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Design and Implementation of Project-Based Collaborative Learning in a Micro-lesson
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作者 凌湘春 《海外英语》 2020年第5期267-268,共2页
Combined with the features and strategies of project-based collaborative learning, micro-lesson—as one of modern teaching means, can display the teaching design and implementation of PBL, thus students can improve th... Combined with the features and strategies of project-based collaborative learning, micro-lesson—as one of modern teaching means, can display the teaching design and implementation of PBL, thus students can improve their awareness and ability of cross-cultural communication in the process of experience PBL. 展开更多
关键词 Micro-lesson project-based collaborative learning CROSS-CULTURAL communication
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Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning
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作者 Fang Hu Siyi Qiu +3 位作者 Xiaolian Yang ChaoleiWu Miguel Baptista Nunes Hui Chen 《Computers, Materials & Continua》 SCIE EI 2024年第8期2897-2915,共19页
As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in dat... As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models. 展开更多
关键词 Blockchain technique federated learning healthcare and medical data collaboration service privacy preservation
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Deep Reinforcement Learning-Based Collaborative Routing Algorithm for Clustered MANETs 被引量:1
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作者 Zexu Li Yong Li Wenbo Wang 《China Communications》 SCIE CSCD 2023年第3期185-200,共16页
Flexible adaptation to differentiated quality of service(QoS)is quite important for future 6G network with a variety of services.Mobile ad hoc networks(MANETs)are able to provide flexible communication services to use... Flexible adaptation to differentiated quality of service(QoS)is quite important for future 6G network with a variety of services.Mobile ad hoc networks(MANETs)are able to provide flexible communication services to users through self-configuration and rapid deployment.However,the dynamic wireless environment,the limited resources,and complex QoS requirements have presented great challenges for network routing problems.Motivated by the development of artificial intelligence,a deep reinforcement learning-based collaborative routing(DRLCR)algorithm is proposed.Both routing policy and subchannel allocation are considered jointly,aiming at minimizing the end-to-end(E2E)delay and improving the network capacity.After sufficient training by the cluster head node,the Q-network can be synchronized to each member node to select the next hop based on local observation.Moreover,we improve the performance of training by considering historical observations,which can improve the adaptability of routing policies to dynamic environments.Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion.In addition,the effectiveness of the routing policy in a dynamic environment is verified. 展开更多
关键词 artificial intelligence deep reinforcement learning collaborative routing MANETS 6G
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PBL(Project-Based Learning)理念融入高职劳动教育的价值意蕴与实践路径
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作者 周琴 《青年创新创业研究》 2023年第2期62-66,共5页
劳动是人维持自我生存和自我发展的唯一手段,劳动教育是推进立德树人根本任务及提升人才培养质量的重要载体。然而教材体系不够成熟、课程体系不够健全、评价体系不够完善是当前高职劳动教育存在的突出问题。将PBL(Pro-ject-Based Learn... 劳动是人维持自我生存和自我发展的唯一手段,劳动教育是推进立德树人根本任务及提升人才培养质量的重要载体。然而教材体系不够成熟、课程体系不够健全、评价体系不够完善是当前高职劳动教育存在的突出问题。将PBL(Pro-ject-Based Learning)项目式教学理念融入劳动教育,契合高职人才培养目标,有助于提高师生参与的积极性,促进劳动教育效果提升。对此,在推进PBL教学理念融入劳动教育教学过程中,加强顶层设计明确目标,聚焦职教特色融通资源,项目驱动理实教育一体化,量身定制项目化测评体系,切实培养好学生的劳动意识、劳动思维、劳动能力。 展开更多
关键词 project-based learning 劳动教育 价值意蕴 实践路径
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The Characteristics of Preschool Children’s Collaborative Problem Solving:A Discourse Analysis in Project-Based Learning
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作者 HE Shanyun CHEN Shuang 《Frontiers of Education in China》 2024年第2期152-173,共22页
Considered a crucial skill in the 21st century,collaborative problem solving(CPS)has been an essential development task for preschool children.This study analyzes preschool children’s discourse in the project-based l... Considered a crucial skill in the 21st century,collaborative problem solving(CPS)has been an essential development task for preschool children.This study analyzes preschool children’s discourse in the project-based learning(PBL)process and presents the following findings.Firstly,in the collaborative dimension,the frequency of children’s discourse on establishing and maintaining shared understanding(U)and taking appropriate action to solve the problem(A)is relatively high,while that on establishing and maintaining team organization(O)is relatively low.Secondly,in the problem solving dimension,the frequency of children’s discourse on planning and executing(P&E)is the highest,while that on monitoring and reflecting(M&R)is the lowest.Thirdly,in terms of turn taking patterns,self-selection accounts for a significantly higher proportion than allocation and continuation.Overall,preschool children’s CPS is characterized by loose collaboration and multilinear problem solving.They are usually keener to strive for opportunities to express their views but lack attention to others’speeches.At the same time,they can constantly come up with new problem solving plans and actions but rarely reflect on their feasibility and actual effects.In addition to children’s collaborative role,teachers’intervention can also impact the CPS processes.Therefore,teachers are recommended to provide children with opportunities for CPS and strengthen monitoring,guidance,and support in children’s CPS processes to facilitate better child engagement in CPS. 展开更多
