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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and Data Mining V.1

作者： Yonghao Liu Fausto Giunchiglia Ximing Li Lan Huang Xiaoyue Feng Renchu Guan Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (纸本)9798400712456

Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among researchers. Recent advancements in graph few-shot learning models have exhibited superior performance across diverse applications. Despite their successes, several limitations still exist. First, existing models in the meta-training phase predominantly focus on instance-level features within tasks, neglecting crucial set-level features essential for distinguishing between different categories. Second, these models often utilize query sets directly on classifiers trained with support sets containing only a few labeled examples, overlooking potential distribution shifts between these sets and leading to suboptimal performance. Finally, previous models typically require necessitate abundant labeled data from base classes to extract transferable knowledge, which is typically infeasible in real-world scenarios. To address these issues, we propose a novel model named STAR, which leverages Set funcTions and optimAl tRansport for enhancing unsupervised graph few-shot learning. Specifically, STAR utilizes expressive set functions to obtain set-level features in an unsupervised manner and employs optimal transport principles to align the distributions of support and query sets, thereby mitigating distribution shift effects. Theoretical analysis demonstrates that STAR can capture more task-relevant information and enhance generalization capabilities. Empirically, extensive experiments across multiple datasets validate the effectiveness of STAR. Our code can be found https://***/KEAML-JLU/STAR here.

关键词： few-shot learning

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Enabling Generalized Zero-Shot Vulnerability Classification

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Hu, Jinghao Guo, Jinsong Luo, Chen Hu, Yang Lanzinger, Matthias Li, Zhanshan Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China University College London Department of Computer Science United Kingdom Amazon United States United Kingdom University of Oxford Department of Computer Science United Kingdom Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Changchun130012 China

Regarding computer security, the growth of code vulnerability types presents a persistent challenge. These vulnerabilities, which may cause severe consequences, necessitate precise classification for effective mitigation. However, the rapid emergence of new vulnerability types complicates the classification process. Traditional methodologies, which often involve human expertise and the manual labeling or generation of example instances, are not only resource-intensive but also struggle to adapt to the dynamic nature of these vulnerabilities. This paper introduces VulnSense, an innovative method that harnesses the capabilities of Generalized Zero-Shot Learning (GZSL) to address the vulnerability classification problem. VulnSense learns about unseen vulnerability classes from the descriptions of these unseen classes, while not requiring to see any instances of these unseen classes. Specifically, VulnSense learns from three main resources: (1) seen classes with labeled code instances;(2) descriptions of these seen classes;and (3) descriptions of "unseen"classes which have no labeled instances. Our experiments underscore VulnSense's superiority over existing GZSL methods in classifying instances of unseen classes. Concurrently, it maintains a performance parity with traditional labeled-example based learning methods in classifying instances of seen vulnerabilities. VulnSense demonstrates the potential of using GZSL for vulnerability classification, while also highlighting challenges that inspire future work. © 2004-2012 IEEE.

关键词： Zero-shot learning

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Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks

arXiv

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arXiv 2025年

作者： Liu, Yonghao Li, Mengyu Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education China

Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled samples for training, which unavoidably leads to overfitting in few-shot scenarios. Recent research has sought to alleviate this issue by simultaneously leveraging graph learning and meta-learning paradigms. However, these graph meta-learning models assume the availability of numerous meta-training tasks to learn transferable meta-knowledge. Such assumption may not be feasible in the real world due to the difficulty of constructing tasks and the substantial costs involved. Therefore, we propose a SiMple yet effectIve approach for graph few-shot Learning with fEwer tasks, named SMILE. We introduce a dual-level mixup strategy, encompassing both within-task and across-task mixup, to simultaneously enrich the available nodes and tasks in meta-learning. Moreover, we explicitly leverage the prior information provided by the node degrees in the graph to encode expressive node representations. Theoretically, we demonstrate that SMILE can enhance the model generalization ability. Empirically, SMILE consistently outperforms other competitive models by a large margin across all evaluated datasets with in-domain and cross-domain settings. Our anonymous code can be found here. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport

arXiv

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arXiv 2025年

作者： Liu, Yonghao Giunchiglia, Fausto Li, Ximing Huang, Lan Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education China

