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Generating 2.5D Photorealistic Synthetic Datasets for Training Machine Vision algorithms 15th

Generating 2.5D Photorealistic Synthetic Datasets for Traini...

引用

15th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Peleka, Georgia Mariolis, Ioannis Tzovaras, Dimitrios Centre for Research and Technology Hellas - CERTH Information Technologies Institute 6th km Charilaou-Thermi Rd. Thessaloniki Greece

ISBN: (纸本)9783030578015

the continued success of deep convolution neural networks (CNN) in computer vision can be directly linked to vast amounts of data and tremendous processing resources for training such non-linear models. However, depending on the task, the available amount of data varies significantly. Particularly robotic systems usually rely on small amounts of data, as producing and annotating them is extremely robot and task specific (e.g. grasping) and therefore prohibitive. Recently, in order to address the aforementioned problem of small datasets in robotic vision, a common practice is to reuse features that are already learned by a CNN within a large-scale task and apply them to different small scale ones. this transfer of learning shows some promising results as an alternative, but nevertheless it can not be compared with the performance of a CNN that is specifically trained from the beginning for that specific task. thus, many researchers turned to synthetic datasets for training, since they can be produced easily and cost effectively. the main issue of such datasets that already exist, is the lack of photorealism both in terms of background and lighting. Herein, we are proposing a framework for the generation of completely synthetic datasets that includes all types of data that state-of-the-art algorithms in object recognition, and tracking need for their training. thus, we can improve robotic perception without deploying the robot in time-consuming real-world scenarios. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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XGBoost and Deep Learning Based-Federated Learning for DDoS Attack Detection in 5G Core Network VNFs

XGBoost and Deep Learning Based-Federated Learning for DDoS ...

引用

IEEE international conference on Computer Communication and the Internet (ICCCI)

作者： Abdoul-Aziz Maiga Edwin Ataro Stanley Githinji Pan African University Institute for Basic Sciences Technology and Innovation Nairobi Kenya Department of Electrical and Electronic Engineering Technical University of Kenya Nairobi Kenya Department of Computing United States International University-Africa (USIU-A) Nairobi Kenya

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

Distributed Denial-of-Service (DDoS) attacks can employ cloud network zombies to compromise the availability of 5G network services and hinder the ability of telecommunication service providers (TSPs) to deliver promised service level agreements (SLAs), which can lead to potential losses for both TSPs and their clients. In this study, a technique for mitigating DDoS attacks in 5G core network Virtual Network Functions (VNFs) was proposed. VNFs are cloud-based network functions that provide 5G services. First, eXtreme Gradient Boosting (XGBoost) is used to extract relevant features, and then the proposed hybrid deep neural network uses the XGBoost-extracted features for DDoS attack detection and mitigation. To address data privacy in heterogeneous 5G networks, where VNFs can be provided and hosted in different cloud computing systems, Federated Learning (FL) is used for the proposed model training. the anti-DDoS framework was evaluated through a simulation using the CICDDoS2019 dataset with 10 VNFs for FL. through extensive experimentation and evaluation, the results demonstrated promising outcomes characterized by high accuracy, low false positive rate, and minimal detection time. the proposed solution offers a robust defense mechanism against evolving DDoS threats that can target 5G core networks, thereby ensuring the availability of critical network infrastructure.

关键词： Training Deep learning Cloud computing 5G mobile communication Federated learning Prevention and mitigation Denial-of-service attack

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Accelerating Machine Learning inference using FPGAs: the PYNQ framework tested on an AWS EC2 F1 41

Accelerating Machine Learning inference using FPGAs: the PYN...

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41st international conference on High Energy Physics, ICHEP 2022

作者： Lorusso, Marco Bonacorsi, Daniele Salomoni, Davide Travaglini, Riccardo Michelotto, Diego Duma, Doina Cristina Veronesi, Paolo INFN Bologna viale Berti Pichat 6/2 Bologna Italy Department of Physics and Astronomy University of Bologna viale Berti Pichat 6/2 Bologna Italy INFN CNAF viale Berti Pichat 6/2 Bologna Italy

