Search

This study seeks to identify the ontological and epistemological foundations of linguistic theory, in light of recent developments in scientific practice that takes language as its object. It requires a reconsideration of the existential concept of language and raises the issue of the existence of ontological and epistemological constants in linguistic theory. Taking into consideration the difference in the nature of linguistic sound and meaning. The approach adopted is historical and comparative, examining the major ontological and epistemological milestones. At the ontological level, the study notes that René Descartes linked the existence of language to mental existence, but the theory of relativity and statistical theory have transformed language into a virtual digital system, calling into question its ontological framework. Not to mention the fact that language is no longer merely a mental product, as evidenced by the existence of automatically generated linguistic texts. Epistemologically, the verification of linguistic knowledge has evolved, moving from logical proof to experimentation, and is now tied to utility and added value, with the virtual simulation of artificial intelligence as the environment for experimentation and proof. [ABSTRACT FROM AUTHOR]

This article presents a study based on evaluating different techniques to automatically recognize the basic emotions of people with Down syndrome, such as anger, happiness, sadness, surprise, and neutrality, as well as the statistical analysis of the Facial Action Coding System, determine the symmetry of the Action Units present in each emotion, identify the facial features that represent this group of people. First, a dataset of images of faces of people with Down syndrome classified according to their emotions is built. Then, the characteristics of facial micro-expressions (Action Units) present in the feelings of the target group through statistical analysis are evaluated. This analysis uses the intensity values of the most representative exclusive action units to classify people's emotions. Subsequently, the collected dataset was evaluated using machine learning and deep learning techniques to recognize emotions. In the beginning, different supervised learning techniques were used, with the Support Vector Machine technique obtaining the best precision with a value of 66.20%. In the case of deep learning methods, the mini-Xception convolutional neural network was used to recognize people's emotions with typical development, obtaining an accuracy of 74.8%. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is revolutionizing micro and macro-aided decision-making processes across various sectors and disciplines worldwide. This paper uses the probability technique to theoretically forecast AI-aided integration program potential to offer accelerated, concrete and individualized integration support for new and 'old' resident immigrants throughout their stay in Finland. The algorithms are base on various taxonomies and topologies for individualized self-paced, life-long situation-specific integration support. This action research article theoretically reviews two high-quality peer-reviewed publications on AI in aging through systematic reviews and meta-analyses, combining results from multiple impact evaluation studies to construct arguments and draw conclusions for the use of AI in accelerating new migrants' integration in Finland. Data is analyzed qualitatively and quantitively to deduce realistic predictions. This article uses grounded theory to test theories that underscore the role and impact of AI in accelerating integration. This article presents the foundation on which future integration programs will be implemented. [ABSTRACT FROM AUTHOR]

The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and outcomes in decision situations, and so they are incomplete. Second, it will be argues that when we attempt to incorporate the knowledge of such causal connections into Bayesian decision theory, a substantial technical problem arises for which there is no currently available solution that does not suffer from some damning objection or other. From a broader perspective, this then throws into question the use of decision theory as a model of human or machine planning. [ABSTRACT FROM AUTHOR]