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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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On the Machine Learning-based Multi-class Classification of Microscopic Colitis

On the Machine Learning-based Multi-class Classification of ...

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IEEE International Conference on Electro-Information Technology

作者： Vivek Tara Dipankar Mitra Aditi Muduganti Padmavathi Mali Srabana Maiti Shuvashis Dey Rahul Gomes Dept. of Physics and Astronomy University of Wisconsin La Crosse WI USA Dept. of Computer Science and Computer Engineering University of Wisconsin La Crosse WI USA Onalaska High School Onalaska WI USA Dept. of Internal Medicine Gundersen Health System La Crosse WI USA Dept. of Electrical and Computer Engineering North Dakota State University Fargo ND USA Dept. of Computer Science University of Wisconsin Eau Claire

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Standard practice for the classification of Microscopic Colitis (MC) as dictated by European guidelines instructs for a complete colonoscopy opposed to the less invasive, but historically inaccurate flexible sigmoidoscopy. A recent study conducted by researchers at Gunderson Health system sought to analyze historical data of patients diagnosed with MC to determine if a complete colonoscopy truly is required to make a diagnosis, or if a flexible sigmoidoscopy would suffice. The results of the study indicated that analysis of patient data obtained through flexible sigmoidoscopy alone provided inaccurate rates of classification of MC. The researchers concluded the results of the study further reinforced European guidelines of performing a complete colonoscopy for the classification of MC. In this work multi-class classification machine learning models are explored to differentiate subtypes of MC on medical data obtained through flexible sigmoidoscopy at Gunderson Health system. The results of the study show that Machine Learning (ML) algorithms have the potential to automate the classification of the traditionally difficult to diagnose disease, MC.

关键词： Accuracy Biological system modeling Microscopy Biopsy Colonoscopy Europe Data models

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MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule

arXiv

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arXiv 2024年

作者： Cai, Guohui Zhang, Ruicheng He, Hongyang Zhang, Zeyu Ergu, Daji Cao, Yuanzhouhan Zhao, Jinman Hu, Binbin Liao, Zhibin Zhao, Yang Cai, Ying College of Computer Science and Artificial Intelligence Southwest Minzu University Chengdu China School of Intelligent System Engineering Sun Yat-sen University Guangzhou China Department of Computer Science University of Warwick United Kingdom Australian National University CanberraACT2601 Australia School of Computer Science and Technology Beijing Jiaotong University Beijing China University of Toronto Toronto Canada University of Adelaide Adelaide Australia La Trobe University Melbourne Australia

Pulmonary nodules are critical for early lung cancer diagnosis, but traditional CT imaging methods suffer from low detection rates and poor localization. Small nodule detection is challenging due to subtle differences in density and issues like occlusion. Existing methods such as FPN, with its fixed feature fusion and limited receptive field, struggle to effectively overcome these issues. To address these challenges, our paper proposed three key contributions: Firstly, we proposed MSDet, a multiscale attention and receptive field network for detecting tiny pulmonary nodules. Secondly, we proposed the extended receptive domain (ERD) strategy to capture richer contextual information and reduce false positives caused by nodule occlusion. We also proposed the position channel attention mechanism (PCAM) to optimize feature learning and reduce multiscale detection errors, and designed the tiny object detection block (TODB) to enhance the detection of tiny nodules. Experiments on the LUNA16 dataset show an 8.8% improvement in mAP over YOLOv8, achieving state-of-the-art performance. The code is available at https://***/CaiGuoHui123/MSDet. © 2024, CC BY-NC-SA.

关键词： Lung cancer

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PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing

arXiv

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arXiv 2025年

作者： Zhang, Yuwei Jin, Zhi Xing, Ying Li, Ge Liu, Fang Zhu, Jiaxin Dou, Wensheng Wei, Jun Affiliated with Nanjing Institute of Software Technology University of Chinese Academy of Sciences Nanjing China Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Computer Science Wuhan University Wuhan China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： computer software maintenance

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CL-MIL: Multi-Instance Learning on Whole-Slide Images with Self-Supervised Contrastive Learning

CL-MIL: Multi-Instance Learning on Whole-Slide Images with S...

