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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O reconhecimento de padões é uma área da inteligência computacional que apoia a resolução de problemas utilizando ferramentas computacionais. Dentre esses problemas podem ser citados o reconhecimento de faces, a identificação de impressões digitais e a autenticação de assinaturas. A autenticação de assinaturas de forma automática tem sua relevância pois está ligada ao reconhecimento de indivíduos e suas credenciais em sistemas complexos e a questões financeiras. Neste trabalho é apresentado um estudo dos parâmetros do Dynamic Time Warping, um algoritmo utilizado para alinhar duas assinaturas e medir a similaridade existente entre elas. Variando-se os principais parâmetros desse algoritmo, sobre uma faixa ampla de valores, foram obtidas as médias dos resultados de erros na classificação, e assim, estas médias foram avaliadas. Com base nas primeiras avaliação, foi identificada a necessidade de se calcular um desses parâmetros de forma dinâmica, o gap cost, a fim de ajustá-lo no uso de uma aplicação prática. Uma proposta para a realização deste cálculo é apresentada e também avaliada. É também proposta e avaliada uma maneira alternativa de representação dos atributos da assinatura, de forma a considerar sua curvatura em cada ponto adquirido no processo de aquisição, utilizando os vetores normais como forma de representação. As avaliações realizadas durante as diversas etapas do estudo consideraram o Equal Error Rate (EER) como indicação de qualidade e as técnicas propostas foram comparadas com técnicas já estabelecidas, obtendo uma média percentual de EER de 3,47%. / Pattern recognition is an important aspect within the computational intelligence area, which helps solving problems that use computing tools. Among these problems we can cite face recognition, fingerprint identication and signature authentication. The relevance of automatic signature authentication is related to the recognition of an individual and his/her role in a complex system and it is often related to financial matters. This work presents a study of the Dynamic Time Warping parameters, which is an algorithm used to align two signatures and measure the similarity between them. In a first stage a set of experiments varied the main parameters of the algorithm in a broad range of values and the resulting averages of classification errors were evaluated. Based on these first evaluations the necessity to calculate dynamically one of these parameters, the gap cost,it was identified in order to adjust it for practical application. A proposal to calculate thisparameter is also presented and evaluated. It is also proposed and evaluated an alternative way to represent the signature attributes, considering the curvature at each point acquired in the acquisition process, using the normal vectors as a form of representation. The evaluations performed in the diverse stages of the study considered the Equal Error Rate (EER) as quality measure and the proposed techniques were compared to well-established ones, obtaining an average EER of 3.47 %.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O reconhecimento de padões é uma área da inteligência computacional que apoia a resolução de problemas utilizando ferramentas computacionais. Dentre esses problemas podem ser citados o reconhecimento de faces, a identificação de impressões digitais e a autenticação de assinaturas. A autenticação de assinaturas de forma automática tem sua relevância pois está ligada ao reconhecimento de indivíduos e suas credenciais em sistemas complexos e a questões financeiras. Neste trabalho é apresentado um estudo dos parâmetros do Dynamic Time Warping, um algoritmo utilizado para alinhar duas assinaturas e medir a similaridade existente entre elas. Variando-se os principais parâmetros desse algoritmo, sobre uma faixa ampla de valores, foram obtidas as médias dos resultados de erros na classificação, e assim, estas médias foram avaliadas. Com base nas primeiras avaliação, foi identificada a necessidade de se calcular um desses parâmetros de forma dinâmica, o gap cost, a fim de ajustá-lo no uso de uma aplicação prática. Uma proposta para a realização deste cálculo é apresentada e também avaliada. É também proposta e avaliada uma maneira alternativa de representação dos atributos da assinatura, de forma a considerar sua curvatura em cada ponto adquirido no processo de aquisição, utilizando os vetores normais como forma de representação. As avaliações realizadas durante as diversas etapas do estudo consideraram o Equal Error Rate (EER) como indicação de qualidade e as técnicas propostas foram comparadas com técnicas já estabelecidas, obtendo uma média percentual de EER de 3,47%. / Pattern recognition is an important aspect within the computational intelligence area, which helps solving problems that use computing tools. Among these problems we can cite face recognition, fingerprint identication and signature authentication. The relevance of automatic signature authentication is related to the recognition of an individual and his/her role in a complex system and it is often related to financial matters. This work presents a study of the Dynamic Time Warping parameters, which is an algorithm used to align two signatures and measure the similarity between them. In a first stage a set of experiments varied the main parameters of the algorithm in a broad range of values and the resulting averages of classification errors were evaluated. Based on these first evaluations the necessity to calculate dynamically one of these parameters, the gap cost,it was identified in order to adjust it for practical application. A proposal to calculate thisparameter is also presented and evaluated. It is also proposed and evaluated an alternative way to represent the signature attributes, considering the curvature at each point acquired in the acquisition process, using the normal vectors as a form of representation. The evaluations performed in the diverse stages of the study considered the Equal Error Rate (EER) as quality measure and the proposed techniques were compared to well-established ones, obtaining an average EER of 3.47 %.

