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International Conference on computer Supported Cooperative Work in Design

作者： Wenhan Hou Bo Cui Yongxin Chen Ru Li Wanshui Song Engineering Research Center of Ecological Big Data Ministry of Education China College of Computer Science Inner Mongolia University Hohhot China College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The rise of Ethereum in various economic and social domains has made it a prime target for illegal activities, particularly phishing scams, which has caused substantial financial losses. Existing methods mainly model transaction records as networks and classify nodes. However, a particular challenge is that not all transactions involved in phishing nodes are illegal, which makes phishing detection very difficult. In order to address the problem, in this paper, we propose a Curriculum Learning-based method for Ethereum phishing detection. We incorporate the Local Outlier Factor to measure the difficulty of nodes, considering the significant feature differences among nodes of the same class. By assigning lower difficulty values to easily identifiable nodes and higher difficulty values to nodes involved in mixed-class transactions, we ensure an effective difficulty measure. Then we gradually increase the number of training nodes input into Graph Convolutional Network in each epoch based on a certain ratio determined by the sorted difficulty scores. Finally, we employ LightGBM as the classifier for identifying phishing nodes. Experimental evaluations on a real-world Ethereum phishing scam dataset demonstrate the superiority of our method over baseline approaches, as evidenced by several evaluation metrics.

关键词： Training Measurement Learning systems Graph convolutional networks Federated learning Phishing Smart contracts

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Fast Sampling of Diffusion Models for Accelerated MRI using Dual Manifold Constraints

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Qiao, Lihong Wang, Rongxuan Shu, Yucheng Li, Baobin Li, Weisheng Gao, Xinbo Cai, Zhanchuan Chongqing University of Posts and Telecom munications Chongqing Big Data Collaborative Innovation Center Chongqing400065 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Macau University of Science and Technology School of Computer Science and Engineering Macau China

Diffusion models show great potential in solving inverse problems, including MRI reconstruction. With its unique characteristics, medical imaging demands both efficiency and accuracy in the reconstruction process. However, existing MRI reconstruction methods based on diffusion models often fall short of fully leveraging the available measurements during sampling. Consequently, these methods suffer from compromised reconstruction quality and elevated bias, especially when dealing with large acceleration factors. In response to these challenges, we propose Dual Manifold Constraints (DMC), a fast MRI reconstruction method based on diffusion models. We treat the sampling process as a combination of denoising and adding noise processes, and we constrain these two processes using both pristine measurements and their noisy counterparts to adapt to the geometry of diffusion. It's worth noting that we propose a method to estimate the noisy measurement that satisfies the sub-sampling process to maintain the current data manifold when performing data consistency constraints. Experimental results show that our method outperforms the latest diffusion-based methods regarding both reconstruction speed and accuracy, and exhibits strong out-of-distribution generalization performance. © 1991-2012 IEEE.

关键词： Inverse problems

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Revenue and User Traffic Maximization in Mobile Short-Video Advertising 21

Revenue and User Traffic Maximization in Mobile Short-Video ...

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21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022

作者： Ran, Dezhi Zheng, Weiqiang Li, Yunqi Bian, Kaigui Zhang, Jie Deng, Xiaotie School of Computer Science Peking University China Department of Computer Science Yale University United States School of Computer Science National Engineering Laboratory for Big Data Analysis and Applications Peking University China Electronics and Computer Science University of Southampton United Kingdom Center on Frontiers of Computing Studies Peking University China

