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Background With the invention of fitness trackers, it has been possible to continuously monitor a user’s biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user’s “activeness”, and investigates the feasibility in modeling and predicting the long-term activeness of the user. Methods...
This paper introduces a vocabulary learning application called “Avocado” that aims to provide suitable learning materials to learners by modeling their language proficiency and topical interests. A learner's vocabulary level is estimated through aggregating words that he identifies as difficult in given text passages; and his topical interests are gathered by utilizing the social network (Facebook)...
In this paper, we propose a service to predict changes in a user's daily activity in consideration of the user's contextual information and recommend appropriate physical activities for improving well-being. Based on the data collected from a smartphone and wearable sensor, our service models an activity pattern of the specific user and predicts the upcoming activity changes of the user by using the...
With the exponential growth in data, we often find ourselves struggling to deal with information overload. Techniques such as timeline summarization tackle this problem by generating short summaries for each time stamp on a timeline. However, we argue that rather than reading a set of blocks of texts, it is easier and quicker for a reader to observe dynamically changing relations between important...
This paper investigates the performance of Elman-type and Jordan-type recurrent neural networks (RNN) in extracting temporal information from textual data. The RNN architectures are applied to two tasks of TempEval-2 challenge: (1) extracting the extent of TIMEX3 tags and its TYPE, and (2) extracting the extent of EVENT tags and its CLASS attribute. For the first task, the performances of the RNN...
Previous research in spam detection, especially in email spam filtering, mainly focused on learning a set of discriminative features that are often present in the spam contents. Nowadays, these commercially oriented spams are well detected; the real challenge lies in filtering rather vague spams that do not exhibit distinctive spam keywords. We investigate two ways of detecting such spams: 1) By comparing...
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