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dataJudge: Cross-Chain data Consistency Verification Based on Extended Merkle Hash Tree 24th

DataJudge: Cross-Chain Data Consistency Verification Based o...

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24th international conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Xu, Yuwei Zeng, Junyu Cao, Jie Dai, Shengjiang Xiang, Qiao Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

Consortium blockchains, with their decentralized characteristics, have found widespread applications across various industries. However, this also forms isolated data islands. These data islands have become obstacles to data sharing between industries, and the issue of cross-chain data consistency verification remains unresolved. Current mainstream cross-chain technologies solve asset transfer issues through complex transaction steps, thus failing to meet the need for efficient data transmission. Existing research has proposed solutions for cross-chain data consistency verification, but two major issues remain. Firstly, these schemes do not consider data changes or support data dynamic operations. Secondly, these schemes involve a significant amount of complex computations, leading to slow efficiency. To address the above issues, we propose dataJudge, a cross-chain data consistency verification scheme based on an extended Merkle hash tree. This scheme not only verifies cross-chain data consistency but also effectively fills the gap in existing schemes regarding dynamic operations. Additionally, we modify the paillier homomorphic encryption. To improve efficiency, we do not introduce many complex computations. Furthermore, the extended Merkle hash tree structure enhances efficiency. Finally, we implement a prototype system and evaluate it. The results demonstrate that dataJudge can effectively verify cross-chain data consistency and outperform existing schemes such as DCIV and BeDCV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data consistency

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Design and Implementation of Coroutine Management Environment

Design and Implementation of Coroutine Management Environmen...

引用

2022 international conference on Computer Graphics, Artificial Intelligence, and data processing, ICCAID 2022

作者： Cai, Bin Qiu, Bingcheng Yao, Jian Fu, Jun Xue, Yunlan Liu, Xiaofei Wu, Ruilong Zhang, Xu Guangdong Guangzhou China Wuhan University Hubei Wuhan China China Hi-Tech Group Co. Ltd. Guangdong Guangzhou China Blue Dot Power Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781510663350

Advances in network and storage technologies enable data centers to provide high-concurrency and low-latency processing responses. To meet user needs, a variety of low-latency technologies can be used to improve the overall concurrent response capability of the system on the premise of ensuring system stability. User mode thread pool is one of the techniques that can be used. In a multi-core processor system, the scheduling management of the user mode thread pool is much more complicated. At present, the scheduling management of the user mode thread pool mainly focuses on the resource utilization of the user mode thread pool itself and the scheduling management of many user-mode threads, such as dynamically adjusting the number and usage of user mode threads in the user mode thread pool according to the load of the system, and replacing the system thread scheduling with user mode thread scheduling, etc. The existing user mode thread scheduling methods do not distinguish the performance requirements of different data processing tasks, treat all user mode thread tasks in the same way, cannot reflect the performance requirements of different user mode thread tasks, and cannot provide more fine-grained user mode thread execution timing control;the existing user mode thread scheduling methods do not provide more effective scheduling control for the host thread, but instead use the thread scheduling mechanism of the operating system itself to control the execution of all kernel threads which is difficult to isolate the host thread and other kernel threads in the system;The existing user mode thread scheduling methods cannot give full play to the parallel execution advantages of the multi-core processor architecture, and it is difficult to implement unified resource allocation management among multiple user mode thread pools. This paper provides a two-level scheduling management method for user mode thread pools that effectively integrates the operating system scheduling m

关键词： Resource allocation

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Maize Growth Simulation Analysis and Yield Prediction Based on Integrated Learning Algorithm and WOFOST Model

