Symbolic Model Research Articles

Learning the laws of lithium-ion transport in electrolytes using symbolic regression

Machine learning to discover equations: we train a symbolic regression model on high-throughput experimental data and find an explainable, simple and accurate analytical expression predicting the ionic conductivity of a Li-ion battery electrolyte.

Конструирование идентичности в детском кинематографе: опыт кино СССР и практика современной России

The article is devoted to the formation spectator-child's identity problem by cinematography means. The authors rely on the social constructionism position. The position allows us to talk about the socio-cultural reality construction by cinematography means: about the creation of an artistic and symbolic world model. The values or ideology system guidelines developed within the framework of the USSR ideology and meeting the tasks socio-political, socio-economic structure is the model based. Cinematography, namely children's cinema, aimed at an educational role, broadcasts these attitudes, including the values of patriotism, diligence, collectivism, etc. In this way, the forming the child's identity (Soviet identity) process is “launched”. After the USSR collapse, cinematography develops under the globalization processes influence: there is a rejection of ideology and the spread of postmodern cultural values. The development of the information environment and the change in the socio-economic system dictate the development of a new language of cinema, which is becoming an actual research field. The expose of the child-viewer's constructing identity specifics on the basis of a comparative the Soviet era and modern Russia children's films is the study purpose according to the authors of the research. The study was conducted on the material of films with similar fairy tale plots, which are analyzed from the position of the following parameters: plot construction, the hero image, the expression used means, as well as the value-semantic attitudes of culture transmitted by the artistic and figurative film production system. The study showed, that modern cinema, unlike the USSR cinema, demonstrates a rejection of binary thinking, does not claim to be clear on the author's position and establish any final meaning of the work, refers to new topics (consumer society, feminism, power) and values (individualism, hedonism, priority of the material over the spiritual).The visual aspect prevails among artistic means to the detriment of the verbal and semantic aspects for a reason the digital culture and computer graphics development. A new socio-cultural reality is constructed, and, consequently, a new child identity in such a cinema space in this way. The “new viewer” is forced to navigate in the meanings multiple stream and images, relying on his own unstable experience.

Formal Verification for Node-Based Visual Scripts Using Symbolic Model Checking

Visual script languages with a node-based interface have commonly been used in the video game industry. We examined the bug database obtained in the development of FINAL FANTASY XV (FFXV), and noticed that several types of bugs were caused by simple mis-descriptions of visual scripts and could therefore be mechanically detected. We propose a method for the automatic verification of visual scripts in order to improve productivity of video game development. Our method can automatically detect those bugs by using symbolic model checking. We show a translation algorithm which can automatically convert a visual script to an input model for NuSMV that is an implementation of symbolic model checking. For a preliminary evaluation, we applied our method to visual scripts used in the production for FFXV. The evaluation results demonstrate that our method can detect bugs of scripts and works well in a reasonable time.

ANALYSIS OF METHODS, METHODS, MECHANISMS, TOOLS THEORIES OF DECISION-MAKING FOR MODELING INFORMATION PROTECTION SYSTEM

The article presents a detailed analysis of methods, methods, mechanisms, tools of decision theory for modeling information security systems. The basic terminological concepts are given, and their detailed definition is given. The combination of elements of decision theory with information security systems is shown. The connecting link for this is probability theory. The issue of decision-making procedure as a process is studied. Emphasis is placed on the qualitative parameters of the decision-making procedure that may be suitable for information protection purposes. Analogies have been made that indicate the applicability of decision theory methods to create a model of information security system. Implementation mechanisms are shown in decision-making algorithms. With the help of decision-making theory tools, it has been established that the modeling process can be formalized since both mathematical icons and verbalization. In general, the step-by-step process of designing an information security system is described. It is concluded that formalization as a type of symbolic modeling simultaneously with the application of decision theory is the best option for the descriptive part of the information security system. Modeling has been found to be the best scientific tool for combining theoretical calculations and the practical application of a wide range of research issues, including information security. To support the decision-making of the decision-maker, in other words the offender, in the field of information protection, it is important that the security officer or system administrator has experience and skills in regulated actions. Such actions are both well-known developments in this field of activity and a synthesis of already known algorithms to achieve the state of information security in general. Automation in decision-making is possible through the introduction of a decision support system that is widely used in automated systems: computer systems and networks, especially where there is a need to analyze significant data flows.

