The crazy, unconscious use of the Internet, and the increase in cybercrime and hacking, which resulted in the loss of a large number of sensitive data, the risk of piracy, etc. were the motivation for protecting right...
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Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different ...
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Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different techniques. While these studies are elegant in theory, they assume an ideal cloud storage model;that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications(e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services.
As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive...
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As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.
In this paper the problem of testing decision making systems for MEC platforms was formulated. Methods and means of organizing the introduction of network delays as part of the emulation system of MEC platforms LWMECP...
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Medical image analysis is essential in the diagnosis and treatment of many diseases, including brain tumors. This work investigates the effectiveness of transformer models in the multiclass classification of brain tum...
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This paper introduces an intelligent traffic flow prediction system that combines data twinning and deep learning, aiming to improve the prediction accuracy and model adaptability by integrating grey prediction model ...
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This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease ...
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This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen
Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...
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Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
In this paper, we investigate the effectiveness of various machine learning methods for the multimodal classification of personality traits using the HEXACO model and open data from social media users. Particular atte...
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ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
In this paper, we investigate the effectiveness of various machine learning methods for the multimodal classification of personality traits using the HEXACO model and open data from social media users. Particular attention is paid to the analysis of data obtained from user profiles, including statistical, visual, and audio characteristics. For each personality trait from the HEXACO model, a machine learning model is trained that determines the manifestation degree of this trait (low, medium, high). The influence of different types of information, both individually and in combination, on the weighted F1-score is considered. The study provides a comparative analysis of traditional machine learning methods that use all features and approaches with the selection of the most significant features. A strategy for combining data from different modalities using early merger is evaluated. The experimental results demonstrate that early merger models using selected features from different modalities provide the best result of the weighted F1-score, which varies depending on the personality trait from 0.76 to 0.85.
Class Title:Radiological imaging method a comprehensive overview *** GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent ...
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Class Title:Radiological imaging method a comprehensive overview *** GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the *** and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed *** recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis ***:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical ***:Radiological imaging is an extremely important tool in modern medicine to assist in medical *** work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.
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