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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jie Dong Wenyu Fang Wanling Zheng Jinkun Liu Yanhua Liu College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm.

关键词： Training data privacy Network topology Denial-of-service attack Feature extraction Real-time systems Topology Labeling Computer crime Long short term memory

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Imposing Total Variation Prior Into Guided Filter

Imposing Total Variation Prior Into Guided Filter

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IEEE International Conference on Image processing

作者： Yuanhao Gong College of Electronics and Information Engineering Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China

Guided filter is a popular filter thanks to its effectiveness in edge preserving and computation efficiency. On the other hand, total variation is a popular prior thanks to its mathematical properties. In this paper, we impose the total variation prior into guided filter, leading to a novel Total Variation Guided Filter (TVGF) that has both advantages from guided filter and total variation prior. First, TVGF has a closed-form expression and also a linear computation complexity. Our experiment confirms that TVGF is 1300+ times faster than traditional iterative solvers for total variation models. Second, thanks to the total variation, TVGF preserves edges better than the original guided filter and leads to much less artifacts. TVGF is also two times faster than the guided filter. We theoretically derive the TVGF and numerically confirm its effectiveness and efficiency. TVGF can be used in a large range of edge-aware applications, such as image smoothing, dehazing, depth estimation and optical flow estimation.

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

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

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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Characteristic Mode Cancellation for Radiation Pattern Decoupling of Asymmetric Dipole Antennas

Characteristic Mode Cancellation for Radiation Pattern Decou...

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Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSRWTC)

作者： Yu-Zhuo Deng Nan Yang Kai Lu Jingchang Nan Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology (School of Microelectronics) Sun Yat-sen University Guangzhou China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

1 × 2 asymmectric dipole antennas with radiation pattern decoupling (RPD) characteristic are presented in this paper. These two dipole antennas are connected by a cross shaped strip. Characteristic mode analysis (CMA) is used to analyze and design this kind of antenna. Observed with CMA, a pair of common and differential characteristic modes superposed together to obtain little current distribution under the coupled dipole antenna. Therefore, RPD characteristic can be realized with high isolation. To verify the concept, a prototype is designed, fabricated, and measured, with good agreement observed. A measured 22.5% 10-dB impedance bandwidth can be obtained, over which the measured isolation is higher than 23 dB. It should be mentioned that RPD effect can be found over entire impedance band, which is highly desired for practical applications.

关键词： Antenna measurements Wireless communication Impedance measurement Philosophical considerations Dipole antennas Prototypes Bandwidth Impedance Gain Antenna radiation patterns

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RECC: A Relationship-Enhanced Content Caching Algorithm Using Deep Reinforcement Learning

RECC: A Relationship-Enhanced Content Caching Algorithm Usin...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jiarui Ren Haiyan Zhang Xiaoping Zhou Menghan Zhu School of Information Engineering Ningxia University Yinchuan China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

Mobile edge caching (MEC) is a promising technology to alleviate traffic congestion in the network. Current studies explored deep reinforcement learning (DRL)-based MEC methods. These methods consider the dynamics of the request size to maximize the cache hit rate. However, they usually ignored the potential request relationships among contents. Two contents with a strong relationship are usually requested sequentially. Inspired by this assumption, this paper proposes a relationship-enhanced content caching algorithm using DRL, named RECC. Our RECC infers user preferences by mining the request relationships among contents. In this work, the relationships are modeled as request sequences, and the request features are learned by using graph embedding. These features will be used as input of state in our DRL-based algorithm. We utilize the Wolpertinger architecture to solve the limitation of large discrete action space. The simulation results indicate that our RECC outperformed the traditional cache policies and state-of-the-art DRL-based method in cache hit rate. Furthermore, the proposed RECC has advantages in long-term stability in the environment where content popularity changes dynamically, and also has a higher cache hit rate when handling the requests with number changes dynamically.

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KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Lin, Shiying Hu, Rong Li, Zuoyong Lin, Qinghua Zeng, Kun Wu, Xiang Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Fuzhou University Affiliated Provincial Hospital Provincial Clinical Medical College Fujian Medical University Department of Urology Fuzhou350001 China

In the field of deep learning-based medical image segmentation, convolutional neural networks (CNNs) extract image features by combining linear convolutional layers with nonlinear activation functions. However, excessive stacking of linear layers in the network limits the model’s ability to capture fine-grained details. In addition, the feature distribution imbalance caused by the traditional fixed grouping strategy (FGS) can affect the deep model’s capacity to perceive the overall structure of the image. To address these challenges, we propose a medical image segmentation framework, called Kolmogorov-Arnold Network with the adaptive group strategy and contextual Transformer based on Unet (KAC-Unet). First, we propose the adaptive group strategy (AGS) to balance the grouping of different input channels, alleviating the performance degradation caused by differences in group information. Then, we propose the Shift Tokenized Kolmogorov-Arnold Network (KAN) Block to capture complex features in medical images through flexible nonlinear transformations and shift operations. Extensive experiments are conducted on three medical image segmentation datasets. The results demonstrate the effectiveness and superiority of our proposed method compared with state-of-the-art algorithms. © 1963-2012 IEEE.

