Tuesday, January 31, 2012
Research Methods and Proposal Writing, Tuesday 2-4pm and Thursday 4-6pm
Victor Aduba, DT286
Andrej Bartko, DT230
John Brogan, DT217
Kenneth Bryne, DT286
Thomas Bryne, DT285
Chenje Cao, DT285
Liam Carey, DT217
Brendan Cregan, DT230
Colclough Doran, DT230
Niall Dowdall, DT210
Garrett Duffy, DT286
Eloho Egivuferai, DT217A
Keith Ellman, DT285
Fatima Emmanuel, DT286
Olu Folarin, DT286
Edward Robert Freyne, DT286
Jelena Haiduroua, DT230
Robert Huczek, DT217A
Frank Kendlin, DT285
Tair Kuanyshev, DT285
Raj Kumar, DT286
Gabriel Lawless, DT202A
Lira Maricar Mariano, DT285
David Marvroudis, DT285
Vincent McKenna, DT217A
Marcus McQuiston, DT217
Eamonn O'Brien, DT230
Philip O'Donnell, DT286
Christina Shannon, DT217
Saturday, January 28, 2012
What scientists say in research papers vs. What they actually mean
http://io9.com/5880128/what-scientists-say-in-research-papers-vs-what-they-actually-mean
There's a secret code hiding in many a scientific research paper, but it's not the key to immortality or a way to turn maple syrup into rocket fuel. No, it's the code that tells you precisely what was going through the researcher's head as he or she was writing the paper. Fair warning: once you've seen what thoughts lurk behind these seemingly innocuous phrases, they cannot be unseen.
While I suspect that whoever is behind these good-natured jabs has written a lot of scientific papers themselves, I'd love to see them take on other academic disciplines and their best crutch phrases.
There's a secret code hiding in many a scientific research paper, but it's not the key to immortality or a way to turn maple syrup into rocket fuel. No, it's the code that tells you precisely what was going through the researcher's head as he or she was writing the paper. Fair warning: once you've seen what thoughts lurk behind these seemingly innocuous phrases, they cannot be unseen.
While I suspect that whoever is behind these good-natured jabs has written a lot of scientific papers themselves, I'd love to see them take on other academic disciplines and their best crutch phrases.
Wednesday, January 25, 2012
History’s Greatest Scientific Articles
You can now access thousands of scientific articles (written by some of history’s greatest minds) for free
http://io9.com/5853566/you-can-now-access-thousands-of-scientific-articles-written-by-some-of-historys-greatest-minds-for-free
When it comes to old academic societies, there isn't an organization on Earth that can hold a candle to Britain's Royal Society. Founded all the way back in 1660, The Royal Society has been pumping out peer-reviewed scientific literature since 1665, when the first edition of Philosophical Transactions of the Royal Society made its debut.
And today, almost 350 years later, The Royal Society has opened up his historical archive of journals to the public, free of charge.
All told, the fully searchable online archive comprises around 60,000 scientific papers. And while complimentary access is limited to those articles published before 1941, don't let that distract you from the incredible collection of publications included in the archive.
Ben Franklin's original paper on his electric kite experiment? It's in there, dating back to 1752. Geological experiments conducted by a young Charles Darwin? Here you go. Isaac Newton's first scientific paper ever? That's there, too.
BBC has a handful of gems that they've already found in the archives, but don't forget, the collection is searchable, so be sure to check it out and see what other historic experiments you can dig up.
http://io9.com/5853566/you-can-now-access-thousands-of-scientific-articles-written-by-some-of-historys-greatest-minds-for-free
When it comes to old academic societies, there isn't an organization on Earth that can hold a candle to Britain's Royal Society. Founded all the way back in 1660, The Royal Society has been pumping out peer-reviewed scientific literature since 1665, when the first edition of Philosophical Transactions of the Royal Society made its debut.
And today, almost 350 years later, The Royal Society has opened up his historical archive of journals to the public, free of charge.
All told, the fully searchable online archive comprises around 60,000 scientific papers. And while complimentary access is limited to those articles published before 1941, don't let that distract you from the incredible collection of publications included in the archive.
Ben Franklin's original paper on his electric kite experiment? It's in there, dating back to 1752. Geological experiments conducted by a young Charles Darwin? Here you go. Isaac Newton's first scientific paper ever? That's there, too.
BBC has a handful of gems that they've already found in the archives, but don't forget, the collection is searchable, so be sure to check it out and see what other historic experiments you can dig up.
