Modeling Language Research Articles

Reviews on Transformer-based Models for Financial Time Series Forecasting

Abstracts. Relationship extraction is a key step in information extraction and aims to identify entities from text data and identify semantic links between them, which is the foundation of knowledge graph construction. Improving relationship extraction efficiency can effectively enhance the quality of information extraction, which has an impact on the construction of a knowledge graph and subsequent downstream tasks. The paper improves the efficiency and quality of relationship extraction by optimizing the model, choosing the way of relationship extraction and proposing new evaluation parameter criteria. The paper summarizes the current state-of-the-art research results, and briefly describes SIEF, an evaluation mechanism that focuses on important sentences, Coreference Aid, which captures the internal structure of entities as well as the external fine-grained information, and FILR, a framework that extracts global document information and performs multi-granular logical reasoning. Aggregate semi-automated data augmentation by integrating large model languages, remote supervision and construction of domain graphs integrating domain information of entities, cross-article entity-centered relationship extraction capable of capturing connections between different articles, and the introduction of mean-accuracy averaging capable of evaluating the comprehensibility of the model are presented. This paper presents a vision of new and better optimization algorithms by describing the latest approaches to relationship extraction optimization and finding that they all have their own limitations.

A Review and Outlook of the Latest Results on Document-level Information Extraction

Abstracts. Relationship extraction is a key step in information extraction and aims to identify entities from text data and identify semantic links between them, which is the foundation of knowledge graph construction. Improving relationship extraction efficiency can effectively enhance the quality of information extraction, which has an impact on the construction of a knowledge graph and subsequent downstream tasks. The paper improves the efficiency and quality of relationship extraction by optimizing the model, choosing the way of relationship extraction and proposing new evaluation parameter criteria. The paper summarizes the current state-of-the-art research results, and briefly describes SIEF, an evaluation mechanism that focuses on important sentences, Coreference Aid, which captures the internal structure of entities as well as the external fine-grained information, and FILR, a framework that extracts global document information and performs multi-granular logical reasoning. Aggregate semi-automated data augmentation by integrating large model languages, remote supervision and construction of domain graphs integrating domain information of entities, cross-article entity-centered relationship extraction capable of capturing connections between different articles, and the introduction of mean-accuracy averaging capable of evaluating the comprehensibility of the model are presented. This paper presents a vision of new and better optimization algorithms by describing the latest approaches to relationship extraction optimization and finding that they all have their own limitations.

On Sequential Structures in Incompressible Multidimensional Networks

In order to deal with multidimensional structure representations of real-world networks, as well as with their worst-case irreducible information content analysis, the demand for new graph abstractions increases. This article investigates incompressible multidimensional networks defined by generalized graph representations. In particular, we mathematically study the lossless incompressibility of snapshot-dynamic networks and multiplex networks in comparison to the lossless incompressibility of more general forms of dynamic networks and multilayer networks, from which snapshot-dynamic networks or multiplex networks are particular cases. Our theoretical investigation first explores fundamental and basic conditions for connecting the sequential growth of information with sequential interdimensional structures such as time in dynamic networks, and secondly it presents open problems demanding future investigation. Although there may be a dissonance between sequential information dynamics and sequential topology in the general case, we demonstrate that incompressibility (or algorithmic randomness) dissolves it, preventing both the algorithmic dynamics and the interdimensional structure of multidimensional networks from displaying a snapshot-like behavior (as characterized by any arbitrary mathematical theory). Thus, beyond methods based on statistics or probability as traditionally seen in random graphs and complex networks models, representational incompressibility implies a necessary underlying constraint in the multidimensional network topology. We argue that the study of how isomorphic transformations and their respective algorithmic information distortions can characterize sequential interdimensional structures in (multidimensional) networks helps the analysis of network topological properties while being agnostic to the chosen theory, algorithm, computation model, and programming language.

From single-objective to multi-objective reinforcement learning-based model transformation

AbstractModel-driven optimization allows to directly apply domain-specific modeling languages to define models which are subsequently optimized by applying a predefined set of model transformation rules. Objectives guide the optimization processes which can range from one single objective formulation resulting in one single solution to a set of objectives that necessitates the identification of a Pareto-optimal set of solutions. In recent years, a multitude of reinforcement learning approaches has been proposed that support both optimization cases and competitive results for various problem instances have been reported. However, their application to the field of model-driven optimization has not gained much attention yet, especially when compared to the extensive application of meta-heuristic search approaches such as genetic algorithms. Thus, there is a lack of knowledge about the applicability and performance of reinforcement learning for model-driven optimization. We therefore present in this paper a general framework for applying reinforcement learning to model-driven optimization problems. In particular, we show how a catalog of different reinforcement learning algorithms can be integrated with existing model-driven optimization approaches that use a transformation rule application encoding. We exemplify this integration by presenting a dedicated reinforcement learning extension for MOMoT. We build on this tool support and investigate several case studies for validating the applicability of reinforcement learning for model-driven optimization and compare the performance against a genetic algorithm. The results show clear advantages of using RL for single-objective problems, especially for cases where the transformation steps are highly dependent on each other. For multi-objective problems, the results are more diverse and case-specific, which further motivates the usage of model-driven optimization to utilize different approaches to find the best solutions.

