One-class collaborative filtering is a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples. In this work, we introduced social networks as a new data source to the one-class collaborative filtering (OCCF) methods and sought ways to benefit from them when dealing with OCCF problems. We divided our research into two parts. In the first part, we proposed different weighting schemes based on social graphs for some well known OCCF algorithms. One of the weighting schemes we proposed outperformed our baselines for some of the datasets we used. In the second part, we focused on the dataset differences in order to find out why our algorithm performed better on some of the datasets. We compared social graphs with the graphs of users and their neighbors generated by the k-NN algorithm. Our research showed that social graphs generated from a specialized domain better improves the recommendation performance than the social graphs generated from a more generic domain.

In the light of the ongoing loss of biodiversity at the global scale, monitoring grasslands is nowadays of utmost importance considering their functional relevance in terms of the ecosystem services that they provide. Here, guidelines of the European Union like the Fauna-Flora-Habitat Directive and the European Agricultural fund for Rural Development with its HNV indicators are crucial. Indeed, they form the legal framework for nature conservation and define grasslands as one of their conservation targets, whose status needs to be assessed and reported by all member states on a regular basis. In the light of these reporting requirements, the need for a harmonised and thorough grassland monitoring is highly demanding since most member states are still currently adopting intensive field surveys or photointerpretation with differing levels of detail for mapping habitat distribution. To this purpose, a cost-effective solution is offered by Earth Observation data for which specific grassland monitoring methodologies shall be then implemented which are capable of processing multitemporal acquisitions collected throughout the entire growing season. Although optical data are most suited for characterising vegetation in terms of spectral information content, they are actually subject to weather conditions (especially cloud coverage), which hinder the possibility of collecting enough information over the full phenological cycle. Furthermore, so far only few studies started employing high and very high resolution optical time series for grassland habitat monitoring since they have become available e.g., from the RapidEye satellites, only in the recent past. To overcome this limitation, SAR systems can be employed which provide imagery independent from weather or daytime conditions, hence enabling vegetation analysis by means of complete time series. Compared to optical data, radar imagery is less affected by the physical-chemical characteristics of the surface, but rather it is sensitive to structural features like geometry and roughness. However, in this context presently only very few techniques have been implemented, which are anyhow not suitable to be employed in an operational framework. Furthermore, to address the classification task, supervised approaches (which require in situ information for all the land-cover classes present in the study area) represent the most accurate methodological solution; nevertheless, collecting an exhaustive ground truth is generally expensive both in terms of time and economic costs and is not even feasible when the test site is remote. However, in many applications the end-users are generally only interested in very few specific targeted land-cover classes which, for instance, have high ecological value or are associated with support actions, subsidies or benefits from national or international institutions. The categorisation of specific grasslands and habitat types as those addressed in this thesis falls within such category of problems, which is defined in the literature as targeted land-cover classification. In this framework, a robust and effective targeted classification system for the automatic identification of grassland types by means of multi-temporal and multi-polarised SAR data has been developed within this thesis. In particular, the proposed system is composed of three main blocks: the preprocessing of the SAR image time series including the Kennaugh decomposition, the feature extraction including multi-temporal filtering and texture analysis, and the hierarchical targeted classification, which consist of two phases where first a one-class classifier is employed to outline the merger of all the grassland types of interest considered as a single information class and then a multi-class classifier is applied for discriminating the specific targeted classes within the areas identified as positive by the one-class classifier. To evaluate the capabilities of the proposed methodology, several experimental trials have been carried out over two test sites located in Southern Bavaria (Germany) and Mecklenburg Western-Pomerania (Germany) for which six diverse datasets have been derived from multitemporal series of dualpol TerraSAR-X as well as dual-/quadpol Radarsat-2 images. Four among the Natura 2000 habitat types of the Fauna-Flora-Habitat Directive as well all High Nature Value grassland types have been considered as targeted classes for this study. Overall, the proposed system proved to be robust and confirmed the effectiveness of employing multitemporal and multi-polarisation VHR SAR data for discriminating habitat types and High Nature Value grassland types, exhibiting high potential for future employment even at larger scales. In particular, it could be demonstrated that the proposed hierarchical targeted classification approach outperforms the available state-of-the-art methods and has a clear advantage with respect to the standard approaches in terms of robustness, reliability and transferability.

