Interoperability middleware for IIoT gateways based on international standard ontologies and standardized digital representation

Recent advances in the areas of microelectronics, information technology, and communication protocols have made the development of smaller devices with greater processing capacity and lower energy consumption. This context contributed to the growing number of physical devices in industrial environments which are interconnected and communicate via the internet, enabling concepts such as Industry 4.0 and the Industrial Internet of Things (IIoT). These nodes have different sensors and actuators that monitor and control environment data. Several companies develop these devices, including diverse communication protocols, data structures, and IoT platforms, which leads to interoperability issues. In IoT scenarios, interoperability is the ability of two systems to communicate and share services. Therefore, communication problems can make it unfeasible to use heterogeneous devices, increasing the project’s financial cost and development time. In an industry, interoperability is related to different aspects, such as physical communication, divergent device communication protocols, and syntactical problems, referring to the distinct data structure. Developing a new standard for solving these matters may bring interoperability-related drawbacks rather than effectively solving these issues. Therefore, to mitigate interoperability problems in industrial applications, this work proposes the development of an interoperability middleware for Edge-enabled IIoT gateways based on international standards. The middleware is responsible for translating communication protocols, updating data from simulations or physical nodes to the assets’ digital representations, and storing data locally or remotely. The middleware adopts the IEEE industrial standard ontologies combined with assets’ standardized digital models. As a case study, a simulation replicates the production of a nutrient solution for agriculture, controlled by IIoT nodes. The use case consists of three devices, each equipped with at least five sensors or actuators, communicating in different communication protocols and exchanging data using diverse structures. The performance of the proposed middleware and its proposed translations algorithms were evaluated, obtaining satisfactory results for mitigating interoperable in industrial applications.Devido a recentes avanços nas áreas de microeletrônica, tecnologia da informação, e protocolos de comunicação tornaram possível o desenvolvimento de dispositivos cada vez menores com maior capacidade de processamento e menor consumo energético. Esse contexto contribuiu para o crescente nú- mero desses dispositivos na industria que estão interligados via internet, viabilizando conceitos como Indústria 4.0 e Internet das Coisas Industrial (IIoT). Esses nós possuem diferentes sensores e atuadores que monitoram e controlam os dados do ambiente. Esses equipamentos são desenvolvidos por diferentes empresas, incluindo protocolos de comunicação, estruturas de dados e plataformas de IoT distintos, acarretando em problemas de interoperabilidade. Em cenários de IoT, interoperabilidade, é a capacidade de sistemas se comunicarem e compartilharem serviços. Portanto, esses problemas podem inviabilizar o uso de dispositivos heterogêneos, aumentando o custo financeiro do projeto e seu tempo de desenvolvimento. Na indústria, interoperabilidade se divide em diferentes aspectos, como comunicação e problemas sintáticos, referentes à estrutura de dados distinta. O desenvolvimento de um padrão industrial pode trazer mais desvantagens relacionadas à interoperabilidade, em vez de resolver esses problemas. Portanto, para mitigar problemas relacionados a intoperabilidade industrial, este trabalho propõe o desenvolvimento de um middleware de interoperável para gateways IIoT baseado em padrões internacionais e ontologias. O middleware é responsável por traduzir diferentes protocolos de comunicação, atualizar os dados dos ativos industriais por meio de suas representações digitais, esses armazenados localmente ou remotamente. O middleware adota os padrões ontológicos industriais da IEEE combinadas com modelos digitais padronizados de ativos industriais. Como estudo de caso, são realizadas simulações para a produção de uma solução nutritiva para agricultura, controlada por nós IIoT. O processo utiliza três dispositivos, cada um equipado com pelo menos cinco sensores ou atuadores, por meio de diferentes protocolos de comunicação e estruturas de dados. O desempenho do middleware proposto e seus algoritmos de tradução foram avaliados e apresentados no final do trabalho, os quais resultados foram satisfatórios para mitigar a interoperabilidade em aplicações industriais

