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Enhancing Security and Privacy in Cloud Computing: Challenges and Solutions in the Digital Age 3

Enhancing Security and Privacy in Cloud Computing: Challenge...

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3rd international conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Ginanjar, Muhamad Gilang Lubis, Muharman Ramadani, Luthfi Dwi Handayani, Dini Oktarina Telkom University Department of Information System Indonesia International Islamic University Malaysia Department of Computer Science Malaysia

ISBN: (纸本)9798350367492

The development of cloud computing offers significant benefits such as reduced operational costs, increased flexibility, and enhanced accessibility to technological resources. With the growing use of data driven by technological advancements and business expansion, organizations adopting cloud computing face various security and privacy challenges due to the involvement of sensitive data. This article aims to discuss the types of cloud computing deployment and services and examine critical security and privacy issues, including governance, data availability, data loss, data breaches, access control, data control, and the trust relationship between customers and service providers. A comprehensive review of existing literature and current practices is conducted to identify these challenges. Specific solutions are proposed for each identified issue to address these challenges effectively. The findings underscore the importance of continuously updating security strategies to protect data privacy. Therefore, it is imperative for organizations utilizing cloud services to adopt these strategies to safeguard their data privacy. © 2024 IEEE.

关键词： Cloud security

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Vehicle Detection and Classification based on C-DSO Dataset using YOLO v3 with SRBD Method for Intelligent Transportation Applications 3

Vehicle Detection and Classification based on C-DSO Dataset ...

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Anitha, R. Prabakaran, P. Vels University v i S T A S Department of Computer Science Chennai India

ISBN: (纸本)9781665494007

The tremendous evolution of computer vision and technology, the most challenging application of vehicle category detection is an important role in our Indian Traffic system. The most of the vehicle detection algorithms facing some struggles with vehicle object detection and recognition rate. Using Deep Learning with Conventional Neural networks to undercover the problem of vehicle detection and to understand the right way of vehicle classification, detection, and vehicle recognition. This paper proposes our model of designing a dataset (C-DSO) and combines the method YOLO(You Only Look Once) V3 algorithm with SRBD to introduce the SRBD-C-DSO algorithm, it detects the smart way of integrating the pre-training of the image, labeling of vehicle image, image smoothing, and Annotation. Furthermore, renovating the dataset, adding the various categories of vehicle images, and the experimental results show a better detection rate and reduction of loss of validation in the dataset. © 2023 IEEE.

关键词： Vehicles

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Single image super-resolution via deep learning 3

Single image super-resolution via deep learning

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Cao, Zhenyue Liu, Xuan Wang, Zhenkun Shenzhen College of International Education Shenzhen China Tongii University Shanghai China South China Agricultural University Guangzhou China

ISBN: (纸本)9781665459112

Single image Super-resolution (SISR) is a computer vision (CV) problem that aims to acquire a high-resolution (HR) image from a distorted low-resolution (LR) image, making it a valuable technology that could be utilized in various fields such as photography, medical imaging, satellite imaging, etc. As a result of the advancement of computing hardware and richer computational power, deep learning-based image super-resolution models have emerged at an unprecedented rate. This paper reviews SISR and its recent development. Three widely used deep architectures: convolutional neural network (CNN), generative adversarial network (GAN), and transformer are explained. Next, six different deep learning-based models that summarize research on SISR are analyzed. Finally, this review concludes with applications of SR, current challenges SISR models encountered, and potential future research directions. © 2022 IEEE.

关键词： Generative adversarial networks

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Who Is to Err? A Limited Effect of Data Labeling in Prediction of Users' Subjective Impressions of Web Designs 3

Who Is to Err? A Limited Effect of Data Labeling in Predicti...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Bortnikova, Anna V. Bakaev, Maxim A. Novosibirsk State Technical University Department of Automated Control Systems Novosibirsk Russia Novosibirsk State Technical University Department of Data Collection and Processing Systems Novosibirsk Russia

ISBN: (纸本)9798331516321

User Experience (UX) researchers and designers who seek to predict users' subjective impressions nowadays turn to Machine Learning (ML) models trained on marked-up data. Labeling of graphical user interfaces (UIs) is a rather time-consuming process that is also prone to errors and inaccuracies, particularly if motivation of the human labelers is low. Correspondingly, the size of datasets in Human-computer Interaction (HCI) rarely exceeds the order of 10,000, and the field largely relies on feature-based models. In our paper, we compare two principal ways of obtaining the features, with respect to their effect on the quality of the models that predict users' impressions of web pages' Complexity, Aesthetics and Orderliness. First, we calculated several layout-related metrics - balance, equilibrium, symmetry - for 368 website screenshots labeled by 40 human volunteers. Second, we obtained the similar metrics from ML-based online services working with raw web UI images. Our results suggest that despite the limitations of the current automated web UI parametrization tools, their metrics can actually yield superior models (having R2s higher by 107% on average). Our key implication for UI/UX designers and ML researchers is that the expected ROI should be carefully weighted before committing to the tedious manual web UI data labeling. © 2024 IEEE.

