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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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Leakage-Resilient Inner-Product Encryption with Attribute-Hiding

SSRN

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SSRN 2022年

作者： Zhang, Yijian Ling, Yunhao Chen, Jie Wang, Luping Software Engineering Institute East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing East China Normal University China School of Electronic and Information Engineering Suzhou University of Science and Technology Jiangsu 215009 China Jiangsu Key Laboratory for Elevator Intelligent Safety Jiangsu 215506 China

In this work, we present two generic frameworks for leakage-resilient inner-product encryption (IPE) over prime-order groups. First, we propose a leakage-resilient IPE with attribute-hiding, which is adaptively secure under $k$-Lin assumption in the bounded leakage model. Prior to this work, no one has yet constructed a leakage-resilient IPE scheme over prime-order groups with both adaptive security and attribute-hiding. Then, we present a leakage-resilient IPE with full security, which possesses stronger continual leakage resilience. Our second framework can also be used for leakage-resilient attribute-based encryption. It is compatible with Zhang et al.'s work [DCC 2018] but more generic. To our best knowledge, previous leakage-resilient frameworks with the similar effect cannot act on some specific schemes while ours manages to do this. Technically, our frameworks are both built on an IPE scheme without leakage-resilience through a method of adding redundancy. For first framework, we manage to obtain bounded leakage resilience while preserving attribute-hiding. For second framework, we improve the method of adding redundancy by which we adapt the underlying IPE to a leakage-resilient one against continual leakage. We observe that this method also acts on the transformation of leakage-resilient attribute-based encryption, which enhances the versatility of our framework. © 2022, The Authors. All rights reserved.

关键词： Cryptography

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Decoupled Prototype Learning for Reliable Test-Time Adaptation

arXiv

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arXiv 2024年

作者： Wang, Guowei Ding, Changxing Tan, Wentao Tan, Mingkui the School of Electronic and Information Engineering South China University of Technology 381 Wushan Road Tianhe District Guangzhou510000 China the Pazhou Lab Guangzhou510330 China the School of Future Technology South China University of Technology 381 Wushan Road Tianhe District Guangzhou510000 China the School of Software Engineering the Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou510006 China

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss’s vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL’s robustness for the small batch sizes often encountered in TTA. We update each class’s pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

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Procedia Computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud Computing Diagnosis Digital Health Edge Computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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Heterogenous multi-source fusion for ship trajectory complement and prediction with sequence modeling 5

Heterogenous multi-source fusion for ship trajectory complem...

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5th IEEE International Conference on Data Science in Cyberspace, DSC 2020

作者： Zheng, Changmeng Peng, Qi Xu, Xuemiao South China University of Technology Key Laboratory of Big Data and Intelligent Robot School of Software Engineering Guangzhou China South China University of Technology School of Computer Science Engineering Guangzhou China

ISBN: (纸本)9781728195582

Effectively monitoring ships and discovering abnormal ship trajectory in time is necessary for marine traffic supervision. The basic work of discovering the ship's abnormal trajectory is to predict the ship's navigation dynamically. Previous works in ship trajectory prediction are basically concern on single-source data, for example, the AIS data. These methods ignore the relations between different sources which may improve the performance of predicting ship trajectory. We propose a neural sequence model based on heterogeneous multisource fusion for ship trajectory completion and prediction. Our method makes better utilization of AIS, GPS and ARPA radar information to predict ship trajectory precisely. We construct a dataset which contains about 8 million ship trajectory samples and the experiments demonstrate that our multi-source fusion model gains promising results. © 2020 IEEE.

关键词： Ships

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Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation

Context Decoupling Augmentation for Weakly Supervised Semant...

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International Conference on Computer Vision (ICCV)

作者： Yukun Su Ruizhou Sun Guosheng Lin Qingyao Wu School of Software and Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education School of Computer Science and Engineering Nanyang Technological University

ISBN: (纸本)9781665428132

Data augmentation is vital for deep learning neural networks. By providing massive training samples, it helps to improve the generalization ability of the model. Weakly supervised semantic segmentation (WSSS) is a challenging problem that has been deeply studied in recent years, conventional data augmentation approaches for WSSS usually employ geometrical transformations, random cropping and color jittering. However, merely increasing the same contextual semantic data does not bring much gain to the networks to distinguish the objects, e.g., the correct image-level classification of "aeroplane" may be not only due to the recognition of the object itself, but also its co-occurrence context like "sky", which will cause the model to focus less on the object features. To this end, we present a Context Decoupling Augmentation (CDA) method, to change the inherent context in which the objects appear and thus drive the network to remove the dependence between object instances and contextual information. To validate the effectiveness of the proposed method, extensive experiments on PASCAL VOC 2012 and COCO datasets with several alternative network architectures demonstrate that CDA can boost various popular WSSS methods to the new state-of-the-art by a large margin. Code is available at https://***/suyukun666/CDA

关键词： Training Deep learning Image segmentation Image recognition Image color analysis Semantics Neural networks

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Self-supervised representation learning for Bayesian quantum architecture search

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Physical Review A 2025年第3期111卷 032403-032403页

作者： Zhimin He Hongxiang Chen Yan Zhou Haozhen Situ Yongyao Li Lvzhou Li School of Electronic and Information Engineering Foshan University Foshan 528000 China School of Computer Science and Artificial Intelligence Foshan University Foshan 528000 China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology Foshan University Foshan 528000 China College of Mathematics and Informatics South China Agricultural University Guangzhou 510642 China School of Physics and Optoelectronic Engineering Foshan University Foshan 528225 China Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China

