Vector Space Explorer Tool

Tags

global weights, local weights, term vector theory, term vectors, tf-idf, vector space explorer, Vector Space Models

Vector Space Explorer Tool is a new tool from Minerazzi, available now at

http://www.minerazzi.com/tools/vector-space-explorer/explorer.php

VSE is aimed at exploring combinations of local (e.g., FREQ, ATF1,…) and global (e.g., IDF, IDFP,…) term weighting schemes for documents and queries. All kind of combinations can be easily explored.

The tool lists results in decreasing order of cosine similarity scores, with or without implementing stopwords removal and parametric corrections.

VSE was developed for computer science students and those interested in information retrieval systems so they can learn how IR systems work.

First-time users may want to try the examples provided by pressing the Try This Example button from the tool. It is possible to cycle through the examples by repetitive pressing this button. Some of the examples list top titles obtained by querying commercial search engines.

Accepting the default settings instructs VSE to remove stopwords and implement the FREQ Model, also known as the Term Count Model. This is one of the simplest vector space implementations where term weights are mere raw frequencies (term counts).

Revamping the Cosine Similarity Calculator Tool

Tags

cosine similarity, cosine similarity calculator, distance metric, similarity-distance transformations

One of the most interesting problems in data mining and cluster analysis relates to the transformation of similarities into distances without breaking the triangular inequality condition for a distance metric.

Some of the transformations found in the literature are based on heuristics and tricks of the trade, or based on assumptions applicable to a given knowledge domain. This topic is discussed in our tutorial on distance and similarity (http://www.minerazzi.com/tutorials/distance-similarity-tutorial.pdf).

We have incorporated to our Cosine Similarity Calculator a simple methodology that easily transforms cosine similarities into distances while obeying the requirements for a distance metric. It all boils down to mean-centering the variables.

Check our revamped and improved Cosine Similarity Calculator tool now at

http://www.minerazzi.com/tools/cosine-similarity/cosine-similarity-calculator.php

RAR Parser |An RSS, ATOM, and RDF Parser

Tags

Atom, feeds parser, news feeds, rar parser, rdf, RSS, web feeds

The RAR Parser is a tool that lets you read RSS, ATOM, and RDF news feeds, without subscribing. It is available at http://minerazzi.com/tools/rar/feeds-parser.php

A practical example: By submitting the MIT Health Sciences and Technology news feeds url http://feeds.nytimes.com/nyt/rss/Health, the following news relevant to COVID-19 were obtained, among others. Results might change as time evolves.

MIT scientist helps build Covid-19 resource to address shortage of face mask http://news.mit.edu/2020/mit-scientist-jill-crittenden-helps-build-covid-19-resource-addressing-face-mask-shortage-0403

MIT initiates mass manufacture of disposable face shields for Covid-19 response http://news.mit.edu/2020/face-shield-ppe-manufacture-covid-19-0331

An experimental peptide could block Covid-19 http://news.mit.edu/2020/peptide-drug-block-covid-19-cells-0327

3 Questions: The risks of using 3D printing to make personal protective equipment http://news.mit.edu/2020/3q-risks-using-3d-printing-make-personal-protective-equipment-0326

Latent Simplex Position Model

Tags

latent simplex position, latent simplex position model

This is an amazing research: Latent Simplex Position Model: High Dimensional Multi-view Clusteringwith Uncertainty Quantification, by Prof. Leo Duan from Department of Statistics University of Florida, Gainesville, FL.

Internet of Senses Miner

Tags

cyborgs, hypersenses, Internet of Senses

Are you ready for the Internet of Senses? Many reports set the year of 2030 as the definitive one for it, though kind of blueprints are already buzzing around. If not ready or to find relevant resources on the topic, we have just launched the Internet of Senses Miner (http://www.minerazzi.com/internet-of-senses). It is a small corpus that, as time goes by, we hope to improve.

Hypersenses are here to stay, along with new technologies, away from keyboard searching, social networks, and more into mind retrieval and cyborgsocials. That looks like the next marketing frontier. New technologies, academic research opportunities, and grants are a corner ahead. Don’t be left behind.