关键词 preschool children collaborative problem solving(CPS) project-based learning(PBL) discourse analysis
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A New Solution to Intrusion Detection Systems Based on Improved Federated-Learning Chain
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作者 Chunhui Li Hua Jiang 《Computers, Materials & Continua》 SCIE EI 2024年第6期4491-4512,共22页
In the context of enterprise systems,intrusion detection(ID)emerges as a critical element driving the digital transformation of enterprises.With systems spanning various sectors of enterprises geographically dispersed... In the context of enterprise systems,intrusion detection(ID)emerges as a critical element driving the digital transformation of enterprises.With systems spanning various sectors of enterprises geographically dispersed,the necessity for seamless information exchange has surged significantly.The existing cross-domain solutions are challenged by such issues as insufficient security,high communication overhead,and a lack of effective update mechanisms,rendering them less feasible for prolonged application on resource-limited devices.This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload.Within this framework,individual nodes solely engage in training local data and subsequently amalgamate the final model employing a federated learning algorithm to uphold enterprise systems with efficiency and security.To curtail the resource utilization of blockchains and deter malicious nodes,a node administration module predicated on the workload paradigm is introduced,enabling the release of surplus resources in response to variations in a node’s contribution metric.Upon encountering an intrusion,the system triggers an alert and logs the characteristics of the breach,facilitating a comprehensive global update across all nodes for collective defense.Experimental results across multiple scenarios have verified the security and effectiveness of the proposed solution,with no loss of its recognition accuracy. 展开更多
关键词 Cross-domain collaboration blockchain federated learning contribution value node management release slack resources
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Pioneering role of machine learning in unveiling intensive care unitacquired weakness
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作者 Silvano Dragonieri 《World Journal of Clinical Cases》 SCIE 2024年第13期2157-2159,共3页
In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machin... In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machine learning methodologies.The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW,focusing on critical variables such as ICU stay duration and mechanical ventilation.This research marks a significant advancement in applying machine learning to clinical diagnostics,offering a new paradigm for predictive medicine in critical care.It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare. 展开更多
关键词 Intensive care unit-acquired weakness Machine learning Multilayer perceptron neural network Predictive medicine Interdisciplinary collaboration
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Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture 被引量:1
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作者 Siyuan Jiang Hongjun Gao +2 位作者 Xiaohui Wang Junyong Liu Kunyu Zuo 《Global Energy Interconnection》 EI CAS CSCD 2023年第1期1-14,共14页
With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provi... With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system. 展开更多
关键词 Cloud-edge collaboration architecture Multi-agent deep reinforcement learning Multi-level dynamic reconfiguration Offline learning Online learning
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Effects of Media and Distributed Information on Collaborative Concept-Learning 被引量:1
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作者 傅小兰 《心理与行为研究》 2005年第4期248-255,共8页
The present study explores the effects of media and distributed information on the performance of remotely located pairs of people′s completing a concept-learning task. Sixty pairs performed a concept-learning task u... The present study explores the effects of media and distributed information on the performance of remotely located pairs of people′s completing a concept-learning task. Sixty pairs performed a concept-learning task using either audio-only or audio-plus-video for communication. The distribution of information includes three levels: with totally same information, with partly same information, and with totally different information. The subjects′ primary psychological functions were also considered in this study. The results showed a significant main effect of the amount of information shared by the subjects on the number of the negative instances selected by the subjects, and a significant main effect of media on the time taken by the subjects to complete the task. 展开更多
关键词 学习观 学习心理学 电视传媒 心理应用