Quantum architecture search (QAS) has shown significant promise in automating quantum circuit design for variational quantum algorithms (VQAs). However, a central challenge remains in the high computational cost of evaluating the ground-truth performance of numerous circuit architectures during the search process. This challenge is further complicated by factors such as inefficient gradient computation, the barren plateau phenomenon, and frequent data exchange between classical and quantum systems within VQAs, all of which restrict the scalability of QAS for increasingly complex VQAs. In this study, we propose an enhanced QAS framework that integrates a performance predictor trained through self-supervised learning with Bayesian optimization to overcome these computational barriers. To address the challenges of circuit performance evaluation, we develop a graph-isomorphism-network-based predictor pretrained using expressibility prediction, an intrinsic measure of a circuit's capacity to explore the Hilbert space uniformly. This pretraining process creates a structured and smooth latent space, significantly improving the predictor's accuracy and generalizability with minimal labeled data. Furthermore, Bayesian optimization is integrated into the QAS framework to refine the predictor iteratively, concentrating the search on regions containing high-performance circuits and thus accelerating the search process. Simulation results for VQAs involving the variational quantum eigensolver and quantum approximate optimization algorithm demonstrate that the proposed framework achieves faster convergence to optimal circuits and substantially reduces computational costs compared to existing QAS methods.

关键词： Bayesian networks

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Self-supervised 3D Skeleton Action Representation Learning with Motion Consistency and Continuity

Self-supervised 3D Skeleton Action Representation Learning w...

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International Conference on Computer Vision (ICCV)

作者： Yukun Su Guosheng Lin Qingyao Wu School of Software and Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education School of Computer Science and Engineering Nanyang Technological University

ISBN: (纸本)9781665428132

Recently, self-supervised learning (SSL) has been proved very effective and it can help boost the performance in learning representations from unlabeled data in the image domain. Yet, very little is explored about its usefulness in 3D skeleton-based action recognition understanding. Directly applying existing SSL techniques for 3D skeleton learning, however, suffers from trivial solutions and imprecise representations. To tackle these drawbacks, we consider perceiving the consistency and continuity of motion at different playback speeds are two critical issues. To this end, we propose a novel SSL method to learn the 3D skeleton representation in an efficacious way. Specifically, by constructing a positive clip (speed-changed) and a negative clip (motion-broken) of the sampled action sequence, we encourage the positive pairs closer while pushing the negative pairs to force the network to learn the intrinsic dynamic motion consistency information. Moreover, to enhance the learning features, skeleton interpolation is further exploited to model the continuity of human skeleton data. To validate the effectiveness of the proposed method, extensive experiments are conducted on Kinetics, NTU60, NTU120, and PKUMMD datasets with several alternative network architectures. Experimental evaluations demonstrate the superiority of our approach and through which, we can gain significant performance improvement without using extra labeled data.

关键词： Representation learning Interpolation Three-dimensional displays Dynamics Transfer learning Force Network architecture

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A Dynamic Neural Network for Solving Time-varying Shortest Path with Hop-constraint 3

A Dynamic Neural Network for Solving Time-varying Shortest P...

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2020 3rd International Conference on Computer Information Science and Artificial Intelligence, CISAI 2020

作者： Xu, Zhilei Huang, Wei Wang, Jinsong School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligent and Novel Software Technology Tianjin University of Technology Tianjin300384 China

This paper proposes a dynamic neural network (DNN) to solve the time-varying shortest path problem with hop-constraint (HC-TSPP). The purpose of HC-TSPP is to find a path with the shortest transmission time and the restricted number of arcs. The proposed DNN is a novel neural network based on dynamic neurons. All neurons on DNN are computing in parallel, and each dynamic neuron is composed of seven parts: input, wave receiver, filter, status memorizer, wave generator, wave sender, and output. Wave is the carrier of neuron communication, and each wave is composed of three parts. The shortest path report is based on the first wave that reaches the destination node and satisfies the hop constraint. The example and experimental results based on the Internet dataset show that the proposed algorithm can arrive at the global optimal solution and outperform the existing algorithm (viz. Dijkstra algorithm). © Published under licence by IOP Publishing Ltd.

关键词： Neurons

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Source-free domain adaptation via avatar prototype generation and adaptation

arXiv

引用

arXiv 2021年

作者： Qiu, Zhen Zhang, Yifan Lin, Hongbin Niu, Shuaicheng Liu, Yanxia Du, Qing Tan, Mingkui School of Software Engineering South China University of Technology School of Computing National University of Singapore Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education Pazhou Laboratory

We study a practical domain adaptation task, called source-free unsupervised domain adaptation (UDA) problem, in which we cannot access source domain data due to data privacy issues but only a pre-trained source model and unlabeled target data are available. This task, however, is very difficult due to one key challenge: the lack of source data and target domain labels makes model adaptation very challenging. To address this, we propose to mine the hidden knowledge in the source model and exploit it to generate source avatar prototypes (i.e., representative features for each source class) as well as target pseudo labels for domain alignment. To this end, we propose a Contrastive Prototype Generation and Adaptation (CPGA) method. Specifically, CPGA consists of two stages: (1) prototype generation: by exploring the classification boundary information of the source model, we train a prototype generator to generate avatar prototypes via contrastive learning. (2) prototype adaptation: based on the generated source prototypes and target pseudo labels, we develop a new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes. Extensive experiments on three UDA benchmark datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Data privacy

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