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Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
13 Aug

Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

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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…
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AyE-Edge: Automated Deployment Space Search Empowering Accuracy yet Efficient Real-Time Object Detection on the Edge
13 Aug

AyE-Edge: Automated Deployment Space Search Empowering Accuracy yet Efficient Real-Time Object Detection on the Edge

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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.…
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DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts
13 Aug

DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts

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[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…
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How Machine Learning is Driving Accuracy in Identifying and Recruiting Talented Candidates
13 Aug

How Machine Learning is Driving Accuracy in Identifying and Recruiting Talented Candidates

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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…
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Improved Adaboost Algorithm for Web Advertisement Click Prediction Based on Long Short-Term Memory Networks
13 Aug

Improved Adaboost Algorithm for Web Advertisement Click Prediction Based on Long Short-Term Memory Networks

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arXiv:2408.05245v1 Announce Type: new Abstract: This paper explores an improved Adaboost algorithm based on Long Short-Term Memory Networks (LSTMs), which aims to improve the prediction accuracy of user clicks on web page advertisements. By comparing it with several common machine learning algorithms, the paper analyses the advantages of the new model in ad click prediction. It is shown that the improved algorithm proposed in this paper performs well in user ad click prediction with an accuracy of 92%, which is an improvement of 13.6% compared to the highest of 78.4% among the other three base models. This significant improvement indicates…
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Enabling Quick, Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mapping
13 Aug

Enabling Quick, Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mapping

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arXiv:2408.05350v1 Announce Type: new Abstract: In order to assess damage and properly allocate relief efforts, mapping the extent of flood events is a necessary and important aspect of disaster management. In recent years, deep learning methods have evolved as an effective tool to quickly label high-resolution imagery and provide necessary flood extent mappings. These methods, though, require large amounts of annotated training data to create models that are accurate and robust to new flooded imagery. In this work, we provide FloodTrace, an application that enables effective crowdsourcing for flooded region annotation for machine learning training data, removing the requirement for…
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From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management
13 Aug

From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management

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arXiv:2408.05328v1 Announce Type: new Abstract: This study explores the potential of Large Language Models (LLMs), specifically GPT-4, to enhance objectivity in organizational task performance evaluations. Through comparative analyses across two studies, including various task performance outputs, we demonstrate that LLMs can serve as a reliable and even superior alternative to human raters in evaluating knowledge-based performance outputs, which are a key contribution of knowledge workers. Our results suggest that GPT ratings are comparable to human ratings but exhibit higher consistency and reliability. Additionally, combined multiple GPT ratings on the same performance output show strong correlations with aggregated human performance ratings,…
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SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks
13 Aug

SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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CAR: Contrast-Agnostic Deformable Medical Image Registration with Contrast-Invariant Latent Regularization
13 Aug

CAR: Contrast-Agnostic Deformable Medical Image Registration with Contrast-Invariant Latent Regularization

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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