A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.

THE RELATIONSHIPS BETWEEN THE POLARIZING FORCE OF CATIONS AND THE ELECTROPHILIC CHARACTER AND CATALYTIC PERFORMANCES OF PROMOTED Pt-Al_2O_3 CATALYST

The electronic modification effect of various metal oxides over Pt-Al;O;catalyst andthe relationships between the polarizing force of cations（PFC）and the electrophiliccharacter（EC）and catalytic performances（CP）of promoted Pt catalyst have been studiecby competitive hydrogenation reaction method（CHRM）and test reaction,i.e.hydrogena-tion of benzene and hydrogenolysis of cyclopentane.

Analysis of Hamlet's Complex Character By Hamlet's Relationship with Other Major Characters

This paper is an attempt to reveal the complexity of the character of Hamlet in Shakespeare's tragedy-Hamlet and the importance of the role that his pretense of lunacy plays in the plot. The different sides of his personality are revealed by the analysis of some of Hamlet's behavior. The argument is also established that the hero's pretense of lunacy is a device in the plot which allows the full display of the character's complexity.

The Female Characters in Heart of Darkness and Victory

The female characters in Conrad's Heart of Darkness and Victory on one hand seem to be either too idealistic or rather uncivilized and insignificant.On the other hand,they have indispensable functions:they prepare the precondition for the plot,they can reverse the gender stereotype,and they help the inner growth of male protagonists.Even so,the focus of Joseph Conrad is still a patriarchal society where women are motivated by men.

A Fast Recognition System for Isolated Printed Characters Using Center of Gravity and Principal Axis

The purpose of this paper is to propose a new multi stage algorithm for the recognition of isolated characters. It was similar work done before using only the center of gravity (This paper is extended version of “A fast recognition system for isolated printed characters using center of gravity”, LAP LAMBERT Academic Publishing 2011, ISBN: 978-38465-0002-6), but here we add using principal axis in order to make the algorithm rotation invariant. In my previous work which is published in LAP LAMBERT, I face a big problem that when the character is rotated I can’t recognize the character. So this adds constrain on the document to be well oriented but here I use the principal axis in order to unify the orientation of the character set and the characters in the scanned document. The algorithm can be applied for any isolated character such as Latin, Chinese, Japanese, and Arabic characters but it has been applied in this paper for Arabic characters. The approach uses normalized and isolated characters of the same size and extracts an image signature based on the center of gravity of the character after making the character principal axis vertical, and then the system compares these values to a set of signatures for typical characters of the set. The system then provides the closeness of match to all other characters in the set.

Visual processing of word and image in the fusiform gyrus

BACKGROUND： The role of the left midfusiform gyrus as a target for visual word processing has been a topic of discussion. Numerous studies have utilized alphabetic writing for subject matter. However, few have addressed visual processing of Chinese characters in the left midfusiform gyrus. OBJECTIVE： To verify visual processing of Chinese characters and images in the left midfusiform gyrus using functional magnetic resonance imaging. DESIGN, TIME AND SETTING： A blocked design paradigm study. Experiments were performed at the Room of Magnetic Resonance, Guangdong Provincial Second People＇s Hospital, China from May to June 2009. PARTICIPANTS： A total of eight undergraduate students were recruited from Guangzhou University of China, comprising two females and six males, aged 20-23 years. The subjects were right-handed which was determined by a Chinese standard questionnaire. None of the subjects had a history of psychoneurosis, familial disease, color blindness, or color weakness. METHODS： A total of eight undergraduates were enrolled as subjects. Picture-naming and verb generation tasks were employed through the use of functional magnetic resonance imaging. Analysis of Functional Neurolmages software was used to process the data. MAIN OUTCOME MEASURES： Visual processing of Chinese characters and images in the left midfusiform gyrus was measured. RESULTS： Picture-naming and verb generation tasks were shown to significantly activate the bilateral midfusiform gyrus. Activation occurred in the visual word form area of the left midfusiform gyrus. CONCLUSION： The left midfusiform gyrus plays a general role in visual processing of Chinese characters and images.