The inflectional morphology representation of individual words in the mental lexicon

Models of complex word recognition can be separated into two wide groups: symbolic and connectionist. Symbolic models presume the existence of an explicit morphological representation of individual words; connectionist models do not and consider morphological effects to be a by-product of interaction between phonological, orthographic and semantic information. This study aimed to test whether there are explicit mental representations of inflected lexical units in the mental lexicon. Accordingly, the method of inflected suffix morphological and semantic priming of nouns in the Serbian language was used. In the morphological priming condition, the prime and the target shared the same inflectional suffix. In Experiment 1 overt priming was used, while in Experiment 2, masked priming. The results showed no significant effects of inflected suffix morphological priming, while significant semantic priming effects were recorded. The results obtained in this research are in line with predictions of the connectionist models.

Spatiotemporal development of suberized barriers in cork oak taproots.

The longevity and high activity of the cork cambium (or phellogen) from Quercus suber L. (cork oak) are the cornerstones for the sustainable exploitation of a unique raw material. Cork oak is a symbolic model to study cork development and cell wall suberization, yet most genetic and molecular studies on these topics have targeted other model plants. In this study, we explored the potential of taproots as a model system to study phellem development and suberization in cork oak, thereby avoiding the time constraints imposed when studying whole plants. In roots, suberin deposition is found in mature endodermis cells during primary development and in phellem cells during secondary development. By investigating the spatiotemporal characteristics of both endodermis and phellem suberization in young seedling taproots, we demonstrated that secondary growth and phellogen activity are initiated very early in cork oak taproots (approx. 8days after sowing). We further compared the transcriptomic profile of root segments undergoing primary (PD) and secondary development (SD) and identified multiple candidate genes with predicted roles in cell wall modifications, mainly lignification and suberization, in addition to several regulatory genes, particularly transcription factor- and hormone-related genes. Our results indicate that the molecular regulation of suberization and secondary development in cork oak roots is relatively conserved with other species. The provided morphological characterization creates new opportunities to allow a faster assessment of phellogen activity (as compared with studies using stem tissues) and to tackle fundamental questions regarding its regulation.

Probabilistic modelling of deception-based security framework using markov decision process

Existing studies using deception are ad-hoc attempts and few theoretical models have been designed to plan and integrate deception. We theorise that a pre-planning stage should be a fundamental part to obtain information about the attackers’ behaviours and the attack process by analysing known attacks. This will help plan and take defence actions by actively interacting with the attackers and predicting their actions using a probabilistic approach. This paper proposes a framework that provides a theoretical understanding to plan and integrate deception systematically and strategically. We also present probabilistic modelling to predict attack actions by formalising a real case of attacks captured on simulated Internet of Things devices as an Markov Decision Process (MDP) and verifying related properties using Probabilistic Symbolic Model Checker (PRISM). MDP’s properties verification results reveal that the associated cost for defence actions can be decreased by successfully predicting attackers’ probable actions. Moreover, we identify several quantification metrics (e.g. cost, reward, trust, incentive and penalty) to evaluate the performance of actions performed by attackers and defenders.

Evaluation of radar image processing efficiency based on intelligent analysis of processes

The paper presents results of development of the method and experimental studies of the system for automatic detection of radar signals of aerial objects and their recognition with the processing of real records in surveillance radars. The relevance of this work consists in creation of algorithms for automatic information processing to ensure effective detection of useful signals due to accumulation of signal (energy) and semantic information. The method is based on the definition of semantic components at the stage of formation and analysis of the symbolic model of signals from point and extended air objects. Signal information is described by the predicate function of process knowledge of the formation and analysis of a symbolic model of a burst of impulse signals from point-like mobile aircraft such as an airplane, a helicopter, a UAV, and from extended atmospheric formations such as angel-echoes, clouds. As a result of semantic analysis of symbolic images of signal marks, classification distinctive features of air objects were obtained. The semantic components of the decision-making algorithm, similar to the decision-making algorithms used by the operator, have been investigated. In the developed algorithm, signal information is described by a predicate function on the set of signal mark pulse amplitudes that have exceeded a certain threshold value. Recognizing of aerial objects is carried out by solving the developed equations of predicate operations. The verification of the developed method was carried out on real data obtained on a survey radar of the centimeter range (pulse duration was 1 μs, probing frequency wass 365 Hz, survey period was 10 s). Based on these data, the types of characteristic marks of radar signals are modeled. According to the results of the experiments, they were all correctly identified.

