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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhao, Hongjian Tao, Yunting Kong, Fanyu Zhang, Guoyan Zhang, Hanlin Yu, Jia Shandong University School of Software Jinan China Binzhou Polytechnic College of Information Engineering Binzhou China Shandong University Shandong Sansec Information Technology Co. Ltd School of Software Jinan China Shandong University School of Cyber Science and Technology Qingdao China Qingdao University College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798331509712

Lattice-based Post-Quantum Cryptography (PQC) can effectively resist the quantum threat to blockchain's underlying cryptographic algorithms. Blockchain node decryption is one of the most commonly used cryptographic computations in blockchain systems, and polynomial multiplication, a time-consuming operation for decryption, is one of the factors limiting blockchain efficiency. This paper proposes a novel distributed computing algorithm for polynomial multiplication, applicable in blockchain decryption. By splitting polynomials into lower-degree terms and delegating tasks to distributed nodes, our approach reduces computation time. A novel verification strategy based on the Karatsuba algorithm ensures result accuracy. The experimental results demonstrate that our proposed scheme improves the execution efficiency of NTT and INTT operations by approximately 47.8% and 52.4%, and reduces Kyber decryption time by up to 23.5%. © 2024 IEEE.

关键词： Encryption algorithms

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Aspect Level Songs Rating Based Upon Reviews in English

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computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-Rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

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computer Modeling in engineering & sciences 2023年第4期135卷 91-108页

作者： Zhichen Hu Huali Ren Jielin Jiang Yan Cui Xiumian Hu Xiaolong Xu School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Institution of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou515021China School of Earth Sciences and Engineering Nanjing UniversityNanjing210023China College of Mathematics and Information Science Nanjing Normal University of Special EducationNanjing210023China

An obviously challenging problem in named entity recognition is the construction of the kind data set of *** some research has been conducted on entity database construction,the majority of them are directed at Wikipedia or the minority at structured entities such as people,locations and organizational nouns in the *** paper focuses on the identification of scientific entities in carbonate platforms in English literature,using the example of carbonate platforms in ***,based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts,this paper designs a literature content extraction method that allows dealing with complex text ***,based on the literature extraction content,we formalize the entity extraction task(lexicon and lexical-based entity extraction)for entity ***,for testing the accuracy of entity extraction,three currently popular recognition methods are chosen to perform entity detection in this *** show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition *** study presents a pilot study of entity extraction,which involves the use of a complex structure and specialized literature on carbonate platforms in English.

关键词： Named entity recognition carbonate platform corpus entity extraction english literature detection

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Low Resolution Face Image Recognition Based on Consistent Discriminant Correlation Analysis with Weight Correction 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Lin, Xiaoan Li, Meihua Pei, Jihong Zhao, Yang College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Consistent Discriminant Correlation Analysis (CDCA) is an effective algorithm for dual-view learning. However, the importance of information from different samples and features may vary. In CDCA, the sample features from both views are equally weighted during training, which may not fully utilize the information contained in certain sample features. This can negatively impact the decision-making and predictive capabilities of the model. Additionally, CDCA is only applicable to dual-view data and cannot effectively utilize multi-view data. To address these issues, this paper extends CDCA to multi-view scenarios and proposes Consistent Discriminant Correlation Analysis with Weight Correction (CDCA-WC). The feature weighting approach used in this paper involves calculating the average weight of each feature using the ReliefF algorithm. The sample weighting method employed is based on a weighted loss function, where a larger weight indicates a stronger classification ability for the corresponding feature or sample. During the training process, CDCA-WC is used to obtain the feature subspace mappings for the training samples from multiple views. The data’s feature and sample dimensions are then adjusted based on their respective weights, and the adjusted data is used for a second round of training. During testing, the similarity between samples is measured using a KNN classifier. Experimental results on three publicly available face recognition datasets demonstrate that CDCA-WC can effectively improve the accuracy of low-resolution face image recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Face recognition

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Cloud Security Service for Identifying Unauthorized User Behaviour

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computers, Materials & Continua 2022年第2期70卷 2581-2600页

作者： D.Stalin David Mamoona Anam Chandraprabha Kaliappan S.Arun Mozhi Selvi Dilip Kumar Sharma Pankaj Dadheech Sudhakar Sengan Department of Computer Science and Engineering IFET College of EngineeringVillupuram605108Tamil NaduIndia Department of Computer Sciences and Software Engineering Faculty of Basic and Applied SciencesInternational Islamic UniversityIslamabadPakistan Department of Information Technology Bannari Amman Institute of TechnologySathyamangalam638401Tamil NaduIndia Department of Computer Science Engineering DMI St.John the Baptist UniversityMangochiLilongweMalawi Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement and Gramothan(SKIT)Jaipur302017RajasthanIndia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Recently,an innovative trend like cloud computing has progressed quickly in *** a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service *** cloud computing environment extracts the execution details of services and systems from end-users and ***,through the system’s virtualization accomplished using resource pooling,cloud computing resources become more *** attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this *** the help of multi-agents,we attempt to represent Open-Identity(ID)design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the *** research aims to determine how authentication and authorization services were provided in an agreeable and preventive *** on attack-oriented threat model security,the evaluation *** considering security for both authentication and authorization systems,possible security threats are analyzed by the proposed security systems.