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International Conference on computer and Communications (ICCC)

作者： Pengfei Xiu Dongying Liu Sheng Wang Hongliang Wang Wei Wang Shun Li Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Department of Gastroenterology Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Histopathological examination of tissue slides is considered the gold standard for clinical cancer diagnosis. However, it heavily relies on pathologists' subjective judgment and involves substantial workload. Existing convolutional neural network (CNN)-based whole slide image (WSI) diagnostic methods suffer from high annotation costs and complex data acquisition processes. Therefore, this study proposes a weakly supervised multiple instance learning method based on self-supervised contrastive learning (CL-MIL) for WSI classification. By integrating self-supervised contrastive learning strategies, leveraging data augmentation through H&E stain separation, and introducing causal intervention via backdoor adjustment to mitigate dataset bias, our approach significantly enhances feature representation and classification performance in pathological images. Experimental results on the Camelyon16 and TCGA lung cancer datasets demonstrate that our method outperforms other comparable WSI classification models across various evaluation metrics, achieving higher diagnostic efficiency and accuracy. Additionally, our method exhibits good interpretability, enabling precise localization of pathological regions and providing robust technical support for clinical applications.

关键词： Measurement Location awareness Pathology Accuracy Weak supervision Lung cancer Contrastive learning Feature extraction Standards Image classification

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Service Migration in Mec: An Approach Based on Deep Reinforcement Learning and Fuzzy Logic

SSRN

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SSRN 2023年

作者： Zhang, Xinyang Bu, Chao Wang, Jinsong Key Laboratory of Computer Vision System of Ministry of Education School of Computer Science and Engineering Tianjin University of Technology China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China

As a new computing paradigm, Mobile Edge Computing (MEC) sinks resources to the network edge to support real-time re-sponses of services. Facing the demand for migrating services caused by the frequent movement of the mobile device, it is a great challenge to reduce the operation costs of migrating services with user requirements satisfied, especially for the continu-ous optimization of services deployed in hot spot areas. In order to address the above challenge, we propose the mechanism of dynamically migrating and optimally deploying services, which optimizes the latency and energy consumption of the service mi-gration. Firstly, we introduce Markov Decision Process (MDP) to model changes of deployment locations of the single-user ser-vice, and devise the approach to determine whether to migrate a service at the current network status based on the dueling Deep Q-Network (DQN). Secondly, we propose the service pre-migration decision approach to determine where to migrate a ser-vice by combining the Long Short-Term Memory (LSTM) network and the attention mechanism, so as to determine the future moving area of the user and perform the migration decision in advance with the impact of migration service on service quality considered. Finally, we devise the distance-weighted matching algorithm based on the fuzzy logic to further optimize the selec-tion of the most appropriate services to migrate in hotspot areas and the corresponding service migration regions, thus, the load pressure of the edge computing node caused by the imbalanced deployment density of service among multiple regions. Simula-tion results show that the proposed mechanism has significant improvements in overall operating costs compared with the cur-rent state of the art. © 2023, The Authors. All rights reserved.

关键词： Fuzzy logic

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YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction

arXiv

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arXiv 2025年

作者： Lin, Ziyu Wu, Yunfan Ma, Yuhang Chen, Junzhou Zhang, Ronghui Wu, Jiaming Yin, Guodong Lin, Liang Guangdong Key Laboratory of Intelligent Transportation System School of intelligent systems engineering Sun Yat-sen University Guangzhou510275 China Department of Architecture and Civil Engineering Chalmers University of Technology Sven Hultins gata 6 GothenburgSE-412 96 Sweden School of Mechanical Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China

Detecting traffic signs effectively under low-light conditions remains a significant challenge. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. Firstly, we introduce the High-Resolution Feature Map for Small Object Detection (HRFM-TOD) module to address indistinct small-object features in low-light scenarios. By leveraging high-resolution feature maps, HRFM-TOD effectively mitigates the feature dilution problem encountered in conventional PANet frameworks, thereby enhancing both detection accuracy and inference speed. Secondly, we develop the Multi-branch Feature Interaction Attention (MFIA) module, which facilitates deep feature interaction across multiple receptive fields in both channel and spatial dimensions, significantly improving the model’s information extraction capabilities. Finally, we propose the Prior-Guided Enhancement Module (PGFE) to tackle common image quality challenges in low-light environments, such as noise, low contrast, and blurriness. This module employs prior knowledge to enrich image details and enhance visibility, substantially boosting detection performance. To support this research, we construct a novel dataset, the Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios, including urban, highway, and rural environments under varying weather conditions. Experimental evaluations demonstrate that YOLO-LLTS achieves state-of-the-art performance, outperforming the previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, and achieving superior results on the CCTSDB2021 dataset. Moreover, deployment experiments on edge devices confirm the real-time applicability and effectiveness of our proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Research Artifacts in Software engineering Publications: Status and Trends

arXiv

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arXiv 2024年

作者： Liu, Mugeng Huang, Xiaolong He, Wei Xie, Yibing Zhang, Jie M. Jing, Xiang Chen, Zhenpeng Ma, Yun Ministry of Education School of Computer Science Peking University Beijing China School of Software and Microelectronics Peking University Beijing China Department of Informatics King’s College London London United Kingdom National Key Laboratory of Data Space Technology and System Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute for Artificial Intelligence Peking University Beijing China