Software composition with templates

Software composition systems are systems that concentrate on the composition of components. Thes.e systems represent a growi~ subfield of software engineering. Traditional software composition approaches define components as black-boxes. Black-boxes are characterised by their visible behaviour, but not their visible structure. They describe what can be done, rather than how it can be done. Basically, black-boxes are structurally monolithic units that can be composed together via provided interfaces. Growing complexity of software systems and dynamically changing requirements to these systems demand better parameterisation of components. State of the art approaches have tried to increase parameterisation of systems with so-called grey-box components (grey-boxes). These types of components introduced a structural configurability of components. Greyboxes could improve composability, reusability, extensibility and adaptability of software systems. However, there is still there is a big gap between grey-box approaches and business. ,' We see two main reasons for this. Firstly, a structurally non-monolithic nature of grey-boxes results in a significantly increased number of components and relationships that may form a software system. This makes grey-box approaches more complex and their development more expensive. There is a lack of tools to decrease the complexity of grey-box approaches. Secondly, grey-box composition approaches are oriented to the experts with a technical background in programming languages and software architectures. Up to now, state-of-the-art approaches have not addressed the question of their efficient applicability by domain experts with no technical background in programming languages. We consider a structural visibility of grey-boxes gives a chance to provide better externalisation of business logic, so that even a non-expert in programming language could design a software system for hislher special domain. In this thesis, we propose a holistic approach, called Neurath Composition Framework, to compose software systems according to well-defined requirements which have been externalised, giving the ownership of the design to the end-user. We show how externalisation of business logic can be achieved using grey-box composition systems augmented with the domain-specific visual interfaces. We define our own grey-box composition system based on the Parametric Code Templates component model and Molecular Operations composition technique. With this composition system awareness 'of a design, comprehensive development and the reuse of program code templates can be achieved. Finally, we present a sample implementation that shows the applicability of the composition framework to solve real-life business tasks.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Action recognition in visual sensor networks: a data fusion perspective

Visual Sensor Networks have emerged as a new technology to bring computer vision algorithms to the real world. However, they impose restrictions in the computational resources and bandwidth available to solve target problems. This thesis is concerned with the definition of new efficient algorithms to perform Human Action Recognition with Visual Sensor Networks. Human Action Recognition systems apply sequence modelling methods to integrate the temporal sensor measurements available. Among sequence modelling methods, the Hidden Conditional Random Field has shown a great performance in sequence classification tasks, outperforming many other methods. However, a parameter estimation procedure has not been proposed with feature and model selection properties. This thesis fills this lack proposing a new objective function to optimize during training. The L2 regularizer employed in the standard objective function is replaced by an overlapping group-L1 regularizer that produces feature and model selection effects in the optima. A gradient-based search strategy is proposed to find the optimal parameters of the objective function. Experimental evidence shows that Hidden Conditional Random Fields with their parameters estimated employing the proposed method have a higher predictive accuracy than those estimated with the standard method, with an smaller inference cost. This thesis also deals with the problem of human action recognition from multiple cameras, with the focus on reducing the amount of network bandwidth required. A multiple view dimensionality reduction framework is developed to obtain similar low dimensional representation for the motion descriptors extracted from multiple cameras. An alternative is proposed predicting the action class locally at each camera with the motion descriptors extracted from each view and integrating the different action decisions to make a global decision on the action performed. The reported experiments show that the proposed framework has a predictive performance similar to 3D state of the art methods, but with a lower computational complexity and lower bandwidth requirements. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Las Redes de Sensores Visuales son una nueva tecnología que permite el despliegue de algoritmos de visión por computador en el mundo real. Sin embargo, estas imponen restricciones en los recursos de computo y de ancho de banda disponibles para la resolución del problema en cuestión. Esta tesis tiene por objeto la definición de nuevos algoritmos con los que realizar reconocimiento de actividades humanas en redes de sensores visuales, teniendo en cuenta las restricciones planteadas. Los sistemas de reconocimiento de acciones aplican métodos de modelado de secuencias para la integración de las medidas temporales proporcionadas por los sensores. Entre los modelos para el modelado de secuencias, el Hidden Conditional Random Field a mostrado un gran rendimiento en la clasificación de secuencias, superando a otros métodos existentes. Sin embargo, no se ha definido un procedimiento para la integración de sus parámetros que incluya selección de atributos y selección de modelo. Esta tesis tiene por objeto cubrir esta carencia proponiendo una nueva función objetivo para optimizar durante la estimación de los parámetros obtimos. El regularizador L2 empleado en la función objetivo estandar se va a remplazar for un regularizador grupo-L1 solapado que va a producir los efectos de selección de modelo y atributos deseados en el óptimo. Se va a proponer una estrategia de búsqueda con la que obtener el valor óptimo de estos parámetros. Los experimentos realizados muestran que los modelos estimados utilizando la función objetivo prouesta tienen un mayor poder de predicción, reduciendo al mismo tiempo el coste computacional de la inferencia. Esta tesis también trata el problema del reconocimiento de acciones humanas emepleando multiples cámaras, centrándonos en reducir la cantidad de ancho de banda requerido par el proceso. Para ello se propone un nueva estructura en la que definir algoritmos de reducción de dimensionalidad para datos definidos en multiples vistas. Mediante su aplicación se obtienen representaciones de baja dimensionalidad similares para los descriptores de movimiento calculados en cada una de las cámaras.También se propone un método alternativo basado en la predicción de la acción realizada con los descriptores obtenidos en cada una de las cámaras, para luego combinar las diferentes predicciones en una global. La experimentación realizada muestra que estos métodos tienen una eficacia similar a la alcanzada por los métodos existentes basados en reconstrucción 3D, pero con una menor complejidad computacional y un menor uso de la red