Unbounded Model Checking for ATL

In this paper, we deal with verification of multi-agent systems represented as concurrent game structures. To express properties to be verified, we use Alternating-Time Temporal Logic (ATL) formulas. We provide an implementation of symbolic model checking for ATL and preliminary, but encouraging experimental results.

Pengkondisian Cara Belajar Anak-Anak TK melalui Teori Belajar Sosial dengan Teknik Modelling

Kindergarten is an educational institution for children that lasts until children are 6 years old. At this age stage, children have a high curiosity about how to act properly in certain situations. For this reason, conditioning is needed to be able to control the development of behavior in each child. So, the purpose of this paper is to find out how to learn through social learning theory with efficient modeling techniques in shaping the desired behaviors in kindergarten age children. The subjects that will be observed are the students of TK Aisyiyah Bustanul Athfal 2 Jetis. The research method used is descriptive qualitative with data collection in the form of interviews and observations. The results of this study, positively, children can apply noble behavior through modeling techniques with live models, symbolic models and verbal description models.

Towards identifying knowledge bases in KIBS through their service development process

PurposeThe purpose of this paper is to explore knowledge-intensive business services (KIBS), to understand how they implement their service development (SD) processes and how these processes are related to their knowledge bases (KB) as not enough studies have focussed on the understanding of KIBS heterogeneity, by analysing the nature and essence of these firms’ activities.Design/methodology/approachThe work adopts the case study approach involving 12 KIBS firms with different KB according to Bjorn Asheim and colleagues’ “synthetic, analytical, symbolic model”, which identifies synthetic, analytical and symbolic primary KB.FindingsThe research results showed how the SD processes are systemised in KIBS companies depending on the KB. The proposed empirical method for defining KB confirmed the assumed existence of a new type or subtype of KB for legal companies “Compliance/Interpretive”.Research limitations/implicationsFurther research, based on a larger number of companies, is needed to confirm or complement the results of this research, as well as to find more methods to define and evaluate KB on an empirical basis.Practical implicationsThe correct classification of the applied KB can help a company to create effective processes for developing innovative services, while understanding of knowledge dynamic in general – to organise a working process that corresponds to employees’ needs and is more adaptable to the changes in society.Originality/valueThe study proposed a new empirical method for defining and evaluating KB in KIBS companies based on their SD processes. This study makes a significant contribution to research by identifying knowledge-integration practices needed to enhance innovation performance.

Symbolism of Colour of the Vietnamese National Garment Áo Dài: Historical Foundations, Development and Interpretations

This paper is an attempt to analyze the color symbolism of traditional Vietnamese o di costume, as well as to identify and characterize the main reasons and peculiarities of transformations in this sphere. The symbolic model of color is considered in the context of language, socio-historical and cultural processes.
 The focus is on women's traditional costume, as it has undergone far more extensive changes than mens and has a much broader scope of use today.

Data Analysis and Symbolic Regression Models for Predicting CO and NOx Emissions from Gas Turbines

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NOx emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NOx emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NOx emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are: R2=0.83 for NOx emissions, R2=0.89 for CO emissions.

Integration duties in the European Union: Four models

Integration is becoming increasingly important in law, due to the growing involvement of the legislative, executive and judicial powers at European, national, regional and local levels. This phenomenon concerns third-country nationals and EU citizens, despite the fundamentally different legal regimes applicable to these groups. In this article, I discuss the expansion of integration duties and the different legal mechanisms which they are based on. I propose four conditions- and obligations-based integration models likely to be found in EU law and national laws: the symbolic model, the meritocratic model, the activation model and the selective model. From a legal perspective, identifying these integration mechanisms is essential for assessing compliance with fundamental rights and EU law. Moreover, such a typology is likely to help researchers, policy-makers and the public obtain a better sense of how the conception of integration evolves in our societies and in the legal field in particular. I conclude by briefly sketching a common trend which I observed throughout the four models: they are imbued with a strong socioeconomic dimension hindering the socioeconomically underprivileged categories.

