Ontology for Nature-Scene Image Retrieval
This paper presents a framework for building an ontology to provide semantic interpretations in image contents. The novelty of this framework comes from building a MPEG-7 ontology for semantic representations of multimedia contents, and from integrating such ontology into an image retrieval system to enable fast, efficient image query and retrieval. The prototype system demonstrated the feasibility of embedding such ontology into an image retrieval system. Its main objective has been achieved by retrieving nature scene images using human readable keywords. Based on the experimental results, we believe that using our ‘bridging’ technique, the high-level non-machine readable human concepts can be seamlessly mapped to low-level machine processable data. This helps to improve the efficiency of our CBIR system compared to conventional methods.

title: Extracting Causal Knowledge from a Medical Database Using Graphical Patterns
abstract: This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences, and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect.

title: Query Expansion by Raw Image Features and Text Annotations in Image Retrieval
abstract: In this paper, we investigate the effect of using several color and texture features in query expansion. We assume that the initial queries are text-based and are matched against the text annotations. The system performs a two-pass retrieval by expanding the initial query using text and image features from the initial retrieved set, assuming that it contains more relevant images than non-relevant ones. We performed extensive experiments using 1019 color pictures with captions, relevance judgments and queries supplied by a national archives agency.

title: Lateral error recovery for application-level multicast
abstract: We consider the delivery of reliable and streaming services using application-level multicast (ALM) by means of UDP, where packet loss has to be recovered via retransmission in a timely manner in order to offer high level of service. Since packets may be lost due to congestion, tree-reconfiguration or node failure, the traditional "vertical" recovery, whereby upstream nodes retransmit the lost packet is no longer effective. We therefore propose and investigate lateral error recovery (LER). In LER, hosts are divided into a number of planes, each of which forms an independent ALM tree. Since the correlation of error among the planes is likely to be low, a node can effectively recover its error "laterally" from nearby nodes in other planes. We employ the technique of global network positioning (GNP) to map the hosts into a coordinate space and identify a set of close neighbors for error recovery by constructing a Voronoi diagram for each plane. We present centralized and distributed algorithm on how to construct the Voronoi diagrams. Using Internet-like topologies, we show via simulations that our system achieves low overheads in terms of relative delay penalty and physical link stress. For reliable service, lateral recovery greatly reduces the average recovery time as compared with vertical recovery schemes. For streaming applications, LER achieves much lower residual loss rate under a certain deadline constraint.

title: Hypertext-assisted video indexing and content-based retrieval
abstract: An effective approach has been designed for the construction of on-line educational video systems. We have made use of the assistance of HTML text for accurate video parsing and complete content extraction. A video system with segmentation/indexing module and browsing/query modules are implemented to demonstrate the idea.

title: A data link control protocol for broadband wireless networks with adaptive coding rate
abstract: One of the challenges of broadband wireless multimedia networks is to design a wireless data link control (W-DLC) protocol which provides reliable data delivery over the noisy wireless link in an efficient manner. This paper presents our proposal of such a W-DLC protocol, based on a new coding scheme called multi-dimensional concatenated zigzag code. Both real-time and non-real-time traffic can be efficiently supported by adopting different error control mechanisms. A distinct feature of the protocol is that the coding rate can be adapted to a wide range of channel conditions. Hence, our protocol utilizes the scarce wireless channel resources more efficiently.

title: A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
abstract: The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS).  extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.

title: On the use of hierarchical information in sequential mining-based XML document similarity computation
abstract: Measuring the structural similarity among XML documents is the task of finding their semantic correspondence and is fundamental to many web-based applications. While there exist several methods to address the problem, the data mining approach seems to be a novel, interesting and promising one. It explores the idea of extracting paths from XML documents, encoding them as sequences and finding the maximal frequent sequences using the sequential pattern mining algorithms. In view of the deficiencies encountered by ignoring the hierarchical information in encoding the paths for mining, a new sequential pattern mining scheme for XML document similarity computation is proposed in this paper. It makes use of a preorder tree representation (PTR) to encode the XML tree’s paths so that both the semantics of the elements and the hierarchical structure of the document can be taken into account when computing the structural similarity among documents. In addition, it proposes a postprocessing step to reuse the mined patterns to estimate the similarity of unmatched elements so that another metric to qualify the similarity between XML documents can be introduced. Encouraging experimental results were obtained and reported.