Bitcoin, Blockchain Technology, and Cryptocurrencies

The blockchain based cryptocurrency known as Bitcoin was theorized in a whitepaper published October 28, 2008, by Satoshi Nakamoto (pseudonym) (Nakamoto, 2008). The paper, titled, “Bitcoin: A Peer-to-Peer Electronic Cash System,” laid out a digital currency creation/exchange structure that employs a decentralized ledger that would later run on the author’s open-source application (Nakamoto, 2008). The main innovation of this technology is found within the security benefits provided by the proof-of-work consensus mechanism that requires solving a mathematic trap-door compression function to verify transactions/blocks added to the blockchain. On January 3, 2009, the genesis block, a term for the first block in any given blockchain, was created using Satoshi’s Bitcoin v0.1 software that actualized the concepts in the Bitcoin whitepaper (Bitcoin Core, 2021)

Bitcoin, Blockchain Technology, and Cryptocurrencies

The blockchain based cryptocurrency known as Bitcoin was theorized in a whitepaper published October 28, 2008, by Satoshi Nakamoto (pseudonym) (Nakamoto, 2008). The paper, titled, “Bitcoin: A Peer-to-Peer Electronic Cash System,” laid out a digital currency creation/exchange structure that employs a decentralized ledger that would later run on the author’s open-source application (Nakamoto, 2008). The main innovation of this technology is found within the security benefits provided by the proof-of-work consensus mechanism that requires solving a mathematic trap-door compression function to verify transactions/blocks added to the blockchain. On January 3, 2009, the genesis block, a term for the first block in any given blockchain, was created using Satoshi’s Bitcoin v0.1 software that actualized the concepts in the Bitcoin whitepaper (Bitcoin Core, 2021)

Towards causal federated learning : a federated approach to learning representations using causal invariance

Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-i.i.d.), and Out of Distribution (OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this work, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyse empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model. Although Federated Learning allows for participants to contribute their local data without revealing it, it faces issues in data security and in accurately paying participants for quality data contributions. In this report, we also propose an EOS Blockchain design and workflow to establish data security, a novel validation error based metric upon which we qualify gradient uploads for payment, and implement a small example of our Blockchain Causal Federated Learning model to analyze its performance with respect to robustness, privacy and fairness in incentivization.L’apprentissage fédéré est une approche émergente d’apprentissage automatique distribué préservant la confidentialité pour créer un modèle partagé en effectuant une formation distribuée localement sur les appareils participants (clients) et en agrégeant les modèles locaux en un modèle global. Comme cette approche empêche la collecte et l’agrégation de données, elle contribue à réduire dans une large mesure les risques associés à la vie privée. Cependant, les échantillons de données de tous les clients participants sont généralement pas indépendante et distribuée de manière identique (non-i.i.d.), et la généralisation hors distribution (OOD) pour les modèles appris peut être médiocre. Outre ce défi, l’apprentissage fédéré reste également vulnérable à diverses attaques contre la sécurité dans lesquelles quelques entités participantes malveillantes s’efforcent d’insérer des portes dérobées, dégradant le modèle agrégé généré ainsi que d’inférer les données détenues par les entités participantes. Dans cet article, nous proposons une approche pour l’apprentissage des caractéristiques invariantes (causales) communes à tous les clients participants dans une configuration d’apprentissage fédérée et analysons empiriquement comment elle améliore la précision hors distribution (OOD) ainsi que la confidentialité du modèle appris final. Bien que l’apprentissage fédéré permette aux participants de contribuer leurs données locales sans les révéler, il se heurte à des problèmes de sécurité des données et de paiement précis des participants pour des contributions de données de qualité. Dans ce rapport, nous proposons également une conception et un flux de travail EOS Blockchain pour établir la sécurité des données, une nouvelle métrique basée sur les erreurs de validation sur laquelle nous qualifions les téléchargements de gradient pour le paiement, et implémentons un petit exemple de notre modèle d’apprentissage fédéré blockchain pour analyser ses performances