Music as a Cultural Inheritance System: A Contextual-Behavioral Model of Symbolism, Meaning, and the Value of Music.

Music is a pervasive cultural practice that has been present in ancient civilizations through to the present, yet its evolutionary significance has not been unequivocally determined. One position suggests that evolution favored music-related behaviors because such behaviors were linked to sexual selection and reproduction. A more recent perspective that is consistent with today’s evolutionary science framework suggests that music is a cultural-level adaptation because of the survival advantages it affords members of a community. This article explores the selection mechanisms responsible for the retention and transmission of music-related behaviors. Music is proposed to be a complex symbolic inheritance system, or an advanced form of relational responding, that required cooperation to develop and further facilitated unity and affinity among groups of people. The aggregate product of this cooperation is then assigned meaning and selected by the contingencies of a particular sociocultural community. Music may thus occasion similar values-consistent behavior (i.e., within the values system of the community) across groups of people. Implications for the role of music in promoting the well-being of a culture are examined.

Symbolic Modeling of Messages and Information Extraction

Symbolic Modeling of Messages and Information Extraction

Predicting the Non-Linear Conveying Behavior in Single-Screw Extrusion: A Comparison of Various Data-Based Modeling Approaches used with CFD Simulations

Abstract The traditional approach to modeling the polymer melt flow in single-screw extruders is based on analytical and numerical analyses. Due to increasing computational power, data-driven modeling has grown significantly in popularity in recent years. In this study, we compared and evaluated databased modeling approaches (i. e., gradient-boosted trees, artificial neural networks, and symbolic regression models based on genetic programming) in terms of their ability to predict – within a hybrid modeling framework – the three-dimensional non-linear throughput-pressure relationship of metering channels in single-screw extruders. By applying the theory of similarity to the governing flow equations, we identified the characteristic dimensionless influencing parameters, which we then varied to create a large dataset covering a wide range of possible applications. For each single design point we conducted numerical simulations and obtained the dimensionless flow rate. The large dataset was divided into three independent sets for training, interpolation, and extrapolation, the first being used to generate and the remaining two to evaluate the models. Further, we added two features derived from expert knowledge to the models and analyzed their influence on predictive power. In addition to prediction accuracy and interpolation and extrapolation capabilities, we evaluated model complexity, interpretability, and time required to learn the models. This study provides a rigorous analysis of various data-based modeling approaches applied to simulation data in extrusion.

Designing Virtual Reality Environments through an Authoring System Based on CAD Floor Plans: A Methodology and Case Study Applied to Electric Power Substations for Supervision

The adoption of Virtual Reality (RV) technologies in prototype design and process revision has contributed to multiple industry areas. Nonetheless, the development of VR systems for engineering is a complex task, as it involves specialized teams handling low-level code development. Given these problems, the goal of this study is presenting a methodology for designing VR, through an Authoring System based on Computer-Aided Design (CAD). The presented methodology provides an easy integration of electric power substation floor plans and Virtual Reality software (VRS), as well as three-dimensional and symbol modeling conventions. Centralized software architecture was developed, composed of the CAD Editor, input manager and VRS. The methodology was evaluated through a case study applied to the conception (elaboration) of electric power substations (EPS) as part of a Research and Development (R&D) project for training and field assets supervision. The results demonstrated visual precision and high integrity in elaboration of a VR environment from the CAD floor plan. This work also presents a comparative analysis between manual conception and the Authoring System.

Multi-gene Genetic Programming based Predictive Models for Full-reference Image Quality Assessment

Abstract Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics utilized and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics utilized, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model ‘goodness of fit’ to data and model ‘complexity’. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.

Interpretable deep-learning models to help achieve the Sustainable Development Goals

We discuss our insights into interpretable artificial-intelligence (AI) models, and how they are essential in the context of developing ethical AI systems, as well as data-driven solutions compliant with the Sustainable Development Goals (SDGs). We highlight the potential of extracting truly-interpretable models from deep-learning methods, for instance via symbolic models obtained through inductive biases, to ensure a sustainable development of AI.