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AVirtual Cloud Storage Architecture for Enhanced Data Security

computer Systems Science & Engineering 2023年第2期44卷 1735-1747页

作者： M.Antony Joans Kumar C.Christopher Columbus E.Ben George T.Ajith Bosco Raj Department of Information Technology St.Xavier’s Catholic College of EngineeringNagercoil629003Tamil NaduIndia Department of Computer Science Engineering PSN College of Engineering and TechnologyTirunelveli627152India Department of Computer Science Engineering University of Technology and Applied SciencesMuscat112Oman Department of Electronics and Communications Engineering PSN College of Engineering and TechnologyTirunelveli627152India

The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and *** proposed Virtual Cloud Storage Archi-tecture is primarily concerned with data integrity and conﬁdentiality,as well as *** provide conﬁdentiality and availability,theﬁle to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct *** the encoded chunks ensured theﬁle integrity,and a newly proposed Circular Shift Chunk Allocation technique was used to determine the order of chunk ***ﬁle could be retrieved by performing the opera-tions in *** the regenerating code,the model could regenerate the missing and corrupted chunks from the *** proposed architecture adds an extra layer of security while maintaining a reasonable response time and sto-rage *** results analysis show that the proposed model has been tested with storage space and response time for storage and *** VCSA model consumes 1.5x(150%)storage *** was found that total storage required for the VCSA model is very low when compared with 2x Replication and completely satisﬁes the CIA *** response time VCSA model was tested with different sizedﬁles starting from 2 to 16 *** response time for storing and retrieving a 2 MBﬁle is 4.96 and 3.77 s respectively,and for a 16 MBﬁle,the response times are 11.06 s for storage and 5.6 s for retrieval.

关键词： Erasure code regenerating code cryptography virtual cloud storage architecture circular shift chunk allocation algorithm

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Research on the Application of computer Big Data technology in the Fiscal Revenue Model Using Mathematical Statistics and Smart Information Systems 2

Research on the Application of Computer Big Data Technology ...

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2nd IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2022

作者： Huang, Changjiang Zhan, Liuchun Guangzhou College of Applied Science and Technology College of Computer Science Guangzhou Guangdong China Guangzhou Huali College College of Computer and Information Engineering Guangzhou Guangdong China

ISBN: (纸本)9781665442763

This paper uses the data of China's three major industries' value-added, employment and China's fiscal revenue from 2000 to 2020 to conduct an empirical study on the factors affecting China's fiscal revenue. This article compares and analyzes the different effects of primary industry added value, secondary industry added value, tertiary industry added value, employment number and other variables on fiscal revenue. It can be seen that the largest impact is the tertiary industry added value, followed by the secondary industry's increase. value. The national fiscal revenue is most affected by the total output value of the service industry and the total population of the country. Finally, we also draw on the relevant principles and technologies of mathematical statistics and information systems to build a fiscal revenue monitoring and early warning system, and try to achieve dynamic monitoring and early warning of government revenue on the computer through quantitative research and system analysis. © 2022 IEEE.

关键词： Big data

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN computer Science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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On Criteria and Tooling for Cryptographic Inventories

On Criteria and Tooling for Cryptographic Inventories

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Die Fachtagung Sicherheit, Schutz und Zuverlassigkeit, SICHERHEIT 2024 - Security, Protection and Reliability Conference, SICHERHEIT 2024

作者： Schmitt, Nicolai Henrich, Johanna Heinz, Dominik Alnahawi, Nouri Wiesmaier, Alexander Darmstadt University of Applied Sciences Department of Computer Science and Information Technology Schöfferstraße 3 Darmstadt64295 Germany

ISBN: (纸本)9783885797395

When cryptography becomes insecure, a migration to new schemes is required. Often the migration process is very complicated, but the time available is very limited. Only if the used cryptographic algorithms, protocols and configurations are known can a system be efficiently and fully adapted to changed security situations. This creates the need for a crypto-inventory that gathers this knowledge. Consequently, the question arises what criteria a crypto-inventory must fulfill to support this adaptation. It also highlights the need for tools to assist compilation. We therefore conducted a literature survey and extracted key requirements. Missing content was supplemented by expanding existing requirements or adding new ones. Furthermore, appropriate metrics were assigned to assess the fulfillment of the requirements for a certain crypto-inventory implementation. Regarding the tooling, we identified five major areas of interest - installed software, connected hardware, communication, stored data and source code scanning - and provide prototypes for semi-automatic creation of crypto-inventories for three of them. This provides organizations with a starting point to understand their cryptographic landscape as a prerequisite for crypto-agility and crypto-migration. However, theoretical design and prototypes have not yet been evaluated. This will be done as a follow-up to this work. All types of organizations are invited to participate. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Cryptography

