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AI is increasingly used to accelerate engineering design by improving decision-making and shortening iteration cycles. Application to marine propeller design, however, remains challenging due to scarce training data and the lack of widely…
Artificial intelligence systems are increasingly deployed in biomedical research. However, current evaluation frameworks may inadequately assess their effectiveness as research collaborators. This rapid review examines benchmarking…
Purpose:This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources. Leveraging recent progress in generative artificial intelligence…
Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and communication technologies (IT/CT) has created an unbroken…
Self-adaptation equips a computing system with a feedback loop that enables it dealing with change caused by uncertainties during operation, such as changing availability of resources and fluctuating workloads. To ensure that the system…
Lived experiences fundamentally shape how individuals interact with AI systems, influencing perceptions of safety, trust, and usability. While prior research has focused on developing techniques to emulate human preferences, and proposed…
Edge computing is projected to become the dominant form of cloud computing in the future because of the significant advantages it brings to both users (less latency, higher throughput) and telecom operators (less Internet traffic, more…
Networked embedded systems endowed with sensing, computing, control and communication capabilities allow the development of various application scenarios and represent the building blocks of the Internet of Things (IoT) paradigm.…
Edge computing was introduced as a technical enabler for the demanding requirements of new network technologies like 5G. It aims to overcome challenges related to centralized cloud computing environments by distributing computational…
We consider the problem of modeling high-speed flows using machine learning methods. While most prior studies focus on low-speed fluid flows in which uniform time-stepping is practical, flows approaching and exceeding the speed of sound…
Edge intelligence delivers low-latency inference, yet most edge analytics remain hard-coded and must be redeployed as conditions change. When data patterns shift or new questions arise, engineers often need to write new scripts and push…
Cloud-native applications have significantly advanced the development and scalability of online services through the use of microservices and modular architectures. However, achieving adaptability, resilience, and efficient performance…
Deep learning-based vulnerability detection has shown great performance and, in some studies, outperformed static analysis tools. However, the highest-performing approaches use token-based transformer models, which are not the most…
Foundation models in digital pathology use massive datasets to learn useful compact feature representations of complex histology images. However, there is limited transparency into what drives the correlation between dataset size and…
Identifying devices such as cameras, printers, voice assistants, or health monitoring sensors, collectively known as the Internet of Things (IoT), within a network is a critical operational task, particularly to manage the cyber risks they…
The paradigm of Intelligent DataPlane (IDP) embeds deep learning (DL) models on the network dataplane to enable intelligent traffic analysis at line-speed. However, the current use of the match-action table (MAT) abstraction on the…
AI's widespread integration has led to neural networks (NNs) deployment on edge and similar limited-resource platforms for safety-critical scenarios. Yet, NN's fragility raises concerns about reliable inference. Moreover, constrained…
The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows,…
This paper explores the integration of AI tools, such as ChatGPT and GitHub Copilot, in the Software Architecture for Embedded Systems course. AI-supported workflows enabled students to rapidly prototype complex projects, emphasizing…
Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning. However, most of these intelligent services-such as computer vision-continually learn with…