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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Yoon, Juyeon Feldt, Robert Yoo, Shin KAIST School of Computing Daejeon Korea Republic of Chalmers University Dept. of Computer Science & Engineering Gothenburg Sweden

ISBN: (纸本)9798331534677

The recent surge of building software systems powered by Large Language Models (LLMs) has led to the development of various testing frameworks, primarily focused on treating prompt templates as the unit of testing. Despite the significant costs associated with test input execution and output assessment, the curation of optimized test suites is yet overlooked in these tools, which calls for tailored test selection or prioritization strategies. In this paper, we show that diversity-based testing techniques, such as Adaptive Random Testing (ART) with appropriate string distance metrics, can be effectively applied to the testing of prompt templates. Our proposed adaptive testing approach adjusts the conventional ART process to this context by selecting new test inputs based on scores derived from existing test suite and their labelling results. Our results, obtained using various implementations that explore several string-based distances, confirm that our approach enables the discovery of failures with reduced testing budgets and promotes the generation of more varied outputs. © 2025 IEEE.

关键词： Software testing

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Sampling-Based Motion Planning for Guide Robots Considering User Pose Uncertainty 25th

Sampling-Based Motion Planning for Guide Robots Considering...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Mosquera-Maturana, Juan Sebastian Hernández Vega, Juan David Romero Cano, Victor Faculty of Engineering Universidad Autónoma de Occidente Cali Colombia School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

ISBN: (纸本)9783031720581

In this paper, we propose a framework to address the problem of guiding a person within a semi-structured environment in a socially acceptable manner that prioritises safety and comfort. We propose an algorithm based on the optimal Rapidly exploring Random Tree (RRT*) algorithm for path planning. Our proposal utilises Dubins curves and takes into account the user during path planning to generate a navigation path that allows the robot to follow a feasible path that can also be navigated by the user. A comparative analysis against standard path planning based on the RRT* algorithm and the Social Force Model validates the efficacy of our proposed algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Microrobots

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Attention Span and it's Correlation with Mental Health 1

Attention Span and it's Correlation with Mental Health

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Shah, Richa Bewoor, Laxmi Rak, Sarthak Patne, Ayush Vishwakarma Institute of Information Technology Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350355611

This paper delves into the correlation between attention span and mental health. Attention span is the ability to focus on a task before being distracted by certain factors. It ranges from 2 seconds to more than 20 minutes. Meanwhile, mental health constitutes a person's psychological, sociological, and emotional well-being. Using a clustering algorithm, this research seeks to provide a concrete relation between the two across an age group of 18-25. © 2025 IEEE.

关键词： Electronic health record

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Dinov2_Mask R-CNN: Self-supervised Instance Segmentation of Diabetic Foot Ulcers 4th

Dinov2_Mask R-CNN: Self-supervised Instance Segmentation of ...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Ban, Yun ji Lee, Seihyoung Park, Jiwon Kim, Jeong Eun Kang, Hyun Seo Han, Seungwan Electronics and Telecommunications Research Institute Gwangju Korea Republic of School of Computer Science and Engineering Mokpo National University Muan-Gun Korea Republic of

ISBN: (纸本)9783031808708

Diabetic foot ulcers (DFUs) are a significant condition where early detection is crucial due to the steadily increasing global prevalence of affected patients. In this paper, we aim to assist in the early detection and treatment of DFU by performing instance segmentation on DFU images using a self-supervised method. Using the pre-trained weights of Dinov2, which is based on a self-supervised learning approach, we connected a Mask R-CNN model to Dinov2’s head for instance segmentation. We divide the images provided by the DFU challenge into training and validation sets, with data augmentation applied to enhance performance. The performance of the model was evaluated using Dice score and IoU metrics. On our generated validation dataset, which consists of 400 images, we achieved a maximum Dice score of 0.8181. On the leaderboard, where performance was measured using the Test set provided by DFUC2024, the highest Dice score was 0.6764. By leveraging the self-supervised learning backbone to capture intrinsic features of the images, we achieved superior performance compared to traditional instance segmentation methods. The model and code we have implemented are publicly available at https://***/seihyoung/dfuc2024. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Self-Guided Graph Refinement with Progressive Fusion for Multiplex Graph Contrastive Representation Learning

