Symbolic Model Research Articles

Pengaruh Teknik Modeling Simbolik terhadap Peningkatan Motivasi Belajar Peserta Didik Kelas X TKI 1 SMK Negeri 3 Madiun

Learning motivation is one of the things that needs to be considered in learning because there are still many who think that motivation is not that important to improve learning achievement. The aim of this research is to study the effect of symbolic modeling techniques in guidance and counseling services on increasing students' learning motivation. This technique is a method in guidance and counseling services that uses pictures, videos or other stories to help students understand the concept of learning motivation. This research uses a guidance and counseling action research approach which involves several cycles, namely planning, action, observation and reflection. The research subjects were class X TKI 1 students at SMK Negeri 3 Madiun with low learning motivation. Data was collected from observations, interviews and special questionnaires regarding learning motivation before and after the intervention.The research results showed that students learning motivation improved significantly after intervention with symbolic modeling techniques.

Modeling of the metal–insulator transition temperature in alio-valently doped VO2 through symbolic regression

The correlated semiconductor vanadium dioxide (VO2) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows.

Формирование логического мышления у старших дошкольников с тяжелыми нарушениями речи на основе информационной теории развития психики Л.М. Веккера

<p style="text-align: justify;"><strong>Objectives. </strong>Contemporary methods of development of logical thinking in preschool children with severe speech disorders (SSD) help to smooth out problems of academic failure at school and social disadaptation. A program for the development of verbal and logical thinking in senior preschool children with SSD was tested. The program was based on the information theory of mental development by L.M. Vekker.</p> <p style="text-align: justify;"><strong>Methods. </strong>The implementation of the experimental program for the development of logical thinking included the creation of psychological and pedagogical conditions: modeling of surrounding relationships and processes; consistent introduction of models of different levels of concretization of the abstraction; creation of problem situations between surrounding objects and phenomena. The study involved children with a diagnosis of SSD (7 boys, 2 girls) aged 6 to 7 years (M = 6.5), attending a preschool educational organization. To identify “deficits” of verbal-logical thinking in children with speech disorders, a group of children without speech disorders (4 boys, 5 girls) of the same age (M=6.5) was used. Two measurements were taken — before and after the implementation of the formative program. To diagnose the parameters of verbal logical thinking, the “Methodology for studying the level of children’s readiness for school” by L.A. Yasyukova was used.</p> <p style="text-align: justify;"><strong>Results</strong>. Comparison of the indicators of the level of development of thinking in children with speech disorders with the results of children without speech disorders at the first stage of the study showed differences (p≤0.01) in the following parameters: speech and visual classifications, abstract thinking. At the second — developmental — stage, a positive shift at a reliable statistical level occurred in the parameters of the methodology: speech analysis-synthesis (p≤0.05), selection of antonyms (p≤0.05), speech analogies (p≤0.01), speech classifications (p≤0.01), visual analysis synthesis (p≤0.01), visual classifications (p≤0.01).</p> <p style="text-align: justify;"><strong>Conclusions</strong>. The results of the study showed that children with speech disorders have learned to use symbolic and speech models to understand logical relationships at the level of visual-effective and visual-figurative thinking. According to the indicator visual analysis-synthesis, children with speech disorders demonstrated results even higher than children without speech disorders. Work is planned to further replenish the children’s vocabulary, to include in the program a set of training in different types of judgments and conclusions.</p>

Establishment of Agro-Eco Industrial Clusters: A Romanian Perspective

The Romanian economy feels the need to formalize links between the situational economic forecast and the forecast of technological events in industry and agriculture. The article studies a “dimension” of the organization for classifications of data, structures and economic / agro-eco-industrial events for clustering with the help of “dimensionality”. Meta-prognostic relationships between economics, forecasting and forecasting agents (mathematical elements) are described and the role of metricity, sub-metricity and ultra-metricity in the observation of an automatic ontology for classifications supporting agro-eco-industrial / food clustering is highlighted. The paper proposes the algorithm of the informal classes of supervised / unsupervised data about agro-eco-industrial / food companies in Romania in order to establish classifications that would motivate the formation of clusters in the field. It is concluded that the mathematical apparatus for classification must be developed because based on symbolic mathematical models can be developed computer / computer programs for calculations of ultra-metricity of transformations for linearization’s of similarities between firms, eliminating reminiscences / redundancies, aiming to reduce space / distances between data that represent, in fact, enterprises that can enter agro-eco-industrial / food clusters. Keywords: agro-eco-industrial clusters, ultra-metricity, clustering, economic forecasting, classification

Approximately Bi-Similar Symbolic Model for Discrete-Time Interconnected Switched System

Approximately Bi-Similar Symbolic Model for Discrete-Time Interconnected Switched System

How Can Deep Neural Networks Inform Theory in Psychological Science?

