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33rd ACM Web Conference, WWW 2024

作者： Liu, Yonghao Huang, Lan Cao, Bowen Li, Ximing Giunchiglia, Fausto 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 of the Ministry of Education China

ISBN: (纸本)9798400701719

Graphs, as a fundamental data structure, have proven efficacy in modeling complex relationships between objects and are therefore found in wide web applications. Graph classification is an essential task in graph data analysis, which can effectively assist in extracting information and mining content from the web. Recently, few-shot graph classification, a more realistic and challenging task, has garnered great research interest. Existing few-shot graph classification models are all supervised, assuming abundant labeled data in base classes for meta-training. However, sufficient annotation is often challenging to obtain in practice due to high costs or demand for expertise. Moreover, they commonly adopt complicated meta-learning algorithms via episodic training to transfer prior knowledge from base classes. To break free from these constraints, in this paper, we propose a simple yet effective approach named SMART for unsupervised few-shot graph classification without using any labeled data. SMART employs transfer learning philosophy instead of the previously prevailing meta-learning paradigm, avoiding the need for sophisticated meta-learning algorithms. Additionally, we adopt a novel mixup strategy to augment the original graph data and leverage unsupervised pretraining on these data to obtain the expressive graph encoder. We also utilize the prompt tuning technique to alleviate the overfitting and low fine-tuning efficiency caused by the limited support samples of novel classes. Extensive experimental results demonstrate the superiority of our proposed approach, significantly surpassing even leading supervised few-shot graph classification models. Our code is available here. © 2024 ACM.

关键词： Graph neural networks

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PierGuard: A Planning Framework for Underwater Robotic Inspection of Coastal Piers

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IEEE Transactions on Automation science and Engineering 2025年 22卷 15941-15952页

作者： Wang, Pengyu Lin, Hin Wang Li, Jialu Wang, Jiankun Shi, Ling Meng, Max Q.-H Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Southern University of Science and Technology Jiaxing Research Institute Jiaxing China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong University of Alberta Canada Department of Electrical and Computer Engineering Canada

Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning within complex environments. Sampling-based path planning methods, such as Rapidly-exploring Random Tree* (RRT*), have demonstrated notable performance in high-dimensional spaces. In recent years, researchers have begun designing various geometry-inspired heuristics and neural network-driven heuristics to further enhance the effectiveness of RRT*. However, the performance of these general path planning methods still requires improvement when applied to highly cluttered underwater environments. In this paper, we propose PierGuard, which combines the strengths of bidirectional search and neural network-driven heuristic regions. We design a specialized neural network to generate high-quality heuristic regions in cluttered maps, thereby improving the performance of the path planning. Through extensive simulation and real-world ocean field experiments, we demonstrate the effectiveness and efficiency of our proposed method compared with previous research. Our method achieves approximately 2.6 times the performance of the state-of-the-art geometric-based sampling method and nearly 4.9 times that of the state-of-the-art learning-based sampling method. Our results provide valuable insights for the automation of pier inspection and the enhancement of maritime safety. (Video1). © 2004-2012 IEEE.

关键词： Motion planning

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DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment 38

DFA-GNN: Forward Learning of Graph Neural Networks by Direct...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Gongpei Wang, Tao Lang, Congyan Jin, Yi Li, Yidong Ling, Haibin Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China School of Computer Science & Technology Beijing Jiaotong University China Department of Computer Science Stony Brook University United States

Graph neural networks (GNNs) are recognized for their strong performance across various applications, with the backpropagation (BP) algorithm playing a central role in the development of most GNN models. However, despite its effectiveness, BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. While several non-backpropagation (non-BP) training algorithms, such as the direct feedback alignment (DFA), have been successfully applied to fully-connected and convolutional network components for handling Euclidean data, directly adapting these non-BP frameworks to manage non-Euclidean graph data in GNN models presents significant challenges. These challenges primarily arise from the violation of the independent and identically distributed (i.i.d.) assumption in graph data and the difficulty in accessing prediction errors for all samples (nodes) within the graph. To overcome these obstacles, in this paper we propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning. The proposed method breaks the limitations of BP by using a dedicated forward training mechanism. Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data. Additionally, for semi-supervised graph learning tasks, we developed a pseudo error generator that spreads residual errors from training data to create a pseudo error for each unlabeled node. These pseudo errors are then utilized to train GNNs using DFA. Extensive experiments on 10 public benchmarks reveal that our learning framework outperforms not only previous non-BP methods but also the standard BP methods, and it exhibits excellent robustness against various types of noise and attacks. © 2024 Neura

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Online Matching based Firefly Algorithm for Dynamic Ridesharing 13

Online Matching based Firefly Algorithm for Dynamic Rideshar...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Abdelmoumène, Hiba Bousena, Amina Computer Science Department University of 8 Mai 1945 Guelma Algeria LabGED Laboratory University of Badji Mokhtar Annaba Algeria

Urban transportation has been a significant contributor to environmental challenges, including traffic congestion and greenhouse gas emissions, exacerbating climate change. In contrast, public transportation, especially in rural areas, often struggles to meet mobility needs effectively. Ridesharing systems have emerged as a response to these pressing issues. By facilitating the sharing of rides among individuals heading in the same direction, ridesharing optimizes vehicle use. This not only offers economic benefits but also addresses environmental concerns. This study addresses the critical challenge of dynamic matching in ridesharing systems, where the objective is to optimize the allocation of drivers to multiple passengers. Recognizing the multifaceted nature of this problem, our approach leverages the Firefly algorithm, a metaheuristic known for its efficacy in tackling complex optimization tasks. In our model, we account for a range of constraints including spatiotemporal limitations, capacity consideration and passenger waiting time restrictions. The primary goal is to minimize both the waiting time for passengers and the total distance traveled. To rigorously evaluate our method, we conducted experiments utilizing real-world data from a geographic area in the city of Guelma, Algeria. The results obtained through our experiments have demonstrated the effectiveness and performance of our system based on the Firefly algorithm. Indeed, our approach enhances the experience of passengers via a notable reduction in waiting times and it contributes to the overall efficiency of the ridesharing system by minimizing the total distance traveled. © 2024 Copyright for this paper by its authors.

