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MEET: Advancing Mobility-Enhanced Edge Intelligence for Efficient and Sustainable 6G Networks

MEET: Advancing Mobility-Enhanced Edge Intelligence for Effi...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Madhu Kumar Vanteru Kavitha Dasari V Asha Navdeep Singh Dalael Saad Abdul-Zahra R Ramprakash Department of ECE Balaji Institute of Technology and Science Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Hilla University College Babylon Iraq Department of ECE Anurag University Hyderabad Telangana

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

A machine learning technique known as federated learning, or collaborative learning, allows edge devices to cooperatively integrate to a global model while retaining local copies of their data. the integration of edge AI, IoT, and communication technologies can be greatly improved via federated learning. Because of the abundance of devices, apps, and services available in the modern digital era, users have higher expectations and demands for quality of service (QoS) from network service providers. As a result, extensive deployment of base stations (BSs) overall edge servers (ESs) is required, which raises the cost of deployment and operation—particularly energy expenditures. In order to add flexibility to the creation of the traffic along with mobility management algorithms, the current work makes use of fuzzy logic. Enhancing the quality of service (QoS) of current mobility management solutions and making optimum use of available network resources are the main goals of the research project. In this study, we examine a novel architecture for the efficient and environmentally friendly 6G systems dubbed Mobility-Enhanced Edge inTelligence (MEET), that takes advantage of the detection, communication, computation, and automatic features of intelligent linked vehicles.

关键词： 6G mobile communication Protocols Federated learning Image edge detection Quality of service Telecommunication traffic throughput Servers Informatics Next generation networking

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6th international conference on Smart Cities and Green ICT systems, SMARTGREENS 2017 and 3rd international conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2017

6th International Conference on Smart Cities and Green ICT S...

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6th international conference on Smart Cities and Green ICT systems, SMARTGREENS 2017 and 3rd international conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2017

ISBN: (纸本)9783030029067

the proceedings contain 17 papers. the special focus in this conference is on Smart Cities and Green ICT systems. the topics include: Intelligent Offloading Distribution of High Definition Street Maps for Highly Automated Vehicles;classification of Automotive Electric/Electronic Features and the Consequent Hierarchization of the Logical System Architecture: From Functional Chains to Functional Networks;user-Based Relocation of Stackable Car Sharing;a Maneuver Based Interaction Framework for External Users of an Automated Assistance Vehicle;a Personal Robot as an Improvement to the Customers’ In-store Experience;road Safety: Human Factors Aspects of Intelligent Vehicle Technologies;Evaluation Methodology for Cooperative ADAS Utilizing Simulation and Experiments;day-Ahead Scheduling of Electric Heat Pumps for Peak Shaving in Distribution Grids;the SAGITTA Approach for Optimizing Solar Energy Consumption in Distributed Clouds with Stochastic Modeling;using Energy Supply Scenarios in an Interdisciplinary Research Process;integrated Electric Vehicle to Small-Scale Energy Management System;An Influence-Based Model for Smart City’s Interdependent Infrastructures: Application in Pricing Design for EV Charging Infrastructures;all Eyes on You! Impact of Location, Camera Type, and Privacy-Security-Trade-off on the Acceptance of Surveillance Technologies;a Strategic Urban Grid Planning Tool to Improve the Resilience of Smart Grid Networks;scenario Interpretation for Automated Driving at Intersections.

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Deep Active Learning Image Classification Algorithm Based on Class-Wise Self-Knowledge Distillation 23

Deep Active Learning Image Classification Algorithm Based on...