Tuning Recurrent Neural Networks for Recognizing Handwritten Arabic Words

Artificial neural networks have the abilities to learn by example and are capable of solving problems that are hard to solve using ordinary rule-based programming. They have many design parameters that affect their performance such as the number and sizes of the hidden layers. Large sizes are slow and small sizes are generally not accurate. Tuning the neural network size is a hard task because the design space is often large and training is often a long process. We use design of experiments techniques to tune the recurrent neural network used in an Arabic handwriting recognition system. We show that best results are achieved with three hidden layers and two subsampling layers. To tune the sizes of these five layers, we use fractional factorial experiment design to limit the number of experiments to a feasible number. Moreover, we replicate the experiment configuration multiple times to overcome the randomness in the training process. The accuracy and time measurements are analyzed and modeled. The two models are then used to locate network sizes that are on the Pareto optimal frontier. The approach described in this paper reduces the label error from 26.2% to 19.8%.

An Exploratory Study on the Electronic Word of Mouth Communication in Promoting Brands in the Online Platforms

This study discusses on the communication method followed by most of the brands called. “Electronic Word of Mouth” in a short form is called as eWOM in order to reach the customers effectively in a short span. Social media having become a new hybrid component of integrated marketing communication allows the brands to establish strong relationship with the customers. With the establishment of customer relationship online, the brands create a platform for the customers to discuss about the product features, quality, price and write a review about the product online. This research analyses about the social media and its impact in spreading the messages about the brands to the end customers and the impact of gender, age groups, income, designation and the demographic details of the customers in trusting the information that is spread through electronic media and the level at which the eWOM helps the customers to select the brand.

The Study of the Effect of Educational Level of Students and English Language Teachers＇ Awareness on True and False Cognates in Iran

The ability to speak two languages in the non-English-speaking countries is a remarkable achievement. There is a good reason to believe that bilingualism is the norm for the majority of people in the world, because 70% of the earth＇s population are supposed to be bilingual or multilingual. Our purpose of the study was to determine the participants＇ awareness of using true and false cognate words of L 1 （the first language） ＆ L2 （the second language） in learning English. Three hundred and eighty-five selected people from 3,789 statistical population participated in 2014--2015 at three levels of A.D., B.A., and M.A. of Islamic Azad University as well as teachers of English in English educational institutions in Zanjan （Iran）. Each educational level was considered as one category with the total sample calculated by using Kokran Formula, and the amount of each category was determined by using appropriate proportion and randomized categorical sampling method. The participants＇ age was between from 18 to 52, with a mean age of 29 years. The materials were 45 words of true and false cognate＇s words from 500 words by doing CVR （content validity ratio） and CVI （content validity index） （Lawshe＇s table with the index of 88% and 82% respectively） for being reliable and valid. ANOVA （Analysis of variance） was used for studying the effect of educational level on the rate of awareness. In other words, the awareness level has been compared in different educational levels. The results of Dankan test show that： There is no significant difference between the awareness of A.D., B.A., and M.A. levels, but there is a significant difference between the level of awareness of teachers group and the other groups （in true cognate words） and for false cognate words, there is no significant difference between the level of awareness of the four groups in this regard.

Sora:从技术哲学到文艺学的思考(笔谈)

哲学学者吴冠军从技术哲学的视角提出:从ChatGPT、DALL·E到Sora,基于转化器的AIGC大模型并不只改变了内容创作,还改变了“世界”在最字面上的意义。世界化成的过程,越来越多被以有损压缩-重新生成为发生学机制的AIGC之创造性活动所介入,这就导致“宇宙”(我们眼里的“物理宇宙”)与“元宇宙”不断深层次地叠加,“自然进程”与“人工创造”不断深层次地交织以至进入无可区分之域。这就意味着,我们所熟悉的那个“世界”,将一去不复返:当AIGC已“深”到参与世界化成本身之中时,讨论“深伪”将越来越没有意义。文艺学学者赵宪章则从“语图关系学”的视角提出:Sora的横空出世及其未来演进,不仅不是图像时代符号危机的解除,更不是语图关系研究的终结。恰恰相反,Sora加深了这一危机,语图关系研究也延宕出新的面向。