关键词： Cloud computing user behaviour access control security model

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Out-of-Distribution Detection with Prototypical Outlier Proxy 39

Out-of-Distribution Detection with Prototypical Outlier Prox...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gong, Mingrong Chen, Chaoqi Sun, Qingqiang Wang, Yue Huang, Hui College of Computer Science and Software Engineering Shenzhen University China School of Engineering Great Bay University China Department of Computer Science University College London United Kingdom

ISBN: (纸本)157735897X

Out-of-distribution (OOD) detection is a crucial task for deploying deep learning models in the wild. One of the major challenges is that well-trained deep models tend to perform over-confidence on unseen test data. Recent research attempts to leverage real or synthetic outliers to mitigate the issue, which may significantly increase computational costs and be biased toward specific outlier characteristics. In this paper, we propose a simple yet effective framework, Prototypical Outlier Proxy (POP), which introduces virtual OOD prototypes to reshape the decision boundaries between ID and OOD data. Specifically, we transform the learnable classifier into a fixed one and augment it with a set of prototypical weight vectors. Then, we introduce a hierarchical similarity boundary loss to impose adaptive penalties depending on the degree of misclassification. Extensive experiments across various benchmarks demonstrate the effectiveness of POP. Notably, POP achieves average FPR95 reductions of 7.70%, 6.30%, and 5.42% over the second-best methods on CIFAR-10, CIFAR-100, and ImageNet-200, respectively. Moreover, compared to the recent method NPOS, which relies on outlier synthesis, POP trains 7.2 times faster and performs inference 19.5 times faster. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Palette-based Color Transfer between Images

arXiv

引用

arXiv 2024年

作者： Lv, Chenlei Zhang, Dan College of Computer Science and Software Engineering Shenzhen University China School of Computer Science Qinghai Normal University China

As an important subtopic of image enhancement, color transfer aims to enhance the color scheme of a source image according to a reference one while preserving the semantic context. To implement color transfer, the palette-based color mapping framework was proposed. It is a classical solution that does not depend on complex semantic analysis to generate a new color scheme. However, the framework usually requires manual settings, blackucing its practicality. The quality of traditional palette generation depends on the degree of color separation. In this paper, we propose a new palette-based color transfer method that can automatically generate a new color scheme. With a redesigned palette-based clustering method, pixels can be classified into different segments according to color distribution with better applicability. By combining deep learning-based image segmentation and a new color mapping strategy, color transfer can be implemented on foreground and background parts independently while maintaining semantic consistency. The experimental results indicate that our method exhibits significant advantages over peer methods in terms of natural realism, color consistency, generality, and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Regularized Loose Coupled Deep Non-negative Basis Matrix Factorization for Low-Resolution Face Image Recognition 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Li, Jianhua Wang, Jinxin Pei, Jihong Zhao, Yang College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Low-resolution face image recognition is a research focus in computer vision and pattern recognition. In this paper, the potential relationship between matched high-resolution face and low-resolution face images is considered to propose the regularized loose coupled deep non-negative basis matrix factorization (RLCDNBMF) architecture, which uses regularized deep non-negative basis matrix factorization for high-resolution images and couples its coefficient matrix with the coefficient matrix obtained from low-resolution non-negative factorization, so that the discriminative information of high-resolution face images can be fully utilized to guide the non-negative matrix factorization of low-resolution face images. Comparative experiments are conducted on three mainstream face databases, and the experimental results show that the method can improve the accuracy of low-resolution face recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Non-negative matrix factorization

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Coupled Non-negative Matrix Factorization with Deep Part-Based Feature Learning for Low-Resolution Image Recognition 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Wang, Jinxin Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

The existing low-resolution (LR) image recognition methods based on coupled relationship learning of high- and low-resolution images usually take similar feature extraction steps for both high- and low-resolution images. However, the high- and low-resolution features thus obtained may differ in terms of information content and information scale. This leads to a robust coupled relationship between the obtained high- and low-resolution images feature is difficult to be learned. Therefore, a coupled non-negative matrix factorization with a deep part-based feature learning method is proposed for the LR image recognition task. This method is divided into two main steps. Firstly, the depth non-negative matrix factorization method by graph convolution is used to obtain a robust depth underlying basis images of HR images for feature representation. Then the coupled relationship between the depth basis images feature of the HR images and the LR basis images feature is learned. Learning More Robust LR Basis images features using depth underlying features of HR images to assist LR images. Improve the expression of LR basis images of features. Experimental results show that the method can provide higher recognition performance. It outperforms existing image recognition methods in image recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Image recognition

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Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan Department of Electrical Engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

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