The Robot Who Knew Too Much: Toward Understanding the Privacy/Personalization Trade-off in Child-Robot Conversation
We explore what happens in the increasingly likely situation that a robot has sensed information about a child of which the child is unaware, then discloses that information in conversation in an effort to personalize the child’s experience.
Semi-Supervised Vocabulary-Informed Learning
We propose the notion of semi-supervised vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot and open set recognition using a unified framework.
Learning Activity Progression in LSTMs for Activity Detection and Early Detection
In this work we improve training of temporal deep models to better learn activity progression for activity detection and early detection tasks.
Harnessing Object and Scene Semantics for Large-Scale Video Understanding
We propose a novel object- and scene-based semantic fusion network and representation.
Learning Online Smooth Predictors for Realtime Camera Planning using Recurrent Decision Trees
We propose a recurrent decision tree framework that can directly incorporate temporal consistency into a data-driven predictor, as well as a learning algorithm that can efficiently learn such temporally smooth models.
Energy-Interference-Free System and Toolchain Support for Energy-Harvesting Devices
Energy-harvesting computers eschew tethered power and batteries by harvesting energy from their environment.
NFC-WISP: A Sensing and Computationally Enhanced Near-Field RFID Platform
This paper presents the NFC-WISP, which is a programmable, sensing and computationally enhanced platform designed to explore new RFID enabled sensing and user interface applications.
3-Dimensional Charging via Multi-Mode Resonant Cavity Enabled Wireless Power Transfer
In this paper, we propose an unexplored type of wireless power transfer system based on electromagnetic cavity resonance.
Resonant Cavity Mode Enabled Wireless Power Transfer
This paper proposes using the electromagnetic resonant modes of a hollow metallic structure to provide wireless power to small receivers contained anywhere inside.
Assessing Tracking Performance in Complex Scenarios using Mean Time Between Failures
In this work we propose ‘mean time between failures’ as a viable summary of solution quality - especially when the goal is to follow objects for as long as possible.
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