Model-driven engineering is a well-established software development methodology that uses models to develop applications where the end-users with visual elements model abstractions from a specific domain. These models are based on domain-specific mod...
The development of modern production systems involves numerous interdependent disciplines, heterogeneous data sources, and frequent design iterations, making the conceptual design phase particularly complex and error-prone. Model-Based Systems Engine...
Electric vehicles (EVs) and their ecosystem have unquestionably made significant technological strides. Indeed, EVs have evolved into sophisticated computer systems with extensive internal and external communication capabilities. This interconnection...
Ontology engineering (OE) plays a critical role in modeling and managing structured knowledge across various domains. This study examines the performance of fine-tuned large language models (LLMs), specifically GPT-4 and Mistral 7B, in efficiently au...
Textual editors are omnipresent in all software tools. Editors provide basic features, such as copy-pasting and searching, or more advanced features, such as error checking and text completion. Current technologies in model-driven engineering can aut...
Traditional methods for urban power grid design have struggled to meet the demands of multi-energy integration and high resilience scenarios due to issues such as delayed updates of terminology and semantic ambiguity. Current techniques for construct...
Background: Malnutrition is a serious health concern among older adults in residential aged care (RAC), and timely identification is critical for effective intervention. Recent advancements in transformer-based large language models (LLMs), such as R...
The final objective of smart cities is to optimize services and improve the quality of life of their citizens, who can play important roles due to the information they can provide. This information can be used in order to enhance many sectors involve...
In the field of edge-cloud computing environments, there is a continuous quest for new and simplified methods to automate the deployment and runtime adaptation to application lifecycle changes. Towards that end, cloud providers promote their own serv...
To support data-driven decision-making in a Manufacturing Execution System (MES) environment, a system that can quickly and accurately analyze a wide range of production, quality, asset, and material information must be deployed. However, existing ME...
This study proposes a large language model, Corr-Lora-RAG, designed to address the complexity and uncertainty inherent in corrosion data. A dedicated corrosion knowledge database (CKD) was constructed, and dataset generation code was provided to enha...
Generative large language models (LLMs) have revolutionized the development of knowledge-based systems, enabling new possibilities in applications like ChatGPT, Bing, and Gemini. Two key strategies for domain adaptation in these systems are Domain-Sp...
Artificial intelligence (AI) has the potential to transform healthcare by supporting more accurate diagnoses and personalized treatments. However, its adoption in practice remains constrained by fragmented data sources, strict privacy rules, and the...
End-user programming may utilize Domain-Specific Modeling Languages (DSMLs) to develop applications in the form of models, using only abstractions found in a specific problem domain. Indeed, the productivity benefits reported from Model-Driven Develo...
Recommender Systems (RSs) have become essential for suggesting personalized recommendations to users across various fields, especially in tourism. Due to the rising popularity of mobile devices, mobile RSs have emerged as a potential research area. H...
Legal text summarization presents distinct challenges due to the intricate and domain-specific nature of legal language. This paper introduces a novel framework integrating dynamic Retrieval-Augmented Generation (RAG) with domain-specific adaptation...
Adapting open-source large language models (LLMs) to specialized domains remains a critical challenge due to domain knowledge gaps, data scarcity, and reference hallucination. Existing approaches often neglect the structural characteristics of domain...
Internet of Things (IoT)-based public transportation systems face distinct challenges within the broader realm of IoT. Developers of such systems encounter a notably intricate development environment compared to general IoT systems, which are inheren...
Application development for the cyber-physical systems (CPS) domain is considered a quite complex procedure, since it not only requires a high level of expertise but also deep knowledge of heterogeneous domains. On the other hand, modern low-code sol...
This review provides a comprehensive overview of the evolution and application of artificial intelligence (AI) and large language models (LLMs) in engineering, with a specific focus on chemical engineering. The review traces the historical developmen...
A common problem in the development of Internet-of-Things (IoT) and cyber–physical system (CPS) applications is the complexity of these domains, due to their hybrid and distributed nature at multiple layers (hardware, network, communication, fr...
Microservice Architecture (MSA) is a rising trend in software architecture design. Applications based on MSA are distributed applications whose components are microservices. MSA has already been adopted with great success by numerous companies, and a...