ISBN: (纸本)9781713854333

A new mobile attention economy has emerged with the explosive growth of short-video apps such as TikTok. In this internet market, three types of agents interact with each other: the platform, influencers, and advertisers. A short-video platform encourages its influencers to attract users by creating appealing content through short-form videos and allows advertisers to display their ads in short-form videos. There are two options for the advertisers: one is to bid for platform advert slots in a similar way to search engine auctions;the other is to pay an influencer to make engaging short videos and promote them through the influencer's channel. The second option will generate a higher conversion ratio if advertisers choose the right influencers whose followers match their target market. Although displaying influencer ads will generate less revenue, it is more engaging than platform ads, which is better for maintaining user traffic. Therefore, it is crucial for a platform to balance these factors by establishing a sustainable business agreement with its influencers and advertisers. In this paper, we develop a two-stage solution for a platform to maximize short-term revenue and long-term user traffic maintenance. In the first stage, we estimate the impact of user traffic generated by displaying influencer ads and characterize the user traffic the platform should allocate to influencers for overall revenue maximization. In the second stage, we devise an optimal (1 - 1/e)-competitive algorithm for ad slot allocation. To complement this analysis, we examine the ratio of the revenue generated by our online algorithm to the optimal offline revenue. Our simulation results show that this ratio is 0.94 on average, which is much higher than (1 - 1/e) and outperforms four baseline algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved

关键词： Multi agent systems

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data center Energy Consumption Prediction Model Based on Deep Neural Network BiLSTM

Data Center Energy Consumption Prediction Model Based on Dee...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Junqiang Zhou Yan Wang JieFeng Lil College of Computer Science Inner Mongolia University Hohhot 010021 China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

With the rapid development of big data and artificial intelligence technologies, a variety of high-performance plat-forms and applications in data centers have significantly increased their energy consumption and operating costs. Energy consumption prediction plays an important role in the optimization and management of energy consumption in data centers. Due to the complexity of power system, there are many complex correlations between various energy consumption parameters and prediction results. In order to improve the accuracy of energy consumption prediction, in this paper, a new prediction model based on BiLSTM is proposed, name SLCOA-BiLSTM (Saltation learning-based coati optimization algorithm and Bi-directionallong short-term memory). Aiming at the minimum prediction error, the model adopts the improved coati optimization algorithm (SLCOA) to optimize the BiLSTM network's learning rate, lstm_layers number, number of units and dropout. In the process of optimization, in order to solve the shortcomings of coati optimization algorithm, Sobol sequence initialization and Salation learning are improved. The experiments are carried out based on the real-time energy consumption data of a data center, and the results show that the model is superior to the relative comparison model in terms of prediction error. Since SLCOA-BiLSTM predicts are stable and accurate, it can be used as an effective tool to solve energy consumption problems.

关键词： Energy consumption data centers Accuracy Computational modeling Software algorithms Predictive models Prediction algorithms

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Dual Contrastive Learning Model for Representation Learning and Background Debiasing in SAR Object Classification

Dual Contrastive Learning Model for Representation Learning ...

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2023 SAR in big data Era, bigSARdata 2023

作者： Wang, Jing Tian, Sirui Feng, Xiaolin Zhang, Bo Wu, Fan Wang, Chao School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Digital Earth Science Beijing100094 China

ISBN: (纸本)9798350326239

As a popular representation learning framework, contrastive learning has been widely used in SAR image interpretation for feature extraction. Recent studies have shown that some features obtained by the model are related to the background information due to the spurious correlation of the backgrounds during the SAR data collection. As a result, the generalization ability of the representation learning model will be greatly affected by the background information and cannot satisfy the requirements of practical applications. This paper constructed a multi-branch dual contrastive learning model that simultaneously compares the similarities between objects and backgrounds of various images. The motivation is to alleviate the impact of the spurious correlation between background information and the object category. The experimental results and interpretable Shapley value demonstrated that the proposed method significantly improved the generalization performance of the SAR object classification algorithm. © 2023 IEEE.

关键词： Learning systems

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Framework for Fuzzy Temporal Association Rule Mining Based on Density Clustering Optimization

Framework for Fuzzy Temporal Association Rule Mining Based o...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Yang Cao Pengzhi Ma School of Computer Science and Engineering Shenyang Jianzhu University(SJZU) Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This paper proposes a framework for fuzzy temporal association rule mining based on density clustering optimization, aimed at improving the fuzzification of continuous attributes and enhancing the efficiency of mining temporal association rules. The first part of the framework effectively handles continuous attribute data and reduces data redundancy by adopting a density peak clustering algorithm and designing an optimized membership function. The second part introduces a fuzzy list ranking structure combined with item lifespan to mine fuzzy temporal association rules. Experimental results demonstrate that the framework exhibits superior performance in mining fuzzy temporal association rules and outperforms advanced algorithms in handling continuous attributes.

