CASH FLOW PLANNING AND OPTIMIZATION THROUGH GENETIC ALGRORITMS

This article describes an intelligent system for financial planning and cashflow optimization named ICF: Intelligent Cash Flow. ICF is a computational tool for decision support which provides short-term and long-term financial managing strategies, considering financial products of the market. The ICF system makes use of Genetic Algorithms to elaborate cash flow projections which improve the company's profit for a specific period. ICF helps to deal with the complex aspects of cash flow planning: the large number of alternatives to consider, i. e. the mix of investments which offer the higher profit rates over a period; the intensive numerical processing involved; the dynamic changes in the Financial Market (e.g. rates, terms and tax regulations); and the changes in the company's daily financial position. The ICF system integrates two models: the financial and the genetic models. The financial model is used to calculate the cash flow profitability, based on the IDC (Interbank Deposit Certificate), by projecting profits and taxes for each kind of investment, for any term in the considered period. The genetic model, on the other hand, is used to search for cash flow plannings which promote profitability and liquidity. The chromosome of the ICF genetic model consists of n genes. Each gene stands for a day in the considered period and has four fields. The first two identify an investment option and its term; the last two identify a resource taking option and its term. For each analyzed day, only two of these fields are used, which depends whether the operational balance is positive or negative that day.According to the Evolutionary Computation theory, problems such as the optimization of the cash flow are highly epistatic, which means that there is a strong interdependency between genes of the respective representation (for example, the investment on day d depends on the availability of financial resources that day, which can be due to the redemption made on day d-n). Such genes consist of genetic patterns that can be set apart by the crossover operator. In order to deal with the epistasy in this problem, the chromosome has been adapted in way that each gene is represented by its allele and by its locus (position in the chromosome). This kind of representation has the objective to relax the positional rigidity of the genes, increasing the chances of distant interdependent genes to come closer to each other. Thus, genetic patterns with high fitness have more chances to proliferate in forthcoming generations. To manipulate this chromosome structure we have employed an extension of the partially-mapped crossover (PMX) operator proposed by Goldberg, which explores important similarities of value and order simultaneously. The mutation operator applied in the ICF implements a random choice of a gene (day) and the random assignment of a new term and a new type of financial application (investment or loan). The fitness function calculates the liquid returns (profit or tax) of each suggested application/loan for each day in the considered period, projected to the last day of the same. A more satisfactory planning is obtained by finding the maximum return value to this function.ICF has been tested and is currently in use by a Brazilian company. The model manages to find cash flow plannings which present profits, in average, 38% higher during evolution, making evident the importance of a such decision-supporting system. Comparing to the random search, the ICF in average leads to profits 50% higher. Many experiments were made for different periods of the year. The results show that the profitability is obviously affected by the company's operational balance, but it is also strongly influenced by the planning strategy. In this point, the ICF was capable of identifying strategies, with matched operations of application and redemption, which increased the cashbox in days of the flow, in which there was the option of highly profitable investments.

Speech Enhancement Using Wavelet Coefficients Masking with Local Binary Patterns

In this paper, we present a wavelet coefficients masking based on Local Binary Patterns (WLBP) approach to enhance the temporal spectra of the wavelet coefficients for speech enhancement. This technique exploits the wavelet denoising scheme, which splits the degraded speech into pyramidal subband components and extracts frequency information without losing temporal information. Speech enhancement in each high-frequency subband is performed by binary labels through the local binary pattern masking that encodes the ratio between the original value of each coefficient and the values of the neighbour coefficients. This approach enhances the high-frequency spectra of the wavelet transform instead of eliminating them through a threshold. A comparative analysis is carried out with conventional speech enhancement algorithms, demonstrating that the proposed technique achieves significant improvements in terms of PESQ, an international recommendation of objective measure for estimating subjective speech quality. Informal listening tests also show that the proposed method in an acoustic context improves the quality of speech, avoiding the annoying musical noise present in other speech enhancement techniques. Experimental results obtained with a DNN based speech recognizer in noisy environments corroborate the superiority of the proposed scheme in the robust speech recognition scenario