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IEEE Transactions on Big Data 2025年

作者： Dai, Qi Gu, Yu Zhu, Xiaofeng Li, Xiaohua Li, Fangfang Yu, Ge The School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu610056 China

Multiplex Graph Contrastive Learning (MGCL) has attracted significant attention. However, existing MGCL methods often struggle with suboptimal graph structures and fail to fully capture intricate interdependencies across multiplex views. To address these issues, we propose a novel self-supervised framework, Multiplex Graph Refinement with progressive fusion (MGRefine), for multiplex graph contrastive representation learning. Specifically, MGRefine introduces a multi-view learning module to extract a structural guidance matrix by exploring the underlying relationships between nodes. Then, a progressive fusion module is employed to progressively enhance and fuse representations from different views, capturing and leveraging nuanced interdependencies and comprehensive information across the multiplex graphs. The fused representation is then used to construct a consensus guidance matrix. A self-enhanced refinement module continuously refines the multiplex graphs using these guidance matrices while providing effective supervision signals. MGRefine achieves mutual reinforcement between graph structures and representations, ensuring continuous optimization of the model throughout the learning process in a self-enhanced manner. Extensive experiments demonstrate that MGRefine outperforms state-of-the-art methods and also verify the effectiveness of MGRefine across various downstream tasks on several benchmark datasets. ©2015 IEEE.

关键词： Contrastive Learning

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Predicting Aesthetic Outcomes in Breast Cancer Surgery: A Multimodal Retrieval Approach 1st

Predicting Aesthetic Outcomes in Breast Cancer Surgery: A M...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Zolfagharnasab, Mohammad Hossein Freitas, Nuno Gonçalves, Tiago Bonci, Eduard Mavioso, Carlos Cardoso, Maria J. Oliveira, Hélder P. Cardoso, Jaime S. University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal Breast Unit Champalimaud Foundation Lisbon Portugal

ISBN: (纸本)9783031777882

Breast cancer treatments often affect patients’ body image, making aesthetic outcome predictions vital. This study introduces a Deep Learning (DL) multimodal retrieval pipeline using a dataset of 2,193 instances combining clinical attributes and RGB images of patients’ upper torsos. We evaluate four retrieval techniques: Weighted Euclidean Distance (WED) with various configurations and shallow Artificial Neural Network (ANN) for tabular data, pre-trained and fine-tuned Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), and a multimodal approach combining both data types. The dataset, categorised into Excellent/Good and Fair/Poor outcomes, is organised into over 20K triplets for training and testing. Results show fine-tuned multimodal ViTs notably enhance performance, achieving up to 73.85% accuracy and 80.62% Adjusted Discounted Cumulative Gain (ADCG). This framework not only aids in managing patient expectations by retrieving the most relevant post-surgical images but also promises broad applications in medical image analysis and retrieval. The main contributions of this paper are the development of a multimodal retrieval system for breast cancer patients based on post-surgery aesthetic outcome and the evaluation of different models on a new dataset annotated by clinicians for image retrieval. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Perception-Enhanced Network for Accurate Human Pose Estimation

Perception-Enhanced Network for Accurate Human Pose Estimati...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sun, Xiaodi Zhong, Baojiang Ma, Kai-Kuang School of Computer Science and Technology Soochow University Suzhou China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368741

Human pose estimation in computer vision is particularly challenging with images containing multiple individuals. Existing methods often integrate spatial and channel attention by simply adding them up through a cascade or parallel connection. The features extracted in this way could lead to less accurate key point predictions, especially in cases where the limbs of different people are obstructed or tangled with each other. To tackle this crucial issue, we develop a novel network that enhances key point detection by combining the spatial and channel attention in a more effective manner. Specifically, our network features a lightweight perception-enhanced module (PEM) that adaptively fuses spatial and channel features through a Hadamard product, thereby refining the overall feature representation. Moreover, by exploiting the initial feature map as a guide to generate the pixel attention, we further boost key point prediction accuracy. Extensive experimental results show that our developed network can clearly outperform the current state-of-the-art methods. © 2025 IEEE.

