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Exploring the convergence of Augmented and Virtual Reality: Applications, Challenges and Future Prospects

Exploring the Convergence of Augmented and Virtual Reality: ...

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2023 international conference on Advanced computing and Communication Technologies, ICACCTech 2023

作者： Jain, Khushi Chirag Mittal, Harish Kumar Vasesi, Sonika Antil, Babita Pandey, Hitesh Chander B M Institute of Engineering and Technology Computer Science and Engineering Sonipat India B M Institute of Engineering and Technology Electronics and Communication Engineering Sonipat India

ISBN: (纸本)9798350380880

This paper presents an in-depth analysis of the convergence of Augmented Reality (AR) and Virtual Reality (VR), two rapidly evolving technologies with significant impact across various sectors. We aim to delineate their unique characteristics, applications, and the challenges they face, while also envisioning their future prospects. In the realm of AR and VR, we explore their distinct technological advancements and the nuances in their applications across industries such as gaming, education, healthcare, and e-commerce. The paper emphasizes the transformative potential of these technologies in enhancing user experiences, offering immersive and interactive environments. A key focus is laid on the comparative analysis of AR and VR, highlighting how each technology caters to different user needs and scenarios. The paper identifies and discusses critical challenges, including technical limitations, user acceptance, and ethical considerations, providing a comprehensive understanding of the obstacles facing these technologies. Furthermore, we delve into the future trajectory of AR and VR, speculating on their development as influenced by emerging trends in artificial intelligence, internet of things, and 5G/6G networks. The potential for these technologies to revolutionize user interaction in digital environments is thoroughly examined. Through this paper, we aim to offer a holistic view of AR and VR, underscoring their current state and future possibilities. The findings and discussions presented here are grounded in a thorough review of existing literature and aim to contribute meaningfully to the ongoing discourse in this dynamic field of study. © 2023 IEEE.

关键词： Augmented reality

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Hybrid Spotted Hyena and Simulated Annealing Optimization Algorithm(HSHOSAA-1) for Efficient Task Scheduling in a Clustered Cloud Environment 3

Hybrid Spotted Hyena and Simulated Annealing Optimization Al...

引用

3rd international conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Sivakumar, T.B. Maria Michael Visuvasam, L. Sangeetha, V. Bhuvana, S. Sathish Kumar, K. Sachet, K. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Dept of CSE Tamil nadu Chennai India RMk College of Engineering and Technology Dept of AIDS Tamil nadu Chennai India Sri Sai Ram Engineering College Dept of AIDS Tamil nadu Chennai India Sriram Engineering College Dept of CSE Tamil nadu Chennai India Sri Sai Ram Engineering College Dept of CSBS Tamil nadu Chennai India

ISBN: (纸本)9798350391565

Efficient task scheduling in the cloud is vital as it maximises the utilisation of resources, guaranteeing optimal allocation of computing resources such as CPU and memory. It improves the efficiency of the system by reducing the amount of time it spends idle and maximising the amount of work that can be done by applications and services. This article presents a new way for optimization algorithms by combining two approaches: the Spotted Hyena Optimisation algorithm (SHO) and the Simulated Annealing algorithm (SA). SHO utilises the cooperative hunting behaviours of hyenas to investigate the issue space and discover the most efficient or nearly perfect solutions for optimising job scheduling. SA enhances SHO by repeatedly refining solutions, resulting in increased convergence rates and attaining superior task scheduling results. The problem involves the selection of appropriate virtual machines (VMs) and the allocation of executors (resources), like a Bin Packing problem, which is a computationally difficult optimization work known as NP-Hard. The objective of our proposed HSHOSA-1 is to evaluate the requirements of tasks and the resources that are available for task scheduling. The main goal is to optimize task scheduling by minimizing expenses, preventing virtual machine overload, and ensuring balanced resource utilization. By conducting simulations on the CloudSim platform, the experimental findings show that the approach effectively decreases the number of virtual machines (VMs) needed to deploy all executors. This methodology efficiently reduces the total expenditure on resources in comparison to conventional scheduling strategies. © 2024 IEEE.

关键词： Simulated annealing

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Deep Reinforcement Learning-based Predictive Maintenance Task Offloading and Resource Allocation 23

Deep Reinforcement Learning-based Predictive Maintenance Tas...

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Zhang, Bo Wang, Chenghao School of Cyber Science and Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350325959

In recent years, the growth of Industrial Internet of Things has enabled automated data collection and analysis, leading to the re-emerging of predictive maintenance. However, IIoT devices have insufficient computing power for computationally intensive maintenance tasks. Consequently, Mobile Edge computing (MEC) has been introduced to relieve the computation burden. This paper focuses on predictive maintenance task offloading and resource allocation optimization based on Deep Deterministic Policy Gradient (DDPG). An Artificial Bee Colony algorithm (ABC) is further incorporated to speed up the learning convergence. The proposed algorithm minimizes the overall maintenance cost in generating the optimal task offloading and resource allocation solution. Experimental results show that this scheme outperforms the baseline scheme in reducing the total system maintenance cost. © 2023 IEEE.