关键词： Zero-shot learning

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Towards High-Accuracy 3D Creation of Aviation Tools via Analysis-by-Synthesis

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IEEE Access 2025年

作者： Hassan, Gundu Mohamed Kim, Yoo Sung Jo, Geun Sik Inha University Knowledge Management System Laboratory Department of Artificial Intelligence Engineering Incheon22212 Korea Republic of Inha University Artificial Intelligence Laboratory Department of Computer Science Engineering Incheon22212 Korea Republic of Augmented Knowledge Corporation Incheon22212 Korea Republic of

The field of education is rapidly shifting toward digital platforms;however, aviation education continues to rely heavily on traditional paper-based manuals. This persistence is largely due to the inherent complexity, scale, and high costs associated with aviation equipment, making digital transformation particularly challenging. In this paper, we utilizing the analysis-by-synthesis method is presented for multi-view 3D reconstruction of solid objects, specifically focusing on aviation tools. To facilitate this research, a custom dataset was generated using Unity, enabling controlled experimentation and performance evaluation. Comparative analysis with existing benchmarks demonstrates that, despite the intricate nature of aviation tools compared to simpler objects, the proposed architecture effectively reconstructs detailed and accurate digital replicas. This advancement has the potential to enhance aviation education by providing immersive, interactive 3D content, thereby bridging the gap between traditional learning materials and modern digital education tools. © 2013 IEEE.

关键词： 3D reconstruction

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Review of advanced drying techniques: a path to lower greenhouse gas emissions in agriculture

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Discover Sustainability 2025年第1期6卷 1-26页

作者： Kaveh, Mohammad Abbaspour-Gilandeh, Yousef Nadimi, Mohammad Shahbazi, Feizollah Paliwal, Jitendra Department of Petroleum Engineering College of Engineering Knowledge University Erbil Iraq Department of Biosystems Engineering College of Agriculture and Natural Resources University of Mohaghegh Ardabili Ardabil Iran Department of Biosystems Engineering University of Manitoba Winnipeg Canada Department of Biosystems Engineering Lorestan University Khoramabbad Iran Department of Applied Computer Science The University of Winnipeg Winnipeg Canada

The agricultural sector has one of the largest carbon footprints among all industries due to the extensive use of fossil fuels, chemical fertilizers, and pesticides. Over the past century, agricultural mechanization has remarkably increased greenhouse gas (GHG) emissions, contributing to global warming and climate change. Among these gases, carbon dioxide (CO2) is the most abundant. Drying is a crucial and widely used method for preserving agricultural products, with broad applications in the food industry. Recent advancements in drying technology offer promising alternatives that enhance product quality, reduce energy use, and mitigate GHG emissions, thus promoting environmental sustainability. This review explores some of the most promising drying techniques that will shape the future of agricultural processes. Efficient and innovative drying of agri-food products can be achieved by hybridizing conventional techniques like hot-air, microwave, infrared, fluid bed, continuum, vacuum, and refractance window drying with pre-treatments such as ultrasound (US), pulsed electric fields (PEF), blanching, and cold plasma (CP). The combined use of these modalities can decrease GHG emissions while producing high-quality, nutritionally rich products. Our synthesis of published information also proposes research and development strategies to mitigate GHGs during the drying process.

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A Comparative Analysis of IoT Data Using Machine Learning Algorithms 15th

A Comparative Analysis of IoT Data Using Machine Learning Al...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Munrathnam, Meram Angeline Kavitha, M. Department of Computer Science and Applications School of CSA REVA University Bangalore India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.K. Valley Kadapa District Andhra Pradesh Idupulapaya516330 India Department of Mathematics Voorhees College Vellore632001 India

ISBN: (纸本)9783031889912

IoT (Internet of Things) is revolutionizing the commercial environment by acquiring vast amounts of data from various sources. However, processing and analyzing this data is challenging due to the large volume of IoT devices. AI-powered IoT creates intelligent machines that mimic human intelligence, aiding decision-making with minimal human input. Both individuals and professionals’ benefit from this combination, as IoT is concerned with communicating online, but AI allows machines to learn from data and experience. This study demonstrates the analysis of IoT data using machine learning algorithms and compares the accuracy of various machine learning algorithms used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Enhancing Kidney Stone Detection in CT scans with Image Processing and the DCNN-RELM Model

