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Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining
Get PDFThis research endeavors to address the persistent dissemination of public opinion within social networks, mitigate the propagation of inappropriate content on these platforms, and enhance the overall service quality of social networks. To achieve these objectives, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques are employed in this research to develop a predictive model for anticipating public opinion crises in social network mining. This model furnishes users with a valuable reference for subsequent decisionmaking processes. The initial phase of this research involves the collection of user behavior data from social networks using IoT technologies, serving as the basis for extensive big data analysis and neural network research. Subsequently, a social network text categorization model is constructed by amalgamating the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture, elucidating the training procedures of deep learning methodologies within CNN and LSTM networks. The effectiveness of this approach is subsequently validated through comparisons with other deep learning techniques. Based on the obtained results and findings, the CNN-LSTM model demonstrates a noteworthy accuracy rate of 92.19% and an exceptionally low loss value of 0.4075. Of particular significance is the classification accuracy of the CNN-LSTM algorithm within social network datasets, which surpasses that of alternative algorithms, including CNN (by 6.31%), LSTM (by 4.43%), RNN (by 3.51%), Transformer (by 40.29%), and Generative Adversarial Network (GAN) (by 4.49%). This underscores the effectiveness of the CNN-LSTM algorithm in the realm of social network text classification
Entrevista con Margaret MacMillan
No full textEntrevista con Margaret MacMillan, una investigadora respetada y una narradora privilegiada. Y también una formidable conversadora. Se hizo célebre analizando las causas que llevaron a los cañones de agosto en 1914, las estructurales y las personales. En "Juegos peligrosos: usos y abusos de la historia" (Ariel), se centró en el liderazgo y la perversión. Y ahora vuelve a sus raíces con "La guerra. Cómo nos han marcado los conflictos "(Turner). A propósito de la publicación de este libro, la historiadora analiza, a luz de las lecciones del pasado, los grandes desafíos del mundo de hoy
Por una vida política con mayor sentido moral. Por una política con mayor énfasis en la ciudadanía, la comunidad y la virtud cívica.
No full textLos argumentos políticos no pueden eludir lo relacionado con una vida lograda y buena. El desarrollo de formas más ricas de socialización democrática pasa por dotar de mayor sentido moral a la vida política colectiva. Hay que combatir el progresivo empobrecimiento del discurso público. Michael J. Sandel. Profesor de Ciencias Políticas en la Universidad de Harvard, premio Princesa de Asturias de Ciencias Sociales y autor de Justicia. ¿Hacemos lo que debemos? (2011) y Lo que el dinero no puede comprar. Los límites morales del mercado (2012). Filosofía pública es una recopilación de artículos breves escritos por Michael J. Sandel que exploran los dilemas morales y cívicos que animan nuestra vida pública y abordan algunas de las cuestiones éticas y políticas más controvertidas de nuestros tiempos, como la discriminación positiva, el suicidio asistido, el aborto, los derechos de los homosexuales, la investigación con células madre, las licencias de contaminación, los límites morales de los mercados, el significado de la tolerancia y la civilidad, los derechos individuales frente a las reivindicaciones de la comunidad y el papel de la religión en la vida pública. Sandel denuncia con su acostumbrada maestría el progresivo empobrecimiento del discurso público que ha acompañado lo que en su opinión es el fracaso del modelo liberal, al tiempo que propone el desarrollo de formas más ricas de socialización democrática. La necesidad de dar mayor sentido moral a la vida política colectiva se hace aún más acuciante desde una perspectiva progresista porque el moralismo y el fundamentalismo tienden a ocupar ese terreno
Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Explaining Biases in Machine Learning
Get PDFMachine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicity
Medication economic burden of antidepressant non-adherence in Spain
Get PDFIntroduction: Non-adherence to antidepressants is associated with worse disease outcomes (morbidity and mortality) and correlates with higher healthcare resource utilization and costs.
Methods: A population-based registry study was conducted to assess non-adherence and to analyze the economic burden of treatment and from non-adherence to antidepressants in 2021. Non-adherence was measured by the Medication Possession Ratio and those below 80% were classified as non-adherent.
Results: In 2021, 246,718 patients (10.60% [95% CI: 10.48–10.72]) received antidepressants at a cost of €29 million. The median antidepressant cost per patient/year was €70.08€, ranging from €7.58 for amitriptyline to €396.66 for agomelatine. Out-of-pocket costs represented 6.09% of total expenditures, with a median copayment of €2.78 per patient. The 19.87% [95% CI 19.52–20.22)] of patients were non-adherent to antidepressants, costing €3.9 million (13.30% of total antidepressant costs). Non-adherence rates exceeded 20% for the tricyclic antidepressants, fluoxetine (23.53%), fluvoxamine (22.42%), and vortioxetine (20.58%). Venlafaxine (14.64%) and citalopram (14.88%) had the lowest non-adherence rates, of less than 15%. The median cost of non-adherent medications per patient/year was €18.96 and ranged from €2.50 (amitriptyline) to €133.42 (agomelatine).
