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Transparent Transaction Processing with A High-Performance Proxy for distributed KVS 8

Transparent Transaction Processing with A High-Performance P...

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8th international Symposium on computing and networking Workshops, CANDARW 2020

作者： Miwa, Ryuya Miyamoto, Motoshi Kawashima, Ryota Matsuo, Hiroshi Nagoya Institute of Technology Nagoya Aichi466-8555 Japan

ISBN: (纸本)9781728199191

distributed consensus protocols require a large amount of communications for transaction processing in distributed KVS (D-KVS). Google Spanner and Amazon DynamoDB alleviate the communication overhead using highly accurate timestamping obtained from atomic clocks. However, introducing special hardware devices complicates operation management and drastic system changes. In this paper, we propose a transparent transaction processing method without depending on distributed consensus protocols and special hardware devices. We provide transaction processing functions that complies with ACID by performing exclusive control and transaction processing on a centralized high-performance proxy. the proxy provides ACID-compliant transaction processing functions to D-KVS by managing locks and transactions on multiple threads and guarantees the performance in medium-scale D-KVS (dozens of nodes). In the evaluation, first, we evaluate the overhead of the proxy with transaction processing functions, the throughput decreased by 0.2% to 12%. Next, we confirmed that the proxy processes the lightweight transaction of Cassandra with about 60% execution time. Finally, we confirmed that the proxy can process trans-actions, compared the transaction processing performance with MariaDB, it operated faster than MariaDB. © 2020 IEEE.

关键词： Processing

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Artificial Intelligence based Facial Emotion Recognition with Deep Neural GAN Augmentation

Artificial Intelligence based Facial Emotion Recognition wit...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： D. Menaka D. Karthika Priya C. Reeda Lenus A Ashwini P. Josephin Shermila Banu Priya Prathaban Department of Electronics and Instrumentation Engineering Noorul Islam Centre for Higher Education Kumaracoil India Department of Artificial Intelligence and Data Science Easwari Engineering College Ramapuram Chennai India Department of Physics S.A.Engineering College Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science R.M.K.College of Engineering and Technology Chennai India Department of Networking and Communication School of computing SRM institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Facial recognition system in computer vision is widely used in human-computer interface and clinical diagnostics. Nonetheless, achieving high accuracy in this task still poses a challenge due to issues of inadequate amount and imbalance of data to train the models as well as issues of generalization and robustness. the findings of this research propose the use of Generative Adversarial Network (GAN) data augmentation for face emotion classification specifically for this approach. the proposed strategy is based on the generation of artificial ‘emotional’ images on the face using Deep Neural Self Attention GANs (DNSAGANs). this way, to capture all the variations of facial expressions the GAN takes a large number of images and thus makes the augmented data richer and more realistic. this larger data set is then used to train a deep learning architecture that performs the face emotion classification thereby enabling the network to learn enhanced discriminative features. To collect face photos the FER2013 dataset is used. the training process, entrained the generator and discriminator to minimize the mean squared error while the discriminator was simultaneously trained to robustly distinguish generated images from real ones. Altogether, the proposed Adam optimizer was more accurate than Adagrad and Adadelta for all epochs. the percentage of accuracy is about $88.64 \%$ for 100 epochs using the proposed optimizer techniques.

关键词： Training Emotion recognition Adaptation models Accuracy Face recognition Computational modeling Generative adversarial networks Data models Robustness Time complexity

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Research and Implementation of Asynchronous Transmission and Update Strategy for Federated Learning

Research and Implementation of Asynchronous Transmission and...

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international conference on Computer and Communications (ICCC)

作者： Yudong Jia Ningbo Zhang Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing China

In today's era, there is a growing concern about data privacy and security. Data is the foundation of machine learning, and due to the complexity of the reality, data often exists in silos. the great development of computing and storage capacity of edge devices has made it possible to train machine learning models on edge devices with data. Federated learning is a special kind of distributed machine learning in which multiple distributed clients use local data to train a model and upload the resulting model parameters to a central server for aggregation and iteration to finally obtain a global model with good generalization performance. the interaction between the clients and the server is model parameters rather than the data itself, which effectively protects data privacy and security. After research, it is found that Federated Learning is mainly divided into two aggregation models: synchronous aggregation and asynchronous aggregation. In this paper, the classical FedAvg synchronous federated average aggregation algorithm is analyzed. the central server needs to wait for all participating nodes to be trained before aggregating and updating the model, which is inconvenient to achieve global synchronization on resource heterogeneous clients. To improve the utilization of computational resources, FedAsync, an asynchronous federated learning aggregation mechanism with a receivable window based on dual weights, which are data volume size weight and staleness weight, is proposed. And FedAvg and FedAsync are tested and analyzed. Based on this, the impact on federated learning due to asynchronous data arrival rate and limited communication resources is further explored.

关键词： Performance evaluation Data privacy Federated learning Computational modeling distributed databases Data models Complexity theory

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Proceedings - 2020 8th IEEE international conference on Mobile Cloud computing, Services, and Engineering, MobileCloud 2020

Proceedings - 2020 8th IEEE International Conference on Mobi...

