9 citations · 17 across the 8 of their papers we have counts for
8 papers
Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models
Takoua Jradi, John Violos, Dimitrios Spatharakis +4
The increasing complexity of smart manufacturing environments demands interfaces that can translate high-level human intents into machine-executable actions. This paper presents a…
Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence
John Violos, Konstantina-Christina Diamanti, Ioannis Kompatsiaris +1
Frugal Machine Learning (FML) refers to the practice of designing Machine Learning (ML) models that are efficient, cost-effective, and mindful of resource constraints. This field a…
A Brief Review for Compression and Transfer Learning Techniques in DeepFake Detection
Andreas Karathanasis, John Violos, Ioannis Kompatsiaris +1
Training and deploying deepfake detection models on edge devices offers the advantage of maintaining data privacy and confidentiality by processing it close to its source. However,…
Reducing Inference Energy Consumption Using Dual Complementary CNNs
Michail Kinnas, John Violos, Ioannis Kompatsiaris +1
Energy efficiency of Convolutional Neural Networks (CNNs) has become an important area of research, with various strategies being developed to minimize the power consumption of the…
A New Approach for Evaluating the Performance of Distributed Latency-Sensitive Services
Theodoros Theodoropoulos, John Violos, Antonios Makris +1
Conventional latency metrics are formulated based on a broad definition of traditional monolithic services, and hence lack the capacity to address the complexities inherent in mode…
Towards Optimal Trade-offs in Knowledge Distillation for CNNs and Vision Transformers at the Edge
John Violos, Symeon Papadopoulos, Ioannis Kompatsiaris
This paper discusses four facets of the Knowledge Distillation (KD) process for Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) architectures, particularly when e…
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques
Anastasios Iliopoulos, John Violos, Christos Diou +1
Anomaly Detection in multivariate time series is a major problem in many fields. Due to their nature, anomalies sparsely occur in real data, thus making the task of anomaly detecti…
Intelligent Proactive Fault Tolerance at the Edge through Resource Usage Prediction
Theodoros Theodoropoulos, John Violos, Stylianos Tsanakas +3
The proliferation of demanding applications and edge computing establishes the need for an efficient management of the underlying computing infrastructures, urging the providers to…