Tuning the Diversity of Open-Ended Responses from the Crowd

Crowdsourcing can solve problems that current fully automated systems cannot. Its effectiveness depends on the reliability, accuracy, and speed of the crowd workers that drive it. These objectives are frequently at odds with one another. For instance, how much time should workers be given to discover and propose new solutions versus deliberate over those currently proposed? How do we determine if discovering a new answer is appropriate at all? And how do we manage workers who lack the expertise or attention needed to provide useful input to a given task? We present a mechanism that uses distinct payoffs for three possible worker actions---propose,vote, or abstain---to provide workers with the necessary incentives to guarantee an effective (or even optimal) balance between searching for new answers, assessing those currently available, and, when they have insufficient expertise or insight for the task at hand, abstaining. We provide a novel game theoretic analysis for this mechanism and test it experimentally on an image---labeling problem and show that it allows a system to reliably control the balance betweendiscovering new answers and converging to existing ones

Cracking the Cocoa Nut: User Interface Programming at Runtime

International audienceThis article introduces runtime toolkit overloading, a novel approach to help third-party developers modify the interaction and behavior of existing software applications without access to their underlying source code. We describe the abstractions provided by this approach as well as the mechanisms for implementing them in existing environments. We describe Scotty, a prototype implementation for Mac OS X Cocoa that enables developers to modify existing applications at runtime, and we demonstrate a collection of interaction and functional transformations on existing off-the-shelf applications. We show how Scotty helps a developer make sense of unfamiliar software, even without access to its source code. We further discuss what features of future environments would facilitate this kind of runtime software development

Direct and gestural interaction with relief: A 2.5D shape display

Actuated shape output provides novel opportunities for experiencing, creating and manipulating 3D content in the physical world. While various shape displays have been proposed, a common approach utilizes an array of linear actuators to form 2.5D surfaces. Through identifying a set of common interactions for viewing and manipulating content on shape displays, we argue why input modalities beyond direct touch are required. The combination of freehand gestures and direct touch provides additional degrees of freedom and resolves input ambiguities, while keeping the locus of interaction on the shape output. To demonstrate the proposed combination of input modalities and explore applications for 2.5D shape displays, two example scenarios are implemented on a prototype system

deForm: An interactive malleable surface for capturing 2.5D arbitrary objects, tools and touch

We introduce a novel input device, deForm, that supports 2.5D touch gestures, tangible tools, and arbitrary objects through real-time structured light scanning of a malleable surface of interaction. DeForm captures high-resolution surface deformations and 2D grey-scale textures of a gel surface through a three-phase structured light 3D scanner. This technique can be combined with IR projection to allow for invisible capture, providing the opportunity for co-located visual feedback on the deformable surface. We describe methods for tracking fingers, whole hand gestures, and arbitrary tangible tools. We outline a method for physically encoding fiducial marker information in the height map of tangible tools. In addition, we describe a novel method for distinguishing between human touch and tangible tools, through capacitive sensing on top of the input surface. Finally we motivate our device through a number of sample applications

PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data

Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging the correlation between the auditory and visual modalities. Despite their many useful downstream tasks, such as video retrieval, AR/VR, and accessibility, the performance and adoption of existing audio-visual models have been impeded by the availability of high-quality datasets. Annotating audio-visual datasets is laborious, expensive, and time-consuming. To address this challenge, we designed and developed an efficient audio-visual annotation tool called Peanut. Peanut's human-AI collaborative pipeline separates the multi-modal task into two single-modal tasks, and utilizes state-of-the-art object detection and sound-tagging models to reduce the annotators' effort to process each frame and the number of manually-annotated frames needed. A within-subject user study with 20 participants found that Peanut can significantly accelerate the audio-visual data annotation process while maintaining high annotation accuracy.Comment: 18 pages, published in UIST'2

Automated Repair of Layout Cross Browser Issues Using Search-Based Techniques

A consistent cross-browser user experience is crucial for the success of a website. Layout Cross Browser Issues (XBIs) can severely undermine a website’s success by causing web pages to render incorrectly in certain browsers, thereby negatively impacting users’ impression of the quality and services that the web page delivers. Existing Cross Browser Testing (XBT) techniques can only detect XBIs in websites. Repairing them is, hitherto, a manual task that is labor intensive and requires significant expertise. Addressing this concern, our paper proposes a technique for automatically repairing layout XBIs in websites using guided search-based techniques. Our empirical evaluation showed that our approach was able to successfully fix 86% of layout XBIs reported for 15 different web pages studied, thereby improving their cross-browser consistency

Augmenting the scope of interactions with implicit and explicit graphical structures

International audienceWhen using interactive graphical tools, users often have to manage a structure, i.e. the arrangement of and relations between the parts or elements of the content. However, the interaction with structures may be complex, and not well integrated with the interaction with the content. Based on contextual inquiries and past works, we have identified a number of concepts and requirements about the interaction with structure. We have explored two interactive tools: a new kind of property sheet that relies on the implicit struc-ture of graphics; and a property delegation graph to enable users to provide an explicit graphical structure. The interac-tions with the tools augment the scope of interactions to multiple objects

SensiCut: Material-Aware Laser Cutting Using Speckle Sensing and Deep Learning

Laser cutter users face difficulties distinguishing between visually similar materials. This can lead to problems, such as using the wrong power/speed settings or accidentally cutting hazardous materials. To support users, we present SensiCut, an integrated material sensing platform for laser cutters. SensiCut enables material awareness beyond what users are able to see and reliably differentiates among similar-looking types. It achieves this by detecting materials' surface structures using speckle sensing and deep learning. SensiCut consists of a compact hardware add-on for laser cutters and a user interface that integrates material sensing into the laser cutting workflow. In addition to improving the traditional workflow and its safety1, SensiCut enables new applications, such as automatically partitioning designs when engraving on multi-material objects or adjusting their geometry based on the kerf of the identified material. We evaluate SensiCut's accuracy for different types of materials under different sheet orientations and illumination conditions