A network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD’s rely on time-domain features and simple thresholds for efficient speech detection however this doesn’t say much about the signal being passed. More sophisticated methods employ machine learning algorithms, but train on specific noises intended for a target environment. Validating speech under a variety of unknown conditions must be possible; as well as differentiating between speech and nonspeech data embedded within the packets. A real-time speech detection method is proposed that relies only on a clean speech model for detection. Through the use of Gammatone filter bank processing, the Cepstrum and several frequency domain features are used to train a One-Class Support Vector Machine which provides a clean-speech model irrespective of environmental noise. A Wiener filter is used to provide improved operation for harsh noise environments. Greater than 90% detection accuracy is achieved for clean speech with approximately 70% accuracy for SNR as low as 5dB

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Les systèmes infrarouges sont essentiels pour fournir aux forces armées une capacité de reconnaissance des menaces. En contexte opérationnel, ces systèmes sont contraints au temps-réel et à l’accès à des taux de fausses alarmes faibles. Ceci implique la détection des menaces parmi de nombreux objets non-pertinents.Dans ce document, nous combinons des OneClass-SVM pour une décision multiclasse avec rejet(préservant la fausse-alarme). En apprentissage, nous sélectionnons les variables pour contrôler la parcimonie du moteur de décision.Nous présentons également un classifieur original, le Discriminative OneClass-SVM, combinant les propriétés du C-SVM et du OneClass-SVM dans le contexte multiclasse. Ce détecteur de nouveauté n’a pas de dépendance au nombre de classes. Ceci permet une utilisation sur des données à grande échelle.Nos expériences sur des données réelles démontrent l’intérêt des propositions pour les systèmes fortement contraints, face aux méthodes de référence. / Infrared systems are keys to provide automatic control of threats to military forces. Such operational systems are constrained to real-time processing and high efficiency (low false-alarm rate) implying the recognition of threats among numerous irrelevant objects.In this document, we combine OneClass Support Vector Machines (SVM) to discriminate in the multiclass framework and to reject unknown objects (preserving the false-alarm rate).While learning, we perform variable selection to control the sparsity of the decision functions. We also introduce a new classifier, the Discriminative OneClass-SVM. It combines properties of both the biclass-SVM and the OneClass-SVM in a multiclass framework. This classifier detects novelty and has no dependency to the amount of categories, allowing to tackle large scale problems. Numerical experiments, on real world infrared datasets, demonstrate the relevance of our proposals for highly constrained systems, when compared to standard methods.

Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: Hydrocyclones are separation devices that are widely used throughout the chemical engineering and mineral processing industries. Although simple in design, the intricate flow structure of the device complicates control. As an alternative to conventional empirical and theoretical modelling, process state monitoring methods have recently been employed as a means to control hydrocyclones. The purpose of process state monitoring methods is to distinguish between the desired operating state with favourable separation, the transition state, and the troublesome operating state of dense flow separation. In comparison to previously employed monitoring techniques, image analysis of the underflow is regarded as a promising approach. Preliminary studies have indicated that the technique complies with hydrocyclone monitoring requirements: sensitivity, non-invasiveness, sampling times less than one second, robustness and low cost. The primary objective of this study was therefore defined as investigating the feasibility of image analysis of hydrocyclone underflow as a monitoring technique. Data collection entailed the recording of hydrocyclone underflow for different operating states. Six case studies were performed in total: Gold, Ilmenite, Platreef, Merensky 1, Merensky 2 and Merensky 3 (with the case study names indicating the different ore types used). An image analysis technique, consisting of feature extraction through motion detection, as well as various noise reduction methods, was consequently developed and applied to the video data. Classification of the various operating states was attempted by performing modelling by one-class support vector machines (SVM). Results indicated that the developed image analysis technique effectively addresses background noise, random noise and system vibration through image enhancement and a motion threshold. Extremely low contrast differences and foreground noise did, however, prove problematic in Ilmenite and Merensky 1 case studies respectively. For the remaining case studies, it was found that the various operating states were identified with high accuracy through one-class SVM classification. This is particularly true for the identification of the troublesome dense flow separation for which extremely low missing alarm rates were obtained (0 % in most cases). In terms of practicality, the technique proved to be sensitive, non-intrusive and economical. The sampling time of 30 frames per second and estimated processing to video time ratio of 1:1, is furthermore satisfactory. Ultimately, the results indicate that the image analysis of hydrocyclone underflow is a viable monitoring technique. The robustness of the technique might further be improved by use of backlighting and an air-knife. It is also recommended that future work should focus on testing the monitoring technique on an industrial hydrocyclone setup. / AFRIKAANSE OPSOMMING: Hidrosiklone is skeidingsapparate wat algemeen gebruik word in chemiese ingenieurswese en mineraalprosesserings industrieë. Alhoewel die apparaat ‘n eenvoudige ontwerp het, bemoeilik die komplekse interne vloeistruktuur die beheer daarvan. Prosestoestandmoniteringsmetodes is vir hidrosikloonbeheer toegepas as alternatief vir konvensionele empiriese en teoretiese modellering. Die doel van prosestoestandmoniteringsmetodes is om te onderskei tussen die gewenste bedryfstoestand met gunstige skeiding, die oorgangstoestand, en die moeilike bedryfstoestand van digtevloeiskeiding. In vergelyking met vorige toegepaste moniteringstegnieke, word beeldverwerking van die ondervloei beskou as ‘n belowende tegniek. Voorlopige studies het aangedui dat die tegniek voldoen aan die hidrosikloonmoniteringvereistes: sensitiwiteit, nie-indringendheid, monsternemingstydperke laer as een sekonde, robuustheid en lae koste. Die primêre doelwit van hierdie studie is daarom gedefineer as die ondersoek van die doenlikheid van beeldverwerking van hidrosikloon ondervloei as ‘n moniteringstegniek. Die data versameling het die afneem van hidrosikloon ondervloei vir verskillende bedryfstoestande behels. Ses gevallestudies is in totaal uitgevoer: Goud, Ilmeniet, Platreef, Merensky 1, Merensky 2 en Merensky 3 (die gevallestudie name dui die verskillende erts tipes wat gebruik is aan). ‘n Beeldverwerkingstegniek, wat bestaan uit kenmerkekstraksie deur bewegingsopsporing, asook verskeie geruisverlagingsmetodes, is gevolglik ontwikkel en toegepas op die video data. Klassifikasie van die verskeie bedryfstoestande is beproef deur modellering met enkelklassteunvektormasjiene. Resultate het aangedui dat die ontwikkelde beeldverwerkingstegniek agtergrond geruis, onreëlmatige geruis en sisteem vibrasie suksesvol aanspreek deur beeldversterking en ‘n bewegingslimiet. Beduidende lae kontrasverskille en voorgrond geruis blyk wel problematies in die Ilmeniet en Merensky 1 gevallestudies onderskeidelik. Vir die orige gevallestudies is gevind dat die verskillinde bedryfstoestande met hoë akkuraatheid geïdentifiseer is deur enkelklassteunvektormasjiene klassifisering. Dit is veral waar vir die identifisering van die moeilike digtevloeiskeiding waarvoor beduidende lae vermiste-alarmmaatstawwe behaal is (0 % in die meeste gevalle). Aangaande die praktiese aspekte, blyk die tegniek sensitief, nie-indringend en ekonomies. Die monsternemingstydperk van 30 raampies per sekonde en die beraamde prosesserings- tot videotyd verhouding van 1:1, is ook voldoende. Ten slotte dui die resultate daarop dat die beeldverwerking van hidrosikloon ondervloei ‘n uitvoerbare moniteringstegniek is. Die robuustheid van die tegniek sou verder verbeter kon word deur gebruik te maak van agtergrondverligting en ‘n lugspuit. Dit word ook aanbeveel dat toekomstige werk op die toetsing van die moniteringstegniek op ‘n industriële hidrosikloon toestel moet fokus.

