Graphical Syntax Research Articles

Exergetic port-Hamiltonian systems for multibody dynamics

AbstractMultibody dynamics simulation plays an important role in various fields, including mechanical engineering, robotics, and biomechanics. Setting up computational models however becomes increasingly challenging as systems grow in size and complexity. Especially the consistent combination of models across different physical domains still requires significant effort. This motivates the study of formal languages that enable a compositional approach to modeling multiphysical systems with basic guarantees. The paper shows how multibody systems, or more precisely assemblies of rigid bodies connected by lower kinematic pairs, can be described as Exergetic Port-Hamiltonian Systems (EPHS). The EPHS modeling language features a straightforward graphical syntax for expressing the energy-based interconnection of hierarchically nested subsystems. This reduces cognitive load and facilitates clearer communication among experts, nonexperts, and computational tools. Hierarchical nesting of systems enables abstraction of lower-level details and promotes the reuse of models at different levels of complexity. At the lowest level, there are three basic kinds of systems, representing energy storage and reversible/irreversible energy exchange. The structured approach guarantees fundamental properties of macroscopic systems, such as conservation of energy and nonnegative entropy production. In combination with the compositional syntax, this makes building and modifying models simpler and less error-prone.

Exploiting Contextual Target Attributes for Target Sentiment Classification

In the past few years, pre-trained language models (PTLMs) have brought significant improvements to target sentiment classification (TSC). Existing PTLM-based models can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. Despite the improvements achieved by these models, we argue that they have their respective limitations. For fine-tuning-based models, they cannot make the best use of the PTLMs’ strong language modeling ability because the pre-train task and downstream fine-tuning task are not consistent. For prompting-based models, although they can sufficiently leverage the language modeling ability, it is hard to explicitly model the target-context interactions, which are widely realized as a crucial point of this task. In this paper, we present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes. Specifically, we design the domain- and target-constrained cloze test, which can leverage the PTLMs’ strong language modeling ability to generate the given target’s attributes pertaining to the review context. The attributes contain the background and property information of the target, which can help to enrich the semantics of the review context and the target. To exploit the attributes for tackling TSC, we first construct a heterogeneous information graph by treating the attributes as nodes and combining them with (1) the syntax graph automatically produced by the off-the-shelf dependency parser and (2) the semantics graph of the review context, which is derived from the self-attention mechanism. Then we propose a heterogeneous information gated graph convolutional network to model the interactions among the attribute information, the syntactic information, and the contextual information. The experimental results on three benchmark datasets demonstrate the superiority of our model, which achieves new state-of-the-art performance.

Inductive Diagrams for Causal Reasoning

The Lamport diagram is a pervasive and intuitive tool for informal reasoning about “happens-before” relationships in a concurrent system. However, traditional axiomatic formalizations of Lamport diagrams can be painful to work with in a mechanized setting like Agda. We propose an alternative, inductive formalization — the causal separation diagram (CSD) — that takes inspiration from string diagrams and concurrent separation logic, but enjoys a graphical syntax similar to Lamport diagrams. Critically, CSDs are based on the idea that causal relationships between events are witnessed by the paths that information follows between them. To that end, we model “happens-before” as a dependent type of paths between events. The inductive formulation of CSDs enables their interpretation into a variety of semantic domains. We demonstrate the interpretability of CSDs with a case study on properties of logical clocks , widely-used mechanisms for reifying causal relationships as data. We carry out this study by implementing a series of interpreters for CSDs, culminating in a generic proof of Lamport’s clock condition that is parametric in a choice of clock. We instantiate this proof on Lamport’s scalar clock, on Mattern’s vector clock, and on the matrix clocks of Raynal et al. and of Wuu and Bernstein, yielding verified implementations of each. The CSD formalism and our case study are mechanized in the Agda proof assistant.

