Tootfinder

Expanding Relevance Judgments for Medical Case-based Retrieval Task with Multimodal LLMs
Catarina Pires, S\'ergio Nunes, Lu\'is Filipe Teixeira
https://arxiv.org/abs/2506.17782

Expanding Relevance Judgments for Medical Case-based Retrieval Task with Multimodal LLMs
Evaluating Information Retrieval (IR) systems relies on high-quality manual relevance judgments (qrels), which are costly and time-consuming to obtain. While pooling reduces the annotation effort, it results in only partially labeled datasets. Large Language Models (LLMs) offer a promising alternative to reducing reliance on manual judgments, particularly in complex domains like medical case-based retrieval, where relevance assessment requires analyzing both textual and visual information. In t…

SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning
Si-Woo Kim, MinJu Jeon, Ye-Chan Kim, Soeun Lee, Taewhan Kim, Dong-Jin Kim
https://arxiv.org/abs/2507.18616

SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning
Zero-shot Image Captioning (ZIC) increasingly utilizes synthetic datasets generated by text-to-image (T2I) models to mitigate the need for costly manual annotation. However, these T2I models often produce images that exhibit semantic misalignments with their corresponding input captions (e.g., missing objects, incorrect attributes), resulting in noisy synthetic image-caption pairs that can hinder model training. Existing dataset pruning techniques are largely designed for removing noisy text in…

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
Xiaoran Xua, In-Ho Rab, Ravi Sankarc
https://arxiv.org/abs/2507.16845 https://arxiv.o…

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
Lung diseases, including lung cancer and COPD, are significant health concerns globally. Traditional diagnostic methods can be costly, time-consuming, and invasive. This study investigates the use of semi supervised learning methods for lung sound signal detection using a model combination of MFCC+CNN. By introducing semi supervised learning modules such as Mix Match, Co-Refinement, and Co Refurbishing, we aim to enhance the detection performance while reducing dependence on manual annotations.…

From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns
Bernardo Leite, Henrique Lopes Cardoso, Pedro Pinto, Abel Ferreira, Lu\'is Abreu, Isabel Rangel, Sandra Monteiro
https://arxiv.org/abs/2506.15598

From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns
While MCQs are valuable for learning and evaluation, manually creating them with varying difficulty levels and targeted reading skills remains a time-consuming and costly task. Recent advances in generative AI provide an opportunity to automate MCQ generation efficiently. However, assessing the actual quality and reliability of generated MCQs has received limited attention -- particularly regarding cases where generation fails. This aspect becomes particularly important when the generated MCQs …

Quality Assessment of Python Tests Generated by Large Language Models
Victor Alves, Carla Bezerra, Ivan Machado, Larissa Rocha, T\'assio Virg\'inio, Publio Silva
https://arxiv.org/abs/2506.14297

Quality Assessment of Python Tests Generated by Large Language Models
The manual generation of test scripts is a time-intensive, costly, and error-prone process, indicating the value of automated solutions. Large Language Models (LLMs) have shown great promise in this domain, leveraging their extensive knowledge to produce test code more efficiently. This study investigates the quality of Python test code generated by three LLMs: GPT-4o, Amazon Q, and LLama 3.3. We evaluate the structural reliability of test suites generated under two distinct prompt contexts: Te…

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Evangelos Froudakis, Athanasios Avgetidis, Sean Tyler Frankum, Roberto Perdisci, Manos Antonakakis, Angelos Keromytis
https://arxiv.org/abs/2506.11325

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Indicators of Compromise (IoCs) are critical for threat detection and response, marking malicious activity across networks and systems. Yet, the effectiveness of automated IoC extraction systems is fundamentally limited by one key issue: the lack of high-quality ground truth. Current extraction tools rely either on manually extracted ground truth, which is labor-intensive and costly, or on automated ground truth creation methods that include non-malicious artifacts, leading to inflated false po…

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Afra Kilic, Kim Batselier
https://arxiv.org/abs/2507.11136 …

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter uncertainty. Further, they require manual tuning of model complexity hyperparameters like tensor rank and feature dimensions, often through trial-and-error or computationally costly methods like cross-validation. We propose Bayesian Tensor Network Kernel Machines, a fully probabil…

Magnetic properties of orphan penumbrae
J. S. Castellanos Dur\'an, B. L\"optien, A. Korpi-Lagg, S. K. Solanki, M. van Noort
https://arxiv.org/abs/2507.08117

Magnetic properties of orphan penumbrae
Orphan penumbrae (OPU) are features resembling sunspot penumbrae, but are not connected to an umbra. Here we compare OPUs and sunspot penumbrae, including their filaments. We also identify and describe the main mechanisms for the formation of OPUs and we characterise their decay process. Our study is based on spectropolarimetric inversions of active regions observed with the Hinode spectropolarimeter. We manually identified 80 individual OPUs, allowing us to study them statistically. In additio…

SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Cheng Wang, Siqi Chen, Donghua Mi, Yang Chen, Yudong Zhang, Yinsheng Li
https://arxiv.org/abs/2506.10325

SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Recent advances in medical imaging have established deep learning-based segmentation as the predominant approach, though it typically requires large amounts of manually annotated data. However, obtaining annotations for intracranial hemorrhage (ICH) remains particularly challenging due to the tedious and costly labeling process. Semi-supervised learning (SSL) has emerged as a promising solution to address the scarcity of labeled data, especially in volumetric medical image segmentation. Unlike …

A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
Lama Alqazlan, Zheng Fang, Michael Castelle, Rob Procter
https://arxiv.org/abs/2506.06083

A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
The availability of big data has significantly influenced the possibilities and methodological choices for conducting large-scale behavioural and social science research. In the context of qualitative data analysis, a major challenge is that conventional methods require intensive manual labour and are often impractical to apply to large datasets. One effective way to address this issue is by integrating emerging computational methods to overcome scalability limitations. However, a critical conc…