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Non-fungible Token Price Prediction with Multivariate LSTM Neural Networks

Non-fungible Token Price Prediction with Multivariate LSTM N...

引用

international conference on Soft computing and Machine Intelligence (ISCMI)

作者： Jerome Branny Rolf Dornberger thomas Hanne MSc Medical Informatics University of Applied Sciences and Arts Northwestern Switzerland Basel Switzerland Institute for Information Systems University of Applied Sciences and Arts Northwestern Switzerland Basel Switzerland Institute for Information Systems University of Applied Sciences and Arts Northwestern Switzerland Olten Switzerland

In this paper, we investigate how to forecast Non-Fungible Token (NFT) sale prices by using multiple multivariate time series datasets containing features related to the NFT market space. We examined eight recent studies regarding the forecasting and valuation of NFTs and compared their most important findings. this laid the fundamental work for two separate machine learning prototypes based on Long Short-Term Memory (LSTM) which are able to forecast the sale price history of an individual NFT asset. Root Mean Squared Errors (RMSE) of 0.2975 and 0.24 were obtained which appears to be promising.

关键词： Training Sentiment analysis Social networking (online) Time series analysis Prototypes Machine learning Predictive models

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Edge computing-Enabled Lightweight Deep Neural Network for Real-Time Video Surveillance in Maritime Cyber-Physical Systems

Edge Computing-Enabled Lightweight Deep Neural Network for R...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Caofei Luo Fumin Wu Renhao Xiong Guanhua Xu Wen Liu CETC (Ningbo) Maritime Electronics Research Institute Co. Ltd. Ningbo China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Navigation Wuhan University of Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the rapid developments of low-end IoT devices and artificial intelligence (AI), the heterogeneous and dynamic data has been increasing drastically, especially in the era of AI-enabled maritime cyber-physical systems (CPS). It is intractable to provide the efficient and flexible computer vision applications through traditional cloud-centric data processing strategies, which commonly suffer from the limitations, e.g., high cost, high- latency communication, and high occupied storage, etc. To meet these challenges, edge computing has meaningfully evolved into a flexible and powerful distributed architecture. It contributes to improvements in bandwidth savings and response time in practical applications. To guarantee efficient video surveillance in AI-powered maritime CPS, the edge computing-based lightweight deep network (termed ShipNet-YOLOv3), incorporated with Kalman filtering and Hungarian algorithm, is proposed to implement real-time detection and tracking of moving ships. In particular, ShipNet-YOLOv3 adopts Darknet-53 as the backbone network (i.e., feature extractor) with three prediction boxes. the channel pruning and fine-tuning strategy are further introduced to balance the trade-off between computational cost, network size, and detection (or tracking) accuracy. Experimental results on both synthetic and realistic scenarios have illustrated that the proposed method could provide superior ship detection and tracking results under different adverse imaging conditions. It thus has the capacity of promoting real-time video surveillance in AI -enabled maritime CPS.

关键词： Performance evaluation Filtering Image edge detection Imaging Video surveillance Feature extraction Real-time systems Kalman filters Marine vehicles Artificial intelligence

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A Comprehensive Performance Evaluation of Different Kubernetes CNI Plugins for Edge-based and Containerized Publish/Subscribe Applications 9

A Comprehensive Performance Evaluation of Different Kubernet...

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9th IEEE international conference on Cloud Engineering (IC2E)

作者： Kang, Zhuangwei An, Kyoungho Gokhale, Aniruddha Pazandak, Paul Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA Real Time Innovat Sunnyvale CA USA

ISBN: (纸本)9781665449700

the growing number of data- and latency-sensitive Internet of things (IoT) applications is posing significant challenges for the edge and cloud deployment of publish/subscribe services, which are required by these applications. Two independently developed technologies show promise in addressing these challenges. First, Kubernetes (K8s) provides a de-facto standard for container orchestration that can manage and scale distributed applications in the cloud. Second, OMG's Data Distribution Service (DDS), a standardized real-time, data-centric and peer-topeer publish/subscribe middleware, is being used in thousands of critical systems around the world. However, the feasibility of running DDS applications within K8s for latency-sensitive edge computing, and specifically the performance overhead of K8s' network virtualization on DDS applications is not yet well-understood. To address this, in this paper we evaluate the performance overhead of several container network interface (CNI) plugins including Flannel, WeaveNet and Kube-Router installed on a hybrid (ARM+AMD) edge/cloud K8s cluster. the paper reports results from a comprehensive set of experiments conducted to measure and analyze the performance (throughput, latency, and CPU/memory usage) of containerized DDS applications from the perspectives of virtualization overhead, reliability (DDS Reliable and BestEffort QoS), transport mechanisms (UDP unicast and multicast), and security. the insights derived from this study provide concrete guidance to developers of DDS-based applications in choosing the right virtual network plugin and configurations when hosting their real-time IoT applications in real-world containerized environments.

