Given a large amount of table data, how can we find the tables that contain the contents we want? A naive search fails when the column names are ambiguous, such as if columns containing stock price information are named “Close” in one table and named “P” in another table.One way of dealing with this problem that has been gaining attention is the semantic annotation of table data columns by using canonical knowledge. While previous studies successfully dealt with this problem for specific types of table data such as web tables, it still remains for various other types of table data: (1) most approaches do not handle table data with numerical values, and (2) their predictive performance is not satisfactory.This paper presents a novel approach for table data annotation that combines a latent probabilistic model with multilabel classifiers. It features three advantages over previous approaches due to using highly predictive multi-label classifiers in the probabilistic computation of semantic annotation. (1) It is more versatile due to using multi-label classifiers in the probabilistic model, which enables various types of data such as numerical values to be supported. (2) It is more accurate due to the multi-label classifiers and probabilistic model working together to improve predictive performance. (3) It is more efficient due to potential functions based on multi-label classifiers reducing the computational cost for annotation.Extensive experiments demonstrated the superiority of the proposed approach over state-of-the-art approaches for semantic annotation of real data (183 human-annotated tables obtained from the UCI Machine Learning Repository).

This article discusses the problem of recognizing groups of people in conversation with each other in an open space. Previous work on this problem takes an approach based on the knowledge that people in the same conversation group often makes a circular formation called an F-formation, by referring to the work in social psychology. Since the F-formation describes spatial and orientational characteristics of the lower bodies of people, their positions and orientations need to be obtained for employing the approach. However, it is difficult to observe lower bodies by cameras, especially for people in a circular formation, due to occlusions between their bodies. We propose to recognize conversation groups while estimating the positions and orientations of the lower bodies of the people only from the information of their head positions and facial orientations observable from a single camera, by considering that the lower bodies of the people are arranged in a circular pattern if they are in the same conversation group.

This article proposes a new concept for a context-aware Web search method that automatically retrieves a webpage related to the daily activity that a user currently is engaged in and displays the page on nearby Internet-connected home appliances such as televisions. For example, when a user is washing a coffeemaker, a webpage is retrieved that includes tips such as “cleaning a coffee maker with vinegar removes stains well,” and the page is displayed on a nearby appliance. In this article, we design and implement a Web search method that employs ubiquitous sensors to monitor a user's daily life. Our proposed method automatically searches for a webpage related to a daily activity by using a query constructed from the use of daily objects employed in the activity that is detected with object-attached sensors. We evaluate the search method with real datasets collected from vast numbers of sensors and achieve very accurate webpage retrieval. We then investigate the usefulness and effectiveness of a daily life Web search with Wizard-of-Oz (WOz)-like experiments. We confirm that the presentation of webpages related to daily activities improves participants' future daily lives and triggers communication among the participants in the experiment.

The new concept proposed in this paper is a query free web search that automatically retrieves a web page including information related to the daily activity that we are currently engaged in for automatically displaying the page on Internet-connected domestic appliances around us such as televisions. When we are washing a coffee maker, for example, a web page is retrieved that includes tips such as `cleaning a coffee maker with vinegar removes stains well.' A method designed on the basis of this concept automatically searches for a web page by using a query constructed from the use of ordinary household objects that is detected by sensors attached to the objects. An in-situ experiment tests a variety of IR techniques and the experiment confirmed that our daily activities can produce related web pages with high accuracy.

This paper proposes new concept of query free web search for daily living. We ordinarily benefit from additional information about our daily activities that we are currently engaged in. When washing a coffee maker, for example, we receive the benefit if we obtain such information as 'cleaning a coffee maker with vinegar removes its stain well.' Our proposed method automatically searches for a web page including such information relates to an activity of daily living when the activity is performed. We assume that wireless sensor nodes are attached to daily objects to detect object use; our method makes a query from the names of objects which are used. Then, the method retrieves a web page relates to the activity of daily living by using the query.

The environment generated media (EGM) are defined here as being generated from a massive amount of and/or incomprehensible environmental data by compressing them into averages or representative values and/or by converting them into such user-friendly media as text, figures, charts, and animations. As an application of EGM, an object-participation-type weblog is introduced, where anthropomorphic indoor objects with sensor nodes post weblog entries and comments about what happened to them in a sensor networked environment.

Context-aware applications in the real world adapt their behavior to contexts abstracted from real-world situations sensed as physical quantities by heterogeneous and distributed sensors. Most of the adaptations are programmed as rules derived from human experience in making environments comfortable and efficient. Preparing sufficient rules, however, is difficult because oversights and exceptional contexts are inevitable. In this paper, we focus on sensor data fluctuations and calculate probabilities that indicate the frequency of such sensor data fluctuations. The results help us find previously unnoticed situations that the context-aware applications should cope with. We applied this method to log data captured in our office in order to evaluate the methods' capabilities and then considered the feasibility of abstracting these situations into rules.

Assuming an environment in which a sensor network always collects data produced by sensors attached to physical objects, the demonstration system presented here searches sensor data for corresponding real-world events using a natural language (NL) words in a query. The system translates each query into a physical quantity representation also introduced here, searches for a sensor data segment that satisfies the description by the representation, and returns the information about the event that is reflected in time series of values from sensor readings.