[pt] A utilização de modelos de inteligência computacional para tarefas de previsão Multi-Step de séries temporais tem apresentado resultados que permitem considerar estes modelos como alternativas viáveis para este tipo de problema. Baseados nos requerimentos computacionais e a melhora de desempenho, recentemente novas áreas de pesquisa têm sido apresentadas na comunidade científica. Este é o caso do Reservoir Computing, que apresenta novos campos de estudo para redes neurais do tipo recorrentes, as quais, no passado, não foram muito utilizados devido à complexidade de treinamento e ao alto custo computacional. Nesta nova área são apresentados modelos como Liquid State Machine e Echo State Networks, que proporcionam um novo entendimento no conceito de processamento dinâmico para redes recorrentes e propõem métodos de treinamento com baixo custo computacional. Neste trabalho determinou-se como foco de pesquisa a otimização de parâmetros globais para o projeto das Echo State Networks. Embora as Echo State Networks sejam objeto de estudo de pesquisadores reconhecidos, ainda apresentam comportamentos desconhecidos, em parte pela sua natureza dinâmica, mas também, pela falta de estudos que aprofundem o entendimento no comportamento dos estados gerados. Utilizando como fundamento o modelo Separation Ratio Graph para análise do desempenho, é proposto um novo modelo, denominado ESN-GA-SRG, que usa como base redes ESN com otimização de parâmetros globais utilizando GA e seleção de topologias para Reservoir por meio de análise de estados empregando SRG. O desempenho deste novo modelo é avaliado na previsão das 11 séries que compõem a versão reduzida do NN3 Forecasting Competition e em 36 séries da competição M3, selecionadas segundo características de periodicidade na amostragem, assimetria, sazonalidade e estacionaridade. O desempenho do modelo ESN-GA-SRG na previsão dessas séries temporais foi superior na maioria dos casos, com significância estatística, quando comparado com outros modelos da literatura. / [en] The use of computational intelligence models for Multi-Step time series prediction tasks has presented results that allow us to consider these models as viable alternatives for this type of problem. Based on computational requirements and performance improvement, new areas of research have recently been presented in the scientific community. This is the case of Reservoir Computing, which presents new fields of study for recurrent-type neural networks, which in the past were not widely used because of training complexity and high computational cost. In this new area are presented models such as Liquid State Machine and Echo State Networks, which provide a new understanding of the concept of dynamic processing for recurring networks and propose methods of training with low computational cost. In this work, we determined the optimization of global parameters for the Echo State Networks project. Although Echo State Networks are the object of study by recognized researchers, they still present unknown behavior, partly due to their dynamic nature, but also due to the lack of studies that deepen behavior understanding of the generated states. Based on the Separation Ratio Graph model for performance analysis, a new model, called ESN-GA-SRG, is proposed, which uses ESN networks with global parameter optimization using GA and selection of topologies for Reservoir through analysis of States employing SRG. The performance of this new model is evaluated to forecast the 11 series that made up the reduced version of the NN3 Forecasting Competition and for 36 series of the M3 competition, selected according to characteristics of periodicity in sampling, asymmetry, seasonality and stationary. The performance of the ESN-GA-SRG model in predicting these time series was superior in most cases, with statistical significance when compared with other models in the literature.

Une cyberattaque est considérée comme multi-étapes si elle est composée d’au moins deux actions différentes. L’objectif principal de cette thèse est aider l’analyste de sécurité dans la création de modèles de détection à partir d’un ensemble de cas alternatifs d’attaques multi-étapes. Pour répondre à cet objectif, nous présentons quattre contributions de recherche. D’abord, nous avons réalisé la première bibliographie systématique sur la détection d’attaques multi-étapes. Une des conclusions de cette bibliographie est la manque de méthodes pour confirmer les hypothèses formulées par l’analyste de sécurité pendant l’investigation des attaques multi-étapes passées. Ça nous conduit à la deuxième de nos contributions, le graphe des scénarios d’attaques abstrait ou AASG. Dans un AASG, les propositions alternatives sur les étapes fondamentales d’une attaque sont répresentées comme des branches pour être évaluées avec l’arrivée de nouveaux événements. Pour cette évaluation, nous proposons deux modèles, Morwilog et Bidimac, qui font de la détection au même temps que l’identification des hypothèses correctes. L’évaluation des résultats par l’analyste permet l’évolution des modèles.Finalement, nous proposons un modèle pour l’investigation visuel des scénarios d’attaques sur des événements non traités. Ce modèle, qui s’appelle SimSC, est basé sur la similarité entre les adresses IP, en prenant en compte la distance temporelle entre les événements. / A cyberattack is considered as multi-step if it is composed of at least two distinct actions. The main goal of this thesis is to help the security analyst in the creation of detection models from a set of alternative multi-step attack cases. To meet this goal, we present four research contributions. First of all, we have conducted the first systematic survey about multi-step attack detection. One of the conclusions of this survey is the lack of methods to confirm the hypotheses formulated by the security analyst during the investigation of past multi-step attacks. This leads us to the second of our contributions, the Abstract Attack Scenario Graph or AASG. In an AASG, the alternative proposals about the fundamental steps in an attack are represented as branches to be evaluated on new incoming events. For this evaluation, we propose two models, Morwilog and Bidimac, which perform detection and identification of correct hypotheses. The evaluation of the results by the analyst allows the evolution of the models. Finally, we propose a model for the visual investigation of attack scenarios in non-processed events. This model, called SimSC, is based on IP address similarity, considering the temporal distance between the events.

