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Parallel and distributed application design is a major area of interest in the domain of high performance scientific and industrial computing. Over the years, various approaches have been proposed to aid parallel program developers to…

Direct Preference Optimization (DPO) has shown effectiveness in aligning multi-modal large language models (MLLM) with human preferences. However, existing methods exhibit an imbalanced responsiveness to the data of varying hardness,…

Industrial process engineering and PLC program development have traditionally favored Function Block Diagram (FBD) programming over classical imperative style programming like the object oriented and functional programming paradigms. The…

The digital transformation of the energy infrastructure enables new, data driven, applications often supported by machine learning models. However, domain specific data transformations, pre-processing and management in modern data driven…

AI agents allow developers to express computational intent abstractly, reducing cognitive effort and helping achieve flow during programming. Increased abstraction, however, comes at a cost: developers cede decision-making authority to…

With the advent of newly introduced programming models like Feature-Oriented Programming (FOP), we feel that it will be more flexible to include the new service invocation function into the service providing server as a Feature Module for…

Context: As the adoption of continuous delivery practices increases in software organizations, different scenarios struggle to make it scales for their products in long-term evolution. This study looks at the concrete software architecture…

Dependency hell is a well-known pain point in the development of large software projects and machine learning (ML) code bases are not immune from it. In fact, ML applications suffer from an additional form, namely, "data source dependency…

The rapidly growing demand for high-quality data in Large Language Models (LLMs) has intensified the need for scalable, reliable, and semantically rich data preparation pipelines. However, current practices remain dominated by ad-hoc…

Extracting valuable insights from vast amounts of information is a critical process that involves acquiring, storing, managing, analyzing, and visualizing data. Providing an abstract overview of data analytics applications is crucial to…

We outline a comprehensive framework for artificial intelligence (AI) Application Operations (AIAppOps), based on real-world experiences from diverse organizations. Data-driven projects pose additional challenges to organizations due to…

In this work we explore the advantages of end-to-end learning of multilayer maps offered by feed forward neural-networks (FFNN) for learning and predicting dynamics from transient fluid flow data.While machine learning in general depends on…

By treating data and models as the source code, Foundation Models (FMs) become a new type of software. Mirroring the concept of software crisis, the increasing complexity of FMs making FM crisis a tangible concern in the coming decade,…

Multimodal Large Language Models (MLLMs) have achieved remarkable advances by integrating text, image, and audio understanding within a unified architecture. However, existing distributed training frameworks remain fundamentally data-blind:…

Given the continuous global degradation of the Earth's ecosystem due to unsustainable human activity, it is increasingly important for enterprises to evaluate the effects they have on the environment. Consequently, assessing the impact of…

Machine Learning (ML) and Artificial Intelligence (AI) have a dependency on data sources to train, improve and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this…

The increasing availability of data and advancements in computational intelligence have accelerated the adoption of data-driven methods (DDMs) in product development. However, their integration into product development remains fragmented.…

We discuss questions about user centric development of business process modeling notations. In the center of our research there is a fully featured multi-enterprise business process platform (ME-BPP) based on the concepts of agent-based…

As generative Artificial Intelligence (AI) technologies evolve, they offer unprecedented potential to automate and enhance various tasks, including coding. Natural Language-Oriented Programming (NLOP), a vision introduced in this paper,…

This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental concepts, traditional methods, and benchmark datasets, then…