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proceedings of the 2023 6th international conference on Artificial Intelligence and Pattern Recognition

作者： Yuliang Pang Ying Liu Yu Hao Yanchao Gong Daxiang Li Zhijie Xu Center for Image and Information Processing Xi'an University of Posts and Telecommunications China Center for Image and Information Processing International Joint-Research Center for Wireless Communication and Information Processing Xi'an University of Posts and Telecommunications China International Joint-Research Center for Wireless Communication and Information Processing University of Huddersfield China

ISBN: (纸本)9798400707674

the success of image classification techniques based on deep learning relies heavily on a large amount of labeled data. However, the cost of data annotation is often expensive. this paper investigates active learning algorithms for image classification to reduce the cost of data annotation. However, traditional active learning algorithms for image classification suffer from overfitting issues due to training deep neural networks with millions of parameters using a small amount of labeled data. this makes it difficult for the model to effectively assess the information richness of unknown unlabeled samples and is detrimental to the selection of high-value samples. To alleviate these issues, this paper proposes a deep active learning-based image classification algorithm with class-wise self-knowledge distillation. this algorithm reduces overfitting and class-wise variations by matching or distilling the predictive distribution between different samples with the same label during training, thereby enabling a more accurate evaluation of the informativeness of unlabeled data by the active learning algorithm and improving the performance of the classification model. Additionally, an efficient Shuffle Attention mechanism is introduced to improve the sample selection strategy by combining spatial and channel feature information of the images. the proposed algorithm is compared with five active learning baselines on CIFAR10, CIFAR100, SVHN, and FashionMNIST datasets. Experimental results demonstrate the proposed algorithm exhibits superior classification performance.

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Sparse Image Reconstruction for the SPIDER Optical Interferometric Telescope 9th

Sparse Image Reconstruction for the SPIDER Optical Interfero...

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9th international conference on Photonics, Optics and Laser Technology (PHOTOPTICS)

作者： Pratley, Luke Mcewen, Jason D. Univ Toronto Dunlap Inst Astron & Astrophys Toronto ON M5S 3H4 Canada Univ Coll London UCL Mullard Space Sci Lab MSSL Holmbury St Mary Dorking RH5 6NT Surrey England

ISBN: (纸本)9789897584923

the concept of a recently proposed small-scale interferometric optical imaging device, an instrument known as the Segmented Planar Imaging Detector for Electro-optical Reconnaissance (SPIDER), is of great interest for its possible applications in astronomy and space science. Due to low weight, low power consumption, and high resolution, the SPIDER telescope could replace the large space telescopes that exist today. Unlike traditional optical interferometry the SPIDER accurately retrieves both phase and amplitude information, making the measurement process analogous to a radio interferometer. State of the art sparse radio interferometric image reconstruction techniques have been gaining traction in radio astronomy and reconstruct accurate images of the radio sky. In this work we describe algorithms from radio interferometric imaging and sparse image reconstruction and demonstrate their application to the SPIDER concept telescope through simulated observation and reconstruction of the optical sky. Such algorithms are important for providing high fidelity images from SPIDER observations, helping to power the SPIDER concept for scientific and astronomical analysis.

关键词： Optical Photonic Circuits Interferometry Imaging Convex Optimization

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6th international KES conference on Smart Education and e-Learning, KES SEEL 2019

6th International KES Conference on Smart Education and e-Le...

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6th international KES conference on Smart Education and e-Learning, KES SEEL 2019

ISBN: (纸本)9789811382598

the proceedings contain 55 papers. the special focus in this conference is on Smart Education and e-Learning. the topics include: A development of instructional games for education students;the methodology of development of electronic educational resources for learning of general scientific disciplines in non-native language;visualization of learning activities in classroom blended with e-learning system;Dosage effects of CAI on literacy skills;construction of a socio-affective profile model of students in a virtual learning environment;smart e-learning in the preparation of novice pop vocalists in the university;Communication with students in smart e-Learning system using LMS moodle;smart learning objects for online and blended learning approach;using ontology engineering to design an artificial intelligence course;Quantitative follow-Up of MOOC students using big data techniques;Development and implementation of BIPV courseware for higher education and professionals;An engineering educational program using “Twin rotor MIMO system” laboratory platform;competency-based approach teaching software engineering;randomized controlled trial of Kindergarten students using literacy technology;mandatory matching between microelectronics industry and higher education in engineering toward a digital society;an approach to educational data mining model accuracy improvement using histogram discretization and combining classifiers into an ensemble;performance evaluation for four types of machine learning algorithms using educational open data;Moving from hardcopy to online submission with a learning analytics enriched Rubric—LAe-R: Students’ perspective;use of mobile devices—a survey study;SEMOGRAPH information system as a platform for network-based linguistic research: A case study of verbal behavior of social network users;overview of recommendation systems.

