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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

共 1769 条记录，以下是441-450 订阅

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Design and Validation of Cloud storage Systems using Rewriting Logic 21

Design and Validation of Cloud Storage Systems using Rewriti...

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21st international symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Olveczky, Peter Csaba Univ Oslo Dept Informat Oslo Norway

ISBN: (纸本)9781728157245

Most large modern-day applications (Facebook, Gmail, eBay, etc.) rely on widely distributed and replicated storage of data for scalability, availability, and disaster tolerance. Since maintaining high degrees of data consistency requires costly communication across distant sites, applications over such distributed and partially replicated data are complex artifacts that must carefully balance the required degrees of consistency and performance. In this paper I summarize work at the University of Illinois Assured Cloud computing center on using rewriting logic and its associated Maude tool environment to formally model and analyze both the correctness and the performance of state-of-the-art distributed transaction systems designs, as well as on how to automatically obtain a correct-by-construction distributed implementation of a promising design.

关键词： cloud computing distributed transaction systems formal methods LTL and statistical model checking performance estimation rewriting logic Maude correct-by-construction code generation

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Autonomous image segmentation by Competitive Unsupervised GrowCut 21

Autonomous image segmentation by Competitive Unsupervised Gr...

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21st international symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Marginean, Radu Andreica, Anca Diosan, Laura Balint, Zoltan Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania Cty Clin Emergency Hosp IMOGEN Res Inst Oradea Romania Babes Bolyai Univ Fac Phys Cluj Napoca Romania

ISBN: (纸本)9781728157245

In this paper, we introduce Competitive Unsupervised GrowCut, a cellular automata-based, unsupervised and autonomous algorithm that combines the label merging component of Unsupervised GrowCut with the soft label propagation mechanism of GrowCut. We evaluated our algorithm on two benchmark image segmentation datasets, along with two related methods proposed in the literature. We also provide a detailed comparative analysis of the three algorithms' segmentation performance and properties. Our analysis identified application-specific regimes that govern the relative performance of the analyzed algorithms.

关键词： image segmentation cellular automata computer vision

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Combining TF-IDF, V-GAN, and XGB to Improve Next-Generation Web Application Firewalls' Ability to Detect SQL Injection Attacks

Combining TF-IDF, V-GAN, and XGB to Improve Next-Generation ...

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ACS/IEEE international Conference on computer Systems and Applications

作者： Emna Fakhfakh Maha Charfeddine Nesrine Tarhouni Mohamed Amine Guettat Securas Technologies Sfax Tunisia Research Groups in Intelligent Machines National Engineering School of Sfax University of Sfax Sfax Tunisia Securas Technologies Plaisir France

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

SQL injection attacks are one of the most devastating vulnerabilities on the web, which can be used to leak sensitive information, gain unauthorized access, and result in financial losses. We utilized five distinct Machine Learning (ML) and Deep Learning (DL) algorithms to detect SQL injection attacks. We extracted features from network traffic and SQL queries through tokenization and regular expressions. Our findings show that combining the Term Frequency-Inverse Document Frequency (TF-IDF), Vanilla Generative Adversarial Network (VGAN), and eXtreme Gradient Boosting (XGB) models resulted in remarkably high performance metrics, including 99.95 % accuracy, 99.92 % precision, 99.97 % recall, and a 99.95 % F1-score. The results not only show the combination's effectiveness but also emphasize the importance of using machine learning techniques to detect SQL injection attacks to improve real-world cybersecurity applications significantly.

关键词： Training structured Query Language Accuracy Generative AI Firewalls (computing) Telecommunication traffic SQL injection Feature extraction Tokenization Testing

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Developing Distributed high-performance computing Capabilities of an Open Science Platform for Robust Epidemic Analysis

Developing Distributed High-performance Computing Capabiliti...