Visual Modeling Languages in Patient Pathways: Scoping Review.

Patient pathways (PPs) are presented as a panacea solution to enhance health system functions. It is a complex concept that needs to be described and communicated well. Modeling plays a crucial role in promoting communication, fostering a shared understanding, and streamlining processes. Only a few existing systematic reviews have focused on modeling methods and standardized modeling languages. There remains a gap in consolidated knowledge regarding the use of diverse visual modeling languages. This scoping review aimed to compile visual modeling languages used to represent PPs, including the justifications and the context in which a modeling language was adopted, adapted, combined, or developed. After initial experimentation with the keywords used to describe the concepts of PPs and visual modeling languages, we developed a search strategy that was further refined and customized to the major databases identified as topically relevant. In addition, we consulted gray literature and conducted hand searches of the referenced articles. Two reviewers independently screened the articles in 2 stages using preset inclusion criteria, and a third reviewer voted on the discordance. Data charting was done using an iteratively developed form in the Covidence software. Descriptive and thematic summaries were presented following rounds of discussion to produce the final report. Of 1838 articles retrieved after deduplication, 22 satisfied our inclusion criteria. Clinical pathway is the most used phrase to represent the PP concept, and most papers discussed the concept without providing their operational definition. We categorized the visual modeling languages into five categories: (1) general purpose-modeling language (GPML) adopted without major extension or modification, (2) GPML used with formal extension recommendations, (3) combination of 2 or more modeling languages, (4) a developed domain-specific modeling language (DSML), and (5) ontological modeling languages. The justifications for adopting, adapting, combining, and developing visual modeling languages varied accordingly and ranged from versatility, expressiveness, tool support, and extensibility of a language to domain needs, integration, and simplification. Various visual modeling languages were used in PP modeling, each with varying levels of abstraction and granularity. The categorization we made could aid in a better understanding of the complex combination of PP and modeling languages. Standardized GPMLs were used with or without any modifications. The rationale to propose any modification to GPMLs evolved as more evidence was presented following requirement analyses to support domain constructs. DSMLs are infrequently used due to their resource-intensive development, often initiated at a project level. The justifications provided and the context where DSMLs were created are paramount. Future studies should assess the merits and demerits of using a visual modeling language to facilitate PP communications among stakeholders and use evaluation frameworks to identify, modify, or develop them, depending on the scope and goal of the modeling need.

Simulation methods realized by virtual reality modeling language for 3D animation considering fuzzy model recognition

The creation of 3D animation increasingly prioritizes the enhancement of character effects, narrative depth, and audience engagement to address the growing demands for visual stimulation, cultural enrichment, and interactive experiences. The advancement of virtual reality (VR) animation is anticipated to require sustained collaboration among researchers, animation experts, and hardware developers over an extended period to achieve full maturity. This article explores the use of Virtual Reality Modeling Language (VRML) in generating 3D stereoscopic forms and environments, applying texture mapping, optimizing lighting effects, and establishing interactive user responses, thereby enriching the 3D animation experience. VRML’s functionality is further expanded through the integration of script programs in languages such as Java, JavaScript, and VRML Script via the Script node. The implementation of fuzzy model recognition within 3D animation simulations enhances the identification of textual, musical, and linguistic elements, resulting in improved frame rates. This study also analyzes the real-time correlation between the number of polygons and frame rates in a virtual museum animation scene. The findings demonstrate that the frame rate of the 3D animation within this virtual setting consistently exceeds 40 frames per second, thereby ensuring robust real-time performance, preserving the quality of 3D models, and optimizing rendering speed and visual effects without affecting the system’s responsiveness to additional functions.

