Domain Specific Languages - Science topic

While the trend of decentralized governance is obvious (cryptocurrencies and blockchains are widely adopted by multiple sovereign countries), initiating governance proposals within Decentralized Autonomous Organizations (DAOs) is still challenging, i.e., it requires providing a low-level transaction payload, therefore posing significant barriers to...

The tensor notation used in several areas of mathematics is a useful one, but it is not widely available to the functional programming community. In a practical sense, the (embedded) domain-specific languages ( dsl s) that are currently in use for tensor algebra are either 1. array-oriented languages that do not enforce or take advantage of tensor...

Simulation is a favoured technique in robotics. It is, however, costly, in terms of development time, and its usability is limited by the lack of standardisation and portability of simulators. We present RoboSim, a diagrammatic tool-independent domain-specific language to model robotic platforms and their controllers. It can be regarded as a profil...

We present a computation method to automatically design the n-qubit realisations of quantum algorithms. Our approach leverages a domain-specific language (DSL) that enables the construction of quantum circuits via modular building blocks, making it well-suited for evolutionary search. In this DSL quantum circuits are abstracted beyond the usual gat...

Compliance with the GDPR privacy regulation places a significant burden on organisations regarding the handling of personal data. The perceived efforts and risks of complying with the GDPR further increase when data processing activities span across organisational boundaries, as is the case in both small-scale data sharing settings and in large-sca...

The evolution of automotive infotainment systems through artificial intelligence integration represents a fundamental transformation in human-vehicle interaction. Modern vehicles have transcended their transportation role to become sophisticated technological ecosystems where AI technologies address longstanding challenges in user experience, cogni...

Annotation  The article presents a semantic framework for prompt engineering that utilizes natural language for creating and managing artificial intelligence systems. The primary goal is to represent prompts as analogs of programming constructs (conditional statements, loops, functions) through the mathematical formalization of their logic and int...

Data driven and autoregressive indoor scene synthesis systems generate indoor scenes automatically by suggesting and then placing objects one at a time. Empirical observations show that current systems tend to produce incomplete next object location distributions. We introduce a system which addresses this problem. We design a Domain Specific Langu...

Autonomous driving (AD) testing constitutes a critical methodology for assessing performance benchmarks prior to product deployment. The creation of segmented scenarios within a simulated environment is acknowledged as a robust and effective strategy; however, the process of tailoring these scenarios often necessitates laborious and time-consuming...

We introduce ArcPro, a novel learning framework built on architectural programs to recover structured 3D abstractions from highly sparse and low-quality point clouds. Specifically, we design a domain-specific language (DSL) to hierarchically represent building structures as a program, which can be efficiently converted into a mesh. We bridge feedfo...

The Abstraction and Reasoning Corpus (ARC) benchmarks general artificial intelligence, presenting a significant challenge to existing machine learning models and program synthesis solvers due to its focus on broad generalization. In this work, we introduce a Multi-Agent System with Reflection (MASR) for ARC. MASR combines Large Language Models (LLM...

The Abstraction and Reasoning Corpus (ARC) benchmarks broad generalization, and poses a significant challenge to existing machine learning models. In this work, we introduce augmented ARC datasets and a new benchmark (AugARC) for large-language models (LLMs), which measures abstraction and reasoning. We evaluate the accuracy of base LLMs on AugARC...

The Abstraction and Reasoning Corpus (ARC) benchmarks broad generalization in artificial intelligence, and presents a significant challenge to existing machine learning models and program synthesis solvers. In this work, we introduce a Reflection System for ARC. It combines Large Language Models (LLMs) and a program synthesis solver based on a Doma...

Formal languages are an integral part of modeling and simulation. They allow the distillation of knowledge into concise simulation models amenable to automatic execution, interpretation, and analysis. However, the arguably most humanly accessible means of expressing models is through natural language, which is not easily interpretable by computers....

Multi-cloud strategies have become a cornerstone of modern enterprise infrastructure, with organizations leveraging multiple cloud service providers to enhance availability, mitigate vendor lock-in, and optimize performance. However, this distributed approach introduces complex governance challenges, including compliance with global regulations, da...

Multi-cloud strategies have become a cornerstone of modern enterprise infrastructure, with organizations leveraging multiple cloud service providers to enhance availability, mitigate vendor lock-in, and optimize performance. However, this distributed approach introduces complex governance challenges, including compliance with global regulations, da...

