Symbolic Model Research Articles

Compositional synthesis of opacity-preserving finite abstractions for interconnected systems

In this paper, we propose a compositional approach to construct opacity-preserving finite abstractions (a.k.a symbolic models) for networks of discrete-time nonlinear control systems. Particularly, we introduce new notions of simulation functions that characterize the distance between control systems while preserving opacity properties across them. Instead of treating large-scale systems in a monolithic manner, we develop a compositional scheme to construct the finite abstractions together with the overall opacity-preserving simulation functions based on those of the smaller subsystems. For a network of incrementally input-to-state stable control subsystems and under some small-gain type condition, an algorithm for designing local quantization parameters is presented to orderly build the local symbolic models of subsystems. We show that the network of those constructed symbolic models simulates the original network for an a-priori defined abstraction accuracy while preserving its opacity properties.

Savitzky-Golay filter energy features-based approach to face recognition using symbolic modeling.

Face recognition is a well-researched domain however many issues for instance expression changes, illumination variations, and presence of occlusion in the face images seriously affect the performance of such systems. A recent survey shows that COVID-19 will also have a considerable and long-term impact on biometric face recognition systems. The work has presented two novel Savitzky–Golay differentiator (SGD) and gradient-based Savitzky–Golay differentiator (GSGD) feature extraction techniques to elevate issues related to face recognition systems. The SGD and GSGD feature descriptors are able to extract discriminative information present in different parts of the face image. In this paper, an efficient and robust person identification using symbolic data modeling approach and similarity analysis measure is devised and employed for feature representation and classification tasks to address the aforementioned issues of face recognition. Extensive experiments and comparisons of the proposed descriptors experimental results indicated that the proposed approaches can achieve optimal performance of 96–97, 92–96, 100, 84–93, and 87–96% on LFW, ORL, AR, IJB-A datasets, and newly devised VISA database, respectively.

Learning Interpretable Models Expressed in Linear Temporal Logic

We examine the problem of learning models that characterize the high-level behavior of a system based on observation traces. Our aim is to develop models that are human interpretable. To this end, we introduce the problem of learning a Linear Temporal Logic (LTL) formula that parsimoniously captures a given set of positive and negative example traces. Our approach to learning LTL exploits a symbolic state representation, searching through a space of labeled skeleton formulae to construct an alternating automaton that models observed behavior, from which the LTL can be read off. Construction of interpretable behavior models is central to a diversity of applications related to planning and plan recognition. We showcase the relevance and significance of our work in the context of behavior description and discrimination: i) active learning of a human-interpretable behavior model that describes observed examples obtained by interaction with an oracle; ii) passive learning of a classifier that discriminates individual agents, based on the human-interpretable signature way in which they perform particular tasks. Experiments demonstrate the effectiveness of our symbolic model learning approach in providing human-interpretable models and classifiers from reduced example sets.

Examining the role of individuals’ perceptions of likelihood of sustained commitment in corporate-nonprofit partnership CSR advertisements

Effective corporate social responsibility (CSR) advertising depends on the nature of the partnership and how it is communicated in messages about CSR initiatives. Guided by the Symbolic Sustainability Model (SSM), this study investigates the effects of communication about CSR initiatives comparing differing CSR approaches through a 2 × 2 between-subjects factorial experiment. After viewing a hypothetical CSR-based advertisement, participants (N = 433) rated their perceptions of initiative effectiveness and attitudes toward the initiative. Compared to a donation-based initiative, a CSR partnership had an indirect, positive effect on audience evaluations, which was mediated by perceptions of the likelihood of the corporation’s sustained commitment to the partnership. The advertising that emphasized a partnership outperformed a donation-focused CSR initiative because participants perceived that the sponsoring corporation was invested in helping the cause for an extended period. The findings indicate that audiences are likely to perceive partnerships and donations positively; however, partnership initiatives may have stronger positive, indirect effects because they may be more likely to signal sustained commitment. The findings support the Symbiotic Sustainability Model (SSM) and demonstrate the efficacy of the SSM for CSR advertising. For practitioners, the findings point to strategies that may be helpful amidst the proliferation of CSR advertising.

Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning

Computing goal-directed behavior is essential to designing efficient AI systems. Due to the computational complexity of planning, current approaches rely primarily upon hand-coded symbolic action models and hand-coded heuristic function generators for efficiency. Learned heuristics for such problems have been of limited utility as they are difficult to apply to problems with objects and object quantities that are significantly different from those in the training data. This paper develops a new approach for learning generalized heuristics in the absence of symbolic action models using deep neural networks that utilize an input predicate vocabulary but are agnostic to object names and quantities. It uses an abstract state representation to facilitate data-efficient, generalizable learning. Empirical evaluation on a range of benchmark domains shows that in contrast to prior approaches, generalized heuristics computed by this method can be transferred easily to problems with different objects and with object quantities much larger than those in the training data.

