EU accession and Poland's external trade policy

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Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

A study of generalization in regression: proposal of a new metric and loss function to better understand and improve generability

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIntuitively Generalization in Machine Learning can be understood as a models ability to apply its trained or acquired knowledge to a previously unseen scenario. In the recent years there has been an exponential growth in machine learning models both efficiency and accuracy, yet the current research is still trying to understand and trust how well models can perform on previously unseen data. For this thesis we propose a study of machine learning’s theoretical background to further expand the notion of generalization and it’s limitation’s, enabling us to derive its commonly accepted approximation, definitions that we will use to present a new generalization metric or score more consistent in detecting and providing understanding of the occurrence of generalization. Additionally a new loss function will be presented in order to mitigate generalization error inherit to a noisy sample, where extensive tests suggest that our loss function has a higher rate of convergence while producing statistically similar or even better results when compared with classical loss functions.Intuitivamente generalização em Aprendizagem Automática pode ser entendida como a capacidade de um modelo em aplicar o seu conhecimento treinado ou adquirido a um cenário nunca antes visto. Nos últimos anos, tem existido um crescimento exponencial tanto na eficiência quanto na precisão dos modelos de Aprendizagem Automática, no entanto a pesquisa atual ainda se debate bastante em como entender e confiar na capacidade de execução dos modelos em dados nunca antes vistos. Para esta tese, propomos um estudo dos fundamentos teóricos da Aprendizagem Automática para expandir ainda mais a noção de generalização e suas limitações, permitindo-nos derivar sua aproximação comummente aceita. Definições estas que usaremos para apresentar uma nova métrica de generalização mais consistente na detecção da ocorrência ou não de generalização. Adicionalmente, uma nova função de perda será apresentada a fim de mitigar o erro de generalização herdado de uma amostra ruidosa, onde testes extensivos sugerem que nossa função de perda tem uma taxa de convergência significantemente mais alta produzindo resultados estatisticamente semelhantes ou até melhores quando comparada com as funções de perda clássicas

Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation

Software bugs and failures cost trillions of dollars every year, and could even lead to deadly accidents (e.g., Therac-25 accident). During maintenance, software developers fix numerous bugs and implement hundreds of new features by making necessary changes to the existing software code. Once an issue report (e.g., bug report, change request) is assigned to a developer, she chooses a few important keywords from the report as a search query, and then attempts to find out the exact locations in the software code that need to be either repaired or enhanced. As a part of this maintenance, developers also often select ad hoc queries on the fly, and attempt to locate the reusable code from the Internet that could assist them either in bug fixing or in feature implementation. Unfortunately, even the experienced developers often fail to construct the right search queries. Even if the developers come up with a few ad hoc queries, most of them require frequent modifications which cost significant development time and efforts. Thus, construction of an appropriate query for localizing the software bugs, programming concepts or even the reusable code is a major challenge. In this thesis, we overcome this query construction challenge with six studies, and develop a novel, effective code search solution (BugDoctor) that assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, we reformulate a given search query (1) by designing novel keyword selection algorithms (e.g., CodeRank) that outperform the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Overflow Q&A site, which were previously untapped. Our experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that our proposed approach can improve the given queries significantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that our approach can outperform the state-of-the-art approaches with a significant margin