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- 作者: kaiwu
- 点击数:988
经典教材:改变心理学的40项研究: 探寻心理学研究的历史
https://www.mendocino.edu/faculty/roger-hock
https://www.goodreads.com/book/show/361298.Forty_Studies_That_Changed_Psychology
https://en.wikipedia.org/wiki/Forty_Studies_That_Changed_Psychology
Forty Studies That Changed Psychology: Explorations Into the History of Psychological Research is an academic textbook written by Roger R. Hock that is currently in its eighth edition. The book provides summaries, critiques, and updates on important research that has impacted the field of psychology. The textbook is used in psychology courses at all levels of education and has been translated into six languages. It is used to properly relate the present knowledge of psychology with the original research that led to it. It is a window into the history of psychology for anyone wishing to expand their understanding of the true roots of psychology
Book Reviews:
Roger R. Hock.(2019). Forty studies that changed psychology: Explorations into the history of psychological research (8th ed.). Pearson.
https://www.amazon.com/dp/0135166489
Roger R. Hock. (2021). 改变心理学的40项研究(第8版) (白学军翻译). 人民邮电出版社.
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- 文章信息
- 作者: kaiwu
- 点击数:2288
for loop
add image
execution time
barchart(ylimit, vertical bar chart, add labels, frequency analysis)
R Notebook
0. load data
for(i in 1:13){
load(file=paste0("D:/datasets/citations_data/tourism_satisfaction2021/M",i,".rda"))
}
1. create m_matrix: first line
m_matrix<-as.data.frame(t(c(1,"AU","M1")))
colnames(m_matrix)<-c("col_id","col_names","from_data")
2. loop for 13 sections
# mlist includes the names of the data frame
mlist<-as.data.frame(c('M1','M2','M3','M4','M5','M6','M7','M8','M9','M10','M11','M12','M13'))
for(i in 1:13){
temp<-get(mlist[i,1])
# temp is the dataframe
c1<-1:ncol(temp) # variable's position
c2<-colnames(temp) # variable's name
c3<-rep(mlist[i,1],ncol(temp)) # section's name
c123<-as.data.frame(cbind(c1,c2,c3))
colnames(c123)<-c("col_id","col_names","from_data")
# rbind
m_matrix<-rbind(m_matrix,c123)
}
# delete the first row
m_matrix<-m_matrix[-1,]
3. frequency analysis
freq_variable<-as.data.frame(table(m_matrix[,2]))
# filter Freq < 13
freq_variable2<-subset(freq_variable,Freq<13)
4. plot of frequency
library(ggplot2)
Plot_variables<-ggplot(freq_variable2, aes(x = Var1, y = Freq)) +
geom_col(fill = "lightblue", colour = "grey60", binwidth=10) +
geom_density(colour="red") +
ylim(0,max(freq_variable2$Freq)*1.1)+
geom_text(aes(label = Freq), hjust = -0.5)+
coord_flip()
Warning: Ignoring unknown parameters: binwidth
Plot_variables
Warning: Groups with fewer than two data points have been dropped.
Warning: Groups with fewer than two data points have been dropped.
Warning: Groups with fewer than two data points have been dropped.
Warning: Groups with fewer than two data points have been dropped.
Warning: Groups with fewer than two data points have been dropped.
Warning: Groups with fewer than two data points have been dropped.
Warning: Groups with fewer than two data points have been dropped.
Warning in max(ids, na.rm = TRUE) :
no non-missing arguments to max; returning -Inf
Warning in max(ids, na.rm = TRUE) :
no non-missing arguments to max; returning -Inf
Warning in max(ids, na.rm = TRUE) :
no non-missing arguments to max; returning -Inf
Warning in max(ids, na.rm = TRUE) :
no non-missing arguments to max; returning -Inf
Warning in max(ids, na.rm = TRUE) :
no non-missing arguments to max; returning -Inf
Warning in max(ids, na.rm = TRUE) :
no non-missing arguments to max; returning -Inf
Warning in max(ids, na.rm = TRUE) :
no non-missing arguments to max; returning -Inf
5. variable_matrix
start_time <- Sys.time()
colid_matrix<-freq_variable
for(i in 1:nrow(m_matrix)){
# all 596 rows
for(j in 1:nrow(mlist)){
# j is for the 13 sections
for(m in 1:nrow(colid_matrix)){
# m is for the final matix: variables
if((colid_matrix[m,1]==m_matrix[i,2]) &(mlist[j,1]==m_matrix[i,3]) ){
colid_matrix[m,mlist[j,1]]<-m_matrix[i,1]
}
}
}
}
end_time <- Sys.time()
end_time - start_time
Time difference of 27.44249 secs
- 文章信息
- 作者: kaiwu
- 点击数:944
From Wikipedia, the free encyclopedia
Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text.