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IEEE international symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Nicholson Collier Justin M. Wozniak Abby stevens Yadu Babuji Mickaël Binois Arindam Fadikar Alexandra Würth Kyle Chard Jonathan Ozik Decision and Infrastructure Sciences Argonne National Laboratory Lemont IL U.S.A Data Science and Learning Argonne National Laboratory Lemont IL U.S.A Computer Science University of Chicago Chicago IL U.S.A Acumes project-team Inria centre at Université Côte d’Azur Sophia Antipolis France

COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among domain experts, mathematical modelers, and scientific computing specialists. Computationally, however, it also revealed critical gaps in the ability of researchers to exploit advanced computing systems. These challenging areas include gaining access to scalable computing systems, porting models and workflows to new systems, sharing data of varying sizes, and producing results that can be reproduced and validated by others. Informed by our team's work in supporting public health decision makers during the COVID-19 pandemic and by the identified capability gaps in applying high-performance computing (HPC) to the modeling of complex social systems, we present the goals, requirements, and initial implementation of OSPREY, an open science platform for robust epidemic analysis. The prototype implementation demonstrates an integrated, algorithm-driven HPC workflow architecture, coordinating tasks across federated HPC resources, with robust, secure and automated access to each of the resources. We demonstrate scalable and fault-tolerant task execution, an asynchronous API to support fast time-to-solution algorithms, an inclusive, multi-language approach, and efficient wide-area data management. The example OSPREY code is made available on a public repository.

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Solar-Powered UV-LEDs for Microbial Disinfection: Design, Challenges and Applications

Solar-Powered UV-LEDs for Microbial Disinfection: Design, Ch...

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international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Trailokya Bhattarai Nita Khanal Abasifreke Ebong Mariya Munir M.Yasin Akhtar Raja Department of Electrical & Computer Engineering UNC Charlotte Charlotte USA Department of Civil and Environment Engineering UNC Charlotte Charlotte NC USA Department of Electrical & Computer Engineering and Phys & OSE UNC Charlotte Charlotte NC USA

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Ultraviolet-Light Emitting Diodes (UV-LEDs) application as sustainable and effective microbial deactivation in healthcare, water treatment, food safety, and surface disinfection has increased recently. This paper explores the feasibility, design, and performance of a solar-powered UV-LED system for microbial deactivation. It emphasizes the use of UV-C LEDs (200–280 nm) for their efficacy in reducing pathogens. Our study addresses the design considerations for integrating solar panels, charge controllers, batteries, UV-LEDs, and sensors to develop an efficient and safe disinfection system. Recent test-results from a 275 nm UV LED system show promising reductions in bacteriophages Phi6 and MS2. That signifies potential applications in water purification, surface sterilization, and emergency settings. However, challenges such as low wall plug efficiency and fabrication difficulties for short-wavelength LEDs need to be addressed to realize the full potential of such technology.

关键词： Pathogens Smart cities Purification Light emitting diodes Sensor systems Sensors Solar panels Surface treatment Robots Plugs

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A Hybrid Intelligence Model Forecasts the Temperature of a Battery Used in Electric Vehicles 21st

A Hybrid Intelligence Model Forecasts the Temperature of a...

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21st international symposium on Distributed computing and Artificial Intelligence, DCAI 2024

作者： Rubiños, Manuel Arcano-Bea, Paula Díaz-Longueira, Antonio Michelena, Álvaro Vega, Rafael Vega Casteleiro-Roca, José-Luis Andújar, José Manuel CTC Department of Industrial Engineering CITIC University of A Coruña EPEF Calle Mendizábal s/n Campus de Esteiro A Coruña Ferrol15403 Spain Department of Electronic Engineering Computer Systems and Automatic University of Huelva Campus de El Carmen Huelva Huelva21071 Spain

ISBN: (纸本)9783031739095

Battery technology is advancing due to the requirement to store reliable energy in electric transportation systems and portable electronics devices. Assessing battery conditions and increasing the autonomy of electric vehicles requires to improve the behavior models. Prognosticating battery performance, nevertheless, is complex due to multiple variables, such as temperature, wear, and usage guidelines. Understanding battery performance requires a depth knowledge of, for example, time series analysis, above all forecasting. This research uses Artificial Neural Networks (ANN) modeled taking into account system dynamics, to create a Lithium Ion Battery (LIB) prediction model. Testing was performed with a 59.2 VDC, 120 Ah LIB, monitoring voltage, current and temperature while simulating charges and discharges. The model used is a hybrid type, which uses clustering to generate internal local models. This model predict the temperature development with very good results in terms of Mean Squared Error. From this research it is deduced that hybrid models are appropriate tools for prognostic battery performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Prediction models

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Personalized Learning Resource Recommendation Based On Learner Profile

Personalized Learning Resource Recommendation Based On Learn...

