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Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples
14 Aug

Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples

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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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CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization
14 Aug

CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization

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arXiv:2408.06576v1 Announce Type: new Abstract: Cyber Threat Intelligence (CTI) summarization task requires the system to generate concise and accurate highlights from raw intelligence data, which plays an important role in providing decision-makers with crucial information to quickly detect and respond to cyber threats in the cybersecurity domain. However, efficient techniques for summarizing CTI reports, including facts, analytical insights, attack processes, etc., have largely been unexplored, primarily due to the lack of available dataset. To this end, we present CTISum, a new benchmark for CTI summarization task. Considering the importance of attack process, a novel fine-grained subtask of attack process summarization…
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Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
14 Aug

Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning

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arXiv:2408.06549v1 Announce Type: new Abstract: Federated Learning (FL) is a distributed machine learning approach that enables devices to collaboratively train models without sharing their local data, ensuring user privacy and scalability. However, applying FL to real-world data presents challenges, particularly as most existing FL research focuses on unimodal data. Multimodal Federated Learning (MFL) has emerged to address these challenges, leveraging modality-specific encoder models to process diverse datasets. Current MFL methods often uniformly allocate computational frequencies across all modalities, which is inefficient for IoT devices with limited resources. In this paper, we propose FlexMod, a novel approach to enhance computational efficiency…
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Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies
14 Aug

Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies

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[Submitted on 12 Aug 2024] View a PDF of the paper titled Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies, by Ahmed Akib Jawad Karim and 2 other authors View PDF HTML (experimental) Abstract:Mosquito-related diseases pose a significant threat to global public health, necessitating efficient and accurate mosquito classification for effective surveillance and control. This work presents an innovative approach to mosquito classification by leveraging state-of-the-art vision transformers and open-set learning techniques. A novel framework has been introduced that integrates Transformer-based deep learning models with comprehensive data augmentation and preprocessing methods, enabling…
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SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model
14 Aug

SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model

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arXiv:2408.06574v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable achievements across various language tasks.To enhance the performance of LLMs in scientific literature services, we developed the scientific literature LLM (SciLit-LLM) through pre-training and supervised fine-tuning on scientific literature, building upon the iFLYTEK Spark LLM. Furthermore, we present a knowledge service system Spark Research Assistant (SparkRA) based on our SciLit-LLM. SparkRA is accessible online and provides three primary functions: literature investigation, paper reading, and academic writing. As of July 30, 2024, SparkRA has garnered over 50,000 registered users, with a total usage count exceeding 1.3 million. Source link…
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Operator Learning Using Random Features: A Tool for Scientific Computing
14 Aug

Operator Learning Using Random Features: A Tool for Scientific Computing

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[Submitted on 12 Aug 2024] View a PDF of the paper titled Operator Learning Using Random Features: A Tool for Scientific Computing, by Nicholas H. Nelsen and 1 other authors View PDF HTML (experimental) Abstract:Supervised operator learning centers on the use of training data, in the form of input-output pairs, to estimate maps between infinite-dimensional spaces. It is emerging as a powerful tool to complement traditional scientific computing, which may often be framed in terms of operators mapping between spaces of functions. Building on the classical random features methodology for scalar regression, this paper introduces the function-valued random features method.…
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S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation
14 Aug

S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation

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arXiv:2408.06447v1 Announce Type: new Abstract: Medical image segmentation has been traditionally approached by training or fine-tuning the entire model to cater to any new modality or dataset. However, this approach often requires tuning a large number of parameters during training. With the introduction of the Segment Anything Model (SAM) for prompted segmentation of natural images, many efforts have been made towards adapting it efficiently for medical imaging, thus reducing the training time and resources. However, these methods still require expert annotations for every image in the form of point prompts or bounding box prompts during training and inference, making it…
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Social Debiasing for Fair Multi-modal LLMs
14 Aug

Social Debiasing for Fair Multi-modal LLMs

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arXiv:2408.06569v1 Announce Type: new Abstract: Multi-modal Large Language Models (MLLMs) have advanced significantly, offering powerful vision-language understanding capabilities. However, these models often inherit severe social biases from their training datasets, leading to unfair predictions based on attributes like race and gender. This paper addresses the issue of social biases in MLLMs by i) Introducing a comprehensive Counterfactual dataset with Multiple Social Concepts (CMSC), which provides a more diverse and extensive training set compared to existing datasets. ii) Proposing an Anti-Stereotype Debiasing strategy (ASD). Our method works by revisiting the MLLM training process, rescaling the autoregressive loss function, and improving data…
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Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction
14 Aug

Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction

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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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