Teoria de opções reais: uma perspectiva para a valoração econômica do meio ambiente sob incerteza

With the process of growing deterioration of nature and of the natural conditions for human survival, the environmental issue is no longer a subject only of economic theory, but is also considered in other research areas, as well as in contemporary society. However, the economic valuation of natural resources through the current valuation techniques traditionally ignores a number of uncertainties in the value of these goods, thus naturally implying an underestimation of the calculated values. This can be circumvented through the use of the key concepts of the real options theory, since its logic allows one to inherently include in the assessment of natural resources and in the analysis of investments the uncertainties associated with the use of the environment or even with its preservation. Thus, the purpose of this study is to present the concepts that are the basis of the real options theory and its relation to the environment as a way to address some of the shortcomings of current techniques of environmental valuation.Keywords: environment, real options, uncertainty, irreversibility.Com o processo de deterioração crescente da natureza e das condições naturais para a sobrevivência humana, o tema ambiental deixou de ser assunto somente da teoria econômica e entranhou- se também em outras áreas de pesquisa, bem como na sociedade atual. Entretanto, a avaliação econômica dos recursos naturais que compõem o meio ambiente, por meio das atuais técnicas de valoração tradicionalmente usadas, desconsidera uma série de incertezas presentes no valor destes recursos, o que naturalmente implica uma subestimação dos valores monetários calculados pelas mesmas. Esta situação pode ser contornada por meio do uso dos conceitos-chave da teoria de opções reais, já que sua lógica permite incluir intrinsecamente, na avaliação dos recursos naturais, bem como na análise de investimentos, as incertezas associadas ao uso do meio ambiente ou até mesmo à sua preservação. Assim, o propósito deste estudo é apresentar os conceitos que são a base da teoria de opções reais, e sua relação com o meio ambiente, como forma de suprir algumas das deficiências das atuais técnicas de valoração ambiental.Palavras-chave: meio ambiente, opções reais, incertezas, irreversibilidade

A Roadmap for AI in Latin America

International audienceIf we want ensure that AI in the upcoming years is a positive factor of the development of Latin America we need to start acting now and stop doing the same thing over and over again. The recent past and the current context in the region clearly indicates that it is unlikely that we see any improvements in the resources and support that AI has, instead, it will probably be aggravated by the impact of the COVID-19 pandemic. Consequently, it is our role as researchers to visit this issue and attempt to propose a road map towards a solution.The driving motivation for this paper is to plant the seeds of a discussion on how to create a bottom-up and inclusive positive momentum for AI in the region, given the existing conditions, while, at the same time, reducing the potential negative impacts that it might have. We present this in the form of a roadmap or workflow that identifies the main obstacles that should be addressed and how they can be overcome by a combination focusing the work AI practitioners on particular research topics and that of decision markers and concern citizens

A GESTÃO SOCIOAMBIENTAL À LUZ DAS TÉCNICAS DE VALORAÇÃO ECONÔMICA DO MEIO AMBIENTE: UMA ANÁLISE DO VALOR DE USO INDIRETO E DO VALOR DE EXISTÊNCIA

Atualmente, o meio ambiente é uma das maiores preocupações da sociedade mundial. Contudo identificar e monitorar os problemas ambientais e a forma como estes interferem na qualidade de vida dos indivíduos é uma questão muito abstrata: não existe uma “receita de bolo” para inserir a gestão econômica na gestão ambiental e nas decisões de investimento. O objetivo deste artigo é desenvolver uma nova metodologia para valoração do meio ambiente a partir dos conceitos base da teoria de opções reais que evite a subestimação do valor dos recursos naturais, ao permitir explicitamente calcular as parcelas do valor de uso indireto, do valor de existência e valor de quasi opção como componentes do valor total do meio ambiente. A proposta deste desenvolvimento é relevante porquanto uma das vantagens dos métodos de valoração é permitir internalizar os custos ambientais decorrentes da atividade econômica. Assim, a partir de um valor monetário, é possível adotar políticas específicas para proteger o meio ambiente, tais como taxação, aumento de impostos, multas e indenizações. Ou até mesmo adiar a decisão de degradar a natureza – ação que, sob certas condições, pode ser a melhor escolha

