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Graph Augmentation Empowered Contrastive Learning for Recommendation

ACM Transactions on Information Systems 2025年第2期43卷 1-27页

作者： Xu, Lixiang Liu, Yusheng Xu, Tong Chen, Enhong Tang, Yuanyan School of Artificial Intelligence and Big Data Hefei University Hefei China State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology University of Science and Technology of China Hefei China Zhuhai UM Science and Technology Research Institute FST University of Macau China

The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the Graph Augmentation Empowered Contrastive Learning (GAECL) for recommendation framework, which uses graph augmentation based on topological and semantic dual adaptation and global co-modeling via structural optimization to co-create contrasting views for better augmentation of the CF paradigm. Specifically, we strictly filter out unimportant topologies by reconstructing the adjacency matrix and mask unimportant attributes in nodes according to the PageRank centrality principle to generate an augmented view that filters out noisy data. Additionally, GAECL achieves global collaborative modeling through structural optimization and generates another augmented view based on the PageRank centrality principle. This helps to filter the noisy data while preserving the original semantics of the data for more effective data augmentation. Extensive experiments are conducted on five datasets to demonstrate the superior performance of our model over various recommendation models. © 2025 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Classification of Sailboat Tell Tail Based on Deep Learning

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Journal of Ocean University of China 2024年第3期23卷 710-720页

作者： CHANG Xiaofeng YU Jintao GAO Ying DING Hongchen LIU Yulong YU Huaming Department of Physical Education Ocean University of ChinaQingdao 266100China College of Big Data Qingdao University of Science and TechnologyQingdao 266061China College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266100China

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail *** is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing *** it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes,affected by strong sunlight and visual *** this case,we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with *** paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system,supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions,including the feature extraction *** the expression capabilities that vary with the computational features in different visual tasks,the paper focuses on five tell tail computing features,which are recoded by an automatic encoder and classified by a SVM *** experimental samples were randomly divided into five groups,and four groups were selected from each group as the training set to train the *** remaining one group was used as the test set for *** highest resolution value of the ResNet network was 80.26%.To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the *** method can be used to assist the sailors in making better judgement about the tell tail changes during sailing.

关键词： tell tail sailboat classification deep learning

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Peer-to-Peer International Trade Model Based on Blockchain and Smart Contracts: Theory and Practice of BTLC 2

Peer-to-Peer International Trade Model Based on Blockchain a...

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2nd International Conference on big data and Privacy Computing, BDPC 2024

作者： Yan, Junjie Wang, Wennan Feng, Duanyang Song, Shiyang Li, Chengyifan Application College Zhuhai College of Science and Technology Alibaba Cloud Big Data Zhuhai China Academy of Management Guangdong University of Science and Technology Dongguan China City University of Macau Faculty of Data Science China

ISBN: (纸本)9798350307597

This study focuses on BTLC, a peer-to-peer international trade model based on blockchain and smart contracts, which aims to reform the traditional let- ter of credit process, improve international trade efficiency, reduce costs and improve security. By elaborating the BTLC model, this study demonstrates how blockchain technology as well as smart contract technology can be applied in the field of international trade to effectively solve the problems existing in the tra- ditional letter of credit process, such as high transaction costs due to too many intermediaries, high transaction risks due to an imperfect trust mechanism, and inefficiency in transactions. In addition, this paper further proves the superiority of the BTLC model in theory and practice through empirical research. It provides an in-depth discussion of the potential application and development direction of the model in future research. However, there are still some challenges and lim- itations in the practical application of the BTLC model. These challenges and limitations include the need for further empirical studies to validate the model's effectiveness and the problems faced. These include technical standards, laws and regulations. Therefore, this study suggests that future research directions should focus on the implementation challenges of blockchain technology, as well as the application of blockchain technology in other fields, in order to promote the wide application of blockchain technology in international trade. © 2024 IEEE.