关键词： Fuzzy sets Systematics Redundancy Memory management Signal processing algorithms Clustering algorithms Signal processing Association rule learning Optimization Sorting

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Blockchain-Enhanced Federated Learning for Secure and Intelligent Consumer Electronics : An Overview

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IEEE Consumer Electronics Magazine 2025年

作者： Zhu, Chenlu Wang, Shuilong Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao He, Yuanyuan Wu, Celimuge Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China The University of Electro-Communications China

With the rapid advancement of Artificial Intelligence (AI) and Internet of Things (IoT), consumer electronics have become essential in daily life. However, these devices generate and store vast amounts of personal data, posing significant security threats. Federated learning, a distributed machine learning method, protects user privacy by training models collaboratively without sharing raw data. However, it still faces numerous challenges. To further enhance data security, the integration of blockchain technology is crucial. Blockchain's decentralized and immutable characteristics enable secure data sharing and privacy protection in consumer electronics. This paper analyzes the application of federated learning in consumer electronics, examines how blockchain can enhance federated learning, and discusses future research directions. The primary contributions of this paper include a detailed application of blockchain-enhanced federated learning in consumer electronics, intended to serve as a reference for future research. © 2012 IEEE.

关键词： Federated learning

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning 39

SADBA: Self-Adaptive Distributed Backdoor Attack Against Fed...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Feng, Jun Lai, Yuzhe Sun, Hong Ren, Bocheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Economics Wuhan Textile University China School of Computer Science and Technology Hainan University China

ISBN: (纸本)157735897X

Backdoor attacks in federated learning (FL) face challenges such as lower attack success rates and compromised main task accuracy (MA) compared to local training. Existing methods like distributed backdoor attack (DBA) mitigate these issues by modifying malicious clients’ updates and partitioning global triggers to enhance backdoor persistence and stealth. The recent full combination backdoor attack (FCBA) further improves backdoor efficiency with a full combination strategy. However, these methods are mainly applicable in small-scale FL. In large-scale FL, small trigger patterns weaken impact, and scaling them requires controlling exponentially more clients, which poses significant challenges, while simply reverting to DBA may decrease backdoor performance. To overcome these challenges, we propose the self-adaptive distributed backdoor attack (SADBA), which achieves similar performance to FCBA with a lower percentage of malicious clients (PMC). It also adapts more flexibly through an optimized model poisoning strategy and a self-adaptive data poisoning strategy. Experiments demonstrate SADBA outperforms state-of-the-art methods, achieving higher or comparable backdoor performance and MA across various datasets with limited PMC. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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A User Behaviour-Based Video Segmentation Strategy for High-Concurrency Environment

A User Behaviour-Based Video Segmentation Strategy for High-...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Danning Shen Wenhao Zhu Yujie Ding Chenyun Liu School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Municipal Big Data Center Shanghai China

With the development of the media industry, maximizing concurrency while ensuring the user’s viewing experience has become an important research topic for each major video website. Currently, one solution is to slice the video, which conserves server bandwidth. Therefore, the design of segment durations has been widely discussed in academia. The research shows that the longer the segment duration is, the better the user experience; however, the increase in video segment duration has a negative impact on the concurrency of the video website. In this paper, a variable length segmentation strategy based on user behaviour is proposed for video segmentation. In the early stage of video browsing, the video loading is ensured, and a short video segmentation strategy is adopted. When the number of users becomes stable, the segment duration is slowly increased to a fixed value to ensure fluency for the users watching the videos. In this paper, the JMeter test tool is used to test the concurrency volume. Through experimental verification, the variable length video segmentation algorithm proposed in this paper is shown to effectively improve the concurrency of the video websites.

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