An Architecture-Altering and Training Methodology for Neural Logic Networks: Application in the Banking Sector

Artificial neural networks have been universally acknowledged for their ability on constructing forecasting and classifying systems. Among their desirable features, it has always been the interpretation of their structure, aiming to provide further knowledge for the domain experts. A number of methodologies have been developed for this reason. One such paradigm is the neural logic networks concept. Neural logic networks have been especially designed in order to enable the interpretation of their structure into a number of simple logical rules and they can be seen as a network representation of a logical rule base. Although powerful by their definition in this context, neural logic networks have performed poorly when used in approaches that required training from data. Standard training methods, such as the back-propagation, require the network’s synapse weight altering, which destroys the network’s interpretability. The methodology in this paper overcomes these problems and proposes an architecture-altering technique, which enables the production of highly antagonistic solutions while preserving any weight-related information. The implementation involves genetic programming using a grammar-guided training approach, in order to provide arbitrarily large and connected neural logic networks. The methodology is tested in a problem from the banking sector with encouraging results

Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control

The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability has lead to its adoption in many non-graphics applications, including wide variety of scientific computing fields. At the same time, a number of important dynamic optimal policy problems in economics are athirst of computing power to help overcome dual curses of complexity and dimensionality. We investigate if computational economics may benefit from new tools on a case study of imperfect information dynamic programming problem with learning and experimentation trade-off that is, a choice between controlling the policy target and learning system parameters. Specifically, we use a model of active learning and control of linear autoregression with unknown slope that appeared in a variety of macroeconomic policy and other contexts. The endogeneity of posterior beliefs makes the problem difficult in that the value function need not be convex and policy function need not be continuous. This complication makes the problem a suitable target for massively-parallel computation using graphics processors. Our findings are cautiously optimistic in that new tools let us easily achieve a factor of 15 performance gain relative to an implementation targeting single-core processors and thus establish a better reference point on the computational speed vs. coding complexity trade-off frontier. While further gains and wider applicability may lie behind steep learning barrier, we argue that the future of many computations belong to parallel algorithms anyway.Graphics Processing Units, CUDA programming, Dynamic programming, Learning, Experimentation

XML-based genetic rules for scene boundary detection in a parallel processing environment

Genetic programming is based on Darwinian evolutionary theory that suggests that the best solution for a problem can be evolved by methods of natural selection of the fittest organisms in a population. These principles are translated into genetic programming by populating the solution space with an initial number of computer programs that can possibly solve the problem and then evolving the programs by means of mutation, reproduction and crossover until a candidate solution can be found that is close to or is the optimal solution for the problem. The computer programs are not fully formed source code but rather a derivative that is represented as a parse tree. The initial solutions are randomly generated and set to a certain population size that the system can compute efficiently. Research has shown that better solutions can be obtained if 1) the population size is increased and 2) if multiple runs are performed of each experiment. If multiple runs are initiated on many machines the probability of finding an optimal solution are increased exponentially and computed more efficiently. With the proliferation of the web and high speed bandwidth connections genetic programming can take advantage of grid computing to both increase population size and increasing the number of runs by utilising machines connected to the web. Using XML-Schema as a global referencing mechanism for defining the parameters and syntax of the evolvable computer programs all machines can synchronise ad-hoc to the ever changing environment of the solution space. Another advantage of using XML is that rules are constructed that can be transformed by XSLT or DOM tree viewers so they can be understood by the GP programmer. This allows the programmer to experiment by manipulating rules to increase the fitness of a rule and evaluate the selection of parameters used to define a solution

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Enhancing the context-aware FOREX market simulation using a parallel elastic network model

[EN] Foreign exchange (FOREX) market is a decentralized global marketplace in which different participants, such as international banks, companies or investors, can buy, sell, exchange and speculate on currencies. This market is considered to be the largest financial market in the world in terms of trading volume. Indeed, the just-in-time price prediction for a currency pair exchange rate (e.g., EUR/USD) provides valuable information for companies and investors as they can take different actions to improve their business. The trading volume in the FOREX market is huge, disperses, in continuous operations (24 h except weekends), and the context significantly affects the exchange rates. This paper introduces a context-aware algorithm to model the behavior of the FOREX Market, called parallel elastic network model (PENM). This algorithm is inspired by natural procedures like the behavior of macromolecules in dissolution. The main results of this work include the possibility to represent the market evolution of up to 21 currency pair, being all connected, thus emulating the real-world FOREX market behavior. Moreover, because the computational needs required are highly costly as the number of currency pairs increases, a hybrid parallelization using several shared memory and message passing algorithms studied on distributed cluster is evaluated to achieve a high-throughput algorithm that answers the real-time constraints of the FOREX market. The PENM is also compared with a vector autoregressive (VAR) model using both a classical statistical measure and a profitability measure. Specifically, the results indicate that PENM outperforms VAR models in terms of quality, achieving up to 930xspeed-up factor compared to traditional R codes using in this field.This work was jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under Grant 20813/PI/18 and by the Spanish MEC and European Commission FEDER under Grants TIN2016-78799-P and TIN2016-80565-R (AEI/FEDER, UE).Contreras, AV.; Llanes, A.; Herrera, FJ.; Navarro, S.; López-Espin, JJ.; Cecilia-Canales, JM. (2020). Enhancing the context-aware FOREX market simulation using a parallel elastic network model. The Journal of Supercomputing. 76(3):2022-2038. https://doi.org/10.1007/s11227-019-02838-1S20222038763Bahrepour M, Akbarzadeh-T MR, Yaghoobi M, Naghibi-S MB (2011) An adaptive ordered fuzzy time series with application to FOREX. Expert Syst Appl 38(1):475–485Bank for International Settlements. https://www.bis.org/ . 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Network-based business process management: embedding business logic in communications networks

Advanced Business Process Management (BPM) tools enable the decomposition of previously integrated and often ill-defined processes into re-usable process modules. These process modules can subsequently be distributed on the Internet over a variety of many different actors, each with their own specialization and economies-of-scale. The economic benefits of process specialization can be huge. However, how should such actors in a business network find, select, and control, the best partner for what part of the business process, in such a way that the best result is achieved? This particular management challenge requires more advanced techniques and tools in the enabling communications networks. An approach has been developed to embed business logic into the communications networks in order to optimize the allocation of business resources from a network point of view. Initial experimental results have been encouraging while at the same time demonstrating the need for more robust techniques in a future of massively distributed business processes.active networks;business process management;business protocols;embedded business logic;genetic algorithms;internet distributed process management;payment systems;programmable networks;resource optimization

Genetic Programming Approach for Classification Problem using GPU

Genetic programming (GP) is a machine learning technique that is based on the evolution of computer programs using a genetic algorithm. Genetic programming have proven to be a good technique for solving data set classification problems but at high computational cost. The objectives of this research is to accelerate the execution of the classification algorithms by proposing a general model of execution in GPU of the adjustment function of the individuals of the population. The computation times of each of the phases of the evolutionary process and the operation of the model of parallel programming in GPU were studied. Genetic programming is interesting to parallelize from the perspective of evolving a population of individuals in paralle