Over the last decade, deep neural networks (DNNs) have transformed the state of the art in artificial intelligence. In domains such as language production and reasoning, long considered uniquely human abilities, contemporary models have proven capable of strikingly human-like performance. However, in contrast to classical symbolic models, neural networks can be inscrutable even to their designers, making it unclear what significance, if any, they have for theories of human cognition. Two extreme reactions are common. Neural network enthusiasts argue that, because the inner workings of DNNs do not seem to resemble any of the traditional constructs of psychological or linguistic theory, their success renders these theories obsolete and motivates a radical paradigm shift. Neural network skeptics instead take this inability to interpret DNNs in psychological terms to mean that their success is irrelevant to psychological science. In this article, we review recent work that suggests that the internal mechanisms of DNNs can, in fact, be interpreted in the functional terms characteristic of psychological explanations. We argue that this undermines the shared assumption of both extremes and opens the door for DNNs to inform theories of cognition and its development.

Good results from sensor data: Performance of machine learning algorithms for regression problems in chemical sensors

Accurately predicting unseen data, instead of mere memorization of training examples, is a critical goal of machine learning. This generalization is particularly important in the field of chemical sensors, where the ability to accurately predict the chemical properties or concentration levels of unknown samples is crucial. The paper presents a comprehensive yet accessible introduction to various machine learning concepts, highlighting the importance of model interpretability and generalization in ensuring reliable and accurate results in this context. Nonlinear sensor array data are utilized to introduce key concepts (e.g., bias-variance tradeoff) and techniques (linear models, partial least squares regression, support vector machines, k-nearest neighbors, decision trees, ensemble methods, automated machine learning, symbolic regression, and artificial neural networks), providing a solid foundation to make informed decisions when selecting machine learning techniques for sensor-specific regression applications. The results clearly indicate a number of conclusions. First, overparameterized deep feedforward neural networks show great accuracy and generalization when trained on a sufficiently large dataset. Second, symbolic regression models proved to be more accurate than deep feedforward neural networks and classical machine learning techniques on smaller datasets. Third, the performance of various machine learning models was dataset-dependent, showing the importance of comparative studies to determine the most suitable approach. It is clear that the optimal model cannot be known a priori. This paper aims to provide a starting point for investigations on the performance of different machine learning techniques in chemical sensor applications.

Efektivitas Konseling Kelompok Teknik Modeling Terhadap Peningkatan Efikasi Diri Pada Siswa MTs Negeri 9 Sleman

The objectives to be achieved in this study are to determine the effectiveness of counseling modeling techniques on improving self-efficacy in students of MTs Negeri 9 Sleman. The type of research in this study is experimental research. The research design used in this study is quasi experiment. The form of design used is Non-equivalent Control Group Design. The study population was VIII grade students of MTs Negeri 9 Sleman consisting of 4 classes, totaling 125 students. The sampling technique in this study used purposive sampling. The criteria for determining the sample in this study were eighth grade students of MTs Negeri 9 Sleman, which indicated a low learning discipline problem. The data collection method uses a self-efficacy scale that has gone through an expert validation process. Data analysis using non-parametric statistics with Man Withney (U) and Wilcoxon tests. The results showed that the counseling of the modeling technique by combining live models and symbolic models proved effective for self-efficacy (sign = 0.027 <0.05). These results can be concluded that group counseling through effective modeling techniques on improving self-efficacy in students of MTs Negeri 9 Sleman

Symbolic metaprogram search improves learning efficiency and explains rule learning in humans

Throughout their lives, humans seem to learn a variety of rules for things like applying category labels, following procedures, and explaining causal relationships. These rules are often algorithmically rich but are nonetheless acquired with minimal data and computation. Symbolic models based on program learning successfully explain rule-learning in many domains, but performance degrades quickly as program complexity increases. It remains unclear how to scale symbolic rule-learning methods to model human performance in challenging domains. Here we show that symbolic search over the space of metaprograms—programs that revise programs—dramatically improves learning efficiency. On a behavioral benchmark of 100 algorithmically rich rules, this approach fits human learning more accurately than alternative models while also using orders of magnitude less search. The computation required to match median human performance is consistent with conservative estimates of human thinking time. Our results suggest that metaprogram-like representations may help human learners to efficiently acquire rules.

