Symbolic Model Research Articles

Turing’s Symbolic Model of Mind and Machine in an Evolutionary Context

Turing’s Symbolic Model of Mind and Machine in an Evolutionary Context

Verifying Computation Tree Logic of Knowledge via Knowledge-Oriented Petri Nets and Ordered Binary Decision Diagrams

Computation Tree Logic of Knowledge (CTLK) can specify many requirements of privacy and security of multi-agent systems (MAS). In our previous papers, we defined Knowledge-oriented Petri Net (KPN) to model MAS, proposed similar reachability graph to verify CTLK, gave their model checking algorithms and developed a related tool. In this paper, we use the technique of Ordered Binary Decision Diagrams (OBDD) to encode similar reachability graph in order to alleviate the state explosion problem, and verify more epistemic operators of CTLK. We design the corresponding symbolic model checking algorithms and improve our tool. We compare our model and method with MCMAS that is the state-of-the-art CTLK model checker, and experiments illustrate the advantages of our model and method. We also explain the reasons why our model and method can obtain better performances.

THE USE AND SYMBOLISM OF ANIMALS IN THE WONDERFUL WIZARD OF OZ

This paper is the result of a qualitative research that follows an animal-based interpretative theory. It sheds the light on the use and symbolism of animal characters in The Wonderful Wizard of Oz written by L. Frank Baum and published in 1900. It seems to be a modern fairy tale with an American setting and charming fantasy characters. It is an immensely popular text among children and adults, so it is considered a classic of children's literature. It also attempts to answer why and how Baum used animals; whether for their qualities or for the sake of creating characters opposite to their symbolism. Therefore, the role and function of animal characters are extensively discussed. The findings show how L. Frank Baum presents a distinguished world from a non-human perspective and at the same time he teaches morality, religion, and accepting diversity in nature in an exciting narrative full of adventures. These animal characters can serve as a symbolic role models for young readers.

Linguistic scale consistency issues in multi-granularity decision making contexts

The symbolic model based on the linguistic scale has been widely used to represent linguistic knowledge to deal with various linguistic decision problems. However, linguistic scales with different granularity may yield inconsistent decision outcomes in the linguistic decision making. Thus, this paper systematically studies the linguistic scale consistency issues in multi-granularity decision making contexts. We first define the concepts of the consistent multi-granularity representation, consistent multi-granularity aggregation and consistent multi-granularity ranking. After that, we analytically present a necessary and sufficient condition to guarantee the consistent multi-granularity representation and a sufficient condition to characterize the intrinsic mechanism of the consistent multi-granularity aggregation. Then, an attitude-based linguistic representation method (ALRM) is proposed to improve the consistent multi-granularity ranking. Finally, a detailed numerical analysis and simulation experiments are presented to show the advantages of the ALRM over the traditional linguistic approach. These results will provide new insights into the use of linguistic scales in the linguistic decision making.

Semantic analysis of fluctuations of a radar pack for identification of air objects

A method for semantic analysis of amplitude fluctuations of the radar pack to identify air objects in surveillance radars has been developed and implemented in software. This method is based on the determination of semantic components at the stage of formation and analysis of the symbolic model of a burst of impulse signals from mobile aircraft. Signal information is described by the predicate function of the process knowledge of the formation and analysis of the symbolic model of a burst of impulse signals from mobile aircraft such as an airplane, helicopter, UAV, and from atmospheric inhomogeneities of the angel-echo type. As a result of semantic analysis of the amplitude fluctuations, classification distinguishing attributes of fluctuations from interfering reflections and air objects are obtained. The semantic components of the decision-making algorithm, which are similar to decision-making algorithms by the operator, are investigated. In the developed algorithm, the signal information is described by a predicate function on the set of amplitudes of burst pulses exceeding a certain threshold value. Identification of the types of fluctuations is carried out by solving the developed equations of predicate operations. Based on these equations, a functional diagram of automatic determination of the fluctuation types is synthesized. The verification of the developed method was carried out on real data obtained on a survey centimeter-band radar (pulse duration 1 μs, sounding frequency 365 Hz, survey period 10 s). Based on these data, types of characteristic packs of radar signals are simulated. According to the results of the experiments, they were all correctly identified.