2018 – https://irthoughts.wordpress.com/2018/10/06/on-mind-retrieval-brainnet/
2017 – https://irthoughts.wordpress.com/2017/02/01/on-mind-retrieval-brain-computer-interfaces/
2016 – https://irthoughts.wordpress.com/2016/08/05/artificial-neurons-ibm-and-mind-retrieval/
2016 – https://irthoughts.wordpress.com/2016/04/28/a-step-closer-to-mind-retrieval/
2015 – https://irthoughts.wordpress.com/2015/06/17/say-hello-to-mind-retrieval/
2010 – https://ithinksearch.wordpress.com/2010/08/10/ideas-entrevista-a-edel-garcia/

On the Myth of d Orbitals Hybridization

With regard to the myth, taught to entire generations of chemistry students and perpetuated online in chemistry portals and by outdated tutorials, textbooks, and in classroom lectures, that elements beyond the second period can expand their octet by utilizing available d orbitals, consider this:

Since the 90’s, quantum chemists have shown this idea to be experimentally incorrect as it is energetically unfeasible to use d-orbitals for extra bonds (Kalemos & Mavridis, 2011; Durrant, 2015; Cowley, 2015; Northumbria, 2015). Indeed, the possibility of extensive d-orbital participation has been discredited more than a quarter century ago (Reed & Schleyer, 1990; Magnusson, 1990). As back then Cooper, Cunnigham, Gerratt, Karadakov, & Raimondi stated in a JACS article published by the ACS (Cooper et. al, 1994):

“Indeed, models based on d2sp3, dsp2, and dsp3 hybrid orbitals are still in widespread use among professional chemists and are described in many of the most widely used textbooks. It is tempting to speculate as to why such models continue to survive when there is so much theoretical evidence which does not support them.”

See references below. Additional references, links, and topic discussion are given at http://www.minerazzi.com/tools/bond-order/calculator.php

References

Cooper, D. L., Cunnigham, T. P., Gerratt, J., Karadakov, P. B., & Raimondi, M. (1994). Chemical Bonding to Hypercoordinate Second-Row Atoms: d Orbital Participation versus Democracy. J. Am. Chem. Soc. Vol. 116, No. 10, pp 4414-4426. doi: 10.1021/ja00089a033.

Kalemos A. & Mavridis, A. (2011). Myths and Reality of Hypervalent Molecules. The Electronic Structure of FClOx, x = 1-3, Cl3PO, Cl3PCH2, Cl3CClO, and C(ClO)4. J. Phys. Chem., 115, (11), pp 2378-2384.

Magnusson, E. (1990). Hypercoordinate molecules of second-row elements: d functions or d orbitals?. J. Am. Chem. Soc., Vol. 112, No. 22, pp. 7940-7951.

Reed, A. E. & Schleyer, P. V. R. (1990). Chemical bonding in hypervalent molecules. The dominance of ionic bonding and negative hyperconjugation over d-orbital participation. J. Am. Chem. Soc., 112, pp. 1434-1445.

Semantic Similarity of Healthcare Data

Tags

cosine similarity, healthcare data, semantic similarity

In “Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching“, published in the International Journal of Medical Informatics 132:104002 DOI: 10.1016/j.ijmedinf.2019.104002, Kiourtis et al. (2019), address the following objective, and quote:

“Healthcare systems deal with multiple challenges in releasing information from data silos, finding it almost impossible to be implemented, maintained and upgraded, with difficulties ranging in the technical, security and human interaction fields.”

The authors propose an elegant mechanism “that promises healthcare interoperability through the transformation of healthcare data into the corresponding HL7 FHIR structure.”

These are great news! Very cool and practical research that can solve so many problems in the healthcare informatics field.

Many thanks for citing our cosine similarity tutorial as reference 52.