Business Process Clustering in Small and Medium Manufacturing Industries Based on Enterprise Resource Planning Concepts: A Systematic Literature Review

Abstract Business process management has become increasingly important in the manufacturing sector, playing a vital role in fostering productivity and facilitating organizational adaptability to technological advancements. Although each company’s business process models vary in uniqueness and complexity, certain similarities can be identified from these differences. This study employs a systematic literature review to aggregate and summarize findings on business process models, modeling languages, clustering techniques, and foundational clustering principles. Results demonstrate the dearth of research on the grouping of business process models in the manufacturing industry. Several studies have focused on sectors such as services, trading, and insurance. Research specifically addressing clustering in the manufacturing sector is limited. Existing clustering efforts in manufacturing revolve around groupings related to product defects, industrial locations, business ecosystems, and similar factors. Analysis of the methods, scope, and criteria used in grouping business process models in the manufacturing industry indicates that most approaches rely on structural or graphic similarities. Follow-up research is lacking once these business process clusters are identified. This study proposes a novel approach to grouping, integrating business process modeling with the implementation of a management information system. Business process management relies on integrating departments within the company through an enterprise resource planning (ERP) system. The next step involves proposing a conceptual framework to categorize business processes and assess the comparability of models in the manufacturing sector. Future research directions are also delineated.

Towards a Fault Management Analysis Tool for Model Centric Systems Engineering

In an effort to effectively develop more complex spacecraft fault management (FM) systems new technologies are sought to enable rapid diagnostic model generation and validation, and provide tools to perform FM analyses Model-based Systems Engineering approaches to FM system development are uniquely suited to be combined with model-based tools currently utilized in the design of other parts of flight systems. Combined tools utilizing information from a common system model can reduce design inconsistencies and gaps in analyses. Tighter integration of FM with other system-level and subsystem-level hardware/software development activities allows crucial redundancy and sensor placement trades to be performed earlier and throughout the mission lifecycle. Our work has been towards the integration of a model-based fault management tool suite called MONSID®, with JPL’s Computer Aided Engineering for Systems ARchitecture (CAESAR ) platform as a way to improve FM system modeling and analysis. MONSID relies on application-specific models of the system being monitored. MONSID models consist of interconnected elements representing system hardware and measurement/command input points, called the topology. Model topology design is currently a manual process and often relies heavily on paper documentation such as hardware/software specs, engineering drawings, and interface control documents. CAESAR is a semantically- driven toolchain for model-based system engineering. At the core is a system model expressed in the Ontological Modeling Language (OML). It is intended to support semantic modeling, consistency validation, and continuous integration. A goal of the combined toolset is to automate FM model development by directly extracting models from CAESAR and then analyzing them in MONSID. Analyses currently available in MONSID include model topology inspection and validation and fault isolation capability based on sensor placement. While we have focused on two specific tools, the integration approaches can be leveraged by other semantically driven model-centric platforms and tools. This paper describes the evolution of our integration approaches as we evaluated different insertion points in the CAESAR toolchain with respect to MONSID model requirements. The MONSID-CAESAR tool is demonstrated on a simplified example of a spacecraft heat reclamation system. Results of the generated MONSID model are discussed, including levels of automation achieved and information surfaced to the users about the extracted model topology.

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.

A Systems Biology Approach Towards a Comprehensive Understanding of Ferroptosis.

Ferroptosis is a regulated cell death process characterized by iron ion catalysis and reactive oxygen species, leading to lipid peroxidation. This mechanism plays a crucial role in age-related diseases, including cancer and cardiovascular and neurological disorders. To better mimic iron-induced cell death, predict the effects of various elements, and identify drugs capable of regulating ferroptosis, it is essential to develop precise models of this process. Such drugs can be tested on cellular models. Systems biology offers a powerful approach to studying biological processes through modeling, which involves accumulating and analyzing comprehensive research data. Once a model is created, it allows for examining the system's response to various stimuli. Our goal is to develop a modular framework for ferroptosis, enabling the prediction and screening of compounds with geroprotective and antiferroptotic effects. For modeling and analysis, we utilized BioUML (Biological Universal Modeling Language), which supports key standards in systems biology, modular and visual modeling, rapid simulation, parameter estimation, and a variety of numerical methods. This combination fulfills the requirements for modeling complex biological systems. The integrated modular model was validated on diverse datasets, including original experimental data. This framework encompasses essential molecular genetic processes such as the Fenton reaction, iron metabolism, lipid synthesis, and the antioxidant system. We identified structural relationships between molecular agents within each module and compared them to our proposed system for regulating the initiation and progression of ferroptosis. Our research highlights that no current models comprehensively cover all regulatory mechanisms of ferroptosis. By integrating data on ferroptosis modules into an integrated modular model, we can enhance our understanding of its mechanisms and assist in the discovery of new treatment targets for age-related diseases. A computational model of ferroptosis was developed based on a modular modeling approach and included 73 differential equations and 93 species.

