Harnessing Big Data and Machine Learning for Event Detection and Localization

Anomalous events are rare and significantly deviate from expected pattern and other data instances, making them hard to predict. Correctly and timely detecting anomalous severe events can help reduce risks and losses. Many anomalous event detection techniques are studied in the literature. Recently, big data and machine learning based techniques have shown a remarkable success in a wide range of fields. It is important to tailor big data and machine learning based techniques for each application; otherwise it may result in expensive computation, slow prediction, false alarms, and improper prediction granularity.First, we aim to address the above challenges by harnessing big data and machine learning techniques for fast and reliable prediction and localization of severe events. Firstly, to improve storage failure prediction, we develop a new lightweight and high performing tensor decomposition-based method, named SEFEE, for storage error forecasting in large-scale enterprise storage systems. SEFEE employs tensor decomposition technique to capture latent spatio-temporal information embedded in storage event logs. By utilizing the latent spatio-temporal information, we can make accurate storage error forecasting without training requirements of typical machine learning techniques. The training-free method allows for live prediction of storage errors and their locations in the storage system based on previous observations that had been used in tensor decomposition pipeline to extract meaningful latent correlations. Moreover, we propose an extension to include severity of the errors as contextual information to improve the accuracy of tensor decomposition which in turn improves the prediction accuracy. We further provide detailed characterization of NetApp dataset to provide additional insight into the dynamics of typical large-scale enterprise storage systems for the community.Next, we focus on another application -- AI-driven Wildfire prediction. Wildfires cause billions of dollars in property damages and loss of lives, with harmful health threats. We aim to correctly detect and localize wildfire events in the early stage and also classify wildfire smoke based on perceived pixel density of camera images. Due to the lack of publicly available dataset for early wildfire smoke detection, we first collect and process images from the AlertWildfire camera network. The images are annotated with bounding boxes and densities for deep learning methods to use. We then adapt a transformer-based end-to-end object detection model for wildfire detection using our dataset. The dataset and detection model together form as a benchmark named the Nevada smoke detection benchmark, or Nemo for short. Nemo is the first open-source benchmark for wildfire smoke detection with the focus of the early incipient stage. We further provide a weakly supervised Nemo version to enable wider support as a benchmark

Multi-teacher knowledge distillation as an effective method for compressing ensembles of neural networks

Deep learning has contributed greatly to many successes in artificial intelligence in recent years. Today, it is possible to train models that have thousands of layers and hundreds of billions of parameters. Large-scale deep models have achieved great success, but the enormous computational complexity and gigantic storage requirements make it extremely difficult to implement them in real-time applications. On the other hand, the size of the dataset is still a real problem in many domains. Data are often missing, too expensive, or impossible to obtain for other reasons. Ensemble learning is partially a solution to the problem of small datasets and overfitting. However, ensemble learning in its basic version is associated with a linear increase in computational complexity. We analyzed the impact of the ensemble decision-fusion mechanism and checked various methods of sharing the decisions including voting algorithms. We used the modified knowledge distillation framework as a decision-fusion mechanism which allows in addition compressing of the entire ensemble model into a weight space of a single model. We showed that knowledge distillation can aggregate knowledge from multiple teachers in only one student model and, with the same computational complexity, obtain a better-performing model compared to a model trained in the standard manner. We have developed our own method for mimicking the responses of all teachers at the same time, simultaneously. We tested these solutions on several benchmark datasets. In the end, we presented a wide application use of the efficient multi-teacher knowledge distillation framework. In the first example, we used knowledge distillation to develop models that could automate corrosion detection on aircraft fuselage. The second example describes detection of smoke on observation cameras in order to counteract wildfires in forests.Comment: Doctoral dissertation in the field of computer science, machine learning. Application of knowledge distillation as aggregation of ensemble models. Along with several uses. 140 pages, 67 figures, 13 table

Intelligent Data Analytics using Deep Learning for Data Science

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization

Ear Biometrics: A Comprehensive Study of Taxonomy, Detection, and Recognition Methods

Due to the recent challenges in access control, surveillance and security, there is an increased need for efficient human authentication solutions. Ear recognition is an appealing choice to identify individuals in controlled or challenging environments. The outer part of the ear demonstrates high discriminative information across individuals and has shown to be robust for recognition. In addition, the data acquisition procedure is contactless, non-intrusive, and covert. This work focuses on using ear images for human authentication in visible and thermal spectrums. We perform a systematic study of the ear features and propose a taxonomy for them. Also, we investigate the parts of the head side view that provides distinctive identity cues. Following, we study the different modules of the ear recognition system. First, we propose an ear detection system that uses deep learning models. Second, we compare machine learning methods to state traditional systems\u27 baseline ear recognition performance. Third, we explore convolutional neural networks for ear recognition and the optimum learning process setting. Fourth, we systematically evaluate the performance in the presence of pose variation or various image artifacts, which commonly occur in real-life recognition applications, to identify the robustness of the proposed ear recognition models. Additionally, we design an efficient ear image quality assessment tool to guide the ear recognition system. Finally, we extend our work for ear recognition in the long-wave infrared domains

