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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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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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Method for Wheat Ear Detection Based on CSR-DETR 6

Method for Wheat Ear Detection Based on CSR-DETR

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Zhao, Zhengshuai Feng, Wei Zou, Xiaobing Li, Qiang School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Mathematics and Big Data Chongqing University of Education Chongqing China School of Artificial Intelligence Chongqing University of Education Chongqing China

ISBN: (纸本)9798350353594

Precise detection of wheat ears is essential for agricultural yield assessment and breeding research. Current object detection technologies still face issues with insufficient accuracy and low detection efficiency when dealing with small targets like wheat ears. To address these issues, this study proposes an improved RT-DETR: CSR-DETR. CSR-DETR integrates CBAM and ScConv into the main network to enhance feature extraction capabilities, and then replaces the Conv module in CCFM with RFCAConv to further improve feature fusion effects. Finally, CSR-DETR is compared with RT-DETR on two wheat ear datasets of varying sizes. The results show that the improved model achieved an mAP of 83.2 % on a dataset of 2690 wheat ear images, an increase of 1.4 percentage points from the original model;and an mAP of 92.4% on a dataset of 6500 wheat ear images, an increase of 1.2 percentage points from the original model. © 2024 IEEE.

关键词： Object detection

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Discovering Personalized Characteristic Communities in Attributed Graphs 40

Discovering Personalized Characteristic Communities in Attri...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Niu, Yudong Li, Yuchen Karras, Panagiotis Wang, Yanhao Li, Zhao School of Computing and Information Systems Singapore Management University Singapore University of Copenhagen Department of Computer Science Copenhagen Denmark School of Data Science and Engineering East China Normal University Shanghai China Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350317152

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of hierarchically structured communities. We first propose a novel compressed COD evaluation approach to accelerate the influence estimation by eliminating redundant computations for overlapping communities. Then, we further devise a local hierarchical reclustering method to alleviate the skewness of hierarchical communities generated by global clustering for a specific query attribute. Extensive experiments confirm the effectiveness and efficiency of our solutions to COD: they find characteristic communities better than existing community search methods by several quality measures and achieve up to 25 x speedups against well-crafted baselines. © 2024 IEEE.

关键词： attributed graph characteristic community community hierarchy influence

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model 8

Sentiment Analysis for TikTok Review Using VADER Sentiment a...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80. © 2023 The Authors. Published by Elsevier B.V.

关键词： Google Play Store Sentiment Analysis SVM TikTok VADER Sentiment

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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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2024 IEEE International Conference on data and Software Engineering, ICoDSE 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Kurnianingrum, Dian Ratnapuri, Chyntia Ika Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Entrepreneurship Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331506407

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus. © 2024 IEEE.

关键词： Decoding

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Bidirectional Authentication Protocol for RFID Systems Based on Mms

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International Journal of Network Security 2025年第1期27卷 72-80页

作者： He, Xiao-ping Liu, Dao-wei Wu, Yang School of Data and Computer Science Guangdong Peizheng College Guangzhou510800 China Engineering College Guangzhou College of Technology and Business Guangzhou510006 China College of Information and Communication National University of Defense Technology Wuhan430010 China

data exchange between electronic tags and the reader-writer in RFID systems is based on the wireless channel. Due to the inherent openness of the wireless channel, the transmitted data information is easy to be eavesdropped by other illegal users. In order to ensure data security, an ultra-lightweight mutual authentication protocol is proposed to solve this problem. In this paper, we independently design a new encryption algorithm, namely the product module synthesis operation. This algorithm is implemented based on bit operations, which can greatly reduce the total computation burden. Using the inherent Hamming weight and anti-Hamming weight information of encrypted parameters, different synthesis operations are carried out based on their product size relationship. Through the protocol analysis, it is shown that the proposed protocol has high security performance and meets the current computational requirements of low-cost RFID systems. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Radio frequency identification (RFID)

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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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Federated Learning for Edge Heterogeneous Object Detection Algorithm 18th

Federated Learning for Edge Heterogeneous Object Detection ...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Hu, Yanxin Liu, Gang Cheng, Chao School of Computer Science and Engineering Changchun University of Technology Changchun130102 China Jilin Province Data Service Industry Public Technology Research Centre Changchun130102 China

ISBN: (纸本)9783031714665

The combination of federated learning and object detection techniques allows for the use of extensive datasets. This approach creates target detection models that are robust and adaptable. It also upholds strict privacy and security protocols. However, the computational resource variance among edge nodes poses significant constraints on the scalability of detection algorithms, thereby impinging on their accuracy and operational efficiency. This discrepancy compels clients, who might otherwise engage complex models, to resort to more simplified algorithms. To navigate this challenge, we introduce a federated learning framework enriched with a knowledge distillation strategy, specifically engineered for the object detection realm. This framework intelligently selects the largest model from the pool of client-contributed models and facilitates model upload and dissemination through a knowledge transfer mechanism orchestrated at the server side. Extensive experimental validations underscore the method’s proficiency in addressing the heterogeneity of detection algorithms within a varied environment, showcasing its viability for real-world application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Faster Local Solvers for Graph Diffusion Equations

arXiv

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arXiv 2024年

作者： Bai, Jiahe Zhou, Baojian Yang, Deqing Xiao, Yanghua The School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Efficient computation of graph diffusion equations (GDEs), such as Personalized PageRank, Katz centrality, and the Heat kernel, is crucial for clustering, training neural networks, and many other graph-related problems. Standard iterative methods require accessing the whole graph per iteration, making them time-consuming for large-scale graphs. While existing local solvers approximate diffusion vectors through heuristic local updates, they often operate sequentially and are typically designed for specific diffusion types, limiting their applicability. Given that diffusion vectors are highly localizable, as measured by the participation ratio, this paper introduces a novel framework for approximately solving GDEs using a local diffusion process. This framework reveals the suboptimality of existing local solvers. Furthermore, our approach effectively localizes standard iterative solvers by designing simple and provably sublinear time algorithms. These new local solvers are highly parallelizable, making them well-suited for implementation on GPUs. We demonstrate the effectiveness of our framework in quickly obtaining approximate diffusion vectors, achieving up to a hundred-fold speed improvement, and its applicability to large-scale dynamic graphs. Our framework could also facilitate more efficient local message-passing mechanisms for GNNs. © 2024, CC BY-NC-SA.

关键词： Iterative methods

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