The thesis illustrates the development of DSP-based systems-¡§Hand Written Number Recognition System,¡¨ and ¡§Pattern Recognition System.¡¨ Hand written number recognition system consists of three sub-systems and recognition algorithm: Image Acquisition System, Image Preprocessing System, Image Segmentation System and Binary Pattern Match Algorithm. Pattern recognition system, as well, consists of three sub-systems and recognition algorithm: Image Acquisition System, Image Preprocessing System, Image Segmentation System, and Visual Dynamic Time Warping Algorithm. From the result of the experiment, both DSP image recognition systems can meet the expectation and gain good recognition and efficiency.

Yes / With the rapid growth of security threats in computer networks, the need for developing efficient security‐warning systems is substantially increasing. Distributed denial‐of‐service (DDoS) and DoS attacks are still among the most effective and dreadful attacks that require robust detection. In this work, we propose a new method to detect TCP DoS/DDoS attacks. Since analyzing network traffic is a promising approach, our proposed method utilizes network traffic by decomposing the TCP traffic into control and data planes and exploiting the dynamic time warping (DTW) algorithm for aligning these two planes with respect to the minimum Euclidean distance. By demonstrating that the distance between the control and data planes is considerably small for benign traffic, we exploit this characteristic for detecting attacks as outliers. An adaptive thresholding scheme is implemented by adjusting the value of the threshold in accordance with the local statistics of the median absolute deviation (MAD) of the distances between the two planes. We demonstrate the efficacy of the proposed method for detecting DoS/DDoS attacks by analyzing traffic data obtained from publicly available datasets. / The Deanship of Scientific Research, King Saud University. The Gulf Science, Innovation, and Knowledge Economy Programme of the U.K. Government

This research explores the temporal patterns of psychophysiological responses to musical excerpts by analyzing the expansive Emotion in Motion dataset, the most comprehensive of its kind. Utilizing the Dynamic Time Warping and T-test analysis techniques, we examined data from participants across seven countries who listened to three distinct musical pieces. During these listening sessions, Electrodermal Activity (EDA) and Pulse Oximetry (POX) readings were collected, complemented by qualitative feedback from the participants. Our analysis focused on detecting recurring patterns and extracting meaningful insights from the data. In addition to this, we compare several Deep Neural Networks to find the one that is best suited for prediction of emotional attributes with EDA and POX signals as input. To further facilitate a comprehensive visualization and analysis of the EDA, POX, and audio signals, we developed a dedicated platform, which features a coordinated multiple view interface, as an integral part of this work. / Master of Science / We explored how people's bodies react over time when they listen to music. We used a large collection of data called the ``Emotion in Motion'' dataset, which has information from people in seven countries who listened to three different music pieces. To understand this data, we used special tools that help detect patterns and changes in how the body responds. During the music sessions, the participant's skin's electrical activity and the amount of oxygen in the blood were recorded, which can give clues about emotional reactions. People also shared their feelings about the music. To make it easier to see and understand all this information together, we created a new web platform that simulates the experiment in real-time. This work aims to help us better understand the deep connection between music and human emotions.

