LVMSR-an efficient algorithm to multicast layered video
Layered video is a video compression technique to encode video data in multiple layers. It typically consists of a base layer and additional layers that provide enhanced video quality. The multicasting operation of a layered video may need to satisfy: (i) bounded end-to-end delay from a source to each receiver, (ii) minimum total cost, and (iii) minimum delay jitter between the various video streams received by the receivers. Because different nodes may request different video quality and because of limited bandwidth on the network's links, different layers of video data may reach their destinations over different distribution trees, and not all receivers may receive all of their requested layers. The problem of computing such data distribution paths is NP-complete, which means that no optimal solution method is available. This paper presents a new heuristic algorithm called LVMSR. With O(Rn/sup 2/) time complexity and O(R/sup 2/) message complexity, where n is the number of nodes in the network and R is the receiver group size. Our simulation results show that the multicast data paths computed by our algorithm can always satisfy the delay constraint with reasonably small total cost.

title: Video loss recovery with FEC and stream replication
abstract: Packet loss is inevitable in video multicast. In this paper, we propose and study an effective feedback-free loss recovery scheme for layered video which combines forward error correction (FEC) and stream replication. In our scheme, the server multicasts the video in parallel with FEC packets and a number of replicated delayed (ReD) version of the stream. Receivers autonomously and dynamically join the FEC and ReD streams to repair their losses. On the server side, we analyze and optimize the number of replicated streams and FEC packets to meet a certain residual loss requirement (i.e., error after correction). On the receiver side, we analyze the optimal combination of FEC and ReD packets to minimize its loss. We also present a fast yet accurate approximation algorithm for receiver to make such decision. We show that FEC combined with merely one or two replicated streams can effectively reduce the residual error rate (by as much as 50%) as compared with pure FEC or replication alone. Both subjective and objective video measures confirm that our recovery scheme achieves much better visual quality.

title: A new motion compensation approach for error resilient video coding
abstract: Multihypothesis motion-compensated prediction (MHMCP) can be used as an error resilience technique for video coding. Motivated by MHMCP, we propose a new error resilience approach named alternative motion-compensated prediction (AMCP), where two-hypothesis and one-hypothesis predictions are alternatively used with some mechanism. Both theory and simulation results show that in case of one frame loss, the expected converged error using AMCP is smaller than that using two-hypothesis MCP.

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

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: Fine-grained device management in an interactive media server
abstract: The use of interactive media has already gained considerable popularity. Interactivity gives viewers VCR controls like slow-motion, pause, fast-forward, and instant replay. However, traditional server-based or client-based approaches for supporting interactivity either consume too much network bandwidth or require large client buffering; and hence they are economically unattractive. We propose the architecture and design of an interactive media proxy (IMP) server that transforms noninteractive broadcast or multicast streams into interactive ones for servicing a large number of end users. For IMP to work cost-effectively, it must carefully manage its storage devices, which are needed for caching voluminous media data. In this regard, we propose a fine-grained device management strategy consisting of three complementary components: disk profiler, data placement, and IO scheduler. Through quantitative analysis and experiments, we show that these fine-grained strategies considerably improve device throughput under various workload scenarios.

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: 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: Mapping XML Schema to Relations Using Genetic Algorithm
abstract: As web-applications grow in number and complexity, there is a need for efficient mappings from XML schemas to the flat relational tables so that existing functions in relational database systems can be utilized. However, many of the existing mapping methods, such as the model-based or the structure-based methods, do not exploit query history for better query performance. In this paper, we propose the use of genetic algorithm (GA) in a cost-based approach for converting a XML schema to relational tables. By formulating the mapping problem as a cost optimization task with respect to a set of weighted frequent queries, we can obtain an efficient mapping that minimizes the queries execution time. In our experiments, we show that the mapping obtained by GA is superior to other non-cost-based approaches. In particular, the GA approach has out-performed the greedy heuristic in the browsing queries where the accessed attributes are many and scattered.

title: Sentiment-based search in digital libraries
abstract: 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)