1998178
A Hierarchical Approach to Internet Distance Prediction
Internet distance prediction gives pair-wise latency information with limited measurements. Recent studies have revealed that the quality of existing prediction mechanisms from the application perspective is short of satisfactory. In this paper, we explore the root causes and remedies for this problem. Our experience with different landmark selection schemes shows that although selecting nearby landmarks can increase the prediction accuracy for short distances, it can cause the prediction accuracy for longer distances to degrade. Such uneven prediction quality significantly impacts application performance. Instead of trying to select the landmark nodes in some "intelligent" fashion, we propose a hierarchical prediction approach with straightforward landmark selection. Hierarchical prediction utilizes multiple coordinate sets at multiple distance scales, with the "right" scale being chosen for prediction each time. Experiments with Internet measurement datasets show that this hierarchical approach is extremely promising for increasing the accuracy of network distance prediction.
1762374
HYPER: A Hybrid Approach to Efficient Content-Based Publish/Subscribe
Publish/Subscribe (pub/sub) is an important paradigm for distributed content delivery. Traditionally, there have been two approaches to supporting pub/sub service: subject-based and content-based. Content-based pub/sub allows fine-grained expressiveness, and thus is a more attractive solution for content dissemination. However, the performance of a content-based pub/sub network is bounded by the expensive matching cost of content messages. In this paper, we propose a hybrid approach capable of minimizing both the matching and forwarding overhead within the pub/sub network and the delay experienced by clients receiving the content. The hybrid approach aims to eliminate redundant matching and forwarding inside the pub/sub network. In particular, it identifies a number of virtual groups by exploring common subscription interests among clients, and messages for each virtual group are only matched once at the group entry point. In addition, for each virtual group, the content delivery tree embedded in the underlying pub/sub network can benefit from shortcutting forwarding-only paths. Simulations have shown that the hybrid approach is highly effective in improving the service efficiency and quality of a content-based pub/sub system
10950819
An Efficient Sequential RBF Network for Gene Expression-Based Multi-category classification
This paper presents a fast and efficient sequential learning method for RBF networks that can perform classification directly for multi-category cancer diagnosis problems based on microarray data. The recently developed algorithm, referred to as Fast Growing And Pruning-RBF (FGAP-RBF) can perform incremental learning on the future data directly. No training of all the previous data is needed. This character can reduce the learning complexity and improve the learning efficiency and is greatly favored in the real implementation of a gene expression-based cancer diagnosis system. We have evaluated FGAP-RBF algorithm on a benchmark multi-category cancer diagnosis problem based on microarray data, namely GCM dataset. The results indicate that compared with the results available in literature FGAP-RBF algorithm produces a higher classification accuracy with reduced training time and implementation complexity.
1032181
An automatic test pattern generation framework for combinational threshold logic networks
We propose an automatic test pattern generation (ATPG) framework for combinational threshold networks. The motivation behind this work lies in the fact that many emerging nanotechnologies, such as resonant tunneling diodes (RTDs) and quantum cellular automata (QCA), implement threshold logic. Consequently, there is a need to develop an ATPG methodology for this type of logic. We have built the first automatic test pattern generator and fault simulator for threshold logic, which has been integrated on top of an existing computer-aided design (CAD) tool. These exploit new fault collapsing techniques we have developed for threshold networks. We perform fault modeling to show that many cuts and shorts in RTD-based threshold gates are equivalent to stuck-at faults at the inputs and output of the gate. Experimental results with the MCNC benchmarks indicate that test vectors were found for all testable stuck-at faults in their threshold network implementations.
8272087
OGSA-based grid workload monitoring
In heterogeneous and dynamic distributed systems like the grid, detailed monitoring of workload and its resulting system performance (e.g. response time) is required to facilitate performance diagnosis and adaptive performance tuning. In this paper, we present a workload monitoring infrastructure for this purpose. The infrastructure classifies and monitors workload across components in grids based on the open grid service architecture (OGSA) in an end-to-end manner. It provides the abilities to assess what components are involved in processing a work unit, to report time elapsed at these components, and to capture concurrency and isolate which components are critical to overall performance observed. These are enclosed in an automatically constructed Response Time Service Petri Net (RT-SPN) model. A tool is provided to accept queries about work units and visualise corresponding RTSPNs. The infrastructure is also designed and implemented so as to be portable, scalable and lightweight.
1035628
Joint categorization of queries and clips for web-based video search
Building a video search engine on the Web is a very challenging problem. Compared with web page search, video search has its unique characteristics (such as high volume of data for each video, existence of multi-modal information including meta-data, visual content, audio, closed caption, etc). In this paper, we investigate some promising approaches to boosting the search relevance of a large scale video search engine on the Web. The contribution of our work is three-fold. (1) We developed a specialized video categorization framework which combines multiple classifiers based on different modalities. (2) By learning users' querying history and clicking log, we proposed an automatic query profile generation technique and applied the profile to query categorization. (3) A highly scalable system was developed, which integrates the online query categorization and offline video categorization. Naive Bayes with mixture of multinomials, Maximum Entropy, and Support Vector Machine categorization methods and the profile learn-ing technique were evaluated on a large scale set of video data on the Web. The evaluation of the developed system and user study has indicated that the joint categorization of queries and video data boosts the video search relevance and user search experience. The high efficiency of our approaches is also demonstrated by the good responsiveness of the system for the video search engine on the Web.
3159534
FAST: Toward more effective and efficient image retrieval
This paper focuses on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region-based approach to improving the retrieval effectiveness and robustness; (2) development of a new hierarchical indexing structure and the corresponding hierarchical, elimination-based A* retrieval (HEAR) algorithm to significantly improve the retrieval efficiency without sacrificing the retrieval effectiveness; it is shown that HEAR is guaranteed to deliver a logarithm search in the average case; (3) employment of user relevance feedback to tailor the effective retrieval to each user's individualized query preference through the novel indexing tree pruning (ITP) and adaptive region weight updating (ARWU) algorithms. Theoretical analysis and experimental evaluations show that FAST methodology holds great promise in delivering fast and semantics-tailored image retrieval in CBIR.