43789567
A Self-Organizing Flock of Condors
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
11225223
Diagnosing Breast Cancer Based on Support Vector Machines
The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.
17591841
State encoding of finite-state machines targeting threshold and majority logic based implementations with application to nanotechnologies
In this paper, we address the problem of state encoding of finite-state machines (FSMs) targeting threshold and majority logic based implementations, which have applications in nanotechnologies. Previous work on state encoding is based on Boolean logic. These methods try to optimize either the literal count or sum-of-product (SOP) terms count, which are more relevant for CMOS technology based circuits. As progress is made in the material and physical understanding of nanoscale devices, functionally-correct nanoscale circuits based on threshold and majority gates are being successfully demonstrated. However, there exists no methodology or design automation tool for state encoding targeting threshold and majority gate based implementations. We propose such a methodology based on an evolutionary algorithm. Experimental results indicate that large reductions in gate count, area and interconnect count are possible, compared to some traditional state encoding methods.
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.
10911475
Stretching Bayesian Learning in the Relevance Feedback of Image Retrieval
This paper is about the work on user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem aiming at refining the retrieval precision by learning through the user relevance feedback data. However, we have investigated the problem by noting two important unique characteristics of the problem: small sample collection and asymmetric sample distributions between positive and negative samples. We have developed a novel approach to stretching Bayesian learning to solve for this problem by explicitly exploiting the two unique characteristics, which is the methodology of BAyesian Learning in Asymmetric and Small sample collections, thus called BALAS. Different learning strategies are used for positive and negative sample collections in BALAS, respectively, based on the two unique characteristics. By defining the relevancy confidence as the relevant posterior probability, we have developed an integrated ranking scheme in BALAS which complementarily combines the subjective relevancy confidence and the objective feature-based distance measure to capture the overall retrieval semantics. The experimental evaluations have confirmed the rationale of the proposed ranking scheme, and have also demonstrated that BALAS is superior to an existing relevance feedback method in the current literature in capturing the overall retrieval semantics.
15964861
Exploiting the cognitive synergy between different media modalities in multimodal information retrieval
This is a position paper reporting an on-going collaboration project between SUNY Binghamton, USA, and Waseda University, Japan, on multimodal information retrieval through exploiting the cognitive synergy across the different modalities of the information, to facilitate an effective retrieval. Specifically, we focus on image retrieval in the applications where imagery data appear along with collateral text. It is noted that these applications are ubiquitous. We have proposed the synergistic indexing scheme (SIS) to explicitly exploit the synergy between the information of imagery and text modalities. Since the synergy we have exploited between the information of imagery and text modalities is subjective and depends on specific cognitive context, we call this type of synergy as cognitive synergy. We have reported part of the empirical evaluation and are in the process of fully implementing the SIS prototype for an extensive evaluation.