Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Blockchain-Enabled Federated Learning for Privacy-Preserving Non-IID Data Sharing in Industrial Internet

Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy concerns.Federated learning(FL)has gained considerable attention because it allows for decentralized training on multiple local datasets.However,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL performance.This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain technology.To overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local models.This approach can significantly improve the accuracy of FL training when there is relatively large dispersion.In addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model parameters.Finally,we evaluate the performance of the proposed scheme using commonly used open-source image datasets.The simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.

Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems

In Decentralized Machine Learning(DML)systems,system participants contribute their resources to assist others in developing machine learning solutions.Identifying malicious contributions in DML systems is challenging,which has led to the exploration of blockchain technology.Blockchain leverages its transparency and immutability to record the provenance and reliability of training data.However,storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs.Additionally,current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data.However,less attention has been paid to the scenario where malicious requesters intentionally manipulate test data during evaluation to gain an unfair advantage.This paper proposes a transparent and accountable training data sharing method that securely shares data among potentially malicious system participants.First,we introduce a blockchain-based DML system architecture that supports secure training data sharing through the IPFS network.Second,we design a blockchain smart contract to transparently split training datasets into training and test datasets,respectively,without involving system participants.Under the system,transparent and accountable training data sharing can be achieved with attribute-based proxy re-encryption.We demonstrate the security analysis for the system,and conduct experiments on the Ethereum and IPFS platforms to show the feasibility and practicality of the system.

PBL(Project-Based Learning)理念融入高职劳动教育的价值意蕴与实践路径

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。

Project-based Language Learning: an Activity Theory Analysis in SOE Language Learning

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.

Federated Learning with Blockchain for Privacy-Preserving Data Sharing in Internet of Vehicles

Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised about the security and privacy of vehicle data.The mobility and real-time characteristics of vehicle data make data sharing more difficult in Io V.The emergence of blockchain and federated learning brings new directions.In this paper,a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in Io V.First,we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of data.Then,we integrate the verification scheme into the consensus process,so that the consensus computation can filter out low-quality models.Experimental data shows that our data-sharing model can better balance the relationship between data availability and privacy,and also has enhanced security.

Alternative Assessment in an EFL Project-based Learning Context: Perceptions and Correlations

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.

Development of Fundamental Competencies for Working Persons Through Project-Based Learning

Universities teach mainly specialized subjects and the liberal arts. Society expects university students to gain certain basic skills important when working for a company. These skills can be divided broadly into action, thinking, and teamwork. The purpose of this paper is to propose a method of project-based education for developing fundamental competencies for working persons. Many studies have been reported on educational methods with project management techniques, but few have considered project-based education aiming at improving fundamental competencies for working persons. If these competencies can be developed through project-based education, it will be possible to develop not only teamwork skills, but also a wide range of skills involving action as well as thinking. The traditional Japanese university curriculum comprises specialized subjects and the liberal arts. The author proposes the addition of project-based education to develop basic skills needed in the workforce. This research proposes an education model for basic competency training and examines the educational outcomes by studying results of a cooking tool project assigned to university students. The model includes Teoriya Resheniya Izobretatelskikh Zadatch （TRIZ, a Russian acronym for the theory of inventive problem solving）, a World Cafe, and the SECI process （a process of knowledge creation comprised of socialization, externalization, combination, and internalization in knowledge management） in the hope that this model will be conducive to implementing effective project-based learning. This research concludes that it is possible to develop the basic skills needed by university students in society through project-based learning under a basic skills education model.

To Cultivate Outstanding Software Engineer Based on Project-based Learning

This thesis sketches the connotation of project-based learning and introduces the basis on which project-based learning is practiced and applied in school of software as well as the plan of further practicing project-based learning.At the same time,this thesis also discusses application of project-based learning in "education and cultivation plan of excellent engineer".

Under "Integration of Doing, Learning and Teaching", Research on the Project-Based Teaching Innovation of "Landscape Planning and Design"

Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroad, combined with literature research, case analysis, system theory and other research methods, the project-based teaching goal, model, content and means of "integration of doing, learning and teaching" in higher vocational education is explored, and the project-based teaching model of "Landscape Planning and Design" is discussed combined with the application of information-based teaching methods. So as to provide references for carrying out the project-based teaching in similar courses in higher vocational colleges and really achieve docking the actual post requirements with the course to provide the basis for achieving the purpose of cultivating skilled talents in higher vocational education.

Design and Implementation of Project-Based Collaborative Learning in a Micro-lesson

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.

Spectrum Sensing Using Optimized Deep Learning Techniquesin Reconfigurable Embedded Systems

The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniques such as spectrum sensing,spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication.IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index,frequency bands,coding rate etc.,to accommodate the above characteristics.Implementing the above learning methods on the embedded chip leads to high latency,high power consumption and more chip area utilisation.To overcome the problems mentioned above,we present DEEP HOLE Radio sys-tems,the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency(RF)environment.DEEP HOLE Radio provides(i)an opti-mized deep learning framework with a good trade-off between latency,power and utilization.(ii)Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance.The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni direc-tional antennas.The whole spectrum of knowledge has been extracted using GNU radio.These extracted features are used to train the proposed optimized deep learning models,which run parallel on Zynq-SoC 7000,consuming less area,power,latency and less utilization area.The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn,Long Term Short Memory(LSTM),Convolutional Neural Networks(CNN)and Deep Neural Networks(DNN).The outcome shows that the proposed framework has outperformed the existing framework regarding the area,power and time.More-over,the experimental results show that the proposed framework decreases the delay,power and area by 15%,20%25%concerning the existing RFlearn and other hardware constraint frameworks.

Multi-agent reinforcement learning for edge information sharing in vehicular networks

To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.

A Distributed Algorithm for Parallel Multi-task Allocation Based on Profit Sharing Learning

经由联盟形成的任务分配是在多代理人系统(妈) 的几应用程序域的基本研究挑战，例如资源分配，灾难反应管理等等。怎么以一种分布式的方式分配许多未解决的任务到一些代理人，主要处理。在这篇论文，我们在自我组织、自我学习的代理人之中建议一个分布式的平行多工分配算法。处理状况，我们在二维的房间地理上驱散代理人和任务，然后介绍为寻找它的任务由的一个单个代理人的分享学习的利润(PSL ) 不断自我学习。我们也在代理人之中为通讯和协商介绍策略分配真实工作量到每个 tasked 代理人。最后，评估建议算法的有效性，我们把它与 Shehory 和 Krau 被许多研究人员在最近的年里讨论的分布式的任务分配算法作比较。试验性的结果证明建议算法罐头快速为每项任务形成一个解决的联盟。而且，建议算法罐头明确地告诉我们每个 tasked 代理人的真实工作量，并且能因此为实际控制任务提供一本特定、重要的参考书。

A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

E-learning——学习型组织的新思路

通过引入E-learing概念,探讨了其在学习型组织中的运用及其系统构建,最后结合Cisco公司的案例给以 具体分析。

Design Agents with Sharing Learning Mechanism

This paper introduces the structure of a multiagent design system with machine learning mechanism and its application in mechanical design. Firs of are it introduces a hierarchical structure of the multiagent design system and takes a mechanical design system as an example. This structure provides a computational platform for cooperative design and sharing learning of multiple design agents. The paper analyses the principle of design activity and puts forward the architecture and learning mechanism of a design agent in datail. The architecture of a design agent is for providing support to learning activity and is based on the analysis of the design activity This is followed by a description of the design knowledge base framework and sharing learning process of multiagent. The main advantages of the system is that complex design task can be done by multiagent in a distributed environment and leaming results can be shared by a group of design agents. This system has partly been implemented in Visual C++ based on Mechanical Desktop 2.0 environment.

基于E-Learning环境的测井应用课程资源共享互动平台建设

测井是石油工业十大主干学科之一,随着复杂非常规油气资源勘探开发的深入,新的地质、工程问题不断出现,测井技术有效解决地质及工程问题的能力及范围不断扩展和延伸,石油企业对测井应用于地质、工程问题分析解决能力的需求更加迫切。本文以"测井地质学"课程为例,利用E-Learning环境,建设了课程资源库、现场实例库、学习记录库及试题信息库等为主的课程共享资源平台,较大限度地激发了学生主动学习的积极性,同时也有效提高了教学资源的利用率和学生的学习效率,加强了教师与学生的课下互动,并使得课程考核的模式多元化,从而突破了传统课堂的限制,促进了教学质量的提升。同时,也是完善地球物理测井"互联网+系列课程"体系及培养测井应用复合型专业人才的实践探索。

基于E-Learning的教学参考信息数据库的建设

互联网络的发展使得网络学习成为一种日益普及的学习途径。高等院校可以凭借自身的资源优势建立教学参考信息数据库,实现网络学习与专业教学的有机结合。本文提出了网络学习环境下建设教学参考信息数据库的基本模式。

E-learning——创建和谐高校图书馆的新思路

介绍了E-learning的概念,探讨了E-learning在和谐高校图书馆中的运用,阐述了图书馆E-learning系统模型的构建。