关键词： Traffic congestion

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Deep Classifier Fusion-based Multi-Classification of Chest X-rays for COVID-19 Detection

Deep Classifier Fusion-based Multi-Classification of Chest X...

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2023 International Conference on Decision Aid sciences and Applications, DASA 2023

作者： Benzebouchi, Nacer Eddine Bourzama, Mohamed Ouassim Zemmal, Nawel Azizi, Nabiha Badji Mokhtar Annaba University Labged Laboratory Computer Science Department Annaba23000 Algeria Badji Mokhtar Annaba University Computer Science Department Annaba23000 Algeria Mohamed Cherif Messaadia University Mathematics and Computer Science Department Souk-Ahras Algeria

ISBN: (纸本)9798350342055

In the last three years, The COVID-19 pandemic has placed immense strain on healthcare systems worldwide. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing COVID-19. Recently, computer Aided Diagnosis systems based on Deep Learning have been widely used to identify COVID-19 from other upper respiratory diseases such as viral pneumonia. To enhance COVID-19 diagnosis using CXR images, this article proposes a methodology that combines three deep learning models renowned for their depth and feature extraction capabilities: ResNet, DenseNet, and VGG. The model' strengths are harnessed using Aggregation techniques such as stacking and voting (majority, weighted, average), to improve prediction accuracy. Moreover, the used dataset was subjected to preprocessing, balancing, and augmentation techniques, including rotation and zooming, to ensure robust recognition of CXR images, even in challenging positions. Experimental results demonstrate that the combination of model ensembles and enhanced dataset leads to superior accuracy in diagnosing COVID-19, with an impressive 97.95% accuracy, 98.2% F1 score, and 99.9% AUC-ROC. This approach not only outperforms individual models but also offers a more reliable and efficient solution for COVID-19 detection, particularly in complex cases where symptoms overlap with other respiratory illnesses. © 2023 IEEE.

关键词： COVID-19

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A NLP and Rule-Based Approach to Extract Spatial Entities and Relationships in Arabic Text 7

A NLP and Rule-Based Approach to Extract Spatial Entities an...

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7th International Conference on Informatics and Applied Mathematics, IAM 2024

作者： Hadji, Atmane Kholladi, Mohamed Khireddine Boumaza, Farid University of Bejaia Faculty of exact sciences Department of Computer Science Bejaia06000 Algeria LISI Laboratory Computer Science Department University Center A. Boussouf Mila Mila43000 Algeria Department of Mathematics and Computer Science Faculty of Exact Sciences University of El-Oued HAMMA Lakhdar El-Oued Algeria MISC Laboratory of Abdelhamid Mehri university of Constantine 2 Algeria Computer Science Department University of Mohamed El Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria LAPECI Laboratory University of Oran1 Oran31000 Algeria

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing101408 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 314100 China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic-like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. First, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic-like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. © 1979-2012 IEEE.

关键词： Latent semantic analysis

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Hybrid Access Control Model for IoT Environments: Formalization and Verification Using Timed Automata

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SN computer science 2025年第3期6卷 1-15页

作者： Krakallah, Mohammed Walid Nait-Bahloul, Safia Department of Mathematics and Computer Science Faculty of Exact Sciences And Computer Science University Abdelhamid Ibn Badis Mostaganem 27000 Algeria Litio Laboratory University Oran1 Ahmed Ben Bella BP 1524 El-M’Naour Oran 31000 Algeria

The Internet of Things (IoT) represents a technological revolution by enabling the interconnection of multiple devices and systems. With this expansion of connected objects, security issues, particularly in access control, become paramount. Due to their complexity and diversity, IoT environments require sophisticated, dynamic, and flexible access control models capable of addressing their specific constraints. This article formalizes and extends the E-UACML model (Emergency Unified Access Control Modelling Language) for IoT environments. Using formal verification techniques, we translate E-UACML into timed automata to validate its properties with the UPPAAL tool. Timed automata enable the integration of timing and synchronization, which are essential for verifying spatio-temporal constraints in IoT systems. Verification, carried out through Computation Tree Logic (CTL), ensures that access control policies meet rigorous security requirements, including spatio-temporal constraints. The results demonstrate the effectiveness of this approach in ensuring flexible and secure access control in IoT environments. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Access control CTL E-UACML Formal verification IoT Timed automata

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Density estimation-based method to determine sample size for random sample partition of big data

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Frontiers of computer science 2024年第5期18卷 57-70页

作者： Yulin HE Jiaqi CHEN Jiaxing SHEN Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing and Decision Sciences Lingnan UniversityHong Kong 999077China

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficient solution for big data processing and ***,a challenge for implementing RSP is determining an appropriate sample size for RSP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

关键词： random sample partition big data sample size Dvoretzky-Kiefer-Wolfowitz inequality kerneldensity estimator probability density function

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