关键词： Web Design

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DMTP: A Distributed Matchmaking Trading Platform 3

DMTP: A Distributed Matchmaking Trading Platform

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Duan, Bowen Du, Aiqing Yang, Peiyu Wang, Jiale Mou, Wenjiei Ju, Haiyu Anhui Normal University Collage of Computer AnHui China Institute of Mathematics and Computer Wuhan Polytechnic University Wuhan China ShangHai Jian Qiao University Collage of Science ShangHai China Northwest University School of Information Science and Technology Xi'an China

ISBN: (纸本)9781665459112

Matchmaking trading system has become an essential paradigm to leverage the massive buyers to obtain the certain trades in a most effective method. Trading matchmaking method is an indispensable issue in commerce platforms owing to sellers and buyers exiting in heterogeneous bidder and prices. Matchmaking method is a fundamental approach in trading platforms, while currently most researchers and systems are considered and relayed on a concentrated servers leads to ignore the server safety in assigning trading platforms and the risk of sensitive information leakage by utilizing a centralized server. In this paper, we concentrate on distributed matchmaking approach for buyers with diversity sellers in trading platforms and propose an allocation method DMTP to maximize the social welfare by utilizing the block chain trading technology with reasonable computation and communication costs. Extensively experimental results indicate that proposed mechanism can greatly enhance the successful ratio of matchmaking trades and compare proposed mechanism social welfare with exiting trading algorithms. © 2022 IEEE.

关键词： Commerce

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An Event-based Tracking control Framework for Multirotor Aerial Vehicles using a Dynamic vision Sensor and Neuromorphic Hardware

An Event-based Tracking Control Framework for Multirotor Aer...

引用

IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Aspragkathos, Sotirios N. Ntouros, Evangelos Karras, George C. Linares-Barranco, B. Serrano-Gotarredona, T. Kyriakopoulos, Kostas J. Natl Tech Univ Athens Sch Mech Engn Control Syst Lab 9 Heroon Polytech Str Athens 15780 Greece Univ Thessaly Dept Informat & Telecommun 3rd Km Old Natl Rd Lamia Athens Lamia 35100 Greece Spanish Res Council Inst Microelect Sevilla Seville Spain Univ Seville Seville Spain

ISBN: (纸本)9781665491907

In this paper, we present an event-based control framework for the efficient tracking of contour-based areas, such as road pavements, using a multirotor aerial vehicle equipped with a bio-inspired Dynamic vision Sensor (DVS). Concerning the detection part, the DVS camera captures events, which are asynchronously fed into a Neuromorphic Hough Transform algorithm running on a SpiNN-3 board and implemented as a Spiking Neural Network (SNN). Next, the asynchronous output of the detection module is fed into an analytically formulated event-based Partitioned Visual Servoing (PVS) algorithm, running on conventional processing hardware, which allows the multirotor to autonomously track and navigate along the detected contour. The proposed architecture achieves efficient tracking of contour-based areas, while constantly maintaining the latter inside the DVS camera's field of view. A set of real-time experiments in various settings employing an octorotor equipped with a downward-looking DVS and a SpiNN-3 board demonstrate the effectiveness of the suggested framework.

关键词： Cameras

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Fault diagnosis of Electro-hydrostatic actuators based on CWT and CNN-Transformer 3

Fault diagnosis of Electro-hydrostatic actuators based on CW...

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3rd international conference on Intelligent Mechanical and Human-computer Interaction Technology, IHCIT 2024

作者： Li, Jiatong Tu, Chaofan Wang, Xingjian Wang, Zhaoyang Xiu, Wanbo School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation Tianjin300131 China China Tianjin Navigation Instrument Research Institute Tianjin300131 China

ISBN: (纸本)9781510683105

Electro-hydraulic actuator (EHA) system is a key component of the ship's electro-hydraulic control system, but the internal failure mechanism of EHA is extremely complex, and it is limited by insufficient detection methods in practical systems, making fault diagnosis of EHA a great challenge. To address this issue, this paper proposes a fault diagnosis method based on Continuous Wavelet Transform (CWT), Convolutional Neural Network (CNN) and Transformer. Firstly, the collected one-dimensional time series signal of the sensor is converted into a two-dimensional time-frequency map through CWT. Then, a hybrid module of CNN-Transformer is used to extract local and global fault features, and finally the classification results are output through softmax. Conduct EHA fault simulation experiments to validate the proposed method. The research results indicate that the proposed method has a fault diagnosis accuracy of 96.33%, achieving high-precision diagnosis of EHA faults. © 2024 SPIE.