Uma abordagem filosófica e histórica da arte cognitiva e informacional

Neste estudo, Uma abordagem filosófica e histórica da arte cognitiva e informacional, abordase a arte feita na intersecção entre a ciência e a tecnologia, habitualmente designada new media art (arte dos novos meios técnicos), e propõe-se a designação de arte cognitiva e informacional. Esta proposta é feita concomitantemente com a análise do projecto definicional para a arte computacional e para a arte digital de Dominic McIver Lopes, presente em A Philosophy of Computer Art, e recorre também a conceitos presentes na teoria ontológica da arte de David Davies, Art as Performance, e no ensaio de Sherri Irvin, The Artist's Sanction in Contemporary Art. Constrói-se de seguida uma narrativa histórica para a arte biológica, com base numa proposta filosófica de Noël Carroll, esperando contribuir-se, através deste sub-género, para que o grande género da arte cognitiva e informacional seja plenamente integrado nas histórias canónicas das artes visuais dos séculos XX e XXI.Abstract: A philosophical and historical approach to cognitive and informational art is a study about the art made in the intersection between science and technology, usually designated new media art. We propose a new designation, cognitive and informational art, during the discussion of the definitions of computer art and digital art, present in Dominic McIver Lopes' A Philosophy of Computer Art; and also by using concepts developed by David Davies in Art as Performance, and in Sherri Irvin's essay The Artist's Sanction in Contemporary Art. We proceed by developing a historical narrative specifically to bioart, hoping that, through this sub-genre, the main genre (cognitive and informational art) can become a part of the canonical historiography on the visual arts of the 20th and 21th centuries

The Authority of Distributed Consensus Systems Trust, Governance, and Normative Perspectives on Blockchains and Distributed Ledgers

The subjects of this dissertation are distributed consensus systems (DCS). These systems gained prominence with the implementation of cryptocurrencies, such as Bitcoin. This work aims at understanding the drivers and motives behind the adoption of this class of technologies, and to – consequently – evaluate the social and normative implications of blockchains and distributed ledgers. To do so, a phenomenological account of the field of distributed consensus systems is offered, then the core claims for the adoption of systems are taken into consideration. Accordingly, the relevance of these technologies on trust and governance is examined. It will be argued that the effects on these two elements do not justify the adoption of distributed consensus systems satisfactorily. Against this backdrop, it will be held that blockchains and similar technologies are being adopted because they are regarded as having a valid claim to authority as specified by Max Weber, i.e., herrschaft. Consequently, it will be discussed whether current implementations fall – and to what extent – within the legitimate types of traditional, charismatic, and rational-legal authority. The conclusion is that the conceptualization developed by Weber does not capture the core ideas that appear to establish the belief in the legitimacy of distributed consensus systems. Therefore, this dissertation describes the herrschaft of systems such as blockchains by conceptualizing a computational extension of the pure type of rational-legal authority, qualified as algorithmic authority. The foundational elements of algorithmic authority are then discussed. Particular attention is focused on the idea of normativity cultivated in systems of algorithmic rules as well as the concept of decentralization. Practical suggestions conclude the following dissertation

Located Lexicon: a project that explores how user generated content describes place

This extended conference paper explores the use and potential of location data in social media contexts. The research involved a series of experiments undertaken to assess the extent to which location information is present in exchanges, directly or indirectly. A prototype application was designed to exploit the insight obtained from the data-gathering experiments. This enabled us to develop a method and toolkit for searching, extracting and visualising mass-generated data for open source use. Ultimately, we were able to generate insights into data quality and ‘scale of query’ for emerging pedagogical research in learning swarms and distributed learners