Fairness is a critical concept in ethics and social domains, but it is also a challenging property to engineer in software systems. With the increasing use of machine learning in software systems, researchers have been developing techniques to assess the fairness of software systems automatically. Nonetheless, many of these techniques rely upon pre...

Multi-cloud strategies have become a cornerstone of modern enterprise infrastructure, with organizations leveraging multiple cloud service providers to enhance availability, mitigate vendor lock-in, and optimize performance. However, this distributed approach introduces complex governance challenges, including compliance with global regulations, da...

Context: Distributed Stream Processing Frameworks (DSPFs) are popular tools for expressing real-time Big Data applications that have to handle enormous volumes of data in real time. These frameworks distribute their applications over a cluster in order to scale horizontally along with the amount of incoming data. Inquiry: Crucial for the performanc...

Human-computer dialog plays a prominent role in interactions conducted at kiosks (e.g., withdrawing money from an atm or filling your car with gas), on smartphones (e.g., installing and configuring apps), and on the web (e.g., booking a flight). Some human-computer dialogs involve an exchange of system-initiated and user-initiated actions. These di...

Deploying machine learning (ML) models on edge devices presents unique challenges, arising from the different environments used for developing ML models and those required for their deployment, leading to a gray area of competence and expertise between ML engineers and application developers. In this paper, we explore the use of model-driven engine...

The use of diverse apps among senior users is increasing. However, despite their diverse age-related accessibility needs and preferences, these users often encounter apps with significant accessibility barriers. Even in the best-case scenarios, they are provided with one-size-fits-all user interfaces that offer very limited personalisation support....

Parallel and signal processing patterns for large-scale radio data applications have been captured with a new domain-specific language (DSL), OptiSDR. The intermediate representations (IR) of the code are optimized at the frontend with the Delite compiler targeted for heterogeneous computing architecture (HCA). The design flow begins at the abstrac...

Our research explores the application of Large Language Models for enhancing legal advice through AI-driven conversational agents, focusing on the analysis and interpretation of user-generated content from a prominent online legal community. By leveraging BERTopic for topic modeling and GPT-3.5 for labeling of user intents, we constructed a dataset...

When building enterprise applications (EAs) on Java frameworks (e.g., Spring), developers often configure application components via metadata (i.e., Java annotations and XML files). It is challenging for developers to correctly use metadata, because the usage rules can be complex and existing tools provide limited assistance. When developers misuse...

Financial Sentiment Analysis (FSA) traditionally relies on human-annotated sentiment labels to infer investor sentiment and forecast market movements. However, inferring the potential market impact of words based on their human-perceived intentions is inherently challenging. We hypothesize that the historical market reactions to words, offer a more...

Recommender systems have become fundamental computational tools deployed across diverse domains, including e-commerce, tourism, and streaming platforms to facilitate personalized content delivery through the analysis of user preferences, behavioral patterns, and interaction data. With the use of Machine Learning (ML) techniques, these Recommender S...

Cyber-physical systems (CPSs) blend digital and physical processes. CPS software is the key to realizing their functionalities. This software needs to evolve to deal with different aspects, such as the implementation of new functionalities or bug fixes. Because of this, design–operation methods, colloquially known as “DevOps,” are paramount to be a...

Large language models (LLMs) have been enormously successful in solving a wide variety of structured and unstructured generative tasks, but they struggle to generate procedural geometry in Computer Aided Design (CAD). These difficulties arise from an inability to do spatial reasoning and the necessity to guide a model through complex, long range pl...

Jjodel is a cloud-based reflective platform designed to address the challenges of Model-Driven Engineering (MDE), particularly the cognitive complexity and usability barriers often encountered in existing model-driven tools. This article presents the motivation and requirements behind the design of Jjodel and demonstrates how it satisfies these thr...

Text correction systems in automotive technical documentation face unique challenges due to domain-specific terminology, complex abbreviations, and context-dependent meanings. This article presents an advanced approach to enhancing text Ravi Sankar Sambangi https://iaeme.com/Home/journal/IJCET 2783 editor@iaeme.com accuracy in automotive documentat...

It is widely acknowledged that LLMs(Large Language Models)assist us in our daily lives and it both save our times and energy,but it still fails in some specific area like DSL(Domain Specific Language). In response to this, the GPThelper was invented, which includes basic knowledge of the language, concepts, and commands used by the language, as wel...

