Symbolic Model Research Articles

Introduction to the Special Section on FM 2021

The first attempts to apply the k-induction method to software verification are only recent. In this paper, we present a novel proof by induction algorithm, which is built on the top of a symbolic context-bounded model checker and uses an iterative ...

The Effect Of Symbolic Modeling Techniques on High School Students’ Altruistic Behavior

The purpose of this study was to determine the effect of group counseling service with symbolic modeling technique on the altruistic behavior of class XI students at SMA Muhammadiyah 1 Sragen in the 2022/2023 academic year. To this end, a pre-experimental study with one group research pre-test and post-test was applied. Participants were ten 11th-grade students in Natural Science and Social Science Departments. Data were collected using questionnaires, interviews, observation, and documentation. Data analysis with paired sample t-test by paired sample t-test, showing t-count of 5.000, which was higher than the t-table (1.833) with a significance value of 0.001 ( 0.05). The comparison value of the pre-test and post-test showed a score of 11.20. To sum up, the pretest and posttest results demonstrated that group guidance with symbolic modeling techniques enhanced the altruistic behavior of class XI students at SMA Muhammadiyah 1 Sragen during the 2022/2023 Academic Year

Preset model of bending force in 6-high universal crown tandem cold rolling mill based on symbolic regression

ABSTRACT Highly accurate bending force presetting is a prerequisite to ensure the well flatness of the strip head, however, there are limitations in the preset model used in industrial sites. According to the flatness control characteristics of work roll and intermediate roll bending forces, this paper integrates the historical production data of a 1420 mm 6-high tandem cold rolling mill and establishes a chain model of bending force preset based on symbolic regression, which implements with age-layered population structure genetic programming (ALPS-GPSR). The advantage of symbolic regression models is the ability to obtain explicit mathematical expressions, which is very important in complex industrial production environments. With the application of ALPS-GPSR based bending force preset model in the industrial site, the hit rate of flatness within 6IU in the strip head for three specifications of the strip has increased by 2.86%, 1.61%, and 2.19% respectively on average, achieving a satisfactory result.

From Floorplan to Navigation Concepts: Automatic Generation of Text-based Games

Abstract. Text-based games are environments in which defining the world, the representation of the world to the player (hereafter, agent) and agent interactions with the environment are all through text. Text-based games expose abstract, executable representations of indoor spaces through verbally referenced concepts. Yet, the ability of text-based games to represent indoor environments of real-world complexity is currently limited due to insufficient support for complex space decomposition and space interaction concepts. This paper suggests a procedure to automate the mapping of real-world geometric floorplan information into text-based game environment concepts, using the Microsoft TextWorld game platform as a case. To capture the complexities of indoor spaces, we enrich existing TextWorld concepts supported by theoretical navigation concepts.We first decompose indoor spaces using skeletonization, and then identify formal space concepts and their relationships. We further enhance the spectrum of supported agent interactions with an extended grammar, including egocentric navigation instructions. We demonstrate and discuss these new capabilities in an evacuation scenario. Our implementation extends the capabilities of TextWorld to provide a research testbed for spatial research, including symbolic spatial modelling, interaction with indoor spaces, and agent-based machine learning and language processing tasks.

First-principles, machine learning and symbolic regression modelling for organic molecule adsorption on two-dimensional CaO surface

Data-driven methods are receiving significant attention in recent years for chemical and materials researches; however, more works should be done to leverage the new paradigm to model and analyze the adsorption of the organic molecules on low-dimensional surfaces beyond using the traditional simulation methods. In this manuscript, we employ machine learning and symbolic regression method coupled with DFT calculations to investigate the adsorption of atmospheric organic molecules on a low-dimensional metal oxide mineral system. The starting dataset consisting of the atomic structures of the organic/metal oxide interfaces are obtained via the density functional theory (DFT) calculation and different machine learning algorithms are compared, with the random forest algorithm achieving high accuracies for the target output. The feature ranking step identifies that the polarizability and bond type of the organic adsorbates are the key descriptors for the adsorption energy output. In addition, the symbolic regression coupled with genetic programming automatically identifies a series of hybrid new descriptors displaying improved relevance with the target output, suggesting the viability of symbolic regression to complement the traditional machine learning techniques for the descriptor design and fast modeling purposes. This manuscript provides a framework for effectively modeling and analyzing the adsorption of the organic molecules on low-dimensional surfaces via comprehensive data-driven approaches.