Friday, January 13, 2012
Language is hardwired to be optimistic, even if people aren’t
Between political disagreements, economic instability, and climate troubles, you might assume every newspaper is full of bad news. Weirdly enough, the exact opposite is true. No matter what's going on in the real world, English is a perversely positive language.
That's the rather counter-intuitive finding of mathematicians at the University of Vermont, who just last month used Twitter data to argue that global happiness had decreased over the last two years. And yet, whatever these short-term trends, English seems to remain "strongly biased toward being positive", as team member Peter Dodds puts it.
Of course, that might seem like such a huge statement that it's impossible. To reach that conclusion, they examined billions of words used in such diverse sources as the last twenty years of The New York Times, 50 years worth of music lyrics, Twitter, and the Google Books Project, which includes millions texts dating as far back as 1520. They then looked at the top 5,000 words for each of these, and then enlisted volunteers to rate on a scale of 1 to 9 the happiness of the 10,222 most common words taken from these four sources.
MORE HERE >>
That's the rather counter-intuitive finding of mathematicians at the University of Vermont, who just last month used Twitter data to argue that global happiness had decreased over the last two years. And yet, whatever these short-term trends, English seems to remain "strongly biased toward being positive", as team member Peter Dodds puts it.
Of course, that might seem like such a huge statement that it's impossible. To reach that conclusion, they examined billions of words used in such diverse sources as the last twenty years of The New York Times, 50 years worth of music lyrics, Twitter, and the Google Books Project, which includes millions texts dating as far back as 1520. They then looked at the top 5,000 words for each of these, and then enlisted volunteers to rate on a scale of 1 to 9 the happiness of the 10,222 most common words taken from these four sources.
MORE HERE >>
Friday, December 9, 2011
IT Has 26 Words for Data Mining
As data proliferate, so do words for handling them
By Paul McFedries
December 2011
Illustration: Brian Stauffer
Intelligence about baseball had become equated in the public mind with the ability to recite arcane baseball stats. What [baseball statistician Bill] James's wider audience had failed to understand was that the statistics were beside the point. The point was understanding; the point was to make life on earth just a bit more intelligible. — Michael Lewis in Moneyball (2003)
Organizations of all sizes are sitting on mountains of data; what they really need are knowledge engineers who can excavate nuggets of valuable information from that data. Earlier this year (in "The Coming Data Deluge," IEEE Spectrum, February 2011), I mentioned the concept of data mining, which uses sophisticated software and database tools to extract nonobvious patterns, correlations, and useful information from large and complex data sets.
Data mining begins with data preprocessing: the gathering of the raw data, which is stored in a data warehouse or data mart. It continues with data cleansing, which removes unrelated or unnecessary data (called dirty data or noise) and looks for missing information.
As the quote from Michael Lewis suggests, the point of data mining is knowledge discovery—the extraction of nonobvious or surprising information hidden in a data set. In data-mining circles, it's axiomatic that the less obvious the knowledge extracted, the more valuable that knowledge is to the organization. Nonobvious patterns represent new opportunities, be it for research, productivity, marketing, or whatever. This is best illustrated by the legendary diapers and beer connection, where data miners allegedly noticed that retail sales of diapers and beer would often spike in tandem. Why? Because new dads asked to pick up diapers on the way home from work would also pick up beer. When retailers stocked the products next to one another, sales of both were said to skyrocket.
Another term for finding previously unseen connections in a data set, especially when there are more than two variables, is pattern mining, and the quarried patterns are called association rules.
Many data sets consist of large amounts of text, such as e-mail, so data-mining projects typically use textual analysis to dredge up connections within that data, a process known as text mining. Another promising avenue is audio mining (also called audio indexing), which is the process of extracting and indexing the words in an audio file and then using that index as data to be otherwise mined. It will come as no surprise that engineers have also come up with ingenious methods for indexing other types of media, including image mining and video mining. If the data set consists of geographical information, it is called spatial (or geospatial) mining. In this increasingly social world, researchers are turning to crowd mining, where they try to unearth useful knowledge from large databases of social information. On a more general level, Web mining refers to the harvesting of useful patterns from data sets of Web content, Web usage (such as server logs), and Web structure (such as hyperlinks).
If a data set is just too large to probe efficiently, data miners can often get away with sampling portions of it, a technique variously known as data dredging, data fishing, or data snooping.
