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Plausible Heterogeneous Graph k-Anonymization for Social Networks

Tsinghua science and technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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Research on cross-project software defect prediction based on feature transfer method 4

Research on cross-project software defect prediction based o...

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4th International Conference on Advanced Information science and System, AISS 2022

作者： Wang, Wennan Zhao, Hanxu Li, Yu Su, Junyu Lu, Jiadong Wang, Baoping Academy of Management Guangdong University of Science and Technology China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology China Institute of Data Science City University of Macau China

ISBN: (纸本)9781450397933

In this paper, the research and experimental analysis of cross-project application software defect prediction is carried out, and the TCA model is used to improve the application function of its prediction. The models pointed out in this paper usually include: normalization processing model and mathematical linear kernel mathematical statistics The difference between the functional SVM classifier and the extended migration component analysis TCA+ model is that the model pointed out in this paper not only satisfies the prediction of software defects within the project suitable for TCA, but also meets the prediction of software defects in the cross-project of TCA+, so the most appropriate normalization can be selected. Optimized processing options to improve cross-project software defect prediction capabilities. © 2022 ACM.

关键词： Forecasting

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FedRKG: A Privacy-Preserving Federated Recommendation Framework via Knowledge Graph Enhancement 18th

FedRKG: A Privacy-Preserving Federated Recommendation Framew...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Yao, Dezhong Liu, Tongtong Cao, Qi Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819998951

Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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The Investigation and Implementation of Automated Pricing and Replenishment Decisions for Vegetable Commodities Relying on Time Series Forecasting and Linear Programming Models

The Investigation and Implementation of Automated Pricing an...

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IEEE Information technology, Networking, Electronic and Automation Control Conference

作者： Yi Tong Jinxuan Zhou Liang Deng Congao Zhang Tao Guo Xueling Long Department of Information Engineering Wuhan Business University Hubei China Major in Software Engineering Major in Data Science and Big DataTechnology Major in Internet of Things Engineering Major in Financial Management

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

In modern supermarkets, given the limited shelf life and the propensity for rapid declines in the sales of perishable goods, such as vegetables, it becomes imperative to conduct daily restocking based on historical sales and consumer demand to preclude the occurrence of undersold items the following day. Consequently, conducting restocking and pricing strategies based on sales and demand holds significant research importance for supermarkets. This study initially employs a multiple linear regression model to investigate the impact on profit by adjusting total sales (Q), sales price (P), and each component of cost-plus pricing (C, T, L, A), thereby determining the relationship between total sales and cost-plus pricing for each vegetable category, as well as the functional relationship between total sales and cost-plus pricing for each vegetable category. Based on the derived functional relationship, time series prediction and linear programming models are utilized to forecast the market demand for each vegetable category over a one-week period, subsequently formulating the daily restocking plan and pricing strategy for each vegetable category in the subsequent week to maximize supermarket profit while satisfying market demand. Finally, an analysis of saleable vegetable items reveals that 29 vegetable items can be displayed, with a total profit of 846.35 yuan. Upon testing the sales data of 300,000 vegetables, compared to the conventional display method sans the use of this model, the overall profit margin surged by 269.75 yuan, an increment of 47% relative to the initial profit, effectively enhancing the operational efficiency and profitability of the supermarket.

关键词： Profitability Time series analysis Linear regression Pricing Predictive models Linear programming data models Information technology Forecasting Testing

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STIOS: A Novel Self-supervised Diffusion Model for Trajectory Imputation in Open Environment Scenarios

STIOS: A Novel Self-supervised Diffusion Model for Trajector...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhijing Hu Hao Yan Yuhao Zheng Hua He Chao Chen Changjun Fan Kuihua Huang Laboratory for Big Data Data and Decision National University of Defense Technology Changsha China School of Computer Science and Engineering Central South University Changsha China Laboratory for Big Data Data and Decision National University of Defense Technology China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

With the growth of location-based services, the accumulation of large amounts of trajectory data comes with the challenge of missing data. Existing trajectory imputation methods rely on deterministic models that cannot take into account open environments scenarios without auxiliary information. This may lead to poor performance in learning the long-term dependencies of trajectories. In this paper, we propose a novel Self-supervised diffusion model for Trajectory Imputation in Open environment Scenarios (STIOS), which combines a deterministic model and a conditional diffusion model. In addition, we propose a self-supervised training method inspired by masked language modelling that divides the data into conditional information and estimation targets. Extensive experiments on two real datasets demonstrate the superiority of STIOS for trajectory imputation.