Maize Growth Simulation Analysis and Yield Prediction Based ...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Haibin Yang Yangfeiyi Chen Tianzi Zhang Fengzhe Zhu Electrical and Information Engineering College JiLin Agricultural Science and Technology University JiLin China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Maize growth status and yield prediction are the key issues in maize planting management. Traditional prediction methods rely on single crop growth models or machine learning algorithms, which have the problems of insufficient prediction accuracy and limited adaptability. The combination of ensemble learning and crop model provides a new idea for improving the prediction accuracy. In this study, an improved maize growth prediction model was proposed based on the WOFOST model, combined with the irrigation model and the ensemble learning algorithm. By introducing K-means clustering and data assimilation technology, the model parameters were optimized to improve the prediction performance. The experimental data are derived from NASA's meteorological data from 1990 to 2020, and the results show that the improved model performs well in the prediction of key indicators such as leaf area index, soil water content and total biomass, and its MAE and MSE are reduced to 2.836 and 7.581, respectively, with a coefficient of determination R—2 It is improved to 0.942, which is significantly better than the traditional WOFOST model and other machine learning algorithms, demonstrating the efficiency and reliability of the improved model in maize growth prediction.

关键词： Adaptation models Machine learning algorithms Computational modeling Biological system modeling Crops Clustering algorithms Predictive models Prediction algorithms data models Ensemble learning

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Domain Shift in Computer Vision models for MRI data Analysis: An Overview 13

Domain Shift in Computer Vision Models for MRI Data Analysis...

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13th international conference on Machine Vision

作者： Kondrateva, Ekaterina Pominova, Marina Popova, Elena Sharaev, Maxim Bernstein, Alexander Burnaev, Evgeny Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9781510640412

Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yet only a few applications are now in clinical use and one of the reasons for that is poor transferability of the models to data from different sources or acquisition domains. Development of new methods and algorithms for the transfer of training and adaptation of the domain in multi-modal medical imaging data is crucial for the development of accurate models and their use in clinics. In present work, we overview methods used to tackle the domain shift problem in machine learning and computer vision. The algorithms discussed in this survey include advanced data processing, model architecture enhancing and featured training, as well as predicting in domain invariant latent space. The application of the autoencoding neural networks and their domain-invariant variations are heavily discussed in a survey. We observe the latest methods applied to the magnetic resonance imaging (MRI) data analysis and conclude on their performance as well as propose directions for further research.

关键词： Invariant Representation MRI Domain Adaptation Transfer Learning Deep Learning Fader Networks

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Engineering data Assets for Public Health Applications: A Covid-19 Case Study

Engineering Data Assets for Public Health Applications: A Co...

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2023 IEEE international conference on Big data, Bigdata 2023

作者： Scriney, Michael Timilsina, Mohan Curry, Edward Porwol, Lukasz Nie, Dongyun Dahley, Darren Fernandez, Jaime B. D'Aquin, Mathieu Roantree, Mark Dublin City University Insight Centre for Data Analytics Dublin Ireland University of Galway Insight Centre for Data Analytics Galway Ireland Dublin City University School of Computing Dublin Ireland Health Protection Surveillance Centre Dublin Ireland Loria University of Lorraine Nancy France Dublin City University Insight Centre for Data Analytics School of Computing Dublin Ireland

ISBN: (纸本)9798350324457

When the global pandemic struck in 2020, most countries established task forces to meet a challenge that impacted governmental resources. It became apparent that data, intelligence gathering, and both modelling and predictive capabilities were required. While artificial intelligence (AI) based solutions had already begun to emerge within the public sector, the Covid-19 pandemic accelerated this process. In particular, modelling of case numbers with the development of predictive algorithms. The development of AI solutions for public sector organizations is inherently multidisciplinary. This is crucial to understanding how solutions can be developed, outputs understood, and the benefits and risks measured. Furthermore, the development of AI solutions often requires data which may not be accessible from a single location. In the case of Covid-19 modelling, data must be extracted from multiple locations to construct data assets. In this research, a collaborative approach to developing machine learning expertise for the public sector is presented. Using Covid-19 as a case study, the role of different government sectors when building data assets is examined along with the use of standard data models, and how this type of cooperation led to the development of a pipeline for data assets to underpin AI solutions for the public sector. © 2023 IEEE.

