Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

‘The uses of ethnography in the science of cultural evolution’. Commentary on Mesoudi, A., Whiten, A. and K. Laland ‘Toward a unified science of cultural evolution’

There is considerable scope for developing a more explicit role for ethnography within the research program proposed in the article. Ethnographic studies of cultural micro-evolution would complement experimental approaches by providing insights into the “natural” settings in which cultural behaviours occur. Ethnography can also contribute to the study of cultural macro-evolution by shedding light on the conditions that generate and maintain cultural lineages

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Music in Evolution and Evolution in Music

Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa. Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon. A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives

Autoencoder-based techniques for improved classification in settings with high dimensional and small sized data

Neural network models have been widely tested and analysed usinglarge sized high dimensional datasets. In real world application prob-lems, the available datasets are often limited in size due to reasonsrelated to the cost or difficulties encountered while collecting the data.This limitation in the number of examples may challenge the clas-sification algorithms and degrade their performance. A motivatingexample for this kind of problem is predicting the health status of atissue given its gene expression, when the number of samples availableto learn from is very small.Gene expression data has distinguishing characteristics attracting themachine learning research community. The high dimensionality ofthe data is one of the integral features that has to be considered whenbuilding predicting models. A single sample of the data is expressedby thousands of gene expressions compared to the benchmark imagesand texts that only have a few hundreds of features and commonlyused for analysing the existing models. Gene expression data samplesare also distributed unequally among the classes; in addition, theyinclude noisy features which degrade the prediction accuracy of themodels. These characteristics give rise to the need for using effec-tive dimensionality reduction methods that are able to discover thecomplex relationships between the features such as the autoencoders. This thesis investigates the problem of predicting from small sizedhigh dimensional datasets by introducing novel autoencoder-basedtechniques to increase the classification accuracy of the data. Twoautoencoder-based methods for generating synthetic data examplesand synthetic representations of the data were respectively introducedin the first stage of the study. Both of these methods are applicableto the testing phase of the autoencoder and showed successful in in-creasing the predictability of the data.Enhancing the autoencoder’s ability in learning from small sized im-balanced data was investigated in the second stage of the projectto come up with techniques that improved the autoencoder’s gener-ated representations. Employing the radial basis activation mecha-nism used in radial-basis function networks, which learn in a super-vised manner, was a solution provided by this thesis to enhance therepresentations learned by unsupervised algorithms. This techniquewas later applied to stochastic variational autoencoders and showedpromising results in learning discriminating representations from thegene expression data.The contributions of this thesis can be described by a number of differ-ent methods applicable to different stages (training and testing) anddifferent autoencoder models (deterministic and stochastic) which, in-dividually, allow for enhancing the predictability of small sized highdimensional datasets compared to well known baseline methods

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

Um modelo de previsão de demanda no varejo do setor de saúde e bem-estar

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de ProduçãoNo setor de varejo, a previsão de demanda é uma informação crítica, podendo afetar diretamente a eficiência operacional das empresas e o nível de serviço prestado ao cliente. Entretanto, prever não é uma tarefa trivial. A crescente variedade e complexidade dos problemas de previsão resultou na necessidade de modelos preditivos cada vez mais complexos e de difícil parametrização. De forma geral, modelos mais complexos requerem mais dados e ajustes para serem treinados e, consequentemente, exigem um maior custo computacional. Quando se há um grande número de itens a serem previstos, o processo de previsão demanda pode demorar várias horas, ou até mesmo dias, o que pode ser prejudicial para a operação. Dessa forma, o presente estudo visa propor um modelo de previsão de demanda no varejo do setor de saúde e bem-estar que melhore a acurácia de predição e seja equilibrado em termos de custo computacional e desempenho. Para isto, o modelo desenvolvido incorpora classes de métodos preditivos como Suavização Exponencial, modelos ARIMA, SARIMA e Redes Neurais Recorrentes. As abordagens de seleção individual, na qual determina-se e aplica-se o melhor modelo preditivo em cada série individual, e seleção agregada, onde o modelo com melhor performance para a população como um todo é determinado e aplicado, foram testadas e comparadas. Técnicas de clusterização de séries temporais foram empregadas com o intuito de aprimorar o método da seleção agregada. Dessa forma, buscou-se o método preditivo a ser aplicado em cada cluster, ao invés de buscar um único método para toda a população. As séries temporais dos centroides obtidos foram utilizadas para eleger o método preditivo a ser utilizado em cada cluster e o resultado dessa abordagem foi comparado com uma árvore de regressão. O custo computacional na seleção agregada foi 96,7% menor comparado com a seleção individual. Em contrapartida, houve um aumento de 9,5% no erro médio de previsão. Em vista dos resultados, empregou-se a abordagem seleção agregada. Em comparação com o modelo corrente na empresa objeto de estudo, os resultados do modelo proposto demonstraram reduções consideráveis no erro de previsão a um custo médio de processamento do modelo de apenas 2,09 segundos por SKU. Ademais, a seleção do método preditivo para cada cluster através dos centroides demonstrou ser uma estimativa com grande potencial de aplicação