Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolu- tionary conservation information for reducing the high false positive rate. We propose that the context of a putative miRNA target in a protein-protein interaction (PPI) network can be used as an additional filter in a computational miRNA target pr ediction algorithm. We compute several graph theoretic measures on human PPI network as indicators of network context. We assess the performance of individual and combined contextual measures in increasing the precision of a popular miRNA target prediction tool, TargetScan, using low throughput and high throughput datasets of experimentally verified human miRNA targets. We used clas- sification algorithms for that assessment. Since there exists only miRNA targets as training samples, this problem becomes a One Class Classification (OCC) problem. We devised a novel OCC method, DiVo, based on simple distance metrics and voting. Comparative analysis with the state of the art methods show that, DiVo attains better classification performance. Our eventual results indicate that topological properties of target gene products in PPI networks are valuable sources of information for filtering out false positive miRNA target genes. We show that, for targets of a number of miRNAs, netwo rk context correlates better with being a target compared to a sequence based score provided by the prediction tool.

Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolu- tionary conservation information for reducing the high false positive rate. We propose that the context of a putative miRNA target in a protein-protein interaction (PPI) network can be used as an additional filter in a computational miRNA target prediction algorithm. We compute several graph theoretic measures on human PPI network as indicators of network context. We assess the performance of individual and combined contextual measures in increasing the precision of a popular miRNA target prediction tool, TargetScan, using low throughput and high throughput datasets of experimentally verified human miRNA targets. We used clas- sification algorithms for that assessment. Since there exists only miRNA targets as training samples, this problem becomes a One Class Classification (OCC) problem. We devised a novel OCC method, DiVo, based on simple distance metrics and voting. Comparative analysis with the state of the art methods show that, DiVo attains better classification performance. Our eventual results indicate that topological properties of target gene products in PPI networks are valuable sources of information for filtering out false positive miRNA target genes. We show that, for targets of a number of miRNAs, network context correlates better with being a target compared to a sequence based score provided by the prediction tool.

Interactive classi&amp / #64257 / cation is an attractive alternative and complementary for automatic classi&amp / #64257 / cation of satellite image content, since the subject is visual and there are not yet powerful computational features corresponding to the sought visual features. In this study, we improve our previous attempt by building a more stable software system with better capabilities for interactive classi&amp / #64257 / cation of the content of satellite images. The system allows user to indicate a few number of image regions that contain a speci&amp / #64257 / c geographical object, for example, a bridge, and to retrieve similar objects on the same satellite images. Retrieval process is iterative in the sense that user guides the classi&amp / #64257 / cation procedure by interaction and visual observation of the results. The classi&amp / #64257 / cation procedure is based on one-class classi&amp / #64257 / cation.

Esse trabalho está baseado na investigação dos detectores de falhas aplicando classificadores de classe única. As falhas a serem detectadas são relativas ao estado de funcionamento de cada componente do circuito, especificamente de suas tolerâncias (falha paramétrica). Usando a função de transferência de cada um dos circuitos são gerados e analisados os sinais de saída com os componentes dentro e fora da tolerância. Uma função degrau é aplicada à entrada do circuito, o sinal de saída desse circuito passa por uma função diferenciadora e um filtro. O sinal de saída do filtro passa por um processo de redução de atributos e finalmente, o sinal segue simultaneamente para os classificadores multiclasse e classe única. Na análise são empregados ferramentas de reconhecimento de padrões e de classificação de classe única. Os classficadores multiclasse são capazes de classificar o sinal de saída do circuito em uma das classes de falha para o qual foram treinados. Eles apresentam um bom desempenho quando as classes de falha não possuem superposição e quando eles não são apresentados a classes de falhas para os quais não foram treinados. Comitê de classificadores de classe única podem classificar o sinal de saída em uma ou mais classes de falha e também podem classificá-lo em nenhuma classe. Eles apresentam desempenho comparável ao classificador multiclasse, mas também são capazes detectar casos de sobreposição de classes de falhas e indicar situações de falhas para os quais não foram treinados (falhas desconhecidas). Os resultados obtidos nesse trabalho mostraram que os classificadores de classe única, além de ser compatível com o desempenho do classificador multiclasse quando não há sobreposição, também detectou todas as sobreposições existentes sugerindo as possíveis falhas. / This work deals with the application of one class classifiers in fault detection. The faults to be detected are related parametric faults. The transfer function of each circuit was generated and the outputs signals with the components in and out of tolerance were analyzed. Pattern recognition and one class classifications tools are employed to perform the analysis. The multiclass classifiers are able to classify the circuit output signal in one of the trained classes. They present a good performance when the fault classes do not overlap or when they are not presented to fault classes that were not presented in the training. The one class classifier committee may classify the output signal in one or more fault classes and may also classify them in none of the trained class faults. They present comparable performance to multiclass classifiers, but also are able to detect overlapping fault classes and show fault situations that were no present in the training (unknown faults).

