Learning to hash for large scale image retrieval

This thesis is concerned with improving the effectiveness of nearest neighbour search. Nearest neighbour search is the problem of finding the most similar data-points to a query in a database, and is a fundamental operation that has found wide applicability in many fields. In this thesis the focus is placed on hashing-based approximate nearest neighbour search methods that generate similar binary hashcodes for similar data-points. These hashcodes can be used as the indices into the buckets of hashtables for fast search. This work explores how the quality of search can be improved by learning task specific binary hashcodes. The generation of a binary hashcode comprises two main steps carried out sequentially: projection of the image feature vector onto the normal vectors of a set of hyperplanes partitioning the input feature space followed by a quantisation operation that uses a single threshold to binarise the resulting projections to obtain the hashcodes. The degree to which these operations preserve the relative distances between the datapoints in the input feature space has a direct influence on the effectiveness of using the resulting hashcodes for nearest neighbour search. In this thesis I argue that the retrieval effectiveness of existing hashing-based nearest neighbour search methods can be increased by learning the thresholds and hyperplanes based on the distribution of the input data. The first contribution is a model for learning multiple quantisation thresholds. I demonstrate that the best threshold positioning is projection specific and introduce a novel clustering algorithm for threshold optimisation. The second contribution extends this algorithm by learning the optimal allocation of quantisation thresholds per hyperplane. In doing so I argue that some hyperplanes are naturally more effective than others at capturing the distribution of the data and should therefore attract a greater allocation of quantisation thresholds. The third contribution focuses on the complementary problem of learning the hashing hyperplanes. I introduce a multi-step iterative model that, in the first step, regularises the hashcodes over a data-point adjacency graph, which encourages similar data-points to be assigned similar hashcodes. In the second step, binary classifiers are learnt to separate opposing bits with maximum margin. This algorithm is extended to learn hyperplanes that can generate similar hashcodes for similar data-points in two different feature spaces (e.g. text and images). Individually the performance of these algorithms is often superior to competitive baselines. I unify my contributions by demonstrating that learning hyperplanes and thresholds as part of the same model can yield an additive increase in retrieval effectiveness

Learning Discriminative Feature Representations for Visual Categorization

Learning discriminative feature representations has attracted a great deal of attention due to its potential value and wide usage in a variety of areas, such as image/video recognition and retrieval, human activities analysis, intelligent surveillance and human-computer interaction. In this thesis we first introduce a new boosted key-frame selection scheme for action recognition. Specifically, we propose to select a subset of key poses for the representation of each action via AdaBoost and a new classifier, namely WLNBNN, is then developed for final classification. The experimental results of the proposed method are 0.6% - 13.2% better than previous work. After that, a domain-adaptive learning approach based on multiobjective genetic programming (MOGP) has been developed for image classification. In this method, a set of primitive 2-D operators are randomly combined to construct feature descriptors through the MOGP evolving and then evaluated by two objective fitness criteria, i.e., the classification error and the tree complexity. Later, the (near-)optimal feature descriptor can be obtained. The proposed approach can achieve 0.9% ∼ 25.9% better performance compared with state-of-the-art methods. Moreover, effective dimensionality reduction algorithms have also been widely used for obtaining better representations. In this thesis, we have proposed a novel linear unsupervised algorithm, termed Discriminative Partition Sparsity Analysis (DPSA), explicitly considering different probabilistic distributions that exist over the data points, simultaneously preserving the natural locality relationship among the data. All these above methods have been systematically evaluated on several public datasets, showing their accurate and robust performance (0.44% - 6.69% better than the previous) for action and image categorization. Targeting efficient image classification , we also introduce a novel unsupervised framework termed evolutionary compact embedding (ECE) which can automatically learn the task-specific binary hash codes. It is regarded as an optimization algorithm which combines the genetic programming (GP) and a boosting trick. The experimental results manifest ECE significantly outperform others by 1.58% - 2.19% for classification tasks. In addition, a supervised framework, bilinear local feature hashing (BLFH), has also been proposed to learn highly discriminative binary codes on the local descriptors for large-scale image similarity search. We address it as a nonconvex optimization problem to seek orthogonal projection matrices for hashing, which can successfully preserve the pairwise similarity between different local features and simultaneously take image-to-class (I2C) distances into consideration. BLFH produces outstanding results (0.017% - 0.149% better) compared to the state-of-the-art hashing techniques