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Enhanced Security Framework for Enabling Facial Recognition in Autonomous Shuttles Public Transportation During COVID-19 17th

Enhanced Security Framework for Enabling Facial Recognition ...

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17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Tsiktsiris, Dimitris Lalas, Antonios Dasygenis, Minas Votis, Konstantinos Tzovaras, Dimitrios Informat Technol Inst Ctr Res & Technol Hellas 6th Km Xarilaou Thermi Thessaloniki 57001 Greece Univ Western Macedonia Dept Elect & Comp Engn Kozani 50100 Greece

ISBN: (纸本)9783030791520;9783030791506;9783030791490

Autonomous Vehicles (AVs) can potentially reduce the accident risk while a human is driving. they can also improve the public transportation by connecting city centers with main mass transit systems. the development of technologies that can provide a sense of security to the passenger when the driver is missing remains a challenging task. Moreover, such technologies are forced to adopt to the new reality formed by the COVID-19 pandemic, as it has created significant restrictions to passenger mobility through public transportation. In this work, an image-based approach, supported by novel AI algorithms, is proposed as a service to increase autonomy of non-fully autonomous people such as kids, grandparents and disabled people. the proposed real-time service, can identify family members via facial characteristics and efficiently ignore face masks, while providing notifications for their condition to their supervisor relatives. the envisioned AI-supported security framework, apart from enhancing the trust to autonomous mobility, could be advantageous in other applications also related to domestic security and defense.

关键词： Autonomous vehicles Public transportation Neural networks Image processing Security

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Reliability of Computer systems during Physical and Informational Recovery of Duplicated Memory 6

Reliability of Computer Systems during Physical and Informat...

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6th international Scientific and Practical conference "Distance Learning Technologies", DLT 2021

作者： Bogatyrev, Vladimir A. Bogatyrev, Stanislav V. Bogatyrev, Anatoly V. ITMO University Kronverksky Pr. 49 bldg. A Saint-Petersburg197101 Russia Saint-Petersburg State University of Aerospace Instrumentation 67 Bolshaya Morskaia str. St Petersburg Russia JSC NEO Saint Petersburg Competence Center 1-Ya Sovetskaya house 6 str. St. Petersburg Russia

the analysis of the influence of various requirements for the continuity of the computing process and the safety of information after failures on the reliability of a fault-tolerant computer system, including a computing node with two redundant storage devices connected to it, is carried out. the peculiarity of restoring storage devices is the need for both their physical and subsequent information recovery, that is, entering the information lost after a failure. three variants of system recovery disciplines are considered, taking into account physical and informational memory recovery. For the first option, the recovered information is not unique and can always be loaded into the physically restored memory from some external source. For the second option, the information accumulated during the operation of the system can be entered into physically restored memory only based on using replicas of information stored in the system. the loss of all copies of the unique data may lead to the inability to restore the information, and as a result, to an unrecoverable failure of the computer system. the third option requires ensuring the continuity of the computing process, which is possible with the operability of the computing node and the availability for it of at least one functioning of the two storage devices containing up-to-date information. A Markov model of a computer system is proposed that reflects the stages of physical and informational recovery of duplicated memory for different criticality of the system to data loss due to memory failures. © 2021 CEUR-WS. All rights reserved.

关键词： Availability

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Efficient k-mer Indexing with Application to Mapping-free SNP Genotyping

Efficient k-mer Indexing with Application to Mapping-free SN...

