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A Differential Privacy Protection Protocol Based on Location Entropy

Tsinghua science and technology 2023年第3期28卷 452-463页

作者： Ping Guo Baopeng Ye Yuling Chen Tao Li Yixian Yang Xiaobin Qian Xiaomei Yu State Key Laboratory of Public Big Data School of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Information Technology Innovation Service Center of Guizhou Province Guiyang 550025China School of Cyberspace Security Beijing University of Posts and TelecommnuicationsBeijing 100000China Guizhou CoVision Science&Technology Co. Ltd.Guiyang 550025China School of Information Science and Engineering Shandong Normal UniversityJinan 250000China

A Location-Based Service(LBS)refers to geolocation-based services that bring both convenience and *** an increase in the scale and value of data,most existing location privacy protection protocols cannot balance privacy and *** solve the revealing problems in LBS,we propose a differential privacy protection protocol based on location ***,we design an algorithm of the best-assisted user selection for constructing anonymity ***,we employ smart contracts to evaluate the credibility of participants,which ensures the honesty of ***,we provide a comprehensive experiment;the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge ***,our protocol improves data ***,it realizes user-controllable privacy protection,which improves privacy protection and strengthens security.

关键词： Location-Based Services(LBS) smart contract location entropy differential privacy privacy protection

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Edge Learning-Based Efficient data Imputation of Water Quality 7th

Edge Learning-Based Efficient Data Imputation of Water Quali...

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7th International Conference on Computing, Control and Industrial Engineering, CCIE 2023

作者： Wang, Yongsheng Chen, Zhen Liu, Limin Gao, Jing Liu, Guangwen Xu, Zhiwei School of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100000 China

ISBN: (纸本)9789819927296

Water quality monitoring is critical for developing effective surface water management strategies, whereas data preprocessing is the foundation for monitoring water quality. Water quality monitoring usually suffers from abnormal and missing values in the collected data of surface water quality, and thus an efficient data imputation method for water quality data is highly desired. To handle these challenges, we propose an edge learning-based data imputation method. By using VAE as the generator of WGAN-LP, the performance of value imputation of water quality is generalized and improved. In addition, we demonstrate the performance of the proposed method on two real-world datasets, collected from Wuliangsu Lake and Taihu Lake in Inner Mongolia. Compared with state of the art, the enhanced imputation accuarcy of water quality is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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Fine-Tuned BERT Model for Hate Speech Detection in Political Discourse

Fine-Tuned BERT Model for Hate Speech Detection in Political...

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International Conference on ICT and Knowledge Engineering

作者： Steve Nwaiwu Nipat Jongsawat Anucha Tungkasthan Jarinya Thaloey Data and Information Science Faculty of Science and Technology RMUTT Pathum Thani Thailand Big Data Analytics and Management Faculty of Science and Technology RMUTT Pathum Thani Thailand

ISBN: (数字)9798350391435

ISBN: (纸本)9798350391442

This research tackles the challenge of detecting hate speech and offensive content in political discussions on social media. By employing natural language processing (NLP) techniques, the study aims to contribute to creating healthier online communities. The researchers started with a manually labeled dataset of Reddit comments centered on political discourse. To address the common issue of class imbalance in classification tasks, they used an oversampling strategy to ensure equal representation of each class. Additionally, they applied data augmentation, specifically synonym replacement, to increase the diversity of the training data, which helps improve the model's generalization. For text preprocessing, the Bidirectional Encoder Represen- tations from Transformers (BERT) tokenizer was used. This tokenizer converts text into subwords, which is especially useful for handling out-of-vocabulary terms. The model used in this study is a fine-tuned version of BERT base, trained with specific hyperparameters: a learning rate of 5e-5, a batch size of 24, and a weight decay of 0.01. Training was carried out over seven epochs using the Hugging Face Transformers library and NVIDIA Tesla P100 GPUs available on Kaggle. The model's performance was assessed using 5-fold cross- validation, resulting in impressive metrics: an average accuracy of 99.34 percent, precision of 99.36 percent, recall of 99.34 percent, F1-score of 99.34 percent, and an ROC-AUC score of 99.96 percent. These results highlight the model's effectiveness in identifying hate speech and offensive content across different data splits. Compared to earlier research, this approach shows significant improvement in performance metrics. It demonstrates the potential of combining BERT with data augmentation and oversampling techniques for detecting hate speech in political discourse on Reddit. This study lays the groundwork for future research into real-time detection systems and cross-lingual models.

