Algorithms in nature: the convergence of systems biology and computational thinking

Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. This Perspectives discusses the recent convergence of these two ways of thinking

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

IoT Health Devices: Exploring Security Risks in the Connected Landscape

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century

A Socio-inspired CALM Approach to Channel Assignment Performance Prediction and WMN Capacity Estimation

A significant amount of research literature is dedicated to interference mitigation in Wireless Mesh Networks (WMNs), with a special emphasis on designing channel allocation (CA) schemes which alleviate the impact of interference on WMN performance. But having countless CA schemes at one's disposal makes the task of choosing a suitable CA for a given WMN extremely tedious and time consuming. In this work, we propose a new interference estimation and CA performance prediction algorithm called CALM, which is inspired by social theory. We borrow the sociological idea of a "sui generis" social reality, and apply it to WMNs with significant success. To achieve this, we devise a novel Sociological Idea Borrowing Mechanism that facilitates easy operationalization of sociological concepts in other domains. Further, we formulate a heuristic Mixed Integer Programming (MIP) model called NETCAP which makes use of link quality estimates generated by CALM to offer a reliable framework for network capacity prediction. We demonstrate the efficacy of CALM by evaluating its theoretical estimates against experimental data obtained through exhaustive simulations on ns-3 802.11g environment, for a comprehensive CA test-set of forty CA schemes. We compare CALM with three existing interference estimation metrics, and demonstrate that it is consistently more reliable. CALM boasts of accuracy of over 90% in performance testing, and in stress testing too it achieves an accuracy of 88%, while the accuracy of other metrics drops to under 75%. It reduces errors in CA performance prediction by as much as 75% when compared to other metrics. Finally, we validate the expected network capacity estimates generated by NETCAP, and show that they are quite accurate, deviating by as low as 6.4% on an average when compared to experimentally recorded results in performance testing

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Matheuristics: using mathematics for heuristic design

Matheuristics are heuristic algorithms based on mathematical tools such as the ones provided by mathematical programming, that are structurally general enough to be applied to different problems with little adaptations to their abstract structure. The result can be metaheuristic hybrids having components derived from the mathematical model of the problems of interest, but the mathematical techniques themselves can define general heuristic solution frameworks. In this paper, we focus our attention on mathematical programming and its contributions to developing effective heuristics. We briefly describe the mathematical tools available and then some matheuristic approaches, reporting some representative examples from the literature. We also take the opportunity to provide some ideas for possible future development

Use of bio-inspired techniques to solve complex engineering problems: industrial automation case study

Nowadays local markets have disappeared and the world lives in a global economy. Due to this reality, every company virtually competes with all others companies in the world. In addition to this, markets constantly search products with higher quality at lower costs, with high customization. Also, products tend to have a shorter period of life, making the demanding more intense. With this scenario, companies, to remain competitive, must constantly adapt themselves to the market changes, i.e., companies must exhibit a great degree of self-organization and self-adaptation. Biology with the millions of years of evolution may offer inspiration to develop new algorithms, methods and techniques to solve real complex problems. As an example, the behaviour of ants and bees, have inspired researchers in the pursuit of solutions to solve complex and evolvable engineering problems. This dissertation has the goal of explore the world of bio-inspired engineering. This is done by studying some of the bio-inspired solutions and searching for bio-inspired solutions to solve the daily problems. A more deep focus will be made to the engineering problems and particularly to the manufacturing domain. Multi-agent systems is a concept aligned with the bio-inspired principles offering a new approach to develop solutions that exhibit robustness, flexibility, responsiveness and re-configurability. In such distributed bio-inspired systems, the behaviour of each entity follows simple few rules, but the overall emergent behaviour is very complex to understand and to demonstrate. Therefore, the design and simulation of distributed agent-based solutions, and particularly those exhibiting self-organizing, are usually a hard task. Agent Based Modelling (ABM) tools simplifies this task by providing an environment for programming, modelling and simulating agent-based solutions, aiming to test and compare alternative model configurations. A deeply analysis of the existing ABM tools was also performed aiming to select the platform to be used in this work. Aiming to demonstrate the benefits of bio-inspired techniques for the industrial automation domain, a production system was used as case study for the development of a self-organizing agent-based system developed using the NetLogo tool. Hoje em dia os mercados locais desapareceram e o mundo vive numa economia global. Devido a esta realidade, cada companhia compete, virtualmente, com todas as outras companhias do mundo. A acrescentar a isto, os mercados estão constantemente à procura de produtos com maior qualidade a preços mais baixos e com um grande nível de customização Também, os produtos tendem a ter um tempo curto de vida, fazendo com que a procura seja mais intensa. Com este cenário, as companhias, para permanecer competitivas, têm que se adaptar constantemente de acordo com as mudanças de mercado, i.e., as companhias têm que exibir um alto grau de auto-organização e auto-adaptação. A biologia com os milhões de anos de evolução, pode oferecer inspiração para desenvolver novos algoritmos, métodos e técnicas para resolver problemas complexos reais. Como por exemplo, o comportamento das formigas e das abelhas inspiraram investigadores na descoberta de soluções para resolver problemas complexos e evolutivos de engenharia. Esta dissertação tem como objectivo explorar o mundo da engenharia bio-inspirada. Isto é feito através do estudo de algumas das soluções bio-inspiradas existentes e da procura de soluções bio-inspiradas para resolver os problemas do dia-a-dia. Uma atenção especial vai ser dada aos problemas de engenharia e particularmente aos problemas do domínio da manufactura. Os sistemas multi-agentes são um conceito que estão em linha com os princípios bio-inspirados oferecendo uma abordagem nova para desenvolver soluções que exibam robustez, flexibilidade, rapidez de resposta e reconfiguração. Nestes sistemas distribuídos bio-inspirados, o comportamento de cada entidade segue um pequeno conjunto de regras simples, mas o comportamento emergente global é muito complexo de perceber e de demonstrar. Por isso, o desenho e simulação de soluções distribuídas de agentes, e particularmente aqueles que exibem auto-organização, são normalmente uma tarefa árdua. As ferramentas de Modelação Baseada de Agentes (MBA) simplificam esta tarefa providenciando um ambiente para programar, modelar e simular, com o objectivo de testar e comparar diferentes configurações do modelo. Uma análise mais aprofundada das ferramentas MBA foi também efectuada tendo como objectivo seleccionar a plataforma a usar neste trabalho

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn