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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Jing Zheng Big Data and Software Engineering Department Zhejiang Wanli University ZJWL Ningbo China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

With the rapid development of remote sensing technology and deep learning, significant progress has been made in land classification of remote sensing images using semantic segmentation models. However, it still faces some deficiencies and challenges in terms of multi-scale features of images, the computational burden of high resolution images, and long-range dependencies. Therefore, in this paper, I design a semantic segmentation network based on encoder and decoder (Encoder-Decoder) architecture, using ConvNeXt as the encoder, i.e., the backbone network, and Mask2Former as the decoder, with the addition of an auxiliary segmentation module (ASM), and at the same time, an image enhancement pipeline is used to assist the training in the image preprocessing stage. Mask2Former is an architecture with good flexibility and generalization ability, which can be easily used in different visual tasks. At the same time, as a convolutional network without the addition of an attention mechanism, ConvNeXt has excellent feature extraction capabilities. I evaluate my model on the widely used high resolution image datasets Potsdam and Vaihinge and compare the results with the current mainstream semantic segmentation models, and the experimental results show that my model can be optimized for the accurate segmentation of land categories. Specifically, my model has an optimal mPA of $\mathbf{9 3 . 7 9 \%}$ and mIoU of $86.80 \%$ on the Potsdam dataset, and an optimal mPA of $82.32 \%$ and mIoU of $\mathbf{9 0 . 0 5 \%}$ on the Vaihinge dataset, which is both significantly higher than the existing models, and provide important references for land classification tasks and land use.

关键词： Training Image resolution Semantic segmentation Computational modeling Land surface Computer architecture Decoding

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer Science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Assessing the Risk of Currency Digitalization in Kazakhstan

Assessing the Risk of Currency Digitalization in Kazakhstan

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Baimaganbetov, Talgat Sodnomova, Mariia Aitmukhanbetova, Elvira Research and Innovation Center 'Big Data and Blockchain Technologies' Astana It University Astana Kazakhstan Astana It University Astana Kazakhstan Astana It University Department of Software Engineering Astana Kazakhstan

ISBN: (纸本)9798350335040

Every year, new technologies in every industry change the model and structure of processes in human life. Including the economy, which interacts so closely with us. After the advent of blockchain technology, many years have passed to test the viability of digital currency and digital wallets. Many countries are already introducing digital currencies at the state level. Since 2021, a study has begun on the need to introduce a national digital currency. A pilot project with basic functions was also developed and test launches of the digital tenge were carried out. © 2023 IEEE.

关键词： Electronic money

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Weakness of Enhanced DNA and ElGamal Cryptosystem for Secure data Storage and Retrieval in Cloud 13

Weakness of Enhanced DNA and ElGamal Cryptosystem for Secure...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Fan, Yibiao Liu, Chenglian Yang, Yuxia Fang, Jie Software Engineering Institute of Guangzhou Department of Networking Technology Guangzhou510990 China School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuqing250300 China

ISBN: (纸本)9798350319156

DNA cryptography has been developed for nearly thirty years, and there is a lot of research on DNA based encryption and decryption. In 2018, Thangavel and Varlakshmi proposed an article 'Enhanced DNA and ElGamal cryptosystem for secure data storage and retrieval in cloud', they provide two cryptosystem schemes DNA and ElGamal. In this paper the authors would like to analyze security to DNA scheme, this is our research goal. We also prove it may occur insecure when the XOR operation using in special environment. The mathematical Proof and experimental evidence be demonstrated in this article, this is our method. The results point out it is not secure to Thangavel and Varlakshmi's DNA protocol. © 2023 IEEE.

关键词： DNA

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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Prediction and Analysis of Stroke Risk Based on Ensemble Learning 15th

Prediction and Analysis of Stroke Risk Based on Ensemble Lea...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Zuo, Xiuji Guo, Xin Yin, Zilong Tseng, Shih-Pang School of Information Science and Technology Sanda University Shanghai201209 China Department of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Software and Big Data Changzhou College of Information Technology Changzhou213164 China

ISBN: (纸本)9789819994113

With the development of science and technology, the application of data mining in medical field is becoming more and more popular. Machine learning methods also plays an important role in disease prediction. Stroke is characterized by high incidence rate, high disability rate, high mortality rate and high recurrence rate, and it is also likely to cause other kinds of complications. In this paper, each feature in the stroke dataset was analyzed in order to find out the factors affecting stroke and conducts classification and prediction research on whether there is a disease risk. Specifically, the PCA (Principal Component Analysis) algorithm is used to extract the main feature components of data, the SMOTE (Synthetic Minority Oversampling Technique) algorithm is used to adjust imbalanced feature categories. Traditional machine learning classification algorithms, such as decision tree, SVM(support vector machines), and various ensemble learning algorithms are used for the prediction of stroke risk, so as to study the relationship between stroke disease and each feature, and the classification prediction model, so that we can prevent strokes in time and reduce the risk of stroke. Among all the models, Bagging (Bootstrap aggregating) has the best performance with an ROC value of 0.97. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features 13

