QoS-Aware Congestion Control with Online Learning

In emerging applications such as industrial control and autonomous driving,end-to-end deterministic quality of service(QoS)transmission guarantee has become an urgent problem to be solved.Internet congestion control algorithms are essential to the performance of applications.However,existing congestion control schemes follow the best-effort principle of data transmission without the perception of application QoS requirements.To enable data delivery within application QoS constraints,we leverage an online learning mechanism to design Crimson,a novel congestion control algorithm in which each sender continuously observes the gap between current performance and pre-defined QoS.Crimson can change rates adaptively that satisfy application QoS requirements as a result.Across many emulation environments and real-world experiments,our proposed scheme can efficiently balance the different trade-offs between throughput,delay and loss rate.Crimson also achieves consistent performance over a wide range of QoS constraints under diverse network scenarios.

Study on Distance Learning Support Services Strategy Based on Effective Interaction

Achieving effective interaction can the students get good learning results,and enhance the quality of distance learning.The paper firstly analyzes the research on distance learning support services and the problems of distance learning interaction in order to clarify the significance of implementing effective interaction.Then it puts forward the learning support services strategies based on effective interaction,which means to promote distance learning interaction and enhance the students'self-learning ability.

Online support vector regression for reinforcement learning

The goal in reinforcement learning is to learn the value of state-action pair in order to maximize the total reward. For continuous states and actions in the real world, the representation of value functions is critical. Furthermore, the samples in value functions are sequentially obtained. Therefore, an online sup-port vector regression （OSVR） is set up, which is a function approximator to estimate value functions in reinforcement learning. OSVR updates the regression function by analyzing the possible variation of sup-port vector sets after new samples are inserted to the training set. To evaluate the OSVR learning ability, it is applied to the mountain-car task. The simulation results indicate that the OSVR has a preferable con- vergence speed and can solve continuous problems that are infeasible using lookup table.

The Research about the Role and influence of Teacher emotional support in Online Learning Environment

At the beginning of 2020,the“COVID-19”came out.Affected by the outbreaks,the universities have to carry out online teaching.Online learning provides students with full freedom and personalized learning space,but at the same time,it also brings problems such as weak feelings between teachers and students and lack of learning experience.To solve these problems,this paper adopts the methods of questionnaire survey,experimental control and behavioral modeling.This paper studies how teachers’emotional support behavior affects students’learning process and learning emotion in online learning environment,and proposes that teachers’emotional support behavior is appealed and desired by students.Positive teachers’emotional support behavior can promote students’learning process and improve students’learning emotion.

Inspiration from Comparative Study of Distance-education Learning Support Services of China and UK

The theory and practice of OUUK learning support service has been widely praised, from the study of composition and dominantof OUUK learning support service system, and as Jiangyin Open Studying center, Wuxi, Jiangsu example, in-depth investigation and analysis ofthe present situation of China＇s distance-education learning support services, explore the reference value of the successful experience of OUUKand inspiration to the distance education in China.

Study on the Strategies of Distance Learning Support Services Based on Effective Interaction

The paper firstly analyzes the problems of distance learning interaction in order to clarify the significance of implementing effective interaction.Then it puts forward the learning support services strategies based on effective interaction,which means to design strategies from the perspective of effective interaction to improve the effect of distance learning.

高校教学参考信息服务保障体系与E-learning平台整合服务研究——高校图书馆资源服务嵌入高校教学过程模式探索

文章总结了高校图书馆服务面临的问题与挑战,分析了高校图书馆开展教学支持服务的现状。通过CALIS高校教学参考信息服务保障体系与E-learning平台的整合实践,探索了图书馆如何为高校教学提供支持服务,将自身的资源与服务嵌入到教学环境中去的途径。

美国公共图书馆Career Online High School在线学历教育服务及启示

近年来美国公共图书馆Career Online High School(COHS)服务得到快速发展,它是一种结合了高中学历与职业培训的在线教育,被称为公共图书馆服务的破坏性创新。本文在分析美国公共图书馆COHS服务内容、流程与特点的基础上,总结出公共图书馆在COHS服务中的作用,并探讨美国公共图书馆COHS服务对我国在线学历教育服务的启示。

