A Metascience Study of the Impact of Low-Code Techniques in Modeling Publications
- URL: http://arxiv.org/abs/2408.05975v1
- Date: Mon, 12 Aug 2024 08:03:01 GMT
- Title: A Metascience Study of the Impact of Low-Code Techniques in Modeling Publications
- Abstract summary: We present a metascience study of Low-Code.
Our study has a two-fold approach: (1) to examine the composition (size and diversity) of the emerging Low-Code community; and (2) to investigate how this community differs from the "classical" model-driven community in terms of people, venues, and types of publications.
- Score: 2.031841135743809
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the last years, model-related publications have been exploring the application of modeling techniques in different domains. Initially focused on UML and the Model-Driven Architecture approach, the literature has been evolving towards the usage of more general concepts such as Model-Driven Development or Model-Driven Engineering. With the emergence of Low-Code software development platforms, the modeling community has been studying how these two fields may combine and benefit from each other, thus leading to the publication of a number of works in recent years. In this paper, we present a metascience study of Low-Code. Our study has a two-fold approach: (1) to examine the composition (size and diversity) of the emerging Low-Code community; and (2) to investigate how this community differs from the "classical" model-driven community in terms of people, venues, and types of publications. Through this study, we aim to benefit the low-code community by helping them better understand its relationship with the broader modeling community. Ultimately, we hope to trigger a discussion about the current and possible future evolution of the low-code community as part of its consolidation as a new research field.
Related papers
- World Action Models: The Next Frontier in Embodied AI [123.5787299299832]
Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning.<n>They learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention.<n>A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline.
arXiv Detail & Related papers (2026-05-12T13:10:52Z) - The Trinity of Consistency as a Defining Principle for General World Models [106.16462830681452]
General World Models are capable of learning, simulating, and reasoning about objective physical laws.<n>We propose a principled theoretical framework that defines the essential properties requisite for a General World Model.<n>Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.
arXiv Detail & Related papers (2026-02-26T16:15:55Z) - A Brief History of the Waterfall Model: Past, Present, and Future [0.16921396880325779]
The waterfall model has played a foundational role in shaping contemporary software engineering practices.<n>The paper contends that the model's enduring relevance lies in its adaptability.
arXiv Detail & Related papers (2025-10-04T18:22:01Z) - A Survey on Generative Model Unlearning: Fundamentals, Taxonomy, Evaluation, and Future Direction [21.966560704390716]
We review current research on Generative Model Unlearning (GenMU)<n>We propose a unified analytical framework for categorizing unlearning objectives, methodological strategies, and evaluation metrics.<n>We highlight the potential practical value of unlearning techniques in real-world applications.
arXiv Detail & Related papers (2025-07-26T09:49:57Z) - Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities [22.476740954286836]
We present a comprehensive survey aimed at guiding future research.<n>We review existing unified models, categorizing them into three main architectural paradigms.<n>We discuss the key challenges facing this nascent field, including tokenization strategy, cross-modal attention, and data.
arXiv Detail & Related papers (2025-05-05T11:18:03Z) - From Task-Specific Models to Unified Systems: A Review of Model Merging Approaches [13.778158813149833]
This paper establishes a new taxonomy of model merging methods, systematically comparing different approaches and providing an overview of key developments.
Despite the rapid progress in this field, a comprehensive taxonomy and survey summarizing recent advances and predicting future directions are still lacking.
arXiv Detail & Related papers (2025-03-12T02:17:31Z) - A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models [74.48084001058672]
The rise of foundation models has transformed machine learning research.<n> multimodal foundation models (MMFMs) pose unique interpretability challenges beyond unimodal frameworks.<n>This survey explores two key aspects: (1) the adaptation of LLM interpretability methods to multimodal models and (2) understanding the mechanistic differences between unimodal language models and crossmodal systems.
arXiv Detail & Related papers (2025-02-22T20:55:26Z) - A Survey of Model Architectures in Information Retrieval [64.75808744228067]
We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
arXiv Detail & Related papers (2025-02-20T18:42:58Z) - How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond [73.5546464126465]
We present a thorough review of human-model cooperation, exploring its principles, formalizations, and open challenges.
We introduce a new taxonomy that provides a unified perspective to summarize existing approaches.
