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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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Evolutionary Multilabel Adversarial Examples: An Effective Black-Box Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 562-572页

作者： Kong, Linghao Luo, Wenjian Zhang, Hongwei Liu, Yang Shi, Yuhui Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation School of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attack. Minor malicious modifications of examples will lead to the DNN misclassification. Such maliciously modified examples are called adversarial examples. So far, the work on adversarial examples is mainly focused on multiclass classification tasks;there is less work in the field of multilabel classification. In this article, for the first time, a differential evolution (DE) algorithm that can effectively generate multilabel adversarial examples is proposed, which is called MLAE-DE. Different from traditional DE, we designed a complementary mutation operator for MLAE-DE, which can improve attack performance and reduce the number of fitness evaluations. As a black-box attack, MLAE-DE does not need to access model parameters and only uses model outputs to generate adversarial examples. Experiments on two typical multilabel classification models and three typical datasets under the black-box settings are conducted in this article. Experimental results demonstrate that, comparing with the existing black-box attack algorithms for multilabel classification models, the attack success rate of our proposed algorithm is much better. © 2020 IEEE.

关键词： Deep neural networks

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A Novel Approach for Photo-Realistic High Quality Sign Language Video Synthesis 3

A Novel Approach for Photo-Realistic High Quality Sign Langu...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Pravine Mukesh, R. Natarajan, B. Elakkiya, R. Vaidhya, G.K. Murali, P. Geetha, K. Amrita Vishwa Vidyapeetham Chennai601103 India School of Computer Science and Engineering Vit University Tamilnadu Chennai India Birla Institute of Technology and Science Department of Computer Science Dubai United Arab Emirates Srm Institute of Science and Technology Vadapalani Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Tamilnadu Chennai India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350361186

This research offers a brand-new framework for producing high-quality, photo-realistic sign language videos using sign language corpora. The framework consists of three main components such as Neural Machine Translation (NMT) using transformer, an open pose estimation approach, and a Generative Adversarial Network (GAN) with VGG-19 classifier. The NMT transformer translates the sign language corpus into a sequence of sign glosses, pose estimation model performs mapping of equivalent signer images and further assists to generate sign images. The GAN with VGG-19 classifier then generates high- quality sign language videos from the sign language images. The proposed method performs at the cutting edge in terms of evaluation on three benchmark dataset using metrics such as PSNR, SSIM, FID, and Inception Score. In addition, the framework offers better translation outcomes with less processing overhead and produces high-quality, photo-realistic sign language videos without the need for animation or avatar techniques. The proposed framework has the potential to offer the uninterrupted communication medium between common people with deaf-mute society. The evaluation scores highlights the improved perfor- mance of the proposed model compared with earlier approaches in terms of visual quality. © 2024 IEEE.

关键词： Generative adversarial networks

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Adaptive Addax-based Deep Fuzzy Clustering for Face Mask Detection

Adaptive Addax-based Deep Fuzzy Clustering for Face Mask Det...

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International Conference on Green Energy, Computing and Intelligent technology 2024, GEn-CITy 2024

作者： Anuradha, B. Gupta, Sudhanshu Kumar, Akhil Bennett University Greater Noida India CMR College of Engineering & Technology Hyderabad India School of Computer Science Engineering and Technology Bennett University India

ISBN: (纸本)9781837242689

Respiratory illness Diseases transmitted by droplets highlighted the necessity of face masks. The traditional methods to ensure mask usage faced challenges like the environment, partial masks, low-resolution, real-time processing, and human error, which hinder accuracy and efficiency dynamically. To handle these challenges, efficient automated mask detection systems are required in real-time across various environments. This work develops such an innovative model named Adaptive Addax Optimization Algorithm enabled Deep Fuzzy Clustering (Adaptive AOA_DFC). In this method, preprocessing is done using a Gaussian filter then undergoes Region of Interest (RoI) extraction to isolate the face region. Subsequently, the mask detection is performed using Deep Fuzzy Clustering (DFC) further trained through the proposed Adaptive AOA method. This algorithm is obtained by integrating the Addax Optimization Algorithm (AOA) with an adaptive mechanism to enhance detection accuracy. The performance evaluation metrics are Accuracy, Dice coefficient, and Jaccard coefficient, which yield the values of 92.39%, 0.950, and 0.951, respectively. © The Institution of engineering & technology 2024.

关键词： Clustering algorithms

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Alzheimer's Disease Prediction from MRI image using Convolutional Deep Neural Network

Alzheimer's Disease Prediction from MRI image using Convolut...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Panda, Sanghamitra Sahu, Rekha Dash, Satya Ranjan Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Bhubaneswar India Silicon University Department of Computer Science and Engineering Odisha Bhubaneswar India KIIT Deemed to Be University School of Computer Application Odisha Bhubaneswar India

ISBN: (纸本)9798350369175

Alzheimer's disease is one sort of neurological disorder which affects the daily activities and it is incurable. But, if it can be detected early, then it may be prevented or made to progress slowly. For detection, deep learning has played a great role now a days. In this paper, we have taken 6400 MRI images of both dementia and non-dementia subjects. The dataset is spilt into ${8 0 \%}$ for training a model and ${2 0 \%}$ for validating and accessing the achievement of the trained model. We have recommended a two-dimensional convolutional neural network architecture in order to apply on the MRI dataset to generate a model for classifying and predicting Alzheimer's disease patients. The model generated using the proposed two-dimensional architecture has given approximately $98 \%$ classification accuracy. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. Again, at training procedure, model loss and accuracy also depicted according to epoch times. The researches already performed on prediction of Alzheimer's disease has given poor performance of ${9 1 \%}$ classification accuracy in comparison to our model. © 2024 IEEE.

