Inteligencia artificial

Ths paper describes the preparatory design and construction work for a of real-time autonomous mobile robot aimed at navigating in real farms with no operator intervention. The agricultural environment being targeted consists of an irregular terrain supporting crops or sparsely populated with objects. This results in complex problems of identification, monitoring and control. In this paper we introduce a fuzzy hierarchical controller for autonomous mobile robot for use in such agricultural environments. This controller utilises co-operating behaviours (obstacle avoidance, edge following, goal seeking) to navigate in tight spaces and navigate towards its target (bales of hay, boxes of fruit/vegetables). The proposed system simplifies fuzzy controller design and provides a control architecture with a fast response, robust performance, and the ability to deal with dynamic environments. This paper describes the preliminary development work in which the proposed controller architecture was tested on an in-door mobile robot. The next phase will concern the transfer of this work to our outdoor electrical and diesel robots.

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation(s) for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a (conditionally) optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions.

We report the magnetic confinement of neutral, ground state hydroxyl radicals (OH) at a density of $\sim3\times10^{3}$ cm$^{-3}$ and temperature of $\sim$30 mK. An adjustable electric field of sufficient magnitude to polarize the OH is superimposed on the trap in either a quadrupole or homogenous field geometry. The OH is confined by an overall potential established via molecular state mixing induced by the combined electric and magnetic fields acting on the molecule's electric dipole and magnetic dipole moments, respectively. An effective molecular Hamiltonian including Stark and Zeeman terms has been constructed to describe single molecule dynamics inside the trap. Monte Carlo simulation using this Hamiltonian accurately models the observed trap dynamics in various trap configurations. Confinement of cold polar molecules in a magnetic trap, leaving large, adjustable electric fields for control, is an important step towards the study of low energy dipole-dipole collisions.

Artificial intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime (AIC). AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space.

Monitoring changes in natural ecosystems is considered essential to natural resource management. Despite the global importance of the lakes' quality monitoring, there is currently a research gap in the simultaneous predictive modeling of lakes' land-use changes and ecosystem measurements. In the present study for projecting the water bodies of lakes and their surrounding ecosystems, the land-use changes and the landscape analysis of different periods, i.e., 1987, 2002, 2018, and 2030, are studied using remote sensing data and various metrics. The trend of land-use and landscape changes is projected for 2030. The results indicate significant degradation of rangelands and forests due to the conversion to agriculture and construction and the declining trend of lakes' water body and their transformation to salt lake and salt lands. The increase of agricultural lands and the overuse of groundwater wells upstream of the lakes could be one of the reasons for this decline. Decreasing the lakes' water body and subsequently increasing salt lands are considered a severe threat to human health and the ecosystem services of the lakes. Besides, the dust generated by salt lands could also decrease crop yield in the study area.

The idea of artificial intelligence for social good (henceforth AI4SG) is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good.

The task of this essay is to respond to the question concerning robots and responsibility—to answer for the way that we understand, debate, and decide who or what is able to answer for decisions and actions undertaken by increasingly interactive, autonomous, and sociable mechanisms. The analysis proceeds through three steps or movements. 1) It begins by critically examining the instrumental theory of technology, which determines the way one typically deals with and responds to the question of responsibility when it involves technology. 2) It then considers three instances where recent innovations in robotics challenge this standard operating procedure by opening gaps in the usual way of assigning responsibility. The innovations considered in this section include: autonomous technology, machine learning, and social robots. 3) The essay concludes by evaluating the three different responses—instrumentalism 2.0, machine ethics, and hybrid responsibility—that have been made in face of these difficulties in an effort to map out the opportunities and challenges of and for responsible robotics.

Financial development, productivity, and growth are interconnected, but the direction of causality remains unclear. The relevance of these linkages is likely different for developing and developed economies, yet comparative cross-country studies are scant. The paper analyses the relationship among credit access, output and productivity in the agricultural sector for a large set of countries, over the period 2000–2012, using an Artificial Neural Networks approach. Empirical findings show that these three variables influence each other reciprocally, although marked differences exist among groups of countries. The role of credit access is more prominent for the OECD countries and less important for countries with a lower level of economic de-elopement. Our analysis allows us to highlight the specific effects of credit in stimulating the development of the agricultural sector: in developing countries, credit access significantly affects production, whereas in developed countries, it also has an impact on productivity.