This thesis presents models and simulations of dielectrophoretic separation of micro and nano particles. The fluid dynamics involved and the dielectric properties of water inside single-walled carbon nanotube are studied as well. Based on the effective dipole moment method, the particle dynamic model focuses on the translational motions of micro particles. The hydrodynamic force between the particles and the particle-particle electrostatic interactions are considered as well. By comparing the dimensionless parameters, the dominating force can be determined. Based on a simplified version of the particle dynamic model, two numerical simulations are carried out to predict the efficiency of dielectrophoretic separation of micro size particles. The first calculation suggests a strategy to improve the trapping efficiency of E.coli bacteria by applying superimposed AC electric fields. The second calculation discusses the concept of mobility and improves the separation rate of particles by a multi-step trapping-releasing dielectrophoresis strategy. The model is extended down scale to calculate the separation of metallic and semiconducting single-walled carbon nanotubes by the modified effective dipole moment method for prolate ellipsoids. The steeply changed gradient of electric field results in the local joule heating therefore creates gradient of dielectric properties in the solution. As a result, certain pattern of fluid flow with a considerable strength is created and affects the motion of carbon nanotubes especially close to the electrode gap, which indicates that the so-called electrothermal flow should be considered in designing the experiment to separate single-walled carbon canotubes. When the length scale of particles is comparable to that of the electrodes, the calculation of dielectrophoretic force by the effective dipole moment is considered not to be accurate since only the electric field in the center point is taken into account. Hence in the thesis a new method based on distributed induced charge is suggested. By approximating a straight slender body as a prolate ellipsoid, the electric field of multiple points along the centerline are all considered in the calculation and the interaction between particles could be concurrently taken care. This method is expected to be an improved method to calculate the dielectrophoretic force of rod-like virus, DNA, nanowires and carbon nanotubes. The dielectric property of water confined in carbon nanotubes is expected to be dramatically different from that of bulk water. The thesis also contains a molecular dynamics study to reveal the difference also a dependence on the diameter of carbon nanotubes. The results show that along the axial direction, both the static permittivity and the relaxation time are larger than the isotropic bulk water, and in the cross-section plane it is opposite. When the radius of the carbon nanotubes increases, the properties of water inside become closer to the bulk water. / QC 20100820

The development and application of portable LCD (Liquid crystal display) technology is the main trend on the market. The goal of this study is to fabricate a compound optical film, and we focus on the design and fabrication of a new type backlight module for side-LED (Light emitting diode) display. The optical efficiency can be improved via the compound optical film. The profile of optical film was determined using commercial optical software, FRED. The mold with multi aspect ratio and multi fill-factor microlens array by LIGA-like process (Lithographie galvanoformung abformung) was produced using THB-126N negative photoresist and AZ-4620 positive photoresist. The study presents many innovative processes, such as the homemade gray scale mask, and multi-step electroforming method, which both produces the microlens array with variable size and aspect ratio. In addition, the embedded micro-void caused light guide and diffusion under the quantitative control during the PDMS (Polydimethylsiloxane) optical film fabrication. The compound optical film with embedded micro-voids, multi aspect ratio and variable size microlens array can be fabricated quickly without substrate. Then the optical properties were analyzed by BM-7A to characterize the luminance, uniformity and optical efficiency.

Abstract The purpose of this study is to determine the exact natural frequencies and mode shapes of multi-span uniform and multi-step Euler-Bernoulli beams with various concentrated elements (such as point masses, rotary inertias, linear springs, rotational springs, spring-mass systems, etc.) by using the matrix assembly method (MAM). To this end, the coefficient matrices for an intermediate pinned support, an intermediate concentrated elements, left-end support and right-end support of a beam are derived, first. Next, the overall coefficient matrix for the whole structural system is obtained by using the assembly technique of the finite element method. Finally, the natural frequencies and the associated mode shapes of the vibrating system are determined by equating the determinant of the last overall coefficient matrix to zero and substituting the corresponding values of integration constants into the associated eigenfunctions respectively. The effects of in-span pinned supports and various concentrated elements on the free vibration characteristics of the beam are also studied.

