This paper introduces the design and functions of HTIP (Home network Topology Identifying Protocol), which can identify the home network topology and check the connectivity to end devices and/or network devices. Applications can utilize this information to perform the rapid, high-resolution fault localization needed if network services are not provided properly. The HTIP-manager agent collects the device and link information required for identifying the network's topology. In HTIP, end devices send device information via UDA while network devices send both device and link information via LLDP. HTIP is light weight, so any low performance device can implement it. We have already standardized HTIP in Japan, and are currently standardizing it in ITU-T.

We present a method for combining two bilingual dictionaries to make a third, using one language as a pivot. In this case we combine a Japanese-English dictionary with a Malay-English dictionary, to produce a Japanese-Malay dictionary suitable for use in a machine translation system. Our method differs from previous methods in its use of semantic classes to rank translation equivalents: word pairs with compatible semantic classes are preferred to those with dissimilar classes. We also experiment with the use of two pivot languages. We have made a prototype dictionary of over 75,000 pairs.

This paper describes an accurate and robust text alignment system for structurally different languages. Among structurally different languages such as Japanese and English, there is a limitation on the amount of word correspondences that can be statistically acquired. The main reason for this is the systems of functional (closed) words are quite different in the two languages. The proposed method makes use of two kinds of word correspondences in aligning bilingual texts. One is a bilingual dictionary of general use. The other is the word correspondences that are statistically acquired in the alignment process. Our method gradually determines sentence pairs (anchors) that correspond to each other by relaxing parameters. The method, by combining two kinds of word correspondences, achieves adequate word correspondences for complete alignment. As a result, texts of various length and of various genres in structurally different languages can be aligned with high precision. Experimental results show our system outperforms conventional methods for various kinds of Japanese–English texts.

This paper describes an accurate and robust text alignment system for structurally different languages. Among structurally different languages such as Japanese and English, there is a limitation on the amount of word correspondences that can be statistically acquired. The proposed method makes use of two kinds of word correspondences in aligning bilingual texts. One is a bilingual dictionary of general use. The other is the word correspondences that are statistically acquired in the alignment process. Our method gradually determines sentence pairs (anchors) that correspond to each other by relaxing parameters. The method, by combining two kinds of word correspondences, achieves adequate word correspondences for complete alignment. As a result, texts of various length and of various genres in structurally different languages can be aligned with high precision. Experimental results show our system outperforms conventional methods for various kinds of Japanese-English texts.