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

AbstractThe negative herding effect tends to cause the spread of negative emotions among netizens, resulting in significant public opinion crises. Therefore, linking the negative herding effect and online public opinion, we aim to exploring the connection between netizens' emotions, opinions, and the negative herding effect. To better distinguish the emotions in texts, a new BiGADG (Bidirectional Gated recurrent unit based on Attention with Distance‐based dependency syntax Graph convolutional network) model is proposed in this article. This model includes primarily the proposed dependency syntax based on dependency distance, Bidirectional Gated Recurrent Unit (BiGRU), multi‐head self‐attention and Graph Convolutional Network (GCN). Particularly, the dependency syntax based on dependency distances incorporates word‐to‐word dependency distances into the traditional dependency syntax, which enriches the syntactic structural features of the text. After the experiments, the accuracy of BiGADG model is 86.53%, which is 3.11% higher compared to BERT model. It is evident that our model for sentiment classification has been improved. Based on the results of sentiment classification, we investigate the existence of negative herding behaviour and its underlying motivation in online public opinion. We have found that the negative herding effect, if not effectively managed, may trigger large‐scale and uncontrollable public opinion crises.

Summarizing source code with Heterogeneous Syntax Graph and dual position

Code summarization attempts to summarize the semantics of source code by automatically producing brief natural-language descriptions. Most existing work proposes to learn from the Abstract Syntax Tree (AST) and plain text of source code for summary generation. However, little attention has been paid to the structural heterogeneity and layout features of source code. In this paper, we present a novel framework titled HetSum to address these issues. Specifically, a Heterogeneous Syntax Graph (HSG) is first built by designing six types of augmented edges in AST, which indicates the heterogeneous structure of source code. Meanwhile, a dual position is designed for each token in the source code by considering the layout information. Moreover, we develop a heterogeneous graph neural network in HetSum to encode the HSG while extracting the code layout features with the Transformer encoder. By assimilating the learned code token vectors into the HSG encoder, HetSum can capture the relations between its two encoders for improved code representation. To facilitate the generation of high-quality summaries, we integrate a copying mechanism into the decoding procedure while expanding the Transformer decoding sublayer. Extensive experiments on the Java and Python datasets prove that HetSum is superior to seventeen state-of-the-art baselines. To promote reproducibility studies, we make the implementation of HetSum available at https://github.com/GJCEXP/HETSUM.

Aspect-Based Sentiment Analysis with Dependency Relation Weighted Graph Attention

Aspect-based sentiment analysis is a fine-grained sentiment analysis that focuses on the sentiment polarity of different aspects of text, and most current research methods use a combination of dependent syntactic analysis and graphical neural networks. In this paper, a graph attention network aspect-based sentiment analysis model based on the weighting of dependencies (WGAT) is designed to address the problem in that traditional models do not sufficiently analyse the types of syntactic dependencies; in the proposed model, graph attention networks can be weighted and averaged according to the importance of different nodes when aggregating information. The model first transforms the input text into a low-dimensional word vector through pretraining, while generating a dependency syntax graph by analysing the dependency syntax of the input text and constructing a dependency weighted adjacency matrix according to the importance of different dependencies in the graph. The word vector and the dependency weighted adjacency matrix are then fed into a graph attention network for feature extraction, and sentiment polarity is predicted through the classification layer. The model can focus on syntactic dependencies that are more important for sentiment classification during training, and the results of the comparison experiments on the Semeval-2014 laptop and restaurant datasets and the ACL-14 Twitter social comment dataset show that the WGAT model has significantly improved accuracy and F1 values compared to other baseline models, validating its effectiveness in aspect-level sentiment analysis tasks.

Understand Me, if You Refer to Aspect Knowledge: Knowledge-Aware Gated Recurrent Memory Network

Aspect-level sentiment classification (ASC) aims to predict the fine-grained sentiment polarity towards a given aspect mentioned in a review. Despite recent advances in ASC, enabling machines to preciously infer aspect sentiments is still challenging. This paper tackles two challenges in ASC: (1) due to lack of aspect knowledge, aspect representation derived in prior works is inadequate to represent aspect’s exact meaning and property information; (2) prior works only capture either local syntactic information or global relational information, thus missing either one of them leads to insufficient syntactic information. To tackle these challenges, we propose a novel ASC model which not only end-to-end embeds and leverages aspect knowledge but also marries the two kinds of syntactic information and lets them compensate for each other. Our model includes four key components: (1) a knowledge-aware gated recurrent memory network recurrently integrates dynamically summarized aspect knowledge; (2) a dual syntax graph network combines both kinds of syntactic information to comprehensively capture sufficient syntactic information; (3) a knowledge integrating gate re-enhances the final representation with further needed aspect knowledge; (4) an aspect-to-context attention mechanism aggregates the aspect-related semantics from all hidden states into the final representation. Experimental results on several benchmark datasets demonstrate the effectiveness of our model, which overpass previous state-of-the-art models by large margins in terms of both Accuracy and Macro-F1. To facilitate further research in the community, we have released our source code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XingBowen714/KaGRMN-DSG_ABSA</uri> .

