This article analyses how distinct argumentation spaces-media, law, and science-interact and collide, using Liebeck v. McDonald's as a legal case study and the hydroxychloroquine COVID-19 debate as a case of scientific dispute. Building on these two case studies, this analysis shows that arguments that intervene in many public debates originate from and move across different spaces, which employ different criteria, rules of evidence, and modes of reasoning for evaluating arguments. Furthermore, our study shows media-driven simplification, reframing, and truncation of nuanced expert exchanges, producing polarized public perceptions and policy consequences. The key features we identified include sliding arguments, repetition-driven strength (clones), shifting proof standards, hedgehog arguments, and zombie information, with implications for formalizing real-world argumentative dynamics and improving cross-space dialogue.
We introduce and develop the theory of Kalman-Galois connections on pseudocomplemented distributive lattices (KGp-algebras). We establish their fundamental properties, study KGp-Boolean filters and KGp-Boolean congruences, and prove a Glivenko-type theorem that relates KGp-algebras to Boolean gebras with additional operators. Using the Kalman construction, we prove categorical equivalence between the algebraic category of KGp-algebras and algebraic category of centered Kleene algebras with intuitionistic negation dowed with a unary operator g (gKANc-algebras). We also extend the Kalman construction to the Sendlewski construction and establish a categorical equivalence between the category of pairs consisting of a KGp-algebra and a Boolean KGp-filter and the algebraic category of gKAN-algebras. Additionally, we troduce the Monteiro construction, which allows us to associate to every gKANalgebra a gKANc-algebra into which it is embedded. These results enable us clarify the relationship between the Kalman and Monteiro constructions in case of gKAN-algebras.
Contradiction separation (CS) and its first-order version S-CS are multi-clause inference schemes for clausal refutation. They isolate a standard contradiction core within a clause set and derive a propagated clause from the remaining literals. This paper develops structural characterizations of non-unit standard contradictions that make such cores explicit and easier to identify. In propositional logic, we introduce a canonical dual-line family and prove that every instance is a standard contradiction. We study admissible literal extensions, define ladder structures as maximal dual-line extensions, and present a regular triple-line family with constructive generation schemes. We also analyze how dual-line cores compose via clause connections and give sufficient conditions under which the composed clause set remains a standard contradiction. In first-order logic, we exhibit clause families that are not standard contradictions syntactically but become standard contradictions after suitable instantiation and controlled clause reuse.
In precision animal husbandry, behavioral monitoring collars have become widely used tools for individualized and real-time herd management. These devices allow for the collection of individual animal activity data to be analyzed to identify behavioral patterns or specific events of interest. One of the most relevant events that can be detected by analyzing behavioral changes is estrus. In this context, the present paper analyzes and compares the performance of several supervised classification techniques combined with feature engineering for early estrus detection in dairy cows from intensive farms using behavioral data obtained from a commercial smart collar. For this purpose, twelve dairy cows were monitored in a Galician intensive farm, and customized models were developed for each animal. Several configurations of both the models and the input data used were evaluated. The results obtained were excellent, reaching, in some cases, F1-Score values of up to 1 in specific configurations and techniques. In addition, this research demonstrates a high variability in model performance between cows, highlighting the need to develop individualized animal models. It is also concluded that to obtain good results, it is essential to provide the model with a temporal context that includes the animal's previous behavior.
This article focuses on optimizing machine learning when each solution evaluation is costly and/or time-consuming. In particular, current deep learning models, which are complex and computationally intensive, rely on a large number of hyperparameters that need to be optimized. The purpose is to maximize the performance of these models by optimizing their hyperparameters and saving computational resources. In this paper, an informed search using Bayesian optimization is applied to tune the hyperparameters of the Convolutional Neural Network (CNN). Firstly, we analyze the performance of the Stochastic Gradient Descent with Momentum (SGDM) optimizer to learn the weights of CNN faster and more accurately. Then, we show the importance of the momentum hyperparameter for CNN learning. Finally, Bayesian optimization is applied to model the generalization performance of CNN as a sample from a Gaussian process (GP). The posterior distribution of the surrogate model is updated sequentially on the basis of a few evaluations of the true expensive function (CNN performance). The results confirm that CNN based on Bayesian optimization has promising performance for hyperparameter optimization when evaluations are expensive or time-limited. We also provide our script code at https://github.com/AmgadMonir/BO-Based-CNN.