Formal knowledge representation struggles to represent the dynamic changes within complex events in a cognitively plausible way. Image schemas, on the other hand, are spatiotemporal relationships used in cognitive science as building blocks to conceptualise objects and events on a high level of abstraction. In this paper, we explore this modelling gap by looking at how image schemas can capture the skeletal information of events and describe segmentation cuts essential for concep-tualising dynamic changes. The main contribution of the paper is the introduction of a more systematic approach for the combination of image schemas with one another in order to capture the conceptual representation of complex concepts and events. To reach this goal we use the image schema logic ISL, and, based on foundational research in cognitive linguistics and developmental psychology, we motivate three different methods for the formal combination of image schemas: merge, collection, and structured combination. These methods are then used for formal event segmentation where the changes in image-schematic state generate the points of separation into individual scenes. The paper concludes with a demonstration of our methodology and an ontological analysis of the classic commonsense reasoning problem of 'cracking an egg.'

This paper presents a proposal of distribution’s estimated algorithm to the extraction of sequential patterns in a database which use a probabilistic model based on graphs which represent the relations among items that form a sequence. The model maps a probability among the items allowing them to adjust the model during the execution of the algorithm using the evolution process of EDA, optimizing the candidate’s generation of solution and extracting a group of sequential patterns optimized.

This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type 1" constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.

that.. .>The results of the assessment indicated that the use of resampling algorithms increases the efficiency of the models. The integrated optimism-bootstrap-BRT, optimism-bootstrap-SVM, and Optimism-Bootstrap-RF models with AUC 0.85, 0.823 and 0.89 respectively, outperformed the cross-validation 5fold (BRT, SVM, RF), Cross-validation 10fold (BRT, SVM, RF) and Bootstrap (BRT, SVM, RF) integrated algorithms.

Agreement technologies refer to a novel paradigm for the construction of distributed intelligent systems, where autonomous software agents negotiate to reach agreements on behalf of their human users. Smart Cities are a key application domain for agreement technologies. While several proofs of concept and prototypes exist, such systems are still far from ready for being deployed in the real-world. In this paper we focus on a novel method for managing elements of smart road infrastructures of the future, namely the case of auction-based road intersections. We show that, even though the key technological elements for such methods are already available, there are multiple non-technical issues that need to be tackled before they can be applied in practice. For this purpose, we analyse legal and ethical implications of auctionbased road intersections in the context of international regulations and from the standpoint of the Spanish legislation. From this exercise, we extract a set of required modifcations, of both technical and legal nature, which need to be addressed so as to pave the way for the potential real-world deployment of such systems in a future that may not be too far away.

This paper examines the relationship between renewable energy consumption and economic growth in Brazil, in the Covid-19 pandemic. Using an Artificial Neural Networks (ANNs) experiment in Machine Learning, we tried to verify if a more intensive use of renewable energy could generate a positive GDP acceleration in Brazil. This acceleration could offset the harmful effects of the Covid-19 global pandemic. Empirical findings show that an ever-greater use of renewable energies may sustain the economic growth process. In fact, through a model of ANNs, we highlighted how an increasing consumption of renewable energies triggers an acceleration of the GDP compared to other energy variables considered in the model.

Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. Here, we nurture the idea of ensemble-based learning by combining bagging and boosting for the purpose of binary classification. Since the former improves stability through variance reduction, while the latter ameliorates overfitting, the outcome of a multi-model that combines both strives toward a comprehensive net-balancing of the bias-variance trade-off. To further improve this, we alter the bagged-boosting scheme by introducing collaboration between the multi-model's constituent learners at various levels. This novel stability-guided classification scheme is delivered in two flavours: during or after the boosting process. Applied among a crowd of Gentle Boost ensembles, the ability of the two suggested algorithms to generalize is inspected by comparing them against Subbagging and Gentle Boost on various real-world datasets. In both cases, our models obtained a 40% generalization error decrease. But their true ability to capture details in data was revealed through their application for protein detection in texture analysis of gel electrophoresis images. They achieve improved performance of approximately 0.9773 AUROC when compared to the AUROC of 0.9574 obtained by an SVM based on recursive feature elimination. Machine learning has been transforming the world by improving our understanding of artificial intelligence 1-3 and by providing solutions for some outstanding problems such as multi-modal parcellation of human cerebral cortex 4 and materials discovery 5. A learning algorithm generalizes if, given access to some training set, it returns a hypothesis whose empirical error is close to its true error 6. There are three main approaches to institute generalization guarantees: (1) by providing bounds of various notions of functional space capacity-most notably, using the VC-dimension 7 ; (2) by establishing connections between the stability of a learning algorithm and its ability to generalize 8-10 , and (3) by considering the compression-scheme method 11. Here we describe an effective way to fuse boosting and bagging ensembles in which algorithmic stability directs a novel process of collaboration between the resulting ensemble's weak/strong components that outperforms best-case boosting/bagging for a broad range of applications and under a variety of scenarios. The algorithms were assessed on various realistic datasets, showing improved performance in all cases, on average of slightly below 40%, compared to the best-case boosting/bagging counterparts. Furthermore, in a medical setting for protein detection in texture analysis of gel electrophoresis images 12 , our approach exhibits surpassing performance of approximately 0.9773 area under the ROC curve (AUROC), compared to three machine-learning feature selection approaches: Multiple Kernel Learning, Recursive Feature Elimination with different classifiers and a Genetic Algorithm-based approach with Support Vector Machines (SVMs) as decision functions, having 0.9574 or less AUROCs. Moreover, when collaboration is effectuated with weak components, our algorithm runs up to more than five times faster than the underlying boosting algorithm. We anticipate our approach to be a starting point for more sophisticated models for generating stability-guided collaborative learning approaches, not necessarily limited to boosting. Ensemble techniques 13-15 show improved accuracy of predictive analytics and data mining applications. In a typical ensemble method, the base inducers and diversity generators are responsible for generating diverse clas-sifiers which represent the generalized relationship between the input and the target attributes. A strong classifier 1