Reconhecimento de ações em vídeos baseado na fusão de representações de ritmos visuais

Orientadores: Hélio Pedrini, David Menotti GomesTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Avanços nas tecnologias de captura e armazenamento de vídeos têm promovido uma grande demanda pelo reconhecimento automático de ações. O uso de câmeras para propó- sitos de segurança e vigilância tem aplicações em vários cenários, tais coomo aeroportos, parques, bancos, estações, estradas, hospitais, supermercados, indústrias, estádios, escolas. Uma dificuldade inerente ao problema é a complexidade da cena sob condições habituais de gravação, podendo conter fundo complexo e com movimento, múltiplas pes- soas na cena, interações com outros atores ou objetos e movimentos de câmera. Bases de dados mais recentes são construídas principalmente com gravações compartilhadas no YouTube e com trechos de filmes, situações em que não se restringem esses obstáculos. Outra dificuldade é o impacto da dimensão temporal, pois ela infla o tamanho dos da- dos, aumentando o custo computacional e o espaço de armazenamento. Neste trabalho, apresentamos uma metodologia de descrição de volumes utilizando a representação de Ritmos Visuais (VR). Esta técnica remodela o volume original do vídeo em uma imagem, em que se computam descritores bidimensionais. Investigamos diferentes estratégias para construção do ritmo visual, combinando configurações em diversos domínios de imagem e direções de varredura dos quadros. A partir disso, propomos dois métodos de extração de características originais, denominados Naïve Visual Rhythm (Naïve VR) e Visual Rhythm Trajectory Descriptor (VRTD). A primeira abordagem é a aplicação direta da técnica no volume de vídeo original, formando um descritor holístico que considera os eventos da ação como padrões e formatos na imagem de ritmo visual. A segunda variação foca na análise de pequenas vizinhanças obtidas a partir do processo das trajetórias densas, que permite que o algoritmo capture detalhes despercebidos pela descrição global. Testamos a nossa proposta em oito bases de dados públicas, sendo uma de gestos (SKIG), duas em primeira pessoa (DogCentric e JPL), e cinco em terceira pessoa (Weizmann, KTH, MuHAVi, UCF11 e HMDB51). Os resultados mostram que a técnica empregada é capaz de extrair elementos de movimento juntamente com informações de formato e de aparência, obtendo taxas de acurácia competitivas comparadas com o estado da arteAbstract: Advances in video acquisition and storage technologies have promoted a great demand for automatic recognition of actions. The use of cameras for security and surveillance purposes has applications in several scenarios, such as airports, parks, banks, stations, roads, hospitals, supermarkets, industries, stadiums, schools. An inherent difficulty of the problem is the complexity of the scene under usual recording conditions, which may contain complex background and motion, multiple people on the scene, interactions with other actors or objects, and camera motion. Most recent databases are built primarily with shared recordings on YouTube and with snippets of movies, situations where these obstacles are not restricted. Another difficulty is the impact of the temporal dimension since it expands the size of the data, increasing computational cost and storage space. In this work, we present a methodology of volume description using the Visual Rhythm (VR) representation. This technique reshapes the original volume of the video into an image, where two-dimensional descriptors are computed. We investigated different strategies for constructing the representation by combining configurations in several image domains and traversing directions of the video frames. From this, we propose two feature extraction methods, Naïve Visual Rhythm (Naïve VR) and Visual Rhythm Trajectory Descriptor (VRTD). The first approach is the straightforward application of the technique in the original video volume, forming a holistic descriptor that considers action events as patterns and formats in the visual rhythm image. The second variation focuses on the analysis of small neighborhoods obtained from the process of dense trajectories, which allows the algorithm to capture details unnoticed by the global description. We tested our methods in eight public databases, one of hand gestures (SKIG), two in first person (DogCentric and JPL), and five in third person (Weizmann, KTH, MuHAVi, UCF11 and HMDB51). The results show that the developed techniques are able to extract motion elements along with format and appearance information, achieving competitive accuracy rates compared to state-of-the-art action recognition approachesDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2015/03156-7FAPES

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included