关键词： COVID-19 data Integration FHIR data Model Pseudonymous data

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Trillion-scale Graph processing Simulation based on Top-Down Graph Upscaling 37

Trillion-scale Graph Processing Simulation based on Top-Down...

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37th IEEE international conference on data Engineering (IEEE ICDE)

作者： Park, Himchan Xiong, Jinjun Kim, Min-Soo Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea IBM Thomas T Watson Res Ctr Cognit Comp Syst Res Yorktown Hts NY USA

ISBN: (纸本)9781728191843

As the number of graph applications increases rapidly in many domains, new graph algorithms (or queries) have become more important than ever before. The current two-step approach to develop and test a graph algorithm is very expensive for trillion-scale graphs required in many industrial applications. In this paper, we propose a concept of graph processing simulation, a single-step approach that generates a graph and processes a graph algorithm simultaneously. It consists of a top-down graph upscaling method called V-Upscaler and a graph processing simulation method following the vertex-centric GAS model called T-GPS. Users can develop a graph algorithm and check its correctness and performance conveniently and cost-efficiently even for trillion-scale graphs. Through extensive experiments, we have demonstrated that our single-step approach of V-Upscaler and T-GPS significantly outperforms the conventional two-step approach, although ours uses only a single machine, while the conventional one uses a cluster of eleven machines.

关键词： conferences Clustering algorithms data engineering data models

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NFT Appraisal Using Machine Learning 23

NFT Appraisal Using Machine Learning

引用

5th Asia Pacific Information Technology conference, APIT 2023

作者： Dawod, Ahmed Dawod Mohammed Munkhdalai, Lkhagvadorj Park, Kwang Ho Ryu, Keun Ho Pham, Van Huy Database and Bioinformatics Laboratory College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9781450399500

Non-Fungible Tokens (NFTs) are digital assets based on a blockchain and those are characterized as unique cryptographic tokens and non-interchangeable. To date, research into the NFT marketplace has been relatively limited. As it is an emerging platform with many unique elements, The NFT market has been impacted due to recent fluctuations in crypto-asset markets more broadly. This current bear market cycle has shed light on concerns around the value of NFTs, profit-based motivation, and environmental sustainability. However, periods of volatility and cyclicality are to be expected with any nascent technology as it develops a product-market fit. consequently, the appraisal of real-price for NFT collections is essential for individual financial security and investment making. In this study, we evaluate the machine learning algorithms to appraise their real-price based on NFT item's characteristics, market event information, and their rarity score data acquired by retrieved from the biggest marketplace OpenSea. Furthermore, the procedures were applied to meet the objectives of this study we built prediction models based on various machine-learning algorithms ranging from Random Forest, XGBoost, SVM, Lasso, ElasticNet, Ridge, Linear Polynomial Regression, TabNet, CatBoost, and LightGBM models. From the results, LightGBM regression model outperformed the other by RMSE around 0.905. The best R2 is only found in this model, which has a value of 0.917. © 2023 ACM.

关键词： Blockchain

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Readability Classification with Wikipedia data and All-MiniLM Embeddings 19th

Readability Classification with Wikipedia Data and All-MiniL...

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19th international conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Vergou, Elena Pagouni, Ioanna Nanos, Marios Kermanidis, Katia Lida Ionian Univ Dept Informat Corfu Greece

ISBN: (纸本)9783031341731;9783031341717;9783031341700

Evaluating the readability of text has been a critical step in several applications, ranging from text simplification, learning new languages, providing school children with appropriate reading material to conveying important medical information in an easily understandable way. A lot of research has been dedicated to evaluating readability on larger bodies of texts, like articles and paragraphs, but the application on single sentences has received less attention. In this paper, we explore several machine learning techniques - logistic regression, random forest, Naive Bayes, KNN, MLP, XGBoost - on a corpus of sentences from the English and simple EnglishWikipedia. We build and compare a series of binary readability classifiers using extracted features as well as generated all-MiniLM-L6-v2-based embeddings, and evaluate them against standard classification evaluation metrics. To the authors' knowledge, this is the first time this sentence transformer is used in the task of readability assessment. Overall, we found that the MLP models, with and without embeddings, as well as the Random Forest, outperformed the other machine learning algorithms.