关键词： Training Social computing Uncertainty Computational modeling Roads Estimation Diffusion models Imputation data models Trajectory

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Personalized Federated Learning with Feature Alignment via Knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated Learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Fast Multivariate Time Series Anomaly Detection Based on Matrix Completion

Fast Multivariate Time Series Anomaly Detection Based on Mat...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： He, Shiming Yi, Tongzhijian Yang, Bo Guo, Chenxi Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Increasingly complex systems contain large numbers of devices that generate great number of multivariate time series that are monitored and recorded. For anomaly detection of these complex time series, deep learning technology is often used to build a learning model for anomaly detection. The method based on deep learning usually takes a long time to train data in practice, and the online service system is deployed or changed very frequently, which makes the online service system need to frequently spend a lot of time updating the anomaly detection model. To overcome this challenge, this paper proposes a fast multivariate time series anomaly detection (FMAD) approach based on matrix completion. After sampling the primitive low-rank matrix, FMAD uses matrix completion to reconstruct the original low-rank matrix to detect anomalies in multivariate time series. We conducted related experimental analysis and discussion. The experimental results on three real data sets show that FMAD has improved the problem of too long model training time and improved the detection efficiency. © 2023 IEEE.

关键词： Anomaly detection

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Model Predictive Current Control for Asymmetric Six-phase Permanent Magnet Motors with Less Computation and Optimized Voltage Vector Magnitude

Model Predictive Current Control for Asymmetric Six-phase Pe...

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2022 International Conference on Smart Energy and Electrical Engineering, SEEE 2022

作者： Sun, Jinyu Chen, Fubing Huai'An Key Laboratory of Big Data Intelligent Computing and Analysis China The School of Computer Science and Technology Huaiyin Normal University Huaian223001 China

For the sake of the fixed magnitude of candidate control sets, the conventional model predictive current control (MPCC) for asymmetric six-phase permanent magnet motors suffers from huge harmonic currents. This article designs a novel MPCC that aims to improve operational performance. First, virtual-voltage vector (VV) sets are synthesized as the new candidate vectors to restrain harmonic currents. Then, a simplified method for evaluating voltage vectors is used to alleviate the computation in the enumeration process. Moreover, the duty cycle modulation with the deadbeat solution is introduced to optimize the magnitude of the VV. The modulation range can be expanded by utilizing null vectors, which will significantly reduce the torque fluctuations and flux ripples. The experiments are conducted to verify the designed control strategy. © Published under licence by IOP Publishing Ltd.

关键词： Vectors

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Structure-aware Table-to-Text Generation with Prefix-tuning 23

Structure-aware Table-to-Text Generation with Prefix-tuning

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2023 4th International Conference on Control, Robotics and Intelligent System, CCRIS 2023

作者： Liu, Cencen Xu, Yi Yin, Wen Zheng, Dezhang University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Sichuan Chengdu China

ISBN: (纸本)9798400708190

Table-to-text generation is designed to generate descriptive natural language for structured tables that conforms to objective facts and follows the source data. The current challenge in this field is to capture the structural information of the table and improve the quality of the generated text. The existing sequence-to-sequence approach is to linearize the table, which leads to miss captured structure information and is not conducive to the model learning contextual semantics. In this paper, we introduce structural-aware self-attention, which focuses on table structure to capture cell relationships between the same row or column. In this way, the generated descriptive text can more accurately reflect the correlation between the cells in the table, discarding irrelevant information. In order to adapt the pre-trained language model to the table-to-text generation task, we introduce prefix-tuning. Traditional fine-tuning methods update all model parameters, which leads to increased training costs. In contrast, using prefix-tuning for a more lightweight approach can improve model performance considerably. Attaching continuous prompts to tokens helps the model better understand the structure and semantics of the input sequence. All of our models are extended based on T5 and have strong competitiveness in the ToTTo dataset and Hitab dataset compared with several classical baselines. © 2023 ACM.

关键词： Semantics

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Hybrid Shunted Transformer embedding UNet for remote sensing image semantic segmentation

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Neural Computing and Applications 2024年第25期36卷 15705-15720页

作者： Zhou, Huacong Xiao, Xiangling Li, Huihui Liu, Xiaoyong Liang, Peng School of Computer Science Guangdong Polytechnic Normal University Guangdong Guangzhou510630 China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Polytechnic Normal University Guangdong Guangzhou510665 China School of Data Science and Engineering Guangdong Polytechnic Normal University Guangdong Guangzhou510630 China Academy of Heyuan Guangdong Polytechnic Normal University Guangdong Heyuan517099 China The Cyberspace Institute of Advanced Technology Guangzhou University Guangdong Guangzhou510006 China

With the development of deep learning, Remote Sensing Image (RSI) semantic segmentation has produced significant advances. However, due to the sparse distribution of the objects and the high similarity between classes, the task of semantic segmentation in RSI is still extremely challenging. In this paper, we propose a novel semantic segmentation framework for RSI called HST-UNet that can overcome the shortcomings of the existing models and extract and recover the global and local features of RSI, which is a hybrid semantic segmentation model with Shunted Transformer as encoder and Multi-Scale Convolutional Attention Network (MSCAN) as decoder. Then, to better fuse the information from the Encoder and the Decoder and alleviate the ambiguity, we design a Learnable Weighted Fusion (LWF) module to effectively connect to the decoder features. Extensive experiments demonstrate that the proposed HST-UNet outperforms the state-of-the-art methods, achieving F1 score/MIoU accuracy of 71.44%/83.00% on the ISPRS Vaihingen dataset and 77.36%/87.09% on ISPRS Potsdam dataset. The code will be available at https://***/HC-Zhou/HST-UNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantic Segmentation

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