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This paper addresses the question of whether generative AI can create copyrightable works by making a comparative analysis between European Union law and U.S. copyright law. First, it is argued that for copyright law it does not seem possible for an entity other than a human being to be the copyright holder, thus ruling out the possibility of generative AI being considered a copyright holder. It is established that in US copyright the door it may be possible to register part of a work made using generative AI, and that in the EU this door remains open and pending evolution, although European jurisprudence already gives some guidelines. Afterwards, this paper explores possible infringement of exploitation rights in the different parts of the training process of generative AI models and in the activities that lead to the generation of content that is like intellectual works. It is concluded that in US law there is a gap in knowledge as to whether fair use would cover these types of activities. In European Law, although there are exceptions such as those in Directive 2001/29, it is not at all clear whether this will be sufficient to cover these activities, or whether the authorization of the rights owner will be necessary. If the latter is true and judging by the wave of lawsuits that have followed against companies and institutions that have made generative AI tools publicly available, it is possible that licensing negotiation may take a central role in the AI industry, with the negative consequences that this may bring for the advancement of science and for the public domain. [ABSTRACT FROM AUTHOR]

From the perspective of a descriptive analysis, and as a starting point to a new research line, this paper examines the potential impact Generative Artificial Intelligence (GAI) technologies may have on academic integrity, manifested in the learning and evaluation processes of law classes at the university level. The article takes as its premise the definition of academic integrity as a set of values and argues that a series of risks arise from the GAI that threaten those values, such as excessive dependence and trust in the GAI, the unreallizability of the pedagogical project and the loss of competitiveness of educational institutions, among others. To minimize or nullify such risks, and thus prevent them from affecting academic integrity, four mitigation measures are identified to be applied in university environments. [ABSTRACT FROM AUTHOR]

Among the malpractices that undermine research integrity, plagiarism is a major threat given its frequency and evolving presentations. Plagiarism implies the intentional grabbing of texts, ideas, images, or data belonging to others and without crediting them. However, the different and even masked forms of plagiarism often difficult a clear identification. Currently, the many kinds of fraud and plagiarism account for most retractions in traditional and open access journals. Further, the rate of retracted articles is higher in the Latin American databases LILACS and Scielo than in PubMed and Web of Science. This difference has been related to the typical laxity of our culture and the lack of English writing skills of non-Anglophone researchers. These features explain the conflict experienced by Latin American students in USA where they face a stricter culture regarding academic and scientific plagiarism. In the internet era, the ease of accessing scientific literature has increased the temptation to plagiarize but this ethical breach has been countered by antiplagiarism software. Now, the so-called "paraphragiarism" prompted by paraphrasing tools exceeds the infamous "copy-paste". For instance, the innovative ChatGPT can be used for plagiarizing and paraphragiarizing. Moreover, its inclusion as coauthor in scientific papers has been banned by prestigious journals and the International Committee of Medical Journal Editors because such chatbot cannot meet the required public responsibility criterium. To avoid plagiarism, it is enough to always give due credit in the proper way. Lastly, I question the ill-fated and now prevailing conjunction of blind faith in progress and zero skepticism that prevents us from foreseeing the negative consequences of technological advances. [ABSTRACT FROM AUTHOR]

The main purpose of this reflection paper is to present a critical analysis of some of the most prominent epistemological and philosophical ideas developed by contemporary transhumanism in relation to the anatomical-functional activity of the brain and the nature of consciousness. From this point of view, this paper considers whether the technologicalinstrumental hermeneutic model promoted by the transhumanist movement is an alternative, comprehensive variant to elucidate the onto-epistemological complexity of consciousness. To this end, this paper explores the way in which transhumanism has focused on certain techno-scientific fields in order to develop a concrete prospective associated with the cognitive potentialities of the human being. In this sense, it discusses certain specific advances in cuttingedge neurotechnology and artificial neuromodulation (neural implants, neuromorphic devices, neuronal reprogramming, controlled laboratory culture of cerebroids, etc.), as well as artificial intelligence. This analysis results in deep scepticism as to whether the transhumanist approach offers a promising model for successfully tackling the age-old problem of the mind-brain relationship because, despite the appeal of some of its boldest speculations about the bioanthropological condition of man and the ontology of the real, it has chosen to update a reductionist and physicalist view of the human mind. [ABSTRACT FROM AUTHOR]