СОЦІАЛЬНА ВІДПОВІДАЛЬНІСТЬ МІФОЛОГІЇ В РЕКЛАМІ

The issues of mythologization of consciousness in our time are considered, which is associated with crisis conditions of life, when a person cannot, on the basis of rationalism, explain changes in the existence of society and appeals to irrational factors, including myths and archetypes. This phenomenon is actively used for the purpose of influence in various fields, including advertising, which determines the purpose of this study - to carry out a philosophical and psychological understanding of the use of images of myths and archetypes in the field of advertisingThe study raises the problems of balancing the components of the rational, real and mythological in the field of advertising; influence on the formation of consumer consciousness; social responsibility of advertising. Based on a study of the use of images of myths and archetypes in advertising, it is shown that the effectiveness of using symbols designed by advertising creators is determined by the fact that the mythological worldview is a type of a person’s syncretic view of the world, when for him there are no fundamental differences between the image created under the influence of myth-making, and objective reality.Therefore, information conveyed by images of myths and archetypes is easily perceived, as if it seems simplified and designed for irrational perception, symbolic modeling of existence. It is concluded that appealing to myths and archetypes in advertising makes it possible to set value orientations, sets of sociocultural norms, appeal to social memory and collective experience, model a system of values, approve certain models of behavior, which actualizes the issue of social responsibility of mythogenesis.

Li-ion battery state of health prediction through metaheuristic algorithms and genetic programming

Predicting the State of Health (SOH) of the Lithium-ion battery with higher accuracy and reduced cost is a challenging and crucial task for ensuring its reliability and safety. To achieve this, a two-stage prediction framework is proposed to find a concise expression of SOH using the health features of the battery through metaheuristic algorithms and genetic programming (GP).In Stage-I, three conflicting objectives are considered concurrently: the root-mean-square error (RMSE) of SOH prediction, the health features selected, and the expressional complexity of the battery capacity. The wrapper structure is utilized for SOH prediction, where a binary multi-objective grey wolf optimization (Binary MOGWO) algorithm is employed to select features and generate the Pareto set. Genetic programming is used to calculate the SOH value using symbolic regression models. In Stage-II, the final compromise solution is filtered from the Pareto set through decision-making approaches. The relationships between selected features and the capacity of the battery are investigated. The NASA Prognostics Center of Excellence battery dataset is chosen to verify the effectiveness of the proposed framework. The simulation results show that the features found through the framework can provide SOH predictions in the form of symbolic equations with higher accuracy, lower cost, and reduced complexity.

Modeling and Verification of Natural Language Requirements based on States and Modes

The relationship between states (status of a system) and modes (capabilities of a system) used to describe system requirements is often poorly defined. The unclear relationship could make systems of interest out of control because of the out of boundaries of the systems caused by the newly added modes. Formally modeling and verifying requirements can clarify the relationship, making the system safer. To this end, an innovative approach to analyzing requirements is proposed. The MoSt language (a Domain Specific Language implemented on the Xtext framework) is firstly designed for requirements modeling and a model validator is realized to check requirements statically. A code generator is then provided to realize the automatic model transformation from the MoSt model to a NuSMV model, laying the foundation for the dynamic checks of requirements through symbolic model checking. Next, a NuSMV runner is designed to connect the NuSMV with the validator to automate the whole dynamic checks. The grammar, the model validator, the code generator, and the NuSMV runner are finally integrated into a publicly available Eclipse-based tool. Two case studies have been employed to illustrate the feasibility of our approach. For each case study, we injected 14 errors. The results show that the static and dynamic checks can successfully detect all the errors.