My only reserve with the paper is that early in the article they suggest adding and averaging similarities, which is a mathematically invalid exercise. Distances are arithmetically additive, but similarities (of the same or different kind or source) are not. We can make similarities additive and average them, but not in the arithmetic sense. Other than that, they work is a noble effort.

CUNY Computational Chemistry Tools

Tags

bond order calculator, chemistry tools, computational chemistry, hydrocarbons parser

If you are a chemist, PhD, or student looking for tools in said discipline, you may want to check the City College Chemistry Web Resource Guide, part of the City College of New York Libraries at CUNY. This is an excellent repository of chemistry resources.

This fall, the college redesigned the site so the web address of the computational chemistry section is now https://library.ccny.cuny.edu/chemistry/computational

Happy to know that two of our chemistry tools are still listed there:

The Bond Order Calculator — http://www.minerazzi.com/tools/bond-order/calculator.php Computes bond orders of diatomic species and their ions having up to 20 electrons, including number of bonding and anti-bonding electrons, without using Molecular Orbital Theory (MOT).

The Hydrocarbons Parser http://www.minerazzi.com/tools/hydrocarbons/parser.php — Calculates boiling points and indicates sigma, pi, single, double, and triple bonds for hydrocarbons, again without using MOT.

I wish that more universities follow in the steps of CUNY and be willing to put together similar repositories, I mean computational chemistry tools, for the benefit of their students and faculties.

Why I chose to be a multidisciplinary scientist?

Tags

Binary Distance Calculator, Binary Similarity Calculator, miner, minerazzi, minerazzi tools, Minerazzi Tutorials

When you are a multidisciplinary scientist or teacher, one way of measuring your success is by looking at what students and others in different fields and countries do with the tools and resources you develop. Satisfaction goes all the way up when these help make a difference in their life.

I’m happy to know that in his 2018 PhD thesis “On Enhancing the Security of Time Constrained Mobile ContactlessTransactions” (https://pure.royalholloway.ac.uk/portal/files/33898207/Iakovos_Gurulian_PhD_Thesis.pdf), the author, Iakovos Gurulian from the Information Security Group, Department of Mathematics at the prestigious Royal Holloway, University of London, developed a Python program capable of running our Binary Similarity Calculator (http://www.minerazzi.com/tools/similarity/binary-similarity-calculator.php), which computes 72 different similarity measures. See pages 87-89, tables 4.1 and 4.2, and reference 118 of the thesis.

The Tutorial on Distance and Similarity (http://www.minerazzi.com/tutorials/distance-similarity-tutorial.pdf) was also cited as reference 60.

According to https://www.topuniversities.com/, Royal Holloway ranks 6/10 in London and 291/1000 in the world. Famous for its Founder’s Building, one of the most spectacular university buildings in the world, the College was officially opened by Queen Victoria in 1886.
https://www.topuniversities.com/sites/default/files/royal-holloway-university-of-london.jpg

A Simple Example of Phonetic Similarity vs. Text Similarity

Tags

levenshtein distance, levenshtein similarity, minerazzi tools, phonetic similarity, tools

The followings sound the same so their phonetic similarity is 1.

(a) r u?
(b) ar u?
(c) are u?
(d) r you?
(e) ar you?
(f) are you?

However, the Levenshtein Distance (LD) and Levenshtein Similarity (LS) of (a) with the other strings differ:

LD(a, b) = 1; LS(a, b) = 0.5
LD(a, c) = 2; LS(a, c) = 0.33
LD(a, d) = 2; LS(a, d) = 0.33
LD(a, e) = 3; LS((a, e) = 0.25
LD(a, f) = 4; LS(a, f) = 0.2

Can you find LD and LS results for other possible combinations?

For i = j, LD(i, j) = 0 and LS(i, j) = 1 so you may want to ignore this case.

Note: LD and LS results were computed with our tool at http://www.minerazzi.com/tools/levenshtein/levenshtein-distance-calculator.php

References
http://www.minerazzi.com/tutorials/levenshtein-distance-tutorial.pdf
http://www.minerazzi.com/tutorials/distance-similarity-tutorial.pdf