Artificial Evolution of Active Filters: A Case Study

This article focuses on the application of artificial evolution to the synthesis of analog active filters. The main objective of this research is the achievement of a new class of systems, with advantageous features compared to conventional ones, such as lower power consumption, higher speed and more robustness to noise. The particular problem of designing the amplifier of an AM receiver is examined in this work. Genetic algorithms are employed as our evolutionary tool and two sets of experiments are described. The first set has been carried out using a single objective, the desired frequency response of the circuit. In a second set of experiments, three other objectives have been included in the system. A new multi-objective evaluation methodology was conceived for this second set of experiments. A second approach for evolving active filters, using programmable chips, is also discussed in this paper

A VLSI architecture for neural network chips

This thesis reports the research for the development of a neural network VLSI design environment where a neural application defined in a high-level programming environment is automatically mapped into custom VLSI chips. This work forms the basis of the UCL hardware investigations in the Esprit II Pygmalion project into VLSI architectures for neural network chips. The ultimate goal is to take a neural network application defined by Pygmalion's neural specification language, and automatically translate it to one or more CMOS integrated circuits. The thesis is composed of four parts: the neural network specification language; the target architecture, formed by the generic neuron model and its corresponding VLSI architecture; a simulator for the architecture; and finally a prototype Back Propagation VLSI chip. The neural network specification language at the centre of Pygmalion has been designed to achieve flexibility and portability to allow an easy translation from a neural network specification to either binary code, for simulation, or to silicon, for execution. The language, named nC, has been defined as a subset of C. The basic concepts and the issues concerning how to use nC are fully examined in this thesis. The target architecture is the critical issue for automatically translating a high-level specification of a neural network into application-specific chips. Consequently, the definition of a generic neuron model incorporating the main features of neural algorithms, and its associated VLSI architecture, form the main scope of this thesis. The architecture's communication strategy and the internal organisation of the processing element are thoroughly investigated. The adequacy of the proposed architectural model is analysed using a simulator implemented in the C language. Simulation results of the Back Propagation execution are presented, verifying the effects of the hardware implementation on the neural network execution. The viability in terms of layout design has been evaluated by designing and fabricating a hand-crafted prototype VLSI chip, performing the "Back Propagation" algorithm. A detailed examination of the layout results is provided, including a full description of the cell library designed. This work is now being advanced in the Esprit II Galatea project where a silicon compiler is to be incorporated to obtain a complete and integrated route from a nC neural model specification into VLSI neuro-chips. This incorporation will lead to a complete integrated programming environment for artificial neural networks

Prototype of Robotic Device for Mobility Assistance for the Elderly in Urban Environments

This study aims to develop a prototype of an autonomous robotic device to assist the locomotion of the elderly in urban environments. Among the achievements presented are the control techniques used for autonomous navigation and the software tools and hardware applied in the prototype. This is an extension of a previous work, in which part of the navigation algorithm was developed and validated in a simulated environment. In this extension, the real prototype is controlled by an algorithm based on fuzzy logic to obtain standalone and more-natural navigation for the user of the device. The robotic device is intended to guide an elderly person in an urban environment autonomously, although it also has a manual navigation mode. Therefore, the device should be able to navigate smoothly without sudden manoeuvres and should respect the locomotion time of the user. Furthermore, because of the proposed environment, the device should be able to navigate in an unknown and unstructured environment. The results reveal that this prototype achieves the proposed objective, demonstrating adequate behaviour for navigation in an unknown environment and fundamental safety characteristics to assist the elderly