Deep Learning Aided SCL Decoding of Polar Codes with Shifted-Pruning

Recently,a generalized successive cancellation list(SCL)decoder implemented with shiftedpruning(SP)scheme,namely the SCL-SP-ωdecoder,is presented for polar codes,which is able to shift the pruning window at mostωtimes during each SCL re-decoding attempt to prevent the correct path from being eliminated.The candidate positions for applying the SP scheme are selected by a shifting metric based on the probability that the elimination occurs.However,the number of exponential/logarithm operations involved in the SCL-SP-ωdecoder grows linearly with the number of information bits and list size,which leads to high computational complexity.In this paper,we present a detailed analysis of the SCL-SP-ωdecoder in terms of the decoding performance and complexity,which unveils that the choice of the shifting metric is essential for improving the decoding performance and reducing the re-decoding attempts simultaneously.Then,we introduce a simplified metric derived from the path metric(PM)domain,and a custom-tailored deep learning(DL)network is further designed to enhance the efficiency of the proposed simplified metric.The proposed metrics are both free of transcendental functions and hence,are more hardware-friendly than the existing metrics.Simulation results show that the proposed DL-aided metric provides the best error correction performance as comparison with the state of the art.

Accurate Machine Learning Predictions of Sci-Fi Film Performance

A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry.

Part-Time Learners’Perceptions of Success During Emergency Remote Teaching and Implications for Future

The Faculty of Lifelong Learning at South East Technological University’s Carlow campus is one of the largest providers of part-time,adult learning in the Irish HE sector.Therefore,the perspectives of our part-time learners offer us valuable insights into adult learner experiences in the Irish HE sector.The outbreak of Covid 19 saw us pivot our provision to an emergency remote teaching(ERT)model in the first wave of the epidemic.The faculty undertook an extensive study of its learners in 2021 to examine the impacts of ERT on learners,and this article takes a qualitative approach to the findings of this study,looking specifically at learners’comments about success,and the barriers to success,which ERT posed.Our study provided an opportunity to learn about our learners’conceptions of student identity,and how they interpret success as part-time learners.

Key Elements for Success in School Learning:An Analysis from the Perspective of Teachers's Caffolding

Success in school learning has attracted considerable attention in educational research.To facilitate success in school learn-ing,researchers have conducted much research on its key elements from various perspectives.Based on relevant previous research,thepaper attempts to analyse the key elements for success in school learning from the perspective of teachers' s caffolding.Two elements ofteachers' scaffolding,stimulating students' motivation for learning and teachers' knowledge related to good teaching,are illustrated.

基于逐次超松弛技术的Double Speedy Q-Learning算法

Q-Learning是目前一种主流的强化学习算法,但其在随机环境中收敛速度不佳,之前的研究针对Speedy Q-Learning存在的过估计问题进行改进,提出了Double Speedy Q-Learning算法。但Double Speedy Q-Learning算法并未考虑随机环境中存在的自循环结构,即代理执行动作时,存在进入当前状态的概率,这将不利于代理在随机环境中学习,从而影响算法的收敛速度。针对Double Speedy Q-Learning中存在的自循环结构,利用逐次超松弛技术对Double Speedy Q-Learning算法的Bellman算子进行改进,提出基于逐次超松弛技术的Double Speedy Q-Learning算法(Double Speedy Q-Learning based on Successive Over Relaxation,DSQL-SOR),进一步提升了Double Speedy Q-Learning算法的收敛速度。通过数值实验将DSQL-SOR与其他算法的实际奖励和期望奖励之间的误差进行对比,实验结果表明,所提算法比现有主流的算法SQL的误差低0.6,比逐次超松弛算法GSQL低0.5,这表明DSQL-SOR算法的性能较其他算法更优。实验同时对DSQL-SOR算法的可拓展性进行测试,当状态空间从10增加到1000时,每次迭代的平均时间增长缓慢,始终维持在10^(-4)数量级上,表明DSQL-SOR的可拓展性较强。

Quality Models for Open, Flexible, and Online Learning

This article is based on research conducted for the European CommissionEducation & Training 2020 working group on digital and online learning(ET2020 WG-DOL) specifically regarding policy challenges, such as thefollowing: 1) Targeted policy guidance on innovative and open learningenvironments under outcome;2) Proposal for a quality assurance modelfor open and innovative learning environments, its impact on specificassessment frameworks and its implication for EU recognition and transparencyinstruments. The article aims to define quality in open, flexible,and online learning, particularly in open education, open educationalresources (OER), and massive open online courses (MOOC). Hence,quality domains, characteristics, and criteria are outlined and discussed,as well as how they contribute to quality and personal learning so thatlearners can orchestrate and take responsibility for their own learningpathways. An additional goal is to identify the major stakeholders directlyinvolved in open online education and to describe their visions, communalities,and conflicts regarding quality in open, flexible, and online learning.The article also focuses on quality in periods of crisis, such as duringthe pandemic in 2020. Finally, the article discusses the rationale and needfor a model of quality in open, flexible, and online learning based on threemajor criteria for quality: excellence, impact, and implementation fromthe learner’s perspective.