Sensor-based human activity recognition has become an important research field within pervasive and ubiquitous computing. Techniques for recognizing atomic activities such as gestures or actions are mature for now, but complex activity recognition still remains a challenging issue. I was a candidate in an activity classification thesis. It collected 4 activities, which included walking on the sidewalk for a set distance, walking up and down a set of stairs, walking on the treadmill at 2.5 mph for 2 minutes, and jogging on the treadmill at 5.5 mph for 1 minute. It took 30 minutes to collect one candidate data. If complex activity data can be made up with atomic activities data, the data collecting process will be simplified. In this thesis, I used methods to mimic a complex activity shapelet by combing atomic activity shapelets. I first collect two candidates walk, jump and skip time series data, in which walk and jump are considered the atomic activities of skip. Time series patterns, shapelets, are extracted using tsshapelet package. Shapelets are small sub-series, or parts of the time-series, that are informative or discriminative for a certain class. They can be used to transform the time-series to features by calculating the distance for each of the time-series you want to classify to a shapelet. In order to create skip representative shapelet, Barycenter Dynamic Time Warping and Weighted Dynamic Time Warping are used to average walk and jump shapelet, and then compare the euclidean distance between skip shapelet with walk shapelet, jump shapelet and, combined-shapelet. Experimental result show that the combined-shapelet is closer to skip shapelet than single walk or jump shapelet. Then I use three evaluation methods to mathematically and statistically show that combined-shapelet and real skip shapelet are similar. Evaluation methods include sliding window, cycle comparison and random comparison. To verify whether combined-shapelet can substitute real skip shapelet, a new labeled time series data is introduced, the result shows that both shapelets have the label accuracy around 70%, accuracy difference is less than 1%.

No / Dynamic speech properties, such as time warping, silence removal and background noise reduction are the most challenging issues in continuous speech signal matching. Among all of them, the time warped speech signal matching is of great interest and has been a tough challenge for the researchers. The literature contains a variety of techniques to measure the similarity between speech utterances, however there are some limitations associated with these techniques. This paper introduces an adaptive framing based continuous speech tracking and similarity measurement approach that uses a Kalman filter (KF) as a robust tracker. The use of KF is novel for time warped speech signal matching and dynamic time warping. A dynamic state model is presented based on equations of linear motion. In this model, fixed length frame of input (test) speech signal is considered as a unidirectional moving object by sliding it along the template speech signal. The best matched position estimate in template speech (sample number) for corresponding test frame at current time is calculated. Simultaneously, another position observation is produced by a feature based distance metric. The position estimated by the state model is fused with the observation using KF along with the noise variances. The best estimated frame position in the template speech for the current state is calculated. Finally, forecasting of the noise variances and template frame size for next state are made according to the KF output. The experimental results demonstrate the robustness of the proposed technique in terms of time warped speech signal matching as well as in computation cost.

Time series are ubiquitous in the day-by-day of human beings. A diversity of application domains generate data arranged in time, such as medicine, biology, economics, and signal processing. Due to the great interest in time series, a large variety of methods for mining temporal data has been proposed in recent decades. Several of these methods have one characteristic in common: in their cores, there is a (dis)similarity function used to compare the time series. Dynamic Time Warping (DTW) is arguably the most relevant, studied and applied distance measure for time series analysis. The main drawback of DTW is its computational complexity. At the same time, there are a significant number of data mining tasks, such as motif discovery, which requires a quadratic number of distance computations. These tasks are time intensive even for less expensive distance measures, like the Euclidean Distance. This thesis focus on developing fast algorithms that allow large-scale analysis of temporal data, using similarity-based methods for time series data mining. The contributions of this work have implications in several data mining tasks, such as classification, clustering and motif discovery. Specifically, the main contributions of this thesis are the following: (i) an algorithm to speed up the exact DTW calculation and its embedding into the similarity search procedure; (ii) a novel DTW-based spurious prefix and suffix invariant distance; (iii) a music similarity representation with implications on several music mining tasks, and a fast algorithm to compute it, and; (iv) an efficient and anytime method to find motifs and discords under the proposed prefix and suffix invariant DTW. / Séries temporais são ubíquas no dia-a-dia do ser humano. Dados organizados no tempo são gerados em uma infinidade de domínios de aplicação, como medicina, biologia, economia e processamento de sinais. Devido ao grande interesse nesse tipo de dados, diversos métodos de mineração de dados temporais foram propostos nas últimas décadas. Muitos desses métodos possuem uma característica em comum: em seu núcleo, há uma função de (dis)similaridade utilizada para comparar as séries. Dynamic Time Warping (DTW) é indiscutivelmente a medida de distância mais relevante na análise de séries temporais. A principal dificuldade em se utilizar a DTW é seu alto custo computacional. Ao mesmo tempo, algumas tarefas de mineração de séries temporais, como descoberta de motifs, requerem um alto número de cálculos de distância. Essas tarefas despendem um grande tempo de execução, mesmo utilizando-se medidas de distância menos custosas, como a distância Euclidiana. Esta tese se concentra no desenvolvimento de algoritmos eficientes que permitem a análise de dados temporais em larga escala, utilizando métodos baseados em similaridade. As contribuições desta tese têm implicações em variadas tarefas de mineração de dados, como classificação, agrupamento e descoberta de padrões frequentes. Especificamente, as principais contribuições desta tese são: (i) um algoritmo para acelerar o cálculo exato da distância DTW e sua incorporação ao processo de busca por similaridade; (ii) um novo algoritmo baseado em DTW para prover invariância a prefixos e sufixos espúrios no cálculo da distância; (iii) uma representação de similaridade musical com implicações em diferentes tarefas de mineração de dados musicais e um algoritmo eficiente para computá-la; (iv) um método eficiente e anytime para encontrar motifs e discords baseado na medida DTW invariante a prefixos e sufixos.