A symbolic interactionist model of White adolescents' digital critical racial literacy and digital anti‐racist action

A symbolic interactionist model of White adolescents' digital critical racial literacy and digital anti‐racist action

Explanation, semantics, and ontology

The terms ‘semantics’ and ‘ontology’ are increasingly appearing together with ‘explanation’, not only in the scientific literature, but also in everyday social interactions, in particular, within organizations. Ontologies have been shown to play a key role in supporting the semantic interoperability of data and knowledge representation structures used by information systems. With the proliferation of applications of Artificial Intelligence (AI) in different settings and the increasing need to guarantee their explainability (but also their interoperability) in critical contexts, the term ‘explanation’ has also become part of the scientific and technical jargon of modern information systems engineering. However, all of these terms are also significantly overloaded. In this paper, we address several interpretations of these notions, with an emphasis on their strong connection. Specifically, we discuss a notion of explanation termed ontological unpacking, which aims at explaining symbolic domain descriptions (e.g., conceptual models, knowledge graphs, logical specifications) by revealing their ontological commitment in terms of their so-called truthmakers, i.e., the entities in one’s ontology that are responsible for the truth of a description. To illustrate this methodology, we employ an ontological theory of relations to explain a symbolic model encoded in the de facto standard modeling language UML. We also discuss the essential role played by ontology-driven conceptual models (resulting from this form of explanation processes) in supporting semantic interoperability tasks. Furthermore, we revisit a proposal for quality criteria for explanations from philosophy of science to assess our approach. Finally, we discuss the relation between ontological unpacking and other forms of explanation in philosophy and science, as well as in the subarea of Artificial Intelligence known as Explainable AI (XAI).

Peningkatan Kedisiplinan Menggunakan Layanan Bimbingan Kelompok Dengan Teknik Modelling Simbolok Pada Siswa SMA N 3 Pati

The objectives of this study are: 1. To describe the researcher's skills in implementing group counseling services symbolic modeling techniques to improve discipline in students 2. increase student discipline. The research subjects were students of class X SMA Negeri 3 Pati. The results showed that the discipline of students attending school increased by 48 from the starting point in the pre-cycle, cycle I, to cycle II. The conclusion is that group guidance services using symbolic modeling techniques can effectively improve the discipline of students entering school at SMA Negeri 3 Pati.

Predicting the Curie temperature of Sm-Co-based alloys via data-driven strategy

Calculating the Curie temperature of rare-earth permanent magnetic materials has remained a big theoretical challenge. In this study, based on a home-built Sm-Co-based alloys database, a data-driven machine learning approach was developed to predict the Curie temperature of Sm-Co-based alloys. High-throughput predictions of Curie temperature were achieved using a genetic program based symbolic regression model. A classification model based on logistic regression was established to quantify the effect of doping on the Curie temperature of Sm-Co-based alloys. The key physical descriptor affecting Curie temperature was extracted from the established machine learning models, and the Curie temperature sensitivity coefficient was defined. It was discovered that the doping elements with large electrical conductivity and similar heat of fusion to that of Sm are likely to increase the Curie temperature of Sm-Co-based alloys. The model predictions were verified quantitatively by the experimental results of a series of prepared Sm-Co-based samples. This work provides a high-efficiency method for developing Sm-Co-based permanent magnets with high Curie temperatures.

Romanul judiciar. Către un nou subgen al romanului istoric românesc

The lifespan and popularity of a subgenre are determined by the inventiveness with which novelists develop and innovate the standard formulas. The judicial novel emerges as an extension of the popularity of the outlaw novel, adding a technical dimension to the misdeeds committed by wrongdoers. The outlaw and judicial novels are anthropomorphized subgenres that set in motion symbolic models, with a central figure dominating the entire course of events. The judicial novel reconfigures the perspective of naturalism, transforming the social milieu into a triggering factor for reprehensible acts. Shocking acts, such as crimes or suicides, and their mysterious circumstances, are analyzed from multiple angles, akin to using a set of parallel mirrors, while the brisk steps of investigations are fascinatingly reconstructed within the eloquent phrases of the novelists. One of the central ideas that the judicial novel seeks to develop is the sacrifice as a social practice, whether symbolic, articulated in the imposition of precarious living conditions leading to extreme gestures. Spatiality and temporality constitute the central pillars that compose the dramatic scenes of the Romanian judicial novel. Crimes are plotted and committed in the moonlight, in neighborhoods or on winding streets. The schematism of gloomy settings and the rudimentary nature of character profiles are countered by allusions to an undefined darkness, the contours of which elude discernment. The judicial subgenre utilizes conventions from the detective novel formula, involving the exaggeration of a cause-and-effect relationship as inevitable and the conquest of a reassuring imaginary, complementary to the moral imperatives discussed by Romanian novelists determined to rehabilitate the reputation of the genre through fictional digressions on morality. In its dimension of historical crime fiction, the Romanian judicial novel not only accentuates its documentary aspect, showing how the institutions responsible for investigations and judicial processes function but also the expansiveness of historicizing interpretations of the analyzed bloody events. Within the context of a dynamically structured novel distinguished by thematic configuration and ideological significance, the incorporation of illustrations transcends mere decorative intent. The judicial novel strategically incorporates illustrations as a highly effective extratextual approach. This not only disrupts the linear narrative trajectory and heightens suspense but also concurrently bestows substance upon the ethereal images adeptly invoked by the literary text.