Conclusion: Reducing non-adherence to antidepressants is critical to improving clinical and economic outcomes. The implementation of interventions and standardized measures, including early detection indicators, is urgently needed. Antidepressants differ with regard to non-adherence and their cost, and this should be considered when prescribing this medication. The Medication Possession Ratio could be used by the healthcare provider and clinician to identify non-adherent patients for monitoring, and to take necessary corrective actions
Las cláusulas de rescisión de los contratos de los deportistas profesionales y su naturaleza jurídica en la doctrina y en la jurisprudencia
No full textIn the world of sports it is obvious that profesional athletes provide their services for certain sports entities or clubs and that the better the athlete in question based on standard such as age, quality, projection, titles won…, the better the club or entity for which he provides his services. Within the framework of these contractual relationships between ahtletes and profesional sports entities, the termination clauses appear as a legal concept that allows a profesional athlete to leave a club in order to provide services to another
one. This opens up big questions about it, as if these clauses are legal, where they come from, who should pay them…, which will be properly analyzed in this paper.En el mundo del deporte es una obviedad que los deportistas profesionales prestan sus servicios para determinadas entidades deportivas o clubs y que, cuanto mejor sea el deportista en cuestión en base a criterios como edad, calidad, proyección, títulos ganados…, mejor será el club o entidad a la que preste sus servicios. En el marco de estas relaciones contractuales entre deportistas y entidades deportivas profesionales, aparecen las cláusulas de rescisión como una figura jurídica que permite a un deportista profesional abandonar un club para poder prestar sus servicios a otro. Así se abren grandes interrogantes al respecto, como si estas cláusulas son legales, de dónde vienen, quién debe abonarlas…, que se analizarán debidamente en el presente trabajo
Nasarre Goicoechea, E. (Ed.) (2022). Por una educación humanista: un desafío contemporáneo. Madrid, Narcea, 209 pp.
Get PDFEducar para la libertad es el desafío del que trata el libro en el que Eugenio Nasarre –licenciado en Filosofía, Derecho y Ciencias Políticas, y dedicado al mundo de la educación– se ha encargado de aunar como editor una compilación de reflexiones que ponen de relieve la crítica situación educativa en la que nos encontramos, las influencias y acontecimientos que nos han conducido a un olvido de quién es el ser humano, y la necesidad de volver a introducir la tradición humanista. La editorial NARCEA refleja en estas páginas su compromiso con la educación, apostando por temáticas controvertidas que ponen en el punto de mira las políticas educativas más recientes
Service anomaly detection in dry bulk terminals: a machine learning approach
Get PDFBulk terminals are complex environments due to a number of variables that affect terminal performance. Although the analysis of big datasets is destined to become an important component of terminal management, previous research has not addressed this issue yet. This paper aims to shed new light on the operation of dry bulk terminals through a two-stage method based on unsupervised machine learning techniques. The first step gives an overview of the terminal's performance, revealing the strongest associations between the variables, while the second calculates an anomaly score for each vessel through an optimised implementation of the isolation forest. As a result, we detect anomalous services which could be directly attributable to the terminal operator. This method can be used to increase transparency in service and assist the terminal operator and ship agents in future contracts
Explainable AI for Human-Centric Ethical IoT Systems
No full textThe current era witnesses the notable transition of society from an information-centric to a human-centric one aiming at striking a balance between economic advancements and upholding the societal and fundamental needs of humanity. It is undeniable that the Internet of Things (IoT) and artificial intelligence (AI) are the key players in realizing a human-centric society. However, for society and individuals to benefit from advanced technology, it is important to gain the trust of human users by guaranteeing the inclusion of ethical aspects such as safety, privacy, nondiscrimination, and legality of the system. Incorporating explainable AI (XAI) into the system to establish explainability and transparency supports the development of trust among stakeholders, including the developers of the system. This article presents the general class of vulnerabilities that affect IoT systems and directs the readers’ attention toward intrusion detection systems (IDSs). The existing state-of-the-art IDS system is discussed. An attack model modeling the possible attacks is presented. Furthermore, since our focus is on providing explanations for the IDS predictions, we first present a consolidated study of the commonly used explanation methods along with their advantages and disadvantages. We then present a high-level human-inclusive XAI framework for the IoT that presents the participating components and roles. We also hint upon a few approaches to upholding safety and privacy using XAI that we will be taking up in our future work. An attack model based on the study of possible attacks on the system is also presented in the article. The article also presents guidelines to choose a suitable XAI method and a taxonomy of explanation evaluation mechanisms, which is an important yet less visited aspect of explainable AI
Impact of the Spanish smoke-free laws on cigarette sales by brands, 2000–2021: Evidence from a club convergence approach
Get PDFINTRODUCTION In January 2006, the Spanish government enacted a tobacco control law that banned the advertising, promotion and sponsorship of tobacco. In January 2011, further legislation on this matter was adopted to provide a more restrictive specification of the ban. In this study, we analyze the effect produced on cigarette sales by these two prohibitions. We address this problem using a cluster time-series analysis to test whether the sales of cigarettes by brands have been homogenized with the prohibition of advertising, promotion, and sponsorship. METHODS The data source used was the official data on legal sales of cigarettes by brands in Spain, from January 2005 to December 2021 (excluding the Canary Islands and the Autonomous Communities of the cities of Ceuta and Melilla). To achieve our objective, we used log(t) test statistics to check if there is global convergence in the three selected periods according to the regulatory changes that have occurred in Spain (2005–2021, 2005–2010 and 2011–2021). Second, once absolute convergence is rejected, we applied a clustering algorithm to test for the existence of subgroup convergence. RESULTS The cigarette brands that have been marketed during the period 20052021 (n=40), can only be grouped into three groups according to the behavior of their sales. When we focus on the period 2005–2010 (n=74), cigarette brands are grouped into five groups according to their sales behavior. Finally, the cigarette brands marketed during the period 2011–2021 (n=67) are grouped into three groups according to the temporal evolution of their sales. These results suggest a greater homogenization of cigarette sales after the application of the law of January 2011. CONCLUSIONS Act 42/2010 (total ban on tobacco advertising, promotion, and sponsorship actions) was associated with greater homogenization of cigarette sales than the application of Act 28/2005 (partial ban). This finding supports what is established in the previous literature that indicates that Act 42/2010 provided a more restrictive specification of the ban than Act 28/2005