- Aug 2022
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www.youtube.com www.youtube.com
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For the sake of simplicity, go to Graph Analysis Settings and disable everything but Co-Citations, Jaccard, Adamic Adar, and Label Propogation. I won't spend my time explaining each because you can find those in the net, but these are essentially algorithms that find connections for you. Co-Citations, for example, uses second order links or links of links, which could generate ideas or help you create indexes. It essentially automates looking through the backlinks and local graphs as it generates possible relations for you.
comment on: https://www.youtube.com/watch?v=9OUn2-h6oVc
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- Jan 2022
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www.taylorfrancis.com www.taylorfrancis.com
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An interesting coming book grouping various application issues (business, crime, military and terrorism).
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- May 2021
- Mar 2021
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positivus.io positivus.io
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<small><cite class='h-cite via'>ᔥ <span class='p-author h-card'>@Currofile</span> in marina @ bliss on Twitter: "@__baileybrooks @SlackHQ This looks in line with something that https://t.co/lo2XmYayhG is building." / Twitter (<time class='dt-published'>03/09/2021 09:27:46</time>)</cite></small>
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- May 2020
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Lanovaz, M., & Turgeon, S. (2020). Tutorial: Applying Machine Learning in Behavioral Research [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9w6a3
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psyarxiv.com psyarxiv.com
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Dhami, M. K., & Mandel, D. R. (2020). UK and US policies for communicating probability in intelligence analysis: A review [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/kuyhb
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- Apr 2020
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Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms [Preprint]. Health Informatics. https://doi.org/10.1101/2020.04.08.20057679
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trello.com trello.com
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Collective Intelligence and COVID-19 | Trello. (n.d.). Retrieved April 20, 2020, from https://trello.com/b/STdgEhvX/collective-intelligence-and-covid-19
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- Aug 2019
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labsblog.f-secure.com labsblog.f-secure.com
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Security Issues, Dangers And Implications Of Smart Systems
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- May 2019
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www.randhome.io www.randhome.io
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Methodology The classic OSINT methodology you will find everywhere is strait-forward: Define requirements: What are you looking for? Retrieve data Analyze the information gathered Pivoting & Reporting: Either define new requirements by pivoting on data just gathered or end the investigation and write the report.
Etienne's blog! Amazing resource for OSINT; particularly focused on technical attacks.
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- Jan 2019
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inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
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the strongest first factor accounted for 86.3% of observed variable variance
I suspect that this factor was so strong because it consisted of only four observed variables, and three of them were written measures of verbal content. All of the verbal cariables correlated r = .72 to .89. Even the "non-verbal" variable (numerical ability) correlates r = .72 to .81 with the other three variables (Rehna & Hanif, 2017, p. 25). Given these strong correlations, a very strong first factor is almost inevitable.
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The weakest first factor accounted for 18.3% of variance
This factor may be weak because the sample consists of Sudanese gifted children, which may have restricted the range of correlations in the dataset.
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The mean sample size of the remaining data sets was 539.6 (SD = 1,574.5). The large standard deviation in relationship to the mean is indicative of the noticeably positively skewed distribution of sample sizes, a finding supported by the much smaller median of 170 and skewness value of 6.297. There were 16,559 females (33.1%), 25,431 males (48.6%), and 10,350 individuals whose gender was unreported (19.8%). The majority of samples—62 of 97 samples (63.9%)—consisted entirely or predominantly of individuals below 18. Most of the remaining samples contained entirely or predominantly adults (32 data sets, 33.0%), and the remaining 3 datasets (3.1%) had an unknown age range or an unknown mix of adults and children). The samples span nearly the entire range of life span development, from age 2 to elderly individuals.
My colleague, Roberto Colom, stated in his blog (link below) that he would have discarded samples with fewer than 100 individuals. This is a legitimate analysis decision. See his other commentary (in Spanish) at https://robertocolom.wordpress.com/2018/06/01/la-universalidad-del-factor-general-de-inteligencia-g/
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- Oct 2015
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cms.whittier.edu cms.whittier.edu
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. Human interactional intelligence is, so far as we know, predicated upon five qualities
So this is basically the through process of analysis/division of how humans take in stimuli in the environment?
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