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22 Nov

BIPro: Zero-shot Chinese Poem Generation via Block Inverse Prompting Constrained Generation Framework

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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

22 Nov

Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition

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[Submitted on 19 May 2023 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition, by Liangqi Yuan and Yuan Wei and Jia Li View PDF HTML (experimental) Abstract:With the emphasis on healthcare, early childhood education, and fitness, non-invasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advantages of simple structure, easy access, visualization application, and harmlessness. This paper introduces a Smart Pressure e-Mat (SPeM) system based on piezoresistive material, Velostat, for human monitoring…

22 Nov

A Multi-scale Information Integration Framework for Infrared and Visible Image Fusion

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[Submitted on 7 Dec 2023 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled A Multi-scale Information Integration Framework for Infrared and Visible Image Fusion, by Guang Yang and 3 other authors View PDF HTML (experimental) Abstract:Infrared and visible image fusion aims at generating a fused image containing the intensity and detail information of source images, and the key issue is effectively measuring and integrating the complementary information of multi-modality images from the same scene. Existing methods mostly adopt a simple weight in the loss function to decide the information retention of each…

22 Nov

Neon: News Entity-Interaction Extraction for Enhanced Question Answering

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[Submitted on 19 Nov 2024 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled Neon: News Entity-Interaction Extraction for Enhanced Question Answering, by Sneha Singhania and 3 other authors View PDF HTML (experimental) Abstract:Capturing fresh information in near real-time and using it to augment existing large language models (LLMs) is essential to generate up-to-date, grounded, and reliable output. This problem becomes particularly challenging when LLMs are used for informational tasks in rapidly evolving fields, such as Web search related to recent or unfolding events involving entities, where generating temporally relevant responses requires access…

22 Nov

Evaluating LLMs Capabilities Towards Understanding Social Dynamics

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arXiv:2411.13008v1 Announce Type: new Abstract: Social media discourse involves people from different backgrounds, beliefs, and motives. Thus, often such discourse can devolve into toxic interactions. Generative Models, such as Llama and ChatGPT, have recently exploded in popularity due to their capabilities in zero-shot question-answering. Because these models are increasingly being used to ask questions of social significance, a crucial research question is whether they can understand social media dynamics. This work provides a critical analysis regarding generative LLM's ability to understand language and dynamics in social contexts, particularly considering cyberbullying and anti-cyberbullying (posts aimed at reducing cyberbullying) interactions. Specifically, we…

22 Nov

Autoassociative Learning of Structural Representations for Modeling and Classification in Medical Imaging

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[Submitted on 18 Nov 2024] View a PDF of the paper titled Autoassociative Learning of Structural Representations for Modeling and Classification in Medical Imaging, by Zuzanna Buchnajzer and 4 other authors View PDF HTML (experimental) Abstract:Deep learning architectures based on convolutional neural networks tend to rely on continuous, smooth features. While this characteristics provides significant robustness and proves useful in many real-world tasks, it is strikingly incompatible with the physical characteristic of the world, which, at the scale in which humans operate, comprises crisp objects, typically representing well-defined categories. This study proposes a class of neurosymbolic systems that learn by…

22 Nov

AIDBench: A benchmark for evaluating the authorship identification capability of large language models

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arXiv:2411.13226v1 Announce Type: new Abstract: As large language models (LLMs) rapidly advance and integrate into daily life, the privacy risks they pose are attracting increasing attention. We focus on a specific privacy risk where LLMs may help identify the authorship of anonymous texts, which challenges the effectiveness of anonymity in real-world systems such as anonymous peer review systems. To investigate these risks, we present AIDBench, a new benchmark that incorporates several author identification datasets, including emails, blogs, reviews, articles, and research papers. AIDBench utilizes two evaluation methods: one-to-one authorship identification, which determines whether two texts are from the same author;…

22 Nov

MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification

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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

22 Nov

EVT: Efficient View Transformation for Multi-Modal 3D Object Detection

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[Submitted on 16 Nov 2024 (v1), last revised 19 Nov 2024 (this version, v2)] View a PDF of the paper titled EVT: Efficient View Transformation for Multi-Modal 3D Object Detection, by Yongjin Lee and 3 other authors View PDF HTML (experimental) Abstract:Multi-modal sensor fusion in bird's-eye-view (BEV) representation has become the leading approach in 3D object detection. However, existing methods often rely on depth estimators or transformer encoders for view transformation, incurring substantial computational overhead. Furthermore, the lack of precise geometric correspondence between 2D and 3D spaces leads to spatial and ray-directional misalignments, restricting the effectiveness of BEV representations. To…

22 Nov

A Benchmark for Long-Form Medical Question Answering

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[Submitted on 14 Nov 2024 (v1), last revised 19 Nov 2024 (this version, v2)] View a PDF of the paper titled A Benchmark for Long-Form Medical Question Answering, by Pedram Hosseini and 6 other authors View PDF HTML (experimental) Abstract:There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on automatic metrics and multiple-choice questions. While valuable, these benchmarks fail to fully capture or assess the complexities of real-world clinical applications where LLMs are being deployed. Furthermore, existing studies on evaluating long-form answer generation in…