E-Learning下社会支持服务体系的实践与探索

E-Learning下为什么要建立社会服务体系?如何建立?本文从实际出发进行了有益的探索,并从实践的角度提出了一些建设的模型与发展原则。

基于MS Analysis Services的OLAP技术在超市中的应用

基于MSAnalysisServices提出了超市OLAP技术的设计思想,并简述了前端客户应用程序的设计方法.该系统能够实现与服务器的自动连接,数据源的选择和立方体的创建、浏览,实现了多维数据查询和图表展现,为管理者提供决策支持.

Machine learning aided design of perovskite oxide materials for photocatalytic water splitting

Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(RH_(2)) still remains a challenge in the field of photocatalytic water splitting(PWS). Herein, we established structural-property models targeted to RH_(2) and the proper bandgap(Eg) via machine learning(ML) technology to accelerate the discovery of efficient perovskite photocatalysts for PWS. The Pearson correlation coefficients(R) of leave-one-out cross validation(LOOCV) were adopted to compare the performances of different algorithms including gradient boosting regression(GBR), support vector regression(SVR), backpropagation artificial neural network(BPANN), and random forest(RF). It was found that the BPANN model showed the highest R values from LOOCV and testing data of 0.9897 and 0.9740 for RH_(2),while the GBR model had the best values of 0.9290 and 0.9207 for Eg. Furtherly, 14 potential PWS perovskite candidates were screened out from 30,000 ABO3-type perovskite structures under the criteria of structural stability, Eg, conduction band energy, valence band energy and RH_(2). The average RH_(2) of these14 perovskites is 6.4% higher than the highest value in the training data set. Moreover, the online web servers were developed to share our prediction models, which could be accessible in http://materialsdata-mining.com/ocpmdm/material_api/ahfga3d9puqlknig(E_g prediction) and http://materials-datamining.com/ocpmdm/material_api/i0 ucuyn3 wsd14940(RH_(2) prediction).

Machine Learning-Based Advertisement Banner Identification Technique for Effective Piracy Website Detection Process

In the contemporary world, digital content that is subject to copyright is facing significant challenges against the act of copyright infringement.Billions of dollars are lost annually because of this illegal act. The currentmost effective trend to tackle this problem is believed to be blocking thosewebsites, particularly through affiliated government bodies. To do so, aneffective detection mechanism is a necessary first step. Some researchers haveused various approaches to analyze the possible common features of suspectedpiracy websites. For instance, most of these websites serve online advertisement, which is considered as their main source of revenue. In addition, theseadvertisements have some common attributes that make them unique ascompared to advertisements posted on normal or legitimate websites. Theyusually encompass keywords such as click-words (words that redirect to installmalicious software) and frequently used words in illegal gambling, illegal sexual acts, and so on. This makes them ideal to be used as one of the key featuresin the process of successfully detecting websites involved in the act of copyrightinfringement. Research has been conducted to identify advertisements servedon suspected piracy websites. However, these studies use a static approachthat relies mainly on manual scanning for the aforementioned keywords. Thisbrings with it some limitations, particularly in coping with the dynamic andever-changing behavior of advertisements posted on these websites. Therefore,we propose a technique that can continuously fine-tune itself and is intelligentenough to effectively identify advertisement (Ad) banners extracted fromsuspected piracy websites. We have done this by leveraging the power ofmachine learning algorithms, particularly the support vector machine with theword2vec word-embedding model. After applying the proposed technique to1015 Ad banners collected from 98 suspected piracy websites and 90 normal orlegitimate websites, we were able to successfully identify Ad banners extractedfrom suspected piracy websites with an accuracy of 97%. We present thistechnique with the hope that it will be a useful tool for various effective piracywebsite detection approaches. To our knowledge, this is the first approachthat uses machine learning to identify Ad banners served on suspected piracywebsites.