In the past few years, using Machine and Deep Learning techniques has become more and more viable, thanks to the availability of tools which allow people without specific knowledge in the realm of data science and complex networks to build AIs for a variety of research fields. this process has encouraged the adoption of such techniques, e.g. in the context of High Energy Physics. In order to facilitate the translation of Machine Learning (ML) models to fit in the usual workflow for programming FPGAs, a variety of tools have been developed. One example is the HLS4ML toolkit, which allows the translation of Neural Networks (NN) built using tools like TensorFlow to a High-Level Synthesis description (e.g. C++) in order to implement this kind of ML algorithms on FPGAs. this paper presents the activity running at the University of Bologna and INFN-Bologna devoted to preliminary studies for the trigger systems of the Compact Muon Solenoid experiment at the CERN LHC accelerator. An open-source project from Xilinx called PYNQ is being tested combined with the HLS4ML toolkit. the PYNQ purpose is to grant designers the possibility to exploit the benefits of programmable logic and microprocessors using the Python language. the use of cloud computing in this work allows us to test the capabilities of this workflow, from the creation and training of a Neural Network and the creation of a HLS project using HLS4ML, to managing NN inference with custom Python drivers. the main application explored in this work lives in the context of the trigger system of the CMS, where new reconstruction algorithms are being developed due to the advent of the High-Luminosity phase of the LHC. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-ND 4.0)

关键词： Field programmable gate arrays (FPGA)

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proceedings - 2020 IEEE 6th Intl conference on Big Data Security on Cloud, BigDataSecurity 2020, 2020 IEEE Intl conference on High Performance and Smart computing, HPSC 2020 and 2020 IEEE Intl conference on Intelligent Data and Security, IDS 2020

Proceedings - 2020 IEEE 6th Intl Conference on Big Data Secu...

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6th IEEE international conference on Big Data Security on Cloud, BigDataSecurity 2020, 6th IEEE international conference on High Performance and Smart computing, HPSC 2020 and 5th IEEE international conference on Intelligent Data and Security, IDS 2020

ISBN: (纸本)9781728168739

the proceedings contain 44 papers. the topics discussed include: multi-generational database inference controllers;adaptive margin based deep adversarial metric learning;research on automatic generation and analysis technology of network attack graph;a survey of intelligent transportation systems security: challenges and solutions;iFLBC: on the edge intelligence using federated learning blockchain network;affinity propagation initialization based proximity clustering for labeling in natural language based big data systems;multi-generational database inference controllers;research on maintenance strategy of distribution network based on Monte Carlo tree;and an improved fuzziness based random vector functional link network for liver disease detection.

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Performance Benchmarking of Visual Human Tracking algorithms for UAVs 22

Performance Benchmarking of Visual Human Tracking Algorithms...

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proceedings of the 26th Pan-Hellenic conference on Informatics

作者： theofanis Kalampokas George Papakostas Vassilios Chatzis Stelios Krinidis MLV Research Group Department of Computer Science International Hellenic University Greece Department of Management Science and Technology International Hellenic University Greece

ISBN: (纸本)9781450398541

With the evolution of robotic systems, unmanned aerial vehicles (UAV) have become a target of interest for domains such as computer vision (CV) and artificial intelligence (AI), contributing to a variety of applications for surveillance, transportation and many more. A very hot topic that is the playground of the proposed benchmark is visual human tracking in images acquired by a camera mounted on a UAV. this target application troubles CV and deep learning (DL) research community in recent years and it has created serious demands for visual tracking algorithms. Some of the most important demands are high performance under hard visual tracking conditions and deployment in edge devices with limited computation resources. these two challenges are the main motivation of the presented paper, where 37 tracking algorithms have been benchmarked in visual object tracking (VOT) images. For each tracking algorithm two metric categories, relative to detection performance and hardware resources consumption, have been considered. the objective of the proposed paper is to highlight the most lightweight and high performance tracking algorithms for usage in UAV based applications.

关键词： Computer Vision UAV Visual Tracking Deep Learning Computational Resources Edge computing

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Multi-aircraft behavior recognition method for airport scene based on ASTERIX data

Multi-aircraft behavior recognition method for airport scene...

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IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Kai Wang Shengwei Li Heng Yue Lieyang Wu Jing Tan Guoqiang Wang The 2nd Research Institute Civil Aviation Administration of China Chengdu China Operation Control Centre of Beijing Capital International Airport Co. Ltd Beijing China Chengdu Tianfu International Airport Chengdu China Jiangxi Traffic Monitoring Command Center Nanchang Jiangxi China The Civil Aviation Chengdu Information Technology Co. Ltd Chengdu China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

To solve the problem that it is difficult to judge the multiple aircraft behaviors through the corresponding trajectory information, all kinds of which are similar in the airport scene video monitoring system, multi-aircraft behavior recognition method for airport scene based on ASTERIX data is proposed. Firstly, ASTERIX data is used to locate the aircraft in the video by the perspective transformation and the historical trajectory information is used to predict the location of aircraft at the next moment, through which can extract the movement area of each aircraft. Namely, the multi-aircraft behavior recognition task is transformed into multiple single-aircraft behavior recognition tasks. Finally, the behavior recognition is completed by the fusion convolution with multi-time scales method in the movement region of each aircraft. the experiment based on a self-made dataset containing various aircraft behaviors at typical airport shows that the proposed method meets the behavior recognition task of multiple targets in the airport scene video monitoring system.