Immersive Design: drawing and perspective through technological devices of Virtual Reality

The human experience is a multisensory process, which shifts according to changesin perception and individual experiences. Experiencing an architectural environment involves these nuances derived from living with a place. This concept of immersion in the environment to promote multisensory stimuli receives from Jon Charles Coe (2012) the name of “Immersive Design”, a term derived from architecture, but applied also to Virtual Reality(VR), Augmented Reality, Mixed Reality, Extended Reality, as well as structuringlanguages for object-oriented design and Human-Computer-Interaction.
 Since the Renaissance technology has become an essential element in the perception of the gaze thanks to the development of optical devices and techniques for representing the perspective. The result can be seen in the paintings, with detailed depictions of depth and textures, as well as in the architecture of cathedrals and other buildings, enabling higher and wider indoor spaces.
 Contemporaneously, based on our own experiences that of other professors, we have identified major problems in teaching perspective and spatial reasoning in drawing classes, either due to the previous deficiency in geometry, or due to the majority use of two-dimensional technologies - such as screens, displays and paper. This scenario can restrict the drawing classes experience, the realization of projects, and even discourage the teaching-learning process.
 The research “Immersive Design, through technological devices of Virtual Reality”, has worked on different methodologies mediated by the use of VR devices for teaching perspective in drawing classes. Among “digital natives” students the use of VR has stimulated new forms of perception and sensitivity thanks to the three- dimensional experience in immersive environments.
 In the virtual space the student can experience in an empirical way the perspective involved in the construction of spatial elements. This happens, for example, with the possibility of drawing directly in three-dimensional space and “walking” along the trace - even controlling the time of the action. Therefore, it becomes possible to unveil the image from various angles by displacing the observer, unlike “static” techniques structurally limited to flat and two dimensional supports.
 Mediated by VR apparatuses the body immerses itself in the digital, creating a synergy between the gestural and the conceptual in three-dimensional virtual environments, that can function simultaneously as a space for representation and embodiment. According to Philippe Quéau (1996), the image becomes an exploitable place that extrapolates its representational condition. The image is no longer a substrate within the experience would be written. This place is itself an image - a kind of symptom of the symbolic model that is at its origin. It is the very experience of that space that allows us to return to the source of its intelligibility, that is, to the model. This is the experience that epigenetically constitutes it as “space”. The synthetic image modifies our relationship with the immediate reality, structuring it as a “drawable” instrument.
 The analysis of the results validates the research hypotheses and has allowed to rescue didactic and perceptual principles related to the perspective. In addition, it indicates new perspectives and potentialities for the use of VR in education, especially, but not limited to, drawing classes.

Effects of psychological behaviour management programme on dental fear and anxiety in children: A randomised controlled clinical trial.

A psychological behaviour management programme with information and communications technology was developed that includes symbolic modelling, tell-show-do, positive reinforcement and distraction, and provides real-time treatment information. We hypothesised that the programme would help patients feel less stressed and show less uncooperative behaviours and subjective pain. Forty-eight paediatric patients were recruited from May 2016 to January 2017, and randomly divided into a control group and an experimental group. In the control, patients watched cartoon animations during the first and second treatments. The experimental group watched cartoon animations during the first treatment, and they used the programme during the second treatment. To measure stress, uncooperative behaviour and subjective pain, we recorded the heart rate, Procedure Behaviour Checklist (PBCL) and Wong and Baker's Faces Pain Rating Scale (FPRS). The experimental group resulted in a significantly lower mean heart rate, uncooperative behaviour and subjective pain in the second treatment than did the control group (p<0.001). The differences in heart rate and uncooperative behaviour between the treatments were also significantly greater in the experimental group than in the control group (p<0.001). The programme was effective in relieving fear and anxiety as well as learning cooperative behaviour.

Toward the Plug-and-Produce Capability for Industry 4.0: An Asset Administration Shell Approach

The fourth industrial revolution, also termed Industry 4.0 (I4.0), will digitalize manufacturing operations and decentralize them from the shop floor to the office and across entire enterprise networks [1]. The proposed system-level digitalization solutions for I4.0 include cyber-physical systems [2] and digital twins [3]. Standardized I4.0 technologies are urgently required to change industry and guide market usage. The asset administration shell (AAS) of the I4.0 component [4] is a German symbolic model that is under development as an international standard in International Electrotechnical Commission (IEC) project 63278-1. Not all engineers, researchers, and entrepreneurs have a basic knowledge of the AAS. Very few understand how to employ an AAS in practice. This article uses AASs to implement an I4.0 scenario, thus plug and produce (PnP) of industrial field devices. A three-layer, AAS-based implementation of PnP manufacture in line with I4.0 is developed. The synergy is to show that the AAS may help expedite I4.0 digitalization and propel its applications forward.