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8th IEEE international conference on Mobile Cloud computing, Services, and Engineering, MobileCloud 2020

ISBN: (纸本)9781728110356

the proceedings contain 13 papers. the topics discussed include: global and secured UAV authentication system based on hardware-security;drone-assisted fog-cloud IoT content service platform for rural communities;scalable and efficient mutual authentication strategy in fog computing;participant comfort adaptation in dependable mobile crowdsensing services;DewROS: a platform for informed dew robotics in ROS;docker container deployment in distributed fog infrastructures with checkpoint/restart;self-sovereign identity specifications: govern your identity through your digital wallet using blockchain technology;and Bloccess: towards fine-grained access control using blockchain in a distributed untrustworthy environment.

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Cloud-Edge Microservice Architecture for DNN-based distributed Multimedia Event Processing 8th

Cloud-Edge Microservice Architecture for DNN-based Distribut...

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1st international Workshop on Edge Adoption and Migration, EdgeWays 2020, 16th international Workshop on Engineering Service-Oriented Applications and Cloud Services, WESOACS 2020, ESOCC 2020 PhD Symposium, ESOCC 2020 EU Projects Track which took place at 8th European conference on Service-Oriented and Cloud computing, ESOCC 2020

作者： Pontes, Felipe Arruda Curry, Edward Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Ireland

ISBN: (纸本)9783030719050

the rise of Big Data, Internet of Multimedia things (IoMT), and Deep Neural Network (DNN) enabled the growth of DNN-based Computer Vision solutions to Multimedia Event Processing (MEP) applications. When these are applied to a real-world scenario we notice the importance of having a system with a satisfactory speed that can fit in the limited resources of most IoMT devices. However, most solutions for distributed MEP are dependent on a Cloud architecture, which makes these applications migration to the Edge more challenging. As a response to this, we present a microservice architecture for DNN-based distributed MEP over heterogeneous Cloud-Edge environments. We describe our solution that allows for an easier deployment both on the Edge and on the Cloud. We show that choosing the proper tools for an Edge-Friendly solution can lead to 100 times less resource utilisation. Our preliminary investigation shows promising results, with a reduction in energy consumption by 8% with a minor drawback of 15% in throughput in the Edge and a negligible increase in energy consumption on the Cloud. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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GAP-AWARE MITIGATION OF GRADIENT STALENESS 8

GAP-AWARE MITIGATION OF GRADIENT STALENESS

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8th international conference on Learning Representations, ICLR 2020

作者： Barkai, Saar Hakimi, Ido Schuster, Assaf Department of Electrical Engineering Technion - Israel Institute of Technology Haifa Israel Department of Computer Science Technion - Israel Institute of Technology Haifa Israel

Cloud computing is becoming increasingly popular as a platform for distributed training of deep neural networks. Synchronous stochastic gradient descent (SSGD) suffers from substantial slowdowns due to stragglers if the environment is non-dedicated, as is common in cloud computing. Asynchronous SGD (ASGD) methods are immune to these slowdowns but are scarcely used due to gradient staleness, which encumbers the convergence process. Recent techniques have had limited success mitigating the gradient staleness when scaling up to many workers (computing nodes). In this paper we define the Gap as a measure of gradient staleness and propose Gap-Aware (GA), a novel asynchronous-distributed method that penalizes stale gradients linearly to the Gap and performs well even when scaling to large numbers of workers. Our evaluation on the CIFAR, ImageNet, and WikiText-103 datasets shows that GA outperforms the currently acceptable gradient penalization method, in final test accuracy. We also provide convergence rate proof for GA. Despite prior beliefs, we show that if GA is applied, momentum becomes beneficial in asynchronous environments, even when the number of workers scales up. © 2020 8th international conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Cloud computing

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Students’ Perspectives on ‘PeVe App’ to Learn English Phrasal Verbs

Students’ Perspectives on ‘PeVe App’ to Learn English Phr...

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international conference on computing, Engineering, and Design (ICCED)

作者： Melania Wiannastiti Teguh Prasandy Yi Ying Krystl Mo Liem Wiwik Andreani Endang Ernawati Language Center The Faculty of Humanity Bina Nusantara University Jakarta Indonesia Informatic Engineering Dept BINUS Online Learning Bina Nusantara University Jakarta Indonesia Chinese Department The Faculty of Humanity Bina Nusantara University Jakarta Indonesia Applied Foreign Languages Department Cheng Shiu University Kaoh Taiwan English Department The Faculty of Humanity Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453905

Learning English Phrasal Verbs (PVs) is one of the biggest challenges for the English as a Foreign Language (EFL) learners in higher education in Indonesia that demands a learning method coping with the development of technology and teaching devices. PeVe app is a new platform designed to support EFL learners to learn English PVs for developing their writing skills. the research aimed to discover the EFL students’ perspectives on the development of first-level content of PeVe apps in learning English PVs and to get their feedback on its development. Extreme Programming was used for developing PeVe Apps and the last phase of testing was carried out by distributing an online questionnaire to 70 students in two classes at one university with 62 responses. A descriptive quantitative was used to analyze the data. the result shows that more than 90% agree that the apps for learning PVs are supportive and more than 80% agree that learning English using the apps is fun and most agree this app supports them in learning English PVs. From the feedback, several things need to be added to the landing page, material, exercises, and the quiz. Since this app is a pilot project, only one at the Beginner level is available. therefore, it is needed to develop to the further levels with various pictures, summaries of tasks, and listening materials. thus, it is suggested for further research to improve PeVe ***— Convolutional Neural Network (CNN), RUU Omnibuslaw, Sentiment Analysis, Twitter.