关键词： Accuracy Target recognition Convolution Surveillance Airports Prediction algorithms Real-time systems Trajectory Safety Aircraft

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IMPACT OF DIGITALIZATION ON FIRMS’ PRODUCTIVITY 22

IMPACT OF DIGITALIZATION ON FIRMS’ PRODUCTIVITY

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proceedings of the 6th international conference on Future Networks & Distributed systems

作者： Zarnigor Akhmadalieva Zulfizar Akhmadalieva Department of World Economy Tashkent State University of Economics Uzbekistan Department of Islamic Economics and Finance International Islamic Academy of Uzbekistan Uzbekistan

ISBN: (纸本)9781450399050

Employing Enterprise Survey data that comprises 1276 firms from four Central Asian countries, this study focuses on the impact of digitalization as well as human capital on firms’ productivity. We have found that digitalization improves firms’ productivity by 44-52 percent on average, statistically significant at 1%. Moreover, analysis shows that the impact is heterogenous, with large and statistically significant effects for firms in retail, nonmetal, and, especially, textile industries. these results imply that digitalization is crucial in improving firms’ productivity and the government could implement policies that encourage and help firms with this regard.

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Car Social Network: Contact a Driver through the License Plate 11

Car Social Network: Contact a Driver Through the License Pla...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing systems - Technology and Applications (IDAACS)

作者： Russo, Michele Mirulla, Giovanni Avanzato, Roberta Beritelli, Francesco VICOSYST SRL Viale Odor Pordenone I-95128 Catania Italy Univ Catania DIEEI Viale Andrea Doria 6 I-95125 Catania Italy

ISBN: (纸本)9781665426053

the paper presents an innovative car social network service that allows getting in touch with a driver through his individual identification based on the car license plate. the Car Social Network architecture is based on a unified cloud communication platform that enables the activation of multimedia communication sessions with the car *** application contexts range from road safety to the reduction of noise pollution in cities.

关键词： Smart Road Vehicular Network Smart Cities Social Networking Service Unified Communications

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Semantic Interoperability of Digital Twins: Ontology-based Capability Checking in AAS Modeling Framework

Semantic Interoperability of Digital Twins: Ontology-based C...

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Industrial Cyber-Physical systems (ICPS)

作者： Yining Huang Saadia Dhouib Luis Palacios Medinacelli Jacques Malenfant Université Paris-Saclay CEA List Palaiseau France Laboratoire d'Informatique de Paris 6 (LIP6) CNRS Sorbonne Université Paris France

Industry 4.0 currently prepares a major shift towards extreme flexibility into production lines management. Digital Twins are one of the key enabling technologies for Industry 4.0. However, the interoperability gap among digital representation of Industry 4.0 assets is still one of the obstacles to the development and adoption of digital twins. If the Asset Administration Shell (AAS), the standard proposed to represent the I4.0 components, caters for syntactic interoperability, a more semantic kind of interoperability is deeply needed to develop flexible and adaptable production lines. In our work, we overcome the limitation of current syntactic-only resource matching algorithms by implementing semantic interoperability based on ontologies i.e., by transforming AAS-based plant models into MaRCO (Manufacturing Resource Capability Ontology) instances and then query the expanded ontology to find the needed resources. this article presents this ontology-based approach as the first step towards the design and implementation of an automated I4.0 flexible plant supervision and control system based on model-driven engineering (MDE) within the “Papyrus for Manufacturing” toolset. We show how an MDE approach can aggregate around digital twin modeling tools from the Papyrus platform both I4.0 technologies and AI (Knowledge Representation and Reasoning) tools. Our platform aligns modeling and ontological elements to get the best of both worlds. this method has two main advantages: (1) to provide semantic descriptions for digital twin models, (2) to complement model-driven engineering tools with automated reasoning. this paper showcases this approach through a robotic cell use case.

关键词： Adaptation models Unified modeling language Semantics Production Ontologies Cognition Digital twins

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HealthRecSys 2019 - proceedings of the 4th international Workshop on Health Recommender systems, co-located with the 13th ACM conference on Recommender systems 2019, RecSys 2019

HealthRecSys 2019 - Proceedings of the 4th International Wor...

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4th international Workshop on Health Recommender systems, HealthRecSys 2019

the proceedings contain 6 papers. the topics discussed include: inspiring healthy habits: data science at WW;utilizing collaborative filtering to recommend opportunities for positive affect in daily life;personalized, health-aware recipe recommendation: an ensemble topic modeling based approach;rethinking hearing aids as recommender systems;evolutionary approach for ‘healthy bundle’ wellbeing recommendations;and an evaluation of recommendation algorithms for online recipe portals.

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