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arXiv:2408.05249v1 Announce Type: new Abstract: Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its…

arXiv:2408.05363v1 Announce Type: new Abstract: Object detection on the edge (Edge-OD) is in growing demand thanks to its ever-broad application prospects. However, the development of this field is rigorously restricted by the deployment dilemma of simultaneously achieving high accuracy, excellent power efficiency, and meeting strict real-time requirements. To tackle this dilemma, we propose AyE-Edge, the first-of-this-kind development tool that explores automated algorithm-device deployment space search to realize Accurate yet power-Efficient real-time object detection on the Edge. Through a collaborative exploration of keyframe selection, CPU-GPU configuration, and DNN pruning strategy, AyE-Edge excels in extensive real-world experiments conducted on a mobile device.…

[Submitted on 9 Aug 2024] View a PDF of the paper titled DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts, by Mohammed Saidul Islam and 4 other authors View PDF HTML (experimental) Abstract:Data-driven storytelling is a powerful method for conveying insights by combining narrative techniques with visualizations and text. These stories integrate visual aids, such as highlighted bars and lines in charts, along with textual annotations explaining insights. However, creating such stories requires a deep understanding of the data and meticulous narrative planning, often necessitating human intervention, which can be time-consuming and mentally taxing. While Large Language Models (LLMs) excel…

While the ongoing generative AI boom has captivated countless industries worldwide, it's actually machine learning (ML) that stands to have a major impact on recruitment over the coming years. The global ML market is expected to reach a value of $209.91 billion by 2029, representing a CAGR of 38.8%. This swift rate of growth will bring a hatful of benefits to digital transformation throughout the recruitment landscape. Machine learning can use its experiences to make recruitment more accurate and efficient without further programming. Instead, the technology learns from data like text, images, or numbers. You've probably already witnessed ML in…