Positive solutions of three-point boundary value problems for the one-dimensional p-laplacian with infinitely many singularities
We consider thesingular three-point boundary value problems 
(φp(y′))′ + a(t)f(y(t)) = 0, 0 < t < 1, y′(0) = 0, y(1) = βy(ƞ), 
where φp(s) = |s|p−2s, p ≥ 2, 0 < β < 1, 0 < η < 1, f ϵ C([0, + ∞), [0, + ∞)), a: [0,1] → [0, + ∞), andhas countably many singularities in [0, frsol|1/2). We show that there exist countably many positive solutions by using the fixed-point index theory.

title: Positive solutions of a nonlinear three-point boundary value problem
abstract: In this paper, by using Krasnoselskii's fixed point theorem in a cone, we study the existence of single and multiple positive solutions to the three-point boundary value problem (BVP)y''(t)+a(t)f(y(t))=0,0

title: Existence and uniqueness of solutionsto first-order multipoint boundary value problems
abstract: In this paper, we give existence and uniqueness results for solutions of multipoint boundary value problems of the form 
x'=f(t,x(t))+e(t),t∈(0,1),∑j=1mAjx(ηj)=0, 
where ƒ : [0,1] × Rn → Rn is a Caratheodory function, Ajs (j = 1, 2,, m) are constant square matrices of order n, 0 < η1 η2 << ηm−1, < ηm ⪯ 1, and e(t) ∈ L1([0,1], Rn). The existence of solutions is proven by the coincidence degree theory. As an application, we also give one example to demonstrate our results.

title: Building text classifiers using positive and unlabeled examples
abstract: We study the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. We first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques.

title: CoDevFrame: An Event-drive Grid Oriented Cooperative Software Development System
abstract: Though twenty years have passed since the birth of CSCW, the original goal of it is not reached as well as people expected. This situation is mostly due to the supporting technology especially the infrastructure. Because of Grid computing and Web services, the globalization of software development has become a reality. Many software projects are now distributed in diverse sites across the globe. The distance between these sites creates several problems that did not exist for previously co-located teams. Problems with the coordination of the activities, as well as with the communication between team members emerge. This paper describes how event-drive servers that are built on grid and web services are a useful technology for supporting global software development. They provide some general yet flexible cooperation related services and organizes them into different layers. In general, advantages of using event-drive servers are described as well as specific problems that they

title: Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification
abstract: Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before they can be used to classify the examples. In the one-against-one algorithm with SVMs, there exists an unclassifiable region where the data samples cannot be classified by its decision function. This paper extends the one-against-one algorithm to handle this problem. We also give the convergence and computational complexity analysis of the proposed method. Finally, one-against-one, fuzzy decision function (FDF), and decision-directed acyclic graph (DDAG) algorithms and our proposed method are compared using five University of California at Irvine (UCI) data sets. The results report that the proposed method can handle the unclassifiable region better than others.

title: An Adaptive Schedule Medium Access Control for Wireless Sensor Networks
abstract: Many media access control (MAC) protocols for wireless sensor networks (WSNs) such as sensor MAC (S-MAC) and carrier sense multiple access with minimum preamble sampling (CSMA-MPS) exploit listen/sleep cycles to conserve energy. The cycles of nodes influence the network performances such as energy efficiency, packet latency and throughput in WSNs. In this paper, an adaptive schedule medium access control (AS-MAC) for WSNs is presented. AS-MAC introduces the adaptation of the cycles in MAC layer to the data traffic. The effect of the protocol is that it can decrease the latency, improve the throughput and energy efficiency when traffic is dynamic. We also compare AS-MAC with S-MAC and CSMA-MPS through simulations and the results show it has better performances than the former protocols.

title: Improving an Association Rule Based Classifier
abstract: Existing classification algorithms in machine learning mainly use heuristic search to find a subset of regularities in data for classification. In the past few years, extensive research was done in the database community on learning rules using exhaustive search under the name of association rule mining. Although the whole set of rules may not be used directly for accurate classification, effective classifiers have been built using the rules. This paper aims to improve such an exhaustive search based classification system CBA (Classification Based on Associations). The main strength of this system is that it is able to use the most accurate rules for classification. However, it also has weaknesses. This paper proposes two new techniques to deal with these weaknesses. This results in remarkably accurate classifiers. Experiments on a set of 34 benchmark datasets show that on average the new techniques reduce the error of CBA by 17% and is superior to CBA on 26 of the 34 datasets. They reduce the error of C4.5 by 19%, and improve performance on 29 datasets. Similar good results are also achieved against RIPPER, LB and a Naive-Bayes classifier.