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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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]