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Artificial Intelligence Role in Software Automation Testing

Artificial Intelligence Role in Software Automation Testing

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Awad, Abdallah Qutqut, Mahmoud H. Ahmed, Ali Al-Haj, Fatima Almasalha, Fadi Computer Science Department West Mishref Hawalli Kuwait Applied Science Private University Faculty of Information Technology Amman Jordan

ISBN: (纸本)9798350369106

Artificial intelligence (AI) significantly influences the systems and applications that underpin modern life. Large datasets are generated from diverse sources. Hence, there is a need for effective data monitoring, processing, and reporting, which is crucial for making influential decisions. In developing complex software systems, AI has become essential for ensuring rigorous testing that aligns with business requirements within constrained timelines. The main advantage of AI in software testing lies in its ability to enhance accuracy and streamline repetitive tasks, ultimately reducing the time required for testing. To this end, this paper explores the role of AI in automated software testing, critically analyzing the limitations of traditional automated testing methods, which often struggle with inconsistent bug detection and have limited adaptability across different environments. By expanding test coverage and addressing these limitations, AI-driven tools can significantly enhance testing efficiency. Additionally, we investigate various AI-based tools designed to optimize software testing and strengthen quality assurance processes. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning Algorithms for Transportation Mode Prediction: A Comparative Analysis

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Informatica (Slovenia) 2024年第6期48卷 117-130页

作者： Murrar, Samer Alhaj, Fatima Qutqut, Mahmoud H. Faculty of Information Technology Applied Science Private University Amman Jordan Faculty of Computer Science University of New Brunswick FrederictonNBE3B 5A3 Canada

This study investigates the performance of various machine learning (ML) algorithms in predicting transportation modes from large datasets. The investigated algorithms include Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Decision Tree (DT), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Logistic Regression. We rigorously evaluated each algorithm’s performance using a robust set of metrics such as precision, recall, and F1-score. This study comprehensively explains the algorithm’s capabilities, strengths, and potential weaknesses across seven transportation categories:’walk’,’bike’,’bus’,’car’,’taxi’,’train’, and’subway’. The DT model consistently outperformed the others, demonstrating superior accuracy and an adequate balance of precision and recall across all modes of transportation. Specifically, it achieved precision, recall, and F1 scores of around 83% to 94% across all categories. These findings underline the suitability of the DT model for this classification task and its potential for further applications in transportation mode prediction based on large datasets. However, other algorithms like LSTM and RNN also showed promising results in certain categories, suggesting the value of continued exploration of different models depending on specific use cases. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Large datasets

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An Attention Mechanism Based AVOD Network for 3D Vehicle Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-13页

作者： Yuan, Di Zhang, Haiping Shu, Xiu Liu, Qiao Chang, Xiaojun He, Zhenyu Shi, Guangming Guangzhou Institute of Technology Xidian University Xian China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China National Center for Applied Mathematics Chongqing Normal University Chongqing China Faculty of Engineering and Information Technology University of Technology Sydney Broadway New South Wales Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

With the continuous advancement of autonomous driving technology, 3D vehicle detection has become of widespread interest. The traditional aggregate view object detection (AVOD) framework has achieved some good results in 3D vehicle detection tasks. However, the complexity of the 3D vehicle detection scenario makes the current detection methods still not meet the actual requirements. To enhance the detection accuracy of 3D vehicle targets, we propose to equip an attention mechanism to improve the representation capability of feature maps, thereby further increasing the precision of 3D vehicle detection. Specifically, we have added the channel attention ECANet, spatial attention SANet, and mixed attention ECANet+SANet respectively into the image-based feature pyramid network of the AVOD detection framework, which can enhance the feature maps representation and improve the detection accuracy observably. The improved AVOD network is verified using the KITTI dataset. By showing the detection results of these attention mechanisms, it is found that the feature pyramid networks (FPN) module in the AVOD network based on Image has the best performance when integrating a mixed attention mechanism. In comparison to the original AVOD network, the detection results on the average precision index of the proposed method have improved by 2.29%, 2.81%, and 1.32% in the three indexes of simple, medium, and difficult, respectively. Extensive experiments have confirmed the practicality and efficacy of the AVOD network to equip the attention mechanisms for 3D vehicle detection. IEEE