阅读伴随词汇学习的词切分:首、尾词素位置概率的不同作用

本研究通过两个平行实验,探讨重复学习新词时首、尾词素位置概率信息作用于词切分的变化模式。采用阅读伴随词汇学习范式,将双字假词作为新词,实验1操纵首词素位置概率高低,保证尾词素相同;实验2操纵尾词素位置概率高低,保证首词素相同。采用眼动仪记录大学生阅读时的眼动轨迹。结果显示:(1)首、尾词素位置概率信息的词切分作用随新词在阅读中学习次数的增加而逐步变小,表现出“熟悉性效应”。(2)首词素位置概率信息的“熟悉性效应”表现在回视路径时间、总注视次数两个相对晚期的眼动指标,而尾词素位置概率信息的“熟悉性效应”则从凝视时间开始,到回视路径时间,再持续到总注视时间。结果表明首、尾词素的位置概率信息均作用于阅读伴随词汇学习的词切分,但首词素的作用时程更长,更稳定,支持了首词素在双字词加工中具有优势的观点。

基于字词向量融合的民航智慧监管短文本分类

为解决民航监管事项所产生的检查记录仅依靠人工进行分类分析导致效率低的问题,提出一种基于数据增强与字词向量融合的双通道特征提取的短文本分类模型,探讨民航监管事项的分类,包括与人、设备设施环境、制度程序和机构职责等相关问题。为解决类别不平衡问题,采用数据增强算法在原始文本上进行变换,生成新的样本,使各个类别的样本数量更加均衡。将字向量和词向量按字融合拼接,得到具有词特征信息的字向量。将字词融合的向量分别送入到文本卷积神经网络(TextCNN)和双向长短期记忆(BiLSTM)模型中进行不同维度的特征提取,从局部的角度和全局的角度分别提取特征,并在民航监管事项检查记录数据集上进行试验。结果表明:该模型准确率为0.9837,F 1值为0.9836。与一些字嵌入模型和词嵌入模型相对比,准确率提升0.4%。和一些常用的单通道模型相比,准确率提升3%,验证了双通道模型提取的特征具有全面性和有效性。

文本相似度计算方法综述

文本相似度计算是自然语言处理的一部分,用来计算两个词、句子及文本之间的相似程度,具有多种应用场景,文本相似度计算的研究对于人工智能的发展有着重要作用。文本相似度计算起初基于字符串表面,随着词向量的提出,文本相似度计算可进行基于统计以及深度学习的建模与计算,也可与预训练模型相结合。首先,将文本相似度计算方法分为基于字符串、基于词向量、基于预训练模型、基于深度学习、其他方法5类,并对这些方法进行简要介绍。然后,根据不同文本相似度计算方法的原理,具体介绍了编辑距离、汉明距离、词袋模型、向量空间模型(VSM)、深度结构语义模型(DSSM)、句子嵌入的简单对比学习(SimCSE)等常见方法。最后,对文本相似度计算常用的数据集以及评价标准进行整理和分析,并对文本相似度计算的未来发展进行展望。

词间空格对汉语母语者和二语者拼音文本阅读的影响——基于眼动的证据

本研究采用眼动跟踪技术考察词间空格对汉语母语者和第二语言学习者汉语拼音文本阅读的影响。实验以中国大学生和在中国学习汉语的外国学生为被试,收集他们阅读三种不同空格条件(词间空格、字间空格和无空格)的汉语拼音句子的眼动数据。研究结果显示,与无空格条件相比,词间空格和字间空格条件的注视时间短,注视次数少,首次注视位置距离词中心近;母语者在词间空格和字间空格条件下阅读效率无显著差异,但二语学习者阅读词间空格文本的阅读效率大于字间空格文本;词间空格对于印欧语母语者的促进作用显著大于泰语母语者。这些结果表明,词间空格对汉语二语者有促进作用,词间空格的促进作用受母语文字背景的影响。

基于多头注意力机制字词联合的中文命名实体识别

针对现有基于字词联合的中文命名实体识别方法会引入冗余词汇干扰、模型网络结构复杂、难以迁移的问题,提出一种基于多头注意力机制字词联合的中文命名实体识别算法.算法采用多头注意力机制融合词汇边界信息,并通过分类融合BIE词集降低冗余词汇干扰.建立了多头注意力字词联合模型,包含字词匹配、多头注意力、融合等模块.与现有中文命名实体识别方法相比,本算法避免了设计复杂的序列模型,方便与现有基于字的中文命名实体识别模型结合.采用召回率、精确率以及F 1值作为评价指标,通过消融试验验证模型各个部分的效果.结果表明,本算法在MSRA和Weibo数据集上F 1值分别提升0.28、0.69,在Resume数据集上精确率提升0.07.

