Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

A Research on Bilingual Dictionary Based Sentence Alignment for Chinese English Parallel Corpus

Parallel corpus is of great importance to machine translation, and automatic sentence alignment is the first step towards its processing. This paper puts forward a bilingual dictionary based sentence alignment method for Chinese English parallel corpus, which differs from previous length based algorithm in its knowledge-rich approach. Experimental result shows that this method produces over 93% accuracy with usual English-Chinese dictionaries whose translations cover 31 88%～47 90% of the corpus.

Transductive Transfer Dictionary Learning Algorithm for Remote Sensing Image Classification

To create a green and healthy living environment,people have put forward higher requirements for the refined management of ecological resources.A variety of technologies,including satellite remote sensing,Internet of Things,artificial intelligence,and big data,can build a smart environmental monitoring system.Remote sensing image classification is an important research content in ecological environmental monitoring.Remote sensing images contain rich spatial information andmulti-temporal information,but also bring challenges such as difficulty in obtaining classification labels and low classification accuracy.To solve this problem,this study develops a transductive transfer dictionary learning(TTDL)algorithm.In the TTDL,the source and target domains are transformed fromthe original sample space to a common subspace.TTDL trains a shared discriminative dictionary in this subspace,establishes associations between domains,and also obtains sparse representations of source and target domain data.To obtain an effective shared discriminative dictionary,triple-induced ordinal locality preserving term,Fisher discriminant term,and graph Laplacian regularization termare introduced into the TTDL.The triplet-induced ordinal locality preserving term on sub-space projection preserves the local structure of data in low-dimensional subspaces.The Fisher discriminant term on dictionary improves differences among different sub-dictionaries through intra-class and inter-class scatters.The graph Laplacian regularization term on sparse representation maintains the manifold structure using a semi-supervised weight graphmatrix,which can indirectly improve the discriminative performance of the dictionary.The TTDL is tested on several remote sensing image datasets and has strong discrimination classification performance.

A robust sparse representation algorithm based on adaptive joint dictionary

Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face recognition.Aiming at the shortcomings of face feature dictionary not‘clean’and noise interference dictionary not‘representative’in sparse representation classification model,a new method named as robust sparse representation is proposed based on adaptive joint dictionary(RSR-AJD).First,a fast lowrank subspace recovery algorithm based on LogDet function(Fast LRSR-LogDet)is proposed for accurate low-rank facial intrinsic dictionary representing the similar structure of human face and low computational complexity.Then,the Iteratively Reweighted Robust Principal Component Analysis(IRRPCA)algorithm is used to get a more precise occlusion dictionary for depicting the possible discontinuous interference information attached to human face such as glasses occlusion or scarf occlusion etc.Finally,the above Fast LRSR-LogDet algorithm and IRRPCA algorithm are adopted to construct the adaptive joint dictionary,which includes the low-rank facial intrinsic dictionary,the occlusion dictionary and the remaining intra-class variant dictionary for robust sparse coding.Experiments conducted on four popular databases(AR,Extended Yale B,LFW,and Pubfig)verify the robustness and effectiveness of the authors’method.

Simultaneous denoising and resolution enhancement of seismic data based on elastic convolution dictionary learning

Enhancing seismic resolution is a key component in seismic data processing, which plays a valuable role in raising the prospecting accuracy of oil reservoirs. However, in noisy situations, existing resolution enhancement methods are difficult to yield satisfactory processing outcomes for reservoir characterization. To solve this problem, we develop a new approach for simultaneous denoising and resolution enhancement of seismic data based on convolution dictionary learning. First, an elastic convolution dictionary learning algorithm is presented to efficiently learn a convolution dictionary with stronger representation capability from the noisy data to be processed. Specifically, the algorithm introduces the elastic L1/2 norm as a sparsity constraint and employs a steepest gradient descent strategy to efficiently solve the frequency-domain linear system with substantial computational cost in a half-quadratic splitting framework. Then, based on the learned convolution dictionary, a weighted convolutional sparse representation paradigm is designed to encode the noisy data to acquire an optimal sparse approximation of the effective signal. Subsequently, a high-resolution dictionary with a broadband spectrum is constructed by the proposed parameter scaling strategy and matched filtering technique on the basis of atomic spectrum modeling. Finally, the optimal sparse approximation of the effective signal and the constructed high-resolution dictionary are used for data reconstruction to obtain the seismic signal with high resolution and high signal-to-noise ratio. Synthetic and field dataset examples are executed to check the effectiveness and reliability of the developed method. The results indicate that this method has a more competitive performance in seismic applications compared with the conventional deconvolution and spectral whitening methods.

The Role of Bilingualised Learner＇s Dictionary in Vocabulary Learning of Chinese College Students as non-English Majors

According to Cowie(2002), choosing which type of dictionary depends on"several factors, including the year of study, the level of linguistic proficiency of the users, and the nature of the study activity"(p. 195). After comparing the features of monolingual and bilingual learner's dictionaries, and examining the definitions and examples of three entry words(‘owe',‘deadlock'and‘pertinent') in six popular learner's dictionaries in China, we make a tentative conclusion that bilingualised dictionary is the better choice in vocabulary learning of Chinese college students as non-English majors. Some further investigations have to be conducted about the status quo of dictionary use among Chinese college students as non-English majors and their vocabulary learning strategies.