Progressive methods of data evaluation based on recent artificial neural networks are introduced to the field of psychology in the current study. Artificial neural networks techniques work on different basis than the classical statistical methods. Particularly, the Kohonen's Self-Organizing Map (SOM), the Modified Group Method of Data Handling (GMDH), and the recent Group of Adaptive Models Evolution (GAME) were used in this study for a self-organized clustering of the measured data and for an analysis of factor significance. Significance of seven various factors for facial expression decoding accuracy was assessed. Gender was considered to be the most significant factor for the correct recognition of facial expressions. Place of origin yielded the second highest significance. Results indicate women to be better decoders than men and persons growing up in urban areas to be better decoders than persons growing up in rural areas.

In this study, a predictive intelligent-based technique, adaptive neuro-fuzzy inference systems (ANFIS) and a hybrid model based on a technique for order performance by similarity to ideal solution (TOPSIS) have been implemented for sustainable supplier selection. Selection of supplier is a crucial task for companies to achieve the objectives of inbound and outbound supply chain system. The process is highly complex owing to the involvement of subjective and objective factors, thereby necessitating a reliable methodology to provide higher accuracy in predictions. This article makes contributions in that direction. The hybrid modeling framework was applied in the fast-moving consumer goods (FMCG) retail industry to select the best possible supplier with regards to relevant triple-bottom-line sustainability factors. Data were sourced from available FCMG retail sector supplier alternatives, then TOPSIS and ANFIS were applied to prioritize the suppliers. Results of the ANFIS analysis indicate that the most dominant sustainability factors in the Nigerian FMCG retail sector are advanced technology, cost, reliability, on-time delivery, and environmental competencies. The research finding would encourage companies in the retail sector to explore sustainability opportunities from supplier selection and to improve their competitiveness. The novelty of this study is the application of ANFIS to the sustainable supplier selection problem in the Nigerian FMCG retail sector. This will ensure that the best possible sustainable supplier is selected and assist managers in the FMCG retail sector to highlight areas of possible sustainability improvements.

This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine-bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.

Por cuenta de la pandemia las firmas tecnológicas y gobiernos están en conversaciones sobre la creación de muchas formas de coletas de datos, incluso los georeferenciados e de salud para hacer monitoreo de lockouts bloqueos, cuarentenas, aislamiento y distancia social. Los datos de salude, localización, compras, transferencias de dinero, biometría facial, certificados digitales pueden si ser una arma poderosa para combate del virus. Sin embargo si los datos colectados y parametrizados no fueren abiertos, con respecto a privacidad y protección de los datos personales o sensibles, si no haber justicia, legalidad y transparencia de cómo los algoritmos están produciendo sus análisis y hasta que punto las decisiones políticas y administrativas están siendo fundadas en las sugerencias tecnológicas, podemos estar caminando para una crisis urgente de derechos digitales. En este texto el caso brasileño será detallado más a fundo a titulo de ejemplo. El estudio de caso en algunos sitios electrónicos brasileños fue hecho en marzo a junio de 2020.