A diesel particulate filter (DPF) is designed to physically remove diesel particulate matter or soot from the exhaust gas of a diesel engine. Frequently replacing DPF is a waste of resource and waiting for full utilization is risky and very costly, so, what is the optimal time/milage to change DPF? Answering this question is very difficult without knowing when the DPF is changed in a vehicle. We are finding the answer with supervised machine learning algorithms for detecting anomalies in vehicles off-board sensor data (operational data of vehicles). Filter change is considered an anomaly because it is rare as compared to normal data. Non-sequential machine learning algorithms for anomaly detection like oneclass support vector machine (OC-SVM), k-nearest neighbor (K-NN), and random forest (RF) are applied for the first time on DPF dataset. The dataset is unbalanced, and accuracy is found misleading as a performance measure for the algorithms. Precision, recall, and F1-score are found good measure for the performance of the machine learning algorithms when the data is unbalanced. RF gave highest F1-score of 0.55 than K-NN (0.52) and OCSVM (0.51). It means that RF perform better than K-NN and OC-SVM but after further investigation it is concluded that the results are not satisfactory. However, a sequential approach should have been tried which could yield better result.

Cette thèse s’intéresse à la problématique de la classification dynamique en environnements stationnaires et non stationnaires, tolérante aux variations de quantités des données d’apprentissage et capable d’ajuster ses modèles selon la variabilité des données entrantes. Pour cela, nous proposons une solution faisant cohabiter des classificateurs one-class SVM indépendants ayant chacun leur propre procédure d’apprentissage incrémentale et par conséquent, ne subissant pas d’influences croisées pouvant émaner de la configuration des modèles des autres classificateurs. L’originalité de notre proposition repose sur l’exploitation des anciennes connaissances conservées dans les modèles de SVM (historique propre à chaque SVM représenté par l’ensemble des vecteurs supports trouvés) et leur combinaison avec les connaissances apportées par les nouvelles données au moment de leur arrivée. Le modèle de classification proposé (mOC-iSVM) sera exploité à travers trois variations exploitant chacune différemment l’historique des modèles. Notre contribution s’inscrit dans un état de l’art ne proposant pas à ce jour de solutions permettant de traiter à la fois la dérive de concepts, l’ajout ou la suppression de concepts, la fusion ou division de concepts, tout en offrant un cadre privilégié d’interactions avec l’utilisateur. Dans le cadre du projet ANR DIGIDOC, notre approche a été appliquée sur plusieurs scénarios de classification de flux d’images pouvant survenir dans des cas réels lors de campagnes de numérisation. Ces scénarios ont permis de valider une exploitation interactive de notre solution de classification incrémentale pour classifier des images arrivant en flux afin d’améliorer la qualité des images numérisées. / This research contributes to the field of dynamic learning and classification in case of stationary and non-stationary environments. The goal of this PhD is to define a new classification framework able to deal with very small learning dataset at the beginning of the process and with abilities to adjust itself according to the variability of the incoming data inside a stream. For that purpose, we propose a solution based on a combination of independent one-class SVM classifiers having each one their own incremental learning procedure. Consequently, each classifier is not sensitive to crossed influences which can emanate from the configuration of the models of the other classifiers. The originality of our proposal comes from the use of the former knowledge kept in the SVM models (represented by all the found support vectors) and its combination with the new data coming incrementally from the stream. The proposed classification model (mOC-iSVM) is exploited through three variations in the way of using the existing models at each step of time. Our contribution states in a state of the art where no solution is proposed today to handle at the same time, the concept drift, the addition or the deletion of concepts, the fusion or division of concepts while offering a privileged solution for interaction with the user. Inside the DIGIDOC project, our approach was applied to several scenarios of classification of images streams which can correspond to real cases in digitalization projects. These different scenarios allow validating an interactive exploitation of our solution of incremental classification to classify images coming in a stream in order to improve the quality of the digitized images.