A coupled framework for symbolic turbulence models from deep-learning

Improvements in turbulence modelling in the recent years has seen an increasing prominence of various machine-learning algorithms. In this work, two different algorithms: tensor basis neural networks (TBNNs) and gene-expression programming (GEP), are combined to extract interpretable Reynolds stress closures. Representations of the high-fidelity Reynolds stress, obtained from deep-learning, are used to learn symbolic expressions for the stress closures. This is in contrast to the previously developed approaches of using either neural networks or symbolic regression algorithms to close Reynolds stresses from high-fidelity data. The involvement of TBNNs as an intermediate step in developing symbolic expressions stems from their ability to maintain the complex relationships between input features and output fields resulting from multiple datasets of different types of flows, which symbolic regression algorithms currently struggle with. The a priori and a posteriori results show that the closures developed by informing GEP with TBNN predictions produce results with similar accuracy to closures developed with GEP purely from high-fidelity data. Additionally, if the high-fidelity database contains multiple flows with uniquely different features, GEP closures developed from TBNN predictions yield better prediction accuracy than if the high-fidelity database (comprising of the multiple flows) was used to train GEP closures. On the point of interpretability, the closures were also examined for the purposes of complexity reduction as well as understanding which terms were responsible for the improvements seen. These results indicate that there is promise in building complex neural networks comprising a catalogue of wide-ranging flow types, which can then be used to extract symbolic closures on a case-by-case basis, thereby reducing the dependency on high-fidelity data for closure modelling.

Generating Music with Data: Application of Deep Learning Models for Symbolic Music Composition

Language models based on deep learning showed promising results for artistic generation purposes, including musical generation. However, the evaluation of symbolic musical generation models is mostly based on low-level mathematical metrics (e.g., the result of the loss function) due to the inherent difficulty in measuring the musical quality of a given performance due to the subjective nature of music. This work sought to measure and evaluate musical excerpts generated by deep learning models from a human perspective, limited to the scope of classical piano music generation. In this assessment, a population of 117 people performed blind tests with musical excerpts of human composition and musical excerpts generated using artificial intelligence models, including the models PerformanceRNN, Music Transformer, MuseNet and a custom model based on GRUs. The experiments demonstrated that musical excerpts generated using models based on the Transformer neural network architecture obtained the greatest receptivity within the tested population, surpassing the results of human compositions. In addition, the experiments also demonstrated that people with greater musical sensitivity and musical experience were more able to identify the compositional origin of the excerpts heard.

Effect of symbolic form model on students’ interest in logic content of the mathematics curriculum

The effect of symbolic form model on students’ interest in logic content of the mathematics curriculum was investigated in this study. The research was guided by three research questions and three hypotheses. The study used a quasi-experimental design. The population of this research was 2,342 SS2 learners in Enugu State, Nigeria. A sample of 172 students was used. Data were collected and analyzed using the SILCMCQ and SPSS software respectively. The symbolic form model in teaching significantly boosted students’ interest in logic content of the mathematics curriculum, according to the findings. The study provided recommendations, one of which is that mathematics teachers should use the symbolic form model of instruction to increase students’ interest when logic content is taught.