This paper identifies key research on the factors that help to explain university dropout and how these factors are being explored by means of artificial intelligence (AI). The study describes the methodology employed to select 31 papers from a repository of 2,745 reported in the literature. The analysis centered on the main AI methods used and four categories of explanatory factors of university dropout: academic factors; factors associated with motivation and study habits; institutional factors; and economic and sociodemographic factors. The conclusion drawn from this literature review is that AI is most commonly used for decision tree-based classification, and most studies focus on predicting university dropout on the basis of explanatory factors. [ABSTRACT FROM AUTHOR]

With digital ecosystems being questioned around the world, this paper examines the EU's role in and contribution to the emerging concept of artificial intelligence (AI) governance. Seen by the EU as the key ingredient for innovation, the adoption of AI systems has altered our understanding of governance. Framing AI as an autonomous digital technology embedded in social structures, this paper argues that EU citizens' trust in AI can be increased if the innovation it entails is grounded in a fundamental rights-based approach. This is assessed based on the work of the High-Level Expert Group on AI (which has developed a framework for trustworthy AI) and the European Commission's recently approved proposal for an Artificial Intelligence Act (taking a risk-based approach). [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has revolutionized our lives and will continue to transform them dramatically for decades to come. Its potential is indisputable and its applications can bring important benefits to people with disabilities. However, AI is a clearly ambivalent reality and along with its friendly face it also presents another one made up of various fundamental risks, many of an ethical nature, capable of creating new types of discrimination that curtail the rights of such groups, always in constant vindication, especially in the face of new realities. This paper will review in a generic sense the advantages and blind spots of artificial intelligence in relation to people with disabilities, with a specific focus on algorithmic biases, a key element with a recognized ability to organize society and influence policies and institutions through autonomous decision-making, based on behavior that is sometimes unfair and discriminatory. Finally, some solutions will be provided to shape a still incipient phenomenon, so that it develops at the service of all people, including those with any type of disability. [ABSTRACT FROM AUTHOR]

Since its first applications, artificial intelligence has presented a vertiginous development, manifesting applications in multiple academic and business fields. In this article we will explore the risks to which the academic community, and especially students, are exposed to the growing wave of artificial intelligent systems, which have materialized through the use of both students and teachers. We will discuss how, through the bad behavior that the models currently present, the problems of interaction with these tools arise, supporting our hypothesis by means of an interaction that was carried out with one of the most popular existing programs among the student community: ChatGPT. This work concludes with the suggestion of three strategies that could be considered when implementing systems powered by artificial intelligence in classrooms, which seek to mitigate the risks presented throughout this paper, so that the pedagogical work of the teacher is optimized, while enhancing the learning process by students. [ABSTRACT FROM AUTHOR]

Artificial intelligence seems to be in vogue as a term lately; however, this term was coined back in 1956 by mathematician John McCarthy, Marvin Minsky, and Claude Shannon. This concept defined the word as the science and engineering of ``making machines intelligent.' its sibling term, artificial life, is equally fundamental. How can we talk about intelligence without an organism to use it after all? Currently, researchers from both fields complement each other and describe a new class of artificial organisms. This paper presents a general overview of advancements achieved in artificial life, the creation of so-called virtual creatures and their possible application to the activities of human beings. [ABSTRACT FROM AUTHOR]

Artificial intelligence is giving rise to intense debates in the doctrine about its compatibility with the humanist ethics prevailing in Western society. This anthropocentric perspective of the paradigm shift posed by artificial intelligence does not, however, exhaust the line of pernicious consequences that may accompany it. Thus, the environment also emerges as a legal asset to be protected against the phenomenon of this new technology. The enormous amount of energy demanded by some of the artificial intelligence models erodes any energy efficiency objectives that may be set in the field of ICTs. For this reason, the aim of this paper will be to analyze this new challenge that environmental law will have to face sooner rather than later. [ABSTRACT FROM AUTHOR]

