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Network-driven Multicast Synchronization with TopoSync-SFC 11

Network-driven Multicast Synchronization with TopoSync-SFC

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11th international conference on Networks of the Future

作者： Bachmann, Felix Bauer, Robert Heseding, Hauke Zitterbart, Martina Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781728180557

this demo showcases TopoSync-SFC, a network-driven approach for SFC-aware multicast synchronization. Synchronization of SFC-processed multicast traffic is an important requirement for several distributed applications like TETRA voice communication or interactive games. that is, packets sent to a multicast group are first processed by a Service Function Chain (SFC) and received approximately at the same time by all group members. the key idea is to exploit properties of the forwarding tree and SFC-embedding in the network for the purpose of synchronization. In particular, we artificially increase the length of some paths in the forwarding tree to achieve synchronization. With this approach, no dedicated support of end devices is required. We use an ONOS-based prototype implementation and a softwarized network emulated with containernet to demonstrate the functionality of TopoSync-SFC. A whack-amole game executed on emulated hosts showcases the impact of synchronized reception on application fairness.

关键词： Synchronized Multicast Reception Service Function Chaining Software Defined networking

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Train throughput Analysis of distributed Reinforcement Learning 11

Train Throughput Analysis of Distributed Reinforcement Learn...

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11th international conference on Information and Communication Technology Convergence (ICTC) - Data, Network, and AI in the age of Untact (ICTC)

作者： Jang, Sooyoung Park, Noh-Sam Elect & Telecommun Res Inst ETRI Intelligence Convergence Res Lab Daejeon South Korea

ISBN: (纸本)9781728167589

distributed deep reinforcement learning can increase the train throughput, which is defined as the timesteps per second used for training, easily by just adding computing nodes to a cluster, which makes it an essential technique for solving complex problems. the more complicated the virtual learning environment and the policy network become, the more the CPU computing power in the rollout phase and the GPU computing power in the policy update phase is required. Recall that the reinforcement learning iterates the phases of acquiring data through rollout in the virtual learning environment and updating the policy from that data over millions of iterations. In this paper, the train throughput analysis is performed with RLlib and IMPALA on two different problems: CartPole, a simple problem, and Pong, a relatively complex problem. the effects of various scalability metrics, clustering, and observation dimensions on train throughput are analyzed. throughout the analysis, we show that 1) the train throughput varies significantly according to the scalability metrics, 2) it is vital to monitor the bottleneck in the train throughput and configure the cluster accordingly, and 3) when the GPU computing power is the bottleneck, reducing the observation dimensions can be a great option as the train throughput increases up to 3 times by reducing the dimension from 84 to 42.

关键词： Train throughput CPU/GPU Utilization distributed Reinforcement Learning

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A Trust computing Model for Future Generation Networks 11

A Trust Computing Model for Future Generation Networks

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11th international conference on computing, Communication and networking Technologies, ICCCNT 2020

作者： Das, Prodipto Debnath, Somen Assam University Department of Computer Science Silchar Assam India Mizoram University Department of Information Technology Aizawl Mizoram India

ISBN: (纸本)9781728168517

Future generation networks are heterogenous in nature due to variety of nodes, terminals, small-sized networks, private networks, virtual networks, IoTs etc. When number of nodes and terminal machines take part in a network, authentication become a major issue. Authentication of node is associated with trust. Trust of a node is a primary behaviour of the node in the network operations. Networks with fewer restrictions in case of user registration may have more trust related issues. A node may exhibit random behaviour at the time of data transmission and forwarding of packets. the major concern is about the assessment of trust value of a node before actual data communication. Trust computing model is the model which gives trust values of nodes in a network. Different trust computing models are designed and developed for different scenarios. In this paper, a model of trust computing is proposed with maximum possible factors. A simulation program in NS3 is written and executed to check the trust values and its effect in the network operations. the results are analysed with and without risk factors to check the accuracy of network performances. It is observed in the experimental work that trust with risk factors is more authenticated solution for the proposed trust model. © 2020 IEEE.

关键词： Authentication

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In-Vehicle Occupancy Detection and Classification Using Machine Learning 11

In-Vehicle Occupancy Detection and Classification Using Mach...