Vergil’s Epicurean Fortunatus

In this paper I reevaluate the relationship between the figures of the felix and the fortunatus at the end of Georgics Book 2. Rather than representing an opposition between Epicurean natural philosophy and traditional religion, the figures are complementary, embodying the physical and ethical teachings of Epicureanism respectively. I first discuss the implications of Vergil’s praise of the felix for his views on traditional religion and analyze allusions to Lucretius in his description of the fortunatus. Then I show that Vergil’s treatment of the divine both has parallels to Epicurean cult practice and follows Lucretius’ poetic model of divine symbolism, the divinities named at 2.494 being symbols for Epicurean ethical ideals. As a whole, my argument indicates a positive attitude towards Epicureanism in the Georgics.

Petri-Net-Based Model Checking for Privacy-Critical Multiagent Systems

Computation Tree Logic of Knowledge (CTLK) can specify many design requirements of privacy and security of multi-agent systems (MAS). In our conference paper, we defined Knowledge-oriented Petri Nets (KPN) to model MAS and proposed Reachability Graphs with Equivalence Relations (RGER) to verify CTLK. In this paper, we use the technique of Ordered Binary Decision Diagrams (OBDD) to encode RGER in order to alleviate the state explosion problem and enhance the verification efficiency. We propose a heuristic method to order those variables in OBDD, which can well improve the time and space performance of producing, encoding and exploring a huge state space. More importantly, our method does not produce and encode any transition or equivalence relation of states when producing and encoding an RGER, and in fact it dynamically produces those transition or equivalence relations that are required in the verification process of CTLK formulas. This policy can save a lot of time and space since the number of transition or equivalence relations of states is much greater than the number of states themselves. We design symbolic model checking algorithms, develop a tool and apply them to two famous examples: Alice-Bob Protocol and Dining Cryptographers Protocol. We compare our tool with MCMAS which is the state-of-the-art model checker of verifying CTLK. The experimental results illustrate the advantages of our model and method. Our tool running in a general PC can totally spend less than 14 hours to verify Dining Cryptographers Protocol with 1200 concurrent cryptographers where there are about $10^{1080}$ states and the two verified CTLK formulas have more than 6000 atomic propositions and more than 3600 operators. These good performances are owed to a combination of the OBDD technique and the structure characteristics of KPN.

A Symbolic Regression Model for the Prediction of Drug Binding to Human Liver Microsomes

It is common practice in the early drug discovery process to conduct in vitro screening experiments using liver microsomes in order to obtain an initial assessment of test compound metabolic stability. Compounds which bind to liver microsomes are unavailable for interaction with the drug metabolizing enzymes. As such, assessment of the unbound fraction of compound available for biotransformation is an important factor for interpretation of in vitro experimental results and to improve prediction of the in vivo metabolic clearance. Various in silico methods have been proposed for the prediction of test compound binding to microsomes, from various simple lipophilicity-based models with moderate performance to sophisticated machine learning models which demonstrate superior performance at the cost of increased complexity and higher data requirements. In this work, we attempt to strike a middle ground by developing easily implementable equations with improved predictive performance. We employ a symbolic regression approach based on a medium-size in-house data set of fraction unbound in human liver microsomes measurements allowing the identification of novel equations with improved performance. We validate the model performance on an in-house held-out test set and an external validation set.

The Effect of Calendar Variation and Sun-Earth Distance on Daily Case Forecasting for COVID-19 Using the Symbolic Time Series Prognosis Model

The Effect of Calendar Variation and Sun-Earth Distance on Daily Case Forecasting for COVID-19 Using the Symbolic Time Series Prognosis Model

Utilizing Continuous Time Markov Chain for analyzing video-on-demand streaming in multimedia systems