关键词： deep reinforcement learning edge computing predictive maintenance resource allocation task offloading

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Flight Delay Prediction Using Machine Learning 8

Flight Delay Prediction Using Machine Learning

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8th IEEE international conference for convergence in technology, I2CT 2023

作者： Reddy, Ravula Tarun Basa Pati, Peeta Deepa, K. Sangeetha, S. Tresa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India University of Technology and Applied Sciences Department of Engineering Al Musannah Oman

ISBN: (纸本)9798350334012

The prediction of flight delay can be considered as one of the most challenging problems to solve. Delay of an aircraft is not only a problem for an airline but also for the passengers. Flights can be delayed due to several reasons, the weather being the primary one. In this paper, our focus is to predict the delay of the flights due to bad weather. The dataset used consists of flight data from JFK airport from the sources Bureau of Transportation statistics and weather data from the National Centers for Environmental Information. The results of different machine learning algorithms like Linear Regression, SVR, Decision Tree Regressor, Random Forest Regressor, Ridge, and Lasso Regressor, for prediction of flight delay, are compared. XGboost regressor had the best performance in all the scenarios with least RMSE score of 0.81. © 2023 IEEE.

关键词： Decision trees

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Unified Framework for End-User Authentication Protocol in Feature-as-a-Service Models 14

Unified Framework for End-User Authentication Protocol in Fe...

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14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Ahn, Jaehyung Min, Junhong Lee, Hyung Tae Paek, Jeongyeup Chung-Ang University Department of Computer Science & Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

Feature-as-a-Service (FaaS) is a new cloud computing model that simplifies the implementation of new features in existing apps with minimal code modifications. One of the most important characteristics of FaaS is that the FaaS server manages its data independently of the service server. However, such a FaaS scheme has a potential issue. Specifically, in the FaaS server, authenticating end-users can be a problem because the service server usually manages user and authentication data. Although several FaaS products requiring end-user authentication have their own authentication methods to address this problem, there is no industry standard for a FaaS server to authenticate an end-user;i.e., commercial FaaS products have individualized end-user authentication methods. In this paper, we analyze existing FaaS products and propose an introductory guideline about end-user authentication methods for FaaS. This guideline aims to help FaaS customers understand the FaaS's authentication and suggest implementation direction. © 2023 IEEE.

关键词： Authentication Cloud computing Feature as a Service (FaaS) Guideline Protocol System as a Service (SaaS)

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An Overview of Opportunities and Challenges of Edge computing in Smart Manufacturing 13

An Overview of Opportunities and Challenges of Edge Computin...

引用

13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Sampedro, Gabriel Avelino Manaig, Raymer Salazar-Manaig, Paulyne Abisado, Mideth B. Kim, Dong-Seong Lee, Jae-Min University of the Philippines-Open University Faculty of Information and Communication Studies Los Baños Philippines Research and Development Center Philippine Coding Camp Manila Philippines Kumoh National Institute of Technology Department of It Convergence Engineering Gumi Korea Republic of College of Computing and Information Technologies National University Manila Philippines

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Shifting to the Smart Industry or Industry 4.0 is becoming a priority in the field of manufacturing. All aspects of manufacturing have greatly benefited from data, which is the intelligence source. Furthermore, innovation in manufacturing increases agility, improves predictability, and promotes energy efficiency, thus creating a higher quality product and improving customer satisfaction. One of the critical challenges in implementing a smart factory is the wide-range, long-lasting, reliable, and near real-time connectivity. On the other hand, network innovation related to the Fifth generation network (5G) and Edge computing creates never-before-seen business opportunities. This study will focus on the benefits, risks, and challenges regarding integrating Smart Manufacturing and Network innovation. © 2022 IEEE.

关键词： Industry 4.0

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BS2: Bit-Serial Architecture Exploiting Weight Bit Sparsity for Efficient Deep Learning Acceleration 21

BS2: Bit-Serial Architecture Exploiting Weight Bit Sparsity ...

引用

21st international System-on-Chip Design conference, ISOCC 2024

作者： Kim, Eunseo Lee, Subean Kim, Chaeyun Lim, HaYoung Nam, Jimin Sim, Jaehyeong Ewha Womans University Artificial Intelligence Convergence Seoul Korea Republic of Ewha Womans University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350377088

Most of weights in deep learning models are small, thus they show high bit sparsity in MSBs. Based on this observation, we propose a bit-serial processing architecture (BS2) that exploits such bit sparsity to maximize computing efficiency. In this architecture, a bit feed front-end (BFFE) logic selects only effectual operands that will be fed into a bit-serial adder-tree. We also introduce an offline weight permutation algorithm to avoid conflicts in the BFFE. Evaluation results show that our design outperforms the previous bit-serial architecture in terms of performance and energy efficiency. © 2024 IEEE.