IoT system for EV charging at shared spaces

In current work, we apply the Internet of Things (IoT) paradigm to handle the electric vehicle (EV) charging process in small shared spaces, such as condominiums without requiring the intervention of an external supervision entity, being that role performed by the condominium management. A Mobile App handles the user interaction with the system, authenticating the request to initiate the EV charging process, a microcontroller connected to set of sensors and an actuator is used for measuring energy consumption and for enabling the charging process and, a Management Unit controls the process end to end, providing the required services to the Mobile App and the microcontroller unit while manages the energy sharing between the EV charging stations accordingly the condominium limitations and processes the energy measures to consolidate the EV charging energy transaction. A minimal user interface allows the users to visualise transactions, manage users' preferences, and configure the platform. Additionally, the conceptual model for a scaled solution is presented, supported on blockchain technologies to handle the financial transitions, allowing current approach to be replicated on broader EV charging scenarios, such as public charging systems in a city. The developed system was tested in a shared space with three EVs using a charging infrastructure for 3.5 months.No presente trabalho, é aplicado um paradigma de Internet Of Things (IOT) para agilizar e controlar o processo de carregamento de Veículos Elétricos (VE) em espaços partilhados de menores dimensões, como por exemplo condomínios residenciais, sem que seja necessária a intervenção (a título de prestação de serviços) de uma entidade externa, sendo todo o processo controlado pela gestão de condomínio. Uma aplicação móvel permite ao utilizador interagir com o sistema, permitindo a este autenticar-se no mesmo é condição necessária para que seja despoletado o processo de carregamento do VE. O sistema implementado com recurso a um microcontrolador encontrase ligado a um conjunto de sensores e um atuador permitindo medir a energia que esta ser consumida para carregamento do VE e simultaneamente, ligar e desligar o dispositivo de carregamento do veículo (através do controlo de um interruptor que entrega a energia entregue a este). O processo é controlado por uma unidade de gestão centralizada, que gera a distribuição de energia pelas estações de carregamento de VEs de acordo com as limitações do condomínio através do ligar e desligar destas e em simultâneo regista e processas as medições da energia consumida para consolidar as informações que constituem a transação de carregamento de VE e respetiva contraparte financeira associada à mesma. Adicionalmente, a unidade de gestão centralizada e a aplicação móvel, disponibilizam interfaces de utilizador mínimas para permitir funções como a consulta de transações, gestão e configuração da plataforma. Complementarmente, é apresentado um modelo conceptual permitindo escalar a solução proposta para espaços partilhados de maior dimensão, com recurso à utilização de tecnologias blockchain para gestão e registo das transações financeiras associadas à operação. Propondo uma abordagem, que poderá ser replicável em cenários mais amplos de utilização como por exemplo, a infraestrutura publica de carregamento de VE de uma cidade. O protótipo desenvolvido foi testado num espaço partilhado com três VE, usando uma infraestrutura de carregamento durante 3,5 meses

Scientific Advances in STEM

Following a previous topic (Scientific advances in STEM: from professors to students; https://www.mdpi.com/topics/advances_stem), this new topic aims to highlight the importance of establishing collaborations among research groups from different disciplines, combining the scientific knowledge from basic to applied research as well as taking advantage of different research facilities. Fundamental science helps us to understand phenomenological basics, while applied science focuses on products and technology developments, highlighting the need to perform a transference of knowledge to society and the industrial sector

Introductory Computer Forensics

INTERPOL (International Police) built cybercrime programs to keep up with emerging cyber threats, and aims to coordinate and assist international operations for ?ghting crimes involving computers. Although signi?cant international efforts are being made in dealing with cybercrime and cyber-terrorism, ?nding effective, cooperative, and collaborative ways to deal with complicated cases that span multiple jurisdictions has proven dif?cult in practic

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available