In multimedia systems, Tree and Mesh-based topologies are the popular ways to organize the peers in an overlay and provide video streaming services. Unlike Content Delivery Networks (CDN), providing streaming services through a Peer-to-Peer (P2P) overlay poses the challenge of unpredictable churn for both provider and consumer peers. The dynamically arriving and departing peers, with Video Cassette Recorder (VCR) actions like jump, pause, forward, rewind, etc., controlled by end users, makes it challenging to reason about such systems. Though techniques like prototype test beds, simulation, etc., are successful to some extent in analyzing the behavior of such complex systems before the actual deployment, they either require lots of effort to set up or fail to capture the finer anomalies. Therefore, there is a need to have a formal framework that can specify the system at the desired abstraction level and capture the stochastic behavior exhibited by the actors of such systems. This paper addresses this issue and provides a platform for analyzing the behavior of real-time Video-on-Demand (VoD) streaming behavior with VCR actions over P2P overlays. To capture the stochastic behavior of actors, the Continuous Time Markov Chain (CTMC) is used to design a model of both the tree and mesh-based systems. This CTMC model is written in the Probabilistic Symbolic Model Checker (PRISM) language, which assists in containing the state space explosion and specifies the QoUX properties using Continuous Stochastic Logic (CSL). The proposed methodology uses the scenarios described in various simulation-based studies and industry standards of VoD systems available in the proposed formal model and finds a striking resemblance in the results. Eventually, given a joining-delay threshold of 170 ms, a P2P-Tree-based architecture resulted in an optimal tree height of 4-5 levels; after that, the startup delay of playback gets affected. Similarly, for a given startup delay threshold of 700 ms after departure and seek-jump actions, the maximum P2P-Tree height should be 5. We have also compared the performance properties of P2P tree and mesh-based VoD streaming systems with industry standards of centralized VoD systems. It is observed that the performance of P2P-tree and P2P-mesh-based VoD streaming systems also meet the industry standards. The results we got from the proposed formal framework are conclusive and establish the applicability of the approach.

Stick: An End-to-End Encryption Protocol Tailored for Social Network Platforms

End-to-End Encryption (E2EE) has become a de facto standard in messengers, especially after the development of the secure messaging protocol – Signal. However, the adoption of E2EE has been limited to messengers, and has not yet seen a noticeable trace in social network platforms, despite the increase in users’ privacy violations. In this paper, we propose, verify, implement and evaluate a novel E2EE protocol – Stick. Stick is a Signal-based protocol tailored for social network platforms. We believe our protocol is the first to support re-establishable encryption sessions in an asynchronous multi-device setting while preserving forward secrecy and introducing backward secrecy. Stick includes several innovative features, including a new session concept, multiple pairwise sessions and refreshing identity keys. We verified Stick using Verifpal - a formal verification tool in the symbolic model. Our security analysis shows our protocol does achieve a form of post-compromise security in many-to-many communications – the trait most group protocols lack. Most importantly, the Stick protocol can re-establish encryption sessions while ensuring authentication and confidentiality. We implemented our protocol as a stand-alone open-source API. Our evaluation shows the Stick protocol can be used in a real-world social network app with no noticeable compromise on usability or performance.

Efektivitas konseling kelompok dengan teknik modeling simbolik berbasis TPACK dalam meningkan kecerdasan emosi anak usia dini

<span>This study was conducted with the aim to examianting the effectiveness of group counselling of TPACK-based symbolic modelling techniques to improve children's emotional intelligence. The research was conducted at Tunas Bangsa Kindergarten A. The research sample used random sampling technique. Determination of the experimental class as a sample of this study using lottery technique, the number of samples taken as many as two classes with 8 students from Kindergarten A2 and 11 people from Kindergarten A1. The experiment was carried out with experimental preparation stages by compiling research instruments, trials, observations, preparing RPLBK counselling guidance services and giving Pre-Test, then implementing experiments and finally the experiment ended. The independent variables in this study are group counselling (A) modelling techniques (A1) and TPACK (A2). The dependent variable (Y) in this study is social and emotional intelligence. Data collection methods were conducted through questionnaires. The data analysis technique used t-test. The results showed that the TPACK-based modelling technique group counselling model is effective for stabilising students' emotional intelligence and there is a significant difference in effectiveness between the TPACK-based modelling technique counselling model and the modelling technique without TPACK-based to stabilise kindergarten children’s emotional intelligence.</span>