Hybrid Connectionist Symbolic Model for Morphologic Recognition by Tactile Sensing

Morphology and texture detection, which are important components of tactile sensing, augment the response of human beings to external stimuli. Similarly, tactile sensing-based information acquisition systems in robots can help enhance the interactions of robots with the surroundings. The main drawback of morphology and texture sensing methods is their inability to explain and quantify sensing information, which makes it difficult to utilize prior knowledge and necessitates a new training process to fit the new task, even if the changes between the existing and new tasks are minuscule. Another drawback is its dependence on large datasets. To solve these problems, a hybrid connectionist symbolic model (HCSM) is proposed herein that combines historic symbolic knowledge and end-to-end neural networks. The symbolic model requires a smaller dataset and possesses an improved transferability of detection. Neural networks can be easily established and exhibit better fault tolerance for non-ideal samples. The HCSM model combines these advantages. Experiments with the tactile-based morphology and texture detection demonstrated that the new method can transfer the detection ability to fit new tasks without requiring additional retraining and has a 16% higher recognition precision than a convolutional neural network, LeNet, AlexNet, VGG16, and ResNet. The HCSM method with these features can broaden the range of applications of tactile sensing.

Holographic Declarative Memory: Distributional Semantics as the Architecture of Memory.

We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high-dimensional space using a distributional semantics model. High-dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high-level cognition, and it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high-level cognition and provide human-readable, explainable models, but scale poorly to naturalistic, non-symbolic, or big data. Vector-symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector-space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our proposed model, Holographic Declarative Memory (HDM), is a vector-space model based on distributional semantics. HDM accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgments, and human performance on an iterated decision task. HDM provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition.

Efficient Parasitic-aware g m / I D - based Hybrid Sizing Methodology for Analog and RF Integrated Circuits

As the primary second-order effect, parasitic issues have to be seriously addressed when synthesizing high-performance analog and RF integrated circuits (ICs). In this article, a two-phase hybrid sizing methodology for analog and RF ICs is proposed to take into account parasitic effect in the early design stage. It involves symbolic modeling and mixed-integer nonlinear programming (MINLP) in the first phase, and a many-objective evolutionary algorithm (many-OEA)-based sizing refiner in the second phase. With the aid of our proposed current density factor and piecewise curve fitting technique, the g m / I D concept, which is typically utilized to solve the analog circuit design problem, can provide theoretical support to our accurate symbolic modeling. Thus, the intrinsic and interconnect parasitics can be accurately considered in our work with moderate modeling effort. A variety of electrical, geometric, and parasitic (including parasitic mismatch) constraints can be conveniently integrated into our MINLP problem formulation. Moreover, numerical simulations are embedded into the many-OEA-based sizing phase, which is able to tackle floorplan co-optimization. With such dynamic floorplan variation, the parasitics accuracy can be sustained along the evolution. The experimental results demonstrate high efficacy of our proposed parasitic-aware hybrid sizing methodology.

The Anomaly of Anomaly of Anomaly

The Anomaly of Anomaly of Anomaly

Symbolic finite element discretization and model order reduction of a multiphysics model for IPMC sensors

The multiphysics model of ionic polymer-metal composite (IPMC) sensors proposed by Zhu has a significant advantage of being able to describe the dynamic sensor response, which highly depends on humidity, by explicitly considering solvent dynamics. However, it is difficult to perform analysis and simulation because Zhu’s model is represented by complex non-linear partial differential equations. This paper describes the symbolic finite element discretization of Zhu’s model and further discusses the essential dynamics of the reduced-order model extracted from the finite element model. The obtained linear ordinary differential equations, or the state equation, can be easily implemented in simulators via common programming languages. The simulation results of an in-house simulator implemented by MATLAB code show good agreement with those of direct numerical simulation by using commercial software, COMSOL. To further simplify the model, the minimum order required for an appropriate approximation is numerically investigated by using a model order reduction technique. This paper reveals that the dynamic response of an IPMC sensor can be consequently approximated by a first-order or second-order linear time-invariant system.

