Hiromi Wakaki

Data allocation scheme based on term weight for P2P information retrieval

Many Peer-to-Peer information retrieval systems that use a global index have already been proposed that can retrieve documents relevant to a query. Since documents are allocated to peers regardless of the query, the system needs to connect many peers to gather the relevant documents. We propose a new data allocation scheme for P2P information retrieval that we call Concordia. Concordia uses a node to allocate a document based on the weight of each term in the document to efficiently assemble all the documents relevant to a query from the P2P Network. Moreover, the node encodes the binary data of a document with an erasure code, and Concordia produces an efficient redundancy for counteracting node failures.

Motion design of a 3D-CG avatar using interactive evolutionary computation

The motion of a 3D-CG avatar is recently used in many games and movies, but it is not easy to generate human motion. Also along with the increasing spread of the Internet, users want to use various expressions on the Web. However the users who do not have special techniques cannot create human motion. A system by which the users can create human motion in an available environment is required. This paper describes a new approach to generating human motion, more easily and semi-automatically by means of interactive evolutionary computation (IEC). In our system the profile of the avatar is based on the Humanoid Animation standard in order to popularize easily.

Abbreviation Generation for Japanese Multi-Word Expressions

This paper proposes a novel method for generating Japanese abbreviations from their full forms with the Log-Linear Model (LLM) in order to take advantage of characteristic patterns of Japanese abbreviation. Our experimental results show that the method is effective for TV program titles that contain colloquial expressions. The proposed method achieved 78.8% recall for the top 30 candidates, whereas a baseline method using Conditional Random Fields (CRFs) achieved 68.3% recall. Moreover, from the results of experiments using six data sets classified according to types of character and semantic categories, we show that each performance of the above two methods depends on the types of the full forms.