Clinical Outcomes After Viewing Video Education on Danger Signs and Symptoms of Worsening Heart Failure and Self-care Actions

Background Patients with heart failure (HF) must understand subtle escalation in fatigue, dyspnea, and edema before they are triggered to control worsening status. Objective The aim of this paper is to examine if video education of HF danger signs or symptoms recognition and control, developed using symbolic modeling (social cognitive theory), leads to between-group differences in functional status, symptoms, and self-efficacy for managing symptoms at 30-days, all-cause, and HF-related hospitalization, emergency department visits, and death at 30, 90, and 180 days. Methods Using a 2-group, randomized controlled, multicenter, single-blind design, patients received video education (VE) plus usual care (UC) or UC alone before hospital discharge. VE patients also had access to content post discharge. Thirty-day functional status, fatigue, dyspnea, and self-efficacy for managing symptoms were assessed using valid, reliable tools. In the analysis, multivariable models were created to compare changes in patient-reported outcomes from baseline to 30-days post discharge and morbidity or mortality outcomes up to 180 days. Results Of 369 VE and 377 UC patients enrolled from 7 sites, mean age was 68.0 (SD 12.4) years, and 206 (55.9%) were male. At 30 days, there were no between-group differences in the change in functional status, fatigue, dyspnea, and self-efficacy from baseline. In multivariable analyses, between-group outcomes did not differ at 30 or 90 days. At 180 days, HF-related events and HF-related hospitalization were higher in the video group—odds ratios (95% CI): 1.42 (1.04, 1.94), P=.03; and 1.44 (1.05, 1.97), P=.03, respectively. In time-to-event adjusted analyses, video patients had earlier HF-related hospitalization, compared to UC patients—hazard ratio (95% CI) 1.32 (1.02, 1.72), P=.04. Conclusions Video education on recognition and control of danger signs or symptoms paradoxically increased HF-related events and hospitalization and decreased time to first HF-related hospitalization. Increased recognition of subtle HF signs or symptoms via video education is valuable but must be connected to patient or family self-care actions that reduce HF signs or symptoms. Trial Registration ClinicalTrials.gov NCT03657459; https://clinicaltrials.gov/ct2/show/NCT03657459 Conflicts of Interest None declared.

Symbolic Regression Model for Predicting Compression Strength of Prismatic Masonry Columns Confined by FRP

The use of Fiber Reinforced Polymer (FRP) materials for the external confinement of existing concrete or masonry members is now an established technical solution. Several studies in the scientific literature show how FRP wrapping can improve the mechanical properties of members. Though there are numerous methods for determining the compressive strength of FRP confined concrete, no generalized formulae are available because of the greater complexity and heterogeneity of FRP-confined masonry. There are two main objectives in this analytical study: (a) proposing an entirely new mathematical expression to estimate the compressive strength of FRP confined masonry columns using symbolic regression model approach which can outperform traditional regression models, and (b) evaluating existing formulas. Over 198 tests of FRP wrapped masonry were compiled in a database and used to train the model. Several formulations from the published literature and international guidelines have been compared against experimental data. It is observed that the proposed symbolic regression model shows excellent performance compared to the existing models. The model is easier, has no restriction and thereby it can be feasibly employed to foresee the behavior of FRP confined masonry elements. The coefficient of determination for the proposed symbolic regression model is determined as 0.91.