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11th international conference on computing, Communication and networking Technologies, ICCCNT 2020

作者： Vamsi, Malneedi Soman, K.P. Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Coimbatore India Centre for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781728168517

Occupancy detection is a difficult problem. there are several mechanisms exists for occupancy detection in vehicles, particularly in Automobiles. Now, safety has become an important and necessary aspect of the automobile industry. Airbag became a basic and important safety measure in cars. Even though airbag is a vehicle safety device, it can kill children below 12 years due to its rapid action by the exerting lot of force. this project explains about detecting the number of passengers sitting in the car and then classifying each person whether he/she is a child or an adult by processing the image taken from the camera. So that the deployment of airbags can be avoided near children. Each time car speeds from 0 Kmph to 20 Kmph, occupancy of the car is determined and each one is classified again. We are using widely used technique Haar Cascades, for detection. First, we detect faces and then classify each occupant adult or child. © 2020 IEEE.

关键词： Classification (of information)

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Driver Inattention Monitoring System Based on the Orientation of the Face Using Convolutional Neural Network 11

Driver Inattention Monitoring System Based on the Orientatio...

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11th international conference on computing, Communication and networking Technologies, ICCCNT 2020

作者： Manjula, P.M. Adarsh, S. Ramachandran, K.I. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Coimbatore India Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781728168517

Driving inattentively is one of the prime reasons for vehicle accidents worldwide and has significant implications for road safety. A prompt alert to the inattentive driver can mitigate many accidents and save numerous lives, and reduce the cost of damages caused by accidents. To achieve this, a proposal of a nonintrusive and noninvasive driver inattention monitoring and alerting system in real time has been put forward. A mobile camera mounted on the windshield captures the video of the driver. Viola-Jones algorithm detects the face in each frame of the video and the Kanade-Lucas-Tomasi (KLT) algorithm tracks the detected face from one frame to another frame. the driver is classified as inattentive or attentive using Convolutional Neural Network (CNN). the transfer learning of the AlexNet Convolutional Neural Network architecture is adopted for the classification. the accuracy, precision, sensitivity, F1 score, and specificity of the system proposed in this paper are 98.24%, 100%, 96.47%, 98.21% and 100%, respectively. © 2020 IEEE.

关键词： Convolutional neural networks

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A Model for computing Skyline Data Items in Cloud Incomplete Databases 11

A Model for Computing Skyline Data Items in Cloud Incomplete...

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11th international conference on Ambient Systems, Networks and Technologies (ANT) / 3rd international conference on Emerging Data and Industry 4.0 (EDI)

作者： Gulzar, Yonis Alwan, Ali A. Abualkishik, Abedallah Zaid Mehmood, Abid King Faisal Univ Dept Management Informat Syst Al Hasa 31982 Saudi Arabia Int Islamic Univ Malaysia Kulliyyah Informat & Commun Technol Kuala Lumpur 53100 Malaysia Amer Univ Emirates Dubai U Arab Emirates

Skyline queries intend to retrieve the most superior data items in the database that best fit with the user's given preference. However, processing skyline queries are expensive and uneasy when applying on large distributed databases such as cloud databases. Moreover, it would be further sophisticated to process skyline queries if these distributed databases have missing values in certain dimensions. the effect of data incompleteness on skyline process is extremely severe because missing values result in un-hold the transitivity property of skyline technique and leads to the problem of cyclic dominance. this paper proposes an efficient model for computing skyline data items in cloud incomplete databases. the model focuses on processing skyline queries in cloud incomplete databases aiming at reducing the domination tests between data items, the processing time, and the amount of data transfer among the involved datacenters. Various set of experiments are conducted over two different types of datasets and the result demonstrates that the proposed solution outperforms the previous approaches in terms of domination tests, processing time, and amount of data transferred. (C) 2020 the Authors. Published by Elsevier B.V.

关键词： Skyline queries incomplete data query processing cloud databases distributed database

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Comparative Analysis of the Efficiency of Modern Fake Detection Algorithms in Scope of Information Warfare

Comparative Analysis of the Efficiency of Modern Fake Detect...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications

作者： Yevhenii Shtefaniuk Ivan Opirskyy Lviv Polytechnic National University Lviv Lviv Oblast

ISBN: (纸本)9781665426060

the problem of detecting fake (false) information passing through various channels of the Internet is becoming increasingly important task. One of the most dangerous types of such information is targeted propaganda, which always has a specific purpose and uses specially prepared resources. To protect a person from such informational influence, we can use already developed tools and algorithms. this article considers the main features of information propaganda and approaches to combating it; the effectiveness of several well-known modern algorithms for recognizing fake news; the comparative efficiency analysis of these algorithms in the context of possibility of their application for counteracting purposeful information influences is carried out. Based on the study, the most promising algorithm for recognizing information propaganda in social networks was selected. this will allow to build more efficient systems of detecting fake news that are delivered in scope of information propaganda campaigns as a part of information warfare in social media networks.