The Relationship between Learning Styles and Students’ Identity Styles

The aim of this study was to investigate the relationship between learning styles and identity styles Students. The study consisted of all high school students and community solidarity (second period) in the 2014-2015 school year resident of the city of Sabzevar that 100 people were selected by multistage random cluster sampling. Kolb’s Learning Styles Inventory Tool questionnaire identity styles Bennion-Adams used the results showed that successful identity styles between style concrete experience, reflective observation, abstract conceptualization and active experimentation students have a significant relationship. Among the components of a successful style only concrete experience, reflective observation and active experimentation to predict and successful identity and disoriented style of abstract conceptualization learning style to predict. Also a significant relationship between the identities of the student field does not exist. Among the students, there is significant gender identity style making.

Conceptual framework for knowledge-based learning environments

The traditional concept of ”one-size-fits-all” educational and training programmes is no more fully adequate to meet the increasing demand worldwide. E-learning, as an alternative approach to traditional face-to-face education, is creating immense challenges for educational institutions to develop new approaches for the production and delivery of cost effective and efficient e-contents. Although, there have been many developments in web-based programmes, they have not fully attained their potential due to a variety of factors. These include: 1) lack of exchangeability between learning materials, 2) delivery mechanisms incompatible with the pedagogical design, 3) low student interaction and insensitive learning processes, 4) absence of intelligent online programme advice and guidance, 5) inflexibility in meeting diverse needs, and 6) institutionally centred ineffective implementation strategies. This paper addresses the critical elements for successful delivery of e-learning environments and then focuses on proposing a framework for the development of an integrated knowledge-based learning environment which has the potential to producer cost effective and personalised training programmes.

紫外吸收光谱结合SPA-ELM算法的水体磷酸根离子检测研究

在工业锅炉中随着水蒸气蒸发,大量的钙镁离子留在炉水中,如果不加处理,在水冷管中会形成水垢,造成爆管停炉。为了保障设备的安全运行,消除安全隐患,工业上通过维持水中一定含量的磷酸根离子来去除锅炉中的钙、镁水垢。传统的磷酸根离子检测技术主要有比色法、分光光度法、色谱法、电位法等,这些方法前期处理步骤较繁琐且耗时较长。光谱法是通过测定物质的吸收光谱并建立和浓度关系的数学模型,对物质浓度定量的一种分析方法。为了满足磷酸根离子快速、实时测量的要求,提出了一种基于紫外吸收光谱结合SPA-ELM算法快速测量磷酸根离子的方法。按照我国《工业锅炉水质GB/T 1576—2018》中所规定的进入热水锅炉前的水质参数要求,配置37份浓度范围在5~100 mg·L^(-1)磷酸根溶液,使用搭建的实验装置,采集紫外吸收光谱。使用SPXY将样品按照7∶3的比例随机划分训练集、测试集;使用Savitzky-Golay(S-G)滤波对数据预处理以提高光谱的信噪比;通过连续投影算法(SPA)压缩光谱数据,筛选出5个与磷酸根强相关的特征波长;使用极限学习机(ELM)将特征波长处的吸光度与样本浓度进行拟合,以决定系数R2和均方根误差RMSE作为模型评价指标,建立磷酸根离子的回归模型。采用所提出的建模方法所建立的模型训练集的R2与RMSE分别为0.9972和1.3015 mg·L^(-1),测试集的R^(2)与RMSE分别为0.9995和0.5174 mg·L^(-1)。为了验证所提出的SPA-ELM预测模型效果,另外建立了LASSO-ELM、PCA-ELM、SPA-PLS和SPA-SVR四种预测模型进行对比。实验结果表明,SPA-ELM建立的预测模型的R2和RMSE均优于其他四种预测模型,说明采用的特征选择方法和回归方法均为最优,能够对磷酸根浓度范围为5~100 mg·L^(-1)的水体进行准确预测,为水中磷酸根离子的检测提供了一种新的解决方法。