Conceptualization of Intelligent Control System for Humanitarian Demining Robotic Complexes Based on Verbal Methods

Introduction. Humanitarian demining is characterized by growing attention to the problems of creating and using robotic systems. The most important aspect of their use is the control method.Problem Statement. At present, the fi rst generation robotic complexes (controlled devices) have been the mostcommon, the second generation complexes (semi-autonomous devices) have been improving. To switch to the third generation complexes (autonomous devices), it is necessary to develop intelligent control systems based on artificial intelligence technologies. The most used for system development are quantitative methods, but such models are “black box models” that do not provide complete clarity about their behavior.Purpose. To develop conceptual model of intelligent control systems for humanitarian demining robotic complexes based on verbal methods.Materials and Methods. Formal logic methods, verbal analysis qualitative methods, and BPMN. Results. Conceptual model of intelligent control systems for humanitarian demining robotic complexes and sym bolic models of representing relevant declarative and procedural knowledge based on verbal methods have been developed.Conclusions. The conceptual model makes it possible to formulate Symbolic Models in the notations of selected verbal methods. At the decision-making level, the ordinary classifi cation method by denotation of the terms used by experts in the selected subject area has been chosen. At the executive level, the BPMN method has been chosen to create process diagrams in graphical notation. The verbal methods make it possible to create “white-box models” that unambiguously interpret the dependence of output and input variables and to explain the model behavior. Symbolic models in the notations of selected verbal methods allow the implementation of an intelligent control systems for a specifi c humanitarian demining robotic complex.

Physics‐constrained symbolic model discovery for polyconvex incompressible hyperelastic materials

AbstractWe present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse experimental data and physical laws. To achieve this goal, we propose a polyconvex neural additive model (PNAM) that enables us to express the hyperelastic model in a learnable feature space while enforcing polyconvexity. An upshot of this feature space obtained via the PNAM is that (1) it is spanned by a set of univariate basis functions that can be re‐parametrized with a more complex mathematical form, and (2) the resultant elasticity model is guaranteed to fulfill the polyconvexity, which ensures that the acoustic tensor remains elliptic for any deformation. To further improve the interpretability, we use genetic programming to convert each univariate basis into a compact mathematical expression. The resultant multi‐variable mathematical models obtained from this proposed framework are not only more interpretable but are also proven to fulfill physical laws. By controlling the compactness of the learned symbolic form, the machine learning‐generated mathematical model also requires fewer arithmetic operations than its deep neural network counterparts during deployment. This latter attribute is crucial for scaling large‐scale simulations where the constitutive responses of every integration point must be updated within each incremental time step. We compare our proposed model discovery framework against other state‐of‐the‐art alternatives to assess the robustness and efficiency of the training algorithms and examine the trade‐off between interpretability, accuracy, and precision of the learned symbolic hyperelastic models obtained from different approaches. Our numerical results suggest that our approach extrapolates well outside the training data regime due to the precise incorporation of physics‐based knowledge.

Finite-step approximately bi-similar symbolic model for switched systems

Obtaining an approximately bi-similar symbolic model for a continuous-state system is a crucial step in symbolic control. Symbolic control aims to design switching logic to achieve temporal logic specifications. Most existing approaches to approximate bi-simulation are developed over infinite time horizons. However, many control tasks are set within finite periods in practical scenarios. Hence, it makes sense to consider the behavior of trajectories within finite-time horizons. Therefore, this paper introduces two new notions: finite-time uniform incremental stability (FUI-stability) and finite-step approximate bi-simulation. Furthermore, a FUI-stability condition for switched system is derived using the multiple incremental Lyapunov-like functions technique. A symbolic model is constructed for switched system by the state space grid technique. Subsequently, a sufficient condition for finite-step approximate bi-simulation between the finite-time uniformly incrementally stable (FUI-stable) switched system and the constructed symbolic model is derived. Finally, the obtained results are illustrated by two numerical examples.