New scalable machine learning methods: beyond classification and regression

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] The recent surge in data available has spawned a new and promising age of machine learning. Success cases of machine learning are arriving at an increasing rate as some algorithms are able to leverage immense amounts of data to produce great complicated predictions. Still, many algorithms in the toolbox of the machine learning practitioner have been render useless in this new scenario due to the complications associated with large-scale learning. Handling large datasets entails logistical problems, limits the computational and spatial complexity of the used algorithms, favours methods with few or no hyperparameters to be con gured and exhibits speci c characteristics that complicate learning. This thesis is centered on the scalability of machine learning algorithms, that is, their capacity to maintain their e ectivity as the scale of the data grows, and how it can be improved. We focus on problems for which the existing solutions struggle when the scale grows. Therefore, we skip classi cation and regression problems and focus on feature selection, anomaly detection, graph construction and explainable machine learning. We analyze four di erent strategies to obtain scalable algorithms. First, we explore distributed computation, which is used in all of the presented algorithms. Besides this technique, we also examine the use of approximate models to speed up computations, the design of new models that take advantage of a characteristic of the input data to simplify training and the enhancement of simple models to enable them to manage large-scale learning. We have implemented four new algorithms and six versions of existing ones that tackle the mentioned problems and for each one we report experimental results that show both their validity in comparison with competing methods and their capacity to scale to large datasets. All the presented algorithms have been made available for download and are being published in journals to enable practitioners and researchers to use them.[Resumen] El reciente aumento de la cantidad de datos disponibles ha dado lugar a una nueva y prometedora era del aprendizaje máquina. Los éxitos en este campo se están sucediendo a un ritmo cada vez mayor gracias a la capacidad de algunos algoritmos de aprovechar inmensas cantidades de datos para producir predicciones difíciles y muy certeras. Sin embargo, muchos de los algoritmos hasta ahora disponibles para los científicos de datos han perdido su efectividad en este nuevo escenario debido a las complicaciones asociadas al aprendizaje a gran escala. Trabajar con grandes conjuntos de datos conlleva problemas logísticos, limita la complejidad computacional y espacial de los algoritmos utilizados, favorece los métodos con pocos o ningún hiperparámetro a configurar y muestra complicaciones específicas que dificultan el aprendizaje. Esta tesis se centra en la escalabilidad de los algoritmos de aprendizaje máquina, es decir, en su capacidad de mantener su efectividad a medida que la escala del conjunto de datos aumenta. Ponemos el foco en problemas cuyas soluciones actuales tienen problemas al aumentar la escala. Por tanto, obviando la clasificación y la regresión, nos centramos en la selección de características, detección de anomalías, construcción de grafos y en el aprendizaje máquina explicable. Analizamos cuatro estrategias diferentes para obtener algoritmos escalables. En primer lugar, exploramos la computación distribuida, que es utilizada en todos los algoritmos presentados. Además de esta técnica, también examinamos el uso de modelos aproximados para acelerar los cálculos, el dise~no de modelos que aprovechan una particularidad de los datos de entrada para simplificar el entrenamiento y la potenciación de modelos simples para adecuarlos al aprendizaje a gran escala. Hemos implementado cuatro nuevos algoritmos y seis versiones de algoritmos existentes que tratan los problemas mencionados y para cada uno de ellos detallamos resultados experimentales que muestran tanto su validez en comparación con los métodos previamente disponibles como su capacidad para escalar a grandes conjuntos de datos. Todos los algoritmos presentados han sido puestos a disposición del lector para su descarga y se han difundido mediante publicaciones en revistas científicas para facilitar que tanto investigadores como científicos de datos puedan conocerlos y utilizarlos.[Resumo] O recente aumento na cantidade de datos dispo~nibles deu lugar a unha nova e prometedora era no aprendizaxe máquina. Os éxitos neste eido estanse a suceder a un ritmo cada vez maior gracias a capacidade dalgúns algoritmos de aproveitar inmensas cantidades de datos para producir prediccións difíciles e moi acertadas. Non obstante, moitos dos algoritmos ata agora dispo~nibles para os científicos de datos perderon a súa efectividade neste novo escenario por mor das complicacións asociadas ao aprendizaxe a grande escala. Traballar con grandes conxuntos de datos leva consigo problemas loxísticos, limita a complexidade computacional e espacial dos algoritmos empregados, favorece os métodos con poucos ou ningún hiperparámetro a configurar e ten complicacións específicas que dificultan o aprendizaxe. Esta tese céntrase na escalabilidade dos algoritmos de aprendizaxe máquina, é dicir, na súa capacidade de manter a súa efectividade a medida que a escala do conxunto de datos aumenta. Tratamos problemas para os que as solucións dispoñibles teñen problemas cando crece a escala. Polo tanto, deixando no canto a clasificación e a regresión, centrámonos na selección de características, detección de anomalías, construcción de grafos e no aprendizaxe máquina explicable. Analizamos catro estratexias diferentes para obter algoritmos escalables. En primeiro lugar, exploramos a computación distribuída, que empregamos en tódolos algoritmos presentados. Ademáis desta técnica, tamén examinamos o uso de modelos aproximados para acelerar os cálculos, o deseño de modelos que aproveitan unha particularidade dos datos de entrada para simplificar o adestramento e a potenciación de modelos sinxelos para axeitalos ao aprendizaxe a gran escala. Implementamos catro novos algoritmos e seis versións de algoritmos existentes que tratan os problemas mencionados e para cada un deles expoñemos resultados experimentais que mostran tanto a súa validez en comparación cos métodos previamente dispoñibles como a súa capacidade para escalar a grandes conxuntos de datos. Tódolos algoritmos presentados foron postos a disposición do lector para a súa descarga e difundíronse mediante publicacións en revistas científicas para facilitar que tanto investigadores como científicos de datos poidan coñecelos e empregalos