关键词： Analytical models Social networking (online) Hate speech Training data Bidirectional control Transformers data augmentation Encoding Natural language processing data models

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MAQMC:Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

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Computer Modeling in Engineering & sciences 2023年第9期136卷 2759-2785页

作者： Zhengkai Ding Qiming Fu Jianping Chen You Lu Hongjie Wu Nengwei Fang Bin Xing School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China School of Architecture and Urban Planning Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling *** reinforcement learning(DRL)methods have recently been proposed to address the HVAC control ***,the application of single-agent DRL formulti-zone residential HVAC controlmay lead to non-convergence or slow *** this paper,we propose MAQMC(Multi-Agent deep Q-network for multi-zone residential HVAC Control)to address this challenge with the goal of minimizing energy consumption while maintaining occupants’thermal *** is divided into MAQMC2(MAQMC with two agents:one agent controls the temperature of each zone,and the other agent controls the humidity of each zone)and MAQMC3(MAQMC with three agents:three agents control the temperature and humidity of three zones,respectively).The experimental results showthatMAQMC3 can reduce energy consumption by 6.27%andMAQMC2 by 3.73%compared with the fixed point;compared with the rule-based,MAQMC3 andMAQMC2 respectively can reduce 61.89%and 59.07%comfort *** addition,experiments with different regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.

关键词： Deep reinforcement learning multi-zone residential HVAC multi-agent energy conservation comfort

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Labelrepair: Sequence Labelling for Compilation Errors Repair 31

Labelrepair: Sequence Labelling for Compilation Errors Repai...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Wu, Zhenyu Yang, Deheng Lei, Yan Xie, Huan Tang, Minghua Li, Maojin Academy of Military Sciences Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China Peng Cheng Laboratory Shenzhen China Kunming University of Science and Technology Kunming China

ISBN: (纸本)9798350330663

Manual fixing of compilation errors could be a tedious and time-consuming task for novice programmers, and even for experienced ones. In recent years, an increasing number of automated repair techniques have been proposed to guide novice programmers and improve the efficiency of software development. Among them, learning-based automated repair techniques have achieved promising results in terms of repair accuracy. However, existing approaches neglect the time efficiency of patch generation, and often treat the compilation errors repair as a neural machine translation task. The end-to-end repair model decoding cannot be parallelized during the inference stage and suffers from redundant decoding search space. Furthermore, the large search space brought by the model poses a potential risk of semantic tampering. To this end, we propose Labelrepair, a novel repair technique that treats the repair of compilation errors as a sequence labelling task. Labelrepair discards the decoding model and converts the search for patches to a search for the error mapping actions between broken code and patch code pairs. In this way, the search space for patch tokens is plum-meted from the entire vocabulary to the size of edit action labels, and the logical semantics of the original program are preserved to some extent. The time complexity of inference is reduced from O(n) to O (1) owing to the parallel generation of edit action labels. Through a comprehensive evaluation of Labelrepair on two datasets, we demonstrate that Labelrepair is able to generate patches instantly (0.71ms on average), which is 28 times faster than existing end-to-end repair models. Compared with existing edit-based repair models, Labelrepair achieves the state-of-art repair accuracy. © 2024 IEEE.

