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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Yuan, X. Ning, Ke Wei, K. He, Yan-Lin Zhu, Qun-Xiong Zhang, Ming-Qing Luo, Yi Zhang, Yang College of Information Science and Technology Beijing University of Chemical Technology Beijing100020 China Ministry of Education of China Engineering Research Center of Intelligent Pse Beijing100020 China Macao Polytechnic University Faculty of Applied Sciences 999078 China Research Institute of Mine Big Data Chinese Institute of Coal Science Beijing100013 China

ISBN: (纸本)9798350361674

Effective anomaly detection of multivariate time series data is crucial for modern industrial applications. However, existing works usually ignore spatial and temporal dependencies of series. The data with complicated spatial-temporal feature put forward higher requirements for accurate detection of anomalies. In this paper, we propose an anomaly detection method for multivariate time series based on spatial-temporal network (STN). Firstly, the time series are reconstructed by Generative Adversarial Networks (GAN). The reconstruction block is used to amplify the influence of the abnormal state and prompt the anomalies more detectable, as poorly reconstructed data of abnormal state further worsen the quality of the final predicted value. Secondly, an adaptive learning graph structure layer is proposed. The graph structure is learned by adaptive method without any prior knowledge and is used for reconstructed data prediction. Thirdly, we use STN to extract spatial and temporal dependencies of series. Especially, attention mechanism is introduced into graph convolution network to enhance the prediction performance by focusing on more important neighbor node message. Finally, our model is tested on 3 public datasets to evaluate performance. The results demonstrate that our method performs better than baselines. © 2024 IEEE.

关键词： Generative adversarial networks

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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Cross-cultural contextualisation for recommender systems

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1659-1670页

作者： Hong, Minsung An, Sojung Akerkar, Rajendra Camacho, David Jung, Jason J. Big Data Research Group Western Norway Research Institute Vestlandsforsking Sogndal6851 Norway Department of Computer Science and Engineering Chung-Ang University 84 Heukseok-ro Heukseok-dong Dongjak-gu Seoul Korea Republic of Information Systems Department Technical University of Madrid Madrid28031 Spain

Cultural Heritage (CH) domain is rapidly moving from traditional heritage sites into smart cultural heritage environment through various technologies. As one of the important technologies in the smart space, Recommender Systems (RSs) have been widely utilised to personalised services and matching visitors’ goals and behaviours. Whereas, cultural difference is often considered a barrier to technology transfer or adoption. However, few studies focus on how the cultural factor influences recommendation despite cultural difference largely affects user preferences in the RSs. Furthermore, existing researches have mainly analysed evaluation results of their recommendation to reveal cultural differences, rather than utilising the cross-cultural factors into RSs. In this paper, we propose a novel concept of cross-cultural contextualisation and a model to compute the cross-cultural factor affecting users (countries or cultures) preferences by using matrix factorisation and clustering techniques. In addition, we discuss how to apply the model to RSs in CH domain through cross-domain recommendation techniques. Note that the two computational techniques were used to analyse cross-cultural factors which impact to user preferences, rather than to recommend items. In other words, the proposed model and computing results capable of utilisation into the other RSs as well as various research fields. Results of experiments with a real-world dataset showed effectiveness of the proposed model and supported that there is cultural difference influencing users’ rating behaviours. Furthermore, a systematic analysis of dataset and the experimental results presented that individual users could be considered as country-wise groups for the model to analyse the cross-cultural factors. © Springer-Verlag GmbH Germany, part of Springer Nature 2019.

关键词： Recommender systems

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DDoS Detection and Defense Based on FLAD and SDN 20

DDoS Detection and Defense Based on FLAD and SDN

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Dong, Jie Fang, Wenyu Zheng, Wanling Liu, Jinkun Liu, Yanhua College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China

ISBN: (纸本)9798350356328

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. © 2024 IEEE.