Enhancing Kidney Stone Detection in CT scans with Image Proc...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Manjula Prabakaran Jesmin Zakaria S Kannan Santhiya P Rajasekar P M V Rathnamma Department of CSE- (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Electronics & Communication Engineering (ECE) NSHM Knowledge Campus Durgapur West Bengal India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Tamilnadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India Department of CSE KLM College of Engineering for Women Kadapa Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

CT of the urinary system, abdomen, and kidneys appears to improve kidney stone screening and identification. Segmentation and categorization are essential for kidney stone identification and management. This work developed a semi-automatic kidney stone detection system using geometric concepts and image processing. The main steps include model training, feature extraction, segmentation, embossing, and preprocessing. Preprocessing involves trimming the input image to remove unnecessary kidney regions to focus on critical CT scan locations. Relief embossing enhanced image details. Segmentation accurately classified veins and blood arteries to define kidney stone boundaries. CNN-RELM was our training model. Compared to CNN and ELM models, the recommended system performed better. Its 93.87% kidney stone detection and localization accuracy prove its reliability. The semi-automatic system's reliable kidney stone detection and segmentation method may improve clinical decision-making. Combining CNN-RELM modeling and enhanced image processing could lead to medical imaging applications.

关键词： Training Image segmentation Accuracy Three-dimensional displays Computed tomography Embossing Data preprocessing Reliability Kidney Biomedical imaging

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Blockchain-Based IoMT Security: BSHS-EODL Method for Secure Data Storage and Analysis

Blockchain-Based IoMT Security: BSHS-EODL Method for Secure ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Biru Rajak M. Shailaja Mustafa Zuhaer Nayef Al-Dabagh Ali Ali Saber Mohammed Malik Bader Alazzam Ponni Valavan M Department of Computer Science Asansol Girls' College Under the Kazi Nazrul University Asansol India Department of Computer Science(DataScience) MallaReddy University Hyderabad India Department of Computer Science College of Science Knowledge University Erbil Iraq Department of Computer Engineering Al Kitab University Kirkuk Iraq Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The integration of the Internet of Medical Things (IoMT) has revolutionized healthcare by improving patient care and system efficiency. However, this progress introduces significant concerns about data security and privacy, particularly with medical data. Traditional methods like Decision Tree, Random Forest, and Naïve Bayes face limitations in scalability, accuracy for large datasets, and energy efficiency. To address these challenges, this research introduces a novel Blockchain-based IoMT framework, BSHS-EODL (Blockchain-Secured Healthcare System with Edge-Oriented Deep Learning). The framework employs Long Short-Term Memory (LSTM) networks for precise healthcare data analysis, optimized using the Grey Wolf Optimization (GWO) algorithm for hyperparameter tuning. The proposed system achieved outstanding results, including an accuracy of 99.42%, precision of 99.51%, recall rate of 99.47%, and F1 score of 99.39%, outperforming conventional methods. Moreover, BSHS-EODL demonstrated remarkable efficiency, this approach not only enhances healthcare data security and diagnostic accuracy The system is implemented using Python integrated with MATLAB.

关键词： Deep learning Accuracy Data security Scalability Memory Internet of Medical Things Long short term memory Optimization Tuning Random forests

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Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection

arXiv

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arXiv 2025年

作者： Hao, Pingting Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University PortlandOR97201 United States

In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the complementary part, which may result in noise due to unclear segmentation. In this paper, we propose a unified model constructed from the perspective of global-view reconstruction. Additionally, while feature selection methods can discern the importance of features, they typically overlook the uncertainty of samples, which is prevalent in realistic scenarios. To address this, we incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Thus, the global-view is reconstructed through the graph structure between samples, sample confidence, and the view relationship. The accurate mapping is established between the reconstructed view and the label matrix. Experimental results demonstrate the superior performance of our method on multi-view datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature Selection

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