Comparing Symbolic Models of Language via Bayesian Inference (Student Abstract)

Given recurring interest in structured representations in computational cognitive models, we extend a Bayesian scoring procedure for comparing symbolic models of language grammar. We conduct a case-study of modeling syntactic principles in German, providing preliminary results consistent with linguistic theory. We also note that dataset and part-of-speech (POS) tagger quality should not be taken for granted.

Kinematic design of a translational parallel mechanism based on sub-kinematic chain determined workspace superposition

This paper presents the kinematic design of a translational parallel mechanism (PM) named Vari-Orthoglide by means of the workspace superposition, according to the sub-kinematic chain (SKC) based PM composition principle. The main topological characteristics of the manipulator with two SKCs under study, such as the position and orientation (POC) characteristics, degree of freedom (DOF) and coupling degree are analyzed, which turns out that the coupling degree equals to 1, implying the partially decoupled motion. With the topological characteristics based kinematic modeling principle, a symbolic model of the kinematics is established to derive its symbolic direct and inverse kinematic solutions. Based upon the direct kinematic solution, the workspaces for the two SKCs can be efficiently found. Moreover, the singularity loci are identified for finding the singularity-free workspace, where a regular workspace is fitted as the task workspace as expected. The presented work shows an approach to design translational parallel mechanisms considering motion decoupling and regular workspace, applicable to other types of parallel mechanisms.

Penerapan Teknik Modeling Simbolis Dalam Pengembangan Perencanaan Karier Siswa SMPN 6 Palangka Raya

Career planning is something that concerns the future in the long term. It must be planned in advance; to plan where someone wants to go and what he wants to achieve.The purpose of this study was to determine the effectiveness of symbolic modeling techniques in the development of student career planning; to choose further studies that suit their interests and talents. The type of the research is qualitative research with observation, interview, and documentation methods. The research subjects were 5 students. This research was carried out based on the phenomenon at SMP Negeri 6 Palangka Raya, where there were students who were still confused about choosing further studies according to their interests and talents. The results showed that after being given symbolic modeling with videos, students experienced changes such as: (1) Students were able to understand their strengths and weaknesses in certain fields; (2) Students begin to be able to plan careers after graduating from school; (3) Students were able to prepare options for the next level of study. From these data the researchers concluded that symbolic modeling was effective in helping students at SMP Negeri 6 Palangka Raya in planning their careers.

Penerapan Teknik Modeling Simbolis Dalam Pengembangan Perencanaan Karier Siswa SMPN 6 Palangka Raya

Career planning is something that concerns the future in the long term. It must be planned in advance; to plan where someone wants to go and what he wants to achieve.The purpose of this study was to determine the effectiveness of symbolic modeling techniques in the development of student career planning; to choose further studies that suit their interests and talents. The type of the research is qualitative research with observation, interview, and documentation methods. The research subjects were 5 students. This research was carried out based on the phenomenon at SMP Negeri 6 Palangka Raya, where there were students who were still confused about choosing further studies according to their interests and talents. The results showed that after being given symbolic modeling with videos, students experienced changes such as: (1) Students were able to understand their strengths and weaknesses in certain fields; (2) Students begin to be able to plan careers after graduating from school; (3) Students were able to prepare options for the next level of study. From these data the researchers concluded that symbolic modeling was effective in helping students at SMP Negeri 6 Palangka Raya in planning their careers.

Penerapan Teknik Modeling Simbolis Dalam Pengembangan Perencanaan Karier Siswa SMPN 6 Palangka Raya

Career planning is something that concerns the future in the long term. It must be planned in advance; to plan where someone wants to go and what he wants to achieve.The purpose of this study was to determine the effectiveness of symbolic modeling techniques in the development of student career planning; to choose further studies that suit their interests and talents. The type of the research is qualitative research with observation, interview, and documentation methods. The research subjects were 5 students. This research was carried out based on the phenomenon at SMP Negeri 6 Palangka Raya, where there were students who were still confused about choosing further studies according to their interests and talents. The results showed that after being given symbolic modeling with videos, students experienced changes such as: (1) Students were able to understand their strengths and weaknesses in certain fields; (2) Students begin to be able to plan careers after graduating from school; (3) Students were able to prepare options for the next level of study. From these data the researchers concluded that symbolic modeling was effective in helping students at SMP Negeri 6 Palangka Raya in planning their careers.