Tuberculosis is a respiratory disease that affects lungs and it is caused by the bacillus Mycobacterium tuberculosis (MTB), which is spread when people who are sick with tuberculosis expel bacteria into the air by coughing. Before the coronavirus (COVID-19) pandemic, tuberculosis was the leading cause of death from infectious agents even ranking above HIV/AIDS. Growing MTB on solid medium is the main diagnostic reference method. In addition, this is the standard method for identifying the bacterial resistance profile. However, the waiting period for the results is long, during which time a patient can be highly contagious. In turn, delaying treatment for tuberculosis can increase disease severity, but treating without diagnostic confirmation can lead to bacterial resistance, a higher rate of adverse events, and higher costs. Therefore, pulmonary tuberculosis disease must be rapidly diagnosed by a costeffective method. This paper proposes models for the detection of pulmonary tuberculosis by using different artificial intelligence techniques. These models can be used to support decision making of doctors and are intended to identify whether a patient has tuberculosis or not. In particular, four supervised learning techniques are used (neural networks, decision trees, and two ensemble methods). Each model allows predicting a positive or negative diagnosis of pulmonary tuberculosis based on previously recorded diagnostic variables taken from patients in Cali, Colombia. According to the results, the Extra Trees method reaches the highest accuracy compared to the other techniques used for the prediction of pulmonary tuberculosis with an area under the ROC curve of 95,63%. [ABSTRACT FROM AUTHOR]

As defined in economic theory, artificial intelligence (AI) can be considered an industry. In this context, two simultaneous processes are observed: concurrence and cooperation. The current dispute between China and the United States for world leadership in AI should be placed against this background. Thus, the description in this paper of concrete policies and measures that the Chinese regime pursued to promote its AI industry and identify its strategy to increase its influence on the standardization processes in the emerging industry is extremely important. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAVs) to monitor forest plantations allows obtaining precise information on different tree attributes. This paper presents a critical review of the potential use of UAVs for monitoring the nutritional and phytosanitary status of forest plantations. A bibliographic search was carried out on the Google Scholar, Scopus, and Science Direct platforms, using keywords such as stress, nutrition, and forestry. Studies were found mainly on the genus Pinus and the Asian continent which use fixed and rotary wing drones to monitor pests and diseases. Experiences in monitoring nutritional deficiencies are few. The future use of UAVs for stress monitoring in forest crops seems to be aimed at automating data collection and combining these with artificial intelligence algorithms. [ABSTRACT FROM AUTHOR]

This paper aims to show the opposition between the Humanist cultural tradition and the main theses currently defended by Posthumanism. The structure of the paper is built upon the corresponding answers provided by both trends to the three main philosophical questions: How do we know reality? What are the world and life, and how are they explained? What is the best way to live one’s life? Gnoseology (or theory of knowledge), Ontology, and Deontology or Ethics have respectively tried to provide answers for such questions. Certain ambiguities of the Posthumanist dehumanizing project are also addressed. The paper concludes with an exposition of the arguments that justify the continuity of Humanist values, principles, and rights, updated according to the demands of our time. This paper aims to show the opposition between the Humanist cultural tradition and the main theses currently defended by Posthumanism. The structure of the paper is built upon the corresponding answers provided by both trends to the three main philosophical questions: How do we know reality? What are the world and life, and how are they explained? What is the best way to live one’s life? Gnoseology (or theory of knowledge), Ontology, and Deontology or Ethics have respectively tried to provide answers for such questions. Certain ambiguities of the Posthumanist dehumanizing project are also addressed. The paper concludes with an exposition of the arguments that justify the continuity of Humanist values, principles, and rights, updated according to the demands of our time. [ABSTRACT FROM AUTHOR]

In this paper, new models for credit risk prediction in Colombia are proposed by using different artificial intelligence techniques. These models can be used to support the risk management area in banks, and they aim to identify clients that could be in default, generating a possible credit risk for financial institutions. Three techniques are used to obtain the models (neuronal networks, decision trees, and support vector machines) that predict the next payment of a client's fee based on basic data from the client and previous recorded installment payments. Decision trees turns out to be more accurate than the other techniques that have been used when predicting credit risk with a ROC area of 88.29%. The proposed models reach accuracies that are like some other papers in the state of the art and in some cases, they overcome models in other studies. [ABSTRACT FROM AUTHOR]

At present, some activities in the livestock sector such as technical assistance and on-site visits, the information is recorded manually using paper formats or on some occasions it is not even carried out. Nowadays in the framework of the Digital economy, information is considered a fixed asset in the business environment, therefore, it is necessary to implement IoT technologies (Internet of Things and AI Artificial Intelligence) that allow recording information corresponding to the activities that allow you to make the right decisions regarding the productivity and marketing of the livestock sector. The technology to be implemented will allow making the right decisions about the zootechnical and health parameters in the livestock environment. [ABSTRACT FROM AUTHOR]

