2,354 research outputs found

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

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    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

    Identifying Purpose Behind Electoral Tweets

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    Tweets pertaining to a single event, such as a national election, can number in the hundreds of millions. Automatically analyzing them is beneficial in many downstream natural language applications such as question answering and summarization. In this paper, we propose a new task: identifying the purpose behind electoral tweets--why do people post election-oriented tweets? We show that identifying purpose is correlated with the related phenomenon of sentiment and emotion detection, but yet significantly different. Detecting purpose has a number of applications including detecting the mood of the electorate, estimating the popularity of policies, identifying key issues of contention, and predicting the course of events. We create a large dataset of electoral tweets and annotate a few thousand tweets for purpose. We develop a system that automatically classifies electoral tweets as per their purpose, obtaining an accuracy of 43.56% on an 11-class task and an accuracy of 73.91% on a 3-class task (both accuracies well above the most-frequent-class baseline). Finally, we show that resources developed for emotion detection are also helpful for detecting purpose

    On the Feature Discovery for App Usage Prediction in Smartphones

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    With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

    Characterization of the Lightning Safety Education Programs in the World as a First Step for the Creation of a Lightning Safety Policy in Colombia

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    En este proyecto de investigación-innovación se presentan los resultados de un estudio sobre los programas de educación en prevención contra rayos que han sido implementados en el mundo. La importancia del estudio de estos programas se fundamenta en las múltiples lesiones asociadas a las descargas eléctricas atmosféricas que se han observado en Colombia durante los últimos años. Una investigación primaria es llevada a cabo para determinar cuáles podrían ser los medios de difusión más pertinentes para transmitir el mensaje de la prevención contra rayos a la población rural, con el fin de tener los elementos necesarios para plantear una metodología educativa que pueda ser el primer paso para la creación de una política pública en prevención y protección contra rayos en Colombia.This research-innovation project presents the results of a study on the education programs on lightning safety that have been implemented in the world. The importance of the study of these programs is based on the multiple injuries associated with atmospheric discharges that have been observed in Colombia during the last years. A primary research is conducted to determine which could be the most pertinent diffusion means to spread the lightning safety message among the rural population, in order to have the necessary elements to propose an educational methodology that may be the first step towards the creation of a lightning safety public policy in Colombia
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