关键词： Convolutional neural networks

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Medical Image Segmentation Based on 3d Pdc with Swin Transformer

SSRN

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SSRN 2025年

作者： Fan, Lin Ding, Xiaojia Wang, Zhongmin Wang, Hai Zhang, Rong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Information Science and Technology Northwest University Shaanxi Xi’an710121 China

3D medical image segmentation is vital for disease diagnosis and effective treatment strategies. Despite the advancements in Convolutional Neural Networks (CNN), their fixed receptive fields constrain global context modeling, leading to suboptimal performance, particularly with complex shapes and multi-scale variations. Transformer enhances the global modeling capability through self-attention mechanism. However, challenges persist in capturing fine-grained local features and extracting edge features. To overcome these issues, In this study, a dual-path encoder is proposed, which consists of a 3D PDC and a Swin Transformer and is combined with a CNN for the decoder. The 3D PDC module extracts local features by calculating the differences between neighboring pixels. To improve the ability of capturing edge structures, three 3D PDC variants (3D CPDC, 3D APDC, and 3D RPDC) are proposed to optimize the processing ability of complex edges, multi-directional edges, and multi-scale structures, respectively. After performance evaluation, the selected CARV combinations (3D CPDC, 3D APDC, 3D RPDC, and 3D ordinary convolution) achieve a compromise between high segmentation accuracy and low computational cost. The Swin Transformer encoder captures global context using a hierarchical shift-window mechanism, while the CNN decoder fuses features progressively to produce precise pixel-level segmentation. The proposed method exceeds the performance of existing state-of-the-art techniques, as shown by experiments conducted on the BTCV, FLARE21, and AMOS22 datasets. © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Design of a clever carbon monoxide monitoring system

Design of a clever carbon monoxide monitoring system

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第35届中国控制与决策会议

作者： Yanxi Cao Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big DataBeijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture

Carbon monoxide is a colorless and odorless gas that is extremely *** monoxide cannot be accurately detected or eliminated by the senses in high concentrations in the body under relatively airtight *** a result,the human body can only inhale it *** monoxide inhalation can deplete the body's oxygen supply,causing symptoms such as nausea,dizziness,headaches,and,in severe cases,*** response to the event's insidiousness,urgency,and ***,because the current carbon monoxide monitoring system is not portable,it is proposed to design a microcontroller as the core,with a concentration collection module,a wireless transmission module,an LCD module,and so *** with information between intelligent monitoring systems via wireless transmission module and mobile phone *** system consists of a microcontroller,concentration acquisition module,wireless transmission module,and LCD display *** the performance of the system is tested by changing the environmental conditions,while a control test is *** results show that the system has a fast and stable transmission rate,small size,and wireless monitoring *** can be informed of environmental conditions via their mobile phones,providing early warning and quick alarms.

关键词： Wi-Fi transmission carbon monoxide monitoring microcontroller unit indoor real-time monitoring

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Community-Aware Heterogeneous Graph Contrastive Learning 19th

Community-Aware Heterogeneous Graph Contrastive Learning

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19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Li, Xinying Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Recently, heterogeneous graph contrastive learning, which can mine supervision signals from the data, has attracted widespread attention. However, most existing methods employ random data augmentation strategies to construct contrastive views, which may destroy the semantic information in heterogeneous graphs. Moreover, they often select positive and negative samples based solely on node-level proximity and overlook hard samples that are difficult to distinguish from anchors. To solve the above problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning model called CAHGCL. In particular, we design an adaptive data augmentation strategy to construct views, including feature augmentation and topology augmentation. To improve the quality of samples, we propose a dynamic sample weighting strategy based on node similarity and community information, capable of identifying both hard positive samples and hard negative samples. Finally, we introduce community-level contrast to improve community cohesion. Extensive experiments and analyses demonstrate that CAHGCL consistently outperforms state-of-the-art baselines on three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Research on energy consumption prediction of public buildings based on improved support vector machine

Research on energy consumption prediction of public building...

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第35届中国控制与决策会议

作者： Liyao Yang Hongyan Ma Yingda Zhang Shengyan Li School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big DataBeijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture

An improved support vector machine model for energy consumption prediction is proposed to achieve efficient energy saving in *** this paper,firstly,a gray correlation analysis model is used to measure the correlation between temperature,humidity,sunlight and other factors and building energy ***,the sparrow search algorithm(SSA) is introduced to optimize the penalty coefficient c and kernel function parameter g of the support vector machine,and then the SSA-SVM energy consumption prediction model is ***,the experimental results are analyzed by comparing with the data derived from the support vector machine prediction(SVM) model and BP neural network energy consumption *** experimental results show that compared with SVM and BP neural network,the prediction results of SSA-SVM model perform better in error index,indicating that the energy consumption prediction of SSA-SVM model has higher prediction accuracy;the maximum relative error of SSA-SVM prediction model is 0.0514,and the maximum relative errors of the other two models are greater than 0.55,indicating that SSA-SVM model has a higher degree of reliability.

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