In this work we developed rule-based algorithms for multiple-instance learning and one-class learning problems, namely, the mi-DS and OneClass-DS algorithms. Multiple-Instance Learning (MIL) is a variation of classical supervised learning where there is a need to classify bags (collection) of instances instead of single instances. The bag is labeled positive if at least one of its instances is positive, otherwise it is negative. One-class learning problem is also known as outlier or novelty detection problem. One-class classifiers are trained on data describing only one class and are used in situations where data from other classes are not available, and also for highly unbalanced data sets. Extensive comparisons and statistical testing of the two algorithms show that they generate models that perform on par with other state-of-the-art algorithms.

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.

This diploma thesis is aimed primarily at the analysis and specification of the management specifics at schools with more grades in one class. Due to the proportionally lower number of those type of schools in the school system, these specifics have a general tendency to be neglected or at least only peripherally mentioned in literature, even though its management undoubtly differs from the one applied at fully organized primary schools. The theoretical part specifies the term school with more grades in one class and pursue to describe its specific features and particularities. A description of the general school management follows, aiming at its most important spheres. In this thesis, school management is being described primarily from the point of view of a headteacher. The research part of this thesis is realized through the method of questionnaire survey aimed at fully-organized schools headteachers and at schools with more grades in one class headteachers in Středočeský region, in order to discover their management specifics. This thesis is not framed as a qualitative comparison of those two school types. The specifies of fully-organized schools management are being elicited only as a default source of inrormation, from which the specifcs of school with more grades in one class management are...

Road Networks play an important role in various applications such as urban and rural planning, infrastructure planning, transportation management, vehicle navigation. Extraction of Roads from Remote Sensed satellite images for updating road database in geographical information systems (GIS) is generally done manually by a human operator. However, manual extraction of roads is time consuming and labor intensive process. In the existing literature, there are a great number of researches published for the purpose of automating the road extraction process. However, automated processes still yield some erroneous and incomplete results and human intervention is still required. The aim of this research is to propose a framework for road network extraction from high spatial resolution multi-spectral imagery (MSI) to improve the accuracy of road extraction systems. The proposed framework begins with a spectral classification using One-class Support Vector Machines (SVM) and Gaussian Mixture Models (GMM) classifiers. Spectral Classification exploits the spectral signature of road surfaces to classify road pixels. Then, an iterative template matching filter is proposed to refine spectral classification results. K-medians clustering algorithm is employed to detect candidate road centerline points. Final road network formation is achieved by Markov Random Fields. The extracted road network is evaluated against a reference dataset using a set of quality metrics.