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Public space security management using digital twin technologies
The applied methodology leverages a Digital Twin of a metro station in Athens, Greece, using the FlexSim simulation software.
Hybrid Semantic-Shannon Communications
Semantic communications are considered a promising beyond-Shannon/bit paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable.
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study
The present study delves into the fundamental principles of these three approaches and proposes an advanced Federated Learning System that utilises different Personalisation methods towards improving the accuracy of AI models and enhancing user experience in real-time NG-IoT applications, investigating the efficacy of these techniques in the local and federated domain.
Leveraging Digital Twin Technologies for Public Space Protection and Vulnerability Assessment
Over the recent years, the protection of the so-called `soft-targets', i. e. locations easily accessible by the general public with relatively low, though, security measures, has emerged as a rather challenging and increasingly important issue.
A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection
One common approach to achieve this is by combining model finetuning with data augmentation strategies.
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions
Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learning models in downstream tasks.
Waveform Design for Over-the-Air Computing
To this end, we examine the theoretical mean squared error (MSE) for OTA transmission under time sampling error and ISI, while also exploring methods for minimizing the MSE in the OTA transmission.
Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting
Specifically, the goal of the proposed method is to enhance model performance for cardiovascular disease prediction by tackling the class-imbalance that typically characterizes datasets used for this purpose, as well as by leveraging patient data available in different nodes of a federated ecosystem without compromising their privacy and enabling more resource sensitive allocation.
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
Federated learning (FL) is a decentralized learning technique that enables participating devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model without revealing their raw data to a third party.
Autonomous AI-enabled Industrial Sorting Pipeline for Advanced Textile Recycling
The escalating volumes of textile waste globally necessitate innovative waste management solutions to mitigate the environmental impact and promote sustainability in the fashion industry.
Evaluating the Efficacy of AI Techniques in Textual Anonymization: A Comparative Study
In the digital era, with escalating privacy concerns, it's imperative to devise robust strategies that protect private data while maintaining the intrinsic value of textual information.
Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches
In the realm of data privacy, the ability to effectively anonymise text is paramount.
Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings
In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity.
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning
Despite the widespread success of deep learning, its intense requirements for vast amounts of data and extensive training make it impractical for various real-world applications where data is scarce.
Evaluation of Environmental Conditions on Object Detection using Oriented Bounding Boxes for AR Applications
The objective of augmented reality (AR) is to add digital content to natural images and videos to create an interactive experience between the user and the environment.
Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions
Despite the fact that Artificial Intelligence (AI) has boosted the achievement of remarkable results across numerous data analysis tasks, however, this is typically accompanied by a significant shortcoming in the exhibited transparency and trustworthiness of the developed systems.
Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook
Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.
Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study
To the best of the authors knowledge, this is the first time that LGSO algorithms are applied to the optimal power allocation problem in IoT networks.
The evolution of argumentation mining: From models to social media and emerging tools
In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media.
Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation
In order to provide related behavioural models, simulation and tracking approaches have been considered in the literature.
