Design and Implementation of a Domain-specific Language for Modelling Evacuation Scenarios Using Eclipse EMG/GMF Tool
- URL: http://arxiv.org/abs/2509.06688v1
- Date: Mon, 08 Sep 2025 13:43:56 GMT
- Title: Design and Implementation of a Domain-specific Language for Modelling Evacuation Scenarios Using Eclipse EMG/GMF Tool
- Authors: Heerok Banerjee,
- Abstract summary: In this paper, a DSL namely, Bmod is introduced, which can be used to model evacuation scenarios.<n>The language is built using Eclipse Modelling Framework (EMF) and Eclipse Modelling Framework (GMF)
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Domain-specific languages (DSLs) play a crucial role in resolving internal dependencies across enterprises and boosts their upfront business management processes. Yet, a lot of development is needed to build modelling frameworks which support graphical interfaces (canvas, pallettes etc.), hierarchical structures and easy implementation to shorten the gap for novice users. In this paper, a DSL namely, Bmod is introduced, which can be used to model evacuation scenarios. The language is built using Eclipse Modelling Framework (EMF) and Eclipse Graphical Modelling Framework (GMF). Furthermore, a comparison is also shown between Eclipse EMF/GMF and other modelling tools such as AToMPM, metaDepth, Sirius etc with respect to expressiveness, learning curve and performance.
Related papers
- ThinkGen: Generalized Thinking for Visual Generation [97.19923474851987]
ThinkGen is a think-driven visual generation framework that explicitly leverages Chain-of-Thought (CoT) reasoning in various generation scenarios.<n>We propose a separable GRPO-based training paradigm, alternating reinforcement learning between the MLLM and DiT modules.<n>Experiments demonstrate that ThinkGen achieves robust, state-of-the-art performance across multiple generation benchmarks.
arXiv Detail & Related papers (2025-12-29T16:08:50Z) - M, Toolchain and Language for Reusable Model Compilation [1.3048920509133806]
M is a toolchain and modeling language designed to support system modeling and multi-target compilation.<n>It provides constructs for modeling system entities, message-based interactions, and time- or state-triggered reactions.
arXiv Detail & Related papers (2025-11-19T09:21:46Z) - MCP4IFC: IFC-Based Building Design Using Large Language Models [10.715011902262617]
MCP4IFC is a comprehensive open-source framework that enables Large Language Models (LLMs) to manipulate Industry Foundation Classes (IFC) data.<n>Our framework is released as open-source to encourage research in BIM-driven design and provide a foundation for AI-assisted modeling.
arXiv Detail & Related papers (2025-10-29T09:14:14Z) - Real-Time World Crafting: Generating Structured Game Behaviors from Natural Language with Large Language Models [0.8869777013253825]
We present a novel architecture for safely integrating Large Language Models into interactive game engines.<n>Our framework mitigates risks by using an LLM to translate commands into a constrained Domain-Specific Language.<n>We evaluate this system in a 2D spell-crafting game prototype.
arXiv Detail & Related papers (2025-10-19T18:09:44Z) - Segment Any Architectural Facades (SAAF):An automatic segmentation model for building facades, walls and windows based on multimodal semantics guidance [17.461797749810327]
This study proposes an automatic segmentation model for building facade walls and windows based on multimodal semantic guidance.<n>We developed an end-to-end training framework that enables the model to autonomously learn the mapping relationship from text descriptions to image segmentation.<n>Our model has made certain progress in improving the accuracy and generalization ability of the wall and window segmentation task.
arXiv Detail & Related papers (2025-06-09T13:16:46Z) - Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework [0.3749861135832073]
The Text2 BIM framework generates 3D building models from natural language instructions.<n>A rule-based model checker is introduced into the agentic workflow to guide the LLM agents in resolving issues.<n>The framework can effectively generate high-quality, structurally rational building models.
arXiv Detail & Related papers (2024-08-15T09:48:45Z) - ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning [72.90823351726374]
We introduce the Unified framework for Large Language Model Embedding (ULLME), a flexible, plug-and-play implementation that enables bidirectional attention across various LLMs.
We also propose Generation-augmented Representation Learning (GRL), a novel fine-tuning method to boost LLMs for text embedding tasks.
To showcase our framework's flexibility and effectiveness, we release three pre-trained models from ULLME with different backbone architectures.
arXiv Detail & Related papers (2024-08-06T18:53:54Z) - CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models.
CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
arXiv Detail & Related papers (2024-04-03T02:21:46Z) - GiT: Towards Generalist Vision Transformer through Universal Language Interface [94.33443158125186]
This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT.
GiT is a multi-task visual model, jointly trained across five representative benchmarks without task-specific fine-tuning.
arXiv Detail & Related papers (2024-03-14T13:47:41Z) - Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception [63.03288425612792]
We propose bfAnyRef, a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references.
Our model achieves state-of-the-art results across multiple benchmarks, including diverse modality referring segmentation and region-level referring expression generation.
arXiv Detail & Related papers (2024-03-05T13:45:46Z) - Towards Automated Support for the Co-Evolution of Meta-Models and
Grammars [0.0]
We focus on a model-driven engineering (MDE) approach based on meta-models to develop textual languages.
In this thesis, we propose an approach that can support the co-evolution of meta-models and grammars.
arXiv Detail & Related papers (2023-12-10T23:34:07Z) - A Vision for Flexibile GLSP-based Web Modeling Tools [0.0]
Web-based modeling tools have started to become increasingly popular for visualizing and editing models adhering to such languages in the industry.
One of the technologies behind this new generation of tools is the Graphical Language Server Platform (GLSP), an open-source client-server framework hosted under the Eclipse foundation.
In this paper, we describe our vision of more flexible modeling tools which is based on our experiences from developing several GLSP-based modeling tools.
arXiv Detail & Related papers (2023-07-03T20:57:44Z) - Language Models are General-Purpose Interfaces [109.45478241369655]
We propose to use language models as a general-purpose interface to various foundation models.
A collection of pretrained encoders perceive diverse modalities (such as vision, and language)
We propose a semi-causal language modeling objective to jointly pretrain the interface and the modular encoders.
arXiv Detail & Related papers (2022-06-13T17:34:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.