关键词： Kubernetes Container networking Data Distribution Service Pub/Sub Performance Evaluation

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the Role of the Data Quality on Model Efficiency: An Exploratory Study on Centralised and Federated Learning

The Role of the Data Quality on Model Efficiency: An Explora...

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international conference on Fog and Mobile Edge computing (FMEC)

作者： Gustav Nilsson Martin Boldt Sadi Alawadi Imagimob Stockholm Sweden Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

this paper investigates the impact that datasets of varying data quality levels have on centralized vs. federated learning models using experiments. We also investigate how the distribution of low-quality data across federated clients affects the models’ accuracy. Within the experiments we create datasets of increasingly worse data quality in terms of the following two data quality metrics; data accuracy and data completeness. this is done by perturbing (i.e., modifying) the datasets in order to decrease the quality of the datasets with regard to these two data quality metrics. then, three experiments are conducted that investigates; i) the impact of decreased data accuracy on the models’ performance, ii) the impact of decreased data completeness, and iii) the effects of different distribution low-quality data on the clients used in the federated learning setup. the results reveal that the centralized model achieves 60.3% validation accuracy with low data accuracy and 58.7% with low data completeness. While the federated model performs better, achieving 69.3% validation accuracy with low data accuracy and 79.2% with low data completeness. the federated model is less affected by low data quality if the data quality is distributed evenly between its clients. Further, the federated learning setup displays certain attributes that make it more robust to data with low quality, compared to centralized learning. Uneven distribution of data quality between clients has a more negative impact on federated learning compared to even distribution.

关键词： Measurement Accuracy Multi-access edge computing Federated learning Data integrity Computational modeling Data models Robustness

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Boosting the Training Time of Weakly Coordinated distributed Machine Learning

Boosting the Training Time of Weakly Coordinated Distributed...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Duriakova, Erika Tragos, Elias Lawlor, Aonghus Smyth, Barry Hurley, Neil Univ Coll Dublin Insight Ctr Data Analyt Dublin Ireland

ISBN: (纸本)9781665439022

In this paper, we propose a novel communication-efficient algorithm for distributed matrix factorisation. Our goal is to find a good trade-off between the communication overhead and the overall model training time. In our setting, the training data is distributed across multiple servers that aim to learn a joint machine learning model. In contrast to standard distributed computation, due to privacy concerns, the participating servers are not allowed to share raw data, however, sharing of the non-personal model parameters is allowed. We investigate the drawbacks of traditional strongly coordinated distributed techniques and compare them to weakly coordinated gossip approaches. the advantage of strongly coordinated approaches is that the learning process closely mimics that of a centralised algorithm and hence this approach can keep the overall training time at a minimum. However, this is at the expense of a large communication footprint of the algorithm. On the other hand, the weakly coordinated gossip approach offers a communication efficient solution that can take a large amount of training time to reach a good accuracy. As a solution, we develop a hybrid approach combining the above two approaches. We apply the hybrid approach on a latent factor model solving a top-N recommendation problem and we show that the hybrid approach achieves good accuracy in relatively short training time with minimal communication overhead particularly on very sparse data.

关键词： Recommender Systems distributed Learning Decentralised Matrix Factorisation Matrix Factorisation Communication Efficiency

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An Ensemble Deep Learning Model for Diabetic Retinopathy Identification

An Ensemble Deep Learning Model for Diabetic Retinopathy Ide...

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international conference on Smart Structures and Systems (ICSSS)

作者： Mohammad Manzoor Hussain P. Shanmugam K. Moorthi U. Sakthivelu A. Rajasekar R. Naresh Kumar Department of Computer Science and Engineering B V Raju Institute of Technology Medak Telangana India Department Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department Electronics and Communication Engineering Sairam Engineering College Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai India

Diabetic Retinopathy (DR) is an eye disease that can potentially cause significant injury in those diagnosed with diabetes. the condition arises due to issues with blood circulation to the retina, the delicate tissue in the posterior part of the eye. DR is recognized as the leading factor contributing to visual impairment among those affected by diabetes. However, timely identification and intervention might mitigate the occurrence of this condition. Conversely, the timely identification of DR poses challenges, while the diagnostic process can be laborious and time-intensive. the automated detection of DR necessitates categorizing colour retinal fundus pictures. this article presents a computer-aided diagnostic strategy that utilizes Deep Learning (DL) algorithms. In order to classify retinal fundus pictures, this study employed a combination of DL models, specifically ResNet50 and Inception V3. the studies are being conducted on the DIARETDB1 dataset. An analysis is conducted to compare the results of the suggested methodology. the comparison study results show that the proposed approach has more significant accuracy levels than the other methods. A high degree of accuracy, that is, 98.37%, has been attained by utilising the proposed techniques.