Conditional Probability Joint Extraction of Nested Biomedical Events: Design of a Unified Extraction Framework Based on Neural Networks.

BackgroundEvent extraction is essential for natural language processing. In the biomedical field, the nested event phenomenon (event A as a participating role of event B) makes extracting this event more difficult than extracting a single event. Therefore, the performance of nested biomedical events is always underwhelming. In addition, previous works relied on a pipeline to build an event extraction model, which ignored the dependence between trigger recognition and event argument detection tasks and produced significant cascading errors.ObjectiveThis study aims to design a unified framework to jointly train biomedical event triggers and arguments and improve the performance of extracting nested biomedical events.MethodsWe proposed an end-to-end joint extraction model that considers the probability distribution of triggers to alleviate cascading errors. Moreover, we integrated the syntactic structure into an attention-based gate graph convolutional network to capture potential interrelations between triggers and related entities, which improved the performance of extracting nested biomedical events.ResultsThe experimental results demonstrated that our proposed method achieved the best F1 score on the multilevel event extraction biomedical event extraction corpus and achieved a favorable performance on the biomedical natural language processing shared task 2011 Genia event corpus.ConclusionsOur conditional probability joint extraction model is good at extracting nested biomedical events because of the joint extraction mechanism and the syntax graph structure. Moreover, as our model did not rely on external knowledge and specific feature engineering, it had a particular generalization performance.

String diagram rewrite theory II: Rewriting with symmetric monoidal structure

AbstractSymmetric monoidal theories (SMTs) generalise algebraic theories in a way that make them suitable to express resource-sensitive systems, in which variables cannot be copied or discarded at will. In SMTs, traditional tree-like terms are replaced by string diagrams, topological entities that can be intuitively thought of as diagrams of wires and boxes. Recently, string diagrams have become increasingly popular as a graphical syntax to reason about computational models across diverse fields, including programming language semantics, circuit theory, quantum mechanics, linguistics, and control theory. In applications, it is often convenient to implement the equations appearing in SMTs as rewriting rules. This poses the challenge of extending the traditional theory of term rewriting, which has been developed for algebraic theories, to string diagrams. In this paper, we develop a mathematical theory of string diagram rewriting for SMTs. Our approach exploits the correspondence between string diagram rewriting and double pushout (DPO) rewriting of certain graphs, introduced in the first paper of this series. Such a correspondence is only sound when the SMT includes a Frobenius algebra structure. In the present work, we show how an analogous correspondence may be established for arbitrary SMTs, once an appropriate notion of DPO rewriting (which we call convex) is identified. As proof of concept, we use our approach to show termination of two SMTs of interest: Frobenius semi-algebras and bialgebras.

String Diagram Rewrite Theory I: Rewriting with Frobenius Structure

String diagrams are a powerful and intuitive graphical syntax, originating in theoretical physics and later formalised in the context of symmetric monoidal categories. In recent years, they have found application in the modelling of various computational structures, in fields as diverse as Computer Science, Physics, Control Theory, Linguistics, and Biology. In several of these proposals, transformations of systems are modelled as rewrite rules of diagrams. These developments require a mathematical foundation for string diagram rewriting: whereas rewrite theory for terms is well-understood, the two-dimensional nature of string diagrams poses quite a few additional challenges. This work systematises and expands a series of recent conference papers, laying down such a foundation. As a first step, we focus on the case of rewrite systems for string diagrammatic theories that feature a Frobenius algebra. This common structure provides a more permissive notion of composition than the usual one available in monoidal categories, and has found many applications in areas such as concurrency, quantum theory, and electrical circuits. Notably, this structure provides an exact correspondence between the syntactic notion of string diagrams modulo Frobenius structure and the combinatorial structure of hypergraphs. Our work introduces a combinatorial interpretation of string diagram rewriting modulo Frobenius structures in terms of double-pushout hypergraph rewriting. We prove this interpretation to be sound and complete and we also show that the approach can be generalised to rewriting modulo multiple Frobenius structures. As a proof of concept, we show how to derive from these results a termination strategy for Interacting Bialgebras, an important rewrite theory in the study of quantum circuits and signal flow graphs.