The thesis is focused on the implementation of method of content and language integrated learning in mathematics in the small school where several grades are taught in one class. The thesis is divided into two parts. The theoretical part deals with issues related to teaching of vocational subject in the foreign language. I define CLIL and show the opening implementation of teaching of this method in schools in the Czech Republic. Then, I concern the advantages and disadvantages of this method, the suitability of different subjects and to implement different variants of CLIL, which I can use while teaching at different stages of education in the Czech school system. The main aim of the practical part is to create educational materials and individual plans of lessons for teaching a curriculum of mathematics in English at the first stage of the primary school for grades 4 and 5, implementation of this method to teaching in selected small school and confirmation or refutation the statements of the authors of publications that, if implemented CLIL teaching at schools, it brings indisputable advantages for the whole educational process.

Sistemas elétricos possuem grande influência no desenvolvimento econômico mundial. Dada a importância da energia elétrica para nossa sociedade, os sistemas elétricos frequentemente são alvos de intrusões pela rede causadas pelas mais diversas motivações. Para minimizar ou até mesmo mitigar os efeitos de intrusões pela rede, estão sendo propostos mecanismos que aumentam o nível de segurança dos sistemas elétricos, como novos protocolos de comunicação e normas de padronização. Além disso, os sistemas elétricos estão passando por um intenso processo de modernização, tornando-os altamente dependentes de sistemas de rede responsáveis por monitorar e gerenciar componentes elétricos. Estes, então denominados Smart Grids, compreendem subsistemas de geração, transmissão, e distribuição elétrica, que são monitorados e gerenciados por sistemas de controle e aquisição de dados (SCADA). Nesta dissertação de mestrado, investigamos e discutimos a aplicabilidade e os benefícios da adoção de Redes Definidas por Software (SDN) para auxiliar o desenvolvimento da próxima geração de sistemas SCADA. Propomos também um sistema de detecção de intrusões (IDS) que utiliza técnicas específicas de classificação de tráfego e se beneficia de características das redes SCADA e do paradigma SDN/OpenFlow. Nossa proposta utiliza SDN para coletar periodicamente estatísticas de rede dos equipamentos SCADA, que são posteriormente processados por algoritmos de classificação baseados em exemplares de uma única classe (OCC). Dado que informações sobre ataques direcionados à sistemas SCADA são escassos e pouco divulgados publicamente por seus mantenedores, a principal vantagem ao utilizar algoritmos OCC é de que estes não dependem de assinaturas de ataques para detectar possíveis tráfegos maliciosos. Como prova de conceito, desenvolvemos um protótipo de nossa proposta. Por fim, em nossa avaliação experimental, observamos a performance e a acurácia de nosso protótipo utilizando dois tipos de algoritmos OCC, e considerando eventos anômalos na rede SCADA, como um ataque de negação de serviço (DoS), e a falha de diversos dispositivos de campo. / Power grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.