关键词： Errors

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A Two-Stage Map-matching Method for Sparse Trajectories with Small-scale Ground Truth data 36

A Two-Stage Map-matching Method for Sparse Trajectories with...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Si, Junjun Xiang, Yang Li, Li Tu, Bo School of Computer and Cyber Sciences Communication University of China Beijing China Big Data Technology Co. Ltd. Beijing China School of Information Management Beijing Information Science & Technology University Beijing China

ISBN: (纸本)1891706594

Trajectory data often suffer from sparsity and a lack of ground truth data, presenting challenges for map-matching. Traditional map-matching approaches struggle with sparse data, and deep learning methods are hindered by data scarcity. To address these issues, we propose a two-stage map-matching method. In the first stage, we use synthetic trajectory data to pre-train a deep learning model, overcoming data scarcity. We introduce a token mixing scheme during pre-training to clarify the interrelations between GPS-based and segment-based trajectory data. In the second stage, we fine-tune the model with limited ground truth data using a Transformer architecture. Empirical evaluations show that our model achieves 3.69%, 2.21% average precision and recall improvements over the best baseline model with various sparseness. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： MapReduce

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Optimization of Performance Models for Interlayer Meltblown Nonwovens with Random Forest 2

Optimization of Performance Models for Interlayer Meltblown ...

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2nd International Conference on Health big data and Intelligent Healthcare, ICHIH 2023

作者： Wang, Pengjie Li, Ya Liang, Yuheng Zhu, Xiaolin Li, Tongfei Lv, Wei Alibaba Cloud College of Big Data Application Zhuhai College of Science and Technology Zhuhai China YGSOFT INC. Information Technology Application Innovation Platform Department Zhuhai China

ISBN: (纸本)9798350309324

In the context of rapid societal and technological advancements, there has been a pronounced elevation in public health awareness. Among various protective measures, meltblown nonwoven materials have emerged as an indispensable component in protective masks, offering a crucial respiratory barrier for users. This study endeavors to provide an in-depth examination of this pivotal material. By systematically analyzing extensive datasets of meltblown fabric manufacturing processes and taking into account key parameters such as receiving distance, air velocity, and material thickness, this paper, grounded on historical production parameters, harnesses advanced machine learning techniques to meticulously design and optimize the performance control model for interlayer melt-blown nonwoven materials. The central aim of this research is to further enhance the efficacy of this material in health protection applications. © 2023 IEEE.

关键词： Neural networks

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Design and Implementation of an Intelligent Document Extraction and Review System Based on Multimodal Large Language Models 24

Design and Implementation of an Intelligent Document Extract...

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5th International Conference on Computer science and Management technology, ICCSMT 2024

作者： Qiu, Han Sun, Chuanqiang Chen, Hongyun Zou, Baoyu Dong, Zizheng Huazhong University of Science and Technology Hubei Wuhan China Shandong Provincial E-Government Big Data Engineering Technology Research Center Shandong Jinan China Shandong Banner Information Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400709999

This study aims to address the issues of low efficiency and high manual involvement in existing government affairs processes by proposing an automated system based on large model *** system integrates modules such as data collection, natural language processing, business rule engine, process generation, and intelligent *** include using large models to transform unstructured government information into executable rules, automatically generating process models and documents, and implementing intelligent approval and automated verification at key *** results indicate that the system significantly enhances the automation level and execution efficiency of government affairs processes, providing a new solution for the intelligent transformation of government management. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Efficiency

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Traceable Ring Signature Scheme in Internet of Vehicles Based on Lattice Cryptography 21

Traceable Ring Signature Scheme in Internet of Vehicles Base...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liao, Wei Han, Lansheng Chen, Peng 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 Wuhan China

ISBN: (纸本)9798350329223

Lattice cryptography, as a recognized Cryptosystem that can resist quantum computation, has great potential for development. Lattice based signature scheme is currently a research focus. In this paper, the traceable ring signature scheme based on lattice cryptography in the Internet of Vehicles (IoV) environment is studied. Firstly, users' public and private keys are generated on the lattice, which effectively reduces the key size and time cost. Secondly, the scheme also achieves traceability by adding additional information. The scheme satisfies strong unforgeability, traceability, and anonymity. In addition, the traceable ring signature scheme based on lattice cryptography is applied in the IoV environment to effectively protect user privacy. Model communication can not only protect user privacy, but also trace the signer when necessary, achieving a balance between security and privacy. The simulation results show that the average delay of IoV of our scheme is better than that of similar schemes. The research in this paper improves the security of data transmission in the IoV environment, enables it to adapt to the complex and changeable new network environment, and effectively guarantees the information security of vehicles and owners. © 2023 IEEE.

关键词： Cryptography

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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