Towards Aggregating Time-Discounted Information in Sensor Networks

Sensor networks are deployed to monitor a seemingly endless list of events in a multitude of application domains. Through data collection and aggregation enhanced with data mining and machine learning techniques, many static and dynamic patterns can be found by sensor networks. The aggregation problem is complicated by the fact that the perceived value of the data collected by the sensors is affected by many factors such as time, location and user valuation. In addition, the value of information deteriorates often dramatically over time. Through our research, we already achieved some results: A formal algebraic analysis of information discounting, especially affected by time. A general model and two specific models are developed for information discounting. The two specific models formalize exponetial time-discount and linear time-discount. An algebraic analysis of aggregation of values that decay with time exponentially. Three types of aggregators that offset discounting effects are formalized and analyzed. A natural synthesis of these three aggregators is discovered and modeled. We apply our theoretical models to emergency response with thresholding and confirm with extensive simulation. For long-term monitoring tasks, we laid out a theoretical foundation for discovering an emergency through generations of sensors, analysed the achievability of a long-term task and found an optimum way to distribute sensors in a monitored area to maximize the achievability. We proposed an implementation for our alert system with state-of-art wireless microcontrollers, sensors, real-time operating systems and embedded internet protocols. By allowing aggregation of time-discounted information to proceed in an arbitrary, not necessarily pairwise manner, our results are also applicable to other similar homeland security and military application domains where there is a strong need to model not only timely aggregation of data collected by individual sensors, but also the dynamics of this aggregation. Our research can be applied to many real-world scenarios. A typical scenario is monitoring wildfire in the forest: A batch of first-generation sensors are deployed by UAVs to monitor a forest for possible wildfire. They monitor various weather quantities and recognize the area with the highest possibility of producing a fire --- the so-called area of interest (AoI). Since the environment changes dynamically, so after a certain time, the sensors re-identify the AoI. The value of the knowledge they learned about the previous AoI decays with time quickly, our methods of aggregation of time-discounted information can be applied to get update knowledge. Close to depletion of their energy of the current generation of sensors, a new generation of sensors are deployed and inherit the knowledge from the current generation. Through this way, monitoring long-term tasks becomes feasible. At the end of this thesis, we propose some extensions and directions from our current research: Generalize and extend the special classes of Type 1 and Type 2 aggregation operators; Analyze aggregation operator of Type 3 and Type 4, find some special applicable candidates; Data aggregation across consecutive generations of sensors in order to learn about events with discounting that take a long time to manifest themselves; Network implications of various aggregation strategies; Algorithms for implementation of some special classes of aggregators. Implement wireless sensor network that can autonomously learn and recognize patterns of emergencies, predict incidents and trigger alarms through machine learning

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Deep learning for large-scale fine-grained recognition of cars

Deep learning (DL) is widely used nowadays, with several applications in image classification and object detection. Among many of these applications is the use of Convolutional Neural Networks (CNNs) whose operation is: for a given input (image) and output (label/class), generate representations that define and allow to distinguish different kinds of objects. Neural Networks are computationally demanding, taking hours to train. Convolutional Neural Networks are even more demanding since their input data are usually images – a rich data type that holds a lot of information. The fast evolution in Computer Vision, using deep learning techniques, and computing power recently allowed to train CNNs which can classify images with high precision. In car classifieds websites images are one of the most important types of content. However, until today, little knowledge/metadata is produced from such images. In order to insert an advert in the platform, the user must upload an image of the car for sale and fill a certain number of fields, among them the vehicle category, the color of the car and its respective make, model and version. In this dissertation, CNNs are used for the recognition of the make, model and version of cars where transfer learning and fine-tuning are two approaches used for transferring the knowledge learned in one task and adapting it to another. We extend the work to also validate the efficacy of these neural networks on the tasks of vehicle category and cars’ color recognition. We pretend to validate how CNNs behave in these different tasks. Approaches like background removal and data augmentation are explored for reducing overfitting. We collected one of the largest datasets to date for the task of make, model and version recognition of cars, composed of 1.2 million images belonging to 790 labels.The results obtained in the scope of this dissertation set a new state-of-the-art performance for this type of task (accuracy of 92.7% on an ensemble method) considering the number of classes to classify and the number of images used. It is demonstrated the efficacy of the recent advances in CNN architectures in fine-grained classification where intra-class variation is small and viewpoint variation is high, when a largescale dataset is used.Deep Learning (DL) é um termo cada vez mais mencionado nos dias de hoje, com vastas aplicações em classificação de imagens e detecção de objectos. Por detrás de muitas destas aplicações está a utilização de Convolutional Neural Networks (CNN) cujo funcionamento é, para um dado input (imagem) e output (nome do objecto representado/classe), produzir representações que definem e permitem distinguir vários tipos de objectos. As redes neuronais são computacionalmente exigentes e podem levar horas a ser treinadas. Convolutional Neural Networks são ainda mais exigentes visto o seu input ser, usualmente, imagens - um tipo de dados rico que contém muita informação. Com a rápida evolução do poder computacional aliada à evolução no campo de Computer Vision com recurso a CNNs é possível, somente nos últimos anos, treinar CNNs para classificação de imagens com alto nível de precisão. Em sites de classificados de carros as imagens são um dos tipos de conteúdo mais importante. Todavia até aos dias de hoje, pouco conhecimento/metadados são gerados a partir das mesmas. O utilizador tem sempre que, para inserir um anúncio na plataforma, preencher um vasto número de campos, entre eles a categoria do veículo, a cor do carro e a respectiva marca, modelo e versão, e inserir uma imagem do carro para venda. Nesta dissertação são utilizadas CNNs para o reconhecimento da marca, modelo e versão de carros em que se utiliza transfer learning e fine-tuning para transferir o conhecimento “aprendido” numa tarefa e adaptá-lo para outra. O trabalho é estendido de forma a demonstrar, também, a eficácia destas redes neuronais para as tarefas de reconhecimento da categoria do veículo e reconhecimento de cor de carros. Pretendemos validar como as CNNs se comportam nestes diferentes tipos de tarefas. Abordagens como remoção do fundo da imagem e data augmentation são utilizadas para reduzir overfitting.É obtido um dos maiores datasets para a tarefa de reconhecimento de marca, modelo e versão de carros, composto por 1,2 milhões de imagens pertencentes a 790 classes. Os resultados apresentados são dos melhores para este tipo de tarefa (precisão de 92.7% com um ensemble) considerando tanto o número de classes a classificar como o número de imagens utilizadas. Os resultados obtidos evidenciam a eficácia das arquitecturas de CNNs modernas para a classificação granular onde a variação intra-classe é reduzida e a variação da perspectiva é elevada, quando é utilizado um dataset de grandes dimensões

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Scholarly Communication and Academic Presses

The objective of the meeting was to identify concrete ways for implementing proposals to foster the international scientific co-operation of universities and academic societies. Against the background of an assumed tendency towards knowledge monopolies, caused by the protection of intellectual property rights by a few publishers, the experts in the Conference discussed the potential of these academic e-presses for improving scientific article collection and treatment as well as facilitating the access to scientific knowledge. The papers in this volume can best be regarded as contributions to the areas of inquiry in a field that is continuing to change very rapidly