Adaptive modelling languages: Abstract syntax and model migration

Adaptive modelling languages: Abstract syntax and model migration

A study on an efficient OSS inspection scheme based on encrypted GML

The importance of Open Source Software (OSS) has increased in recent years. OSS is software that is jointly developed and maintained globally through open collaboration and knowledge sharing. OSS plays an important role, especially in the Information Technology (IT) field, by increasing the efficiency of software development and reducing costs. However, licensing issues, security issues, etc., may arise when using OSS. Some services analyze source code and provide OSS-related data to solve these problems, a representative example being Blackduck. Blackduck inspects the entiresource code within the project and provides OSS information and related data included in the whole project. Therefore, there are problems such as inefficiency due to full inspection of the source code and difficulty in determining the exact location where OSS is identified. This paper proposes a scheme to intuitively analyze source code through Graph Modelling Language (GML) conversion to solve these problems. Additionally, encryption is applied to GML to performsecure GML-based OSS inspection. The study explains the process of converting source code to GML and performing OSS inspection. Afterward, we compare the capacity and accuracy of text-based OSS inspection and GML-based OSS inspection. Signcryption is applied to performsafe, GML-based, efficient OSS inspection.

Research on modeling method of providing oxygen functional architecture based on model

Abstract The provision of oxygen is an important function of commercial aircraft, which is defined as providing the necessary oxygen for the safety of everyone on board, including crew oxygen, passenger oxygen, and portable oxygen. This article adopts SysML modelling language and proposes a model-based architecture modelling method for providing oxygen function for commercial aircraft. It transforms traditional experience and document-based system engineering work into functional architecture system engineering work, and utilises the correlation and traceability relationships of models to achieve state machine modelling for providing oxygen functional architecture. Through simulation verification, the functional architecture modelling method that provides oxygen can be effectively applied to the architecture of other systems.

Generative AI in the Software Modeling Classroom: An Experience Report With ChatGPT and Unified Modeling Language

Generative AI in the Software Modeling Classroom: An Experience Report With ChatGPT and Unified Modeling Language

Model management to support systems engineering workflows using ontology-based knowledge graphs

System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber-Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in a plethora of different formalisms, each with their own methods, techniques and tools. Storing knowledge on these workflows can reduce considerable effort during system development not only to allow their repeatability and replicability but also to access and reason on data generated by their execution. In this work, we propose a framework to manage modelling artefacts generated from workflow executions. The basic workflow concepts, related formalisms and artefacts are formally defined in an ontology specified in OML (Ontology Modelling Language). This ontology enables the construction of a knowledge graph that contains system engineering data to which we can apply reasoning. We also developed several tools to support system engineering during the design of workflows, their enactment, and artefact storage, considering versioning, querying and reasoning on the stored data. These tools also hide the complexity of manipulating the knowledge graph directly. Finally, we have applied our proposed framework in a real-world system development scenario of a drivetrain smart sensor system. Results show that our proposal not only helped the system engineer with fundamental difficulties like storage and versioning but also reduced the time needed to access relevant information and new knowledge that can be inferred from the knowledge graph.

Expressive rule-based modeling and fast simulation for dynamic compartments.

Compartmentalization is vital for cell biological processes. The field of rule-based stochastic simulation has acknowledged this, and many tools and methods have capabilities for compartmentalization. However, mostly, this is limited to a static compartmental hierarchy and does not integrate compartmental changes. Integrating compartmental dynamics is challenging for the design of the modeling language and the simulation engine. The language should support a concise yet flexible modeling of compartmental dynamics. Our work is based on ML-Rules, a rule-based language for multi-level cell biological modeling that supports a wide variety of compartmental dynamics, whose syntax we slightly adapt. To develop an efficient simulation engine for compartmental dynamics, we combine specific data structures and new and existing algorithms and implement them in the Rust programming language. We evaluate the concept and implementation using two case studies from existing cell-biological models. The execution of these models outperforms previous simulations of ML-Rules by two orders of magnitude. Finally, we present a prototype of a WebAssembly-based implementation to allow for a low barrier of entry when exploring the language and associated models without the need for local installation.

