检索结果-内蒙古大学图书馆

IEEE Symposium on Computers and Communications (ISCC)

作者： Yuxuan Deng Xiuhua Li Chuan Sun Jinlong Hao Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

引用

SN Computer science 2023年第4期4卷 363页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

来源：评论

学校读者我要写书评

暂无评论

Capability-based IoT access control using blockchain

引用

Digital Communications and Networks 2021年第4期7卷 463-469页

作者： Yue Liu Qinghua Lu Shiping Chen Qiang Qu Hugo’Connor Kim-Kwang Raymond Choo He Zhang School of Computer Science and Engineering University of New South WalesAustralia Data61 CSIROAustralia Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesChina Department of Information Systems and Cyber Security University of Texas at San AntonioUSA Software Institute Nanjing UniversityChina

Internet of Things(IoT)devices facilitate intelligent service delivery in a broad range of settings,such as smart offices,homes and ***,the existing IoT access control solutions are mainly based on conventional identity management schemes and use centralized *** are knowm security and privacy limitations with such schemes and architectures,such as the single-point failure or surveillance(e.g.,device tracking).Hence,in this paper,we present an architecture for capability-based IoT access control utilizing the blockchain and decentralized identifiers to manage the identity and access control for IoT ***,we propose a protocol to provide a systematic view of system interactions,to improve *** also implement a proof-of-concept prototype of the proposed approach and evaluate the prototype using a real-world use *** evaluation results show that the proposed solution is feasible,secure,and scalable.

关键词： Blockchain Internet of things Capability-based access control Identity management Architecture design

来源：评论

学校读者我要写书评

暂无评论

Dual Latent State Learning: Exploiting Regional Network Similarities for QoS Prediction

arXiv

引用

arXiv 2023年

作者： Wang, Ziliang Zhang, Xiaohong Zhang, Kechi Li, Ze Shi Yan, Meng Ministry of Education School of Computer Science Peking University Beijing China Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing401331 China The Department of Computer Science the University of Victoria Canada

Individual objects (users or services) within a region usually have similar network states to each other because they usually come from the same region. Despite the similarity within regional networks, many existing techniques overlook this potential, thereby limiting the accuracy of Quality of Service (QoS) predictions. In this paper, we introduce the regional-based dual latent state learning network(R2SL), a novel deep learning framework designed to overcome the pitfalls of traditional individual object-based prediction techniques for QoS prediction. R2SL first captures the regional network states by deriving two distinct regional network latent states: the physical-regional latent state and the virtual-regional latent state. These states are built using aggregated data from a physical area (such as the same city) or a virtual network area (such as the same service provider) rather than individual object data. Then R2SL provides a specific Mixture of Experts(MOE) network based on sparse activation to realize the classification learning task of latent features of different regions. Thirdly, R2SL adopts an enhanced smooth Huber loss function that adjusts Huber loss’s linear loss component to alleviate the impact caused by label imbalance. Finally, the perceptual network was used to interpret the integrated features, thereby realizing the QoS prediction. Experimental results show that compared with the typical methods and the latest methods, the proposed method can obtain higher prediction accuracy, thus improving the research level of QoS prediction. © 2023, CC0.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning-Enhanced IoT Wearable Device for Monitoring and Providing Feedback on Stuttering

A Machine Learning-Enhanced IoT Wearable Device for Monitori...

引用

Computational Modelling, Simulation and Optimization (ICCMSO), International Conference on

作者： Hussain Suttarwala Ritesh Kumar Madhavi L. Deorankar Dikshendra Daulat Sarpate Pallavi Shingade Surender Kumar Sharma Vrushali More Uday Chandrakant Patkar Deepak K. Sharma Senior Software Engineer American Express Phoenix AZ USA Dept. of AI and Data Science Dr. Akhilesh Das Gupta Institute of Professional Studies New Delhi Delhi India Dept. of Electronics and Telecommunications Marathwada Mitramandal College of Engineering Pune Maharashtra India Dept. of AI and Data Science Zeal College of Engineering and Research Narhe Pune Maharashtra India Dept. of AI and Data Science D Y Patil College of Engineering Akurdi Pune India Dept. of Electrical Engineering Poornima University Jaipur India Dept. of AI and Data Science Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Department of Computer Engineering Bharati Vidyapeeth’s College of Engineering Lavale Pune Dept. of AI and Data Science Savitribai Phule Pune University Pune Maharashtra India

ISBN: (数字)9798350361391

ISBN: (纸本)9798350361407

This study introduces an innovative IoT-enabled wearable device tailored for monitoring and providing feedback on stuttering, leveraging advanced machine learning algorithms to enhance precision and real-time responsiveness. The device integrates an array of hardware and software elements, including sensors, microcontrollers, and communication protocols, to accurately capture speech patterns and movements. Utilizing machine learning techniques such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), the device performs real-time analysis to identify stuttering instances. Detailed discussions cover the criteria for selecting machine learning models, the training and testing procedures, and the metrics used for performance evaluation. The prototype underwent extensive testing and validation, showcasing high accuracy, sensitivity, and specificity in differentiating stuttered from fluent speech. User feedback emphasized the ergonomic design, user-friendly interface, and real-time feedback features, highlighting its potential to significantly enhance stuttering therapy outcomes. Comparative analysis with existing solutions revealed the device’s superior performance, indicating strong potential for clinical application. The study discusses the implications for stuttering therapy, acknowledges the limitations, outlines future research directions, and underscores the device’s contributions to personalized and effective stuttering interventions. This research advances the field of assistive technologies in speech therapy and demonstrates the promise of IoT-based wearable devices integrated with machine learning for improving the quality of life for individuals with stuttering disorders.