The extraction of Adverse Drug Reactions from biomedical text is a critical task in the field of healthcare and pharmacovigilance. It serves as a cornerstone for improving patient safety by enabling the early identification and mitigation of potential risks associated with pharmaceutical treatments. This process not only helps in detecting harmful...

Scientists across domains are often challenged to master domain-specific languages (DSLs) for their research, which are merely a means to an end but are pervasive in fields like computational chemistry. Automated code generation promises to overcome this barrier, allowing researchers to focus on their core expertise. While large language models (LL...

Tensor networks provide a powerful framework for compressing multi-dimensional data. The optimal tensor network structure for a given data tensor depends on both the inherent data properties and the specific optimality criteria, making tensor network structure search a crucial research problem. Existing solutions typically involve sampling and vali...

Domain-specific languages (DSLs) play a crucial role in facilitating a wide range of software development activities in the context of model-driven engineering (MDE). However, a systematic understanding of their evolution is lacking, which hinders methodology and tool development. To address this gap, we performed a comprehensive investigation into...

The complexity of multi-layered, data-intensive systems demands frameworks that ensure flexibility, scalability, and efficiency. DATCloud is a model-driven framework designed to facilitate the modeling, validation, and refinement of multi-layered architectures, addressing scalability, modularity, and real-world requirements. By adhering to ISO/IEC/...

Despite the increasing interest in blockchain and smart contracts, their inherent complexity has impeded widespread adoption. In order to mitigate this issue, this work introduces SmaC, a model-based framework for the development of smart contracts in Solidity that enables the treatment of contracts as models, opening up new possibilities for their...

We consider the inverse reinforcement learning (IRL) problem, where an unknown reward function of some Markov decision process is estimated based on observed expert demonstrations. In most existing approaches, IRL is formulated and solved as a nonconvex optimization problem, posing challenges in scenarios where robustness and reproducibility are cr...

Natural Language Processing (NLP) is transforming scientific communication by enhancing accessibility, efficiency, and collaboration within research communities. This article explores the multifaceted role of NLP in scientific communication, detailing its applications such as automated summarization, sentiment analysis, and machine translation. It...

In real-world settings, vision language models (VLMs) should robustly handle naturalistic, noisy visual content as well as domain-specific language and concepts. For example, K-12 educators using digital learning platforms may need to examine and provide feedback across many images of students' math work. To assess the potential of VLMs to support...

Systems interacting with humans, such as assistive robots or chatbots, are increasingly integrated into our society. To prevent these systems from causing social, legal, ethical, empathetic, or cultural (SLEEC) harms, normative requirements specify the permissible range of their behaviors. These requirements encompass both functional and non-functi...

Generative artificial intelligence (AI) systems are capable of synthesizing complex artifacts such as text, source code or images according to the instructions provided in a natural language prompt. The quality of the input prompt, in terms of both content and structure, has a large impact on the quality of the output. This has given rise to prompt...

Version Control Systems (VCSs) are used by development teams to manage the collaborative evolution of source code, and there are several widely used industry standard VCSs. In addition to the code files themselves, metadata about the changes made are also recorded by the VCS, and this is often used with analytical tools to provide insight into the...

When developing general-purpose robot software components, we often lack complete knowledge of the specific contexts in which they will be executed. This limits our ability to make predictions, including our ability to detect program bugs statically. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loo...

Mobile app developers often encounter a significant challenge in developing well-structured mobile apps capable of supporting multiple platforms and diverse functional requirements. The main current practice involves coding versions for different platforms separately using traditional software development methods. Implementing any changes across th...

P4, a domain-specific language (DSL) for programming network devices, offers flexibility in defining packet processing behaviors. This paper demonstrates the use of P4 to achieve modular eCPRI protocol processing and enhanced PTP-1588 synchronization, both critical for 5G fronthaul applications in Open Radio Access Network (O-RAN) environments. By...

The exponential growth of unstructured textual data has necessitated the advancement of Natural Language Understanding (NLU) techniques to extract meaningful insights from big data. AI-driven approaches, including deep learning, transformer-based models, and knowledge graphs, have revolutionized NLU by enabling machines to comprehend context, seman...

Background: Large Language Models (LLMs) are emerging as promising tools in hardware design and verification, with recent advancements suggesting they could fundamentally reshape conventional practices. Objective: This study examines the significance of LLMs in shaping the future of hardware design and verification. It offers an extensive literatur...

BharatSim is an open-source agent-based modelling framework for the Indian population. It can simulate populations at multiple scales, from small communities to states. BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, th...