The Software engineering (SE) community has been embracing the open science policy and encouraging researchers to disclose artifacts in their publications. However, the status and trends of artifact practice and quality remain unclear, lacking insights on further improvement. In this paper, we present an empirical study to characterize the research artifacts in SE publications. Specifically, we manually collect 1,487 artifacts from all 2,196 papers published in top-tier SE conferences (ASE, FSE, ICSE, and ISSTA) from 2017 to 2022. We investigate the common practices (e.g., URL location and format, storage websites), maintenance activities (e.g., last update time and URL validity), popularity (e.g., the number of stars on GitHub and characteristics), and quality (e.g., documentation and code smell) of these artifacts. Based on our analysis, we reveal a rise in publications providing artifacts. The usage of Zenodo for sharing artifacts has significantly increased. However, artifacts stored in GitHub tend to receive few stars, indicating a limited influence on real-world SE applications. We summarize the results and provide suggestions to different stakeholders in conjunction with current guidelines. © 2024, CC BY.

关键词： Software engineering

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Affinity-Based Power Flow Optimization in Reconfigurable Picogrid

Affinity-Based Power Flow Optimization in Reconfigurable Pic...

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IEEE/SICE International Symposium on system Integration

作者： Ryota Kokubu Kaito Oeda Yuusuke Kawakita Shinji Yokogawa Yoshito Tobe Haruhisa Ichikawa Graduate School of Science and Engineering Aoyama Gakuin University Sagamihara Japan Department of Information and Computer Sciences Kanagawa Institute of Technology Atsugi Japan Info-Powered Energy system Research Center The University of Electro-Communications Chofu Japan

In recent years, there has been an increasing demand to adopt renewable energy resources to power grids for environmental issues and energy concerns. A virtual grid system, which is designed to utilize decentralized resources, is proposed as an approach. This system enables users to construct their power grids with virtual grid hubs (VG Hub) which interconnect via commercially available cables according to USB Power Delivery (USB PD). A prototype of VG Hub is capable of synthesizing and distributing DC power for connected devices, and the power flow control is managed by a microcontroller. Our previous study proposed a method called Minimum-Hop Power Path Routing (MHPPR). This method determines the power flow that uses the closest source in candidates which can fulfill the demand when a load connects to the network to minimize power loss due to transmission. However, this could result in unproductive power flow, such as a flow that consumes power stored in batteries prior to surplus power from generators. Therefore, we assumed that there are factors to value a source for a load other than demand and supply capacity, and inferred that the factor derives from the type of device. We defined this factor as the affinity between devices. The objective of this study is to solve the power path optimization problem while taking affinity into account. In this study, we discussed device classification and affinity between types and defined affinity coefficients. We then extended MHPPR by incorporating this affinity coefficient. The results have shown that the proposed method selected the sources following the priority defined by the affinity.

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Machine Learning Techniques Using the Rapid Miner Tool for Solar Production Forecasting

Machine Learning Techniques Using the Rapid Miner Tool for S...

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Digital Age & Technological Advances for Sustainable Development (ICDATA), International Conference on

作者： Younes Ledmaoui Asmaa El Fahli Adila Elmaghraoui Mohamed El Aroussi Rachid Saadane Abdellah Chehri Laboratory Engineering system Hassania School of Public Works Casablanca Morocco Green Tech Institute (GTI) Mohammed VI Polytechnic University Benguerir Morocco Mathematics and Computer Science Kingston ON Canada

The Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBOOST), Support Vector Machine (SVM), and Random Forest (RF) machine learning (ML) algorithms are used in this research to give a comparative study of solar energy production forecasting in Morocco. The models were developed, trained, and then assessed. In this paper, two metrics were employed to evaluate the models' performance: Mean absolute error (MAE), and root mean squared error (RMSE). The models' performances show that ANN is the most reliable predictive model for energy forecasting in similar scenarios.

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