关键词： Readability classification Text simplification Embeddings

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Unsupervised Feature Selection via Feature-Grouping and Orthogonal Constraint 26

Unsupervised Feature Selection via Feature-Grouping and Orth...

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26th international conference on Pattern Recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Yuan, Aihong Huang, Jiahao Wei, Chen Zhang, Wenjie Zhang, Naidan You, Mengbo Northwest A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the fields of machine learning and data mining, unsupervised feature selection plays an important role in processing large amounts of high-dimensional unlabeled data. This paper proposes an original and novel unsupervised feature selection based on feature grouping and orthogonal constraints. We consider the domain relationship in the original data and reconstruct the similarity matrix based on the correlation between the features. We use a generalized incoherent regression model based on orthogonal constraints. Furthermore, a graph regularization term with local structure preservation constraints is added to ensure that the feature subset does not lose local structural features in the original data space. Besides, an iterative algorithm is proposed to solve the optimization problem by iteratively updating the global similarity matrix, and constructing weight matrix, pseudo-label matrix and transformation matrix. Through experiments on 6 benchmark datasets, the clustering performance of the proposed method outperforms state-of-the-art unsupervised feature selection methods. The source code is available at: https://***/misteru/FGOC.

关键词： Correlation Source coding Machine learning Benchmark testing Feature extraction data structures data models

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Smart data Driven System for Pathological Voices Classification 2nd

Smart Data Driven System for Pathological Voices Classificat...

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2nd international conference on Optimization, Learning algorithms and Applications (OL2A)

作者： Fernandes, Joana Candido Junior, Arnaldo Freitas, Diamantino Teixeira, Joao Paulo Inst Politecn Braganca Res Ctr Digitaizat & Intelligent Robot CeDRI P-5300 Braganca Portugal Univ Porto Fac Engn P-4200465 Porto Portugal Sao Paulo State Univ Inst Biosci Language & Phys Sci Sao Jose Do Rio Preto SP Brazil Inst Politecn Braganca Res Ctr Digitaizat & Intelligent Robot CeDRI Appl Management Res Unit UNIAG P-5300 Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Regioes Montanha Su P-5300 Braganca Portugal

ISBN: (纸本)9783031232350;9783031232367

Classifying and recognizing voice pathologies non-invasively using acoustic analysis saves patient and specialist time and can improve the accuracy of assessments. In this work, we intend to understand which models provide better accuracy rates in the distinction between healthy and pathological, to later be implemented in a system for the detection of vocal pathologies. 194 control subjects and 350 pathological subjects distributed across 17 pathologies were used. Each subject has 3 vowels in 3 tones, which is equivalent to 9 sound files per subject. For each sound file, 13 parameters were extracted (jitta, jitter, Rap, PPQ5, ShdB, Shim, APQ3, APQ5, F0, HNR, autocorrelation, Shannon entropy and logarithmic entropy). For the classification between healthy and pathological, several classifiers were used (Decision Trees, Discriminant Analysis, Logistic Regression Classifiers, Naive Bayes Classifiers, Support Vector Machines, Nearest Neighbor Classifiers, Ensemble Classifiers, Neural Network Classifiers) with various models. For each patient, 118 parameters were used (13 acoustic parameters * 9 sound files per subject, plus the subject's gender). As pre-processing of the input matrix data, the Outliers treatment was used using the quartile method, then the data were normalized and, finally, Principal Component Analysis (PCA) was applied in order to reduce the dimension. As the best model, the Wide Neural Network was obtained, with an accuracy of 98% and AUC of 0.99.

关键词： Speech pathologies Machine learning Speech features Principal component analysis Vocal acoustic analysis

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