Time series are ubiquitous in the day-by-day of human beings. A diversity of application domains generate data arranged in time, such as medicine, biology, economics, and signal processing. Due to the great interest in time series, a large variety of methods for mining temporal data has been proposed in recent decades. Several of these methods have one characteristic in common: in their cores, there is a (dis)similarity function used to compare the time series. Dynamic Time Warping (DTW) is arguably the most relevant, studied and applied distance measure for time series analysis. The main drawback of DTW is its computational complexity. At the same time, there are a significant number of data mining tasks, such as motif discovery, which requires a quadratic number of distance computations. These tasks are time intensive even for less expensive distance measures, like the Euclidean Distance. This thesis focus on developing fast algorithms that allow large-scale analysis of temporal data, using similarity-based methods for time series data mining. The contributions of this work have implications in several data mining tasks, such as classification, clustering and motif discovery. Specifically, the main contributions of this thesis are the following: (i) an algorithm to speed up the exact DTW calculation and its embedding into the similarity search procedure; (ii) a novel DTW-based spurious prefix and suffix invariant distance; (iii) a music similarity representation with implications on several music mining tasks, and a fast algorithm to compute it, and; (iv) an efficient and anytime method to find motifs and discords under the proposed prefix and suffix invariant DTW. / Séries temporais são ubíquas no dia-a-dia do ser humano. Dados organizados no tempo são gerados em uma infinidade de domínios de aplicação, como medicina, biologia, economia e processamento de sinais. Devido ao grande interesse nesse tipo de dados, diversos métodos de mineração de dados temporais foram propostos nas últimas décadas. Muitos desses métodos possuem uma característica em comum: em seu núcleo, há uma função de (dis)similaridade utilizada para comparar as séries. Dynamic Time Warping (DTW) é indiscutivelmente a medida de distância mais relevante na análise de séries temporais. A principal dificuldade em se utilizar a DTW é seu alto custo computacional. Ao mesmo tempo, algumas tarefas de mineração de séries temporais, como descoberta de motifs, requerem um alto número de cálculos de distância. Essas tarefas despendem um grande tempo de execução, mesmo utilizando-se medidas de distância menos custosas, como a distância Euclidiana. Esta tese se concentra no desenvolvimento de algoritmos eficientes que permitem a análise de dados temporais em larga escala, utilizando métodos baseados em similaridade. As contribuições desta tese têm implicações em variadas tarefas de mineração de dados, como classificação, agrupamento e descoberta de padrões frequentes. Especificamente, as principais contribuições desta tese são: (i) um algoritmo para acelerar o cálculo exato da distância DTW e sua incorporação ao processo de busca por similaridade; (ii) um novo algoritmo baseado em DTW para prover invariância a prefixos e sufixos espúrios no cálculo da distância; (iii) uma representação de similaridade musical com implicações em diferentes tarefas de mineração de dados musicais e um algoritmo eficiente para computá-la; (iv) um método eficiente e anytime para encontrar motifs e discords baseado na medida DTW invariante a prefixos e sufixos.