Aspect-Level Sentiment Analysis via a Syntax-Based Neural Network

Aspect-level sentiment classification (ALSC); a fine-grained task of sentiment analysis holds the promise of machines communicating knowledge of different aspects of a product/service with humans. Specifically, ALSC aims at inferring the sentiment expressed toward a specific aspect term in a user-generated textual content. It is a long-standing problem in sentiment analysis, and its usefulness cannot be underestimated. Recent studies employ the dependency tree and restructure it to model the syntactic relationship among words in text. However, the restructuring process destructs the structural information of the original dependency tree. As a solution, we first construct a syntax graph that preserves the structural information of the dependency tree. We then propose a reliable syntax-based neural network model that performs a thorough search on the syntax graph to effectively find the relevant contextual information with respect to the aspect term for the sentence encoding. Noting that dependency trees parsed from existing dependency parsers may contain incorrect syntactic dependencies due to grammatical errors in a sentence, we adopt a convolutional layer that takes into account the relations among features of words in a local neighborhood to mitigate the issues brought by incorrect syntactic dependencies. Our results on benchmark datasets demonstrate that our model outperforms the previous methods and achieves state-of-the-art results for the ALSC task.

Exergetic Port-Hamiltonian Systems: Navier-Stokes-Fourier Fluid

The Exergetic Port-Hamiltonian Systems modeling language combines a graphical syntax inspired by bond graphs with a port-Hamiltonian semantics akin to the GENERIC formalism. The syntax enables the modular and hierarchical specification of the composition pattern of lumped and distributed-parameter models. The semantics reflects the first and second law of thermodynamics as structural properties. Interconnected and hierarchically defined models of multiphysical thermodynamic systems can thus be expressed in a formal language accessible to humans and computers alike. We discuss a composed model of the Navier-Stokes-Fourier fluid on a fixed spatial domain as an example of an open distributed-parameter system. At the top level, the system comprises five subsystems which model kinetic energy storage, internal energy storage, thermal conduction, bulk viscosity, and shear viscosity.

Aspect-guided Syntax Graph Learning for Explainable Recommendation

Explainable recommendation systems provide explanations for recommendation results to improve their transparency and persuasiveness. The existing explainable recommendation methods generate textual explanations without explicitly considering the user's preferences on different aspects of the item. In this paper, we propose a novel explanation generation framework, namely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>A</u></b> spect-guided <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>E</u></b> xplanation generation with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>S</u></b> yntax <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>G</u></b> raph ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AESG</b> ), for explainable recommendation. Specifically, AESG employs a review-based syntax graph to provide a unified view of the user/item details. An aspect-guided graph pooling operator is proposed to extract the aspect-relevant information from the review-based syntax graphs to model the user's preferences on an item at the aspect level. Then, an aspect-guided explanation decoder is developed to generate aspects and aspect-relevant explanations based on the attention mechanism. The experimental results on three real datasets indicate that AESG outperforms state-of-the-art explanation generation methods in both single-aspect and multi-aspect explanation generation tasks, and also achieves comparable or even better preference prediction accuracy than strong baseline methods.

The challenges of visualizing DMLA models

Abstract Multi-layer metamodeling is a metamodeling method that provides great flexibility for domain prototyping, while it also ensures rigid validation of domain rules. Visualization of multi-layer models would be essential in order to promote this new modeling technique in industrial setups; however, the traditional way of representing model entities is not sufficient here. Dynamic Multi-Layer Algebra is a multi-layer metamodeling approach that addressed the challenges of multi-layer modeling, but uses a text-based language to create models. This paper presents Dynamo, a visual notation for Dynamic Multi-Layer Algebra, which is also applicable to other multi-layer metamodeling approaches. The paper elaborates the graphical syntax and the design decisions.