Data mining sounds innocent enough on the surface, but privacy advocates warn that it can be used for nonbenign purposes. When Internet service providers and companies such as Google hoard massive data sets that detail the online activities of hundreds of millions of people, automated data mining methods can analyze that data to look for patterns of suspicious activity. As computer scientist Jonathan Zittrain has pointed out, "When governments begin to suspect people because of where they were at a certain time, it can get very worrying."
Whether it's a boon or a bane, informative or intrusive, you've seen here that the field of data mining is a rich source of new words and phrases. As I see it, my job here at IEEE Spectrum is to sift through the raw material of articles, papers, blogs, and books to uncover new lexical gems and then present them to you in this column. Call it word mining.
http://spectrum.ieee.org/at-work/innovation/it-has-26-words-for-data-mining
By Paul McFedries
December 2011
Intelligence about baseball had become equated in the public mind with the ability to recite arcane baseball stats. What [baseball statistician Bill] James's wider audience had failed to understand was that the statistics were beside the point. The point was understanding; the point was to make life on earth just a bit more intelligible. — Michael Lewis in Moneyball (2003)
Organizations of all sizes are sitting on mountains of data; what they really need are knowledge engineers who can excavate nuggets of valuable information from that data. Earlier this year (in "The Coming Data Deluge," IEEE Spectrum, February 2011), I mentioned the concept of data mining, which uses sophisticated software and database tools to extract nonobvious patterns, correlations, and useful information from large and complex data sets.
Data mining begins with data preprocessing: the gathering of the raw data, which is stored in a data warehouse or data mart. It continues with data cleansing, which removes unrelated or unnecessary data (called dirty data or noise) and looks for missing information.
As the quote from Michael Lewis suggests, the point of data mining is knowledge discovery—the extraction of nonobvious or surprising information hidden in a data set. In data-mining circles, it's axiomatic that the less obvious the knowledge extracted, the more valuable that knowledge is to the organization. Nonobvious patterns represent new opportunities, be it for research, productivity, marketing, or whatever. This is best illustrated by the legendary diapers and beer connection, where data miners allegedly noticed that retail sales of diapers and beer would often spike in tandem. Why? Because new dads asked to pick up diapers on the way home from work would also pick up beer. When retailers stocked the products next to one another, sales of both were said to skyrocket.
Another term for finding previously unseen connections in a data set, especially when there are more than two variables, is pattern mining, and the quarried patterns are called association rules.
Many data sets consist of large amounts of text, such as e-mail, so data-mining projects typically use textual analysis to dredge up connections within that data, a process known as text mining. Another promising avenue is audio mining (also called audio indexing), which is the process of extracting and indexing the words in an audio file and then using that index as data to be otherwise mined. It will come as no surprise that engineers have also come up with ingenious methods for indexing other types of media, including image mining and video mining. If the data set consists of geographical information, it is called spatial (or geospatial) mining. In this increasingly social world, researchers are turning to crowd mining, where they try to unearth useful knowledge from large databases of social information. On a more general level, Web mining refers to the harvesting of useful patterns from data sets of Web content, Web usage (such as server logs), and Web structure (such as hyperlinks).
If a data set is just too large to probe efficiently, data miners can often get away with sampling portions of it, a technique variously known as data dredging, data fishing, or data snooping.
Data mining sounds innocent enough on the surface, but privacy advocates warn that it can be used for nonbenign purposes. When Internet service providers and companies such as Google hoard massive data sets that detail the online activities of hundreds of millions of people, automated data mining methods can analyze that data to look for patterns of suspicious activity. As computer scientist Jonathan Zittrain has pointed out, "When governments begin to suspect people because of where they were at a certain time, it can get very worrying."
Whether it's a boon or a bane, informative or intrusive, you've seen here that the field of data mining is a rich source of new words and phrases. As I see it, my job here at IEEE Spectrum is to sift through the raw material of articles, papers, blogs, and books to uncover new lexical gems and then present them to you in this column. Call it word mining.
http://spectrum.ieee.org/at-work/innovation/it-has-26-words-for-data-mining
Tuesday, December 6, 2011
Stanford University: Natural Language Processing course
The following online course on Natural Language Processing from Stanford University (sorry for advertising a non-DIT course!) may be useful for some dissertations:
There is a range of other courses listed at the bottom that sound really interesting if you have time such as Probabilistic Graphic Models, Machine Learning and Information Theory.
Course Description
The course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.
Friday, November 25, 2011
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