We present ARCAS (Automated Root Cause Analysis System), a diagnostic platform based on a Domain Specific Language (DSL) built for fast diagnostic implementation and low learning curve. Arcas is composed of a constellation of automated troubleshooting guides (Auto-TSGs) that can execute in parallel to detect issues using product telemetry and apply...

Industrial control systems (ICSs) increasingly rely on digital technologies vulnerable to cyber attacks. Cyber attackers can infiltrate ICSs and execute malicious actions. Individually, each action seems innocuous. But taken together, they cause the system to enter an unsafe state. These attacks have resulted in dramatic consequences such as physic...

PyPM is a Python-based domain specific language (DSL) for building rewrite-based optimization passes on machine learning computation graphs. Users define individual optimizations by writing (a) patterns that match subgraphs of a computation graph and (b) corresponding rules which replace a matched subgraph with an optimized kernel. PyPM is distingu...

Аннотация: Цель проекта – апробация подхода к генерации кода, реализующего пользовательские модели визуализации данных, на основе метамоделей визуальных предметно-ориентированных языков (DSL), созданных для описания моделей визуализации, и описаний формальных грамматик целевых текстовых языков, представленных в многоаспектной онтологии. Онтология в...

Аннотация: Цель проекта – апробация подхода к разработке средств автоматизации создания предметно-ориентированных языков (DSL) для создания средств визуализации данных, настраиваемых на потребности пользователей. Основная идея языково-ориентированного подхода в том, что для описания новых моделей визуализации должны быть разработаны визуальные пред...

This paper presents HyperGraphOS, a significant innovation in the domain of operating systems, specifically designed to address the needs of scientific and engineering domains. This platform aims to combine model-based engineering, graph modeling, data containers, and documents, along with tools for handling computational elements. HyperGraphOS fun...

Domain-specific languages that use a lot of specific terminology often fall into the category of low-resource languages. Collecting test datasets in a narrow domain is time-consuming and requires skilled human resources with domain knowledge and training for the annotation task. This study addresses the challenge of automated collecting test datase...

In Voice Assistant (VA) platforms, when users add devices to their accounts and give voice commands, complex interactions occur between the devices, skills, VA clouds, and vendor clouds. These interactions are governed by the device management capabilities (DMC) of VA platforms, which rely on device names, types, and associated skills in the user a...

Understanding and extracting the grammar of a domain-specific language (DSL) is crucial for various software engineering tasks; however, manually creating these grammars is time-intensive and error-prone. This paper presents Kajal, a novel approach that automatically infers grammar from DSL code snippets by leveraging Large Language Models (LLMs) t...

Process algebras have been widely used to verify security protocols in a formal manner. However they mostly focus on synchronous communication based on the exchange of messages. We present an alternative approach relying on asynchronous communication obtained through information available on a shared space. More precisely this paper first proposes...

Categories and categorical structures are increasingly recognized as useful abstractions for modeling in science and engineering. To uniformly implement category-theoretic mathematical models in software, we introduce GATlab, a domain-specific language for algebraic specification embedded in a technical programming language. GATlab is based on gene...

The deployment of complex autonomous systems into open environment calls for robust, modular and verifiable software architectures. Skillset models, which encapsulate the capabilities of the autonomous robots into modular Skills, have emerged to address these challenges. Expressed within an intermediary layer of the robot's architecture, Skillsets...

Exploring, analyzing, and interpreting law can be tedious and challenging, even for legal scholars, since legal texts contain domain-specific language, require knowledge of tacit legal concepts, and are sometimes intentionally ambiguous. In related, text-based domains, Visual Analytics (VA) and large language models (LLMs) have become essential for...

Document databases are increasingly popular in various applications, but their queries are challenging to write due to the flexible and complex data model underlying document databases. This paper presents a synthesis technique that aims to generate document database queries from input-output examples automatically. A new domain-specific language i...

This paper presents HyperGraphOS, an innovative Operating System designed for the scientific and engineering domains. It combines model based engineering, graph modeling, data containers, and computational tools, offering users a dynamic workspace for creating and managing complex models represented as customizable graphs. Using a web based archite...

Despite the success of the O-RAN Alliance in developing a set of interoperable interfaces, development of unique Radio Access Network (RAN) deployments remains challenging. This is especially true for military communications, where deployments are highly specialized with limited volume. The construction and maintenance of the RAN, which is a real t...