Evolutionary Multiobjective Feature Selection Assisted by Un...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Duan, Xuan Liu, Songbai Ji, Junkai Li, Lingjie Lin, Qiuzhen Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets. © 2024 IEEE.

关键词： Feature Selection

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One-Handed Gesture Recognition Based on Improved ST-GCN and CA Attention Mechanism 3

One-Handed Gesture Recognition Based on Improved ST-GCN and ...

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3rd International Conference on Automation, Robotics and Computer engineering, ICARCE 2024

作者： Wang, Guixin School of Big Data and Computer GuangDong BaiYun University Department of Software Engineering Guangzhou China

ISBN: (纸本)9798331529505

The article takes one-handed gesture video as the research object, and mainly focuses on the recognition process and method of 'isolated words' and static gesture mixtures. Firstly, the algorithm of extracting the key point positions of frame pictures is proposed by grouping rules of frame sequences of different gestures. This algorithm solves the problem of invalid gesture images affecting the recognition due to the redundancy of gesture images when extracting gestures from traditional gesture videos. Finally, a new network (ST-GCN-CA) is proposed, which improves the traditional spatio-temporal graph convolutional network (ST-GCN) and adds the CA attention mechanism. The method and network in this paper are applied to the Chinese isolated word sign language dataset (CSL-500) single-handed video, and the experimental results show that the recognition accuracies in the isolated word gesture video and the hybrid video reach 94.3% and 94.1%, respectively, which improves the recognition accuracies in comparison with other methods in the public domain. © 2024 IEEE.

关键词： Gesture recognition

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Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model

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Computers, Materials & Continua 2023年第9期76卷 3605-3622页

作者： Abdulgbar A.R.Farea Gehad Abdullah Amran Ebraheem Farea Amerah Alabrah Ahmed A.Abdulraheem Muhammad Mursil Mohammed A.A.Al-qaness School of Big Data&Software Engineering Chongqing UniversityChongqing401331China Department of Management Science Engineering Dalian University of TechnologyDalian116024China Software College Northeastern UniversityShenyang110169China Department of Information Systems College of Computer and Information ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Management Science and Engineering South China University of TechnologyGuangzhou510641China Department of Computer Engineering and Mathematics University of Rovira i VirgiliTarragonaSpain College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China

E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely *** web developers may have varying levels of understanding when it comes to securing an online *** Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these *** published articles focused on these attacks’binary *** article described a novel deep-learning approach for detecting SQL injection and XSS *** datasets for SQL injection and XSS payloads are combined into a single *** dataset is labeledmanually into three labels,each representing a kind of *** work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a *** model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload *** payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and *** outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious ***’s high performance was demonstrated by its accuracy of 99.26%.

关键词： Web security SQL injection XSS deep learning RNN LSTM BiLSTM

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One-Handed Gesture Recognition Based on Improved ST-GCN and CA Attention Mechanism

One-Handed Gesture Recognition Based on Improved ST-GCN and ...

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Automation, Robotics and Computer engineering (ICARCE), International Conference on

作者： Guixin Wang Department of Software Engineering School of Big Data and Computer GuangDong BaiYun University Guangzhou China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

The article takes one-handed gesture video as the research object, and mainly focuses on the recognition process and method of “isolated words” and static gesture mixtures. Firstly, the algorithm of extracting the key point positions of frame pictures is proposed by grouping rules of frame sequences of different gestures. This algorithm solves the problem of invalid gesture images affecting the recognition due to the redundancy of gesture images when extracting gestures from traditional gesture videos. Finally, a new network (ST-GCN-CA) is proposed, which improves the traditional spatio-temporal graph convolutional network (ST-GCN) and adds the CA attention mechanism. The method and network in this paper are applied to the Chinese isolated word sign language dataset (CSL-500) single-handed video, and the experimental results show that the recognition accuracies in the isolated word gesture video and the hybrid video reach 94.3% and 94.1%, respectively, which improves the recognition accuracies in comparison with other methods in the public domain.

关键词： Sign language Attention mechanisms Accuracy Image recognition Automation Graph convolutional networks Convolution Redundancy Robots

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