基于深度字词融合的小麦种质信息实体关系联合抽取

为获得结构化的小麦品种表型和遗传描述,针对非结构化小麦种质数据中存在的实体边界模糊以及关系重叠问题,提出一种基于深度字词融合的小麦种质信息实体关系联合抽取模型WGIE-DCWF(wheat germplasm information extraction model based on deep character and word fusion)。模型编码层通过深度字词融合和上下文语义特征融合,提高密集实体特征识别能力;模型三元组抽取层建立层叠指针网络,提高重叠关系的提取能力。在小麦种质数据集和公开数据集上的一系列对比实验结果表明,WGIE-DCWF模型能够有效提高小麦种质数据实体关系联合抽取效果,同时拥有较好的泛化性,可以为小麦种质信息知识库构建提供技术支撑。

主题方面共享的领域主题层次模型

层次主题模型是构建主题层次的重要工具.现有的层次主题模型大多通过在主题模型中引入nCRP构造方法,为文档主题提供树形结构的先验分布,但无法生成具有明确领域涵义的主题层次结构,即领域主题层次.同时,领域主题不仅存在层次关系,而且不同父主题下的子主题之间还存在子领域方面共享的关联关系,在现有主题关系研究中没有合适的模型来生成这种领域主题层次.为了从领域文本中自动、有效地挖掘出领域主题的层次关系和关联关系,在4个方面进行创新研究.首先,通过主题共享机制改进nCRP构造方法,提出nCRP+层次构造方法,为主题模型中的主题提供具有分层主题方面共享的树形先验分布;其次,结合nCRP+和HDP模型构建重分层的Dirichlet过程,提出rHDP(reallocated hierarchical Dirichlet processes)层次主题模型;第三,结合领域分类信息、词语语义和主题词的领域代表性,定义领域知识,包括基于投票机制的领域隶属度、词语与领域主题的语义相关度和层次化的主题-词语贡献度;最后,通过领域知识改进rHDP主题模型中领域主题和主题词的分配过程,提出结合领域知识的层次主题模型rHDP_DK(rHDP with domain knowledge),并改进采样过程.实验结果表明,基于nCRP+的层次主题模型在评价指标方面均优于基于nCRP的层次主题模型(hLDA,nHDP)和神经主题模型(TSNTM);通过rHDP_DK模型生成的主题层次结构具有领域主题层次清晰、关联子主题的主题词领域差异明确的特点.此外,该模型将为领域主题层次提供一个通用的自动挖掘框架.

“膊”字历时记词功能研究

文章考察了“膊”字的历时记词情况,归纳为以下八种:第一,记{膊},音pò,义为“曝肉”“干肉”“晒”“陈尸示众”“打开、剖开”“泛指杀”;第二,记{伯},音bó,义为“兄”;第三,记录单纯词{膊膊},音bó,用同{旁薄};第四,作为“膞”字讹写记录{膞}{腨}等词;第五,记{髆},音bó,义为“肩膀”“上臂”“胳膊”;第六,记录{膊膊}{腷膊}{腷腷膊膊}等拟声词,音bó;第七,记{脖},音bó,义为“脖子”;第八,记{拨},音bō,义为“调转”。“膊”字历时记词情况考察的结果对于释读文献、完善辞书具有重要价值,文章对《汉语大字典》《汉语大词典》提出修订建议。

城市性格特质与居民生活满意度的关系研究

居民生活满意度提升是城市高质量发展的重要内容之一。作为一个有机体的城市,其性格特质对居民生活满意度提升具有重要作用。研究发现:城市性格特质包括城市意志、城市情绪、城市态度三个维度,性格特质整体及三个维度对居民生活满意度提升均起到显著的正向效应,且效应从大到小依次为城市情绪、城市态度、城市意志,效应大小在不同地区的城市表现出异质性。研究结论支撑了城市性格特质理论架构的建立,也为城市性格特质和居民生活满意度关系的探索提供了证据。