Airborne LiDAR produced large amounts of data for archaeological research over the past decade. Labeling this type of archaeological data is a tedious process. We used a data set from Pacunam LiDAR Initiative survey of lowland Maya region in Guatemala. The data set contains ancient Maya structures that were manually labeled, and ground verified to a large extent. We have built and compared two deep learning-based models, U-Net and Mask R-CNN, for semantic segmentation. The segmentation models were used in two tasks: identification of areas of ancient construction activity, and identification of the remnants of ancient Maya buildings. The U-Net based model performed better in both tasks and was capable of correctly identifying 60-66% of all objects, and 74-81% of medium sized objects. The quality of the resulting prediction was evaluated using a variety of quantifiers. Furthermore, we discuss the problems of re-purposing the archaeological style labeling for production of valid machine learning training sets. Ultimately, we outline the value of these models for archaeological research and present the road map to produce a useful decision support system for recognition of ancient objects in LiDAR data.

The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM10, PM2.5, and NO2 concentrations in New York state using city-level daily data through two Machine Learning experiments. PM2.5 and NO2 are the most significant pollutant agents responsible for facilitating COVID-19 attributed death rates. Besides, we found only six out of many tested causal inferences to be significant and true within the AUPRC analysis. In line with the causal findings, a unidirectional causal effect is found from PM2.5 to Deaths, NO2 to Deaths, and economic growth to both PM2.5 and NO2. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM2.5 to Deaths, NO2 to Deaths) to accelerate COVID-19 deaths. In contrast, we found evidence that unsustainable economic growth predicts the dynamics of air pollutants. This shows how unsustainable economic growth could increase environmental pollution by escalating emissions of pollutant agents (PM2.5 and NO2) in New York state.

This paper explores a practical application of a weak, or narrow, artificial intelligence (AI) in the news media. Journalism is a creative human practice. This, according to widespread opinion, makes it harder for robots to replicate. However, writing algorithms are already widely used in the news media to produce articles and thereby replace human journalists. In 2016, Wordsmith, one of the two most powerful news-writing algorithms, wrote and published 1.5 billion news stories. This number is comparable to or may even exceed work written and published by human journalists. Robo-journalists' skills and competencies are constantly growing. Research has shown that readers sometimes cannot differentiate between news written by robots or by humans; more importantly, readers often make little of such distinctions. Considering this, these forms of AI can be seen as having already passed a kind of Turing test as applied to journalism. The paper provides a review of the current state of robo-journalism; analyses popular arguments about " robots' incapability " to prevail over humans in creative practices; and offers a foresight of the possible further development of robo-journalism and its collision with organic forms of journalism.

Este es un libro que traduje, no un libro que escribí. La traducción, sin embargo, junto con la biografía de Von Neumann que también traduje, contribuyó a que la industria editorial española dejara de hablar de "ordenadores" y se refiriera de allí en más a "computadoras", que es como siempre debió ser.

Aun cuando el lector se incline hacia las ciencias sociales y no hacia la informática (y sobre todo en ese caso), los artículos que componen esta compilación resultan esenciales para comprender la dualidad de los procesos que ocurren en la mente: por un lado, las operaciones lógicas del modelo simbólico; por el otro, el reconocimiento de patrones favorecido por el conexionismo. Esta dualidad deviene clave, asimismo, para comprender mejor el contraste entre los modelos mecánicos y los estadísticos. En el momento del debate ese contraste definía para qué lado irían a parar los fondos que subsidiaban la investigación; hoy, con los ánimos no del todo serenados, es una heurística para comprender mejor la pluralidad de lo que llamamos mente y el carácter compuesto y complejo de la cognición.

El libro incluye casi exactamente los textos que yo mismo habría escogido para representar a las dos partes en pugna. Es por ello un documento valioso para comprender mejor tanto los postulados de la Inteligencia Artificial clásica como los fundamentos filosóficos de las redes neuronales, así como sus respectivos logros: los sistemas de demostración automática de teoremas, los sistemas expertos (en particular los sistemas de diagnóstico clínico) y los programas de computadora capaces de aprendizaje y de reconocimiento de patrones.

In recent years, there has been a growing interest in the application of foundational ontologies, i.e., formal ontological theories in the philosophical sense, to provide a theoretically sound foundation for improving the theory and practice of conceptual modeling and knowledge representation. This paper addresses one particular foundational theory of events termed UFO-B, which has been successfully employed as a reference model for addressing problems from complex media management, enterprise architecture, software engineering, and modeling of events in petroleum exploration. Despite its success, there is still no formalization of UFO-B in a decidable knowledge representation language that could support reasoning about complex events and event relations. We address this gap by proposing a number of alternative translations from UFO-B's original axiomatization (in first-order logic and in the Alloy formal language) to the description logic SROIQ, which is the formal underpinning of OWL 2 DL. Additionally, to support practical applications, we translated these SROIQ theories to OWL 2 DL TBoxes, which were validated by showing that all the intended models of UFO-B (the logical models of the UFO-B specification in Alloy) that we generated are consistent with these UFO-B TBoxes. In a sense, the specification in Alloy implements the specification in first-order logic, while the OWL 2 TBoxes implement the SROIQ specifications. Incidentally, the methodology that we designed for the translation from UFO-B's original axiomatization in FOL and Alloy to SROIQ came to be a key contribution of this work by providing us evidence of the inadequacy of DLs for the specification of comprehensive foundational ontologies.

