Look into the Mirror:Evolving Self-Dual Bent Boolean Functions

Bent Boolean functions are important objects in cryptography and coding theory, and there are several general approaches for constructing such functions. Metaheuristics proved to be a strong choice as they can provide many bent functions, even when the size of the Boolean function is large (e.g., more than 20 inputs). While bent Boolean functions represent only a small part of all Boolean functions, there are several subclasses of bent functions providing specific properties and challenges. One of the most interesting subclasses comprises (anti-)self-dual bent Boolean functions. This paper provides a detailed experimentation with evolutionary algorithms with the goal of evolving (anti-)self-dual bent Boolean functions. We experiment with two encodings and two fitness functions to directly evolve self-dual bent Boolean functions. Our experiments consider Boolean functions with sizes of up to 16 inputs, and we successfully construct self-dual bent functions for each dimension. Moreover, when comparing with the evolution of bent Boolean functions, we notice that the difficulty for evolutionary algorithms is rather similar. Finally, we also tried evolving secondary constructions for self-dual bent functions, but this direction provided no successful results

Contribuições de aprendizado por reforço em escolha de rota e controle semafórico

A área de sistemas inteligentes de transporte há muito investiga como empregar tecnologias da informação e comunicação a fim de melhorar a eficiência do sistema como um todo. Isso se traduz basicamente em monitorar e gerenciar a oferta (rede viária, semáforos etc.) e a demanda (deslocamentos de pessoas e mercadorias). A esse esforço, mais recentemente, estão sendo adicionadas técnicas de inteligência artificial. Essa tem o potencial de melhorar a utilização da infraestrutura existente, a fim de melhor atender a demanda. Neste trabalho é fornecido um panorama focado especificamente em duas tarefas onde a inteligência artificial tem contribuições relevantes, a saber, controle semafórico e escolha de rotas. Os trabalhos aqui discutidos objetivam otimizar a oferta e/ou distribuir a demanda.The field of of intelligent transportation systems has long investigated how to employ information and communication technologies to improve the efficiency of the system as a whole. This basically means to monitor and manage both supply (traffic network, traffic signals etc.) and demand (vehicles, people and goods). More recently, artificial intelligence techniques are being added to this effort, as they have the potential to improve the usage of existing infrastructure to meet the corresponding demand. In this paper, an overview is given, focusing specifically on two tasks where artificial intelligence has made relevant contributions, namely, traffic signal controls and route choices. The works discussed here aim at optimize the supply and/or distribute the demand

A survey of metaheuristic algorithms for the design of cryptographic Boolean functions

Boolean functions are mathematical objects used in diverse domains and have been actively researched for several decades already. One domain where Boolean functions play an important role is cryptography. There, the plethora of settings one should consider and cryptographic properties that need to be fulfilled makes the search for new Boolean functions still a very active domain. There are several options to construct appropriate Boolean functions: algebraic constructions, random search, and metaheuristics. In this work, we concentrate on metaheuristic approaches and examine the related works appearing in the last 25 years. To the best of our knowledge, this is the first survey work on this topic. Additionally, we provide a new taxonomy of related works and discuss the results obtained. Finally, we finish this survey with potential future research directions.</p

Interpreting Housing Prices with a MultidisciplinaryApproach Based on Nature-Inspired Algorithms and Quantum Computing

Current technology still does not allow the use of quantum computers for broader and individual uses; however, it is possible to simulate some of its potentialities through quantum computing. Quantum computing can be integrated with nature-inspired algorithms to innovatively analyze the dynamics of the real estate market or any other economic phenomenon. With this main aim, this study implements a multidisciplinary approach based on the integration of quantum computing and genetic algorithms to interpret housing prices. Starting from the principles of quantum programming, the work applies genetic algorithms for the marginal price determination of relevant real estate characteristics for a particular segment of Naples’ real estate market. These marginal prices constitute the quantum program inputs to provide, as results, the purchase probabilities corresponding to each real estate characteristic considered. The other main outcomes of this study consist of a comparison of the optimal quantities for each real estate characteristic as determined by the quantum program and the average amounts of the same characteristics but relative to the real estate data sampled, as well as the weights of the same characteristics obtained with the implementation of genetic algorithms. With respect to the current state of the art, this study is among the first regarding the application of quantum computing to interpretation of selling prices in local real estate markets