I introduce an extended version of the Type-Theory of Acyclic Recursion and Acyclic Algorithms (TTAR / TTAA) and its reduction calculi, by adding an extended chain-reduction rule. TTAA provides specialized recursion terms for stepwise, mutually recursive computations. The results of the recursive computations are saved in memory slots, according to assignments, and can be reused, by accessing the corresponding memory slots. The chain-like assignments copy values of terms from one memory slot to another, without any other essential algorithmic steps. The extended reduction calculus eliminates repeated, chain-like assignments of copies of functional values in memory slots. The primary applications of the chain-free type theory of recursion are for algorithmic semantics of formal and natural languages, including of programming languages and parts of compilers that transform recursive programs into iterative ones.
The main objective of this article is to provide an overview of how Generative Artificial Intelligence techniques can be used to create content in a controlled manner for applications that require high reliability and accuracy. Generative Artificial Intelligence, capable of generating various types of content, such as text, images, video, and audio, has revolutionised many areas of knowledge, but it must be considered that these systems can suffer from 'hallucinations' and produce false or inconsistent information. For this reason, having a system that allows their controlled use in sensitive contexts is of vital importance. This paper presents a methodology that allows the user to use a Generative Artificial Intelligence system to create new content according to the user's needs based on controlled and user-specified sources. The operation of the proposed system is illustrated by means of a concrete example of application in education, the automatic generation of test-type exams.
Quality assurance stands as a pivotal phase across all manufacturing processes, particularly within the textile industry. Presently, textile inspections heavily rely on human visual assessment due to deficiencies in commercial solutions. In response, this research advocates for the adoption of Deep Learning models to automate fabric quality control within dynamic and contemporary production environments. Specifically, Convolutional Neural Networks are scrutinized using authentic images sourced from the Batavia and Sarga weave production lines. The experimental results identify DenseNet121 and Incep-tionV3 as the most effective models for Batavia and Sarga weaves, respectively. DenseNet121 demonstrates balanced performance across key metrics for Batavia weave, while InceptionV3 excels in Sarga weave, particularly in F1-score and AU-ROC. These findings underscore the potential of DL models to enhance the accuracy and efficiency of textile quality control.
The integration of digital technologies in the shipbuilding industry has become essential to improve efficiency and ensure precision in complex manufacturing processes. This study explores the application of computer vision techniques for the identification and traceability of minor and simple subassemblies in shipbuilding. We developed a dual approach combining 3D point cloud analysis and 2D deep learning based instance segmentation to detect and classify components. A depth camera was used to acquire 3D point clouds and high-quality 2D images. For the 3D approach, we used surface and edge matching techniques, and for the 2D approach, we fine-tuned advanced instance segmentation models such as YOLO11 and MaskRCNN2. The results show that this combined approach significantly improves the reliability of subassembly identification, which is essential for improving traceability, reducing errors, and optimizing production workflows in the shipbuilding sector.