Current state-of-the-art methods solve spatio-temporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called action micro-tubes. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera do. Assuming we evaluate n 2D anchors in each frame, then the number of possible transitions from each 2D anchor to he next, for a sequence of f consecutive frames, is in the order of O(n f), expensive even for small values of f. To avoid this problem we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from O(n f) to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, and making detection translation-invariant. Finally, we show that our network is able to handle sparse annotations such as those available in the DALY dataset, while allowing for both dense (accurate) or sparse (efficient) evaluation within a single model. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.

Artificial intelligence has dramatically changed the world as we know it, but is yet to fully embrace ‘hot’ cognition, i.e., the way an intelligent being's thinking is affected by their emotional state. Artificial intelligence encompassing hot cognition will not only usher in enhanced machine-human interactions, but will also promote a much needed ethical approach. Theory of Mind, the ability of the human mind to attribute mental states to others, is a key component of hot cognition. To endow machines with (limited) Theory of Mind capabilities, computer scientists will need to work closely with psychiatrists, psychologists and neuroscientists. They will need to develop new models, but also to formally define what problems need to be solved and how the results should be assessed.

Resumen: Actualmente, la convergencia de tres ámbitos de aplicación tecnológica —Big Data, Inteligencia Artificial e Internet de las Cosas— permite hiperconectivizar, algoritmizar y dataficar los distintos ámbitos de actividad humana para optimizar sus procesos productivos, comunicativos, asistenciales y/o relacionales, y, con ello, aumentar su eficiencia, eficacia y sostenibilidad. El éxito cosechado en sectores como el industrial o el retail y las enormes expectativas que abre su implementación en otros como el político o el asistencial, empero, ha extendido la idea de que incluso es posible discernir mejor qué es lo justo y bueno a través del análisis y procesamiento algorítmico de datos masivos sobre lo moral procedentes de los individuos hiperconectados Sin embargo, este enfoque dataficado de ética alberga dudas razonables respecto a su viabilidad, posibilidad y eficacia. Pero, sobre todo, produce inquietud su aceptación y puesta en marcha acrítica por las importantes anomalías que produce en el mundo de la vida, especialmente en la concreción y perdurabilidad del saber moral. Este estudio pretende mostrar los restos, límites y consecuencias que subyacen a la dataficación del ámbito moral.

Palabras clave: algoritmización, dataficación, ética discursiva, hiperconectividad, sociedad digital.

We propose a new autofocus and fusion algorithm to enhance the characteristics of the image in less run-time. The autofocus method selects the best-focused image (BFI) in a stack of images captured at different distances from the object. A vector is defined for each image in the stack, which contains elements selected by spiral scanning of the image. The spectrum of each vector is calculated using the Fourier transform then applying non-linear correlation to the reference vector spectrum and those of each of the corresponding to the images in the stack the BFI is determined. The fusion is carried out with a subset of images that have a focus measure value close to the BFI ones. The parabolic filter is applied to determine the relevant elements of the images that will be included in the fusion. The results evaluation is conducted using a multi-image metric, a quality measure that represents the percentage improvement of the fused image. The comparison was made with other fusion methods such as standard wavelets, resulting in that the autofocus and fusion algorithm (AFA) method obtained the highest quality indices. It is concluded that the AFA improves the quality of the images in less time than the conventional fusion methods.

"Muchas series de tiempo con tendencia y ciclos estacionales son exitosamente modeladas y pronosticadas usando el modelo airline de Box y Jenkins; sin embargo, la presencia de no linealidades en los datos son despreciadas por este modelo. En este artículo, se propone una nueva versión no lineal del modelo airline; para esto, se reemplaza la componente lineal de promedios móviles por un perceptrón multicapa. El modelo propuesto es usado para pronosticar dos series de tiempo benchmark; se encontró que el modelo propuesto es capaz de pronosticar las series de tiempo con mayor precisión que otras aproximaciones tradicionales"