Sparse Binary Features for Image Classification

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes improvements based on an efficient descriptor representation. The efficient representation is created using dimensionality reduction techniques, entropy analysis and decorrelated sampling. In a second part, the problem of image classification is tackled. The traditional approach uses machine learning algorithms to create classifiers, and some works already propose to use a compact image representation using feature extraction as preprocessing. The second contribution of this work is to show that binary features, while being very compact and low dimensional (compared to traditional representation of images), still provide a very high discriminant power. This is shown using various learning algorithms and binary descriptors. These years a scheme has been widely used to perform object recognition on images, or equivalently image classification. It is based on the concept of Bag of Visual Words. More precisely, an image is described using an unordered set of visual words, that are generally represented by feature descriptions. The last contribution of this work is to use binary features with a simple Bag of Visual Words classifier. Tests of performance for the image classification are performed on a large database of images

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Improved Detection for Advanced Polymorphic Malware

Malicious Software (malware) attacks across the internet are increasing at an alarming rate. Cyber-attacks have become increasingly more sophisticated and targeted. These targeted attacks are aimed at compromising networks, stealing personal financial information and removing sensitive data or disrupting operations. Current malware detection approaches work well for previously known signatures. However, malware developers utilize techniques to mutate and change software properties (signatures) to avoid and evade detection. Polymorphic malware is practically undetectable with signature-based defensive technologies. Today’s effective detection rate for polymorphic malware detection ranges from 68.75% to 81.25%. New techniques are needed to improve malware detection rates. Improved detection of polymorphic malware can only be accomplished by extracting features beyond the signature realm. Targeted detection for polymorphic malware must rely upon extracting key features and characteristics for advanced analysis. Traditionally, malware researchers have relied on limited dimensional features such as behavior (dynamic) or source/execution code analysis (static). This study’s focus was to extract and evaluate a limited set of multidimensional topological data in order to improve detection for polymorphic malware. This study used multidimensional analysis (file properties, static and dynamic analysis) with machine learning algorithms to improve malware detection. This research demonstrated improved polymorphic malware detection can be achieved with machine learning. This study conducted a number of experiments using a standard experimental testing protocol. This study utilized three advanced algorithms (Metabagging (MB), Instance Based k-Means (IBk) and Deep Learning Multi-Layer Perceptron) with a limited set of multidimensional data. Experimental results delivered detection results above 99.43%. In addition, the experiments delivered near zero false positives. The study’s approach was based on single case experimental design, a well-accepted protocol for progressive testing. The study constructed a prototype to automate feature extraction, assemble files for analysis, and analyze results through multiple clustering algorithms. The study performed an evaluation of large malware sample datasets to understand effectiveness across a wide range of malware. The study developed an integrated framework which automated feature extraction for multidimensional analysis. The feature extraction framework consisted of four modules: 1) a pre-process module that extracts and generates topological features based on static analysis of machine code and file characteristics, 2) a behavioral analysis module that extracts behavioral characteristics based on file execution (dynamic analysis), 3) an input file construction and submission module, and 4) a machine learning module that employs various advanced algorithms. As with most studies, careful attention was paid to false positive and false negative rates which reduce their overall detection accuracy and effectiveness. This study provided a novel approach to expand the malware body of knowledge and improve the detection for polymorphic malware targeting Microsoft operating systems

