Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

基于Online-GRU信道预测的星上自适应功率控制方法

针对传统卫星功率控制方法存在资源浪费、时延长的问题,提出一种基于在线-门控循环单元(Online-GRU)信道预测的星上自适应功率控制方法,通过在线训练更新网络参数来解决离线预测算法存在的累积误差的问题。仿真结果表明,提出的在线训练算法比离线算法预测精确度提升了38.30%,相比在线-长短期记忆网络(Online-LSTM)节约了63.21%的训练时间;提出的自适应功率控制方法比固定发射功率的方法节约了55.74%的发射功率;同时,相比基于地面定时反馈信道状态的自适应功率控制方法具备更好的鲁棒性。

Building a Smart Management System for the Field Training Course at the Faculty of Specific Education-Mansoura University

Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training.Currently,trainee students attendance in field training is recordedmanually through signatures on attendance sheets.However,thismethod is prone to impersonation,time wastage,and misplacement.Additionally,traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes.Field training also lacks modern technology that the supervisor can use in case of his absence from school to monitor the trainee students’implementation of the required activities and tasks.These shortcomings do not meet the needs of the digital era that universities are currently experiencing.As a result,this paper presents a smart management system for field training based on Internet of Things(IoT)and mobile technology.It includes three subsystems:attendance,monitoring,and evaluation.The attendance subsystem uses an R307 fingerprint sensor to record trainee students’attendance.The Arduino Nano microcontroller transmits attendance data to the proposed Android application via an ESP-12F Wi-Fi module,which then forwards it to the Firebase database for storage.The monitoring subsystem utilizes Global Positioning System(GPS)technology to continually track trainee students’locations,ensuring they remain at the school during training.It also enables remote communication between trainee students and supervisors via audio,video,or text by integrating video call and chat technologies.The evaluation subsystem is based on three items:an online exam,attendance,and implementation of required activities and tasks.Experimental results have demonstrated the accuracy and efficiency of the proposed management system in recording attendance,as well as in monitoring and evaluating trainee students during field traiing.

An Effective Online Collaborative Training in Developing Listening Comprehension Skills

The COVID-19 outbreak severely affected formal face-to-face classroom teaching and learning.ICT-based online education and training can be a useful measure during the pandemic.In the Pakistani educational context,the use of ICT-based online training is generally sporadic and often unavailable,especially for developing English-language instructors’listening comprehension skills.The major factors affecting availability include insufficient IT resources and infrastructure,a lack of proper online training for speech and listening,instructors with inadequate academic backgrounds,and an unfavorable environment for ICT-based training for listening comprehension.This study evaluated the effectiveness of ICT-based training for developing secondary-level English-language instructors’listening comprehension skills.To this end,collaborative online training was undertaken using random sampling.Specifically,60 private-school instructors in Chakwal District,Pakistan,were randomly selected to receive online-listening training sessions using English dialogs.The experimental group achieved significant scores in the posttest analysis.Specifically,there were substantial improvements in the participants’listening skills via online training.Given the unavailability of face-to-face learning during COVID-19,this study recommends using ICT-based online training to enhance listening comprehension skills.Education policymakers should revise curricula based on online teaching methods and modules.

Online split-and-merge expec tation-maximization training of Gaussian mixture model and its optimization

This paper presents a new online incremental training algorithm of Gaussian mixture model （GMM）, which aims to perform the expectation-maximization（EM） training incrementally to update GMM model parameters online sample by sample, instead of waiting for a block of data with the sufficient size to start training as in the traditional EM procedure. The proposed method is extended from the split-and-merge EM procedure, so inherently it is also capable escaping from local maxima and reducing the chances of singularities. In the application domain, the algorithm is optimized in the context of speech processing applications. Experiments on the synthetic data show the advantage and efficiency of the new method and the results in a speech processing task also confirm the improvement of system performance.

Airway management training program for nurses via online course in COVID-19 preparedness

BACKGROUND Nursing officers are an integral component of any medical team.They participate in taking care of basic airway management and assist in advanced airway management,specifically amidst the current coronavirus disease 2019(COVID-19)pandemic.AIM To assess the efficacy of a standardized web-based training module for nurses in preparedness to fight against COVID-19.METHODS The training was held in three sessions of 1 h each,consisting of live audio-visual lectures,case scenarios,and skill demonstrations.The sequence of airway equipment,drug preparation,airway examination,and plans of airway management was demonstrated through mannequin-based video-clips.RESULTS Pre-and post-test scores as well as objective structured clinical examination scores were analyzed using Student’s t-test and the Likert scale was used for feedback assessment.It was found that the mean score out of the total score of 20 was 8.47±4.2 in the pre-test,while in the post-test it was 17.4±1.8(P value<0.001).The participants also felt self-reliant in executing the roles of airway assistant(63.3%)and drug assistant(74.3%).Fear of self-infection with COVID-19 was also high,as 66%of participants feared working with the patient’s airway.CONCLUSION Amidst this COVID-19 emergency,when the health care systems are being persistently challenged,training of nursing staff in the safe conduct of airway management can ensure delivery of life-saving treatment.

