A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
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: Modeling and Dimensioning Hierarchical Storage Systems for Low-Delay Video Services
abstract: In order to cost-effectively accommodate a large number of titles in a video system, a hierarchical storage system can be used. In this system, not-so-popular video files are stored in a tertiary level such as a disk/tape library. These files are transferred, or "staged," to a secondary level composed of magnetic disks before being streamed to the users. This system overcomes the current limitations in using disk/tape libraries to stream videos and resolves the bandwidth difference between staging and streaming. We present, via analysis, a model of the system and determine the minimum storage and bandwidth required, at each level, to meet a given user delay goal. We also analyze a number of system operations pertaining to whether or not a file is played while it is being staged (i.e., stage-streaming) and whether or not the displayed segments are deleted (i.e., trail-deletion). We show that stage-streaming and trail-deletion can achieve substantially lower bandwidth and storage requirements. In order to further increase the streaming and storage scalability, a distributed storage system can be used where multiple local servers are put close to user pools and get their files from one of the libraries through a network. We extend the models developed to such a system and specify the resource requirements to meet a given delay goal.

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: 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: Improved refinement search for H.263 to H.264/AVC transcoding based on the minimum cost tendency search
abstract: An improved refinement search method for transcoding from H.263 to H.264/AVC is proposed in this paper. Many existing motion re-estimation methods refine the input motion vector (MV) with a small search range, which is usually input MV biased. Motion estimation (ME) in H.263 usually does not consider the rate required for coding the MV, and hence, the input MV may incur a large cost in H.264/AVC. To overcome this problem, we introduce a refinement search method, called minimum cost tendency search (MCTS), which takes the difference between the cost functions for ME in H.263 and H.264/AVC into consideration. The input MV and the predictor MV are used as two anchor points. The proposed MCTS starts searching from the anchor point with a higher cost to another. Finally, the best point is chosen as the center for further refinement. The performance of MCTS is evaluated by comparing with full search, FME in JM software and refinement scheme using small diamond pattern around the input MV (RSD). Experimental results show the proposed MCTS performs more stable than FME and RSD over a wide range of output video quality