It is the third-generation language prediction model in the GPT-n series (and the successor to GPT-2) created by OpenAI, a San Francisco-based artificial intelligence research laboratory.[2] GPT-3's full version has a capacity of 175 billion machine learning parameters. GPT-3, which was introduced in May 2020, and was in beta testing as of July 2020,[3] is part of a trend in natural language processing (NLP) systems of pre-trained language representations.
This is a real example, see https://www.ambrogiorobot.com/en. Disclosure: LF owns one.
See https://en.wikipedia.org/wiki/ELIZA. A classic book still worth reading on the ELIZA effect and AI in general is (Weizenbaum 1976). In 2014 some people claimed, mistakenly, that a chatbot had passed the test. Its name is “Eugene Goostman”, and you can check it by yourself, by playing with it here: http://eugenegoostman.elasticbeanstalk.com/. When it was tested, I was one of the judges, and what I noticed was that it was some humans who failed to pass the test, asking the sort of questions that I have called here “irreversible”, such as (real examples, these were asked by a BBC journalist) “do you believe in God?” and “do you like ice-cream”. Even a simple machine tossing coins would “pass” that kind of test.
See for example the Winograd Schema Challenge (Levesque et al. 2012).
For an excellent, technical and critical analysis, see McAteer (https://matthewmcateer.me/blog/messing-with-gpt-3/ ." data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020">2020). About the “completely unrealistic expectations about what large-scale language models such as GPT-3 can do” see Yann LeCun (Vice President, Chief AI Scientist at Facebook App) here: https://www.facebook.com/yann.lecun/posts/10157253205637143.
The following note was written by the journalists, not the software: “[…] GPT-3 produced eight different outputs, or essays. Each was unique, interesting and advanced a different argument. The Guardian could have just run one of the essays in its entirety. However, we chose instead to pick the best parts of each, in order to capture the different styles and registers of the AI. Editing GPT-3’s op-ed was no different to editing a human op-ed. We cut lines and paragraphs, and rearranged the order of them in some places. Overall, it took less time to edit than many human op-eds.” (GPT-3 2020).
For some philosophical examples concerning GPT-3, see http://dailynous.com/2020/07/30/philosophers-gpt-3/.
For a more extended, and sometimes quite entertaining, analysis see (Lacker https://lacker.io/ai/2020/07/06/giving-gpt-3-a-turing-test.html ." data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020">2020).
For an interesting analysis see (Elkins and Chun 2020).
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- 文章信息
- 作者: kaiwu
- 点击数:3087
软件名称:Scimago Graphica
官方网站:www.graphica.app
开发机构:SCImago Lab (www.scimagolab.com)
软件大小:264兆
更新日期:2021-9-30
A new way to explore, visually communicate and make sense of data. Scimago Graphica is completely free, but if you need even more features, we are working on a pro version.
Scimago Graphica作为Scimago Journal & Country Rank(SJR期刊排名)的数据可视化工具(www.scimagojr.com/viztools.php),完全免费,支持windows,mac和linux系统,目前提供了30种图形和示例数据,支持交互功能,很多方面已经很接近商业软件tableau了(www.tableau.com)。
- 文章信息
- 作者: kaiwu
- 点击数:538
https://gs.statcounter.com/browser-market-share/desktop/worldwide/#monthly-200901-202301
The market share of web browsers for desktop.
全球桌面电脑网页浏览器软件的市场份额,数据来自statcounter.com
1.Google chrome
https://www.google.com/chrome/
https://pc.qq.com/detail/1/detail_2661.html
2.Microsoftware IE,Edge_Legacy,Edge
https://www.microsoft.com/edge
3.Firefox
https://www.mozilla.org/en-US/firefox/new/
http://www.firefox.com.cn/
4.Apple Safari
https://www.apple.com.cn/safari/
5.Opera
https://www.opera.com/browsers/opera