Chiara Camerotahttps://camerotachiara.github.io/Recent content on Chiara CamerotaHugo -- 0.114.0enFri, 12 Jul 2024 00:00:00 +0000Addressing Data Security in IoT: Minimum Sample Size and Denoising Diffusion Models for Improved Malware Detectionhttps://camerotachiara.github.io/papers/paper1/Mon, 01 Jan 0001 00:00:00 +0000https://camerotachiara.github.io/papers/paper1/This paper investigates on how to define the miimum sample siz and improving the data agumentation for Traffic Images. Published 20th International Conference on Network and Service Management, 2024.The intrinsic convenience of federated learning in malware IoT detectionhttps://camerotachiara.github.io/papers/paper2/Mon, 01 Jan 0001 00:00:00 +0000https://camerotachiara.github.io/papers/paper2/This paper investigates on how to implement a federated malware recognition system. Publishing 1st Workshop on Integrated Wireless Networking and Computing (IWNC), 2024.Automating Heterogeneous Internet of Things Device Networks from Multiple Brokers with Multiple Data Modelshttps://camerotachiara.github.io/papers/paper3/Mon, 01 Jan 0001 00:00:00 +0000https://camerotachiara.github.io/papers/paper3/This paper ris pubblished in Global IoT Summit, 2022.Taming Bandwidth Bottlenecks in Federated Learning via ECN-based Gradient Compressionhttps://camerotachiara.github.io/papers/paper_cnsm/Mon, 01 Jan 0001 00:00:00 +0000https://camerotachiara.github.io/papers/paper_cnsm/The paper addresses communication bottlenecks in Federated Learning and introduces Selective Compression via Adaptive Lightweight Protocol, which dynamically selects compression levels based on local gradient variance and uplink bandwidth. Published on 2025 21st International Conference on Network and Service Management (CNSM).Load Profile Generation for Robust Optimization: A Stochastic Approach Based on Conditional Probability Approximationhttps://camerotachiara.github.io/papers/paper_el/Mon, 01 Jan 0001 00:00:00 +0000https://camerotachiara.github.io/papers/paper_el/This paper investigates the generation of load profiles using a stochastic approach, focusing on creating robust estimations that rely solely on CPU resources. The methodology outlined emphasizes computational efficiency and is designed for practical implementation in resource-constrained environments. This work will be presented at the 2025 IEEE International Conference on Environment and Electrical Engineering and the 2025 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), highlighting its relevance in the context of renewable energy management and optimization.A Convolutional Neural Network to Locate Unbalance in Turbomachinery Supported by AMBshttps://camerotachiara.github.io/papers/paper4/Mon, 01 Jan 0001 00:00:00 +0000https://camerotachiara.github.io/papers/paper4/This paper studies the fault recognitions in an ABMs system. Published in 2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT), 2024.A Convolutional Neural Network for Electrical Fault Recognition in Active Magnetic Bearing Systemshttps://camerotachiara.github.io/papers/paper5/Mon, 01 Jan 0001 00:00:00 +0000https://camerotachiara.github.io/papers/paper5/This paper presents a case study on the application of Convolutional Neural Networks (CNNs) for the recognition of electrical faults in Active Magnetic Bearing systems. Published in the Sensors, 2023.List of Irregular Verbs Across Romance Languageshttps://camerotachiara.github.io/data/data1/Thu, 07 Mar 2013 00:00:00 +0000https://camerotachiara.github.io/data/data1/This dataset contains all irregular verbs in known Romance languages.