关键词： Differential privacy

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Hybrid feature-based neural network regression method for load profiles forecasting

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Energy Informatics 2025年第1期8卷 1-20页

作者： Satan, Aidos Zhakiyev, Nurkhat Nugumanova, Aliya Friedrich, Daniel Department of Science and Innovation Astana IT University Astana010000 Kazakhstan Big Data and Blockchain technologies Research Center Astana IT University Astana Kazakhstan School of Engineering Institute for Energy Systems University of Edinburgh EdinburghEH93DW United Kingdom Department of Energy Engineering University of Genova Savona17100 Italy

This study addresses the critical need for improved demand forecasting models that can accurately predict energy consumption, particularly in the context of varying geographical and climatic conditions. The work introduces a novel demand forecasting model that integrates clustering techniques and feature engineering into neural network regression, with a specific focus on incorporating correlations with air temperature. Evaluation of the model’s efficacy utilized a benchmark dataset from Tetouan, Morocco, where existing forecasting methods yielded RMSE values ranging from 6429 to 10,220 [MWh]. In contrast, the proposed approach achieved a significantly lower RMSE of 5168, indicating its superiority. Subsequent application of the model to forecast demand in Astana, Kazakhstan, as a case study, showcased its efficacy further. Comparative analysis against a baseline neural network method revealed a notable improvement, with the proposed model exhibiting a MAPE of 5.19% compared to the baseline’s 17.36%. These findings highlight the potential of the proposed approach to enhance demand forecasting accuracy, particularly across diverse geographical contexts, by leveraging climate-related inputs, the methodology also demonstrates potential for broader applications, such as flood forecasting, agricultural yield prediction, or water resource management. © The Author(s) 2025.

关键词： Demand response

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Semantics Guided Diffusion for Deep Joint Source and Channel Coding Design 16

Semantics Guided Diffusion for Deep Joint Source and Channel...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Zhang, Maojun Zhu, Guangxu Han, Kaifeng Jin, Richeng Chen, Xiaoming College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Shenzhen Research Institute of Big Data Shenzhen China China Academy of Information and Communications Technology Beijing China

ISBN: (纸本)9798350390643

Semantic communication (SemCom) offers a promising avenue for enhancing transmission efficiency, especially as traditional bit-level communication approaches its theoretical limits. A key enabling technology in SemCom is deep learning-based joint source and channel coding (DeepJSCC). However, existing DeepJSCC methods lack interpretability because they transmit semantic information implicitly by mapping data to latent features. Additionally, the black-box nature of neural networks makes it challenging to ensure the reliable transmission of critical semantics. To address these challenges, we propose advancing DeepJSCC towards a more 'semantic' approach. Specifically, we suggest transmitting interpretable and lightweight semantics as side information alongside JSCC latent features. At the receiver end, we introduce a novel latent diffusion model designed for wireless communication, trained from scratch to integrate seamlessly with DeepJSCC. Using the semantic side information, the receiver employs the proposed semantics-guided latent diffusion for denoising. Furthermore, since accurate channel state information (CSI) is essential in practice, we propose estimating CSI directly from the channel output. The estimated CSI is then used for step matching in the denoising diffusion process, enabling CSI-free transmission. Finally, the denoised feature is fed into the JSCC decoder to reconstruct the image. Numerical results demonstrate that our proposed scheme can achieve comparable performance without accurate CSI. Additionally, guided by semantic information and leveraging the powerful diffusion model, our method surpasses current DeepJSCC schemes, delivering satisfactory reconstruction performance even at SNR = -5dB. This proposed scheme highlights the potential of incorporating diffusion models in future SemCom systems and suggests several promising applications. © 2024 IEEE.

