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Fractional-Order Image Moments and Applications 1

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Xu, Liyun Zhang, Min Institute of Big Data Science and Industry School of Computer and Information Technology Shanxi University Taiyuan030006 China

ISBN: (数字)9783031533112

ISBN: (纸本)9783031533105

Image moments, as a global feature descriptor for images, have become a powerful tool for pattern recognition and image analysis. Most of the currently existing fractional-order image moments are polynomial-based. Three novel moments, namely Zernike fractional Fourier moment, Merlin fractional Fourier moment, and exponential fractional Fourier moment, combined with classical moments and fractional Fourier transform, are introduced based on angular functions. Additionally, we propose a zero watermarking algorithm based on these moments. We present robustness and comparative analysis experiments, including the effects of noise, filtering, rotation, and scaling. The experimental results demonstrate that the zero watermarking algorithm utilizing fractional-order moments can effectively withstand image processing attacks and geometric attacks. In both cases, their performance surpasses that of their corresponding integer-order image moments. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Security of data

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Network Diffusion Framework to Simulate Spreading Processes in Complex Networks

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big data Mining and Analytics 2024年第3期7卷 637-654页

作者： MichałCzuba Mateusz Nurek Damian Serwata Yu-Xuan Qiu Mingshan Jia Katarzyna Musial Radosław Michalski Piotr Bródka Department of Artificial Intelligence Wrocław University of Science and TechnologyWrocław 50-370Poland Data Science Institute University of Technology SydneyUltimoNSW 2007Australia

With the advancement of computational network science,its research scope has significantly expanded beyond static graphs to encompass more complex *** introduction of streaming,temporal,multilayer,and hypernetwork approaches has brought new possibilities and imposed additional *** instance,by utilising these advancements,one can model structures such as social networks in a much more refined manner,which is particularly relevant in simulations of the spreading ***,the pace of advancement is often too rapid for existing computational packages to keep up with the functionality *** results in a significant proliferation of tools used by researchers and,consequently,a lack of a universally accepted technological stack that would standardise experimental methods(as seen,e.g.,in machine learning).This article addresses that issue by presenting an extended version of the Network Diffusion ***,a survey of the existing approaches and toolkits for simulating spreading phenomena is shown,and then,an overview of the framework ***,we report four case studies conducted with the package to demonstrate its usefulness:the impact of sanitary measures on the spread of COVID-19,the comparison of information diffusion on two temporal network models,and the effectiveness of seed selection methods in the task of influence maximisation in multilayer *** conclude the paper with a critical assessment of the library and the outline of still awaiting challenges to standardise research environments in computational network science.

关键词： computational framework seed selection influence maximisation spreading models temporal networks multilayer networks network science network control

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Seismology and Climatology: A Study of Seismological Impacts of Climate Change in Indonesia

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Atmospheric and Climate sciences 2024年第2期14卷 210-220页

作者： Lukundo Mtambo Xingxiang Tao The Department of Big Data Science Zhejiang University of Science and Technology Hangzhou China

Climate change has been a matter of discourse for the last several decades. Much research has been conducted regarding the causes and impacts of climate change around the world. The current research contributes to the knowledge of the influence of climate change on our environment, with emphasis on earthquake occurrences in the region of Indonesia. Using global temperature anomaly as a measure of climate change, and earthquake data in Indonesia for the period 1900-2022, the paper seeks to find a relationship (if any) between the two variables. Statistical methods used include normal distribution analysis, linear regression and correlation test. The results show peculiar patterns in the progression of earthquake occurrences as well as global temperature anomaly occurring in the same time periods. The findings also indicated that the magnitudes of earthquakes remained unaffected by global temperature anomalies over the years. Nonetheless, there appears to be a potential correlation between temperature anomalies and the frequency of earthquake occurrences. As per the results, an increase in temperature anomaly is associated with a higher frequency of earthquakes.

