6 accepted papers
This paper proposes a semantic pilot design for data-aided channel estimation in text-inclusive data transmission, using a large language model (LLM). In this scenario, channel impairments often appear as typographical errors in the decoded text, which can be corrected using an LLM. The proposed met…
Recent research has shown a growing interest in per-instance differential privacy (pDP), highlighting the fact that each data instance within a dataset may incur distinct levels of privacy loss. However, conventional additive noise mechanisms apply identical noise to all query outputs, thereby deter…
Deep neural networks (DNNs) have a risk of remembering sensitive data from their training datasets, inadvertently leading to substantial information leakage through privacy attacks like membership inference attacks. DP-SGD is a simple but effective defense method, incorporating Gaussian noise into g…
Object detection in aerial images is a growing area of research, with maritime object detection being a particularly important task for reliable surveillance, monitoring, and active rescuing. Notwithstanding astonishing advances of computer vision technologies, detecting ships and floating matters i…
By virtue of their ultra high resolution, scanning electron microscopes (SEMs) are essential to study topography, morphology, composition, and crystallography of materials, and thus are widely used for advanced researches in physics, chemistry, pharmacy, geology, etc. The major hindrance of using SE…
As smart cameras are becoming ubiquitous in mobile robot systems, there is an increasing concern in camera devices invading people's privacy by recording unwanted images. We want to fundamentally protect privacy by blurring unwanted blocks in images, such as faces, yet ensure that the robots can und…