A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings

Memetic algorithms are a class of well-studied metaheuristics which combine evolutionary algorithms and local search techniques. A meme represents contagious piece of information in an adaptive information sharing system. The canonical memetic algorithm uses a fixed meme, denoting a hill climbing operator, to improve each solution in a population during the evolutionary search process. Given global parameters and multiple parametrised operators, adaptation often becomes a crucial constituent in the design of MAs. In this study, a self-adaptive self-configuring steady-state multimeme memetic algorithm (SSMMA) variant is proposed. Along with the individuals (solutions), SSMMA co-evolves memes, encoding the utility score for each algorithmic component choice and relevant parameter setting option. An individual uses tournament selection to decide which operator and parameter setting to employ at a given step. The performance of the proposed algorithm is evaluated on six combinatorial optimisation problems from a cross-domain heuristic search benchmark. The results indicate the success of SSMMA when compared to the static Mas as well as widely used self-adaptive Multimeme Memetic Algorithm from the scientific literature

Domain-aware ontology matching

During the last years, technological advances have created new ways of communication, which have motivated governments, companies and institutions to digitalise the data they have in order to make it accessible and transferable to other people. Despite the millions of digital resources that are currently available, their diversity and heterogeneous knowledge representation make complex the process of exchanging information automatically. Nowadays, the way of tackling this heterogeneity is by applying ontology matching techniques with the aim of finding correspondences between the elements represented in different resources. These approaches work well in some cases, but in scenarios when there are resources from many different areas of expertise (e.g. emergency response) or when the knowledge represented is very specialised (e.g. medical domain), their performance drops because matchers cannot find correspondences or find incorrect ones. In our research, we have focused on tackling these problems by allowing matchers to take advantage of domain-knowledge. Firstly, we present an innovative perspective for dealing with domain-knowledge by considering three different dimensions (specificity - degree of specialisation -, linguistic structure - the role of lexicon and grammar -, and type of knowledge resource - regarding generation methodologies). Secondly, domain-resources are classified according to the combination of these three dimensions. Finally, there are proposed several approaches that exploit each dimension of domain-knowledge for enhancing matchers’ performance. The proposals have been evaluated by matching two of the most used classifications of diseases (ICD-10 and DSM-5), and the results show that matchers considerably improve their performance in terms of f-measure. The research detailed in this thesis can be used as a starting point to delve into the area of domain-knowledge matching. For this reason, we have also included several research lines that can be followed in the future to enhance the proposed approaches

Automatic schema matching utilizing hypernymy relations extracted from the web

This thesis explores how a large corpus of Is-a statements can be exploited for the task of schema matching

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

Ontology-based similarity measures and their application in bioinformatics

Genome-wide sequencing projects of many different organisms produce large numbers of sequences that are functionally characterized using experimental and bioinformatics methods. Following the development of the first bio-ontologies, knowledge of the functions of genes and proteins is increasingly made available in a standardized format. This allows for devising approaches that directly exploit functional information using semantic and functional similarity measures. This thesis addresses different aspects of the development and application of such similarity measures. First, we analyze semantic and functional similarity measures and apply them for investigating the functional space in different taxa. Second, a new software program and a new database are described, which overcome limitations of existing tools and simplify the utilization of similarity measures for different applications. Third, we delineate two applications of our functional similarity measures. We utilize them for analyzing domain and protein interaction datasets and derive thresholds for grouping predicted domain interactions into low- and high-confidence subsets. We also present the new MedSim method for prioritization of candidate disease genes, which is based on the observation that genes and proteins contributing to similar diseases are functionally related. We demonstrate that the MedSim method performs at least as well as more complex state-of-the-art methods and significantly outperforms current methods that also utilize functional annotation.Die Sequenzierung der kompletten Genome vieler verschiedener Organismen liefert eine große Anzahl an Sequenzen, die mit Hilfe experimenteller und bioinformatischer Methoden funktionell charakterisiert werden. Nach der Entwicklung der ersten Bio-Ontologien wird das Wissen über die Funktionen von Genen und Proteinen zunehmend in einem standardisierten Format zur Verfügung gestellt. Dadurch wird die Entwicklung von Verfahren ermöglicht, die funktionelle Informationen direkt mit Hilfe semantischer und funktioneller Ähnlichkeit verwenden. Diese Doktorarbeit befasst sich mit verschiedenen Aspekten der Entwicklung und Anwendung solcher Ähnlichkeitsmaße. Zuerst analysieren wir semantische und funktionelle Ähnlichkeitsmaße und verwenden sie für eine Analyse des funktionellen Raumes verschiedener Organismengruppen. Danach beschreiben wir eine neue Software und eine neue Datenbank, die Limitationen existierender Programme überwinden und den Einsatz von Ähnlichkeitsmaßen in verschiedenen Anwendungen vereinfachen. Drittens schildern wir zwei Anwendungen unserer funktionellen Ähnlichkeitsmaße. Wir verwenden sie zur Analyse von Domän- und Proteininteraktionsdatensätzen und leiten Grenzwerte ab, um die Domäninteraktionen in Teilmengen mit niedriger und hoher Konfidenz einzuteilen. Außerdem präsentieren wir die MedSim-Methode zur Priorisierung von potentiellen Krankheitsgenen. Sie beruht auf der Beobachtung, dass Gene und Proteine, die zu ähnlichen Krankheiten beitragen, funktionell verwandt sind. Wir zeigen, dass die MedSim-Methode mindestens so gut funktioniert wie komplexere moderne Methoden und die Leistung anderer aktueller Methoden signifikant übertrifft, die auch funktionelle Annotationen verwenden