Also, we discuss potential frontier areas and their corresponding challenges.
arXiv Detail & Related papers (2025-01-10T05:15:14Z) - Exploring Model Kinship for Merging Large Language Models [73.98345036483299]
We study model evolution through iterative merging, drawing an analogy to biological evolution.<n>We show that model kinship is closely linked to the performance improvements achieved by merging.<n>We propose a new model merging strategy: Top-k Greedy Merging with Model Kinship.
arXiv Detail & Related papers (2024-10-16T14:29:29Z) - Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities [89.40778301238642]
Model merging is an efficient empowerment technique in the machine learning community.
There is a significant gap in the literature regarding a systematic and thorough review of these techniques.
arXiv Detail & Related papers (2024-08-14T16:58:48Z) - A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning [136.89318317245855]
MoErging aims to recycle expert models to create an aggregate system with improved performance or generalization.
A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application.
This survey includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method.
arXiv Detail & Related papers (2024-08-13T17:49:00Z) - Spatio-Temporal Graphical Counterfactuals: An Overview [11.616701619068804]
Counteractual is a critical yet challenging topic for artificial intelligence to learn knowledge from data.
Our aim is to investigate a survey to compare thinking and discuss different counterfactual models, theories and approaches.
arXiv Detail & Related papers (2024-07-02T01:34:13Z) - Coding for Intelligence from the Perspective of Category [66.14012258680992]
Coding targets compressing and reconstructing data, and intelligence.
Recent trends demonstrate the potential homogeneity of these two fields.
We propose a novel problem of Coding for Intelligence from the category theory view.
arXiv Detail & Related papers (2024-07-01T07:05:44Z) - Explore In-Context Segmentation via Latent Diffusion Models [132.26274147026854]
latent diffusion model (LDM) is an effective minimalist for in-context segmentation.
We build a new and fair in-context segmentation benchmark that includes both image and video datasets.
arXiv Detail & Related papers (2024-03-14T17:52:31Z) - Advancing Graph Representation Learning with Large Language Models: A
Comprehensive Survey of Techniques [37.60727548905253]
The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures.
This collaboration harnesses the sophisticated linguistic capabilities of LLMs to improve the contextual understanding and adaptability of graph models.
Despite a growing body of research dedicated to integrating LLMs into the graph domain, a comprehensive review that deeply analyzes the core components and operations is notably lacking.
arXiv Detail & Related papers (2024-02-04T05:51:14Z) - A Survey of Reasoning with Foundation Models [235.7288855108172]
Reasoning plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
We introduce seminal foundation models proposed or adaptable for reasoning.
We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models.
arXiv Detail & Related papers (2023-12-17T15:16:13Z) - Advancing State of the Art in Language Modeling [0.0]
Generalization is arguably the most important goal of statistical language modeling research.
Publicly available benchmarks and papers published with an open-source code have been critical to advancing the field.
We propose a simple framework that should help advance the state of the art in language modeling in terms of generalization.
arXiv Detail & Related papers (2023-11-28T12:30:43Z) - In Pursuit of Unification of Conceptual Models: Sets as Machines [0.0]
This manuscript is a sequel in a research venture that belongs to the second approach.
It uses a model called thinging machines founded on Stoic and Lupascian logic.
arXiv Detail & Related papers (2023-06-24T01:28:31Z) - Foundation Models for Decision Making: Problems, Methods, and
Opportunities [124.79381732197649]
Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks.
New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning.
Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
arXiv Detail & Related papers (2023-03-07T18:44:07Z) - Image Quality Assessment in the Modern Age [53.19271326110551]
This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA)
We will first revisit several subjective quality assessment methodologies, with emphasis on how to properly select visual stimuli.
Both hand-engineered and (deep) learning-based methods will be covered.
arXiv Detail & Related papers (2021-10-19T02:38:46Z) - What's New? Summarizing Contributions in Scientific Literature [85.95906677964815]
We introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work.
We extend the S2ORC corpus of academic articles by adding disentangled "contribution" and "context" reference labels.
We propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs.
arXiv Detail & Related papers (2020-11-06T02:23:01Z) - A Theoretical Approach for a Novel Model to Realizing Empathy [0.0]
This paper introduces a theoretical concept as a proposed model that visualizes the process of realizing empathy.
The intended purpose of this proposed model, is to create an initial blueprint that may be applicable to a range of disciplines.
arXiv Detail & Related papers (2020-09-03T17:21:49Z) - From Theory to Behaviour: Towards a General Model of Engagement [3.9198548406564604]
We operationalize engagement mechanistically by linking it directly to human behaviour.
We show that the construct of engagement can be used for shaping and interpreting data-driven methods.
arXiv Detail & Related papers (2020-04-27T08:44:30Z)
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.