关键词： Wavelet transforms

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A study of algorithms for generating descriptions of emotionally stylized images with labels 3

A study of algorithms for generating descriptions of emotion...

引用

3rd international conference on Optics and Machine vision, ICOMV 2024

作者： Wang, S. Huanxin Zheng, R. Gengsheng School of Computer Science and Engineering Wuhan Institute of Technology No. 206 Optics Valley 1st Road East Lake High-tech Development Zone Jiangxia District Wuhan430205 China

ISBN: (纸本)9781510680319

Generating stylized captions for images is a challenging task, in order to solve this problem, this paper proposes a new stylized image captioning method M-tag. we design a memory module M containing a set of embedding vectors for encoding stylistically relevant phrases in a training corpus. To obtain style-related phrases, we develop a sentence decomposition algorithm P, which divides the stylized sentence into a style-related part that reflects the linguistic style and a content-related part that contains the visual content. Transformer encoder is used to encode the target of target features within the image and swin-Transformer encoder is used to encode the relational features within the image to jointly encode different aspects of information within the image from different perspectives. The stylized target features encoded by the target Transformer are fused with the relational features encoded by the swin-Transformer through the splicing method to achieve the purpose of fusion of intra-image relational features and local target *** generating the caption, the content-related style knowledge is first extracted from the memory module through the noticing mechanism, and the extracted style features are then integrated into the language model, and finally the fused encoded features are decoded to generate the corresponding image description using the Transformer decoder. © 2024 SPIE.

关键词： Signal encoding

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Distance-aware Multilayer Aggregation Transformer for Image Captioning 3

Distance-aware Multilayer Aggregation Transformer for Image ...

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3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Xu, Honglong Xue, Lixia Wang, Ronggui Yang, Juan School of Computer and Information Hefei University of Technology Hefei230601 China

ISBN: (纸本)9781510681880

Recently, the Transformer based on grid features has achieved great su ccess in image captioning, but it still has some problems: the flattening operation of Transformer will destroy the positional information among visual objects, and only the output of the last layer encoder is sent to the decoder, which will lose low-level semantic information. To solve the above problems, we first introduce Distance-aware self-attention (DA), which considers the original geometric distance between visual objects in a two-dimensional image during the self-attention modeling process, and integrates distance information into attention calculation through a mapping function, better capturing the relational information among visual objects. Second, we propose the Multilayer Aggregation (MA) module, which aggregates the output of the encoder and establishes a weighted residual connection as the final output, sent to the decoder separately. It aggregates information from different encoder layers to achieve cross-layer semantic complementarity, features with rich semantics can be explored simultaneously from both low-level and high-level coding layers. To verify the validity of our proposed two designs, we applied them to a standard Transformer and conducted extensive experiments on MS-COCO, a benchmark dataset for image captioning. The experimental results demonstrate the effectiveness of our proposed Distance-aware Multilayer Aggregation Transformer (DMAT) model. © 2024 SPIE.

关键词： Aggregates

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Design Strategy for Identification and Tracking of Video Objects over Crowded Video Scenes using a Novel Feature-Learning Algorithm 3

Design Strategy for Identification and Tracking of Video Obj...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Divyaprabha Guruprasad, S. Sri Sriddhartha Institute of Technology Dept. of Electronics and Communication Engg Tumkur India Sri Sriddhartha Institute of Technology Dept. of Biomedical Engg Tumkur India

ISBN: (纸本)9798350317022

The idea of tracking video objects has evolved to facilitate the area of surveillance systems. It is mainly observed that the existing video tracking models are more inclined towards improving the accuracy where the consideration of a more significant proportion of mobile objects dynamics in motion over the crowded video frame sequence is mainly overlooked, which is essential to study a specific movement pattern of mobile objects appearing in the video frame sequence. Moreover, research solutions mostly need more effectiveness when it comes to the cost of computation. The study thereby introduces a unique, simplified video tracking strategy capable of assessing a specific pattern of mobile objects movement over complex and crowded video scenes. The research findings show that, unlike the existing system, the proposed tracking model attains approximately 11.4523% improvement over the precision score and 0.882202 % over the F1 measure while reducing the feature computation time to 0.128124 sec. © 2023 IEEE.

关键词： computer vision Learning Mobile Object Dynamics Tracking Video Video Tracking

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