关键词： Earthquakes Climatology Climate Change Seismology Correlation Linear Regression Indonesia

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Real-time distance field acceleration based free-viewpoint video synthesis for large sports fields

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Computational Visual Media 2024年第2期10卷 331-353页

作者： Yanran Dai Jing Li Yuqi Jiang Haidong Qin Bang Liang Shikuan Hong Haozhe Pan Tao Yang School of Telecommunications Engineering Xidian UniversityXi’an 710071China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology SAIIPthe School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710129China

Free-viewpoint video allows the user to view objects from any virtual perspective,creating an immersive visual *** technology enhances the interactivity and freedom of multimedia ***,many free-viewpoint video synthesis methods hardly satisfy the requirement to work in real time with high precision,particularly for sports fields having large areas and numerous moving *** address these issues,we propose a freeviewpoint video synthesis method based on distance field *** central idea is to fuse multiview distance field information and use it to adjust the search step size *** step size search is used in two ways:for fast estimation of multiobject three-dimensional surfaces,and synthetic view rendering based on global occlusion *** have implemented our ideas using parallel computing for interactive display,using CUDA and OpenGL frameworks,and have used real-world and simulated experimental datasets for *** results show that the proposed method can render free-viewpoint videos with multiple objects on large sports fields at 25 ***,the visual quality of our synthetic novel viewpoint images exceeds that of state-of-the-art neural-rendering-based methods.

关键词： free-viewpoint video view synthesis camera array distance field sports video

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Credit card fraud detection: an evaluation of SMOTE resampling and machine learning model performance

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International Journal of Business Intelligence and data Mining 2023年第1期23卷 1-13页

作者： Alshameri, Faleh Xia, Ran Department of Information Technology Data Science and Cybersecurity School of Business and Technology Marymount University United States

Credit card fraud has been a noted security issue that requires financial organisations to continuously improve their fraud detection system. In most cases, a credit transaction dataset is expected to have a significantly larger number of normal transactions than fraud transactions. Therefore, the accuracy of a fraud detection system depends on building a model that can adequately handle such an imbalanced dataset. The purpose of this paper is to explore one of the techniques of dataset rebalancing, the synthetic minority oversampling technique (SMOTE). To evaluate the effects of this technique on model training, we selected four basic classification algorithms, complement naïve Bayes (CNB), K-nearest neighbour (KNN), random forest and support vector machine (SVM). We then compared the performances of the four models trained on the rebalanced and original dataset using the area under precision-recall curve (AUPRC) plots. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Support vector machines

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Machine Learning Based Predictive Analysis of Consumer Behavior in Webcasting Bandwagons 24

Machine Learning Based Predictive Analysis of Consumer Behav...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Zhu, Lixing Yang, Xue Jiang, Xue Chen, Shufen Chen, Jie Xiao, Yunqing Guangdong University of Science and Technology Dongguan China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9798400717147

This paper addresses the problem of predicting consumer behavior in live webcast banding and explores the application of machine learning algorithms in this field and their effectiveness. The study mainly focuses on the use of two machine learning models, Decision Tree and Random Forest, to analyze and predict consumer purchase behavior in live streaming bandwagon campaigns. By simulating a dataset containing 15,000 records, this study conducted detailed training and testing of the models to evaluate their performance on key metrics such as accuracy, recall, precision and F1 score. The experimental results show that the Random Forest model outperforms the Decision Tree model on all the evaluated metrics, especially on accuracy and F1 score. This finding highlights the advantages of integrated learning methods when dealing with complex datasets. In addition, the feature selection process revealed that live streaming viewing duration, number of interactions, and anchor ratings are key factors influencing consumers' purchasing decisions. The findings of this study provide valuable insights for the optimization of marketing strategies for webcast bandwagons. By understanding the key factors influencing consumer purchasing behavior, e-commerce platforms and companies can more effectively position their target markets and optimize their advertising and product promotion strategies. © 2024 ACM.

关键词： Decision trees

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Range Proof-Based Noise Filtering Mechanism for loT Differential Privacy

Range Proof-Based Noise Filtering Mechanism for loT Differen...

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2024 IEEE Cyber science and technology Congress, CyberSciTech 2024

作者： Wei, Jianqi Chen, Yuling Luo, Yun Li, Zaidong State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (纸本)9798331532093

With the rapid development of Internet of Things (loT) technology, the vast amount of data generated by its devices has raised widespread concern for user privacy pro-tection. Differential Privacy, as a stringent privacy protection measure, plays a significant role in safeguarding individual data. However, implementing Differential Privacy in the loT environment faces challenges in ensuring data utility. This paper focuses on proposing a Verifiable Differential Privacy (VDP) scheme based on zero-knowledge proofs, under the premise of ensuring data utility. The scheme requires data publishing entities to provide publicly verifiable proofs to confirm the reliability of the dataset and the effectiveness of privacy protection. By introducing a commitment mechanism and range proofs, our model not only protects the interests of data users but also enhances trust in the enforcement of privacy protection measures. The experimental results show that the scheme can effectively filter out unreasonable Differential Privacy noise, ensuring the privacy and reliability of data, offering a new approach to data privacy protection in the loT field. © 2024 IEEE.