拉曼光谱结合机器学习对植物油的分类鉴别

该研究采集了六类(38个品牌)常见植物油的551份拉曼光谱,并根据光谱数据分别建立了正交偏最小二乘判别和支持向量机模型,对比了连续投影法和竞争性自适应重加权采样法对模型识别正确率的影响。基于算法改进的偏最小二乘判别模型的总体预测准确率为82.53%、83.13%,低于基于全光谱数据建立的偏最小二乘判别模型。竞争性自适应重加权采样法结合支持向量机对玉米油、橄榄油、葵花籽油和芝麻油的品牌分类测试集正确率均达到100%;椰子油和花生油的测试集正确率为22.22%、63.64%。两类特征提取算法均可以减少建立分类模型所需的变量数目和计算资源,但以提取后变量建立分类模型可能会导致识别正确率下降。在解决样本间相似度较高的多分类问题时,支持向量机模型优于正交偏最小二乘判别模型。正确率差异可能和生产商所使用的生产工艺以及植物油原料相关。面对案件侦办中品牌种类多样的油脂物证,基于拉曼光谱分析和特征提取算法的支持向量机模型可为可食用植物油的无损快速检验提供一定的参考与借鉴。

基于高光谱成像的烟丝中梗签分类识别研究

针对烟丝中梗签分类识别检测问题,利用高光谱成像技术,结合机器学习方法对烟丝中掺杂的梗签进行分类快速识别。首先,基于短波近红外高光谱成像技术,采用标准正态变化(SNV)对烟丝和梗签光谱数据进行预处理,消除光谱散射和漫反射的影响,减少检测干扰信息。然后,利用连续投影算法(SPA)进行特征波长选择,融合极端梯度提升(XGBoost)算法,提出了1种基于XGboost算法的烟丝中梗签分类模型。最后,采用Mean-shift均值漂移算法和形态学梯度算法相结合的方式作为后处理方法,对模型的分类结果进行后处理,实现烟丝中梗签的快速智能检测。结果表明:建立的SNV-SPA-XGBoost分类模型,训练集和测试集准确率分别达到100%和99.32%,经后处理后对A_(1)(1.0~1.5 cm)、A_(2)(0.5~1.0 cm)、A_(3)(<0.5 cm)梗签检测准确率分别达100%、95.50%和86%。

面向簇化移动机器人的网络资源调度算法

工厂自动化要求超可靠低时延通信,使控制中心能够可靠实时地向移动机器人传输控制指令。为此,面向移动机器人作业的工厂环境,提出基于深度增强学习的资源调度算法(DRL-RS)。DRL-RS算法由两个阶段构成:在第一阶段,一起作业的移动机器人形成簇群,将簇群内多个移动机器人的指令包融合成一个包,再将此包传输至簇群的领导者;在第二阶段,领导者向它簇群成员广播指令包。DRL-RS算法引用深度增强学习算法优化资源调度。领导者扮演Agent,通过向环境学习,择优选择接入点以及子信道和传输功率,进而最大化向所有机器人传输指令包的成功率。性能分析结果表明,DRL-RS算法传输指令包成功率逼近于穷搜索法。

基于干扰消除辅助稀疏连接神经网络的大规模MIMO信号检测

近年来,深度学习成为无线通信领域的关键技术之一。在基于深度学习的一系列MIMO信号检测算法中,大多未充分考虑相邻天线之间的干扰消除问题,无法彻底消除多用户干扰对误码率性能的影响。为此,该文提出一种将深度学习与串行干扰消除(SIC)算法进行结合的方法用于大规模MIMO系统上行链路信号检测。首先,通过优化传统的检测网络(DetNet)及改进ScNet检测算法,该文提出一种基于深度神经网络(DNN)的检测算法,称为ImpScNet。在此基础上,进一步将SIC思想应用到深度学习框架结构设计中,提出一种基于深度学习的大规模MIMO多用户SIC检测算法,称为ImpScNet-SIC。此算法在每个检测层上分为两级,其中,第1级由该文提出的ImpScNet算法提供初始解,再将初始解解调至相应的星座点上作为SIC的输入,由此构成该算法的第2级。此外,在SIC中也使用了ImpScNet算法估计传输符号,以便获得最优性能。仿真结果表明,与已有的各种典型代表算法相比,该文所提ImpScNet-SIC检测算法特别适合大规模MIMO信号检测,具有收敛速度快、收敛稳定及复杂度相对较低的优势,并且在10–3误码率上有至少0.5 dB以上的增益。