Foreign exchange (FX) rate forecasting has been a challenging area of study in the past. Various linear and nonlinear methods have been used to forecast FX rates. As the currency data are nonlinear and highly correlated, forecasting through nonlinear dynamical systems is becoming more relevant. The nearest neighbor (NN) algorithm is one of the most commonly used nonlinear pattern recognition and forecasting methods that outperforms the available linear forecasting methods for the high frequency foreign exchange data. The basic idea behind the NN is to capture the local behavior of the data by selecting the instances having similar dynamic behavior. The most relevant k number of histories to the present dynamical structure are the only past values used to predict the future. Due to this reason, NN algorithm is also known as the k-nearest neighbor algorithm (k-NN). Here k represents the number of chosen neighbors. In the k-nearest neighbor forecasting procedure, similar instances are captured through a distance function. Since the forecasts completely depend on the chosen nearest neighbors, the distance plays a key role in the k-NN algorithm. By choosing an appropriate distance, we can improve the performance of the algorithm significantly. The most commonly used distance for k-NN forecasting in the past was the Euclidean distance. Due to possible correlation among vectors at different time frames, distances based on deterministic vectors, such as Euclidean, are not very appropriate when applying for foreign exchange data. Since Mahalanobis distance captures the correlations, we suggest using this distance in the selection of neighbors. In the present study, we used five different foreign currencies, which are among the most traded currencies, to compare the performances of the k-NN algorithm with traditional Euclidean and Absolute distances to performances with the proposed Mahalanobis distance. The performances were compared in two ways: (i) forecast accuracy and (ii) transforming their forecasts in to a more effective technical trading rule. The results were obtained with real FX trading data, and the results showed that the method introduced in this work outperforms the other popular methods. Furthermore, we conducted a thorough investigation of optimal parameter choice with different distance measures. We adopted the concept of distance based weighting to the NN and compared the performances with traditional unweighted NN algorithm based forecasting. Time series forecasting methods, such as Auto regressive integrated moving average process (ARIMA), are widely used in many ares of time series as a forecasting technique. We compared the performances of proposed Mahalanobis distance based k-NN forecasting procedure with the traditional general ARIM- based forecasting algorithm. In this case the forecasts were also transformed into a technical trading strategy to create buy and sell signals. The two methods were evaluated for their forecasting accuracy and trading performances. Multi-step ahead forecasting is an important aspect of time series forecasting. Even though many researchers claim that the k-Nearest Neighbor forecasting procedure outperforms the linear forecasting methods for financial time series data, and the available work in the literature supports this claim with one step ahead forecasting. One of our goals in this work was to improve FX trading with multi-step ahead forecasting. A popular multi-step ahead forecasting strategy was adopted in our work to obtain more than one day ahead forecasts. We performed a comparative study on the performance of single step ahead trading strategy and multi-step ahead trading strategy by using five foreign currency data with Mahalanobis distance based k-nearest neighbor algorithm.

The research presented in this thesis addresses inherent problems in signaturebased intrusion detection systems (IDSs) operating in heterogeneous environments. The research proposes a solution to address the difficulties associated with multistep attack scenario specification and detection for such environments. The research has focused on two distinct problems: the representation of events derived from heterogeneous sources and multi-step attack specification and detection. The first part of the research investigates the application of an event abstraction model to event logs collected from a heterogeneous environment. The event abstraction model comprises a hierarchy of events derived from different log sources such as system audit data, application logs, captured network traffic, and intrusion detection system alerts. Unlike existing event abstraction models where low-level information may be discarded during the abstraction process, the event abstraction model presented in this work preserves all low-level information as well as providing high-level information in the form of abstract events. The event abstraction model presented in this work was designed independently of any particular IDS and thus may be used by any IDS, intrusion forensic tools, or monitoring tools. The second part of the research investigates the use of unification for multi-step attack scenario specification and detection. Multi-step attack scenarios are hard to specify and detect as they often involve the correlation of events from multiple sources which may be affected by time uncertainty. The unification algorithm provides a simple and straightforward scenario matching mechanism by using variable instantiation where variables represent events as defined in the event abstraction model. The third part of the research looks into the solution to address time uncertainty. Clock synchronisation is crucial for detecting multi-step attack scenarios which involve logs from multiple hosts. Issues involving time uncertainty have been largely neglected by intrusion detection research. The system presented in this research introduces two techniques for addressing time uncertainty issues: clock skew compensation and clock drift modelling using linear regression. An off-line IDS prototype for detecting multi-step attacks has been implemented. The prototype comprises two modules: implementation of the abstract event system architecture (AESA) and of the scenario detection module. The scenario detection module implements our signature language developed based on the Python programming language syntax and the unification-based scenario detection engine. The prototype has been evaluated using a publicly available dataset of real attack traffic and event logs and a synthetic dataset. The distinct features of the public dataset are the fact that it contains multi-step attacks which involve multiple hosts with clock skew and clock drift. These features allow us to demonstrate the application and the advantages of the contributions of this research. All instances of multi-step attacks in the dataset have been correctly identified even though there exists a significant clock skew and drift in the dataset. Future work identified by this research would be to develop a refined unification algorithm suitable for processing streams of events to enable an on-line detection. In terms of time uncertainty, identified future work would be to develop mechanisms which allows automatic clock skew and clock drift identification and correction. The immediate application of the research presented in this thesis is the framework of an off-line IDS which processes events from heterogeneous sources using abstraction and which can detect multi-step attack scenarios which may involve time uncertainty.

