Branch and bound based coordinate search filter algorithm for nonsmooth nonconvex mixed-integer nonlinear programming problems

Publicado em "Computational science and its applications – ICCSA 2014...", ISBN 978-3-319-09128-0. Series "Lecture notes in computer science", ISSN 0302-9743, vol. 8580.A mixed-integer nonlinear programming problem (MINLP) is a problem with continuous and integer variables and at least, one nonlinear function. This kind of problem appears in a wide range of real applications and is very difficult to solve. The difficulties are due to the nonlinearities of the functions in the problem and the integrality restrictions on some variables. When they are nonconvex then they are the most difficult to solve above all. We present a methodology to solve nonsmooth nonconvex MINLP problems based on a branch and bound paradigm and a stochastic strategy. To solve the relaxed subproblems at each node of the branch and bound tree search, an algorithm based on a multistart strategy with a coordinate search filter methodology is implemented. The produced numerical results show the robustness of the proposed methodology.This work has been supported by FCT (Fundação para a Ciência e aTecnologia) in the scope of the projects: PEst-OE/MAT/UI0013/2014 and PEst-OE/EEI/UI0319/2014

PREDICTING SEQUENTIAL PATTERN CHANGES IN DATA STREAMS

Data streams are utilized in an increasing number of real-time information technology applications. Unlike traditional datasets, data streams are temporally ordered, fast changing and massive. Due to their tremendous volume, performing multiple scans of the entire data stream is impractical. Thus, traditional sequential pattern mining algorithms cannot be applied. Accordingly, the present study proposes a new sequential pattern mining model for predicting sequential pattern changes in data streams. The experimental results show that the prediction performance of the proposed model is better than that of two linear regression-based models

A New Clustering Validity Index for Cluster Analysis Based on a Two-Level SOM

Self-Organizing Map (SOM) is a powerful tool for the exploratory of clustering methods. Clustering is the most important task in unsupervised learning and clustering validity is a major issue in cluster analysis. In this paper, a new clustering validity index is proposed to generate the clustering result of a two-level SOM. This is performed by using the separation rate of inter-cluster, the relative density of inter-cluster, and the cohesion rate of intra-cluster. The clustering validity index is proposed to find the. optimal numbers of clusters and determine which two neighboring clusters can be merged in a hierarchical clustering of a two-level SOM. Experiments show that, the proposed algorithm is able to cluster data more accurately than the classical clustering algorithms which is based on a two-level SOM and is better able to find an optimal number of clusters by maximizing the Clustering validity index

Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information

Indoor location determination has emerged as a significant research topic due to the wide-spread deployment of wireless local area networks (WLANs) and the demand for context-aware services inside buildings. However, prediction accuracy remains a, primary issue surrounding the practicality of WLAN-based location determination systems. This study proposes a novel scheme that utilizes mobile user orientation information to improve prediction accuracy. Theoretically, if the precise orientation of a user can be identified, then the location determination system can predict that user's location with a high degree of accuracy by using the training data of this specific-orientation. In reality, a mobile user's orientation can be estimated only by comparing variations in received signal strength; and nevertheless the predicted orientation may be incorrect. Incorrect orientation information causes the accuracy of the entire system to decrease. Therefore, this study presents an accumulated orientation strength algorithm which can utilize uncertain estimated orientation information to improve prediction accuracy. Implementation of this system is based on the Bayesian model, and the experimental results indeed show the effectiveness of our proposed approach. (c) 2007 Elsevier Inc. All rights reserved

An index selection method without repeated optimizer estimations

The index selection problem (ISP) concerns the selection of an appropriate index set to minimize the total cost for a given workload containing read and update queries. Since the ISP has been proven to be an NP-hard problem, most studies focus on heuristic algorithms to obtain approximate solutions. However, even approximate algorithms still consume a large amount of computing time and disk space because these systems must record all query statements and frequently request from the database optimizers the cost estimation of each query in each considered index. This study proposes a novel algorithm without repeated optimizer estimations. When a query is delivered to a database system, the optimizer evaluates the costs of various query plans and chooses an access path for the query. The information from the evaluation stage is aggregated and recorded with limited space. The proposed algorithm can recommend indexes according to the readily available information without querying the optimizer again. The proposed algorithm was tested in a Post-greSQL database system using TPC-H data. Experimental results show the effectiveness of the proposed approach. (C) 2009 Elsevier Inc. All rights reserved

PORE: a personal ontology recommender system for digital libraries

Purpose - As library collections increase rapidly, personalized recommender systems have become a very important service for library patrons. The purpose of this paper is to design and implement a personal ontology recommender (PORE) system by building personal ontologies based on patrons' borrowing records. Design/methodology/approach - In the PORE system, the traditional cataloging scheme, classification for Chinese libraries, is used as the reference ontology. This reference ontology is transformed to a unique personal ontology for each user based on the mining results from library borrowing records of that user. Findings - A personal ontology represents a unique user interest on specific subjects. The personal ontology can be used to filter out unsuitable recommendations based only on a keyword matching method. Besides, the recommended books can be organized into the personal ontology, and provide the patron with a user-friendly interface to access library collections. Research limitations/implications - The PORE system is currently implemented for Chinese collections. From this paper builds a new version to support English collections by adopting the Library of Congress Classification as the reference ontology. Originality/value - This paper represents a practical method of building a user's personal ontology and explains the functional use of ontology knowledge

A password authentication scheme over insecure networks

Authentication ensures that system's resources are not obtained fraudulently by illegal users. Password authentication is one of the simplest and the most convenient authentication mechanisms over insecure networks. The problem of password authentication in an insecure networks is present in many application areas. Since computing resources have grown tremendously, password authentication is more frequently required in areas such as computer networks, wireless networks, remote login, operation systems, and database management systems. Many schemes based on cryptography have been proposed to solve the problem. However, previous schemes are vulnerable to various attacks and are neither efficient, nor user friendly. Users cannot choose and change their passwords at will. In this paper, we propose a new password authentication scheme to achieve the all proposed requirements. Furthermore, our scheme can support the Diffie-Hellman key agreement protocol over insecure networks. Users and the system can use the agreed session key to encrypt/decrypt their communicated messages using the symmetric cryptosystem. (C) 2005 Elsevier Inc. All rights reserved

Detecting rogue access points using client-side bottleneck bandwidth analysis

A rogue access point (AP) is an unauthorized AP plugged into a network. This poses a serious security threat. To detect an AP, a network manager traditionally takes the electric wave sensor across an entire protected place. This task is very labor-intensive and inefficient. This study presents a new AP detection method without extra hardware or hard work. This new method determines whether the network packets of an IP address are routed from APs, according to client-side bottleneck bandwidth. The network manager can perform his job from his office by monitoring the packets passing through the core switch. The accuracies remain above 99% when the parameter, sliding window size, of the proposed algorithm is larger than 20, according to experimental results. The proposed method effectively reduces the network manager's workload, and increases network security. (C) 2008 Elsevier Ltd. All rights reserved

Security enhancement on a new authentication scheme with anonymity for wireless environments

In a paper recently published in the IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, Zhu and Ma proposed a new authentication scheme with anonymity for wireless environments. However, this paper shows that Zhu and Ma's scheme has some security weaknesses. Therefore, in this paper, a slight modification to their scheme is proposed to improve their shortcomings. As a result, the scheme proposed in this paper can enhance the security of Zhu and Ma's scheme. Finally, the performance of this scheme is analyzed. Compared with the Zhu-Ma scheme, this scheme is also simple and efficient