Predatory Journals Miner

Tags

Predatory Conferences, Predatory Journals, Predatory Publishers

Predatory Journals Miner. Our most recent miner. Find, research, and judge for yourself if a journal is predatory.
http://www.minerazzi.com/predatory-journals/

The problem is pretty bad, particularly when those publishing in said journals are rewarded with career promotions and job security.

It is all about the money, from all sides (authors, journals, publishers, and conferences).

Over 5,000 Japanese articles published in predatory journals
https://mainichi.jp/english/articles/20180903/p2a/00m/0na/010000c

Predatory Publishers
http://blogs.sciencemag.org/pipeline/archives/2018/08/30/india-tries-to-deal-with-predatory-publishers

Predatory Conferences
http://www.universityworldnews.com/article.php?story=20180922053255197

The problem is not unique to science conferences and “experts”. There are others out there that qualify as predatory; for instance, some predatory marketing conferences, some SEM/SEO “experts”, blah, blah,… Nothing new under the sun.

On more positive matters, here is a nice third party tool to verify a journal
http://miar.ub.edu/

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This one is predatory. That one isn’t. Really? Are all journals predatory?

Tags

Marketing, Predatory Conferences, Predatory Journals, Scientific Journals

Researching the origins of so-called trusted publishers (https://www.theguardian.com/science/2017/jun/27/profitable-business-scientific-publishing-bad-for-science) helped me understand the
mentality behind alleged open access predatory journals & publishers which are beating them in their own game.

There is nothing new under the sun. It is all about the money (https://en.wikipedia.org/wiki/Robert_Maxwell). Time to build a miner on that.

#predatoryjournals

#predatoryconferences

Regression & Correlation Calculator: Updates and Improvements

Tags

Correlation Coefficients, Pearson Correlation, Spearman Correlation

We have updated and improved our Regression & Correlation Calculator to demonstrate, as shown in the above figure, that a Spearman’s Correlation Coefficient is just a Pearson’s Correlation Coefficient computed from ranks.

The tool uses an algorithm that converts values to ranks and averages any ties that might be present before calculating the correlations. This comes handy when we need to compute a Spearman’s Correlation Coefficient from ranks with a large number of ties.

We have explained in the “What is Computed?” section of the page’s tool that as the number of ties increases the classic textbook formula for computing Spearman’s correlations

increasingly overestimates the results, even if ties were averaged.

By contrast, computing a Spearman’s as a Pearson’s always work, even in the presence or absence of ties.

To illustrate the above, consider the following two sets:

X = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Y = [1, 1, 1, 1, 1, 1, 1, 1, 1, 2]

using Spearman’s classic equation r_s = 0.6364 ≈ 0.64.
By contrast, r_s = 0.5222 ≈ 0.52 when computed as a Pearson coefficient derived from ranks. This is a non trivial difference.

Accordingly, we can make a case as to why we should ditch for good Spearman’s classic formula.

We also demonstrate in the page’s tool why we should never arithmetically add or average Spearman’s correlation coefficients. The same goes for Pearson’s.

Early articles in the literature of correlation coefficients theory failed to recognize the non-additivity of Pearson’s and Spearman’s Correlation Coefficients.

Sadly to say, this is sometimes reflected in current research articles, textbooks, and online publications. The worst offenders are some marketers and teachers that, in order to protect their failing models, resist to consider up-to-date research on the topic.

PS. Updated on 09-14-2018 to include the numerical example and to rewrite some lines.

Chemistry at the Intersection of Similarity-Based Classification

Tags

chemistry, Chemometrics, Classification, Data Mining, Similarity

I got a copy of this nice research work written as a book chapter, Building Classes of Similar Chemical Elements from Binary Compounds and their Stoichiometries from its author, Guillermo Restrepo.

It is great to see chemistry research at the intersection of similarity-based classification studies.

Read it. It is a nice work!

Google’s Data Sets Search Engine

Tags

Data Mining, data sets, open data sets, rss mining

This is a great tool from Google
https://www.blog.google/products/search/making-it-easier-discover-datasets/

I will try to see how the feed flattener can benefit from it.
http://www.minerazzi.com/tools/flattener/feed-flattener.php

Chemistry Organizations Miner

Tags

Chemistry Collections, Chemistry Organizations, Chemistry Student Organizations, miner, minerazzi

The Chemistry Organizations Miner (http://www.minerazzi.com/chemorgs/) is our newest productivity-driven search engine.

This micro-index helps you find worldwide chemistry organizations, societies, industry groups, chemistry student organizations, and more.

Its news channels can help you search for research, articles, events, and other types of news relevant to chemistry.

Recrawl its search results and build your own curated collection of resources about chemistry organizations.

