to gesture elicitation studies. We also discuss how to compute interval estimates for these coefficients and how to use them for statistical inference

The Drawing Assistant: Automated Drawing Guidance and Feedback from Photographs

International audienceWe present an interactive drawing tool that provides automated guidance over model photographs to help people practice traditional drawing-by-observation techniques. The drawing literature describes a number of techniques to support this task and help people gain consciousness of the shapes in a scene and their relationships. We compile these techniques and derive a set of construction lines that we automatically extract from a model photograph. We then display these lines over the model to guide its manual reproduction by the user on the drawing canvas. Finally, we use shape-matching to register the user's sketch with the model guides. We use this registration to provide corrective feedback to the user. Our user studies show that automatically extracted construction lines can help users draw more accurately. Furthermore, users report that guidance and corrective feedback help them better understand how to draw

Stretchis: Fabricating Highly Stretchable User Interfaces

International audienceRecent advances in materials science research have enabled the production of highly stretchable sensors and displays. However, such technologies are not yet accessible to non-expert makers. We present a novel and inexpensive fabrication method for creating Stretchis, highly stretchable user interfaces that combine sensing capabilities and visual output. We use Polydimethylsiloxan (PDMS) as the base material for a Stretchi and show how to embed stretchable touch and proximity sensors and stretchable electroluminescent displays. Stretchis can be ultra-thin, flexible, and fully customizable, enabling non-expert makers to add interaction to elastic physical objects, shape-changing surfaces, fabrics, and the human body. We demonstrate the usefulness of our approach with three application examples that include ubiquitous computing, wearables and on-skin interaction

INKSPLORER : VERS LE PAPIER INTERACTIF POUR LA COMPOSITION MUSICALE DÉMONSTRATION

International audienceFigure 1 : Utilisation d'InkSplorer avec Max/MSP pour contrôler l'enveloppe temporelle d'un son. Lors de la sélection d'un tracé avec le stylo, l'enveloppe est appliquée et le son est joué. Il est ainsi possible de comparer parmi plusieurs alternatives pour obtenir le résultat souhaité en interagissant directement sur le papier. RÉSUMÉ Nous présentons InkSplorer, un nouveau système utilisant la richesse et la souplesse du papier lors de l'expérimentation avec OpenMusic et Max/MSP. Ink-Splorer permet aux compositeurs d'utiliser et de réutiliser directement sur le papier des courbes dessinées comme données de contrôle

ProS: Data Series Progressive k-NN Similarity Search and Classification with Probabilistic Quality Guarantees

Existing systems dealing with the increasing volume of data series cannot guarantee interactive response times, even for fundamental tasks such as similarity search. Therefore, it is necessary to develop analytic approaches that support exploration and decision making by providing progressive results, before the final and exact ones have been computed. Prior works lack both efficiency and accuracy when applied to large-scale data series collections. We present and experimentally evaluate ProS, a new probabilistic learning-based method that provides quality guarantees for progressive Nearest Neighbor (NN) query answering. We develop our method for k-NN queries and demonstrate how it can be applied with the two most popular distance measures, namely, Euclidean and Dynamic Time Warping (DTW). We provide both initial and progressive estimates of the final answer that are getting better during the similarity search, as well suitable stopping criteria for the progressive queries. Moreover, we describe how this method can be used in order to develop a progressive algorithm for data series classification (based on a k-NN classifier), and we additionally propose a method designed specifically for the classification task. Experiments with several and diverse synthetic and real datasets demonstrate that our prediction methods constitute the first practical solutions to the problem, significantly outperforming competing approaches. This paper was published in the VLDB Journal (2022)

Progressive Similarity Search on Time Series Data

International audienceTime series data are increasing at a dramatic rate, yet their analysis remains highly relevant in a wide range of human activities. Due to their volume, existing systems dealing with time series data cannot guarantee interactive response times, even for fundamental tasks such as similarity search. Therefore , in this paper, we present our vision to develop analytic approaches that support exploration and decision making by providing progressive results, before the final and exact ones have been computed. We demonstrate through experiments that providing first approximate and then progressive answers is useful (and necessary) for similarity search queries on very large time series data. Our findings indicate that there is a gap between the time the most similar answer is found and the time when the search algorithm terminates, resulting in inflated waiting times without any improvement. We present preliminary ideas on computing probabilistic estimates of the final results that could help users decide when to stop the search process, i.e., deciding when improvement in the final answer is unlikely, thus eliminating waiting time. Finally, we discuss two additional challenges: how to compute efficiently these probabilistic estimates, and how to communicate them to users

PaperComposer: Creating Interactive Paper Interfaces for Music Composition

National audienceInteractive paper technologies offer new opportunities for supporting the highly individual practices of creative artists, such as contemporary music composers, who express and explore their ideas on both paper and the computer. We introduce PaperComposer, a graphical interface builder that allows users to create a personalized interactive paper interface that they can connect to their own computer-based musical data. We also present an API that facilitates the development of interactive paper components for PaperComposer. We describe one public demonstration of a novel musical interface designed for children and our collaborations with composers to create two novel interactive music interfaces that reflected their individual composition styles

WRIST : Watch-Ring Interaction and Sensing Technique for wrist gestures and macro-micro pointing

Funding: Next-Generation In-ormation Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7066316) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-01270, WISE AR UI/UX Platform Development for Smartglasses).To better explore the incorporation of pointing and gesturing into ubiquitous computing, we introduce WRIST, an interaction and sensing technique that leverages the dexterity of human wrist motion. WRIST employs a sensor fusion approach which combines inertial measurement unit (IMU) data from a smartwatch and a smart ring. The relative orientation difference of the two devices is measured as the wrist rotation that is independent from arm rotation, which is also position and orientation invariant. Employing our test hardware, we demonstrate that WRIST affords and enables a number of novel yet simplistic interaction techniques, such as (i) macro-micro pointing without explicit mode switching and (ii) wrist gesture recognition when the hand is held in different orientations (e.g., raised or lowered). We report on two studies to evaluate the proposed techniques and we present a set of applications that demonstrate the benefits of WRIST. We conclude with a discussion of the limitations and highlight possible future pathways for research in pointing and gesturing with wearable devices.Postprin

User-Controlled Link Adaptation

This paper introduces an adaptable hypermedia approach applied to adaptive link annotation techniques. This approach suggests that the combination of direct manipulation with automated link annotation affords greater user control over page adaptation. In turn, this direct control better supports user focus in information discovery tasks. Unlike adaptive-only systems, our approach lets users both define multiple topics of interest and then manipulate how these topics' associated links are presented in a page. We discuss how the approach can be applied both to pages viewed as well as to the user's history list, thereby relieving users from the task of either adding to or organizing bookmarks. We describe the prototype developed to support these manipulations, as well as the adaptive architecture developed to support these controls