learning
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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The threat landscape continues to evolve, and companies around the world face escalating risks heading into 2025. As AI enables more malware and phishing campaigns, and attacks become even more sophisticated across enterprises and supply chains, cybersecurity teams need to enhance their strategies to keep up with the dynamic and complex threat landscape. Here are…
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The arrival of AI has received a lukewarm welcome from teachers and parents – Copyright AFP/File EVARISTO SA
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FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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Unsurprisingly, lack of skills is cited as the biggest challenge. Issues around data governance and challenges around clear metrics follow the top challenge areas. All of these relate to the lack of experience with AI. As organisations embark on their journeys, they have to learn what is needed to ensure a successful project.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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Jia-Lin Chan, Wai-Kong Lee, Denis C.-K Wong, Wun-She Yap, Bok-Min Goi ePrint Report Advancements in deep learning (DL) not only revolutionized many aspects in our lives, but also introduced privacy concerns, because it processed vast amounts of information that was closely related to our daily life. Fully Homomorphic Encryption (FHE) is one of the promising…
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Fixed point arithmetic (FPA) is essential to enable practical Privacy-Preserving Machine Learning. When multiplying two fixed-point numbers, truncation is required to ensure that the product maintains correct precision. While multiple truncation schemes based on Secure Multiparty Computation (MPC) have been proposed, which of the different schemes offers the best trade-off between accuracy and efficiency on…
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[Submitted on 30 Nov 2024 (v1), last revised 4 Dec 2024 (this version, v2)] View a PDF of the paper titled MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption, by Siddhant Dutta and 4 other authors
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4 Ways To Facilitate a Successful Learning Review – The New Stack
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[Submitted on 2 Dec 2024 (v1), last revised 3 Dec 2024 (this version, v2)] View a PDF of the paper titled Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks, by Wenhan Dong and 4 other authors
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Over the coming decades, security risks associated with AI systems will be a major focus of researchers’ efforts. One of the least explored risks today is the possibility of trojanizing an AI model. This involves embedding hidden functionality or intentional errors into a machine learning system that appears to be working correctly at first glance.…
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The rocketing price of energy is one of the biggest contributors to the rising cost of living. However, a smart thermostat can go a long way towards driving energy efficiency at your home. While Cyber Monday is over and the deals are in their final hours, this incredible discount on the Google Nest Learning Thermostat…
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COMMENTARY As a child, airplanes fascinated me — I was taken by their gravity-defying magic, their technical wonders, their sleek designs, and the adventures they unlocked. I dreamed of flying one myself.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.