Perfectoid Spaces, Arithmetic Geometry, and Quantum Theory

Tags

Arithmetic Geometry, Perfectoid Spaces, Quantum Theory

Possible connections and applied research resources:

Perfectoid Spaces, Arithmetic Geometry, and Quantum Theory

Quantum Geometric Langlands Correspondence
https://ncatlab.org/nlab/show/quantum+geometric+Langlands+correspondence

Grand Unification of Mathematics and Physics
http://www.math.columbia.edu/~woit/wordpress/?p=7114

A new approach to Quantum Mechanics I : Overview
http://vixra.org/pdf/1803.0626v1.pdf

Is the tone appropriate? Is the mathematics at the right level?
https://mathematicswithoutapologies.wordpress.com/2018/06/02/is-the-tone-appropriate-is-the-mathematics-at-the-right-level/

Other useful references:
http://arxiv.org/abs/1201.6343
http://math.berkeley.edu/courses/fall-2014-math-274-001-lec
http://mathoverflow.net/questions/162803/p-adic-string-theory-and-the-string-orientation-of-topological-modular-forms-tm
http://ncatlab.org/nlab/show/LanglandsOxford2014
http://ncatlab.org/nlab/show/Weil
http://ncatlab.org/nlab/show/differential
http://ncatlab.org/nlab/show/function+field+analogy
http://ncatlab.org/nlab/show/geometric+Langlands+correspondence#GerasimovLebedevOblezin0
http://ncatlab.org/nlab/show/string+orientation+of+tmf
http://sciencematters.berkeley.edu/archives/volume2/issue12/story3.php
http://www.msri.org/programs/276
http://www.msri.org/workshops/710

Perfectoid Spaces Miner

Tags

Arithmetic Geometry, miner, number theory, p-adic, Perfectoid Spaces

The Perfectoid Spaces Miner is available at

http://www.minerazzi.com/perfectoid-spaces/

Use it to find research articles, video lectures, seminars, tutorials, events, and more relevant to this breakthrough new field that connects numbers with shapes.

Algebro-geometric fractal objects are here to stay!

Harris provides a basic introduction at
https://www.math.columbia.edu/~harris/otherarticles_files/perfectoid.pdf

See also link resources at a previous post.

Peter Scholze and Perfectoid Spaces: A math genius among us.

Tags

Arithmetic Geometry, fractals, Perfectoid Spacers, Peter Scholze

I’m reading with great interest biographic notes and work of Peter Scholze who at the age of 30 is one of the youngest Fields Medal Award Laureates. He has already won most of the top awards in Mathematics.

He currently is a Max Planck Institute for Mathematics director and a Hausdorff Chair at the University of Bonn. Super impressive!

Scholze’s key innovation is a class of fractal structures that he calls perfectoid spaces (2011 PhD thesis) which has far-reaching ramifications in the field of Arithmetic Geometry.

To help others learn about his awesome research work, the following links were indexed in the Math Bios (http://www.minerazzi.com/mathbios) miner.

https://en.wikipedia.org/wiki/Peter_Scholze

https://www.quantamagazine.org/peter-scholze-becomes-one-of-the-youngest-fields-medalists-ever-20180801/

https://www.wired.com/2016/07/the-oracle-of-arithmetic/

https://www.scopus.com/authid/detail.uri?authorId=45561671200

https://www.mpim-bonn.mpg.de/node/8461

http://www.hcm.uni-bonn.de/uploads/tx_bzdstaffdirectory/d2ff97eae17a476570c2107b25d84778.pdf

http://www.math.uni-bonn.de/people/scholze/PerfectoidSpaces.pdf (2011 PhD thesis).

A miner on its own class and focused on perfectoid spaces is more than meritorious, I believe. Don’t you think so?

PS. Here is an introductory note by Jared Weinstein on perfectoid spaces:

https://www.msri.org/system/cms/files/83/files/original/141109_Emissary-Fall-2014-Web.pdf

Here are some notes about his greatness

https://www.mathunion.org/fileadmin/IMU/Prizes/Fields/2018/scholze-final.pdf

And here is a chat on perfectoid spaces:

https://mathoverflow.net/questions/65729/what-are-perfectoid-spaces

I decided to go ahead and build the perfectoid spaces miner. It should be ready pretty soon.

 

 

Cosine Similarity Tutorial (citations)

Cosine similarity is one of those basic resemblance measures with many practical applications, and relevant to many research problems.

However, its meaning in the context of uncorrelated and orthogonal variables, as its connection with the non-additivity nature of correlation coefficients are often overlooked.

Happy to see the research papers below are still citing Minerazzi’s Cosine Similarity Tutorial, revamped a few years ago.

Continuous Real-Time Vehicle Driver Authentication Using Convolutional Neural Network Based Face Recognition.

F8-DP-2017-Krakora-Vojtech-thesis.pdf

Factors Contributing to Elevated Concentrations of Mercury and PCBs in Fish in the Inland Lakes of Michigan’s Upper Peninsula and Lake Superior.

Plagiarism Detection Tool for AMHARIC Text.

Extract reordering rules of sentence structure using neuro-fuzzy machine learning system.

Verification of upper Citarum River discharge prediction using climate forecast system version 2 (CFSv2) output.

Building Machine Learning System with Deep Neural Network for Text Processing.

Deep neural based name entity recognizer and classifier for English language.

Machine translation using deep learning: An overview.

An Analytical Method for Probabilistic Modeling of the Steady-State Behavior of Secondary Residential System

Big data analytic untuk pembuatan rekomendasi koleksi film personal menggunakan Mlib. Apache Spark.

Use of Data Warehousing to Analyze Customer Complaint Data of CFPB of USA.