Application of the Just-in-Time Teaching(JiTT)Model in Bilingual Courses of Chinese-Foreign Cooperative Education

The teaching abilities of bilingual teachers have emerged as one of the major bottlenecks hindering the further development of Chinese-foreign cooperative education projects.Implementing the JiTT(Just-in-Time Teaching)model in bilingual courses of Chinese-foreign cooperative education can effectively integrate information technology with traditional classroom teaching.This integration enhances teaching quality and effectiveness,encourages university instructors to improve their diverse and collective capabilities,promotes digital development,and contributes to the enhancement of students’learning abilities and overall improvement.

A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

Unconstrained face images are interfered by many factors such as illumination,posture,expression,occlusion,age,accessories and so on,resulting in the randomness of the noise pollution implied in the original samples.In order to improve the sample quality,a weighted block cooperative sparse representation algorithm is proposed based on visual saliency dictionary.First,the algorithm uses the biological visual attention mechanism to quickly and accurately obtain the face salient target and constructs the visual salient dictionary.Then,a block cooperation framework is presented to perform sparse coding for different local structures of human face,and the weighted regular term is introduced in the sparse representation process to enhance the identification of information hidden in the coding coefficients.Finally,by synthesising the sparse representation results of all visual salient block dictionaries,the global coding residual is obtained and the class label is given.The experimental results on four databases,that is,AR,extended Yale B,LFW and PubFig,indicate that the combination of visual saliency dictionary,block cooperative sparse representation and weighted constraint coding can effectively enhance the accuracy of sparse representation of the samples to be tested and improve the performance of unconstrained face recognition.

Exploring Bilingual Instruction for Intermediate Microeconomics in the Context of Sino-Foreign Collaborative Education

With the establishment of intermediate microeconomics courses in an increasing number of economics majors at universities,its importance is becoming more prominent.This article analyzes the current situation and existing problems of bilingual teaching in intermediate microeconomics.It also proposes improvement suggestions for bilingual teaching in intermediate microeconomics under Sino-foreign collaborative education,addressing aspects such as teaching mode innovation,teaching team building,bilingual teaching differentiation,and diverse process assessments.

基于数据挖掘的痛经用药规律研究

目的:基于数据挖掘技术分析《中医方剂大辞典》中治疗痛经方剂的用药规律。方法:根据纳入与排除标准筛选出《中医方剂大辞典》中治疗痛经的方剂,对其进行方药名称规范,运用Excel、IBM SPSS Modeler 18.0、IBM SPSS Statistics 26.0等进行频数统计、关联规则分析、聚类分析及因子分析。结果:共纳入151首治疗痛经的方剂,涉及中药191味;其中出现频次≥10的中药为当归、川芎、延胡索、白芍、香附、甘草等共36味,药效以补虚药、活血化瘀药、理气药为主,药性偏温、寒、平,药味则偏辛、苦、甘,多入肝、脾、心经。选取出现频次≥25的13味高频药物进行关联规则分析,得到“当归-川芎“”当归-香附-川芎“”当归-熟地黄-白芍-川芎”等常用药对21组,并通过提升关联规则的支持度得到“当归-川芎-白芍”核心组合。进行高频药物聚类分析得出4类药物组合,C1:白芍、熟地黄、川芎、当归;C2:牡丹皮、茯苓;C3:香附、甘草;C4:桃仁、红花,因子分析得到5个公因子。结论《:中医方剂大辞典》中治疗痛经以补血活血、化瘀散结、疏肝理气为主。

“双一流”背景下生物化学双语教学改革探索

生物化学是生命科学及医学的基础及前沿学科,为实现“双一流”建设目标及适应经济和科技发展的全球化趋势,运用双语教学对生命科学培养高素质人才具有重要的意义。该文指出在生物化学双语教学中存在的问题,提出建立以能力为导向的教学体系,在教学中采用多样化的授课方式并融入思政元素,尽力提高学生的课堂参与度,同时还建立多样化的学习效果评价体系。这些措施使学生的学习成绩明显提高,说明我们在探索双语教学方法的改革中做出的尝试是行之有效的。

基于知识图谱的多特征融合谣言检测方法

为了解决谣言检测中由于缺乏外部知识而导致模型难以感知内隐信息,进而限制了模型挖掘深层信息的能力这个问题,提出了基于知识图谱的多特征融合谣言检测方法(KGMRD)。首先,对于每个事件,将帖子和评论共同构建为一个文本序列,并利用分类器从中提取情感特征,利用ConceptNet基于文本构造其知识图谱,将知识图谱中的实体表示利用注意力机制与文本的语义特征进行聚合,进而得到增强的语义特征表示;其次,在传播结构方面,对于每个事件,基于帖子的传播转发关系构建传播结构图,使用DropEdge对传播结构图进行剪枝,从而得到更有效的传播结构特征;最后,将得到的特征进行融合处理得到一个新的表示。在Weibo、Twitter15和Twitter16三个真实数据集上,使用SVM-RBF等七个模型作为基线进行了对比实验。结果表明:对比当前效果最好的基线,KGMRD方法在Weibo数据集的ACC指标提升了1.1%;在Twitter15和Twitter16数据集的ACC指标上提升了2.2%,证明了KGMRD方法是合理的、有效的。