关键词： Bit Serial Bit Sparsity Deep Learning Acceleration

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Federated Learning convergence on IID features via optimized local model parameters

Federated Learning convergence on IID features via optimized...

引用

IEEE international conference on Big Data and Smart computing (BigComp)

作者： Ullah, Shan Kim, Deok-Hwan Inha Univ Dept Elect Engn Incheon 2212 South Korea

ISBN: (纸本)9781665421973

Federated learning enables collaborative training of global models without sharing local training data. In this paper, we propose a method to enhance the convergence of federated learning algorithm by modifying the training method at the client's side, such as we have deployed two different methods at local models, i) filter out batch normalization parameters and remove them from weight decay, and ii) zero initialize the last batch normalization in each residual branch (in case of resnet) so that the residual branch starts with zeros and each residual block behaves as identity. The following two methods were previously proposed for distributed training. In this paper, we propose to deploy the method in federated learning to enhance the convergence of the federated aggregation method. The proposed method is implemented at local model training and has shown significant improvements in the convergence, keeping the same rounds of federated learning. We have rigorously evaluated the efficacy of the proposed algorithm using a range of image classification models such as Resnet (-18, -50) and MobileNetv2 under the CIFAR-10 dataset. For all experiments, we have used an independent and identically distributed (IID) configuration for distributing the data among contributing clients and fedavg as a federated aggregation algorithm. We have deployed our code based on the open-source library "Flower" for federated learning and communication. We have presented detailed results on various performance metrices such convergence under pretrained networks and without pretrained networks with different number of federated rounds, hyperparameter optimization, impact on different models to provide generability. The proposed algorithm outperforms state-of-the art algorithms in terms of convergence for federated learning by increasing up to 1.5% accuracy in limited federated rounds.

关键词： federated learning deep learning batch normalization IID

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Augmented Reality for Public Engagement in Sustainable City Planning: Cloud and Machine Learning Integration

Augmented Reality for Public Engagement in Sustainable City ...

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2024 international conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Sabitha, R. Gowriswari, S. Yuvaraj, S. Gayathri Devi, M. Babuji, R. Murugan, S. Rajalakshmi Engineering College Department Of Computer Science And Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department Of Instrumentation And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Electronics And Communication Engineering Kattankulathur Tamil Nadu Chennai India Department Of Biomedical Engineering Tamil Nadu Rasipuram India R.M.K. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

This paper explores how the convergence of augmented reality (AR), cloud computing, and machine learning might transform public engagement in sustainable city planning in today's dynamic urban environment. Citizens may experience proposed urban developments in unprecedented detail, including their possible social, environmental, and infrastructural impacts, due to the use of augmented reality. The potential for AR, cloud computing, and Support Vector Machines (SVM) to dramatically increase citizen participation in green urban design is investigated. With AR, individuals may explore potential new developments in virtual cityscapes. Intricate linkages within the dynamic dataset created during public participation are captured by the system's usage of SVM to evaluate and understand user comments. The ability of SVM to deal with non-linear patterns is crucial to gleaning useful insights from stakeholders' complex and varied contributions. In order to ensure that all parties involved in a system have access to the most up-to-date information, cloud computing plays a crucial role by enabling real-time collaboration and changes to the AR models This all-encompassing method not only improves the effectiveness of eco-friendly city planning, but also encourages openness and participation in policymaking. The suggested system represents a paradigm shift, demonstrating that innovative technology may be combined with informed citizenry to produce ecologically responsible and socially dynamic cities. © 2024 IEEE.

关键词： Support vector machines

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Improvement of Confidence of Learners of Oral English in College English Integrating Speech Emotion Recognition technology 3

Improvement of Confidence of Learners of Oral English in Col...

引用

3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Huang, Jingjing Wuhan Polytechnic Wuhan China

ISBN: (纸本)9798350343922

In traditional English oral learning, only limited feedback information can be provided, which cannot deeply analyze and diagnose the emotional characteristics of learners' speech expression, directly affecting students' confidence. By automatically analyzing learners' speech data, including intonation, speaking speed, tone, etc., combined with emotion recognition algorithms, learners' oral expression can be evaluated and fed back, helping them improve their oral ability and enhance their confidence. Three dimension convolutional neural network with residual network (Res3DCNN) was utilized to extract spectral-temporal features of emotional speech from spectrograms. Then, the long-term dependency relationships of students' speech emotional features were extracted, and emotional features in oral expression were automatically detected and classified, such as confidence, tension, excitement, etc. Through experiments, it has been proven that the use of speech emotion recognition technology has improved the learners' confidence of oral English in college English. Through this approach, learners can gain a more comprehensive understanding of their emotional state in oral expression, thereby improving and adjusting their oral language. © 2023 IEEE.

关键词： Contrastive Learning

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