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1. chinaXiv:202003.00049 [pdf]

基于自我介绍视频的人格预测技术研究

温业业; 陈德元; 李保滨; 汪晓阳; 刘晓倩; 朱廷劭
Subjects: Psychology >> Applied Psychology

人格影响着个体的工作生活方式,对于个体的心理疏导、职业发展等具有重要指导意义。传统方法通过量表测评人格得分存在个体拒绝回答、盲目作答等问题,近年来随着机器学习的发展为人格识别提供了新的思路。本文使用被试者自我介绍视频和大五人格量表得分,经过关键点提取、特征降维、建模、迭代调参等步骤,针对不同人格维度得到不同的预测模型。测试结果表明,基于自我介绍视频的人格预测模型在各维度都接近或达到中等相关,能够提供无侵扰的人格自动识别,为人格测量提供了新的思路。

submitted time 2020-03-08 Hits7883Downloads202 Comment 0

2. chinaXiv:202003.00048 [pdf]

自监督图像增强网络:仅需低照度图像进行训练

张雨; 遆晓光; 张斌; 王春晖
Subjects: Computer Science >> Other Disciplines of Computer Science

本文提出了一种基于深度学习的自监督低照度图像增强方法。受信息熵理论和Retinex模型的启发,我们提出了一种基于信息熵最大的Retinex模型。利用该模型,一个非常简单的网络可以将照度图和反射图分离开来,且仅用低照度图像就可以进行训练。为了实现自监督学习,我们在模型中引入了一个约束条件:反射图的最大值通道与低照度图像的最大值通道一致,且其熵最大。我们的模型非常简单,不依赖任何精心设计的数据集(即使是一张低照度图像也能完成网络的训练),网络仅需进行分钟级的训练即可实现图像增强。实验证明,该方法在处理速度和效果上均达到了当前最新水平。

submitted time 2020-03-06 Hits6284Downloads109 Comment 0

3. chinaXiv:202002.00063 [pdf]

Perspectives on Active Preventive Measures of Wuhan People against COVID-19 Epidemic at Home: A Comparative Study

zhidong,Xue; Lei,Zhao; Tailang,Yin; Yan,Fu; Zehua,Lyu; yiping,Dang; Yujiang,Zeng; Silou,Huang; Bing,Qu; Hongya,Lyu; Chen,Huang; Zhiyou,Kong; Kepei,Xu; Feipeng,Zhou; Hexun,Dong; He,Hu; Jing,Tang; Senyuan,Xue; Zhixiang,Fang; Jinxiang,Lu
Subjects: Computer Science >> Computer Application Technology

Background:The COVID-19 Epidemic emerged in Wuhan, Hubei province, China. Ever since Wuhan lockdown on January 23rd, mass quarantines were exercised on Wuhan and other epidemic areas of China. We aimed to clarify how ordinary Wuhan people defend against COVID-19 epidemic at home through the Internet survey. Methods:A questionnaire survey, consisting of 30 questions were posted on the Internet. The following aspects were investigated: household preventive measures, self-monitoring of discomfort symptoms, immunity boosting against the epidemic, frequency and reasons of outgoing and mental status of the isolated people. The questionnaire was circulated on Wechat. We marked the areas based on the surveyed network IP addresses and categorized respondents into group A(Wuhan), B(Hubei Province excluding Wuhan ), C, and D based on the epidemic severity of their areas announced by Baidu.com at 17:00 on February 8, 2020. And a comparative study was conducted to illustrate how Wuhan people took the anti-COVID-19 strategies and how efficient these preventive measures were. Findings:In terms of discomfort symptoms, Wuhan, as Group A, had the lowest asymptomatic percentages (70.2%), compared to the average 78.5% (±7%). Considering the three typical symptoms for the COVID-19, i.e., cough, fever and fatigue, Wuhan (9.67%) greatly deviated from the average (7.68%). The fatigue was the most significant factor in the deviation, exceeding the average by 1.35%. In terms of household protection measures, most people or families were able to take effective protection measures with very low frequency of going out, but the percentage of those who took this practice was obviously smaller in Wuhan and Hubei Province. From the aspect of going out, most of the people in Wuhan only went out for shopping and work, with a small number of people for social gathering. In terms of immunity boosting, compared with Group C and D, it was relatively lower in Wuhan. Overall, most people chose to enhance their immunity through regular schedule, exercise, sufficient nutrition. Only 33.44% of people in Group A did not go out, and 59.97% had to go out for living supplies, which was the highest level among the four groups. However, the percentage of people who went out for work and unnecessary activities remains the lowest while 1% of the population went out for public welfare activities, higher than other groups. Worry about the family health topped all the parameters for all the groups. Among them, Wuhan has reached a maximum of 49.61%, higher than the average level of 36.62% (± 10.69%). Mental status except for feeling bored and lonely were the highest in Wuhan. Suggestions:When the epidemic prevention and control is still in a sticky state, and Wuhan started a stricter control measure, the closed management of communities, on Feb 11, 2020, it is expected that our findings can provide some insights into the current household preventive actions and arouse more attentions of the public to some ignored preventive precautions. Unnecessary outgoing should be strictly abandoned. Regular schedule, exercises and nutrition were the top 3 measures participants would choose to enhance their own immunity system. It seems that people in Wuhan would choose nutrition and regular scheduler rather than exercises as the primary immunity-boosting ways. Exercise should be especially advocated as an effective way to enhance the immunity system. In terms of physical condition, people in Wuhan should take more active measures when symptoms occurred. The mentality is also an important aspect requiring intensive attention with the conduct of stricter control management in Wuhan while the rest groups gradually resume to work and ordinary life.