Machine Learning-Aided Parametrically Homogenized Crystal Plasticity Model (PHCPM) for Single Crystal Ni-Based Superalloys

This article establishes a multiscale modeling framework for the parametrically homogenized crystal plasticity model (PHCPM) for single crystal Ni-based superalloys. The PHCPMs explicitly incorporate morphological statistics of the $$\gamma -\gamma '$$ intragranular microstructure in their crystal plasticity constitutive coefficients. They enable highly efficient and accurate calculations for image-based polycrystalline microstructural simulations. The single crystal PHCPM development process involves: (1) construction of statistically equivalent RVEs or SERVEs, (2) image-based modeling with a dislocation-density crystal plasticity model, (3) identification of representative aggregated microstructural parameters, (4) selection of a PHCPM framework and (5) self-consistent homogenization. Novel machine learning tools are explored at every development phase. Supervised and unsupervised learning methods, such as support vector regression, artificial neural networks, k-means, and symbolic regression, enhanced optimization, model emulation and sensitivity analysis methods are all critical components of the multiscale modeling pipeline. The integration of machine learning tools with physics-based models enables the creation of powerful single crystal constitutive models for polycrystalline simulations.

Predicate model of process knowledge when detecting and recognizing extended objects such as clouds, angel-echoes in surveillance radars

A predicate model of the process knowledge of inter-period processing of radar signals in the detection and recognition of extended objects and a decision-making method based on precedents have been developed. The main features and structural elements of the process knowledge model are presented. It is shown that the advantages of this model are related to the configuration and hierarchical representation of the process for studying the possible structures of single or groups of impulse signals within the same radar field of view based on the intelligent analysis of signals using the algebra of finite predicates. It is shown how this approach can be used to automate the process of detecting and recognizing extended objects such as clouds, atmospheric inhomogeneities of the angel-echo type. A method for processing process knowledge has been developed as a tool for creating universal algorithms for inter-period processing of signal information to ensure effective detection and recognition of various extended objects, including atmospheric inhomogeneities of the angel-echo type, by accumulating both signal (energy) and logical information in the analyzed cell and in its vicinity. The developed technology includes procedures for formalizing and analyzing the symbolic model of observed objects for making decisions based on precedents. Depending on the types of connections used in the model, classifying and functional networks are distinguished, where some elements of logical and network models are used. The idea of inference rules or decision rules is borrowed from logical models, and the description of knowledge in the form of a semantic neural network is borrowed from network models. In this combined model, procedural information is clearly highlighted. Instead of a logical conclusion, a conclusion or a decisive rule on knowledge appears. As a result of solving the system of predicate equations of process knowledge, we find the place, geometric dimensions and type of the symbolic model of an extended object.

Dini ve Dünyevi "Hac"

Türkiye’deki köylülerin Mekke’ye yaptıkları hac ile Avrupa'daki göçmen Türk köylülerinin doğdukları köylere yaptıkları yolculuklar karşılaştırıldığında bu iki yolculuğun hacı ve göçmen kategorilerinde olduğu gibi “dinî” ve “dünyevi” kategorileri arasındaki sınırları da bulanıklaştırdığı iddia edilebilir. Göçmenler ile hacıların ülke sınırlarını aştığı gibi bilim insanları da disiplin sınırlarını aşmalıdır. Bu köylüler için Mekke ve köyleri, onlarda gurbet hissi uyandıran, dışarıdaki merkezleridir. Oraya tam olarak yerleşmeme ama gidebildiklerinde orayı ziyaret etme nedenleri, zihinlerindeki bu imajın yok olmaması için olmalıdır. Böylece onlar, yaşadıkları yerin kendi evleri olmadığını düşünecek, ilk, gerçek ve nihai evlerinin neresi olduğunu hep zihinlerinde canlı tutabileceklerdir. C. Delaney, köylülerin Mekke’ye yaptıkları hac gibi göçmenlerin evlerine yaptıkları bu zorlu yolculuğun da ritüelistik ve zorunlu karakterini örtülü şekilde yapılandıran ve anlaşılır hale getiren sembolik bir model sunduğunu ileri sürmektedir. Her biri kendine ait retorik ve sembolleri olan göç ve hac gibi ayrı alanlar yerine farklı anlaşılırlık dereceleriyle hayal gücünü şekillendiren ve Müslümanların davranışını motive eden bazı güçlü anahtar semboller olabileceğini öne sürerek böyle bir projeye girişmektedir.