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IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： WeiJie Peng HaiKun Liu Chao Qi School of Computer Science Shaanxi Normal University Xi’an China

The online learning platform is overloaded with learning resources, and learners are prone to “information maze” when facing massive learning resources. An effective way to solve information maze is to recommend resources that conform to learners’ own characteristics from a large number of learning resources. Therefore, we proposes a personalized learning resource recommendation algorithm based on learner profile. Firstly, the learner portrait is modeled based on two characteristics of learner, learning interest preference and knowledge point mastery level. Secondly, K nearest neighbor learners who are most similar to the target learner are found according to the learner portrait. Thirdly, using the rating information of these K learners on the learning resources, the predicted scores of the target learner on the learning resources are calculated. Finally, the learning resources are sorted by predicted scores, the first N learning resources are the final recommended results. The algorithm we proposed is verified in EdNet, a data set published by Santa. The experimental results show that the algorithm has significantly improved the recommendation performance.

关键词： Prediction algorithms Ubiquitous computing

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Anonymous Hierarchical Identity-based Encryption EHR Sharing Mechanism Based on SM9

Anonymous Hierarchical Identity-based Encryption EHR Sharing...

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IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Zuobin Ying Minghao Liu Siofong Kong Shengmin Xu Jinbo Xiong Faculty of Data Science City University of Macau Macau China College of Computer and Cyber Security Fujian Normal University Fuzhou China

The identity-based encryption (IBE) scheme SM9 is a commercial encryption standard in China and has been used as a basic building block for authentication. Currently, to accommodate large-scale application scenarios, a variant hierarchical identity-based encryption (HIBE) scheme has been constructed on the basis of SM9. However, there still a problem that the ciphertext may leak the identity of the recipients. An anonymous HIBE has been developed based on SM9 to realize the anonymization of data recipients. Moreover, the anonymous HIBE scheme will be proven to have adaptive secure under the standard model. The performance of the anonymous HIBE is evaluated, the experimental results are comparable to the classical AHIBE algorithm which show the practicability of the scheme.

关键词： Privacy Data privacy Ciphers Identity-based encryption Authentication Ubiquitous computing Information filtering

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Knowledge Tracing with Multi-Feature Fusion and Question Difficulty

Knowledge Tracing with Multi-Feature Fusion and Question Dif...

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IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Haikun Liu Weijie Peng Chao Qi School of Computer Science Shaanxi Normal University Xi’an China

Knowledge Tracing (KT) aims to trace students’ knowledge state and predict their performance on questions in online tutoring system. For the moment, most methods assume that all questions are equal to every student, that is to say, question itself has not any effect on students’ knowledge state. And they have not considered the effect of question difficulty (QD) on students’ knowledge state. To address this issue, some researchers have worked on the effect of question difficulty on students’ knowledge state. But, they have not considered the effect of question difficulty and knowledge concept difficulty (KCD) on students’ knowledge acquisition ability and knowledge state. In this paper, we propose a novel model called Question Difficulty Estimation Knowledge Tracing (QDEKT) model, which comprehensively considers question difficulty and knowledge concept difficulty and introduces students’ real response and predicted response on questions into the model, to evaluate students’ knowledge acquisition ability and dynamically update students’ knowledge state. Finally, extensive experiments show the effectiveness of our model.

关键词： Visualization Knowledge acquisition Computational modeling Estimation Predictive models Ubiquitous computing

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Group-based Sparse Coding with Adaptive Dictionary Learning for Image Denoising

Group-based Sparse Coding with Adaptive Dictionary Learning ...

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international symposium on Distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Jiaying Wang Meiqing Wang Hang Cheng Rong Liu Fei Chen College of Mathematics and Statistics Fuzhou University Fuzhou China College of Computer Science and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9781665454636

Group sparse coding (GSC) is a powerful mechanism that has achieved great success in many low-level vision tasks, showing great potential in image denoising. Traditional group sparse coding generally uses overcomplete dictionaries and $l_{1}$ -norm to regularize sparse coefficients. But this is only an estimate of the solution, which cannot obtain a sparse solution and has a high computational cost. In this paper, we use a GSC framework with adaptive dictionary learning for image denoising. In order to improve the accuracy of obtaining sparse coefficients, the dictionary used in this paper is learned from the input image, which can be obtained by applying SVD once for each patch group. Then use ADMM algorithm to solve the objective function. Experimental results show that the PSNR value of our approach not only is competitive with many advanced image denoising methods but also achieves better visual effects.

关键词： Image coding Dictionaries Noise reduction Machine learning Visual effects Minimization Linear programming

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