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Research progress on interdisciplinary cooperative learning in nursing
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作者 Jing-Mei Li Xiang-Shu Cui 《Nursing Communications》 2024年第24期1-7,共7页
Multidisciplinary and interprofessional collaboration among medical staff is an effective way to reduce the incidence and complication of complex diseases and improve the quality of life.Therefore,it is very important... Multidisciplinary and interprofessional collaboration among medical staff is an effective way to reduce the incidence and complication of complex diseases and improve the quality of life.Therefore,it is very important to carry out interdisciplinary cooperative learning for nursing students in the education stage.This paper expounds the current situation of cross-disciplinary nursing education at home and abroad from the aspects of preparation and influencing factors of cross-disciplinary cooperative learning,teacher team building,teaching content and teaching methods,implementation time and place,in order to put forward suggestions for carrying out cross-disciplinary cooperative learning in college courses,and provide reference for many educators to carry out cross-disciplinary education and improve the comprehensive ability of nursing students. 展开更多
关键词 interprofessional collaboration learning nursing students EDUCATION PROPOSAL REVIEW
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Project-based Language Learning: an Activity Theory Analysis in SOE Language Learning
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作者 陈苡晴 《海外英语》 2016年第10期215-217,220,共4页
This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activ... This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activity Theory. Moreover,Data has been collected and categorized based on the components of complex human activity: the subject, object, tools(signs,symbols, and language), the community in which the activity take place, division of labor, and rules. The findings theoretically support the outcome of project-based language learning which align with the object of the activity. 展开更多
关键词 ACTIVITY THEORY project-based learning SOE LANGUAGE learning
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Alternative Assessment in an EFL Project-based Learning Context: Perceptions and Correlations
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作者 韦启卫 皮拉萨·西里约丁 安德鲁·彼得·莱恩 《海外英语》 2019年第12期272-277,共6页
This study aims to explore Chinese university EFL learners'perceptions toward alternative assessment in a context of a project-based learning digital storytelling presentation in Speaking Course.It also seeks to c... This study aims to explore Chinese university EFL learners'perceptions toward alternative assessment in a context of a project-based learning digital storytelling presentation in Speaking Course.It also seeks to compare the relationship between alternative assessment and teacher assessment.The findings showed that a strong correlation between alternative assessment and teacher assessment occurred.Alternative assessment activities are viewed by students as"authentic"assessments,as they mimic how the student will be using their knowledge in the future.Alternative assessment as a form of formative assessment can be a powerful day-to-day tool for teachers and students.Alternative assessment is an enabler of process of learning.The study suggests that alternative assessment can encourage learners to become more fully responsible for their learning and can result in more and better learning.Alternative assessment can thus be used as a golden key to the"deaf and dumb"phenomenon for Chinese university EFL learners. 展开更多
关键词 alternative assessment EFL project-based learning digital storytelling speaking course
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On the Task-based Collaborative Learning 被引量:1
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作者 曲囡囡 马卓 《语言与文化研究》 2008年第2期149-152,共4页
Task-based language teaching(TBLT) has been a prevalent teaching practice in the TEFL field in the recent years and its momentum for striving to be the legitimate one has never ceased. The present study tries to provi... Task-based language teaching(TBLT) has been a prevalent teaching practice in the TEFL field in the recent years and its momentum for striving to be the legitimate one has never ceased. The present study tries to provide a theoretical foundation for its application in the communicative learning approach of English as the second language(ESL),namely the collaborative learning mode. 展开更多
关键词 TBLT collaborative learning TASK
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Collaborative Spectrum Sensing for Illegal Drone Detection: A Deep Learning-Based Image Classification Perspective 被引量:6
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作者 Huichao Chen Zheng Wang Linyuan Zhang 《China Communications》 SCIE CSCD 2020年第2期81-92,共12页
Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can pro... Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations. 展开更多
关键词 illegal drones detection deep learning collaborative spectrum sensing
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The MOOC/SPOC Based"1+M+N"Multi-University Collaborative Teaching and Learning Mode:Practice and Experience 被引量:11
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作者 Xiaofei Xu Dechen Zhan +2 位作者 Ce Zhang Dianhui Chu Weihua Guo 《计算机教育》 2018年第12期1-6,共6页