Inertial Parameter Identification in Robotics: A Survey

This work aims at reviewing, analyzing and comparing a range of state-of-the-art approaches to inertial parameter identification in the context of robotics. We introduce “BIRDy (Benchmark for Identification of Robot Dynamics)”, an open-source Matlab toolbox, allowing a systematic and formal performance assessment of the considered identification algorithms on either simulated or real serial robot manipulators. Seventeen of the most widely used approaches found in the scientific literature are implemented and compared to each other, namely: the Inverse Dynamic Identification Model with Ordinary, Weighted, Iteratively Reweighted and Total Least-Squares (IDIM-OLS, -WLS, -IRLS, -TLS); the Instrumental Variables method (IDIM-IV), the Maximum Likelihood (ML) method; the Direct and Inverse Dynamic Identification Model approach (DIDIM); the Closed-Loop Output Error (CLOE) method; the Closed-Loop Input Error (CLIE) method; the Direct Dynamic Identification Model with Nonlinear Kalman Filtering (DDIM-NKF), the Adaline Neural Network (AdaNN), the Hopfield-Tank Recurrent Neural Network (HTRNN) and eventually a set of Physically Consistent (PC-) methods allowing the enforcement of parameter physicality using Semi-Definite Programming, namely the PC-IDIM-OLS, -WLS, -IRLS, PC-IDIM-IV, and PC-DIDIM. BIRDy is robot-agnostic and features a complete inertial parameter identification pipeline, from the generation of symbolic kinematic and dynamic models to the identification process itself. This includes functionalities for excitation trajectory computation as well as the collection and pre-processing of experiment data. In this work, the proposed methods are first evaluated in simulation, following a Monte Carlo scheme on models of the 6-DoF TX40 and RV2SQ industrial manipulators, before being tested on the real robot platforms. The robustness, precision, computational efficiency and context of application the different methods are investigated and discussed.

Reading the Human through Robots

Reading the Human through Robots

Reusing Solutions Modulo Theories

In this paper we propose an approach for reusing formula solutions to reduce the impact of Satisfiability Modulo Theories (SMT) solvers on the scalability of symbolic program analysis. SMT solvers can efficiently handle huge expressions in relevant logic theories, but they still represent a main bottleneck to the scalability of symbolic analyses, like symbolic execution and symbolic model checking. Reusing proofs of formulas solved during former analysis sessions can reduce the amount of invocations of SMT solvers, thus mitigating the impact of SMT solvers on symbolic program analysis. Early approaches to reuse formula solutions exploit equivalence and inclusion relations among structurally similar formulas, and are strongly tighten to the specific target logics. In this paper, we present an original approach that reuses both satisfiability and unsatisfiability proofs shared among many formulas beyond only equivalent or related-by-implication formulas. Our approach straightforwardly generalises across multiple logics. It is based on the original concept of distance between formulas, which heuristically approximates the likelihood of formulas to share either satisfiability or unsatisfiability proofs. We show the efficiency and the generalisability of our approach, by instantiating the underlying distance function for formulas that belong to most popular logic theories handled by current SMT solvers, and confirm the effectiveness of the approach, by reporting experimental results on over nine millions formulas from five logic theories.

The Effectiveness of Psychoeducational Group with Symbolic Modeling and Bibliotherapy Techniques to Improve Career Decision Making Self-Efficacy of Junior High School Students

Students who have high self-efficacy can design career plans and show persistent efforts. However, this efficacy is not always owned by every single student since there found many students having difficulties in making career decision. Thus, this study tried to examine the effectiveness of psychoeducational group with symbolic modelling and bibliotherapy techniques to improve the career decision making of Junior High School students. In carrying out the study, the researchers employed randomized pretest-posttest comparison group design by involving 42 students divided into three experimental groups. Students’ data were collected using career decision making self-efficacy scale. Based on Wilcoxon test results, the implementation of the psychoeducational groups with symbolic modelling technique, bibliotherapy, and the combination of both techniques were effective to improve students’ career decision making self-efficacy. In details, Kruskal wallis test showed that symbolic modelling and bibliotherapy techniques were more effective than their combination to improve the career decision making self-efficacy of SMP Negeri 6 Bogor students. According to these findings, counselors are recommended to apply symbolic modelling and bibliotherapy techniques separately to improve students’ career decision making self-efficacy