关键词： Semantics

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CMTJ:Simulation package for analysis of multilayer spintronic devices

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npj Computational Materials 2023年第1期9卷 1804-1813页

作者： Jakub Mojsiejuk Sławomir Ziętek Krzysztof Grochot Witold Skowroński Tomasz Stobiecki Institute of Electronics AGH University of Science and TechnologyAl.Mickiewicza 30Kraków 30-059Poland Faculty of Physics and Applied Computer Science AGH University of Science and TechnologyAl.Mickiewicza 30Kraków 30-059Poland

We present CMTJ—a simulation package for large-scale macrospin analysis of multilayer spintronics *** from conventional simulations,such as magnetoresistance and magnetisation hysteresis loops,CMTJ implements a mathematical model of dynamic experimental techniques commonly used for spintronics devices characterisation,for instance:spin diode ferromagnetic resonance,pulse-induced microwave magnetometry,or harmonic Hall voltage *** find that macrospin simulations offer a satisfactory level of agreement,demonstrated by a variety of *** a unified simulation package,CMTJ aims to accelerate wide-range parameter search in the process of optimising spintronics devices.

关键词： multilayer analysis hysteresis

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Generative AI and Gamification for Personalized Learning: Literature Review and Future Challenges

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SN computer Science 2024年第8期5卷 1-12页

作者： Abbes, Feriel Bennani, Souha Maalel, Ahmed RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia University of Sousse Higher Institut of Applied Science and Technology Sousse Tunisia

The integration of Artificial intelligence (AI) in education (AIEd) opens the possibilities for creating new content, virtual teaching, and personalized learning experiences. This paper studies the different innovative methods used in education where we investigate the integration of Generative Artificial Intelligence (GAI) into education, and its transformative influence in producing personalized learning experiences through the use of its models like BERT, ChatGPT and GANs that have unique role in creating new content like text, image, and video, unlike discriminative algorithms that focus only on classification tasks. Also, the paper explores the power of gamification to enhance academic systems by generating new dynamic and interesting learning environments by using specific design that make learner learn and have fun which adjusts their needs, their personality, values and motivation. Even though the importance,The development of a dynamic adaption engine model for GAI and adaptive gamification is a pressing matter in this particular domain. Thus, This review aims the incorporation of generative AI into gamification to generate personalized content and improve the quality of the entire learning experience. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive gamification Adaptive learning AIED Artificial Intelligence (AI) Education Generative AI Learning Personalized learning Teaching

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Case Study Based Investigation on Self-Healing Cloud Deployments for Edge-Based Software Development

Case Study Based Investigation on Self-Healing Cloud Deploym...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Shabariram, C.P. Vrinda, S. Srivatsan, S. Manimeghalai, R. PSG Institute of Technology and Applied Research Department of Computer Science and Engg Coimbatore India Sap Labs Karnataka Bengaluru India

ISBN: (纸本)9798350378177

Cloud Computing has enabled many application providers to deploy web applications in cloud data centers with the advent of on-demand resource allocation and self-healing techniques that provide high availability and flexibility. However, web applications frequently have dynamic workloads that are difficult to forecast. Researchers and Cloud service providers are working hard to find ways to cut costs without sacrificing Quality of Service (QoS). Self-healing is one of the key issues in Cloud computing for application deployment, which is still in its early phases and necessitates extensive research on taxonomy, methodologies, and resource requirements. This article provides a complete overview of self-healing approaches for cloud application deployment. This work aims to assist the scientific community in identifying the elements required for efficient self-healing strategies. An overview of evaluated studies is presented, including various self-healing strategies, resources, monitoring tools, experiments, workloads, metrics, and other pertinent factors. This work provides useful insights and lays the groundwork for future research in this area. In this work, an existing platform is deployed using several self-healing deployment methodologies and responses from various stakeholders are collected and analyzed. The analysis of stakeholders response indicates that self-healing strategies provide better acceptance and deployment with increased customer delight. After self-healing cloud deployment. The survey result indicates self healing improves issue detection, down time and overall system reliability by 90%, 85% and 70%. The operational overhead was reduced by 70%. © 2024 IEEE.

关键词： Cloud platforms

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