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Reliability and Incentive of Performance Assessment for Decentralized Clouds

Journal of computer science & technology 2022年第5期37卷 1176-1199页

作者： Jiu-Chen Shi Xiao-Qing Cai Wen-Li Zheng Quan Chen De-Ze Zeng Tatsuhiro Tsuchiya Min-Yi Guo Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science China University of GeosciencesWuhan 430074China Graduate School of Information Science and Technology Osaka UniversityOsaka 565-0871Japan

Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing *** any user or enterprise can be the cloud provider in the decentralized cloud,the performance assessment of the heterogeneous computing resources is of vital ***,with the consideration of the untrustworthiness of the participants and the lack of unified performance assessment metric,the performance monitoring reliability and the incentive for cloud providers to offer real and stable performance together constitute the computational performance assessment problem in the decentralized *** this paper,we present a robust performance assessment solution RODE to solve this *** mainly consists of a performance monitoring mechanism and an assessment of the claimed performance(AoCP)*** performance monitoring mechanism first generates reliable and verifiable performance monitoring results for the workloads executed by untrusted cloud *** on the performance monitoring results,the AoCP mechanism forms a unified performance assessment metric to incentivize cloud providers to offer performance as *** extensive experiments,we show RODE can accurately monitor the performance of cloud providers on the premise of reliability,and incentivize cloud providers to honestly present the performance information and maintain the performance stability.

关键词： decentralized cloud computing robust performance assessment trusted execution environment(TEE)

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Prediction of Oil Palm Conditions Using Deep Learning Based on the Visible Atmospherically Resistant Index on UAV Imagery

Prediction of Oil Palm Conditions Using Deep Learning Based ...

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2023 International Conference on Informatics Engineering, science and technology, INCITEST 2023

作者： Mimboro, Prasetyo Soewito, Benfano Soeparno, Haryono Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

Artificial neural network (ANN)-based computer vision techniques are becoming increasingly popular for palm oil disease detection and classification. Deep learning models' capacity to automatically learn and extract relevant image features has enabled accurate and efficient detection and classification of palm oil diseases. In this research, research was conducted to test the deep learning method to predict the condition of oil palm plantations based on the visible atmospherically resistant index on the Unmanned Aerial Vehicle Image. Some diseases that can attack oil palm trees are root disease or oil palm root rot (blast disease), basal stem rot (ganoderma), bud rot (spear rot), yellow line disease (patch yellow). This study aims to predict the condition of oil palm trees based on the VARI so that the process of detecting the spread of disease in oil palm trees can be accelerated. In this study, the prediction model for the condition of oil palm trees using the ANN algorithm succeeded in predicting the condition of oil palm trees and provided satisfactory prediction results, namely an accuracy rate of 94.7% and a loss of 21.58%. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Transfer Learning Method for Malaria Identification 8

Transfer Learning Method for Malaria Identification

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Herli, Cecilia Audrey Wijaya, Daniel Zerge Hansen Angela, Michelle Pragusma, Zakaria Berlam Wulandari, Ajeng Anggreainy, Maria Susan Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350342604

Malaria is a severe disease caused by parasites of the genus Plasmodium, which are transmitted to humans through the bite of an infected female Anopheles mosquito. Symptoms of malaria begin to appear at least within 10 to 15 days. If malaria is not treated immediately, it is feared that it will cause respiratory problems, shortness of breath, and death. To avoid the occurrence of these events, the idea arose to create an AI (Artificial Intelligence) project that can recognize the presence of malaria parasites in blood cells. Thus, the main objective of this project is to find out how to create a Machine Learning model that can efficiently identify malaria parasites in the human body. The AI project uses CNN (Convolutional Neural network) as an algorithm to recognize the presence or absence of parasites in blood cell images that will be inputted by the user. Process of implementing CNN, using VGG19 which is an advanced CNN that has pre-trained layers and a good understanding of describing an image, both the shape, color, and structure of the image. After implementing the Transfer Learning algorithm on the dataset, the result is a Transfer Learning algorithm that can detect the presence of Malaria parasites in blood cells with an accuracy rate of 92 percent a specificity of 95 percent, and a sensitivity of 89 percent. The accuracy can still increase depending on the diversity of the data provided. The more often we train and input test data as train data, the accuracy of AI will also increase. © 2023 IEEE.

关键词： Convolutional neural networks

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs 1

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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1st IEEE International Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Abdullah, Roni Reza Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia National Institute of Science and Technology Faculty of Informatics Engineering Jakarta12630 Indonesia Bina Nusantara University Binus Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience. © 2023 IEEE.

