DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally, customizing ACO for a specific problem requires the expert design of knowledge-driven heuristics. In this paper, we propose DeepACO, a generic framework that leverages deep reinforcement learning to automate heuristic designs. DeepACO serves to strengthen the heuristic measures of existing ACO algorithms and dispense with laborious manual design in future ACO applications. As a neural-enhanced meta-heuristic, DeepACO consistently outperforms its ACO counterparts on eight COPs using a single neural model and a single set of hyperparameters. As a Neural Combinatorial Optimization method, DeepACO performs better than or on par with problem-specific methods on canonical routing problems. Our code is publicly available at https://github.com/henry-yeh/DeepACO.Comment: Accepted at NeurIPS 202

Evolutionary Computation for Overlapping Community Detection in Social and Graph-based Information

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 26-06-2017Esta tesis tiene embargado el acceso al texto completo hasta el 26-12-201

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

Cyber Security and Critical Infrastructures

This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain

Development of a Multi-UAV Simulator to Analyze the Behavior of Operators in Coastal Surveillance Missions

This Master Thesis 1 presents the design and development of a computer simulator created for executing and supervising missions carried out by multiple Unmanned Aerial Vehicles (UAVs). The aim of this simulator is to provide an open, simple and accessible environment to train and analyze the performance and evolution of low-experienced human operators supervising and controlling a team of UAVs. This work is divided into two parts. The rst one is focused on describing the simulator mechanisms and architecture. To accomplish the required accessibility of this tool for novice users, the simulator has been implemented following a web architecture, where only a web browser is needed to execute it. Also, in order to engage and challenge the operator, some gami cation elements have been added, bringing the simulation closer to a videogame experience. The second part of this work uses the developed simulator to carry out several experiments with novice users. A set of performance metrics is designed to de ne the pro le of a user, and based on those pro les, we run and validate some clustering algorithms to obtain groups of users with common performance pro les. These results are analyzed to extract behavioral patterns that distinguish and rank the di erent users in the experiment, allowing the identi cation and selection of potential expert operators.El presente Trabajo Fin de Máster 2 presenta el diseño y desarrollo de un simulador creado con el fin de ejecutar y supervisar misiones llevadas a cabo por múltiples Vehículos Aéreos no Tripulados (UAVs). El objetivo de este simulador es ofrecer un entorno simple y accesible donde entrenar y analizar el rendimiento y la evolución de operadores inexpertos mientras supervisan y controlan un equipo de UAVs. Este trabajo se divide en dos partes. La primera está enfocada en describir el funcionamiento del simulador y su arquitectura. Para lograr la accesibilidad que esta herramienta requiere de cara a usuarios inexpertos, el simulador ha sido implementado siguiendo una arquitectura web, donde solamente se requiere un navegador web para ejecutarlo. Además, para atraer y retar al operador, se han introducido algunos elementos de gamificación, que acercan este simulador a una experiencia propia del mundo de los videojuegos. La segunda parte del trabajo se basa en el simulador desarrollado para llevar a cabo varios experimentos con usuarios inexpertos. Se ha diseñado un conjunto de métricas de rendimiento con las cuales se de fine el perfil de un usuario. Usando estos perfiles, se ejecutan y validan algoritmos de clustering para obtener grupos de usuarios con perfiles de rendimiento comunes. Los resultados se analizan de cara a extraer patrones de comportamiento que distingan a los diferentes usuarios del experimento, permitiendo la identificación y selección de operadores expertos potenciales

Ausgewählte Chancen und Herausforderungen der digitalen Transformation für die Produktentwicklung und Unternehmensorganisation im Finanzdienstleistungssektor

Vor dem Hintergrund der digitalen Transformation sind Finanzdienstleistungsunternehmen auf unterschiedlichen Ebenen zahlreichen Chancen sowie Herausforderungen ausgesetzt. Während der Einsatz neuer Technologien die Optimierung bestehender Geschäftsprozesse sowie das Angebot digitalisierter Finanzdienstleistungen ermöglicht, geht dies zugleich mit veränderten Arbeitsbedingungen innerhalb der Unternehmensorganisation einher. Darüber hinaus sind Finanzdienstleister dazu angehalten die sich ändernden Kundenerwartungen bei den bisherigen Geschäftsaktivitäten sowie bei der Produktentwicklung zu berücksichtigen. Das Ziel der vorliegenden kumulativen Dissertation ist es, bestehende Forschungsdesiderate hinsichtlich der Auswirkungen der digitalen Transformation auf den Finanzdienstleistungssektor, differenziert nach der Kunden- und Produktperspektive sowie der internen Unternehmensperspektive, vertiefend zu analysieren. Das Technology-Organization-Environment (TOE)-Framework von DePietro et al. (1990) wird dabei als theoretischer Rahmen zur Einordnung und Strukturierung der Forschungsmodule verwendet. Die Ergebnisse der acht Module zeigen, dass die Kundenbedürfnisse und –erwartungen im Finanzdienstleistungssektor verstärkt von der digitalen Transformation beeinflusst werden. Dies zeigt sich in der Beratungstätigkeit bspw. durch das Angebot neuer Kundenkanäle sowie der aus dem steigenden Wettbewerbsdruck resultierenden erhöhten Preistransparenz. Im Rahmen der Produktentwicklung sind zudem u. a. ESG-Risiken und Silent Cyber-Risiken zu beachten. Aus der Analyse der Auswirkungen der digitalen Transformation auf die Unternehmensorganisation geht hervor, dass über den Einsatz digitaler Innovationen innerhalb des Backoffice die Realisation von Effizienzgewinnen sowie das Entgegenwirken eines Personalmangels möglich ist. Darüber hinaus wird in den Modulen der Einfluss des Faktors Mensch auf die Cyber-Sicherheit hervorgehoben. Während dieser einerseits als „schwächstes Glied“ und potenzielles Angriffsziel im Sicherheitskonstrukt der Unternehmen dargestellt wird, ist andererseits das Potenzial der Beschäftigten zur Frühwarnung zu berücksichtigen