关键词： attention mechanism coordinate classification human pose estimation

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TSR-Jack: An In-Browser Crypto-Jacking Detection Method Based on Time Series Representation Learning 26th

TSR-Jack: An In-Browser Crypto-Jacking Detection Method Base...

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26th International Conference on Information and Communications Security, ICICS 2024

作者： Cui, Bo Liu, Shuai Engineering Research Center of Ecological Big Data Ministry of Education Beijing China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9789819788002

As the economic value of cryptocurrencies continues to ascend, an increasing number of cybercriminals exploit malicious browser scripts to commandeer the system and network resources of victims for unauthorized cryptocurrency mining. This form of browser-based mining, termed crypto-jacking, imparts substantial impact and harm to society, enterprises, and users. However, existing research methods lead to poor transferability because they require model training using labeling data from detection devices. To solve this problem, we introduce a crypto-jacking detection method based on time series representation learning, called TSR-jack. Specifically, we first collect hardware runtime system data for a period of time and generate datasets. After slicing the time series datasets, they are used to train a time series representation model, which introduces the loss of temporal information to extract temporal features. The temporal features can capture similarities between different devices and therefore address model transfer difficulties. Finally, the time vectors obtained from representation learning are fed into SVM for classification. Experimental results show that our TSR-jack method outperforms similar dynamic feature detection methods, especially when considering transferability, with a precision improvement of more than 5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Assessment of ingestion dose from 226Ra, 228Ra, and 40K in vegetables consumed in Koya district, Iraq

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Environmental science and Pollution Research 2025年第19期32卷 11972-11980页

作者： Saleh, Dedawan Salam Salh, Hemn Smail, Jahfer Majeed Ahmad, Saddon Taha Department of Physics Faculty of Science and Health Koya University Kurdistan Region KoyaKOY45 Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Faculty of Medicine Koya University KoyaKOY45 Iraq

The present work comprises the measurement of activity concentrations of three radionuclides, 22⁶Ra, 228Ra, and 4⁰K in 33 species of vegetables commonly used in Koya district, the Iraqi Kurdistan region. The analysis of the samples was performed with high purity germanium (HPGe) gamma spectrometry. Among the analyzed samples, mushrooms presented the highest activity concentrations of 22⁶Ra (3.34 ± 1.18 Bq·kg⁻1), 228Ra (1.24 ± 0.5 Bq·kg⁻1), and 4⁰K (1481.93 ± 14.46 Bq·kg⁻1) while legumes had the lowest levels of these radionuclides. The estimated annual effective dose from the consumption of these vegetables was computed to be 97.216 µSv·y⁻1 and this is much lower than the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) global reference level of 290 µSv·y⁻1. Therefore, it can be concluded that the radiological risk from the consumption of vegetables in this area is very low thus giving a reassurance to the people on the safety implication on the radiological health hazards. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Radiation effects

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Optimizing Hiring Practices: A Machine Learning Approach for Candidate Selection 3

Optimizing Hiring Practices: A Machine Learning Approach for...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dogiparthy, Naga Datta Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Recruitment is one of the most crucial factors in shaping efficient and high-performing teams within organizations. However, the traditional recruitment processes are often plagued by biases and subjective judgments that lead to inconsistent and suboptimal hiring decisions. This project uses machine learning(ML) to make recruitment more objective, efficient, and data-driven. By taking the age, gender, education level, experience, interview scores, and personality traits of the applicants as key features, the model predicts candidate suitability without bias. The methodology uses robust preprocessing, Chi-Square feature selection, and the SMOTE technique to overcome class imbalance in the dataset. The proposed system uses various different machine learning algorithms like CatBoost, KD-Trees, Random Forest, and Logistic Regression and many more. All these models are trained and fine-tuned with GridSearchCV for hyperparameter optimization. Among them, the best model was CatBoost with 95% accuracy. This work shows how machine learning can change the recruitment game for organizations to make unbiased, data-driven hiring decisions and ultimately increase workforce efficiency and organizational success. © 2025 IEEE.

关键词： Adversarial machine learning

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