大学生学业归因与学习倦怠的相关性分析

对307名大学生的问卷调查显示:不同类型学业归因大学生在学习倦怠的三个维度上均存在显著性差异;大学生学业归因与学习倦怠存在显著相关;将学业成功归因于能力强、足够努力、身心状况佳、外界环境有利能够显著降低大学生的学习倦怠,归因于题目简单则会显著提高大学生的学习倦怠;将学业失败归因于题目太难、不够努力、外界环境不利均会显著提高大学生的学习倦怠。依据大学生学业归因与学习倦怠之间的相关性,进行有针对性的归因训练,能有效降低大学生的学习倦怠水平,提高其学习积极性。

基于近红外光谱的SSA-RELM的菠萝含水率快速检测

目的:建立快速无损检测菠萝含水率的方法。方法:提出一种基于连续投影法的特征波长选择和麻雀搜索算法(SSA)优化正则化极限学习机(RELM)的菠萝含水率检测模型。针对菠萝近红外光谱数据具有维度高、冗余信息多的特点,分别对比连续投影法、主成分分析法和全波段等筛选特征波长的结果,确定菠萝近红外光谱特征波长筛选方法;针对RELM模型性能受其输入层权值和隐含层偏置的影响,运用麻雀搜索算法优化RELM模型的输入层权值和隐含层偏置,提出一种基于麻雀搜索算法改进正则化极限学习机的菠萝含水率检测模型。结果:与遗传算法改进正则化极限学习机(GA-RELM)、粒子群算法改进正则化极限学习机(PSO-RELM)和RELM相比,基于麻雀算法改进正则化极限学习机(SSA-RELM)的菠萝含水率检测模型的检测精度最高。结论:麻雀搜索算法优化RELM模型可以有效提高RELM模型的菠萝含水率检测精度。

连续投影与SSA-ELM结合的玉米氮平衡指数高光谱估测

玉米氮平衡指数的快速、无损估测,对监测植株长势和指导田间氮肥施用具有重要现实意义。通过田间试验观测各生育期(拔节期、抽雄期、乳熟期、完熟期)玉米叶片氮平衡指数和对应光谱反射率,应用特征波段和植被指数,构建不同类型的玉米氮平衡指数高光谱反演模型。结果表明:不同氮平衡指数下的叶片高光谱反射特征基本一致,在可见光波段反射率较低、近红外波段反射率较高,不同生育期氮平衡指数与光谱反射率的相关性在抽雄期最高,乳熟期、完熟期次之、拔节期最低;连续投影算法具有良好的降维效果,筛选出的各生育期建模光谱参数为8~20个;各生育期多因素模型均优于单因素模型,抽雄期各类模型效果最佳,其中基于麻雀搜索算法优化的极限学习机回归模型是最优模型,其建模R^(2)与验证R^(2)均为0.93,相对预测偏差分别为2.57、2.31,表明该模型对氮平衡指数具有极好的预测能力。

神经网络视域下互联网安全态势预测方法分析

传统层次化网络安全态势评估模型在应用时,主要利用入侵检测系统和警报系统发挥态势评估作用,对警告要素关联性缺乏关注。文章以神经网络为基础背景探讨互联网安全态势预测,意在完善层次化态势评估模型,融入模糊层,为提升网络安全态势评估质量提供保障。通过实践分析可知,模糊层构建后,能够通过警报匹配模式衡量警报成功率数值指标,并且进一步对警报威胁性、警报成功率、警报周期3项指标进行明确,确认其对网络安全态势影响程度。最后,在不同模型结构层级上计算出综合警报态势值,达到优化错报漏报问题、提升评估结果准确度的目标。

创业学习、创业胜任力与创业成功:少数民族大学生创业能力提升路径

创业有助于提升民族地区自我发展能力,但是作为“双创”有生力量的少数民族大学生是否胜任创业活动,其学习意愿与能力是否能够提升创业胜任力、提高创业成功率等值得探讨。文章通过对少数民族大学生创业者的问卷调查以及利用结构方程模型开展实证分析,确认了少数民族大学生创业者创业学习对创业成功的正向影响以及创业胜任力在该作用机制中的中介作用。研究认为学校、家庭与社会应该进一步把握少数民族大学生的特性,加强理论与实践互动,提升大学生创业学习效率及创业胜任力,助力大学生实现创业成功。