Author: Leo Hudson

Authors: Vladislav Li、Georgios Tsoumplekas、Ilias Siniosoglou、Vasileios Argyriou、Anastasios Lytos、Eleftherios Fountoukidis、Panagiotis Sarigiannidis Paper: https://arxiv.org/abs/2408.10940 Introduction In the rapidly evolving landscape of Artificial Intelligence (AI), the ability to train models efficiently and effectively is paramount. This is especially true in domains where data scarcity is a significant challenge, such as the industrial and healthcare sectors. Traditional AI models often require extensive datasets and computational resources, which can be both costly and energy-intensive. To address these challenges, low-shot learning (LSL) and few-shot learning (FSL) have emerged as promising solutions. These approaches leverage prior knowledge to generalize from a small amount of labeled data, significantly reducing…

Authors: Andrew Kiruluta、Eric Lundy、Andreas Lemos Paper: https://arxiv.org/abs/2408.10619 Introduction Change detection in remote sensing is a critical task that involves comparing satellite images taken at different times to identify changes in the observed scene. This capability is essential for various applications, including environmental monitoring, urban expansion analysis, disaster management, and land use classification. Traditional change detection methods, such as image differencing and ratioing, often struggle with noise and fail to capture complex variations in imagery. Recent advancements in machine learning, particularly generative models like diffusion models, offer new opportunities for enhancing change detection accuracy. In this paper, we introduce a novel…

Authors: Xinyu Liu、Ke Jin Paper: https://arxiv.org/abs/2408.10921 MTFinEval: A Comprehensive Multi-domain Chinese Financial Benchmark Introduction In the rapidly evolving field of economics, Large Language Models (LLMs) have emerged as powerful tools for providing insights and enhancing the efficiency of economic industry development. However, with the increasing specialization of these models, a critical question arises: how can we effectively measure their performance and ensure their reliability in real-world applications? Traditional benchmarks, often focused on specific scenarios, fail to capture the theoretical depth and generalization capabilities required for comprehensive economic analysis. This study introduces MTFinEval, a new benchmark designed to evaluate the foundational…

Authors: Anusree P.S.、Bikram Keshari Parida、Seong Yong Moon、Wonsang You Paper: https://arxiv.org/abs/2408.09358 Introduction In the realm of dental health care, imaging modalities such as Cone Beam Computed Tomography (CBCT) and Panoramic X-rays are indispensable tools. CBCT provides three-dimensional views of a patient’s head, enhancing diagnostic capabilities, while Panoramic X-rays capture the entire maxillofacial region in a single image with minimal radiation exposure. However, the need for both imaging techniques can lead to increased radiation exposure and patient discomfort. This study introduces a novel method to synthesize Panoramic X-rays from existing CBCT data, thereby eliminating the need for additional scans and reducing radiation…

Authors: Karthik Shivashankar、Antonio Martini Paper: https://arxiv.org/abs/2408.09134 Better Python Programming for All: Enhancing Maintainability with Large Language Models Introduction The advent of Large Language Models (LLMs) has revolutionized automated programming, offering unprecedented assistance in generating syntactically correct and functionally robust code. However, concerns about the maintainability of the code produced by these models persist. Maintainability is crucial for the long-term success of software projects, affecting factors such as technical debt and the cost of future modifications. While existing research has extensively explored the functional accuracy and testing efficacy of LLM-generated code, maintainability has often been overlooked. This study aims to address…

Authors: Wenxuan Xie、Gaochen Wu、Bowen Zhou Paper: https://arxiv.org/abs/2408.07930 Introduction Text-to-SQL is a challenging task that involves generating SQL queries from natural language questions. This task is crucial for retrieving database values without human intervention. There are two main categories of approaches for Text-to-SQL: In-Context Learning (ICL) and Supervised Fine-Tuning. While earlier work has achieved human-level performance on simpler datasets like Spider, there remains a significant gap on more complex datasets such as BIRD. To address these challenges, the authors propose MAG-SQL, a multi-agent generative approach that incorporates soft schema linking and iterative Sub-SQL refinement. Methodology The MAG-SQL framework consists of four…

Authors: Andrea Hrckova、Jennifer Renoux、Rafael Tolosana Calasanz、Daniela Chuda、Martin Tamajka、Jakub Simko Paper: https://arxiv.org/abs/2408.06847 Introduction AI research is currently grappling with a reproducibility crisis, which poses significant risks not only to the field itself but also to other scientific domains that increasingly rely on AI systems. This paper investigates the challenges faced by AI doctoral students in Europe, focusing on the reproducibility and responsibility of AI research. The study surveyed 28 PhD candidates from 13 European countries, uncovering critical issues in the quality and findability of AI resources, difficulties in replicating experiments, and the lack of trustworthiness and interdisciplinarity in AI research. Methodology…

Authors: Alimjan Mattursun、Liejun Wang、Yinfeng Yu Paper: https://arxiv.org/abs/2408.06851 Introduction In the realm of speech processing, background noise and room reverberation pose significant challenges, degrading the clarity and intelligibility of speech. This degradation impacts various applications such as conferencing systems, speech recognition systems, and speaker recognition systems. Speech enhancement (SE) tasks aim to extract clean speech from noisy environments, thereby improving speech quality and intelligibility. Recently, deep neural network (DNN) models have shown superior denoising capabilities in complex noise environments compared to traditional methods. This study introduces a novel cross-domain feature fusion and multi-attention speech enhancement network, termed BSS-CFFMA, which leverages self-supervised…