Search

We propose a method for selecting home appliances using a smart glass, which facilitates the control of network-connected appliances in a smart house. Our proposed method is image-based appliance selection and enables smart glass users to easily select a particular appliance by just looking at it. The main feature of our method is that it achieves high precision appliance selection using user contextual information such as position and activity, inferred from various sensor data in addition to camera images captured by the glass because such contextual information is greatly related in the home appliance that a user wants to control in her daily life. We design a state-of-the-art appliance selection method by fusing image features extracted by deep learning techniques and context information estimated by non-parametric Bayesian techniques within a framework of multiple kernel learning. Our experimental results, which use sensor data obtained in an actual house equipped with many network-connected appliances, show the effectiveness of our method.

Most commercial search engines have a query suggestion feature, which is designed to capture various possible search intents behind the user's original query. However, even though different search intents behind a given query may have been popular at different time periods in the past, existing query suggestion methods neither utilize nor present such information. In this study, we propose Time-aware Structured Query Suggestion (TaSQS) which clusters query suggestions along a timeline so that the user can narrow down his search from a temporal point of view. Moreover, when a suggested query is clicked, TaSQS presents web pages from query-URL bipartite graphs after ranking them according to the click counts within a particular time period. Our experiments using data from a commercial search engine log show that the time-aware clustering and the time-aware document ranking features of TaSQS are both effective.

Accelerated by the technological advances in the domain, the size of the biomedical literature has been growing rapidly. As a result, it is not feasible for individual researchers to comprehend and synthesize all the information related to their interests. Therefore, it is conceivable to discover hidden knowledge, or hypotheses, by linking fragments of information independently described in the literature. In fact, such hypotheses have been reported in the literature mining community; some of which have even been corroborated by experiments. This paper mainly focuses on hypothesis ranking and investigates an approach to identifying reasonable ones based on semantic similarities between events which lead to respective hypotheses. Our assumption is that hypotheses generated from semantically similar events are more reasonable. The validity of our approach is demonstrated in comparison with those based on term frequencies, often adopted in the related work.