In recent years, there has been an exponential growth in developments aimed at the creation of textual, graphic, audio, and audiovisual content using artificial intelligence. These extraordinary technological achievements offer huge potential opportunities but, at the same time, are vulnerable to certain questionable uses that constitute real threats. This paper examines recent initiatives targeting the simulation of human writing, the creation of deepfake videos, voice cloning, and the generation of images from text prompts. Then, the potential benefits of these developments as well as the main dangers resulting from their misuse are discussed. [ABSTRACT FROM AUTHOR]

This paper aims to problematize the role of contemporary technologies and machines, especially Artificial Intelligence (AI), in the dynamics of value production and in the production processes of subjectivities. According to the Marxian position, it is the human labour power as variable capital that produces value, while the machine as constant capital only preserves value. However, the emergence of the current machinic automatisms could complicate the productive conditions of the political economy, as well as influence the behavior of the subjects. In order to specify this double problem, we will analyze the work of Félix Guattari, who precisely proposes the concept of "machinic surplus value" (the production of value by the machine) and "desire value" (the activity of the subjects as unproductive work outside the workplace). The article is divided into five parts. In the first part we offer a contextualization of the proposed topic. In the second part we discuss the Guattarian conceptualization of the machine, the machinic surplus value and the value of desire. In the third we revisit the Marxian approach to the machine and constant capital in Grundrisse and Das Kapital. In the fourth we confront the Guattarian and Marxian position in order to think about the subjective consequences of human work in digital and machinic contexts. Finally, we offer some conclusions. [ABSTRACT FROM AUTHOR]

The increasing dispute between the United States of America and China finds a main area of competition around 5G, which will enable a substantial improvement in the capacity for vehicle auto-guidance, remote operations, robotics, artificial intelligence and other dual-use applications. While 5G is still emerging, the global shaping of the network infrastructure, regulations, patents and standards involves the Global South. It is an eminently global dispute for markets, but also for national security, which has gained a central place on the foreign policy agenda of most of the countries around the globe. In this context, the objective of this text is to analyze the emergent conditions of 5G in two of the main countries of the Global South, India and Brazil, within the dispute between the US and China. Through the contextualization of this dispute and its implications in India and Brazil, we seek to extract a series of preliminary reflections for other countries of the Global South. Thus, this paper contributes to a key area understanding the emergent conditions of a new phase of the mobile telecommunications paradigm developing two main cases still unexplored systematically by the literature in Spanish. [ABSTRACT FROM AUTHOR]

Performance tests are crucial to measure the quality of software developments, since they allow identifying aspects to be improved in order to achieve customer satisfaction. The objective of this research was to identify the optimal Machine Learning technique to predict whether or not a software development meets the customer's acceptance criteria. A dataset with information obtained from web services performance tests and the F1-score quality metric were used. This paper concludes that, although the Random Forest technique obtained the best score, it is not correct to state that it is the best Machine Learning technique; the quantity and quality of the data used in the training play a very important role, as well as an adequate processing of the information. [ABSTRACT FROM AUTHOR]

Artificial intelligence offers great opportunities for the supply chain, making it a competitive advantage for today's changing market. This paper aims to identify the impacts and opportunities that artificial intelligence software can offer to supply chain in the Colombian banana sector to facilitate the operation and improve the performance. The searching method consists of six steps getting 72 investigations finally. The sources of information were four databases. The main conclusion is the supply chain of the banana sector has everything for implementation of solutions based on intelligent software in order to achieve adaptation, flexibility and context awarenes and execution domain. [ABSTRACT FROM AUTHOR]

This paper reflects on the understanding of the tourism phenomenon from social research in the era of big data, analyzing the use of artificial intelligence to identify emotions, social networks on the internet to draw user profiles and scoring systems to compute ratings. New research practices, arising from marketing, may relegate the measurement instruments that social scientists have traditionally used. Conditioned by their business purpose, these innovative techniques contain ethical and methodological problems that, in the field of tourism, are related to attempts to analyze emotions and opinions quantitatively. Problems that can become an object of analysis for the sociology of tourism. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of artificial intelligence (AI) on journalism and disinformation. To achieve this, methodological design begins with a review of the scientific literature that is complemented by the analysis of three interrelated perspectives: journalistic, economic and ethical. The results reveal disinformation to be a polysemic phenomenon, complicating the design of public policies to counter it. AI's emergence presents an opportunity to contribute to this and develop the concept of citizenship along three lines: 1) improving the state of professional journalism; 2) managing privacy; and 3) an ethical approach to technological development, which helps make informative decision-making conscious and free from biases. [ABSTRACT FROM AUTHOR]