关键词： Smart contract

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An Improved Apriori Algorithm Based on Goal Orientation and Double Pruning

An Improved Apriori Algorithm Based on Goal Orientation and ...

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2023 International Conference on Networks, Communications and Intelligent Computing, NCIC 2023

作者： Deng, Shiyu Li, Li Xiong, Yichen Zhang, Jian School of Computer Science Beijing International Science and Technology Cooperation Base of Intelligent Decision and Big Data Application Beijing Information Science and Technology University Laboratory of Big Data Decision Making for Green Development Beijing China School of Economics and Management Beijing International Science and Technology Cooperation Base of Intelligent Decision and Big Data Application Beijing Information Science and Technology University Laboratory of Big Data Decision Making for Green Development Beijing China

ISBN: (纸本)9798350371949

Aiming at solving the problem of high time complexity of association rule mining among item-sets in Apriori algorithm and obtaining the set of goal-association relations of single-to-single point type, this paper improves the Apriori algorithm based on Goal Orientation and Double Pruning (GODP-Apriori). Oriented by the type of goal, the GODP-Apriori uses double pruning to remove the redundant item set during the calculation of the frequency of item set support, which greatly improves the efficiency of obtaining the frequent item set;in addition to this, by sacrificing space for time, it also records the set of transaction items corresponding to the item when calculating the support degree, which avoids the repetitive scanning in the subsequent calculations and then effectively increases the computation efficiency of traditional Apriori algorithm. The effectiveness of GODP-Apriori algorithm in improving the calculation time efficiency of traditional Apriori algorithm was verified by using a real household electricity consumption data at the end of this research. © 2023 IEEE.

关键词： Association rules

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An enhanced dung beetle optimizer for cloud task schedule 4

An enhanced dung beetle optimizer for cloud task schedule

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Xie, Rongxiang Li, Shaobo Wu, Fengbin Wu, Libang Xiong, Xuan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

ISBN: (纸本)9781510681637

Aiming to address the shortcomings of Dung Beetle Optimizer (DBO) in the areas of accuracy and convergence speed in dealing with cloud computing task scheduling issues, this paper suggests an advanced Dung Beetle Optimizer (DGTDBO)based cloud computing task scheduling method by combining the differential strategy and the perturbation strategy. Among them, the differential strategy strengthens the global exploration performance of the method, while the perturbation strategy improves the capability of the algorithm to escape from the local optimal trap. Through validation on different sizes of cloud computing systems, it is confirmed that the suggested DGTDBO can enhance the solution accuracy and speed of the algorithm effectively. It is considered as a promising cloud scheduling method. © 2024 SPIE.

关键词： Cloud computing

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data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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Computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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Integrated Sensing-Communication-Computation for Edge Artificial Intelligence

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IEEE Internet of Things Magazine 2024年第4期7卷 14-20页

作者： Wen, Dingzhu Li, Xiaoyang Zhou, Yong Shi, Yuanming Wu, Sheng Jiang, Chunxiao School of Information Science and Technology ShanghaiTech University China Shenzhen Research Institute of Big Data China Beijing University of Posts and Telecommunications China Tsinghua University Beijing National Research Center for Information Science and Technology China

Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI inference, depends on the quality of three highly coupled processes, i.e., sensing for data acquisition, computation for information extraction, and communication for information transmission. However, these three modules need to compete for network resources for enhancing their own quality-of-services. To this end, integrated sensing-communication-computation (ISCC) is of paramount significance for improving resource utilization as well as achieving the customized goals of edge AI tasks. By investigating the interplay among the three modules, this article presents various kinds of ISCC schemes for federated edge learning tasks and edge AI inference tasks in both application and physical layers. © 2018 IEEE.

关键词： Artificial intelligence

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Targeted Universal Adversarial Attack Based on Fourier Transform 2nd

Targeted Universal Adversarial Attack Based on Fourier Tran...