智慧图书馆背景下学习支持服务研究

文章通过梳理国内外高校图书馆学习支持服务现状,总结其服务特点,结合当今AI科技发展的新趋势,探讨智慧图书馆背景下的学习支持服务发展新模式。

未来学习中心理念下图书馆教学支持研究

在多元智能技术驱动下,图书馆需抓住未来学习中心的发展契机,发挥海量资源优势,重塑以学习者为中心的教学支持环境,实现用户、空间和知识三元互联的发展愿景。文章回溯未来学习中心文献理论研究,分析图书馆未来教学支持研究的必要性。在智慧学习的理念下探索未来教学支持空间建设和服务平台架构。通过融合传统教学、发展特色学科、培养创新及应用能力,打造互动、开放的新型智慧教学空间,搭建智能技术立体互联、5G课程资源体系为基础、智慧馆员深度嵌入的服务平台架构,最后根据教学过程,开发定制式资源匹配、课程跟踪、跨学科交流及交互体验的未来教学支持场景。

开放大学远程教育学习支持服务发展研究

中国高等教育已踏上以提高质量为核心的内涵式发展之路。面对新形势,开放大学远程教育要抓住教育样态和学习方式变革的新机遇,主动转变教育质量观及质量保障策略,为学生提供学习支持服务。通过深入研究学习支持服务的组织形式、活动内容及评估评价策略,针对发展中存在的问题,提出构建路径,旨在真正实现开放大学远程教育学习支持服务“一切为了学生、为了学生的一切”。

基于马斯洛需求层次理论的高校图书馆学习支持服务模型研究

用户需求是图书馆提供学习支持服务的基础。由于用户需求具有多样性、层级性等特征,因此,图书馆在提供学习支持服务时不仅要满足用户的基本需求,还应全方位考虑并满足用户更高层次的需求。文章以马斯洛需求层次理论为基础,结合高校图书馆学习支持服务现状,构建高校图书馆学习支持服务结构框架,提出高校图书馆学习支持服务系统建设策略:努力提升用户体验,搭建多元协作社交服务平台及智慧参考咨询平台,创新学习支持服务。

局部观测的车辆边缘计算在线服务迁移决策

针对多用户、全局信息缺失的复杂车辆边缘计算场景,提出了一种基于多智能体强化学习的车辆边缘计算服务迁移策略,使车辆在仅获得部分观察的情况下仍能进行不完整系统信息学习并进行分布式在线迁移决策。该策略使用Gumbel-Softmax采样改进的多智能体深度确定性策略梯度算法实现,用户间通过协作和竞争来达到共同的目标,从而使系统更加灵活,提高系统的整体收益和稳定性。真实数据集的仿真实验结果表明,该策略收敛速度快,在不同用户数量和任务到达率场景下均表现出较好的性能,鲁棒性和稳定性优于其他对比策略。

一种改进近端优化的多目标流QoS调度策略

软件定义网络可以搭载灵活的流调度策略来提升网络服务系统的服务质量,但随着业务流量复杂度的提升,现有的流调度算法会因场景匹配度的下降而导致性能受到影响。为此提出一种基于深度强化学习的智能路由策略。该策略通过软件定义网络收集各链路信息,基于长短期记忆网络与近端策略优化算法实现特征提取与状态感知,最终决策生成符合业务场景下服务质量(quality of service,QoS)目标的动态流量调度策略,并实现QoS最大化。实验结果表明,所提的方案与现有的路由策略相比可以使整套系统QoS指标提升7.06%,有效地提升了业务系统的吞吐率。

基于多模态大模型的精准教学支持体系构建研究

随着人工智能大模型从单一模态向多模态融合的通用人工智能演变,多模态大模型的发展有望推动教育领域的变革。在技术进步的推动和智能时代教学原理的指导下,多模态大模型有望实现规模化教育与个性化培养之间的有机结合,并彻底转变精准教学和个性化学习的方式。然而,在实际应用中仍然面临着教育环境的实际限制等诸多挑战。为此,提出了基于多模态大模型的精准教学支持系统框架和面向个性化教育的云边协同基础设施架构。这两个互补架构能够协同工作,为构建更高效、规模化且个性化的精准教学体系奠定基础。此外,为基于多模态大模型的教育模式变革提供了更广泛的对话起点,为该领域的未来研究和发展提供了思路。