Debate abounds surrounding the future of work, motivated by the so-called Fourth Industrial Revolution, and especially by the challenges that technology and Artificial Intelligence (AI) pose. The development of AI in recent years threatens to replace human labor, which not only has social consequences, but also anthropological ones due to work's importance for human life. This paper compares AI with practical reason -i.e., properly human intelligence - in order to suss out if such a threat is real. First, it takes a theoretical approach to practical reason in light of classical philosophy, using Carlos Llano's theoretical framework as a reference. Then it contrasts this analysis with perceptions about AI gleaned from in-depth interviews conducted with executives and professionals at influential technology companies in Mexico City. [ABSTRACT FROM AUTHOR]

The purpose of this system is to satisfy the expectations and requirements of the customers of computer assembly, which is a procedure that consists on correctly placing all the parts of a computer in order such that everything works efficiently. Then, it will be able to install the operating system and other programs according to the user's needs, thus a computer selection is based on information on compatibility and assembly of computers. The information has been compiled from books, scientific papers, and based on research tools such as: surveys, interviews, for the collection of information. The development of this system serves as a tool that in the future may bring some comfort to users with little knowledge of hardware to make purchases according to the needs and customer's economy, which is an important aspect in people's daily lives. The Expert System of requirements and compatibility in assembly of computers realizes what an expert engineer in assembly of computers can do, but the system does it in a fast way and it is always available. [ABSTRACT FROM AUTHOR]

In Colombia, social networks have become a powerful tool to disseminate political opinions, especially during the campaign period in the national presidential elections. This paper proposes a hybrid model to predict the outcome of the first round of presidential elections in Colombia in 2018, which aims to minimize absolute error and improve the quality of the final prediction. User activities on Twitter and Facebook were recorded and analyzed with artificial intelligence algorithms, resulting in an accurate prediction consistent with reality. As a core result is highlighted that the RMSE of the hybrid model is around 2.47%, surpassing on average the RMSE of the country's most prominent traditional polling firms. Additionally, the value of electoral abstentionism was also predicted with a differential error of 1.72% in relation to the real value, demonstrating the reliability of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper describes a process of semi-automatic systematic reviews in scientific research. On the one hand, it is presented a software prototype based on artificial intelligence, semi-automatic systematic reviews tool. On the other hand, a study case of a phd thesis project shows the maps and information with aggregate value. In addition, the software prototype were evaluated by 40 students of master and Ph.D projects, they report the utility of the software tool by a survey. This work presents the modules and functionalities, software architecture and software deployment. Furthermore, a comparative study of a set of similar software tools has been conducted according to the following criteria: input files, analysis and mapping, and software license. Finally, it is discussed the results of the comparative study, the implementation of artificial intelligence techniques, the software scalability and the easiness of include new analysis and maps. [ABSTRACT FROM AUTHOR]

The business world competitiveness demands have suitable staff, especially when production is organized in projects. The labor competencies assessment is a human resources management important activity. For its implementation, have been proposed methods, models and algorithms that use artificial intelligence techniques. However, many cases represent the oretical proposals or invasive mechanisms class again stor project efficiency. This paper presents amodel for evaluating job competencies starting from evidences project that analyze stasks staff, using indicators and artificial intelligence techniques. The proposal is implemented on Xedro-GESPRO: an open source tool, for the integrated management projects, developed in the country and used by several development projects organizations. The experimental implementation of the model showed acceptable levels of effectiveness and a significant increase in efficiency compared to methods such as 360-degrees. [ABSTRACT FROM AUTHOR]

The characterization of city borders is of especial interest, since it may provide hints to understand the urban growing taking place in peripheries and it also allows to identify continuity and discontinuity relationships between cities and their surroundings. Within this context, many different approaches could be adopted to undertake this kind of analysis. In this paper, we explore the possibilities offered by the use of Self-Organized Maps built from the results of the application of different centrality measures in the mixed road network, which is composed by streets, metropolitan roads and rural roads. The application of centrality measures to this mixed road network is already an innovative exercise, considering that the centrality analysis tend to be applied just on intra-urban contexts. The spatial representation of the profiles obtained by the Self-Organized Maps allows us to identify different structural characteristics throughout the city border. The proposed method is tested in the city of Granada (Spain), in the city border in contact with the Vega de Granada, an agricultural landscape linked to the Genil River. [ABSTRACT FROM AUTHOR]