关键词： Learning systems Sentiment analysis Social networking (online) Education Writing Animation Convolutional neural networks

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A Local Interpretability Model-Based Approach for Black-Box Adversarial Attack 8th

A Local Interpretability Model-Based Approach for Black-Box ...

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8th international conference on Data Mining and Big Data, DMBD 2023

作者： Duan, Yuanjie Zuo, Xingquan Huang, Hai Wu, Binglin Zhao, Xinchao School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819708437

Deep learning models are vulnerable to adversarial examples due to their fragility. Current black-box attack methods typically add perturbations to the whole example, and added perturbations may be large and easily detected by human eyes. this study proposes a Local Interpretable Model-agnostic Explanations (LIME)-based approach for black-box adversarial Attack (LIME-Attack). the approach can reduce the size of perturbations via adding perturbations in discriminative regions of an example. First, LIME is used to interpret a black-box model to obtain discriminative regions of an example. then, the gradient information of the example is estimated by a derivative-free optimization method (Nature Evolution Strategy). Utilizing the gradient information, two white-box attack methods are adapted to generate perturbations, which are added in discriminative regions of the example to form an adversarial example. LIME-Attack is applied to several typical neural network models. Experiments show that it can achieve a high attack success rate with perturbation size 10%–30% lower than that of comparative methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lime

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Cervical Cancer Identification using Deep Learning Approaches

Cervical Cancer Identification using Deep Learning Approache...

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international conference on computing, Engineering, and Design (ICCED)

作者： Shien Nie Kong Dini Oktarina Dwi Handayani Hou Kit Mun Pei Pei Chong Teddy Mantoro School of Computer Science & Engineering Faculty of Innovation & Technology Taylor's University Subang Jaya Selangor Computer Science Department Kuliyyah of Information Communication and Technology International Islamic University Malaysia Kuala Lumpur Department of Mechatronic and Biomedical Engineering Faculty of Engineering and Science Universiti Tunku Abdul Rahman Bandar Sungai Long Selangor School of Biosciences Faculty of Health and Medical Sciences Taylor's University Subang Jaya Selangor Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia

ISBN: (纸本)9781665453905

the presence of cervical cancer is not apparent as its incubation period is long. A pap smear screening is the only diagnostic method; examining the pap smear slides uses a microscope. However, problems happen where humans make mistakes during the diagnostic process, causing inaccurate results and delaying the individual who needs to receive comprehensive treatments. With hopes to improve the current situation, assisting cervical cancer diagnosis with artificial intelligence techniques is suggested. this paper will use ResNet101v2, an upgraded residual network from ResNet, to develop a cervical cancer detection model to predict the severity of cervical cells. the Herlev dataset distributed 70% into training and 30% into validation; remaining 98 unique images will be used during the testing stage. Transfer learning techniques were introduced to develop the model. Using the testing images, the model reached 71.4% accuracy contributing better accuracy compared to other research studies in predicting the cervical cells using 7 classification classes. the model shows potential for clinical cytotechnologists’ during the pap smear diagnosis.

关键词： Training Systematics Microscopy Transfer learning Learning (artificial intelligence) Predictive models Market research

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Economic Denial of Sustainability (EDoS ) Detection using GANs in SDN-based Cloud 8

Economic Denial of Sustainability (EDoS ) Detection using GA...

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8th IEEE international conference on Communications and Electronics (IEEE ICCE)

作者： Dinh, Phuc Trinh Park, Minho Soongsil Univ Dept Informat Commun Mat & Chem Convergence Techn Seoul 156743 South Korea Soongsil Univ Sch Elect Engn Seoul 156743 South Korea

ISBN: (纸本)9781728154718

Cloud computing is now considered to be the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. Specifically, a newly discovered type of attack, called an economic-denial-of-sustainability attack known as EDoS, exploits the pay-per-use model to scale up the resource usage over time to the degree that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, we propose an effective solution in the SDN-based cloud computing environment. We first introduce a machine-learning-based approach adopting a framework called MAD-GAN which applies an unsupervised multivariate anomaly detection technique based on Generative Adversarial Networks (GANs), using the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) to detect EDoS attacks. Its main idea is to produce an anomaly score at each time step by learning a multivariate attribute. We then generate a dynamic threshold score to compare with the anomaly score produced to classify the network traffic as EDoS traffic or normal traffic. By realistic tests, our proposed scheme is demonstrated to outperform existing methods for EDoS attack detection. the detailed experiments conducted with different levels of EDoS attacks show that the proposed scheme is an efficient, innovative approach to defend EDoS attacks in the SDN-based cloud.

关键词： Cloud computing security economic denial of sustainability (EDoS) software-defined networking machine learning network function virtualization

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