Since 2012, the MOOCs, the massive open online courses, have brought big influences on the higher education in the world. How to use MOOCs to help universities rather than bother them to improve their education level ... Since 2012, the MOOCs, the massive open online courses, have brought big influences on the higher education in the world. How to use MOOCs to help universities rather than bother them to improve their education level and quality becomes an important issue. In China, many universities have explored the new modes and approaches for MOOC/SPOC-based teaching and learning. Especially, the China MOOC Association on Computing Education(CMOOC association), established in 2014, has done a set of successful practice and achieved fruitful experiences on MOOC courses development and computer education reform. Based on the practical experiences, a MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode is presented, which is adapted to the real situation of Chinese university education. In the paper, the practices and experiences of CMOOC association are introduced, the MOOC/SPOC based "1+M+N" multi-university collaborative teaching and learning mode and its approaches are described. Finally, the suggestions for MOOCs development and applications are also presented. 展开更多
关键词 MOOCs SPOCs CMOOC Association "1+M+N"collaborative TEACHING and learning model flipped CLASSROOM based TEACHING approaches
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Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple Object Tracking by Detection Using Deep Learning 被引量:8
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作者 Imran Ahmed Sadia Din +2 位作者 Gwanggil Jeon Francesco Piccialli Giancarlo Fortino 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第7期1253-1270,共18页
Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It a... Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines. 展开更多
关键词 collaborative robotics deep learning object detection and tracking top view video surveillance
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Hidden Two-Stream Collaborative Learning Network for Action Recognition 被引量:4
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作者 Shuren Zhou Le Chen Vijayan Sugumaran 《Computers, Materials & Continua》 SCIE EI 2020年第6期1545-1561,共17页
The two-stream convolutional neural network exhibits excellent performance in the video action recognition.The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extr... The two-stream convolutional neural network exhibits excellent performance in the video action recognition.The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extracted by the frames,to train a model each,and to finally integrate the outputs of the two models.Nevertheless,the reliance on the pre-extraction of the optical flow impedes the efficiency of action recognition,and the temporal and the spatial streams are just simply fused at the ends,with one stream failing and the other stream succeeding.We propose a novel hidden two-stream collaborative(HTSC)learning network that masks the steps of extracting the optical flow in the network and greatly speeds up the action recognition.Based on the two-stream method,the two-stream collaborative learning model captures the interaction of the temporal and spatial features to greatly enhance the accuracy of recognition.Our proposed method is highly capable of achieving the balance of efficiency and precision on large-scale video action recognition datasets. 展开更多
关键词 Action recognition collaborative learning optical flow
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Collaborative Clustering Parallel Reinforcement Learning for Edge-Cloud Digital Twins Manufacturing System 被引量:1
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作者 Fan Yang Tao Feng +2 位作者 Fangmin Xu Huiwen Jiang Chenglin Zhao 《China Communications》 SCIE CSCD 2022年第8期138-148,共11页
To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge serv... To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments. 展开更多
关键词 edge-cloud collaboration digital twins job shop scheduling parallel reinforcement learning
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Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment 被引量:1
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作者 Thavavel Vaiyapuri 《Computers, Materials & Continua》 SCIE EI 2021年第7期487-503,共17页
The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means t... The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems. 展开更多
关键词 Neural collaborative filtering cold-start problem data sparsity multilayer perception generalized matrix factorization autoencoder deep learning ensemble learning top-K recommendations
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A Multi-Agent Reinforcement Learning-Based Collaborative Jamming System: Algorithm Design and Software-Defined Radio Implementation 被引量:1
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作者 Luguang Wang Fei Song +5 位作者 Gui Fang Zhibin Feng Wen Li Yifan Xu Chen Pan Xiaojing Chu 《China Communications》 SCIE CSCD 2022年第10期38-54,共17页
In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent ja... In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect. 展开更多
关键词 multi-agent reinforcement learning intelligent jamming collaborative jamming software-defined radio platform
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