关键词： Convolutional neural networks

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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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Hierarchical multi-granularity classification based on bidirectional knowledge transfer

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Multimedia Systems 2024年第4期30卷 1-12页

作者： Jiang, Juan Yang, Jingmin Zhang, Wenjie Zhang, Hongbin School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Fujian Zhangzhou363000 China Department of Electronic Engineering National Taipei University of Technology Taipei China

Hierarchical multi-granularity classification is the task of classifying objects according to multiple levels or granularities. The class hierarchy is vital side information for hierarchical multi-granularity classification. The existing hierarchical multi-granularity classification research utilizes class hierarchy to classify from coarse to fine or fine to coarse. Although these methods are effective in many cases, there are still two issues: (1) multi-task learning for hierarchical multi-granularity classification leads to decreased classification performance;(2) class hierarchy transfer learning is prone to error propagation. In this paper, we propose a bidirectional knowledge transfer model framework to address these issues. Firstly, we improve classification performance through data augmentation. Specifically, by learning the similarity between the original image and the enhanced image, better learn discriminative features, which is beneficial for subsequent classification. Secondly, using class hierarchy trees, we propose reverse hierarchical knowledge transfer to correct some errors in forward hierarchical propagation and improve hierarchical consistency. In addition, we also construct a hierarchical network that adds features from coarse-grained levels to fine-grained levels. The experimental results on six datasets with different class hierarchies demonstrate the effectiveness and superiority of the proposed model. Especially on the CUB-200-2011 and Cifar-100 datasets, our model improved classification accuracy by 3.61% and 4.17% compared to the suboptimal model. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Classification (of information)

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SmokerViT: A Transformer-Based Method for Smoker Recognition

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computers, Materials & Continua 2023年第10期77卷 403-424页

作者： Ali Khan Somaiya Khan Bilal Hassan Rizwan Khan Zhonglong Zheng College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua321004China School of Electronics Engineering Beijing University of Posts and TelecommunicationsBeijing100876China Department of Electrical Engineering and Computer Science Khalifa University of Science and TechnologyAbu Dhabi127788United Arab Emirates Key Laboratory of Intelligent Education of Zhejiang Province Zhejiang Normal UniversityJinhua321004China

Smoking has an economic and environmental impact on society due to the toxic substances it *** Neural Networks(CNNs)need help describing low-level features and can miss important ***,accurate smoker detection is vital with minimum false *** answer the issue,the researchers of this paper have turned to a self-attention mechanism inspired by the ViT,which has displayed state-of-the-art performance in the classification *** effectively enforce the smoking prohibition in non-smoking locations,this work presents a Vision Transformer-inspired model called SmokerViT for detecting ***,this research utilizes a locally curated dataset of 1120 images evenly distributed among the two classes(Smoking and NotSmoking).Further,this research performs augmentations on the smoker detection dataset to have many images with various representations to overcome the dataset size *** convolutional operations used in most existing works,the proposed SmokerViT model employs a self-attention mechanism in the Transformer block,making it suitable for the smoker classification ***,this work integrates the multi-layer perceptron head block in the SmokerViT model,which contains dense layers with rectified linear activation and linear kernel regularizer with L2 for the recognition *** work presents an exhaustive analysis to prove the efficiency of the proposed SmokerViT *** performance of the proposed SmokerViT performance is evaluated and compared with the existing methods,where it achieves an overall classification accuracy of 97.77%,with 98.21%recall and 97.35%precision,outperforming the state-of-the-art deep learning models,including convolutional neural networks(CNNs)and other vision transformer-based models.

关键词： Smoker recognition SmokerViT deep learning transformer for vision

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A survey on event connections and their discovery for cloud service

A survey on event connections and their discovery for cloud ...

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2023 International Conference on Electronic Information engineering and Data Processing, EIEDP 2023

作者： Zhang, Zhen Xia, Chunhe Li, Shuyu Liu, Zhiying Huang, Mingzhi Huang, Chengling Li, Xiaojian School of Computer Science & Engineering Guangxi Normal University Guilin China Beijing Key Laboratory of Network Technology School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781510666160

Connection is the effect between events. It is an important basis for judging of violations and source-trace of failure in cloud service accountability. This paper presents a survey on the event connections including the features, method of discovery and correction. Most of recent research on event connections in cloud services relying on manually labeling and correcting to event connections, and a few of them have achieved automatically labeling of event connections. Finally, we give out a taxonomy of the recent research, some problems in the method of discovery and correction, and an approach in the paper. © 2023 SPIE.

关键词： Distributed database systems

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