In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a realworld environment, collect a large dataset of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms in localizing a mobile device. We have found algorithms that give a mean error as accurate as 0.76 meters, outperforming other indoor localization systems reported in the literature. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance-based methods, allowing for fast and accurate localization. Further, we determine how smaller datasets collected with less density affect accuracy of localization, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment.

Answer: Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors


Question: We present a neural network architecture to predict a point in color space from the sequence of characters in the color’s name. Using large scale color–name pairs obtained from an online color design forum, we evaluate our model on a “color Turing test” and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at http://colorlab.us.

Answer: Character Sequence Models for Colorful Words


Question: The problem of recommending tours to travellers is an impor-<lb>tant and broadly studied area. Suggested solutions include<lb>various approaches of points-of-interest (POI) recommenda-<lb>tion and route planning. We consider the task of recommend-<lb>ing a sequence of POIs, that simultaneously uses information<lb>about POIs and routes. Our approach unifies the treatment<lb>of various sources of information by representing them as fea-<lb>tures in machine learning algorithms, enabling us to learn<lb>from past behaviour. Information about POIs are used to<lb>learn a POI ranking model that accounts for the start and<lb>end points of tours. Data about previous trajectories are used<lb>for learning transition patterns between POIs that enable us<lb>to recommend probable routes. In addition, a probabilistic<lb>model is proposed to combine the results of POI ranking and<lb>the POI to POI transitions. We propose a new F1 score on<lb>pairs of POIs that capture the order of visits. Empirical re-<lb>sults show that our approach improves on recent methods,<lb>and demonstrate that combining points and routes enables<lb>better trajectory recommendations.

Answer:
Learning Points and Routes to Recommend Trajectories