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13th international conference on Bioinformatics Models, Methods and algorithms (BIOINFORMATICS) held as part of 15th international Joint conference on Biomedical Engineering systems and Technologies (BIOSTEC)

作者： Marcolin, Mattia Andreace, Francesco Comin, Matteo Univ Padua Dept Informat Engn Via Gradenigo 6-A I-35100 Padua Italy

ISBN: (纸本)9789897585524

Advances in sequencing technologies and computational methods have enabled rapid and accurate identification of genetic variants. Accurate genotype calls and allele frequency estimations are crucial for population genomics analyses. One of the most demanding step in the genotyping pipeline is mapping reads to the human reference genome. Recently mapping-free methods, like Lava and VarGeno, have been proposed for the genotyping problem. they are reported to perform 30 times faster than a standard alignment-based genotyping pipeline while achieving comparable accuracy. Moreover, these methods are able to include known genomic variants in the reference making read mapping, and genotyping variant-aware. However, in order to run they require a large k-mers database, of about 60GB, to be loaded in memory. In this paper we study the problem of genotyping using new efficient data structures based on k-mers set compression, and we present a fast mapping-free genotyping tool, named GenoLight. GenoLight reports accuracy results similar to the standard pipeline, but it is up to 8 times faster. Also, GenoLight uses between 5 to 10 times less memory than the other mapping-free tools, and it can be run on a laptop. Availability: https://***/CominLab/GenoLight.

关键词： Genotyping Mapping-free k-mers Set Representation

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ICMAI 2019 - proceedings of 2019 4th international conference on Mathematics and Artificial Intelligence

ICMAI 2019 - Proceedings of 2019 4th International Conferenc...

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4th international conference on Mathematics and Artificial Intelligence, ICMAI 2019

ISBN: (纸本)9781450362580

the proceedings contain 43 papers. the topics discussed include: gesture recognition based on multi-channel convolutional neural network;an effective method for complex network community detection based on hierarchical splitting;analysis of spatial distribution of China's station-free bike-sharing by clustering algorithms;concept clustering algorithm based on the factor space theory;research on matching accuracy of underwater terrain matching algorithm based on unscented kalman filter;a novel deep learning approach for lung cancer recognition based on 1-D deep convolutional neural network;extraction of transitional relations in healthcare processes from Chinese medical text based on deep learning;SOM clustering collaborative filtering algorithm based on singular value decomposition;and deep reinforcement learning based delay-sensitive task scheduling and resource management algorithm for multi-user mobile-edge computing systems.

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Age-Aware Query Evaluation for Big Data Analytics in Mobile Edge Clouds 22

Age-Aware Query Evaluation for Big Data Analytics in Mobile ...

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22nd IEEE international conference on High Performance computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on Data Science and systems, HPCC-SmartCity-DSS 2020

作者： Xia, Qiufen Ren, Wenhao Li, Mingchu Ren, Jiankang Dalian University of Technology International School of Information Science and Engineering Dalian116621 China Dalian University of Technology School of Software Dalian116621 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

ISBN: (纸本)9781728176499

the advancement of 5G technology and extensive usage of smart devices have led to a deluge of data. It is invaluable to analyze such big data by issuing queries to derive real-Time business insights for better, smarter and fact-based decisions. Timely big data analytics are crucial in many service domains, thus the age of data (AoD) required by queries is emerging as a novel metric that measures the freshness of data and evaluates the quality of data analytics. Traditional big data analytics that are performed in remote clouds cannot satisfy the AoD requirements of queries, due to the congested core networks and long transmission latency between users and clouds. the technique of mobile edge computing (MEC) is expected to reduce the age and guarantee the timeliness of queries for big data analytics, by processing data at edge cloudlets close to users. In this paper, we investigate a problem of age-Aware query evaluation for big data analytics in a mobile edge cloud network. We first formulate the problem with certain data processing latency as an Integer Linear Program (ILP). We then develop an online learning algorithm for the problem with uncertain data processing latency. We finally evaluate the performance of proposed algorithms against existing studies by simulations and testbed implementations. Evaluation results show that the proposed algorithms outperform existing works by achieving 20% lower AoD. © 2020 IEEE.

关键词： Data Analytics

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