The rapid pace of urbanization and the expansion of economic activities have significantly contributed to rising levels of air pollution, posing serious threats to human health and the environment. Among the key pollutants, Nitrogen Dioxide (NO2) stands out due to its strong association with respiratory and cardiovascular diseases and its role in exacerbating climate change. NO(2 )is predominantly emitted through fossil fuel-powered transportation and industrial processes. At the economic level, air pollution entails high costs, such as lost productivity in key sectors and increased public spending on environmental remediation initiatives. In this context, Artificial Intelligence (AI) tools, such as Machine Learning (ML) and Deep Learning (DL), are now indispensable for analyzing environmental data and offering accurate predictions about pollution levels. Hence, this work focuses on the design, tuning, and evaluation of five DL models, namely Multi-Layer Perceptron (MLP), LSTNet, Temporal Convolutional Networks (TCN), Transformers, and Transformers combined with Long Short-Term Memory (LSTM), to forecast NO(2 )concentrations in the city of Porto up to two days in advance. The results reveal that the MLP model demonstrated the highest performance, achieving a Root Mean Square Error (RMSE) of 7.65 mu g/m3, outperforming more complex architectures. These findings underscore the effectiveness of DL in pollutant forecasting, contributing to more informed decision-making and air pollution mitigation strategies.
Ground-based telescopes face a significant challenge posed by atmospheric turbulence, resulting in acquired images appearing distorted and lacking sharpness. Adaptive optics technology is employed to mitigate this issue by effectively correcting wavefront aberrations through adjustments to the surface of a deformable mirror. Furthermore, the integration of neural networks into the control system has demonstrated notable enhancements in both atmospheric correction and turbulence prediction. Specifically, in this work, a 2D-LSTM network structure is utilized, which has shown good efficiency in slope prediction over a time sequence. The objective of this study is to address the prediction of turbulence data without the noise introduced by reading instruments. Through the experiments conducted in this research, it is demonstrated that such neural models are capable of learning to a certain extent the noise patterns of the system. Thus, the obtained data closely resemble real-world turbulence conditions.
The purpose of this article is to introduce and study monadic cx-filters in a monadic residuated lattice. The notion of monadic co-annihilators in a monadic residuated lattice is introduced and some of their related properties are shown. Moreover, the concept of hyperarchimedean residuated lattices are proposed and some equivalent conditions are given for hyperarchimedean residuated lattices. The notion of monadic cx-filters in a monadic residuated lattice is presented and some characterizations are derived for monadic cx-filters. Particularly, the lattice of monadic cx-filters is investigated and prime monadic cx-filter theorems are established.
This paper extends the literature on the strict-tolerant logical approach by applying its methods to intuitionistic and minimal logic. In short, the strict-tolerant approach modifies the usual notion of logical consequence by stipulating that, in order for an inference to be valid, from the truth of the premises must follow the non-falsity of the conclusion. This notion can also be generalized to define strict-tolerant metainferences, metametainferences and so on, which may or may not generate logics distinct from those obtained on the inferential level. It is already known that strict-tolerant definitions can make the notion of inference for non-classical logics collapse into the classical notion, but the strength of this effect is not yet fully known. This paper shows that intuitionistic strict-tolerant inferences also collapse into classical ones, but minimal ones do not. However, minimal strict-tolerant logic has the property that no inferences are valid (which is not carried over to the metainferential level). Additionally, it is shown that the logics obtained from intuitionistic, minimal and classical logic at at the metainferential level are distinct from each other.
In this paper, we study the variety of pseudocomplemented distributive lattices with existential and universal quantifiers, called monadic pseudocomplemented distributive lattices. We introduce the variety of monadic KANalgebras, which turns out to be different from the class studied in [Gomez C., Marcos M., San Martin H.J.: On the relation of negations in Nelson algebras. Rep. Math. Logic 56 (2021), 15-56], and prove that the category of monadic pseudocomplemented distributive lattices is equivalent to the category of centered monadic KAN-algebras, extending the results given in [Calomino I., Pelaitay G.: A new categorical equivalence for Stone algebras. Accepted in Mathematica Slovaca (2025)].
In this article, we provide methods for constructing t-norms and t-conorms on bounded lattices via t-subnorms and t-superconorms, respectively. These methods have certain conditions, we prove that they are sufficient and necessary, and we use examples to illustrate them. Ultimately, we assert and show that the proposed construction methods are not adaptable to an altered ordinal sum for t-norms and t-conorms through inductive reasoning on bounded lattices.