A Semi-Automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database

To tackle the issues of semantic collision and inconsistencies between ontologies and the original data model while learning ontology from relational database (RDB), a semi-automatic semantic consistency checking method based on graph intermediate representation and model checking is presented. Initially, the W-Graph, as an intermediate model between databases and ontologies, was utilized to formalize the semantic correspondences between databases and ontologies, which were then transformed into the Kripke structure and eventually encoded with the SMV program. Meanwhile, description logics (DLs) were employed to formalize the semantic specifications of the learned ontologies, since the OWL DL showed good semantic compatibility and the DLs presented an excellent expressivity. Thereafter, the specifications were converted into a computer tree logic (CTL) formula to improve machine readability. Furthermore, the task of checking semantic consistency could be converted into a global model checking problem that could be solved automatically by the symbolic model checker. Moreover, an example is given to demonstrate the specific process of formalizing and checking the semantic consistency between learned ontologies and RDB, and a verification experiment was conducted to verify the feasibility of the presented method. The results showed that the presented method could correctly check and identify the different kinds of inconsistencies between learned ontologies and its original data model.

A Hierarchical Multicast Key Distribution Protocol

In secure group communication, group keys (GK) are used to ensure the confidentiality of communication. The group key distribution (GKD) is responsible for updating and distributing new group keys when the group membership changes. Most well-known GKD protocols are based on a logical key hierarchy (LKH), where only one group controller (GC) is used. These protocols have various issues, including a single point of failure, meaning that the GC often has a huge workload and can be easily overwhelmed. In this paper, we propose a hierarchical multicast key distribution protocol that supports multi-level controllers to manage a group. Each controller just needs to manage the next-level nodes, and if one fails, the superior controller can replace it with minimal work. The proposed protocol effectively balances the work of controllers, greatly improves the reliability of the group key distribution, and also allows group members to build dynamic conferences without controllers. We provide a security proof of the proposed protocol in a symbolic security model and compare it to other protocols in terms of efficiency, functionality, and security.

Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process

Hybrid semiparametric models integrate physics-based (“white-box”, parametric) and data-driven (“black-box”, non-parametric) submodels. Black-box models are often implemented using artificial neural networks (ANNs). In this work, we investigate the fitness of symbolic regression (SR) for black-box modelling. The main advantage of this approach is that a trained hybrid model can be expressed in closed form as an algebraic equation. We examine and test the idea on a simple example, namely the v-shape bending of a metal sheet, where an analytical solution for the stamping force is readily available. We explore unconstrained and hybrid symbolic regression modelling to show that hybrid SR models, where the regression tree is partly fixed according to a-priori knowledge, perform much better than purely data-driven SR models based on unconstrained regression trees. Furthermore, the generation of algebraic equations by this method is much more repeatable, which makes the approach applicable to process knowledge discovery.

Encoding-based memory for recurrent neural networks

Learning to solve sequential tasks with recurrent models requires the ability to memorize long sequences and to extract task-relevant features from them. In this paper, we study memorization from the point of view of the design and training of recurrent neural networks. We study how to maximize the short-term memory of recurrent units, an objective difficult to achieve using backpropagation. We propose a new model, the Linear Memory Network, which features an encoding-based memorization component built with a linear autoencoder for sequences. Additionally, we provide a specialized training algorithm that initializes the memory to efficiently encode the hidden activations of the network. Experimental results on synthetic and real-world datasets show that the chosen encoding mechanism is superior to static encodings such as orthogonal models and the delay line. The method also outperforms RNN and LSTM units trained using stochastic gradient descent. Experiments on symbolic music modeling show that the training algorithm specialized for the memorization component improves the final performance compared to stochastic gradient descent.

Sensitivity to geometric shape regularity in humans and baboons: A putative signature of human singularity

Among primates, humans are special in their ability to create and manipulate highly elaborate structures of language, mathematics, and music. Here we show that this sensitivity to abstract structure is already present in a much simpler domain: the visual perception of regular geometric shapes such as squares, rectangles, and parallelograms. We asked human subjects to detect an intruder shape among six quadrilaterals. Although the intruder was always defined by an identical amount of displacement of a single vertex, the results revealed a geometric regularity effect: detection was considerably easier when either the base shape or the intruder was a regular figure comprising right angles, parallelism, or symmetry rather than a more irregular shape. This effect was replicated in several tasks and in all human populations tested, including uneducated Himba adults and French kindergartners. Baboons, however, showed no such geometric regularity effect, even after extensive training. Baboon behavior was captured by convolutional neural networks (CNNs), but neither CNNs nor a variational autoencoder captured the human geometric regularity effect. However, a symbolic model, based on exact properties of Euclidean geometry, closely fitted human behavior. Our results indicate that the human propensity for symbolic abstraction permeates even elementary shape perception. They suggest a putative signature of human singularity and provide a challenge for nonsymbolic models of human shape perception.

Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming

Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes. While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion. With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes. A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.