submitted time 2020-02-24 Hits6160Downloads288 Comment 0

4. chinaXiv:202002.00009 [pdf]

一种新型冠状病毒肺炎(COVID-19)药物与天然产物快速发现的计算药理学方法

全源; 梁峰吉; 熊江辉
Subjects: Biology >> Virology

新发传染病爆发流行期间,亟需提出候选药物功效与机制的科学假说。疫苗或新药研发均需要一定时间,因而药物重定位(老药新用)策略有其独特价值。但是新发疾病其病原体、宿主反应的临床数据不充分,制约了候选药物假设的提出。此阶段常根据病人临床特征进行广谱抗病毒药物的尝试。本文借鉴人工智能领域常见的启发式搜索思路,提出一种新方法(aCODE),基于前期有一定疗效提示的广谱抗病毒药,获得其宿主靶蛋白集合,在全基因组尺度上搜索与之相关性最高的基因模块组合,进而对候选化合物(如已批准上市药物、天然产物)进行模式匹配与统计检验排序。本方法可根据临床实践的进展更新输入药物,迭代输出更精准结果,输出的天然产物或中药、药食同源成分结合其它信息后可实施快速测试,形成敏捷研发测试闭环。本方法的第二版更新及其与文献证据的比对分析请参考:http://chinaxiv.org/abs/202002.00024。

submitted time 2020-02-21 Hits11729Downloads1080 Comment 0

5. chinaXiv:202002.00015 [pdf]

人工智能在新型冠状病毒(2019-nCoV)肺炎的应用进展:需求和机遇

王永桂; 李强; 余晴; 杨水化; 徐子怡; 谢天奕; 胡珊
Subjects: Computer Science >> Computer Application Technology

[目的]探究人工智能在新型冠状病毒(2019-nCoV)的诊断、治疗和控制中的应用场景和进展,以利用人工智能为新型冠状病毒肺炎的防控提供助力。 [方法]剖析新型冠状病毒肺炎防控的技术需求,从人工智能基因测序、辅助诊断、远程专家系统、药物筛查与研制等方面,分析当前的应用进展,挖掘应用的机遇。 [结果]中国是新型冠状病毒疫情最严重的国家,存在诸多的技术短板有待科技助力,AI能在疫情防控中发挥出重要的作用,但目前处于初步阶段,缺乏经过验证的落地成果;AI辅助诊断领域重复性研发较多,其他方面研究较少。 [局限]当前应用的数据大部分来自网站报道,如有更多的学术性成果,进展的分析将更全面。 [结论]需要加大投入和调控,在数据、算法和算力共享的基础上,各方面全面展开研发。

submitted time 2020-02-15 Hits11315Downloads542 Comment 0

6. chinaXiv:202002.00006 [pdf]

运输,病原微生物,文化:2019-nCoV传播的动态图模型

杨晓飞; 徐暾; 贾鹏; 夏涵; 郭立; 叶凯
Subjects: Computer Science >> Computer Application Technology

自从武汉市爆发新型冠状病毒疫情以来,迅速蔓延的事态已经造成300多人死亡,一万多人感染。在中国之外有一百多起病例,影响了全球十几个国家。研究人员已经报道了冠状病毒的全基因组序列,并且正在迅速开发快速诊断试剂盒、有效的治疗方法以及预防性疫苗。最初快速增长的确诊病例触发了武汉及附近城市的封锁。世界各地的科学家尝试建立数学模型来预测未来几天内的感染病例数。但是,交通和文化习俗等主要因素尚未得到足够的权衡。我们的模型并不是为了精确预测感染病例数量,而是旨在模拟公共流行紧急情况下的动态情况以及不同影响因素的贡献。我们希望我们的模型和模拟能够为全球公共卫生机构提供更多的见解和观点信息,以便设计出更好的预防和控制解决方案。

submitted time 2020-02-03 Hits7797Downloads594 Comment 0

7. chinaXiv:202001.00075 [pdf]