关键词： Sensitive data

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Anonymous TollPass: A Blockchain-Based Privacy-Preserving Electronic Toll Payment Model

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computers, Materials & Continua 2024年第6期79卷 3495-3518页

作者： Jane Kim Soojin Lee Chan Yeob Yeun Seung-Hyun Seo Department of Electronic&Electrical Engineering Graduate SchoolHanyang UniversitySeoul04763Korea Department of Electrical Engineering and Computer Science Khalifa University of Science and TechnologyAbu Dhabi127788United Arab Emirates School of Electrical Engineering Hanyang UniversityERICAAnsan15588Korea

As big data,Artificial Intelligence,and Vehicle-to-Everything(V2X)communication have advanced,Intelligent Transportation Systems(ITS)are being developed to enable efficient and safe transportation *** Toll Collection(ETC),which is one of the services included in ITS systems,is an automated system that allows vehicles to pass through toll plazas without stopping for manual *** ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system *** ensure secure and efficient toll payments,designing a distributed model for sharing toll payment information among untrusted toll service providers is ***,the current ETC system operates under a centralized ***,both toll service providers and toll plazas know the toll usage history of *** raises concerns about revealing the entire driving routes and patterns of *** address these issues,blockchain technology,suitable for secure data management and data sharing in distributed systems,is being applied to the ETC *** enables efficient and transparent management of ETC ***,the public nature of blockchain poses a challenge where users’usage records are exposed to all *** tackle this,we propose a blockchain-based toll ticket model named AnonymousTollPass that considers the privacy of *** proposed model utilizes traceable ring signatures to provide unlinkability between tickets used by a vehicle and prevent the identity of the vehicle using the ticket from being identified among the ring members for the ***,malicious vehicles’identities can be traced when they attempt to reuse *** conducting simulations,we show the effectiveness of the proposed model and demonstrate that gas fees required for executing the proposed smart contracts are only 10%(when the ring size is 50)of the fees required in previous studies.

关键词： Blockchain electronic toll collection smart contract traceable ring signature

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Applications of Artificial Intelligence in Human Resource Management: Theoretical Framework and Future Research Directions 4

Applications of Artificial Intelligence in Human Resource Ma...

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4th International Conference on computer science and Management technology, ICCSMT 2023

作者： Zhang, Xuming Wang, Shilong Peng, Ling School of Management Guangdong University of Science and Technology Dongguan523083 China Graduate School of Computer Science and Technology Harbin Institute of Technology Harbin150000 China

ISBN: (纸本)9798400709517

With the rapid advancement of artificial intelligence (AI) technologies, their incorporation into human resource management (HRM) has grown increasingly prevalent. AI tools have substantially enhanced the precision of talent acquisition, predictive performance analytics, and the formulation of responsive HR strategies and human resource management (HRM). However, challenges persist, including data privacy concerns, potential biases in AI algorithms, and retaining a human-centric approach amidst automation. This paper delves into the diverse applications of AI within HRM, encompassing areas such as recruitment screening, employee engagement analysis, and forward-thinking HR planning. Looking ahead, organizations must deeply contemplate the ethical implications of these technologies, endeavoring to design transparent, accountable, and unbiased AI systems while striking a balance between technological efficiency and human intuition and judgment. © 2023 ACM.

关键词： Human resource management

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Image Fuzzy Enhancement Based on Membrane Computing Particle Swarm Optimization Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 658-672页

作者： Hou, Xiao-Mao Cai, Liu-Ping Wang, Yan-Hui Automation and Information Engineering College Hunan Chernical Vocational Technology College Zhuzhou412000 China Graduate School Department Saint Paul University Tuguegarao3500 Philippines School of Computer Science and Engineering Hunan University of Information Technology Changsha410151 China

