Sentiment-based search in digital libraries
Several researchers have developed tools for classifying/ clustering Web search results into different topic areas (such as sports, movies, travel, etc.), and to help users identify relevant results quickly in the area of interest. This study follows a similar approach, but is in the area of sentiment classification - automatically classifying on-line review documents according to the overall sentiment expressed in them. This paper presents a prototype system that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended (or non-recommended) information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents, by using an automatic classifier based on a supervised machine learning algorithm, support vector machine (SVM)

title: Semantic Analysis of Basketball Video Using Motion Information
abstract: This paper presents a new method for extracting semantic information from basketball video. Our approach consists of three stages: shot and scene boundary detection, scene classification and semantic video analysis for event detection. The scene boundary detection algorithm is based on both visual and motion prediction information. After the shot and scene boundary detection, a set of visual and motion features are extracted from scene or shot. The motion features, describing the total motion, camera motion and object motion within the scene respectively, are computed from the motion vector of the compressed video using an iterative algorithm with robust outlier rejection. Finally, the extracted features are used to differentiate offensive/defensive activities in the scenes. By analyzing the offensive/defensive activities, the positions of potential semantic events, such as foul and goal, are located. Experimental results demonstrate the effectiveness of the proposed method.

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: 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: Ontology for Nature-Scene Image Retrieval
abstract: 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: 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 Cost-Based Evaluation of End-to-End Network Measurements in Overlay Multicast
abstract: Application-layer multicast (ALM, or overlay multicast) has been proposed to overcome limitations in IP multicast. While much measurement work (such as delay or connectivity measurement) has been conducted to build efficient ALM trees, several interesting questions therein are left unclear: (1) What are the measurement costs of the different measurement methods? (2) What is the major improvement to an ALM tree by using a certain measurement method? (3) To achieve the target tree performance, what is the most cost-efficient measurement method? In this paper, we study three representative measurement methods, i.e., delay measurement, connectivity measurement and available bandwidth measurement. We select six typical ALM protocols each adopting at least one of the measurement methods and evaluate their performance on Internet-like topologies. Our study shows that delay and connectivity measurements can effectively reduce end-to-end delay in overlay trees with low measurement costs. Only using delay measurement may lead to a tree consuming much network resource. The use of connectivity and bandwidth measurements can build a tree with low resource consumption.

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: Accessing Hidden Web Documents by Metasearching a Directory of Specialty Search Engines
abstract: Many valuable Web documents have not been indexed by general search engines and are only accessible through specific search interfaces. Metasearching groups of specialty search engines is one possible way to gain access to large amount of such hidden Web resources. One of the key issues for returning quality metasearch results is how to select the most relevant specialty search engines for a given query. We introduce a method for categorizing specialty search engines automatically into a hierarchical directory for metasearching. By utilizing the directory, specialty search engines that have a high possibility of having relevant information and resources can be easily selected by a metasearch engine. We evaluate our algorithm by comparing the directory built by the proposed algorithm with another one that was built by human-judgments. In addition, we present a metasearch engine prototype, which demonstrates that such a specialty search engine directory can be beneficial in locating essential but hidden Web resources.

title: Distributed servers architecture for networked video services
abstract: In an on-demand video system, the video repository generally has limited streaming capacities and may be far from the users. In order to achieve higher user capacity and lower network transmission cost, distributed servers architecture can be used, in which multiple local servers are placed close to user pools and, according to their local demands, dynamically cache the contents streamed from the repository. We study a number of caching schemes as applied in the local servers depending on whether the repository is able to multicast movie contents to the local servers or not, and whether the local servers can exchange their cached contents among themselves or not. Our caching schemes keep a circular buffer of data for the movie requested, and hence movies are partially cached. By adjusting the size of the buffer, such caching is able to achieve better tradeoff between network channels and local storage as compared to the traditional caching in which a movie is treated as an entity. For each caching scheme, we study the tradeoff between the local storage and the network channels, and address how the total cost of the system can be minimized by appropriately sizing the buffer. As compared to a number of traditional operations (request batching and multicasting, true-VOD, etc.), we show that distributed servers architecture is able to achieve much lower system cost to offer on-demand video services.