关键词： Training Social networking (online) distributed databases Manuals Feature extraction Internet Reliability

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Sentiment Analysis of Restaurant Reviews using Combined CNN-LSTM 11

Sentiment Analysis of Restaurant Reviews using Combined CNN-...

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11th international conference on computing, Communication and networking Technologies, ICCCNT 2020

作者： Hossain, Naimul Bhuiyan, Md. Rafiuzzaman Tumpa, Zerin Nasrin Hossain, Syed Akhter Daffodil International University Dept. Name of CSE Dhaka Bangladesh Daffodil International University Dept. of CSE Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168517

the combination of machine learning approach and natural language processing is applied to analyze the sentiment of text for particular sentences. In this particular area lots of work done in recent times. Restaurant business was always a popular business in Bangladesh. these business is now Leaning towards online delivery services and the overall quality of restaurants are now judged by reviews of customers. One try to understand the quality of a restaurant by the reviews from other customers. these opinions of customers organizing in structured way and to understand perception of customers reviews and reactions is the main motto of our work. Collecting data was the first thing we have done for deploying this piece of work. then making a dataset which we harvested from websites and tried to deploy with deep learning technique. In this piece of research, a combined CNN-LSTM architecture used in our dataset and got an accuracy of 94.22%. Also used some other performance metrics to evaluate our model. © 2020 IEEE.

关键词： Sentiment analysis

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Internet of things (IoT): Vulnerabilities, Security Concerns and things to Consider 11

Internet of Things (IoT): Vulnerabilities, Security Concerns...

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11th international conference on computing, Communication and networking Technologies, ICCCNT 2020

作者： Ahmed, Eshtiak Islam, Ashraful Ashraf, Mohsena Chowdhury, Atiqul Islam Rahman, Mohammad Masudur Daffodil International University Dhaka Bangladesh Ahsanullah University of Science and Technology Dhaka Bangladesh United International University Dhaka Bangladesh Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9781728168517

In recent years, Internet of things (IoT) is rapidly gaining popularity as it has a vast potential. But without proper security and privacy measures, IoT can facilitate cyberattacks as it possesses some serious issues that need to be tackled well to utilize its fullest potential. In this study, an attempt to briefly discuss the basic concept and the pros and cons of IoT was taken by the authors. Moreover, this research also highlights the basic architectural issues, vulnerabilities, and future security concerns of IoT as IoT has a cost when it comes to the case of security and privacy. In addition to that, major threats and attacks of IoT e.g. Denial of Service (DoS), Man in the Middle (MiTM), Supervisory Control and Data Acquisition (SCADA) attack were explained in brief. At last, probable measures that can be employed in order to strengthen the security of IoT were underlined so that the advantages of IoT can be reaped mostly in terms of security and privacy. © 2020 IEEE.

关键词： Internet of things

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Fog Services Provider Architecture for IoT 11

Fog Services Provider Architecture for IoT

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11th international conference on Networks of the Future

作者： Le, Hoan Achir, Nadjib Boussetta, Khaled Univ Sorbonne Paris Nord L2TI Villetaneuse France

ISBN: (纸本)9781728180557

the prominence of Internet of things (IoT) devices is characterized by a wide diversity of network access technologies, including Wi-Fi, Cellular, Lo-Ra, ZigBee, or Bluetooth. Dealing with such heterogeneity is still very challenging. Current cloud computing solutions can sustain a considerable amount of diverse data that are generated by geographically spread IoT devices. However, this task is quite challenging for time-sensitive services. Moreover, IoT heterogeneous devices are always managed by different application systems, and there are no unified storage and management solutions for information on IoT devices. In this paper, we propose a microservice architecture based on Fog computing paradigm named Fog services Provider (FSP) for IoT devices to perceive and flexibly manage heterogeneous devices in ubiquitous environments. A novel aspect of this service architecture is to build a general data model to describe and store data of IoT heterogeneous devices based on Semantic Web technologies. Besides, we also propose a data converting algorithm from relational databases to a unified format, and demonstrates procedures with effective management to register and access those devices into FSP architecture.

关键词： Fog computing device management heterogeneity distributed management Internet of things Semantic Web Interoperability

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