Inputting query terms on a mobile terminal is frustrating because of device limitations though mobile web search is becoming popular. Query prediction is a promising approach for mobile search. In the literature, however, little attention has been paid to the first query term, though there are many reports on query expansion or second query recommendation. In this paper, we propose a first query prediction method that enables users to search related information for the current browsed webpage with an easier interface such as touch operation without any background search processes. The proposed method consists of body-text extraction, candidate query term extraction, and a scoring process. We also implemented a one-touch search application that works for Japanese webpages at a practical speed on a smartphone. According to our closed evaluation with 299 webpages and open evaluation with 14 users, our method achieved practical quality.

This poster presents an overview of the characteristics of a one-button information retrieval interface with closed captions from TV watching activities, which is intended to lighten the burden of remembering and entering query terms while watching TV. We investigated this interface with an experimental system named Video Bookmarking Search, which estimates query terms from closed captions with named-entity recognition and sentence labeling techniques. According to an empirical evaluation for 1,138 search queries from 206 bookmarks using seven actual TV shows on city life, travel, health, and cuisine, we found wider queries and search results are acceptable through the query-input-free interface, despite the fact that the number of queries and search results that are directly relevant to the users' original intentions is not high. The main reason is a watching user's interest is wider than what is expressed with query terms.

Observation study of human infants tells us that they can successfully acquire lexicon; understanding the relationship between the meaning and the uttered word from only one teaching by caregiver, even though there are many other possible mappings. This paper proposes a lexical acquisition model which makes use of curiosity to associate visual features of observed objects with the labels that are uttered by a caregiver. A robot changes its attention and learning rate based on curiosity. In the experiment with a real humanoid robot, the visual features are represented with self organizing maps which adaptively represents the shape of observed objects independent of the viewpoints.

This paper proposes a method of behavior generation for humanoids in which a robot learns sensorimotor maps in each motion module as the forward and inverse relationships between optic flows in the robot's view and motion parameters. Humanoids use these maps to determine appropriate motion parameters that generate a desired flow given by a planner. Each module consists of a planner and sensorimotor maps of primitives and can accomplish a simple task. Using a predefined module transition rule, humanoids can accomplish a complex task. Passing a ball (face-to-face pass) between two humanoids which have different camera lens and body parameters is realized as an example task.