Bialgebraic foundations for the operational semantics of string diagrams

Turi and Plotkin's bialgebraic semantics is an abstract approach to specifying the operational semantics of a system, by means of a distributive law between its syntax (encoded as a monad) and its dynamics (an endofunctor). This setup is instrumental in showing that a semantic specification (a coalgebra) is compositional.In this work, we use the bialgebraic approach to derive well-behaved structural operational semantics of string diagrams, a graphical syntax that is increasingly used in the study of interacting systems across different disciplines. Our analysis relies on representing the two-dimensional operations underlying string diagrams in various categories as a monad, and their semantics as a distributive law for that monad.As a proof of concept, we provide bialgebraic semantics for a versatile string diagrammatic language which has been used to model both signal flow graphs (control theory) and Petri nets (concurrency theory).

Replicate, Walk, and Stop on Syntax: An Effective Neural Network Model for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ALSC) aims at predicting the sentiment polarity of a specific aspect term occurring in a sentence. This task requires learning a representation by aggregating the relevant contextual features concerning the aspect term. Existing methods cannot sufficiently leverage the syntactic structure of the sentence, and hence are difficult to distinguish different sentiments for multiple aspects in a sentence. We perceive the limitations of the previous methods and propose a hypothesis about finding crucial contextual information with the help of syntactic structure. For this purpose, we present a neural network model named RepWalk which performs a replicated random walk on a syntax graph, to effectively focus on the informative contextual words. Empirical studies show that our model outperforms recent models on most of the benchmark datasets for the ALSC task. The results suggest that our method for incorporating syntactic structure enriches the representation for the classification.

A Graphical Toolkit for IEC 62264-2

Among the plethora of industrial standards available in the context of smart manufacturing, one series of standards is consistently being mentioned for dealing with manufacturing operations management: IEC 62264. Its second part provides a conceptual model for the description of production systems and their capabilities, including runtime information such as concrete maintenance schedules or achieved production goals. In this work, we present a concrete graphical syntax and toolkit for the creation and presentation of IEC 62264-2 compliant models, using techniques from model-driven (software) engineering. We have evaluated our tool by conducting a user study for assessing its usability and effectiveness.

Categorical models for process planning

Process plans provide a structure for 1) identifying the tasks involved in a given process, 2) the resources needed to accomplish them, and 3) a variety of relationships and constraints between these. This information guides important operational decisions across various organizational levels, from the factory floor to the global supply chain. Efficient use of this information requires a concrete analytical model that can be easily represented in digital form.In this paper we present a modeling framework for process plans based on a branch of mathematics called category theory (CT). Specifically, string diagrams provide an intuitive yet precise graphical syntax for describing symmetric monoidal categories (SMCs), mathematical structures which support serial and parallel composition. Ideal for process representation, these structures also support a powerful mathematical toolkit. Here we use these tools to analyze the relationship between different levels of abstraction in process planning hierarchy. We also model some spatiotemporal aspects of planning, dynamic decision-making inside the process hierarchy and the updating procedure in the event of errors or extensions in the production line.

From a BPMN Black Box to a Smalldb State Machine

The state of a state machine is a path between two actions; however, it is the rest of the world who walks the path. The development of a web application, especially of an information system, starts with use cases, i.e., model scenarios of how users interact with the application and its entities. The goal of this paper is to turn the use cases into a useful specification and automatically convert them into a model layer of a web application, in our case using finite automata. Business Process Model and Notation (BPMN) provides a graphical syntax to capture the use cases, which is based on the theoretical background of Petri Nets. However, because BPMN does not capture the state of the modeled entities, it is impractical to use it as a specification of a persistent storage and model layers of the web application. To overcome this problem, we propose a new STS algorithm to infer a finite automaton that implements a chosen participant in a BPMN diagram that represents a given entity of the web application.

USL: A Domain-Specific Language for Precise Specification of Use Cases and Its Transformations

A use case model is often represented by a UML use case diagram and loosely structured textual descriptions. The use case model expressed in such a form contains ambiguous and imprecise parts. This prevents integrating it into model-driven approaches, where use case models are often taken as the source of transformations. In this paper, we introduce a domain-specific language named the Use case Specification Language (USL) to precisely specify use cases. We define the abstract syntax of USL using a metamodel together with OCL wellformedness rules and then provide a graphical concrete syntax for the usability goal. We also define a precise semantics for USL by mapping USL models to Labelled Transition Systems (LTSs). It opens a possibility to transform USL models to software artifacts such as test cases and design models. We focus on a transformation from a USL model to a template-based use case description in order to illustrate our method. A language evaluation of USL is also performed in this paper.