Event Detection and Tracking Detection of Dangerous Events on Social Media

Online social media platforms have become essential tools for communication and information exchange in our lives. It is used for connecting with people and sharing information. This phenomenon has been intensively studied in the past decade to investigate users’ sentiments for different scenarios and purposes. As the technology advanced and popularity increased, it led to the use of different terms referring to similar topics which often result in confusion. We study such trends and intend to propose a uniform solution that deals with the subject clearly. We gather all these ambiguous terms under the umbrella of the most recent and popular terms to reach a concise verdict. Many events have been addressed in recent works that cover only specific types and domains of events. For the sake of keeping things simple and practical, the events that are extreme, negative, and dangerous are grouped under the name Dangerous Events (DE). These dangerous events are further divided into three main categories of action-based, scenario-based, and sentiments-based dangerous events to specify their characteristics. We then propose deep-learning-based models to detect events that are dangerous in nature. The deep-learning models that include BERT, RoBERTa, and XLNet provide valuable results that can effectively help solve the issue of detecting dangerous events using various dimensions. Even though the models perform well, the main constraint of fewer available event datasets and lower quality of certain events data affects the performance of these models can be tackled by handling the issue accordingly.As plataformas online de redes sociais tornaram-se ferramentas essenciais para a comunicação, conexão com outros, e troca de informação nas nossas vidas. Este fenómeno tem sido intensamente estudado na última década para investigar os sentimentos dos utilizadores em diferentes cenários e para vários propósitos. Contudo, a utilização dos meios de comunicação social tornou-se mais complexa e num fenómeno mais vasto devido ao envolvimento de múltiplos intervenientes, tais como empresas, grupos e outras organizações. À medida que a tecnologia avançou e a popularidade aumentou, a utilização de termos diferentes referentes a tópicos semelhantes gerou confusão. Por outras palavras, os modelos são treinados segundo a informação de termos e âmbitos específicos. Portanto, a padronização é imperativa. O objetivo deste trabalho é unir os diferentes termos utilizados em termos mais abrangentes e padronizados. O perigo pode ser uma ameaça como violência social, desastres naturais, danos intelectuais ou comunitários, contágio, agitação social, perda económica, ou apenas a difusão de ideologias odiosas e violentas. Estudamos estes diferentes eventos e classificamos-los em tópicos para que a ténica de deteção baseada em tópicos possa ser concebida e integrada sob o termo Evento Perigosos (DE). Consequentemente, definimos o termo proposto “Eventos Perigosos” (Dangerous Events) e dividimo-lo em três categorias principais de modo a especificar as suas características. Sendo estes denominados Eventos Perigosos, Eventos Perigosos de nível superior, e Eventos Perigosos de nível inferior. O conjunto de dados MAVEN foi utilizado para a obtenção de conjuntos de dados para realizar a experiência. Estes conjuntos de dados são filtrados manualmente com base no tipo de eventos para separar eventos perigosos de eventos gerais. Os modelos de transformação BERT, RoBERTa, e XLNet foram utilizados para classificar dados de texto consoante a respetiva categoria de Eventos Perigosos. Os resultados demonstraram que o desempenho do BERT é superior a outros modelos e pode ser eficazmente utilizado para a tarefa de deteção de Eventos Perigosos. Salienta-se que a abordagem de divisão dos conjuntos de dados aumentou significativamente