Learning Method For Local Identity Through Traditional Food In Tulungagung “Ayam Lodho”

This research aims to look into the traditional food of Tulungagung. Ayamlodho is a traditional Tulungagung dish that is very popular among the locals. AyamLodho is also served with urap-urap and has a distinctive grilled and spicy sauce. AyamLodho became popular among Tulungagung residents as the main course menu at special events. The purpose of this research is to examine the meaning of AyamLodho as well as the meaning of the word Lodho. This study employs a qualitative approach. In-depth interviews, observation, document studies, literature studies, and recording sources are all used to collect data. Functional analysis is a method for deciphering the meaning of Ayamlodhofood.The goal of this review is to analyze the meaning of the traditional food "AyamLodho" using this symbolic model so that the meaning obtained is the meaning of Bronislaw Malinowski. Malinowski's theory is a progression of functionalism in terms of methods for describing the various functional relationships of cultural elements in a living social system, with a focus on cultural objects and tradition. The role of the reader is the most important object of research in this theory, and it is the role of the reader who will trigger various views on the meaning of AyamLodho's traditional food. According to the findings, the meaning of Ayam lodho with a functionalism approach is an expression of gratitude for the people of Tulungagung Regency. As for this theory, cultural and traditional sociological heritage are not biological heritage. Beliefs or religion, traditional customs, and social structures are examples of social heritage forces that influence and shape the personality born into the community.

In search of a data-driven symbolic multi-fluid ten-moment model closure

The inclusion of kinetic effects into fluid models has been a long standing problem in magnetic reconnection and plasma physics. Generally, the pressure tensor is reduced to a scalar which is an approximation used to aid in the modelling of large scale global systems such as the Earth's magnetosphere. This unfortunately omits important kinetic physics which have been shown to play a crucial role in collisionless regimes. The multi-fluid ten-moment model, however, retains the full symmetric pressure tensor. The ten-moment model is constructed by taking moments of the Vlasov equation up to second order, and includes the scalar density, the vector bulk-flow and the symmetric pressure tensor for a total of ten separate components. Use of the multi-fluid ten-moment model requires a closure which truncates the cascading system of equations. Here we look to leverage data-driven methodologies to seek a closure which may improve the physical fidelity of the ten-moment multi-fluid model in collisionless regimes. Specifically, we use the sparse identification of nonlinear dynamics (SINDy) method for symbolic equation discovery to seek the truncating closure from fully kinetic particle-in-cell simulation data, which inherently retains the relevant kinetic physics. We verify our method by reproducing the ten-moment model from the particle-in-cell (PIC) data and use the method to generate a closure truncating the ten-moment model which is analysed through the nonlinear phase of reconnection.

Learning dynamics from coarse/noisy data with scalable symbolic regression

Distilling equations from data can provide insights into physics systems, helping validate theoretical modeling, infer unknown system properties, and explore indeterminate processes. Noisy or downsampled data have been a bottleneck limiting wide applications of symbolic regression, since identified equations are sensitively affected by data statistics. Coarse and noisy data can deteriorate equation reliability by magnifying errors in derivative estimation. While physics-informed surrogate models have been introduced in the literature to reconstruct augmented data that are high-resolution, less affected by noise, and more consistent with underlying physics mechanism (“physics-consistent”) for symbolic regression, dominant symbolic regression methods with data augmentation features are reluctant to consider wide function search space. This is due to the optimization burden in nontrivial function search, which is further exacerbated when dynamics exhibit chaotic or rapid oscillation. In this paper, a novel physics-informed equation learning method is proposed to address these issues. Specifically, leveraging a Fourier feature mapping enables a regular and accessible fully-connected neural network (the surrogate model) to learn dynamics with various frequency components. A neural-network-based symbolic model is improved to efficiently represent and separate function combinations in the form of polynomial series. Joint training of the surrogate model and the symbolic model enables “physics-consistent” data augmentation to the original low-quality data and lays the ground for a more reliable equation discovery. The proposed method is demonstrated by numerical and experimental systems parameterized by ordinary differential equations. Compared with baseline methods, such as sparse Bayesian learning and physics-informed neural network with dictionaries, it is found that the proposed method possesses evident competitive edges regarding optimization tractability for scalable function search. Codes will be publicly available upon publication.