Psda: A tool for extracting knowledge from symbolic data with an application in Brazilian educational data

Symbolic polygonal data analysis is a new type of framework to extract valuable knowledge from a new structure of data using regular polygon built from data in class, big data, and complex data. This paper introduces a toolbox for symbolic polygonal data, named psda, that contains the main descriptive measures for this type of variable, e.g., mean, variance, correlation, and a polygonal linear regression model (plr). It is applied at the Brazilian Basic Education Assessment System (SAEB), giving a new perspective to the managers of the counties to realize the public policy in the Brazilian educational system. The hypothesis test showed that the polygonal linear regression model presented the best performance compared to some symbolic interval regression models in the SAEB application.

The Sacramentality of Preaching : A Sacramental Theological Approach to Preaching as the Word of God

This paper aims to investigate the significance of peaching as the Word of God in terms of sacramentality, the concept that indicates the divine encounter in and through the physical matter or action. Drawing on the expanded concept of sacramentality, I attempt to claim that preaching as the Word of God can be restated as the sacramentality of preaching. In particular, I investigate how Martin Luther, Karl Barth, and David Buttrick understood the sacramentality of preaching and what common ground they share with their contemporary sacramental theology. While Luther claimed of the real presence of Christ in preaching, as he claimed of Christ's real presence in the sacraments, Karl Barth developed the Reformed confession of preaching as the Word of God to the preaching as the Word-event, emphasizing the eventfulness of the revelation. Meanwhile, David Buttrick articulated preaching as the symbolic event or the hermeneutical event, in which the Christ-event is reconstructed in human consciousness. These homiletical discussions are related to the sacramental theological conversations in each period. While Luther's conviction of the real presence of Christ in preaching was a reaction to the Thomist objectivism, Barth's dialectical theology of the revelation was the reaction to liberal theology and impacted Roman Catholic sacramental theology, as Karl Rahner and Edward Schilleebeckx show. Also, Buttrick's hermeneutical and symbolic model for preaching runs on the same track with the sacramental theologians who attempted to explain the sacramentality by employing Heideggerian hermeneutical phenomenology. These scholarly models show that homiletics and sacramental theology have a point of contact for the discussion of preaching as the Word of God in terms of sacramentality, as preaching functions as a mode of revelation as the sacrament does.

Evaluating Lateral Interactions of Motorized Two-Wheelers using Multi-Gene Symbolic Genetic Programming

Complex maneuvering patterns are typical of motorized two-wheelers (MTWs), and their widespread adoption in many countries has spurred a growing response from transport researchers to model their dynamic behavior realistically. Considering the increased vulnerability of MTW drivers in dense urban mixed traffic systems, proper evaluation and modeling of lateral interactions between the drivers/riders moving abreast need to be addressed. A proper investigation can essentially help in understanding the behavioral aspects of riders in accepting shorter lateral clearances, design of exclusive motorcycle lanes, improved reliability of microsimulation models, and safety evaluation of the riders in a cognitive architecture. The current study therefore attempts to develop a novel symbolic regression model using a multigene genetic programming algorithm to generate and evaluate lateral clearance models naturally from field data for MTW interactions at mid-block sections, data being collected using video recorders. A binary logit model is initially developed to investigate the factors associated with the riders’ decisions to accept critical lateral clearances. Considering highly non-linear variations in data, the symbolic regression models were further developed and a comparison with the existing linear regression based lateral clearance models indicated that the symbolic model could generalize the non-linear structure of the data realistically and performed significantly better than the existing models.

Neural Components of Reading Revealed by Distributed and Symbolic Computational Models.

Determining how the cognitive components of reading - orthographic, phonological, and semantic representations - are instantiated in the brain has been a longstanding goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit non-symbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling-sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded with neural activity. However, ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.