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Sociobiology, universal Darwinism and their transcendence: An investigation of the history, philosophy and critique of Darwinian paradigms, especially gene-Darwinism, process-Darwinism, and their types of reductionism towards a theory of the evolution of evolutionary processes, evolutionary freedom and ecological idealism

Based on a review of different Darwinian paradigms, particularly sociobiology, this work, both, historically and philosophically, develops a metaphysic of gene-Darwinism and process-Darwinism, and then criticises and transcends these Darwinian paradigms in order to achieve a truly evolutionary theory of evolution. Part I introduces essential aspects of current sociobiology as the original challenge to this investigation. The claim of some sociobiologists that ethics should become biologized in a gene-egoistic way, is shown to be tied to certain biological views, which ethically lead to problematic results. In part II a historical investigation into sociobiology and Darwinism in general provides us, as historical epistemology', with a deeper understanding of the structure and background of these approaches. Gene-Darwinism, which presently dominates sociobiology and is linked to Dawkins' selfish gene view of evolution, is compared to Darwin's Darwinism and the evolutionary' synthesis and becomes defined more strictly. An account of the external history of Darwinism and its subparadigms shows how cultural intellectual presuppositions, like Malthusianism or the Newtonian concept of the unchangeable laws of nature, also influenced biological theory' construction. In part III universal 'process-Darwinism' is elaborated based on the historical interaction of Darwinism with non-biological subject areas. Building blocks for this are found in psychology, the theory of science and economics. Additionally, a metaphysical argument for the universality of process- Darwinism, linked to Hume's and Popper's problem of induction, is proposed. In part IV gene-Darwinism and process-Darwinism are criticised. Gene-Darwinism—despite its merits—is challenged as being one-sided in advocating 'gene-atomism', 'germ-line reductionism' and 'process-monism'. My alternative proposals develop and try to unify different criticisms often found. In respect of gene-atomism I advocate a many-level approach, opposing the necessary radical selfishness of single genes. I develop the concept of higher-level genes, propose a concept of systemic selection, which may stabilise group properties, without relying on permanent group selection and extend the applicability of a certain group selectionist model generally to small open groups. Proposals of mine linked to the critique of germ-line reductionism are: 'exformation', phenotypes as evolutionary factors and a field theoretic understanding of causa formalis (resembling Aristotelian hylemorphism). Finally the process-monism of gene-Darwinism, process-Darwinism and, if defined strictly, Darwinism in general is criticised. 1 argue that our ontology and ethics would be improved by replacing the Newtoman-Paleyian deist metaphor of an eternal and unchangeable law of nature, which lies at tire very heart of Darwinism, by a truly evolutionary understanding of evolution where new processes may gain a certain autonomy. All this results in a view that I call 'ecological idealism', which, although still very much based on Darwinism, clearly transcends a Darwinian world view