Examining the Effectiveness of Virtual Training under the Kirkpatrick Model:A Post-COVID Study

Teaching and training through online tools were used by most Higher Education Institute(HEIs)worldwide during COVID-19 to cater to the needs of students who stay far away from educational institutions.After the pandemic,the virtual model of education and training became a trend.The students and teachers are also more used to this trend which is giving more opportunities to both learners and instructors.One of the notable benefits of virtual teaching and learning platform is that it provides a flexible environment to gain knowledge,skills,and attitude simultaneously along with formal off-line education.From the earlier studies it is found that the majority of studies have focused on traditional,offline training methods and only a few studies have focused on virtual training.Therefore,the present study examined the effectiveness of virtual training using the Kirkpatrick model.For this purpose,a research instrument based on the Kirkpatrick model was constructed and distributed among the UG and PG students in Mangalore City.A total of 143 responses were collected,of which 132 were completed responses and all of them were considered for analysis.Descriptive statistics and one sample t-test are employed to analyse and interpret the data.The findings revealed that virtual training is more effective compared to traditional and offline training methods.Henceforth,the training and education provided through virtual platforms made significant contributions to the employability of youngsters.

Online SVM在实时入侵检测中的应用研究

Online支持向量机作为一种新的分类方法可以在异常入侵检测中提供良好的分类效果。根据Online算法对传统支持向量机、Robust支持向量机和One-class支持向量机进行改进,将改进后的算法与原始算法进行比较,然后使用1999DARPA数据作为评估数据。通过实验和比较发现,改进后的支持向量机可以实现在线训练,而且使用更少的支持向量,训练时间也有效缩短,在噪声数据存在的情况下检测正确率和虚警率比未改进前有一定程度的提升。

用于在线产品评论质量分析的Co-training算法

在线评论广泛存在于电子商务网站平台,其中包含着客户对产品的评价及偏好.高效分析在线评论数据并满足客户需求,对许多谋求立足于竞争激烈的国际化市场的企业来说至关重要.但因在线评论的质量不一,使得如何分析在线评论的质量成为一项重要工作.从两个方面提取特征对在线评论进行描述,并构建了一种Co-training算法来判断评论的质量.通过对比实验验证了该算法相对于单一分类算法的优势.

Online LS-SVM for function estimation and classification

An online algorithm for training LS-SVM (Least Square Support VectorMachines) was proposed for the application of function estimation and classification. Online LS-SVMmeans that LS-SVM can be trained in an incremental way, and can be pruned to get sparseapproximation in a decremental way. When a SV (Support Vector) is added or removed, the onlinealgorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Onlinealgorithm is especially useful to realistic function estimation problem such as systemidentification. The experiments with benchmark function estimation problem and classificationproblem show the validity of this online algorithm.

Spam Filtering:Online Naive Bayes Based on TONE

The naive, Bayes （NB） model has been successfully used to tackle spare, and is very accurate. However, there is still room for improwment. We use a train on or near error （TONE） method in online NB to enhance the perfornmnee of NB and reduce the number of training emails. We conducted an experiment to determine the performanee of the improved algorithm by plotting （I-ROCA）% curves. The resuhs show that the proposed method improves the performanee of original NB.

高校大型仪器开放实验室网络培训体系的探索

大型仪器在高校人才培养和科研测试服务中发挥着重要作用,系统的培训体系是高校大型仪器共享平台高效运行的基本保障,也是高校推进一流教育和拔尖人才培养的客观要求。系统性规范化的网络培训模式有利于提升培训的效果。通过构建包含安全培训及考核、仪器使用培训及考核、仪器原理及应用技巧、仪器测试成果与前沿展示互动交流于一体的网络资源平台,探索基于此平台的培训管理体系和长效完备的学习支撑体系,为高校大型仪器开放共享与人才培养体系建设提供了新思路。

数字化转型背景下县域城乡教师网络研修:样态、困境与破解

县域城乡师资均衡配置是义务教育优质均衡发展的重点,也是难点。网络研修为突破这一难点提供了数字化战略选择。如何推进县域城乡教师网络研修,是县域义务教育数字化转型不得不面对的问题,必须予以高度重视。县域城乡教师网络研修主要存在区域常规式网络研修、名师引领式网络研修和校际协同式网络研修三种样态。县域城乡教师网络研修“形象”虽已数字化,但“灵魂”依旧停留在工业化阶段,整体上还是依附于原有的教师研修体制机制,沿用了传统教师研修的方式、方法。数字化与工业化的矛盾引发了县域城乡教师网络研修的诸多困境。破解困境的根本出路在于实施供给改革,由原来的供给驱动转变为需求驱动;关键在于统筹规划,以教学实践为核心构建“教—研—训”一体化研修体系;落脚点在于以方法创新推进数字技术与教师研修深度融合。