Optimization based on Ant Colony two stages (TS -ac), it represents a way to improve exploration strategy meta-heuristic optimization based on Ant Colony (ACO). TS-ACO, it is based on the methodology of "divide and conquer" and its effectiveness has been proven in several discrete optimization problems. In this paper, three approaches to TS-ACO are presented and appropriate adjustments are made to test their effectiveness in solving the problem of Coverage Sets (SCP). These approaches use partial results (only sub solution (S-TS-ACO) or only pheromone trail (P-TS-ACO) or both (PS-TS-ACO)), obtained in the first phase, in order to guide exploration in the second phase of the search process. Experimental results show that the best solutions are achieved by the variant P-TS-ACO. Finally a comparative study of the two-stage strategy and the original ACO strategy was performed. The results show that the proposed P-TS-ACO, you get significantly better results than the original strategy, in the same execution time and under the same parameter values. [ABSTRACT FROM AUTHOR]

This paper offers a comparative analysis of the effectiveness of eight popular forecasting methods: univariate, linear, discriminate and logit regression; recursivepartitioning, rough sets, artificial neural networks, and DEA. Our goals are: clarify the complexity-effectiveness balance of each methodology; identify a reduced set of independent variables that are significant predictors whatever the methodology is; and discuss and relate these findings to the financial theory, to help consolidate the foundations of a theory of financial failure. Our results indicate that, whatever the methodology is, reliable predictions can be made using four variables; these ratios convey informatión about profitability, financial structure, rotation, and operating cash flows. [ABSTRACT FROM AUTHOR]

In this paper is shown the application of neural networks in order to predict academic marks that will be obtained for the students in the subjects of Data Structures I and II, included both in the Informatics Engineering curricula at Higher Polytechnic Institute José Antonio Echeverría in the Republic of Cuba. The main motivation for the present work is justified because selected subjects have a high level of complexity, demanding from the student to be rigorous and a permanent dedication. As a consequence the academic results obtained at the present time are not satisfactory. To reach the goal mentioned above a software based on MATLAB tool was developed and the marks obtained previously by students in some subjects and others data of interest were used. Two neural networks were employed, both with the same architecture, but each one trained with the specific data of each subject (Data Structures I and II). A group of experiments was carried out to contrast the behavior of the neural networks regarding some specific statistics in the data of the sample. An overall effectiveness in prediction superior to 78% for the case of the first subject was achieved, while for the second one effectiveness superior to 75% was reached. [ABSTRACT FROM AUTHOR]

Memory tasks are closely linked to all our cognitive processes. The study of human memory is immersed in countless theories and models of every sort, so it is necessary to clear the outlook of study to create better applications in education and other related areas. This article is about computational models of human memory, and is part of a line of research on human memory, which goal is to demonstrate the importance of the most recent studies in the area. With this investigation, we made a critical review of 37 papers about subjects of computation and memory, using an analysis instrument which we called "tamiz." We identified the most relevant aspects found in these papers and concluded with general observations about what we found. The results show a large reliance on the mind-computer metaphor to explain the cognitive processes of human beings. [ABSTRACT FROM AUTHOR]

This paper will present the problem of virtuality from a philosophical perspective to think contemporary forms of understanding of the term related to technological developments in recent history. This perspective pretends to establish a thoughtful critique of the levity with which some concepts related to virtuality (such as "possibility", "potential", "reality", "actual", "cyberspace", "artificial intelligence", etc...) are taken. [ABSTRACT FROM AUTHOR]