Modern enterprise systems are likely to have a very long life. Their specifications therefore need to employ mechanisms that allow them to evolve during their lifetime; where they exploit generic components, these must be adaptable for use in novel situations. The paper looks at some of the issues that arise from this requirement, and how the explo...

The increasing volume of clinical documentation in healthcare systems has created the need for automated solutions to extract critical information efficiently. Natural Language Processing (NLP) techniques have emerged as powerful tools to summarize large amounts of unstructured clinical text, improving information retrieval and aiding in decision-m...

Over the past decade, different domain-specific languages (DSLs) were proposed to formally specify requirements stated in legal contracts, mainly for analysis but also for code generation. Symboleo is a promising language in that area. However, writing formal specifications from natural-language contracts is a complex task, especial for legal exper...

New programming languages are often designed to keep up with technological advancements and project requirements while also learning from previous attempts and introducing more powerful expression mechanisms. However, most existing dynamic programming languages rely on English keywords and lack features that facilitate easy translation of language...

Predicting spatial representations in literature is a challenging task that requires advanced machine learning methods and manual annotations. In this paper, we present a study that leverages manual annotations and a BERT language model to automatically detect and recognise non-named spatial entities in a historical corpus of Swiss novels. The anno...

Given a natural language query, code search aims to find matching code snippets from a codebase. Recent works are mainly designed for mainstream programming languages with large amounts of training data. However, code search is also needed for domain-specific programming languages, which have fewer training data, and it is a heavy burden to label a...

This study investigates the application of machine learning (ML) models in stock market forecasting, with a focus on their integration using PineScript, a domain-specific language for algorithmic trading. Leveraging diverse datasets, including historical stock prices and market sentiment data, we developed and tested various ML models such as neura...

Event logs often record the execution of business process instances. Detecting traces in the event logs that do not comply with access control policies, such as role-based access control (RBAC) policies, is essential to ensuring system security. Moreover, process mining has been extensively utilized for security analysis in recent years. However, p...

Aggregate implements an efficient fast Fourier transform (FFT)-based algorithm to approximate compound probability distributions. Leveraging FFT-based methods offers advantages over recursion and simulation-based approaches, providing speed and accuracy to otherwise time-consuming calculations. Combining user-friendly features and an expressive dom...

Programs, like people, get old. The same is true for models, which can become obsolete due to a diversity of factors such as changing requirements, data drift or evolution of the domain itself. Preventing or addressing obsolescence as early as possible helps to reduce the significant costs, risks, and uncertainties incurred by obsolete models and t...

For half a century, artificial intelligence research has attempted to reproduce the human qualities of abstraction and reasoning - creating computer systems that can learn new concepts from a minimal set of examples, in settings where humans find this easy. While specific neural networks are able to solve an impressive range of problems, broad gene...

Chatbots have emerged as ubiquitous tools for enhancing user interaction across various platforms, from customer service to personal assistance. They are computer programs that simulate and process human conversation, either written, spoken or both. However, developing efficient chatbots remains a challenge, primarily due to the intricate nature of...

Natural Language Processing (NLP) is increasingly essential in financial analysis as vast amounts of unstructured data-such as news articles, regulatory filings, social media, and earnings reports-shape investment decisions and risk management strategies. Financial text analysis with NLP techniques enables the extraction of valuable insights from t...

With the increasing proliferation of mobile applications in our everyday experiences, the concerns surrounding ethics have surged significantly. Users generally communicate their feedback, report issues, and suggest new functionalities in application (app) reviews, frequently emphasizing safety, privacy, and accountability concerns. Incorporating t...

Smart contracts are undoubtedly one of the most successful and popular applications of the blockchain industry. They consist of computer programs that are stored in blockchain, typically immutable, allowing the creation of decentralized applications (DApps). Their source code describes how the blockchain’s global state should evolve as a consequenc...

Traditional compilers, designed for optimizing low-level code, fall short when dealing with modern, computation-heavy applications like image processing, machine learning, or numerical simulations. Optimizations should understand the primitive operations of the specific application domain and thus happen on that level. Domain-specific languages (DS...

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 that needs to be analyzed and processed in a systematic way by government officials. Artificial Intel...

Security controls are mechanisms or policies designed for cloud based services to reduce risk, protect information, and ensure compliance with security regulations. The development of security controls is traditionally a labor-intensive and time-consuming process. This paper explores the use of Generative AI to accelerate the generation of security...