内科专业住院医师规范化培训指导医师线上DOPS培训的效果研究

目的探讨线上工作坊模式对内科专业住院医师规范化培训(简称住培)指导医师临床操作技能直接观察评估(direct observation of procedural skills,DOPS)师资培训的应用效果。方法选取2022年11月参加广东省内科专业住培普通师资培训班的45名指导医师为研究对象,采用线上分组讨论与教学视频评价反馈相结合的线上工作坊模式进行DOPS师资培训,采用问卷自评的方式评价指导医师对DOPS的掌握程度,并通过问卷调查了解培训前后指导医师对线上DOPS的培训需求与满意度。结果培训前仅17.8%的指导医师使用过DOPS量表;指导医师在培训后对DOPS的掌握程度明显高于培训前[(3.71±0.69)分比(2.29±0.87)分,P<0.01];且95.6%及以上的指导医师表示能够理解“DOPS评分项目的均质化制定方法”和“形成性反馈技巧”;100.0%的指导医师表示在今后住培教学中愿意尝试使用DOPS。课程的整体满意度评分为(8.40±1.70)分。结论线上工作坊模式对内科专业住培指导医师DOPS师资培训的短期效果满意,可进一步在住培形成性评价师资培训中推广应用。

基于异质性因素的高校线上线下教学模式研究

高校是高素质专业人才培养的重要平台,承担着“为党育才、为国育才”的重大历史使命,教学过程及教学模式是践行育才使命的重要环节。该文从异质性因素的角度剖析教学内容异质性、教学主体异质性、教学环境异质性和教学评价体系异质性在线上线下教学模式中的表现和影响,在实证分析的基础上提出从四个层面提高高校线上线下教学模式适宜性的策略,为提升教学效果人才培养质量提供借鉴参考。

以学习动机为驱动的基础化学实验课程教学改革与实践

课程是人才培养的核心要素,深化课程改革是推进我国高等教育高质量发展的重要内容之一。鉴于基础课程在人才培养方面的重要性,对基础化学实验课程普遍存在的共性问题进行了教学分析与思考,开展了以学习动机为驱动的基础化学实验课程教学改革研究与实践,为持续提升教学质量和人才培养质量提供了有效途径。

线上产后康复促进项目活动方案的设计及实施

目的探讨线上产后康复促进项目活动方案对产妇产后母乳喂养、形体恢复的效果。方法以便利抽样法抽取符合纳入及排除标准的产妇70例,将产妇随机分为产康组与对照组各35例。对照组实施常规产褥期护理及随访,产康组按照线上产后康复促进项目活动方案实施护理干预。产后6个月比较两组纯母乳喂养率、婴儿体格发育情况、产妇盆底肌肌力及形体恢复情况。结果产后6个月,纯母乳喂养率对照组为42.86%,产康组为88.57%,两组差异有统计学意义(P<0.05)。产康组产妇臀围、腰围显著小于对照组,而盆底肌肌力评分显著高于对照组(均P<0.05)。两组婴儿身长及体质量差异无统计学意义(均P>0.05)。结论对产妇实施线上产后康复促进项目,可提升产妇纯母乳喂养率,促进产后形体恢复、改善盆底肌肌力。

线上与线下麻醉见习教学的效果评价

目的 比较非麻醉专业本科生线上见习与传统见习效果。方法 将2022年7月4日和2020年6月28日在首都医科大学附属北京佑安医院麻醉科见习的首都医科大学公共卫生医学事业管理专业46名学生分成两组,2022年7月4日的见习学生24名为传统组,采用线下麻醉科见习方法;2020年6月28日的见习学生22名为线上组,采用“腾讯会议直播+操作视频分享+班级微信群答疑”的线上麻醉见习方法。采用问卷调查方式对教学整体满意度和考试成绩进行评价。结果 传统组与线上组麻醉见习整体满意度分别为100%和95.45%(P>0.05)。两组试卷成绩和正确率相比较差异无统计学意义(P>0.05)。线上组86.36%学生有参与传统见习的意愿。结论 线上见习与传统见习相比较,整体满意度较高、考试成绩较好,可作为传统见习无法进行时的替代方案。

基于互联网云平台的教学模式在静脉治疗专科护士规范化培训中的应用

目的探讨基于互联网云平台的教学模式在静脉治疗专科护士规范化培训中的应用效果。方法以参加2019年和2020年各1期的静脉治疗专科护士培训班的62名学员为对照组,实施传统线下培训方法;以参加2021年共2期静脉治疗专科护士培训班的64名学员为试验组,实施云平台教学方法。培训结束后比较两组的理论、操作、临床实践、结业理论、病例汇报成绩以及对整体培训的满意度情况。结果试验组学员在理论考核、操作考核、临床实践考核、结业理论成绩得分均高于对照组(P<0.05);试验组学员在病例汇报考核以及整体培训满意度评价上均优于对照组(P<0.05)。结论基于互联网云平台的教学模式能更好地提升静脉治疗专科护士理论知识水平、操作能力以及综合运用知识解决问题的能力,提高培训学员满意度及整体规范化培训质量。