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15 Aug

LiveFC: A System for Live Fact-Checking of Audio Streams

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arXiv:2408.07448v1 Announce Type: new Abstract: The advances in the digital era have led to rapid dissemination of information. This has also aggravated the spread of misinformation and disinformation. This has potentially serious consequences, such as civil unrest. While fact-checking aims to combat this, manual fact-checking is cumbersome and not scalable. While automated fact-checking approaches exist, they do not operate in real-time and do not always account for spread of misinformation through different modalities. This is particularly important as proactive fact-checking on live streams in real-time can help people be informed of false narratives and prevent catastrophic consequences that may cause…

15 Aug

An Introduction to Time Series Forecasting with Generative AI

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An Introduction to Time Series Forecasting with Generative AITime series forecasting has been a cornerstone of enterprise resource planning for decades. Predictions about future demand guide critical decisions such as the number of units to stock, labor to hire, capital investments into production and fulfillment infrastructure, and the pricing of goods and services. Accurate demand forecasts are essential for these and many other business decisions.However, forecasts are rarely if ever perfect. In the mid-2010s, many organizations dealing with computational limitations and limited access to advanced forecasting capabilities reported forecast accuracies of only 50-60%. But with the wider adoption of the…

15 Aug

Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction

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arXiv:2408.07100v1 Announce Type: new Abstract: In recent years, deep learning has increasingly gained attention in the field of traffic prediction. Existing traffic prediction models often rely on GCNs or attention mechanisms with O(N^2) complexity to dynamically extract traffic node features, which lack efficiency and are not lightweight. Additionally, these models typically only utilize historical data for prediction, without considering the impact of the target information on the prediction. To address these issues, we propose a Pattern-Matching Dynamic Memory Network (PM-DMNet). PM-DMNet employs a novel dynamic memory network to capture traffic pattern features with only O(N) complexity, significantly reducing computational overhead…

15 Aug

Enhanced Scale-aware Depth Estimation for Monocular Endoscopic Scenes with Geometric Modeling

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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

15 Aug

Exploring Retrieval Augmented Generation in Arabic

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[Submitted on 14 Aug 2024] View a PDF of the paper titled Exploring Retrieval Augmented Generation in Arabic, by Samhaa R. El-Beltagy and Mohamed A. Abdallah View PDF Abstract:Recently, Retrieval Augmented Generation (RAG) has emerged as a powerful technique in natural language processing, combining the strengths of retrieval-based and generation-based models to enhance text generation tasks. However, the application of RAG in Arabic, a language with unique characteristics and resource constraints, remains underexplored. This paper presents a comprehensive case study on the implementation and evaluation of RAG for Arabic text. The work focuses on exploring various semantic embedding models in…

15 Aug

Bearing Fault Diagnosis using Graph Sampling and Aggregation Network

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arXiv:2408.07099v1 Announce Type: new Abstract: Bearing fault diagnosis technology has a wide range of practical applications in industrial production, energy and other fields. Timely and accurate detection of bearing faults plays an important role in preventing catastrophic accidents and ensuring product quality. Traditional signal analysis techniques and deep learning-based fault detection algorithms do not take into account the intricate correlation between signals, making it difficult to further improve detection accuracy. To address this problem, we introduced Graph Sampling and Aggregation (GraphSAGE) network and proposed GraphSAGE-based Bearing fault Diagnosis (GSABFD) algorithm. The original vibration signal is firstly sliced through a fixed…

15 Aug

Ensemble architecture in polyp segmentation

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arXiv:2408.07262v1 Announce Type: new Abstract: In this research, we revisit the architecture of semantic segmentation and evaluate the models excelling in polyp segmentation. We introduce an integrated framework that harnesses the advantages of different models to attain an optimal outcome. More specifically, we fuse the learned features from convolutional and transformer models for prediction, and we view this approach as an ensemble technique to enhance model performance. Our experiments on polyp segmentation reveal that the proposed architecture surpasses other top models, exhibiting improved learning capacity and resilience. The code is available at https://github.com/HuangDLab/EnFormer. Source link lol

15 Aug

Knowledge in Superposition: Unveiling the Failures of Lifelong Knowledge Editing for Large Language Models

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arXiv:2408.07413v1 Announce Type: new Abstract: Knowledge editing aims to update outdated or incorrect knowledge in large language models (LLMs). However, current knowledge editing methods have limited scalability for lifelong editing. This study explores the fundamental reason why knowledge editing fails in lifelong editing. We begin with the closed-form solution derived from linear associative memory, which underpins state-of-the-art knowledge editing methods. We extend the solution from single editing to lifelong editing, and through rigorous mathematical derivation, identify an interference term in the final solution, suggesting that editing knowledge may impact irrelevant knowledge. Further analysis of the interference term reveals a close…

15 Aug

Active Metadata – The New Unsung Hero of Successful Generative AI Projects

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(BEST-BACKGROUNDS/Shutterstock) In the rapidly advancing world of technology, one silent powerhouse is revolutionizing how organizations manage and utilize data: active metadata. As generative AI (GenAI) and large language models (LLMs) become integral to data management practices, the role of active metadata in ensuring the success of these initiatives cannot be overstated. By leveraging active metadata, organizations can validate AI outputs, align AI capabilities with business goals by providing relevant context to LLMs, and significantly enhance data management efficiency. But what exactly is it and why does it matter? Active metadata refers to the dynamic information that provides organizations with real-time…

15 Aug

Attention Please: What Transformer Models Really Learn for Process Prediction

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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