In recent years, head mounted displays (HMDs) have become widely used among general users thanks to the release of low-cost and high-performance versions. It is expected that virtual reality (VR) experiences will often be performed in a relaxed posture, while sitting on a reclining seat at home or in the supine position on a bed in rehabilitation institutions. However, there is no clear knowledge on the subject of how humans perceive the horizontal plane of a virtual world while using HMDs in reclining postures. In this study, we investigated how the angle of the subjective horizontal plane changes in relation to the angle of the upper body in a reclining seat. As a result, we found that the angle of the perceived horizontal plane changes depending on the angle of the upper body, and the physical direction of gravity has little effect on this perception.

We have been investigating about the visual horizontal plane when wearing HMD. We investigated about the change of the visual horizontal plane in the reclining position and the upright position, but there is no clear knowledge as to whether the change in the subjective horizontal plane is due to the change of head angle or the change of posture. In this study, we investigate the effect of head angle on subjective horizontal plane in reclining position and upright position.

Humans have always desired to guess the future in order to adapt their behavior and maximize chances of success. In this paper, we conduct exploratory analysis of future-related information on the web. We focus on the future-related information which is grounded in time, that is, the information on forthcoming events whose expected occurrence dates are already known. We collect data by crawling search engine index and analyze collective view of future time-referenced events discussed on the web.

In this paper, we will propose a novel technique for Future Opinion Extraction, a new task of Information Extraction. The system we built can extract automatically future opinions, building automatic queries for the Search API. We obtained an F-Measure of 81.8% for the filtering of future opinion candidates. Our method provides future opinions allowing us to identify future problems and future goals of our society in the forty next years. The total system, called ChronoSeeker, contains +20,000 future opinions.

Entity resolution (ER) matches and merges records that refer to the same real-world entities, and is typically a compute-intensive process due to complex matching functions and high data volumes. We present a family of algorithms, D-Swoosh, for distributing the ER workload across multiple processors. The algorithms use generic match and merge functions, and ensure that new merged records are distributed to processors that may have matching records. We perform a detailed performance evaluation on a testbed of 15 processors. Our experiments use actual comparison shopping data provided by Yahoo!.