关键词： Channel coding

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Masked Conditional Diffusion Model with GNN for Spatial Transcriptomics data Imputation

Masked Conditional Diffusion Model with GNN for Spatial Tran...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Xiaoyu Min, Wenwen Wang, Shunfang Wang, Changmiao Xu, Taosheng Yunnan University School of Information Science and Engineering Yunnan Kunming650500 China The Shenzhen Research Institute of Big Data Shenzhen518172 China Chinese Academy of Science The Hefei Institutes of Physical Science Hefei230031 China

ISBN: (纸本)9798350386226

Spatially resolved transcriptomics represents a significant advancement in single-cell analysis by offering both gene expression data and their corresponding physical locations. However, this high degree of spatial resolution entails a drawback, as the resulting spatial transcriptomic data at the cellular level is notably plagued by a high incidence of missing values. Furthermore, most existing imputation methods either overlook the spatial information between spots or compromise the overall gene expression data distribution. To address these challenges, our primary focus is on effectively utilizing the spatial location information within spatial transcriptomic data to impute missing values, while preserving the overall data distribution. We introduce stMCDI, a masked conditional diffusion model for spatial transcriptomics data imputation, which employs a denoising network trained using randomly masked data portions as guidance, with the unmasked data serving as conditions. Additionally, it utilizes a GNN encoder to integrate the spatial position information, thereby enhancing model performance. Compared with baseline methods, our model achieves state-of-the-art performance in all evaluation metrics on six real-world datasets. The results obtained from spatial transcriptomics datasets elucidate the performance of our methods relative to existing approaches. Our code can be accessed at https://***/wenwenmin/stMCDI. © 2024 IEEE.

关键词： Gene expression

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research on Enhanced Perception and Attention-Driven Person Re-Identification 20

Research on Enhanced Perception and Attention-Driven Person ...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Li, Dong Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness. © 2024 IEEE.

关键词： Reamers

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GNN-based Structured Bayesian Inference for Multi-grid Localized Statistical Channel Modeling

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IEEE Transactions on Wireless Communications 2025年

作者： Wang, Yiheng Xue, Ye Zhang, Shutao Chang, Tsung-Hui The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Guangdong Shenzhen518172 China The Chinese University of Hong Kong-Shenzhen Shenzhen Research Institute of Big Data School of Science and Engineering Guangdong518172 China

Localized statistical channel modeling (LSCM) is an efficient channel modeling framework recently proposed for wireless network optimization which learns the angular power spectrum (APS) of the downlink channel from the beam-wise reference signal receiving power (RSRP). However, the conventional LSCM is only based on RSRP measurements from one single geographical grid and ignores the inherent property of spatial consistency over wireless channels, resulting in suboptimal performance. To this end, we consider the LSCM in a manner of multiple geographical grids and further propose a novel graph-based approach for the multi-grid LSCM, called the accelerated Markovian variational Bayesian graph neural network (AMVB-GNN). The AMVB-GNN leverages a heterogeneous Markovian graph representation to capture the structured sparsity in the channel APSs and employs refined variational Bayesian inference (VBI) to learn the APSs of multiple grids. Notably, the design of AMVB-GNN eliminates the exact matrix inversion operations required in conventional VBI, thereby enhancing computational efficiency. Additionally, we demonstrate the partial permutation equivalence of AMVB-GNN, ensuring both interpretability and reliability. To address the issue of the demand for ground-truth APSs labels, we propose an unsupervised training loss function. Extensive simulation experiments validate the effectiveness and efficiency of the proposed AMVB-GNN model. © 2002-2012 IEEE.

关键词： Graph neural networks

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Steel Surface Defect Detection Based on Improved YOLOv8 20

Steel Surface Defect Detection Based on Improved YOLOv8

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Zhang, Jichen Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

There are some defects on the surface of steel that are difficult to detect, so we propose an improved algorithm based on YOLOv8 for detecting defects that are difficult to be detected. This improvement includes the introduction of an SPD module to minimize the problem of information loss due to downsampling;the addition of a small object detection head combined with EMA attention to further enhance the detection capabilities for small targets;the use of BiFPN to replace the original PANet, applying different weights to different features to strengthen feature fusion. Experiments were conducted on the NEU-DET dataset, and the algorithm improved mAP to 80.2%, an improvement of 3.9% over the baseline model. © 2024 IEEE.

关键词： Change detection

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