In this project, three primary problems were addressed to improve battery data management and software performance evaluation. All solutions used voltage values in time together with various device characteristics. Battery replacement labeling was performed using Hidden Markov Models. Both deep reinforcement learning, specifically TD3 with an LSTM layer, and traditional models were employed to predict future battery voltages. These predictions subsequently informed a developed novel method for early evaluation of software impact on battery performance. A baseline model was also introduced for optimal battery replacement timing. Results indicated that the TD3-LSTM model achieved a mean absolute percentage error below 5%, on par with traditional methods. The battery replacement labeling had above 85% correctly labeled replacements, impact on battery performance was above 90% correct in software comparisons. TD3-LSTM proved a viable choice for multi-step predictions requiring online learning, albeit requiring potentially more tuning. / I detta projekt behandlades tre primära problem i syfte att förbättra batteridatahantering och utvärdering av mjukvaruprestanda. Alla lösningar använde spänningsvärden i tid tillsammans med olika enhetsegenskaper. Batteribytesmärkning utfördes med hjälp av Hidden Markov Models. Både deep reinforcement learning, särskilt TD3 med ett LSTM-lager, och traditionella modeller användes för att förutsäga framtida batterispänningar. Dessa förutsägelser användes sedan i en framtagen ny metod för tidig utvärdering av mjukvarans påverkan på batteriprestanda. En basmodell introducerades också för optimal batteribytestid. Resultaten indikerade att TD3-LSTM modellen uppnådde ett genomsnittligt absolut procentfel under 5%, i nivå med traditionella metoder. Batteribytesmärkningen hade över 85% korrekt märkta batteribyten, inverkan på batteriprestanda var över 90% korrekt i mjukvarujämförelser. TD3-LSTM visade sig vara ett hållbart val för flerstegsförutsägelser som kräver onlineinlärning, även om det krävde potentiellt mer justering.

Some scholars argue that the principle of voluntary informed consent is rooted in the Western ethos of liberal individualism; that it would be difficult to implement this requirement in societies where the norms of decision-making emphasize collective rather than individual decision-making (for example, Sub-Saharan Africa); that it would amount to “cultural imperialism” to seek to implement the principle of voluntary informed consent in non-Western societies. This thesis rejects this skepticism about the possibility of implementing the informed consent requirement in non-Western environments and argues that applying the principle of voluntary informed consent in human subjects’ research in Sub-Saharan African communal culture could serve as an effective measure to protect vulnerable subjects from possible abuses or exploitations. The thesis proposes the “multi-step” approach to informed consent as the best approach to the implementation of the principle in the African communal setting. The thesis argues that the importance of the “multi-step” approach lies in the fact that it is one that is sensitive to local culture and customs. On the question of whether the principle of voluntary informed consent should be made compulsory in research, the thesis answers that we have no choice in the matter.

<p>Some scholars argue that the principle of voluntary informed consent is rooted in the Western ethos of liberal individualism; that it would be difficult to implement this requirement in societies where the norms of decision-making emphasize collective rather than individual decision-making (for example, Sub-Saharan Africa); that it would amount to “cultural imperialism” to seek to implement the principle of voluntary informed consent in non-Western societies. This thesis rejects this skepticism about the possibility of implementing the informed consent requirement in non-Western environments and argues that applying the principle of voluntary informed consent in human subjects’ research in Sub-Saharan African communal culture could serve as an effective measure to protect vulnerable subjects from possible abuses or exploitations. The thesis proposes the “multi-step” approach to informed consent as the best approach to the implementation of the principle in the African communal setting. The thesis argues that the importance of the “multi-step” approach lies in the fact that it is one that is sensitive to local culture and customs. On the question of whether the principle of voluntary informed consent should be made compulsory in research, the thesis answers that we have no choice in the matter.</p>