Biomass, as an alternative source of energy, can be converted into biogas through the anaerobic digestion of organic material. The device used for this purpose is called biodigestor, and several protocols have been studied aimed at estimating the volume and quantity of the gas obtained. These protocols use a combination of the origin of the biomass and equations involving environment temperature, biological load and retention time. The treatment of organic residues and the systematization of the activity on livestock farms requires the intervention of expert users or the implementation of intelligent systems, which allow for quantitative and qualitative variables. In this paper, we use Artificial Neural Networks (ANN) to estimate the production volume of a bio-digester ball. The networks are designed for this use on both livestock and farming residues. The efficiency of the estimation is evaluated using R Correlation Coefficient. Results show that the technique is reliable and can be used in practice. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive evaluation of supervised classification algorithms without semantic information for opinion mining on Twitter. The evaluation will assess the potential of this approach to tackle the given problem. Also, as result of evaluating, the impact of different variants of the text preprocessing is assessed, as well as different alternatives of dimension reduction techniques in the problem solution. Finally, the results obtained in the experiments offer evidence to direct the use of supervised classification to classify opinions and its valence in a domain of short messages such as Twitter. [ABSTRACT FROM AUTHOR]

One of the most fundamental problems formulated to machine learning is the development of habits and abilities in complex systems modeling. At the present paper a system named Generator of Problems Hypermedia is described and developed. This software system allows automating the process of optimization problem construction and automatic generation of problem statements for complex problem situations using the multimedia technology tools and intelligent interfaces. The work possesses significant importance in the introduction of computers in Education and in the development of Mathematical Modeling. Their successful application increases considerably the efficiency of the teaching and learning process. By means of the rational introduction of the Multimedia Technology tools students have exercised the construction and representation process of is used with the technology .net for Windows, as welloptimization problem situations. The system C as diverse multimedia technologies. [ABSTRACT FROM AUTHOR]

One of the most important economic strategic sectors in any economy is the electricity market. Its main feature is its oligopolistic character favoured by the returns to scale which act as an entry barrier. As a result, the energy generators can use their power market in order to increase their benefits through the daily offered price and quantity of energy for each of their power plants. This paper presents a methodology for estimating the daily offered price of the most important power stations in Colombia (hydraulic and thermal) by applying artificial intelligence techniques: Fuzzy Logic and Neural Networks. Such techniques are found to be partially useful particularly for price tendencies. It also compares the results with autoregressive models that turned out inappropriate for the case of study. [ABSTRACT FROM AUTHOR]

The paper presents the modeling of the energy behavior and determination of equivalent circuit parameters for a photovoltaic panel using artificial intelligence techniques. For this purpose, it was performed a genetic algorithm in Matlab, which calculates the physical parameters not provided by manufacturers to implement mathematical functions that approve the dynamic behavior of a solar panel. In addition, the algorithm can determine the efficiency of the maximum power point tracking algorithm of the inverter, which is a parameter of not simple obtaining or not determined under field conditions. Finally, the algorithm was successfully tested in a monocrystalline solar 50W panel with excellent results, compared to the experimental model for different levels of solar irradiation. The results demonstrate that the developed process is a tool that can be applied to photovoltaic panels under field conditions. [ABSTRACT FROM AUTHOR]

The University of Computer Sciences is responsible for the training of computer professionals as well as for the direct participation in the development of national computerization projects and export commitments as part of the industry. The integration model of formation-production- research processes expresses that students must perform a role in the software development process, linked to a real project. The objective of this paper is to establish a proposal of a methodology that systematizes the process of assigning students to a role. It has as a starting point the recruitment processes in the industry and a model for the comparison and prediction of student's behavior in each role. The methods used were document analysis, systemic method, among others. The analysis of the impact of the results of the methodology is analyzed and discussed. [ABSTRACT FROM AUTHOR]

This paper presents an incremental k-most similar neighbor classifier, for mixed data and similarity functions that are not necessarily distances. The algorithm presented is suitable for processing large data sets, because it only stores in main memory the k most similar neighbors processed until step t, traversing only once the training data set. Several experiments with synthetic and real data are presented. [ABSTRACT FROM AUTHOR]

This paper shows a model of grey fuzzy logic for water and quality management in the Aburrá river (Colombia). Grey fuzzy logic considers the uncertainty of the environmental parameters. This uncertainty produces a lot of conflicts between environmental agencies and polluters of water bodies. The application of this model was done in the Aburrá river, the campaing data includes discharges, dissolved oxygen and biochemical oxygen demand. The model takes the tributaries to Aburrá river like polluter points. Also, this model implies an optimization, in accordance with the goals and objectives of both polluters and environmental agencies.The model was developed using Matlab. The general conclusion is that the model has a great potential for water quality management in the river and for achieving the environmental agencies goals. [ABSTRACT FROM AUTHOR]

In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network. [ABSTRACT FROM AUTHOR]