关键词： Performance evaluation Machine learning algorithms Accuracy Medical treatment Prototypes Machine learning Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Enabled Healthcare Balancing Patient Privacy and data Utility

Machine Learning Enabled Healthcare Balancing Patient Privac...

引用

International Conference on science Technology engineering and Management (ICONSTEM)

作者： Manoj Ram Tammina Bhargavi Posinasetty Prabha Shreeraj Nair Santosh Kumar Pavithra G Harpreet Kaur Sr Software Developer Innovation Bread Financial Columbus Ohio Clinical Data Management Sr. Clinical Data Manager PROMETRIKA LLC Department of IT Noida Institute of Engineering and Technology Greater Noida Computer Science ERA University Lucknow Uttar Pradesh Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore Karnataka Lovely Professional University Punjab Phagwara

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

When applied to healthcare, machine learning ushers in a new age of data-driven medical practice that holds great promise for better patient outcomes and individualized treatment. However, this evolution isn't without significant difficulties, such as the difficulty of striking a balance between patient confidentiality and data use. In this study, we use -Differential Privacy as a privacy-protecting technique and a number of machine learning models to quantify the value of the data collected. Our research demonstrates a subtle trade-off, where more stringent privacy safeguards often result in less useful data, and vice versa. We stress the need for ethical frameworks, patient permission, and flexible privacy restrictions as means of negotiating this space. Achieving responsible and successful machine learning-enabled healthcare calls for a number of future steps, including optimization of privacy settings, adoption of federated learning, data ownership through blockchain, validations in the actual world, and extensive ethical advice.

关键词： data privacy Ethics Privacy Federated learning Medical services data models Blockchains

来源：评论

学校读者我要写书评

暂无评论

Feature Noise Resilient for QoS Prediction with Probabilistic Deep Supervision

arXiv

引用

arXiv 2023年

作者： Wang, Ziliang Zhang, Xiaohong Li, Ze Shi Huang, Sheng Yan, Meng Ministry of Education School of Computer Science Peking University Beijing China The Department of Computer Science The University of Victoria Canada Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise. In this paper, we present the Probabilistic Deep Supervision Network (PDS-Net), a robust framework designed to effectively identify and mitigate feature noise, thereby improving QoS prediction accuracy. PDS-Net operates with a dual-branch architecture: the main branch utilizes a decoder network to learn a Gaussian-based prior distribution from known features, while the second branch derives a posterior distribution based on true labels. A key innovation of PDS-Net is its condition-based noise recognition loss function, which enables precise identification of noisy features in objects (users or services). Once noisy features are identified, PDS-Net refines the feature's prior distribution, aligning it with the posterior distribution, and propagates this adjusted distribution to intermediate layers, effectively reducing noise interference. Extensive experiments conducted on two real-world QoS datasets demonstrate that PDS-Net consistently outperforms existing models, achieving an average improvement of 8.91% in MAE on dataset D1 and 8.32% on dataset D2 compared to the ate-of-the-art. These results highlight PDS-Net's ability to accurately capture complex user-service relationships and handle feature noise, underscoring its robustness and versatility across diverse QoS prediction environments. © 2023, CC0.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Imitation Learning from Purified Demonstrations

arXiv

引用

arXiv 2023年

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong China School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

URLLC and eMBB Multiplexing with RIS: Modeling, Analysis, and Optimization

URLLC and eMBB Multiplexing with RIS: Modeling, Analysis, an...

引用

International Conference on Wireless and Optical Communications ( WOCC)

作者： Kaiwen Zhuang Linpeng Zhong Jiaxin Wen Limin Li Haoran Peng College of Data Science and Artificial Intelligence Wenzhou University of Technology China School of Information and Software Engineering University of Electronic Science and Technology of China College of Internet of Things Nanjing University of Posts and Telecommunications China College of Electrical and Electronic Engineering Wenzhou University China Department of Information Engineering The Chinese University of Hong Kong SAR Hong Kong

ISBN: (数字)9798331539658

ISBN: (纸本)9798331539665

Reconfigurable Intelligent Surfaces (RISs)-assisted technology has the potential to enhance service coverage’ reduce latency, and save energy in Ultra-Reliable and Low-Latency Communications (URLLC) and Enhanced Mobile Broadband (eMBB) multiplexing systems. However, in URLLC and eMBB multiplexing systems, low-priority eMBB services can experience interruptions from tem-porarily inserted high-priority URLLC services, resulting in significant transmission delays and traffic congestion. Furthermore, varying the services capabilities between different nodes can lead to increased latency and backlog violations. This paper offers an overview of RIS-assisted URLLC and eMBB multiplexing systems, discussing their applications, challenges, and solutions. In this paper, we propose optimizing spectrum sharing and resource allocation based on accurate latency estimation, followed by performing primary simulations to demonstrate the effectiveness.

关键词： Multiplexing Wireless communication Analytical models Accuracy Estimation Ultra reliable low latency communication Reconfigurable intelligent surfaces Resource management Optimization Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

引用

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T. Ganesan N. Murali S. Palani Murugan M. Chandrakumar Peter P. Anand Raj S. Manikandan Department of Computer Science & Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Artificial Intelligence & Data Science E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Business Systems E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：