基于苹果应用商店大数据的APP词典融媒创新分析

手机终端的APP词典是目前最主流、最贴近融媒辞书发展趋势的数字化词典形态。利用Data.ai平台对苹果应用商店2360个APP词典的调查研究发现,自2008年首个产品发行以来,APP词典在数字开发商科技实力的巨大推动下迅速发展,展现了六大融媒创新变革。侧重词典本体内链“融合”的创新表现为:基于语言加工的多语种服务融合,基于集成整合的多词典类型融合,基于逻辑媒体的多模态内容融合和基于查询场景的多检索手段融合;侧重词典外部世界外链“融通”的创新表现为:数字工具之间的功能融通和词典交际主体之间的身份融通。APP词典要实现融媒辞书的跨越转型,还需要考虑:如何在利用技术追求“美颜”式发展的同时做到内外兼修,如何在编纂传统规范和身份创新融通之间实现编用平衡,如何达到海量内容与精准服务的信息平衡,如何保证泛在服务与知识工具的定位平衡。

新工科背景下基于OBE模式的双语课程考核评价研究——以“机场概论”课程为例

新工科背景下我国高校开设了许多双语课程,但双语教学的理论研究和实践探索还处于起步阶段,双语教学模式还不够完善,而且对双语课程较为重要的考核评价方式尚不明确。以“机场概论”课程为突破口,探讨双语课程考核评价模式确立的策略及制度保障,提出基于OBE教育模式下的双语课程考核评价方式的确定方法,并将OBE理念与新工科教育目标相结合,以期提高工程教育人才培养质量,进一步实现新工科教育教学与当前经济社会发展的相互耦合。

探寻中国译学话语的发展之路写在《中国译学大辞典》(第二版)出版之前

一部术语史就是一部专业话语的历史。术语是专业话语的基本要素,术语系统的建设是反映相关学科进步和走向的一面镜子。本文概述了《中国译学大辞典》(第二版)的编纂思想,介绍了该辞典的编辑方针、收选原则和编辑实践,从中可以看出我国译学术语的积累与译学话语体系的完善是同步的。这部辞典见证了翻译学科酝酿-诞生-发展的过程。

基于超星平台的“面向对象程序设计基础”双语课程建设

“面向对象程序设计基础”课程是计算机网络技术专业的专业核心基础课,以培养计算机网络技术专业复合型国际化高素质技术技能人才为目的。为了国际化人才培养,满足培养本土国际化人才,也为输出国际通用的课程、教学资源,该课程进行了基于超星平台的双语课程建设。从课程建设基础出发,结合课程的内容和特点,系统地介绍了从需求分析与课程规划、素材收集与整理到课程设计与开发的建设过程,并对课程建设成果进行了总结,最后分析了在双语课程建设中出现的问题以便后续对课程进行持续优化、更新与推广。

基于“虚拟教学+双语教学”的教学方式探索——以扬州大学“牧草及饲料作物栽培学”实验教学为例

“牧草及饲料作物栽培学”是草学专业的核心专业课程。双语教学是现代高校人才培育和满足与国际接轨需求的一种重要的教学模式。虚拟现实教学则是突破传统教学局限性,基于先进数字技术实现教学目标的全新教学手段。“虚拟现实教学+双语教学”的多元教学是“双一流”建设背景下草学专业课程教学改革的必然趋势。以精心设计的课前、课中及课后教学课件为实例,以“学生为中心”为教学切入点,探讨多元教学模式如何促进“双一流”建设背景下的草学专业课“牧草及饲料作物栽培学”教学实践。

基于CLIL理论的中医院校留学生教育双语师资发展体系构建探索

双语师资短缺已成为制约高等中医院校留学生教育的瓶颈问题。基于内容与语言融合式教学(Content and language integrated learning,CLIL),构建包括培训目标、课程内容模块、教材和教师、教学方法与考核方式的中医留学教育双语师资发展体系,旨在提高参训老师的中医药学科知识、英语语言技能和中医英语教学能力。

日汉对译辞书《杂字类编》中的词语误认现象及成因

《杂字类编》收录的汉语词以近代汉语口语词和俗语词为主,杂以部分文言词和方言词,也存在一些非“纯汉语”成分和词语误认现象。《杂字类编》中的词语误认现象主要有两种类型:误认非词形式为词、误认词语对应关系。导致词语误认的常见因素有:文献来源与文本用字的影响、常用词和常用义的干扰、句法结构的重新分析,各种因素的“影响因子”存在差异,根本原因是对目标语言及其载体的“不完善学习”。结合相关文献,考察日汉对译辞书的词汇及词语误认现象,有助于梳理中日语言文化接触与相互影响的发展脉络,也有助于正确认识双语辞书的语言成分及其语料价值。