基于大规模古文语料库的词典构建及分词技术研究

邢付贵; 朱廷劭
Subjects: Psychology >> Applied Psychology

古文献的研究有助于传统文化的继承与发扬,而古文分词则是利用自然语言处理技术对古文献进行分析的重要环节,但由于缺少规范的数据资料而没有像现代汉语分词取得突破性进展。当前互联网拥有大量古汉语文本和词典方面的数据资料,但是这些数据分散,没有得到有效地整合。本文提出采集互联网非结构化古汉语数据,经过数据清洗和预处理抽取出一个古汉语基础词典,然后再利用互信息、信息熵、位置成词概率相结合的新词发现方法从大规模古籍文本中抽取古汉语候补词典,最终将基础词典与候补词典融合,利用正向最大匹配实现对古文的分词。与开源的分词器甲言在基于词典的分词方面比较后F值提高了14%,取得了良好的效果,结果证明本文构建的分词器可以应用在古汉语文本分词上。

submitted time 2020-01-07 Hits7492Downloads259 Comment 0

8. chinaXiv:201912.00027 [pdf]

古文LIWC词典的构建及初步分析

范妙榕; 邢付贵; 刘兴云; 朱廷劭
Subjects: Psychology >> Applied Psychology

[背景]LIWC(基于语词计量的文本分析)以关键词的词频统计为基础,可对个体和群体的表达语句的心理学意义等方面进行量化分析。由于文言文的表达方式与现代汉语存在明显的差异,为了分析文言文文本的心理学意义,我们在简体中文LIWC词典(Simplified Chinese LIWC 2015年版本, 简称SC-LIWC)的基础上,构建了古文LIWC(Classical Chinese LIWC,以下简称CC-LIWC)词典。[目的]本研究的目的是探究如何构建CC-LIWC词典并介绍如何使用该词典对古文文本进行分析。[方法]获取在线汉语词典的全部词汇及其对应解释,保留文言文词及其现代文译文,并从译文中寻找SC-LIWC词,将SC-LIWC词与文言文词进行匹配。对匹配结果进行人工标注,确保结果的一致性与准确性。[结果]最终生成的CC-LIWC包含了81个词类与49136个文言文词条。[局限]古文中一词多义、一词多性的情况较为普遍,对词典中词汇的分类存在一定影响。[结论]使用CC-LIWC对《论语(节选)》、《孤愤》进行词频分析,分析结果体现了儒家的中庸与法家的注重逻辑辩证的区别,说明CC-LIWC词典能够有效区分文本的表达倾向。

submitted time 2019-12-20 Hits7372Downloads334 Comment 0

9. chinaXiv:201911.00097 [pdf]

Review of Machine-Vision-Based Plant Detection Technologies for Robotic Weeding

Li,Nan; Zhang, Xiaoguang ; Zhang, Chunlong; Ge, Luzhen; He, Yong; Wu,Xinyu
Subjects: Computer Science >> Computer Application Technology

Controlling weeds with reduced reliance on herbicides is one of the main challenges to move toward a more sustainable agriculture. Robotic weeding is a thought to be a viable way to reduce the environmental loading of agrochemicals while keeping the operation efficiency high. One of the key technologies for performing robotic weeding is automatic detection of crops and weeds in fields. This paper presents an overview on various methods for detecting plants based on machine vision, mainly concentrating on two main challenges: dealing with changing light and crop/weed discrimination. To overcome the first challenge, both physical and algorithmic methods have been proposed. Physical methods can result in a more cumbersome machine while algorithmic methods are less robust. For crop/weed discrimination, deep-learning-based methods have shown obvious advantages over traditional methods based on hand-crafted features. However, traditional methods still hold some merits that can be leveraged to deep-learning-based methods. With the fast development of hardware technologies, researchers should take full advantage of advanced hardware to ease the algorithm design. In the future, the identification of crops and weeds can be more accurate and fine-grained with the support of online databases and computing resources based on the advances in artificial intelligence and communication technologies.

submitted time 2019-11-23 Hits8248Downloads335 Comment 0

10. chinaXiv:201910.00076 [pdf]

Masked Sentence Model based on BERT for Move Recognition in Medical Scientific Abstracts

Yu, Gaihong; Zhang, Zhixiong; Liu, Huan ; Ding, Liangping
Subjects: Computer Science >> Natural Language Understanding and Machine Translation

Purpose: Move recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language unit. To improve the performance of move recognition in scientific abstracts, a novel model of move recognition is proposed that outperforms BERT-Base method. Design: Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences. In this paper, inspired by the BERT's Masked Language Model (MLM), we propose a novel model called Masked Sentence Model that integrates the content and contextual information of the sentences in move recognition. Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps. And then compare our model with HSLN-RNN, BERT-Base and SciBERT using the same dataset. Findings: Compared with BERT-Base and SciBERT model, the F1 score of our model outperforms them by 4.96% and 4.34% respectively, which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-the-art results of HSLN-RNN at present. Research Limitations: The sequential features of move labels are not considered, which might be one of the reasons why HSLN-RNN has better performance. And our model is restricted to dealing with bio-medical English literature because we use dataset from PubMed which is a typical bio-medical database to fine-tune our model. Practical implications: The proposed model is better and simpler in identifying move structure in scientific abstracts, and is worthy for text classification experiments to capture contextual features of sentences. Originality: The study proposes a Masked Sentence Model based on BERT which takes account of the contextual features of the sentences in abstracts in a new way. And the performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.

submitted time 2019-10-29 Hits17897Downloads404 Comment 0

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