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2nd International Conference on data Security and Privacy Protection, DSPP 2024

作者： Sun, Yancheng Chen, Yuling Liu, Ziyang Dou, Hui Tan, Chaoyue The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819785391

The existence of adversarial example reveals the fragility in neural networks, and the exploration of their theoretical origins increases the interpretability of deep learning, enhances researchers’ understanding of neural networks, and contributes to the development of next-generation artificial intelligence, which has attracted widespread research in various fields. The targeted adversarial attack problem based on sample features faces two problems: on the one hand, the difference in the model’s attention to different features in the example;On the other hand, the bias that occurs in adversarial attacks can have an impact on targeted attacks. The mechanism of the human eye relies more on the shape information of the image. However, in the past, artificial intelligence models based on convolutional neural networks often relied on the texture features of image examples to make decisions. At present, general optimize adversarial attack algorithms do not distinguish different types of features based on different parts of the image, but only process the entire example in a general manner, making it difficult to effectively utilize the effective features in the example, resulting in poor algorithm performance and interpretability. This article optimizes the adversarial attack algorithm based on optimization iteration, as follows: Firstly, different types of information in adversarial examples are studied, and fourier transform technology is used to process the attacked original image and obtain its low-frequency information. The obtained low-frequency examples are randomly cropped to obtain some feature examples. Then, the clustering effect was studied when the examples were attacked without targets, and an inter-class smoothing loss was designed to improve the success rate of target attacks. This Rebalance Universal Feature Method (RFM) is based on fourier low pass filtering and inter-class smoothing, which effectively improves the ability of optimization iteration bas

关键词： Adversarial machine learning

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Continuous and generalized visual odometry based on FLANN_PSC-RANSAC_LSTM

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 15-28页

作者： Li, Dengao Feng, Ran Wu, Fanming Zhao, Jinhua Zhao, Jumin College of Computer Science and Technology College of Data Science Taiyuan University of Technology Shanxi Taiyuan China College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan University of Technology Shanxi Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China

In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations. © 2024 - IOS Press. All rights reserved.

关键词： Long short-term memory

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CBPF: A Novel Method For Filtering Poisoned data Based on Composite Backdoor Attacks

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IEEE Internet of Things Journal 2025年第13期12卷 25136-25147页

作者： Xia, Hanfeng Hong, Haibo Wang, Ruili Sun, Yiru Ding, Hao Zhejiang Gongshang University Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology School of Computer Science and Technology Hangzhou China Massey University School of Mathematical and Computational Sciences Auckland New Zealand

Backdoor attacks involve the injection of a limited quantity of poisoned samples containing triggers into the training dataset. During the inference stage, backdoor attacks can uphold a high level of accuracy for normal examples, yet when presented with trigger-containing instances, the model may erroneously predict them as the targeted class designated by the attacker. This paper addresses the challenge of backdoor attacks by developing a novel method for filtering poisoned samples. We primarily leverage two key characteristics of backdoor attacks: 1) Multiple backdoors can exist simultaneously within a single model;2) The discovery through Composite Backdoor Attack (CBA) that altering two triggers in a sample to new target labels does not compromise the original functionality of the triggers, yet enables the prediction of the data as a new target class when both triggers are present simultaneously. Therefore, a novel three-stage poisoning data filtering approach, known as Composite Backdoor Poisoning Filtering (CBPF), is proposed as an effective solution. Firstly, utilizing the identified distinctions in output between poisoned and clean samples, a subset of data is partitioned to include both poisoned and clean data. Subsequently, benign triggers are incorporated and labels are adjusted to create new target and benign target classes, thereby prompting the poisoned and clean data to be classified as distinct entities during the inference stage. The experimental results indicate that CBPF is successful in filtering out poisoned data produced by seven advanced attacks on CIFAR-10, GTSRB and ImageNet-12. On average, CBPF attains a notable filtering success rate of 99.88% for these attacks on CIFAR-10. Additionally, the model trained on the uncontaminated samples exhibits sustained high accuracy levels. © 2014 IEEE.

关键词： Wiener filtering

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