Image fuzzy enhancement is a research hotspot in the field of image processing, which aims to recover enhanced beginning clear images from degraded images. Based on the research of traditional particle swarm optimization algorithm and fuzzy enhancement algorithm, an image fuzzy enhancement method based on membrane computing particle swarm algorithm is proposed. Firstly, in order to make full use of the sparse characteristics of the clear image, the coefficient decomposition under wavelet domain and tightly supported wavelet domain is performed on the image respectively. Then, a joint optimisation model is constructed using L1 parametric constraints to achieve pretzel noise cancellation. Next, the MMH-PSO algorithm is designed by improving the particle swarm algorithm using membrane computing and Metropolis-Hastings sampling. Based on the simulated annealing algorithm temperature drop process, Metropolis-Hastings sampling is used to add randomness to the particle swarm algorithm so that it has the ability to jump out of the local optimum. The use of membrane computing enhances the parallelism of the particle swarm algorithm and can reduce the time complexity in solving complex problems. Finally, MMH-PSO is used to simultaneously search out the magnitude of the two fuzzy parameters in the traditional fuzzy enhancement algorithm in order to improve the accuracy of the algorithm. The experimental results show that the proposed algorithm has better SSIM values than the traditional fuzzy enhancement algorithm, which effectively improves the image quality and makes the image edge information more abundant. © 2024, J. Network Intell. All rights reserved.

关键词： Simulated annealing

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A Dynamic Bayesian-Based Comprehensive Trust Evaluation Model for Dispersed Computing Environment

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China Communications 2023年第2期20卷 278-288页

作者： Hongwen Hui Zhengxia Gong Jianwei An Jianzhong Qi School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Graduate School of University of Science and Technology Beijing Guangdong 528399China China Academy of Information and Communications Technology(CAICT) Beijing 100191China Department of Information Engineering North China University of TechnologyBeijing 100144China

Dispersed computing is a new resourcecentric computing *** to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its *** trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing *** this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing ***,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the *** the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding ***,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary *** experiments are introduced to verify the performance of the *** with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.

关键词： dispersed computing trust evaluation model malicious node interaction success rate detection rate

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Dynamic Graph Neural Networks with Neighborhood Constraints 21

Dynamic Graph Neural Networks with Neighborhood Constraints

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Ren, Jiankang Lv, Xian Zhang, Jun Li, Xu Ni, Zheng Zhang, Haijun School of Computer Science and Technology Dalian University of Technology Dalian China Dalian University of Technology Key Laboratory of Social Computing and Cognitive Intelligence Dalian China School of Computer Science and Technology Xinjiang Normal University Xinjiang China Graduate School of Education Dalian University of Technology Dalian China

ISBN: (纸本)9798350365221

Graph data are well suited to represent complex interactions and dependencies owing to its inherent relational structure. Graph neural networks (GNNs) can effectively explore the topological structure and node feature information of graph data to perform perceptual inference tasks. In GNNs, the state of each node is iteratively updated by aggregating information from its neighbors. Note that some noise may be introduced due to the information aggregation of irrelevant neighbor nodes, leading to poor performance with higher computational overhead. This paper proposes a dynamic graph generation method based on neighborhood constraints, which learns the degrees of the nodes and imposes the neighborhood constraints to limit the neighbor-hood space of the nodes, resulting in a sparse graph with dynamic neighborhoods. Based on the sparse graph, the dynamic graph neural network selectively aggregates the information of the most important neighbors to improve the performance of the model. Furthermore, to ensure neighborhood constraints satisfaction during the learning process, a loss function is designed to penalize the nodes whose degrees exceed a specific threshold. The evaluation results demonstrate that our proposed method achieves a higher inference accuracy with the same or higher sparsity compared to state-of-the-art methods. © 2024 IEEE.

关键词： Graph neural networks

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Combining DistilBERT and LSTM to Enhanced Detection of Depression in Social Media Text 9

Combining DistilBERT and LSTM to Enhanced Detection of Depre...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Kurniadi, Felix Indra Satyaning Pradnya Paramita, Ni Luh Putu Angela Sihotang, Erna Fransisca Anggreainy, Maria Susan Zhan, Rongfang School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Statistics Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science University of North Texas Department of Rehabilitation and Health Service United States

ISBN: (纸本)9798350390025

Depression affects a significant number of people, and it has a significant impact not only on their lives but also on society as a whole. In light of this, we require more advanced methods that are capable of locating individuals who are depressed in a timely and accurate manner. This research investigates the effectiveness of Long Short-Term Mem- ory (LSTM) networks used in conjunction with DistilBERT, a small transformer-based model, in identifying indications of melancholy in posts made on social networking platforms. The text was cleaned up using a dataset that was obtained from Reddit, and then we used the mixed DistilBERT+LSTM model to evaluate how well it performed in comparison to the DistilBERT model by itself. The findings that we obtained indicate that both approaches produce results that are comparable but not flawless, with only slight variations in terms of accuracy, precision, recall, and F1 scores. The fact that this is the case demonstrates that there are issues with optimizing the model and displaying the data. © 2024 IEEE.

关键词： Social networking (online)

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