Data collection from distributed automated production systems is one of the main prerequisites to leverage information gain from data analysis in the context of Industrie 4.0, e.g., for the optimization of product quality. However, the realization of...
The rollout of smart grid solutions has already started and new methods are deployed to the power systems of today. However, complexity is still increasing as focus is moving from a single system, to a system of systems perspective. The results are i...
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval to improve factual consistency and reduce hallucinations. Despite growing interest, its use in healthcare remains fragmented. This...
Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, their performance in domain-specific contexts, such as E-learning, is hindered by the lack of specific domain knowledge. Thi...
As research on biological unmanned aerial vehicle (UAV) swarm control has blossomed, professionals face increasing time and cognitive pressure in mastering the rapidly growing domain knowledge. Although recent general large language models (LLMs) may...
Recent advances in large language models (LLMs) have shown promise in specialized fields, yet their effectiveness is often constrained by limited domain expertise. We present a renewable and hydrogen energy-focused LLM developed by fine-tuning LLaMA...
This paper introduces a novel framework for addressing domain adaptation challenges in large language models (LLMs), emphasising privacy-preserving synthetic data generation and efficient fine-tuning. The proposed framework employs a multi-stage appr...
Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the e...
In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the co...
Achieving the Sustainable Development Goals (SDGs) requires collaboration among various stakeholders, particularly governments and non-state actors (NSAs). This collaboration results in but is also based on a continually growing volume of documents t...
9 August 2024
Background: In biomedical imaging research, experimental biologists generate vast amounts of data that require advanced computational analysis. Breakthroughs in experimental techniques, such as multiplex immunofluorescence tissue imaging, enable deta...
Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we d...
In this paper, we present a novel Artificial Intelligence (AI) -empowered system that enhances large language models and other machine learning tools with rules to provide primary care diagnostic advice to patients. Specifically, we introduce a novel...
Large language models (LLMs) such as Chat Generative Pre-Trained Transformer (ChatGPT) are increasingly used across domains, yet their generic training data and propensity for hallucination limit reliability in safety-critical fields like forestry. T...
In Smart Cities Systems, one of the main problems of its development is the integration of different devices and sensors. Many of them are incompatible with each other (different platforms, communication protocols, etc.). In this paper a Domain Speci...
Unlike conventional software, robotic software suffers from a lack of methods and processes that could systematize and facilitate development. Thus, the application of software engineering techniques is at the heart of current issues in robotics. The...
Amid the current industrial revolution, a total disruption of the existing production lines may seem to be the easiest approach, as the potential possibilities seem limitless when starting from the ground up. On the business side, an adaptation of ex...
Developing secure and well-structured web applications using Spring Boot presents a significant challenge, as it requires developers to manage multiple layers, employ framework-specific annotations, and ensure authentication, authorization, and compl...
Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices’ hardware and software when combi...
The emergence of large language models (LLMs) has introduced new possibilities for government-oriented question-answering (QA) systems. Nonetheless, limitations in retrieval accuracy and response quality assessment remain pressing challenges. This st...
Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networ...
Existing Retrieval-Augmented Generation (RAG) systems face challenges in enterprise settings due to limited retrieval scope and data security risks. When relevant internal documents are unavailable, the system struggles to generate accurate and compl...
In the field of Natural Language Processing (NLP), the lack of support for minority languages, especially Uyghur, the scarcity of Uyghur language corpora in the agricultural domain, and the lightweight nature of large language models remain prominent...
Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML...
We consider the problem of solving Natural Language Understanding (NLU) tasks characterized by domain-specific data. An effective approach consists of pre-training Transformer-based language models from scratch using domain-specific data before fine-...
This paper proposes a speech recognition method based on a domain-specific language speech network (DSL-Net) and a confidence decision network (CD-Net). The method involves automatically training a domain-specific dataset, using pre-trained model par...
At 2000 m depth in the oceans, one can hear biological, seismological, meteorological, and anthropogenic activity. Acoustic monitoring of the oceans at a global scale and over long periods of time could bring important information for various science...
Question answering over domain-specific knowledge graphs implies several challenges. It requires sufficient knowledge of the world and the domain to understand what is being asked, familiarity with the knowledge graph’s structure to build a cor...
of 16