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A Novel Color Image Encryption based on Chaos and DNA Mutation

A Novel Color Image Encryption based on Chaos and DNA Mutati...

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IEEE international conference on Signal and Image Processing (ICSIP)

作者： Mingliang Sun Jie Yuan Xiaoyong Li Dongxiao Liu Xinghai Wei Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

the security of the color images is challenged by the illegal copying and distribution over various communication networks. this work proposed a novel color image encryption scheme based on the chaos and Deoxyribonucleic acid (DNA). Firstly, A novel four dimensions chaotic system is constructed and analyzed. Secondly, the DNA coding rules are controlled by the chaos-based pseudorandom sequence to substitute the image pixels. the column and row rotation are designed to disturb the image pixels. Finally, the DNA matrix coded from the color image is divided into blocks to implement the XOR and addition operation with the pseudorandom sequence. Its security is simulated and compared with other schemes. the comparison show that the proposed scheme has a higher key sensitivity, lower correlation of adjacent pixels, and higher NPCR and information entropy. therefore, the proposed image encryption scheme is more capable against statical attacks and efficiency while keeping high robustness.

关键词： Sensitivity Image coding Chaotic communication DNA Color Robustness Matrices

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Modification Method for Internal Network Flow Anomaly Detection Based on Multi-timescale Analysis

Modification Method for Internal Network Flow Anomaly Detect...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Xinyu Zhang Junmei Ding Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the continuous development of information technology, security issues from internal networks are becoming more and more important. Many anomaly detection algorithms are designed to identify anomalies, but these algorithms usually do not care about temporal information, or only care about fine-grained temporal information. On the other hand, deep-learning-based algorithms must rely on complex neural networks to capture temporal information, which is difficult to deploy and maintain in actual internal network environments. Addressing the aforementioned problems, this paper proposes a multi-timescale analyzer using wavelet packet transform and semi-supervised learning, and a method that uses the analyzer to modify the original anomaly scores called Multi-timescale Modification (MTM) method. the method is validated using the network flow dataset CIC-IDS 2017 and a real-world dataset. Experimental evaluations show that using the proposed method can obtain better performance than the original algorithms.

关键词： Hands Neural networks Cyberspace Semisupervised learning Data science Wavelet analysis Wavelet packets Security Information technology Anomaly detection

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Integrating Trusted computing Mechanisms with Trust Models to Achieve Zero Trust Principles

Integrating Trusted Computing Mechanisms with Trust Models t...

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international conference on Internet of things: Systems, Management and Security

作者： Muntaha Alawneh Imad M. Abbadi Computer Engineering Al Ain University Abu Dhabi UAE Security Risk & Compliance Practice Hewlett-Packard Enterprise (HPE) Dubai UAE

One of the main pillars for creating a sustainable model to address changes in the threat landscape is the Zero Trust principle of "Never Trust/Always Verify". Zero Trust focuses on addressing insiders and creeping insiders problem including those existing in clouds, IoT and social networks environment. We noticed Zero Trust did not yet get enough attention from academia and most of the work done on Zero Trust is coming from industry. Majority of the work on this domain are vendor specific and not well covered from an enterprise security architecture point of view. this results in narrow focus and inconsistencies. the use of trust models to implement the "Never Trust" principle, and the "Always Verify" concept utilizing trusted computing techniques is our major contribution. the integration of Trusted computing mechanisms with Trust Models would strongly help in achieving the Zero Trust principles.

关键词： Industries Trusted computing Social networking (online) Computational modeling Computer architecture Organizations Zero Trust

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IPV6 DUAL-STACK DEPLOYMENT for the distributed computing CENTER 9

IPV6 DUAL-STACK DEPLOYMENT for the DISTRIBUTED COMPUTING CEN...

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9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Kotliar, Anna Kotliar, Viktor Institute for High Energy Physics named by A.A. Logunov National Research Center "Kurchatov Institute" Nauki Square 1 Protvino Moscow region 142281 Russia

computing center of the Institute for High Energy Physics in Protvino provides computing and storage resources for various HEP experiments (Atlas, CMS, Alice, LHCb) and currently operates more than 150 working nodes with around 3000 cores and provides near 2.5 PB of disk space. All these resources are connected through two 10 GB/s links to LHCONE and other research networks. IHEP computing center has IPv4 address space limited to one C-sized network and all computing nodes working behind the NAT that has some drawbacks for production use. To optimize routing, switching and to get higher network throughput for data transfer the IPv6 dual-stack was deployed on the computing farm. this work shows the full cycle of the real IPv6 dual-stack deployment from zero to production. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Data transfer

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