A two-tier database architecture framework for combating cyber threats

The 21st century has witnessed a lot of advancements in science and technology. One may not be wrong to assert that the field of Information and Communications Technology (ICT) and its allied disciplines like Artificial Intelligence (AI) and Nanotechnology have taken the lead as we witness new developments in these areas year by year. The products and services we utilize and enjoy are evidence of the usefulness of this advancement in science and technology. It is worthy of note that the interest of many (both good and bad in the discipline of ICT), is being ignited by the fact that ICT finds its applicability virtually in every area of human endeavor. In this study, we propose a two tier database architecture that employs an Object-Oriented Analysis Design Methodology (OOADM) using the Unified Modelling Language (UML). The choice of this methodology is because we need to present every stage of the study from analysis to the design phases. With the help of the chosen methodology, we analyzed the existing frameworks, identified the weaknesses, and as a result, we further surveyed algorithms used for reliable data security. From the results obtained from the survey, we employed the SHA-256 algorithm because of its strength in data security, and the Argon2 hashing algorithm which is one of the best algorithms for securing database credentials. In the end, we provide an enhanced framework made up of the chosen algorithms and a two-dimensional database (2-DDb), which could be implemented to effectively combat attacks on important databases.

Developing a Graphical Domain-Specific Modeling Language for Efficient Lightweight Block Cipher Schemas Configuration: LWBCLang

The lightweight block cipher is an encryption technique with negligible computational overhead. Despite its advantages, it faces a substantial challenge. Correct handwriting of the script code for the cipher scheme is a challenge for programmers. In this research, we suggest a new graphical domain-specific modeling language to make it easier for both non-technical users and domain specialists to implement lightweight block cipher schemes. The proposed language, called LWBCLang, is a modular and extensible language that offers graphical components for constructing three essential types of inner block cipher structures. Seven different methods of keystream generation and all the tests of the NIST suite with performance analysis are provided. In the context of its meta-model, LWBCLang's abstract and concrete syntaxes are specified. LWBCLang has been implemented as an internal DSML with Python as the host language. The evaluation of LWBCLang is based on qualitative analysis to demonstrate the language's effectiveness and efficiency. Further benefits of this proposed language are evaluated and discussed in depth in this research.

Library Information System at SDN 101826 Tuntungan Web-Based

The development of technology that is so advanced has a real effect on human life. Libraries can also take advantage of this technological advancement, by building a website-based library information system. The SDN library 101826 still managed manually, this is very inefficient, because in addition to wasting paper, data is inaccurate, archiving is not neatly organized and messy. When employees want to find borrowed books, they have to check and search manually. It is very draining of energy and time, because many books are lost because it is not known who the borrower of the book is. Based on these problems, the author provides a solution by building a library information system at the elementary school based on the web. Data collection methods include observation, interviews, and literature studies. Meanwhile, the system development method uses Waterfall, and in its design utilizes Unified Modeling Language. The programming language used in coding is PHP with MySQL database. This research requires a library information system that can help library officers in carrying out their activities. The system that has been built can speed up, make it easier and more efficient for visitors in the book search process.

Do help options facilitate computer‐based L2 listening comprehension? Evidence from an activity theory perspective

AbstractDespite the proliferation of help options for computer‐based second language (L2) listening comprehension, little attention has been given to a comprehensive understanding of the pedagogical effects. To gain a thorough understanding of the overall and moderator effects, drawing on the activity theory (AT), this study conducted a meta‐analysis of 33 valid independent studies on help options for computer‐based L2 listening comprehension with 3697 participants involved. Results demonstrated that (a) compared with those traditional methods, help options are found to be more facilitative for L2 listening comprehension with a small‐to‐moderate effect of g = 0.449, 95% CI [0.303, 0.596]; and (b) the effects of help options for computer‐based L2 listening comprehension are moderated by option types, feedback types, language distance types, intervention durations and learning activity types. Motivated by the results obtained, this study also discussed pedagogical implications for future studies. Practitioner notesWhat is already known about this topic The improved pedagogical effects of help options for computer‐based second language (L2) listening comprehension have been well‐documented. A large number of studies on the use of help options for computer‐based L2 listening comprehension have been published. There has been no research aggregating the overall effects of help options for computer‐based L2 listening comprehension. Some potential factors that may moderate the effects of help options for computer‐based L2 listening comprehension remain underexplored. What this paper adds This paper aims to aggregate the overall and moderator effects from the existing publications of help options for computer‐based L2 listening comprehension. Compared with those traditional methods, help options are found to be more facilitative for L2 listening comprehension with a small‐to‐moderate effect of g = 0.449. The effects of help options for computer‐based L2 listening are moderated by option types, feedback types, language distance types, intervention durations and learning activity types. Implications for practice and/or policy Help options have been confirmed to facilitate computer‐based L2 listening comprehension, especially among learners of primary and secondary levels. Call for more empirical studies investigating how different types of options, feedback, language distance and intervention durations along with learning activity moderate the effectiveness. Further attempts should investigate help options for computer‐based L2 listening comprehension with the integration of generative AI, large language model (LLM) and natural language processing (NLP) techniques.