关键词： Differential privacy

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Green technology innovation guiding policies under the synergy of government and financial institutions

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Sustainable Operations and Computers 2024年第1期5卷 269-277页

作者： Ma, Yanhong Zhao, Hui School of Economics and Management Beijing Information Science and Technology University Beijing100192 China Beijing Key Lab of Green Development Decision Based on Big Data Beijing100192 China

Guiding enterprises towards green technology innovation (GTI) is a pivotal strategy for mitigating pollution and energy wastage, thereby facilitating the transition towards a green and low-carbon economy. In order to solve the single irrationality of the existing guidance policy and improve its implementation effect, parameters of environmental regulation and green credit policy are hereby introduced to construct a tripartite evolutionary game model of the government, financial institutions, and enterprises. The evolutionary stability of the government and financial institutions in guiding enterprises' GTI is analyzed. Simulation analysis method is used to visualize the evolution path of the game system under different policy options. The study results show that increasing interest rate subsidies can promote the financial institutions and the government to formulate GTI guidance policies. Besides, there is a significant synergistic effect between interest rate subsidies and green credits, which can efficiently foster GTI within enterprises and decrease their innovation costs. Building on this foundation, further increasing the carbon tax rate can lead to a reduction in government subsidy expenditures, improve the evolution rate of the ideal strategy, and expedite the realization of a green, low-carbon transformation. © 2024

关键词： Green economy

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Application of improved virtual sample and sparse representation in face recognition

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1391-1402页

作者： Yongjun Zhang Zewei Wang Xuexue Zhang Zhongwei Cui Bob Zhang Jinrong Cui Lamin LJanneh College of Computer Science and Technology Guizhou UniversityGuiyangChina Big Data Science and Intelligent Engineering Research Institute Guizhou Education UniversityGuiyangChina School of Mathematics and Big Data Guizhou Education UniversityGuiyangChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina School of Mathematics and Information South China Agricultural UniversityGuangzhouChina

Sparse representation plays an important role in the research of face *** a deformable sample classification task,face recognition is often used to test the performance of classification *** face recognition,differences in expression,angle,posture,and lighting conditions have become key factors that affect recognition ***,there may be significant differences between different image samples of the same face,which makes image classification very ***,how to build a robust virtual image representation becomes a vital *** solve the above problems,this paper proposes a novel image classification ***,to better retain the global features and contour information of the original sample,the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample,thus generating a virtual ***,by the principle of sparse representation,the linear expression coefficients of the original sample and the virtual sample can be calculated,*** obtaining these two types of coefficients,calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test *** two distances are converted into distance ***,a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual *** fused score will determine the final classification *** experimental results show that the proposed method outperforms other typical sparse representation classification methods.

关键词： representation sample image

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The Evaluation Benchmark for SP ARQL Federated Query

The Evaluation Benchmark for SP ARQL Federated Query

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2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Xu, Jiao Tian, Pingfang Gu, Jinguang Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665457033

In SP ARQL federated query system, cardinality estimation is one of the most critical steps that affect query performance. In recent years, a large number of federated query cardinality estimation methods have been produced, which is particularly important for evaluating the performance of different methods. However, the existing research shows that there is no unified test benchmark when evaluating cardinality estimation methods, and the commonly used precision index Q-error has also been proved to be unable to generate the optimal query plan. In order to solve the above problems, this paper proposes a test benchmark (FQCEB) for SP ARQL federated query cardinality estimation. FQCEB consists of three components: benchmark data set, query generator and evaluation index, including 32 templates created manually, 1600 unique queries and more than 200K query subplans. The experimental results show that when the performance of radix estimation method is evaluated by using the test benchmark in this paper, it shows the applicability of the benchmark to various methods, and the P-error evaluation index in the benchmark shows the correlation with the query running time, and the correlation coefficient is as high as 85%, which proves that P-error is more suitable for evaluating the actual performance of radix estimation method than Q-error. © 2023 IEEE.

关键词： Errors

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