Fault Diagnosis in DSL Networks using Support Vector Machines

The adequate operation for a number of service distribution networks relies on the e�ective maintenance and fault management of their underlay DSL infrastructure. Thus, new tools are required in order to adequately monitor and further diagnose anomalies that other segments of the DSL network cannot identify due to the pragmatic issues raised by hardware or software misconfigurations. In this work we present a fundamentally new approach for classifying known DSL-level anomalies by exploiting the properties of novelty detection via the employment of one-class Support Vector Machines (SVMs). By virtue of the imbalance residing in the training samples that consequently lead to problematic prediction outcomes when used within two-class formulations, we adopt the properties of one-class classification and construct models for independently identifying and classifying a single type of a DSL-level anomaly. Given the fact that the greater number of the installed Digital Subscriber Line Access Multiplexers (DSLAMs) within the DSL network of a large European ISP were misconfigured, thus unable to accurately flag anomalous events, we utilize as inference solutions the models derived by the one-class SVM formulations built by the known labels as flagged by the much smaller number of correctly configured DSLAMs in the same network in order to aid the classification aspect against the monitored unlabelled events. By reaching an average over 95% on a number of classification accuracy metrics such as precision, recall and F-score we show that one-class SVM classifiers overcome the biased classification outcomes achieved by the traditional two-class formulations and that they may constitute as viable and promising components within the design of future network fault management strategies. In addition, we demonstrate their superiority over commonly used two-class machine learning approaches such as Decision Trees and Bayesian Networks that has been used in the same context within past solutions. Keywords: Network management, Support Vector Machines, supervised learning, one-class classifiers, DSL anomalie

Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines

In many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for quality assessment (QA) in manufacturing industries. In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set. Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided

Fault diagnosis in DSL networks using support vector machines

The adequate operation for a number of service distribution networks relies on the effective maintenance and fault management of their underlay DSL infrastructure. Thus, new tools are required in order to adequately monitor and further diagnose anomalies that other segments of the DSL network cannot identify due to the pragmatic issues raised by hardware or software misconfigurations. In this work we present a fundamentally new approach for classifying known DSL-level anomalies by exploiting the properties of novelty detection via the employment of one-class Support Vector Machines (SVMs). By virtue of the imbalance residing in the training samples that consequently lead to problematic prediction outcomes when used within two-class formulations, we adopt the properties of one-class classification and construct models for independently identifying and classifying a single type of a DSL-level anomaly. Given the fact that the greater number of the installed Digital Subscriber Line Access Multiplexers (DSLAMs) within the DSL network of a large European ISP were misconfigured, thus unable to accurately flag anomalous events, we utilize as inference solutions the models derived by the one-class SVM formulations built by the known labels as flagged by the much smaller number of correctly configured DSLAMs in the same network in order to aid the classification aspect against the monitored unlabeled events. By reaching an average over 95% on a number of classification accuracy metrics such as precision, recall and F-score we show that one-class SVM classifiers overcome the biased classification outcomes achieved by the traditional two-class formulations and that they may constitute as viable and promising components within the design of future network fault management strategies. In addition, we demonstrate their superiority over commonly used two-class machine learning approaches such as Decision Trees and Bayesian Networks that has been used in the same context within past solutions

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC