基于Web2.O的题库应用

针对传统的题库并未考虑到学员之间在学习上的协同作用,现介绍了web2．0的概念和理念,探讨了应用Web2．O的技术和软件,创建信息共享和协同学习的题库系统构架的方法.     

基于Web2．0的题库应用

针对传统的题库并未考虑到学员之间在学习上的协同作用,现介绍了Web2．0的概念和理念,探讨了应用Web2．0的技术和软件,创建信息共享和协同学习的题库系统构架的方法。     

基于协同学习的频谱智能感知方法

目前无线通信网络频谱环境时空分布复杂多变,现有多用户协同感知方法数据预处理繁琐,感知效率低下。为此,在由用户感知层和边缘融合层构成的系统架构下,提出了一种基于协同学习的频谱智能感知算法。用户感知层采用多分支卷积循环门控神经网络,利用原始归一化能量信号的底层结构信息,实现本地感知。边缘融合层基于自注意力机制进行消息传播,融合用户感知层中各个非授权用户的感知结果得出最终决策。实验表明,在信噪比为-20 dB以及5个用户协同感知的情况下,该方法能在虚警概率为1.91%时达到18.3%的检测概率,相比对比模型提升了6.1%,且不需要对原始数据额外预处理,降低了算法的复杂度。     

基于协同联想的分类器算法

文中首先从协同学角度出发,提出了一种协同联想记忆分类器（SAMC）的实现算法。为了验证SAMC算法的有效性,针对汽车车牌图像的识别系统,构建了一个基于SAMC的解决方案。实验结果表明,用SAMC实现算法识别效果好,适合于对有噪声的车牌图像的处理;该算法训练简单、联想能力强,对具有背景噪声和视角引起的图像畸变失真有一定的抗干扰能力,识别结果令人满意。     

基于双核协同学习模型的红外目标跟踪算法

在红外目标跟踪中,如何鲁棒地跟踪上目标,对提升武器装备战斗力意义重大.本文在核相关目标跟踪算法(KCF)的基础上提出了一种有效的多特征协同学习核相关红外目标跟踪算法,该算法通过KCF模型将HOG(Histogram of Oriented Gradient)特征与Haar-like特征整合到一个框架中,解决了单一特征不足以表征目标外观变化,同时大大提升了红外目标跟踪的准确性与稳定性.同时,本文也提出了一种自适应学习因子策略,增强了模型的泛化能力.大量定性定量实验结果表明本文所提算法在重叠率准则(OR)和跟踪中心误差(CLE)准则上超过现有大多数算法,同时其跟踪速度也超过大多数算法.     

基于神经网络模型测试生成的学习策略

本文描述一种基于组合电路的Ｈｏｐｆｉｅｌｄ神经网络模型的测试生成系统，重点介绍了系统中实现神经网络学习并记忆基于电路拓扑的知识信息的学习策略，从而将基于电路拓扑的知识与数学计算结合起来，最后给出了实验结果。     

具备视角协同学习能力的多视角TSK型模糊系统

传统模糊系统建模方法本质上是一种单视角学习模式,面向适合多视角处理的场景时,它们通常只能将每一视角割裂开来进行独立建模,这导致其所得系统泛化性能往往不令人满意。针对此缺陷,该文探讨具备多视角学习能力的模糊系统建模方法。为此,基于经典的L2型TSK模糊系统,通过引入具备多视角学习能力的协同学习项,该文提出了核心的多视角TSK型模糊系统(MV-TSK-FS)建模方法。MV-TSK-FS不仅能有效地利用各视角不同特征构成的独立样本信息,还能充分地利用各视角间由于相互关联而存在内在信息,以最终达到提高系统泛化性能的效果。在模拟数据集与真实数据集上的实验结果验证了较之于传统单视角模糊建模方法该多视角模糊系统有着更好的泛化性和适用性。     

基于伪逆的协同神经网络学习算法

本文改进了Ｈａｋｅｎ协同神经网络的算法。该学习算法在Ｈａｋｅｎ算法的基础上引进反馈机制,对权值矩阵反复训练,使权值矩阵能更有效地进行图像识别,并增大了网络容量。     

基于Web的超媒体教学CAI课件系统设计

为促进基于Web的超媒体课件在高校教学改革中的实际应用,以Windows XP为开发平台,Tomcat为服务器引擎,采用JSdP/Servlet作为服务器端执行语言,SQLServer2000为后台数据库作为技术支撑,开发设计并实现了基于Intemet/Intranet标准协议的三层(Brower/Server,B/S)体系结构的超媒体教学课件.该超媒体课件系统经实践应用表明.能够激发学生学习兴趣,减轻教师考试和答疑负担,有助于利用网络信息技术改进教学方法和提高教学质量.     

基于Web的超媒体教学CAI课件系统设计

为促进基于Web的超媒体课件在高校教学改革中的实际应用,以WindowsXP为开发平台,Tomcat为服务器引擎,采用JSP／Servlet作为服务器端执行语言,SQL Server2000为后台数据库作为技术支撑,开发设计并实现了基于Intemet／Intranet标准协议的三层（Brower／Server,B／S）体系结构的超媒体教学课件。该超媒体课件系统经实践应用表明,能够激发学生学习兴趣,减轻教师考试和答疑负担,有助于利用网络信息技术改进教学方法和提高教学质量。     

基于多Agent的协作学习支持系统研究

基于CSCL的教学系统普遍存在着协作评价困难、答疑低效以及缺乏知识管理的问题。据此提出了一种基于Agent的协作学习支持系统模型：ICLS-Agent。就ICLS-Agent模型的结构、以及协作评价、智能答疑处理、分布式的知识管理的实现策略进行了探讨。并结合各个实现策略及多Agent通信机制，设计了ICLS-Agent的协作交互模型。     

A decentralised iterative learning control framework for collaborative tracking

Collaborative tracking control involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. Decentralised iterative learning control schemes have demonstrated highly accurate collaborative tracking by using past experience gained over repeated attempts at the task. However they impose highly restrictive constraints on the system dynamics, and their reliance on inverse dynamics has degraded their robustness to model uncertainty.This paper proposes the first general decentralised iterative learning framework to address this problem, thereby enabling a wide range of existing iterative learning control methodologies to be applied in a decentralised manner to collaborative subsystems. This framework is illustrated through the derivation of a variety of new decentralised iterative learning control algorithms which balance collaborative tracking performance with optimisation of a general objective function. The framework is illustrated by application to wearable stroke rehabilitation technology in which each subsystem is a muscle artificially activated by electrical stimulation. These verify the framework’s simplified design and reduced hardware and communication overheads.     

Prerequisites between learning objects: Automatic extraction based on a machine learning approach

One standing problem in the area of web-based e-learning is how to support instructional designers to effectively and efficiently retrieve learning materials, appropriate for their educational purposes. Learning materials can be retrieved from structured repositories, such as repositories of Learning Objects and Massive Open Online Courses; they could also come from unstructured sources, such as web hypertext pages. Platforms for distance education often implement algorithms for recommending specific educational resources and personalized learning paths to students. But choosing and sequencing the adequate learning materials to build adaptive courses may reveal to be quite a challenging task.In particular, establishing the prerequisite relationships among learning objects, in terms of prior requirements needed to understand and complete before making use of the subsequent contents, is a crucial step for faculty, instructional designers or automated systems whose goal is to adapt existing learning objects to delivery in new distance courses. Nevertheless, this information is often missing. In this paper, an innovative machine learning-based approach for the identification of prerequisites between text-based resources is proposed. A feature selection methodology allows us to consider the attributes that are most relevant to the predictive modeling problem. These features are extracted from both the input material and weak-taxonomies available on the web. Input data undergoes a Natural language process that makes finding patterns of interest more easy for the applied automated analysis. Finally, the prerequisite identification is cast to a binary statistical classification task. The accuracy of the approach is validated by means of experimental evaluations on real online coursers covering different subjects.     

网络化协同系统分析与设计

基于网络的协同系统是提升产品协同设计能力的可行途径,采用基于网络的疏松耦合式协同设计框架,建立模块化、基于网络的协同设计系统的通用模型,能够改善企业与企业间在线协同产品开发的困难局面,增强产品协同研发效率。     

网络化QC管理与协同优化技术及其应用

针对不锈钢管行业的现状和特点,对一些关键工序的生产设备进行接口技术改造,实现质量信息的数据采集与传输以供分析、处理和共享。在此基础上,实现信息共享的网络化QC管理系统平台,并在此平台上实现故障模式及影响分析、故障报告和纠正措施以及工艺参数的协同优化技术,从而实现企业对产品质量管理的"可知、可控、可管"。     

Combination strategy of active learning for hyperspectral images classification

In order to improve the phenomena of jitter and instability of the traditional active learning single strategy algorithm in selecting the most valuable unlabeled samples.The idea of weighted combination of ensemble learning classifier and proposes a joint selection based on the combination strategy method (ESAL,ensemble strategy active learning) was introduced,the combination of the model was extended to the combination of the strategy so as to achieve the fusion of multiple strategies in a single model and achieve higher stability.By analyzing the classification results of hyperspectral remote sensing images,the ESAL algorithm can save 25.4% of the cost compared with the single strategy algorithm and reduce the jitter frequency to 16.67% when the same accuracy threshold is obtained,and the jitter is obviously improved.ESAL algorithm is out of good stability.     

Review on application progress of federated learning model and security hazard protection

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy. As data privacy becomes more important, it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security. Data is forced to be stored independently between companies, creating “data silos”. With the goal of safeguarding data privacy and security, the federated learning framework greatly expands the amount of training data, effectively improving the shortcomings of traditional machine learning and deep learning, and bringing AI algorithms closer to our reality. In the context of the current international data security issues, federated learning is developing rapidly and has gradually moved from the theoretical to the applied level. The paper first introduces the federated learning framework, analyzes its advantages, reviews the results of federated learning applications in industries such as communication and healthcare, then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications, and finally looks into the future of federated learning and the application layer.     

基于标签与协同过滤算法的淘书吧应用

随着互联网的不断发展,电子商务的流行使人们从线下交易逐渐转为线上交易。电子商务中的推荐系统对人们日益多元化的网络消费起到了至关重要的作用。本文在传统协同过滤推荐算法基础上,加入商品标签属性,构建用户,商品,标签三者之间的关联模型。先构建用户商品评分矩阵,在计算用户对商品兴趣度时增加入标签作为权重系数,提高淘书吧应用推荐准确性。实验结果表明,该方法能有效地改进现有的推荐算法,达到更好的推荐效果。     

A group key exchange and secure data sharing based on privacy protection for federated learning in edge-cloud collaborative computing environment

Federated learning (FL) is widely used in internet of things (IoT) scenarios such as health research, automotive autopilot, and smart home systems. In the process of model training of FL, each round of model training requires rigorous decryption training and encryption uploading steps. The efficiency of FL is seriously affected by frequent encryption and decryption operations. A scheme of key computation and key management with high efficiency is urgently needed. Therefore, we propose a group key agreement technique to keep private information and confidential data from being leaked, which is used to encrypt and decrypt the transmitted data among IoT terminals. The key agreement scheme includes hidden attribute authentication, multipolicy access, and ciphertext storage. Key agreement is designed with edge-cloud collaborative network architecture. Firstly, the terminal generates its own public and private keys through the key algorithm then confirms the authenticity and mapping relationship of its private and public keys to the cloud server. Secondly, IoT terminals can confirm their cryptographic attributes to the cloud and obtain the permissions corresponding to each attribute by encrypting the attributes. The terminal uses these permissions to encrypt the FL model parameters and uploads the secret parameters to the edge server. Through the storage of the edge server, these ciphertext decryption parameters are shared with the other terminal models of FL. Finally, other terminal models are trained by downloading and decrypting the shared model parameters for the purpose of FL. The performance analysis shows that this model has a better performance in computational complexity and computational time compared with the cited literature.     

Ubiquitous learning: A systematic review

Ubiquitous learning, labeled as u–learning, takes advantage of digital content, physical surroundings, mobile devices, pervasive components, and wireless communication to deliver teaching–learning experiences to users at anytime, anywhere, and anyway. U–learning represents an emergent paradigm that spreads education in diverse settings, where users are situated in authentic learning contexts to face immersive experiences in order to accomplish meaningful learning. With the aim at disseminating such a revolutionary arena, this systematic review analyzes its nature, application, and evolution throughout a longitudinal study, where 176 approaches built since 2010 up to the third quarter of 2017 date are gathered, classified, and characterized to disclose labor traits, outcome patterns, and field tendencies. These five results are grounded respectively in a representative collection, a proposed taxonomy, a suggested pattern, statistical interpretations, mining findings, and critical analysis. The conclusions reveal: u–learning is able to transform traditional education provided at classroom level and by e–learning. Principally, this is because students, pertaining to diverse academic levels experience real and authentic settings, are immersed in dual reality sceneries, benefit from context–aware support, learn diverse educational domains, follow suitable learning paradigms, deal with diverse effects, and interact with different devices and technologies in a blended fashion. All of this with the purpose of enhancing users’ apprenticeship.