o desempenho dos modelos. Existem diversos métodos propostos para a deteção de eventos. A deteção destes eventos (ED) são maioritariamente classificados na categoria de supervisonado e não supervisionados, como demonstrado nos metódos supervisionados, estão incluidos support vector machine (SVM), Conditional random field (CRF), Decision tree (DT), Naive Bayes (NB), entre outros. Enquanto a categoria de não supervisionados inclui Query-based, Statisticalbased, Probabilistic-based, Clustering-based e Graph-based. Estas são as duas abordagens em uso na deteção de eventos e são denonimados de document-pivot and feature-pivot. A diferença entre estas abordagens é na sua maioria a clustering approach, a forma como os documentos são utilizados para caracterizar vetores, e a similaridade métrica utilizada para identificar se dois documentos correspondem ao mesmo evento ou não. Além da deteção de eventos, a previsão de eventos é um problema importante mas complicado que engloba diversas dimensões. Muitos destes eventos são difíceis de prever antes de se tornarem visíveis e ocorrerem. Como um exemplo, é impossível antecipar catástrofes naturais, sendo apenas detetáveis após o seu acontecimento. Existe um número limitado de recursos em ternos de conjuntos de dados de eventos. ACE 2005, MAVEN, EVIN são alguns dos exemplos de conjuntos de dados disponíveis para a deteção de evnetos. Os trabalhos recentes demonstraram que os Transformer-based pre-trained models (PTMs) são capazes de alcançar desempenho de última geração em várias tarefas de NLP. Estes modelos são pré-treinados em grandes quantidades de texto. Aprendem incorporações para as palavras da língua ou representações de vetores de modo a que as palavras que se relacionem se agrupen no espaço vectorial. Um total de três transformadores diferentes, nomeadamente BERT, RoBERTa, e XLNet, será utilizado para conduzir a experiência e tirar a conclusão através da comparação destes modelos. Os modelos baseados em transformação (Transformer-based) estão em total sintonia utilizando uma divisão de 70,30 dos conjuntos de dados para fins de formação e teste/validação. A sintonização do hiperparâmetro inclui 10 epochs, 16 batch size, e o optimizador AdamW com taxa de aprendizagem 2e-5 para BERT e RoBERTa e 3e-5 para XLNet. Para eventos perigosos, o BERT fornece 60%, o RoBERTa 59 enquanto a XLNet fornece apenas 54% de precisão geral. Para as outras experiências de configuração de eventos de alto nível, o BERT e a XLNet dão 71% e 70% de desempenho com RoBERTa em relação aos outros modelos com 74% de precisão. Enquanto para o DE baseado em acções, DE baseado em cenários, e DE baseado em sentimentos, o BERT dá 62%, 85%, e 81% respetivamente; RoBERTa com 61%, 83%, e 71%; a XLNet com 52%, 81%, e 77% de precisão. Existe a necessidade de clarificar a ambiguidade entre os diferentes trabalhos que abordam problemas similares utilizando termos diferentes. A ideia proposta de referir acontecimentos especifícos como eventos perigosos torna mais fácil a abordagem do problema em questão. No entanto, a escassez de conjunto de dados de eventos limita o desempenho dos modelos e o progresso na deteção das tarefas. A disponibilidade de uma maior quantidade de informação relacionada com eventos perigosos pode melhorar o desempenho do modelo existente. É evidente que o uso de modelos de aprendizagem profunda, tais como como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Tem sido evidente que a utilização de modelos de aprendizagem profunda, tais como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Em geral, o BERT tem um desempenho superior ao do RoBERTa e XLNet na detecção de eventos perigosos. É igualmente importante rastrear os eventos após a sua detecção. Por conseguinte, para trabalhos futuros, propõe-se a implementação das técnicas que lidam com o espaço e o tempo, a fim de monitorizar a sua emergência com o tempo