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, frameworks, etc.). This complexity often leads to implementation errors, some of which result in undesired states of Read more…
Background/Objectives: Behavioral variant frontotemporal dementia (bvFTD), the most prevalent clinical subtype within the frontotemporal lobar degeneration spectrum disorders, is characterized by early and prominent changes that significantly disrupt everyday functioning. This study aims to identify the key correlates of functional status in bvFTD by investigating the relative contributions of cognitive deficits, Read more…
The Software Engineering (SE) domain increasingly adopts low-code and no-code approaches to simplify application development and deployment. Two dominant paradigms have emerged in this space: Model-driven Engineering (MDE), leveraging Domain-specific Languages (DSLs) to abstract implementation and reduce the knowledge and expertise required, and LLM-based vibe coding, where developers interact with Read more…
The early identification of cognitive decline is crucial for well-timed intervention and diagnosis, particularly in the context of preclinical Alzheimer’s disease (AD). In this study, we investigated the complex interplay between trait affect, objective cognitive performance, and subjective memory estimations in a sample of 105 older adults. Using path analysis, Read more…
Knee osteoarthritis (KOA) is a most prevalent chronic muscoloskeletal disorder causing pain and functional impairment. Accurate predictions of KOA evolution are important for early interventions and preventive treatment planning. In this paper, we propose a novel dynamic hypergraph-based risk model (DyHRM) which integrates the encoder–decoder (ED) architecture with hypergraph convolutional Read more…
The data landscape has changed, as more and more information is produced in the form of continuous data streams instead of stationary datasets. In this context, several online machine learning techniques have been proposed with the aim of automatically adapting to changes in data distributions, known as drifts. Though effective Read more…
Over the last decade, a transformation in the automotive industry has been witnessed, as advancements in artificial intelligence and sensor technology have continued to accelerate the development of driverless vehicles. These systems are expected to significantly reduce traffic accidents and associated costs, making their integration into future transportation systems highly Read more…
Conversational Assistants (CAs) are continuously gaining traction across industries and are promptly adopted in the corporate world, offering intuitive interactions enabled by advancements in NLP and AI. However, as CAs handle sensitive tasks and enterprise data, special attention should be paid to security challenges, including inadequate authentication, authorization, and data Read more…
In recent years, system intelligence has revolutionized various domains, including the automotive industry, which has fully incorporated intelligence through the emergence of Advanced Driver Assistance Systems (ADAS). Within this transformative context, Autonomous Parking Systems (APS) have emerged as a foundational component, revolutionizing the way vehicles navigate and park with precision Read more…
We’re excited to participate in RSCy2025 and present our latest research on food security, sustainability, and digital innovation in agriculture! Our Contributions: • Best paper award : A hybrid Edge-to-Cloud Architecture towards low-code development of global food-security applications: The Nostradamus case • Towards a Big Data Infrastructure and Monitoring Dashboard Read more…