In this paper, we discuss the relationship between quantum B-algebras and pseudo BCI-algebras, and prove that finite normal quantum B-algebras are pseudo BCI-algebras. We introduce a notion of the pseudo closure (denoted as p-closure) for non-empty subsets F of a quantum B-algebra X, denoted by F-pc, and explore some of its properties. As an application of p-closure, we characterize the conditions under which a quantum B-algebra becomes a pseudo BCI-algebra and a pseudo BCK-algebra through p-closure. We prove that the p-closure of a subalgebra is still a subalgebra, and the p-closure of a filter is still a filter. Based on the concept of p-closure, we establish the conditions under which a p-closure operator qualifies as a closure operator. Finally, we show that the set of all closed filters of X which F-pc = F , is a complete lattice.
Steen's (2018) properties for Hintikka sets for Church's type theory based on primitive equality are reduced to the alternative, technically different, Hintikka set properties of Brown (2007). Using this reduction, a model existence theorem for Steen's properties is derived. In related work by Steen and Benzm & uuml;ller (2021) this model existence result has been employed to prove completeness of the higher-order paramodulation calculus underlying the Leo-III prover.
This work addresses the interaction between two significant areas of research in Knowledge Representation and Reasoning: Belief Revision and Argumentation. Both areas focus on determining the validity of the beliefs within an agent's knowledge base. In belief revision, the emphasis is on modifying beliefs to sustain a coherent knowledge base for the agent. This process generally involves incorporating new information while eliminating existing beliefs. Conversely, argumentation research focuses on assessing the epistemic state of the beliefs within that knowledge base. The decision to accept a belief is made after evaluating all arguments both in favor of and against the claim. Each area contributes tools that model human commonsense reasoning as applied in everyday situations. In recent years, efforts have been made to formalize the interaction between these two areas by applying belief revision strategies to argumentative systems and utilizing formal argumentation techniques to guide change processes in belief revision. One such approach employs stratified belief bases, enhanced with argumentative inference mechanisms. Here, a definition is proposed for the introspective revision of stratified belief bases. The concept involves the introduction of change revision operators that can be applied to a stratified belief base. These operators are designed to either eliminate conflicting arguments or modify certain beliefs to accommodate new beliefs, resulting in a change in the status of specific arguments.
Deep Neural Networks (DNNs) in general, and transformers in particular, have revolutionised AI by achieving high levels of performance across a wide range of tasks. However, they remain limited in their capacity to handle domain general real world reasoning. They also require large amounts of training data to obtain reasonable learning outcomes. A number of researchers have attempted to combine DNNs with symbolic representations and rule systems to overcome these limitations. Hybrid models of this kind fall into two broad classes. The first includes DNNs in which symbolic representations and constraints are injected directly into the internal processing operations of the system, or they are incorporated into the data on which it is trained. In the second class, DNNs and symbolic reasoning systems operate autonomously, with the independence of each sustained. DNNs extract features from the input, which are made accessible to a symbolic rule system through an interface. I consider several instances of each type of hybrid neuro-symbolic model, with application to a number of AI tasks. The available evidence suggests that while the first class of models has, in general, not yielded substantial improvements over their non-hybrid counterpart systems, the second variety has produced more hopeful results. I will briefly consider the implications of this contrast in the architecture of hybrid models for future research in deep learning.
This is the first paper in a series exploring the following generic question for a given type of combination of logics: Given tableaux systems for two logics and a combination of these logics of a given type, how to combine systematically and uniformly the tableaux systems for component logics into a tableaux system for the combined logic by preserving important properties of the components? Here I consider the case of general fibring of logics, introduced by Dov Gab-bay. Starting with the basic cases of propositional merger and simple nesting of logics, I present natural generic versions of fibring of tableaux for these constructions, illustrate them with some examples, and establish respective results on preservation of soundness, completeness, and termination from the component tableaux to the combined tableau. Then I extend the combined tableau construction to the general case of fibring by iterating the basic cases and mention some potential applications.