"A Nova Eletricidade: Aplica\c{c}\~oes, Riscos e Tend\^encias da IA Moderna -- "The New Electricity": Applications, Risks, and Trends in Current AI

The thought-provoking analogy between AI and electricity, made by computer scientist and entrepreneur Andrew Ng, summarizes the deep transformation that recent advances in Artificial Intelligence (AI) have triggered in the world. This chapter presents an overview of the ever-evolving landscape of AI, written in Portuguese. With no intent to exhaust the subject, we explore the AI applications that are redefining sectors of the economy, impacting society and humanity. We analyze the risks that may come along with rapid technological progress and future trends in AI, an area that is on the path to becoming a general-purpose technology, just like electricity, which revolutionized society in the 19th and 20th centuries. A provocativa compara\c{c}\~ao entre IA e eletricidade, feita pelo cientista da computa\c{c}\~ao e empreendedor Andrew Ng, resume a profunda transforma\c{c}\~ao que os recentes avan\c{c}os em Intelig\^encia Artificial (IA) t\^em desencadeado no mundo. Este cap\'itulo apresenta uma vis\~ao geral pela paisagem em constante evolu\c{c}\~ao da IA. Sem pretens\~oes de exaurir o assunto, exploramos as aplica\c{c}\~oes que est\~ao redefinindo setores da economia, impactando a sociedade e a humanidade. Analisamos os riscos que acompanham o r\'apido progresso tecnol\'ogico e as tend\^encias futuras da IA, \'area que trilha o caminho para se tornar uma tecnologia de prop\'osito geral, assim como a eletricidade, que revolucionou a sociedade dos s\'eculos XIX e XX.Comment: In Portugues

Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains

The relationship between words in a sentence often tell us more about the underlying semantic content of a document than its actual words individually. Natural language understanding has seen an increasing effort in the formation of techniques that try to produce non-trivial features, in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. These new dense vector representations indeed leverage the baseline in natural language processing, but they still fall short in dealing with intrinsic issues in linguistics, such as polysemy and homonymy. Systems that make use of natural language at its core, can be affected by a weak semantic representation of human language, resulting in inaccurate outcomes based on poor decisions. In this subject, word sense disambiguation and lexical chains have been exploring alternatives to alleviate several problems in linguistics, such as semantic representation, definitions, differentiation, polysemy, and homonymy. However, little effort is seen in combining recent advances in token embeddings (e.g. words, documents) with word sense disambiguation and lexical chains. To collaborate in building a bridge between these areas, this work proposes a collection of algorithms to extract semantic features from large corpora as its main contributions, named MSSA, MSSA-D, MSSA-NR, FLLC II, and FXLC II. The MSSA techniques focus on disambiguating and annotating each word by its specific sense, considering the semantic effects of its context. The lexical chains group derive the semantic relations between consecutive words in a document in a dynamic and pre-defined manner. These original techniques' target is to uncover the implicit semantic links between words using their lexical structure, incorporating multi-sense embeddings, word sense disambiguation, lexical chains, and lexical databases. A few natural language problems are selected to validate the contributions of this work, in which our techniques outperform state-of-the-art systems. All the proposed algorithms can be used separately as independent components or combined in one single system to improve the semantic representation of words, sentences, and documents. Additionally, they can also work in a recurrent form, refining even more their results.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/149647/1/Terry Ruas Final Dissertation.pdfDescription of Terry Ruas Final Dissertation.pdf : Dissertatio