This paper presents a proposal for monitoring temperature in electric power transmission lines by using artificial intelligence. With a temperature monitoring system, the operator can know the current state of the line and carry out control actions to improve reliability, safety and quality of the service. The proposed methodology in the training stage uses optical sensors and devices for recording and storing temperature data. With the historical data, a fuzzy classifier is trained and, the obtained classes are associated with states of normal or failure operation by an expert. In the on line stage, the classifier processes each new sample and assigns an operation state, and then with this information the operator determines the right action on the electric system. The methodology is tested on a section of emulated transmission line in a high voltage laboratory. [ABSTRACT FROM AUTHOR]

Every day organizations have more information because their systems produce a large amount of daily operations which are stored in transactional databases. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. In the other hand, Data Warehouses are not able to perform predictive analysis for themselves, but machine learning techniques can be used to classify, grouping and predict historical information in order to improve the quality of analysis. This paper depicts architecture of a Data Warehouse useful to perform an analysis of students' academic performance. The Data Warehouse is used as input of a Neural Network in order to analyze historical information and forecast. The results show the viability of using Data Warehouse for academic performance analysis and the feasibility of predicting the number of approved courses for students using only their own historical information. [ABSTRACT FROM AUTHOR]

Information and Communication Technologies (ICT) are intended to develop, maintain and administrate information using computer systems, communication networks, portable dispositives, etc. In addition to that, this paper deals with ICT's power to create new contexts: what is called digital ambiences. According to the author the design and even the concept of these intelligent ambiences has problems that have to do both with the use of reasoning and the display of inferential and interpretative processes to interact with the social context in an individual or a collective way. Solving these philosophical problems could guide the development and implantation of ubiquitous and embodied technologies that would be able to amplify the cognitive and motive capacity of people. [ABSTRACT FROM AUTHOR]

This paper discusses how two artificial intelligence techniques were combined, neural networks and genetic algorithms for the development of a computational tool used for the estimation of mechanical properties such as tensile strength, uniaxial compressive strength and triaxial compressive strength in sandstones, from petrophysical properties using data from tests of Rock Mechanics Laboratory of the Colombian Petroleum Institute - Ecopetrol SA as training data, to improve the design of non-destructive testing with some degree of confidence and resulting in cost reduction. [ABSTRACT FROM AUTHOR]

Fault location on distribution systems has become an important issue due to the negative impact due to perception, economy and quality issues that it is implied in a low continuity service. There are many techniques and strategies proposed for this problem, however, it is not clear which one is the best. This paper presents a review of some techniques that use a steady state analysis and techniques that use transient state analysis to locate short-circuit faults with low fault resistance in radial systems. The main conclusion is that, due to the commitment with the precision of the techniques, a combination of some strategies is needed for a real system implementation. [ABSTRACT FROM AUTHOR]

This paper presents a methodology that integrates several available techniques to manage the massive amount of alarm signals in electrical power dispatch control centers, as well as the contribution of each entity involved in the system. Artificial intelligence techniques that can be used in this problem are reviewed here, based on the information resources available. The final objective is to find the root cause of avalanches of alarms (Alarm Tree) and to reduce their number through grouping or clustering techniques so that the EEMUA 191 standards are followed. Even though other contributions in this topic have been made before, the alarm management problem continues to be practically unsolved for many applications in industry. Here, the integration is developed using the ontology of each system domains, i.e., the ontology corresponding to the alarms, controls, events, energy flow and trigger sequence. Additionally, in this methodology, a rule based expert system is used to treat the alarms with a neural net based approach to treat the historical database of alarms and failures. [ABSTRACT FROM AUTHOR]

Since the 1980s, cognitive analysis of public policies has become increasingly fashionable. This paper examines this current in public policy and its relationship to other cognitive sciences such as neurosurgery, cognitive psychology and artificial intelligence. We ask what kinds of theoretical transfer have taken place between these to date. After defining four theories of public-policy analysis as a function of their cognitive content cases of integration of these cognitive sciences within social sciences, we argue that, in most of cases, such analysis does not tend to apply the concepts if cognitive sciences to their field in any real way but rather uses them as metaphors. Nevertheless, in some cases, assumptions of cognitive sciences are used to explain the development of public policy. These cases are extremely interesting since they shed a new light on rationality by proposing an alternative vision of policy change. [ABSTRACT FROM AUTHOR]