Esse trabalho está baseado na investigação dos detectores de falhas aplicando classificadores de classe única. As falhas a serem detectadas são relativas ao estado de funcionamento de cada componente do circuito, especificamente de suas tolerâncias (falha paramétrica). Usando a função de transferência de cada um dos circuitos são gerados e analisados os sinais de saída com os componentes dentro e fora da tolerância. Uma função degrau é aplicada à entrada do circuito, o sinal de saída desse circuito passa por uma função diferenciadora e um filtro. O sinal de saída do filtro passa por um processo de redução de atributos e finalmente, o sinal segue simultaneamente para os classificadores multiclasse e classe única. Na análise são empregados ferramentas de reconhecimento de padrões e de classificação de classe única. Os classficadores multiclasse são capazes de classificar o sinal de saída do circuito em uma das classes de falha para o qual foram treinados. Eles apresentam um bom desempenho quando as classes de falha não possuem superposição e quando eles não são apresentados a classes de falhas para os quais não foram treinados. Comitê de classificadores de classe única podem classificar o sinal de saída em uma ou mais classes de falha e também podem classificá-lo em nenhuma classe. Eles apresentam desempenho comparável ao classificador multiclasse, mas também são capazes detectar casos de sobreposição de classes de falhas e indicar situações de falhas para os quais não foram treinados (falhas desconhecidas). Os resultados obtidos nesse trabalho mostraram que os classificadores de classe única, além de ser compatível com o desempenho do classificador multiclasse quando não há sobreposição, também detectou todas as sobreposições existentes sugerindo as possíveis falhas. / This work deals with the application of one class classifiers in fault detection. The faults to be detected are related parametric faults. The transfer function of each circuit was generated and the outputs signals with the components in and out of tolerance were analyzed. Pattern recognition and one class classifications tools are employed to perform the analysis. The multiclass classifiers are able to classify the circuit output signal in one of the trained classes. They present a good performance when the fault classes do not overlap or when they are not presented to fault classes that were not presented in the training. The one class classifier committee may classify the output signal in one or more fault classes and may also classify them in none of the trained class faults. They present comparable performance to multiclass classifiers, but also are able to detect overlapping fault classes and show fault situations that were no present in the training (unknown faults).

Sistemas elétricos possuem grande influência no desenvolvimento econômico mundial. Dada a importância da energia elétrica para nossa sociedade, os sistemas elétricos frequentemente são alvos de intrusões pela rede causadas pelas mais diversas motivações. Para minimizar ou até mesmo mitigar os efeitos de intrusões pela rede, estão sendo propostos mecanismos que aumentam o nível de segurança dos sistemas elétricos, como novos protocolos de comunicação e normas de padronização. Além disso, os sistemas elétricos estão passando por um intenso processo de modernização, tornando-os altamente dependentes de sistemas de rede responsáveis por monitorar e gerenciar componentes elétricos. Estes, então denominados Smart Grids, compreendem subsistemas de geração, transmissão, e distribuição elétrica, que são monitorados e gerenciados por sistemas de controle e aquisição de dados (SCADA). Nesta dissertação de mestrado, investigamos e discutimos a aplicabilidade e os benefícios da adoção de Redes Definidas por Software (SDN) para auxiliar o desenvolvimento da próxima geração de sistemas SCADA. Propomos também um sistema de detecção de intrusões (IDS) que utiliza técnicas específicas de classificação de tráfego e se beneficia de características das redes SCADA e do paradigma SDN/OpenFlow. Nossa proposta utiliza SDN para coletar periodicamente estatísticas de rede dos equipamentos SCADA, que são posteriormente processados por algoritmos de classificação baseados em exemplares de uma única classe (OCC). Dado que informações sobre ataques direcionados à sistemas SCADA são escassos e pouco divulgados publicamente por seus mantenedores, a principal vantagem ao utilizar algoritmos OCC é de que estes não dependem de assinaturas de ataques para detectar possíveis tráfegos maliciosos. Como prova de conceito, desenvolvemos um protótipo de nossa proposta. Por fim, em nossa avaliação experimental, observamos a performance e a acurácia de nosso protótipo utilizando dois tipos de algoritmos OCC, e considerando eventos anômalos na rede SCADA, como um ataque de negação de serviço (DoS), e a falha de diversos dispositivos de campo. / Power grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.