A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The paper introduces CoMoSVC, a consistency model-based Singing Voice Conversion (SVC) method aimed at achieving high-quality generation and high-speed sampling. The authors first design a diffusion-based teacher model specifically for…
Continue readingThis paper proposes a new Privacy-Preserving TFGC (Target Fine-grained Classification) Framework based on Federated Learning, named PRFL. The framework is designed to help clients learn global and local knowledge, improving…
Continue readingThis academic article delves into the potential and challenges of task-oriented and semantic communications for next-generation (NextG) networks. It discusses the integration of multi-task learning, using deep neural networks for…
Continue readingThe research article presents a novel method for Out-of-distribution (OOD) detection in neural networks, inspired by the phenomenon of Neural Collapse (NC). Existing OOD detection methods have utilized the fact…
Continue readingThe article by Marc Schmitt published in December 2023, discusses the use of Artificial Intelligence (AI) and Machine Learning (ML) in securing digital ecosystems against cyber threats. It focuses on…
Continue readingThe article presents IoTGeM, a methodology for modelling IoT network attacks, focusing on generalizability and improved detection performance. It introduces an improved rolling window approach for feature extraction and a…
Continue readingThe article focuses on the rise of Machine Learning as a Service (MLaaS) and its potential vulnerability to backdoor attacks due to advancements in Artificial Intelligence (AI). The paper introduces…
Continue readingThis paper discusses the development of ChronoCTI, a system designed to mine temporal attack patterns from cyberthreat intelligence reports. These patterns provide structured and actionable information on past cyberattacks, aiding…
Continue readingThe article discusses a new training scheme for distributed robot systems in open-world scenarios. It uses a teacher-student model where a ‘student’ robot can ask other ‘teacher’ robots for guidance…
Continue readingThis paper presents SAM-G, a new framework that uses the Segment Anything Model (SAM) to improve the generalization abilities of visual reinforcement learning (RL) agents. SAM-G uses image features from…
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