Gesture-aware Interactive Machine Teaching with In-situ Object Annotations

Interactive Machine Teaching (IMT) systems allow non-experts to easily create Machine Learning (ML) models. However, existing vision-based IMT systems either ignore annotations on the objects of interest or require users to annotate in a post-hoc manner. Without the annotations on objects, the model may misinterpret the objects using unrelated features. Post-hoc annotations cause additional workload, which diminishes the usability of the overall model building process. In this paper, we develop LookHere, which integrates in-situ object annotations into vision-based IMT. LookHere exploits users' deictic gestures to segment the objects of interest in real time. This segmentation information can be additionally used for training. To achieve the reliable performance of this object segmentation, we utilize our custom dataset called HuTics, including 2040 front-facing images of deictic gestures toward various objects by 170 people. The quantitative results of our user study showed that participants were 16.3 times faster in creating a model with our system compared to a standard IMT system with a post-hoc annotation process while demonstrating comparable accuracies. Additionally, models created by our system showed a significant accuracy improvement ($\Delta mIoU=0.466$) in segmenting the objects of interest compared to those without annotations.Comment: UIST 202

For What It's Worth: Humans Overwrite Their Economic Self-interest to Avoid Bargaining With AI Systems

As algorithms are increasingly augmenting and substituting human decision-making, understanding how the introduction of computational agents changes the fundamentals of human behavior becomes vital. This pertains to not only users, but also those parties who face the consequences of an algorithmic decision. In a controlled experiment with 480 participants, we exploit an extended version of two-player ultimatum bargaining where responders choose to bargain with either another human, another human with an AI decision aid or an autonomous AI-system acting on behalf of a passive human proposer. Our results show strong responder preferences against the algorithm, as most responders opt for a human opponent and demand higher compensation to reach a contract with autonomous agents. To map these preferences to economic expectations, we elicit incentivized subject beliefs about their opponent's behavior. The majority of responders maximize their expected value when this is line with approaching the human proposer. In contrast, responders predicting income maximization for the autonomous AI-system overwhelmingly override economic self-interest to avoid the algorithm

Manipulation, Learning, and Recall with Tangible Pen-Like Input

International audienceWe examine two key human performance characteristics of a pen-like tangible input device that executes a different command depending on which corner, edge, or side contacts a surface. The manipulation time when transitioning between contacts is examined using physical mock-ups of three representative device sizes and a baseline pen mock-up. Results show the largest device is fastest overall and minimal differences with a pen for equivalent transitions. Using a hardware prototype able to sense all 26 different contacts, a second experiment evaluates learning and recall. Results show almost all 26 contacts can be learned in a two-hour session with an average of 94% recall after 24 hours. The results provide empirical evidence for the practicality, design, and utility for this type of tangible pen-like input

Motivation through gamification: A Self-Determination Theory perspective for the design of an adaptive reward system

Research on the nature and origins of human motivation has addressed the role of rewards in learning and behaviour. Gamification finds its raison d'être in being able to leverage motivational theories, to foster motivation in users through the use of game elements. One of the main criticisms moved to the use of gamification for learning purposes is related to the one-size-fits-all approach that tends to characterize many gamified applications. In this paper we explore the possibilities that can arise from the convergence of Self-Determination Theory principles and machine learning, to improve the efficacy of gamification reward systems

Copyright c 2011 by Koji YataniAbstract Spatial Tactile Feedback Support for Mobile Touch-screen Devices

Mobile touch-screen devices have the capability to accept flexible touch input, and can provide a larger screen than mobile devices with physical buttons. However, many of the user interfaces found in mobile touch-screen devices require visual feedback. This raises a number of user interface challenges. For instance, visually-demanding user interfaces make it difficult for the user to interact with mobile touch-screen devices without looking at the screen—a task the user sometimes wishes to do particularly in a mobile setting. In addition, user interfaces on mobile touch-screen devices are not generally accessible to visually impaired users. Basic tactile feedback (e.g., feedback produced by a single vibration source) can be used to enhance the user experience on mobile touch-screen devices. Unfortunately, this basic tactile feedback often lacks the expressiveness for generating vibration patterns that can be used to convey specific information about the application to the user. However, the availability of richer information accessible through the tactile channel would minimize the visual demand of an application. For example, if the user can perceive which button she is touching on the screen through tactile feedback, she would not need to view the screen, and can instead focus her visual attentio

Intuitive Interaction Techniques for Mobile Devices with Human Gestures

報告番号: ; 学位授与年月日: 2005-03- ; 学位の種別: 修士 ; 学位の種類: 修士() ; 学位記番号: ; 研究科・専攻: 新領域創成科学研究科基盤情報学専