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I must admit that I am incredibly jealous of those developers who happen to live near active user groups (e.g. the ALT.NET guys in Austin). I often read blog posts and listen to podcasts that reference these in-person meetings and find myself wishing that I could sit in and participate as well. But it just isn't realistic to fly across the country to meet a few guys for a couple hours in a pub to talk about patterns and practices.
So I was wondering if there was a similar discussion forum for those who don't happen to live near an active user group. After all, blogs and books only go so far, and for the most part are a one-way avenue of communication. True, you can use comments, e-mails, tweets, and IM to get some interaction, but there is something to be said about face-to-face real-time interaction that will get lost in all of these mediums.
I guess what I'm looking for is some sort of video-conferencing deal where people who share an interest in a specific field of software development can get together to talk and interact without having to live right next door to each other. Does anything like this exist?
There's a .NET usergroup in SecondLife. Of course this depends how you feel about second life.
I haven't had a chance to check it out, but the Virtual ALT.NET group sounds promising.
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I hoping to create an application that would listen to ambient sound and detect if music is being played. It is not important to identify the music being played; just detecting that some music is being played is enough.
I looked around for existing solutions but couldn't find any. Does anyone know algorithms that I can use to solve this problem? If source code is available, all the better.
I found are a couple of academic papers and implemented solutions suggested in them. But the results I obtained were not satisfactory.
PS:
i) It would be a bonus if the algorithm is not computationally intensive; if algorithm is completely in time-domain that would be wonderful. ii) It is okay if the solution is not very accurate; occasional false-positives are okay.
Under the assumption that music is made of a bunch of chords instead of single pitch (like monophonic MIDI), multiple pitches at the same time (aka, the chords) may be a good candidate to be detected and differentiated from pure noise. Actually there is a very good Harmony Progression Analyser software package in which chords are detected based on a chromagram. Hope it helps.
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Our site allows users to enter the company they work for as a free form text entry.
Historically we gathered around a few millions of unique entries. Since we put no constraints we ended up with a lot of variations, typos (e.g. over 1000 distinct entries just for McDonald's)
We realized we could provide our users with a great feature if only we could tie these variations together. We compiled a clean list of companies as a starting point using various online sources [Dictionary]
Now, we're trying to find out a best way to deal with the user data source. We thought about assigning some similarity score:
- comparing each entry with [Dictionary], calculating a lexical distance (possibly in Hadoop job)
- taking advantage of some search database (e.g. Solr)
And associate the user enter text this way.
What we're wondering is did anyone go through similar "classification" exercise and could share any tips?
Thanks,
Piotr
I'd use simple Levenshtein distance (http://en.wikipedia.org/wiki/Levenshtein_distance).
A few millions entries - you should be able to process it easily on one computer (no hadoop, or other heavy-weight tools).
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I was wondering if anyone had knew of a website that provides a great review of data structures and algorithms. I would like it to specifically geared towards interview questions with regards to data structures and algorithms. Would implementation of all of these data structures be something good to review?
Thanks!
This page is a good starting point:
This webpage covers the space and time Big-O complexities of common algorithms used in Computer Science. When preparing for technical interviews in the past, I found myself spending hours crawling the internet putting together the best, average, and worst case complexities for search and sorting algorithms so that I wouldn't be stumped when asked about them. Over the last few years, I've interviewed at several Silicon Valley startups, and also some bigger companies, like Yahoo, eBay, LinkedIn, and Google, and each time that I prepared for an interview, I thought to msyelf "Why oh why hasn't someone created a nice Big-O cheat sheet?". So, to save all of you fine folks a ton of time, I went ahead and created one.
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I wish to use Google 2-grams for my project; but the data size renders searching expensive both in terms of speed and storage.
Is there a Web-API available for this purpose (in any language) ? The website http://books.google.com/ngrams/graph renders an image, can I get data values?
Well, I got a round about way of doing that, using Google BigQuery
In that, trigrams are available in public domain. Using Command line access did the job for me.
I found a great alternative: Microsoft Web N-Gram
It can be queried in different ways, including a straighforward GET call through the REST interface.
For instance, calling the URL:
http://weblm.research.microsoft.com/weblm/rest.svc/bing-body/apr10/1/jp?u={YOUR_TOKEN}&p=red+panda
returns
-9.005
which is the log likelihood of the phrase red panda.
Furthermore, it is handier than Google N-Grams, as for a given phrase it does not simply output its absolute frequency, but it can output its joint probability, conditional probability and even the most likely words that follow.
Disclaimer: I am not a Microsoft employee, I simply think that I just found an awesome service.
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I'm new to web programming. Where is the best place online can I find examples of algorithms created to solve specific problems ie. reputation system for a user controlled forum.
On my opinion a good introduction to this issue you can fing in this book:
Paperback: 336 pages
Publisher: Yahoo Press; 1 edition (March 16, 2010)
Language: English
ISBN-10: 059615979X
ISBN-13: 978-0596159795
Table of Contents:
Part I. Reputation Defined and Illustrated
Reputation Systems Are Everywhere
A (Graphical) Grammar for Reputation
Part II. Extended Elements and Applied Examples
Building Blocks and Reputation Tips
Common Reputation Models
Planning Your System’s Design
Objects, Inputs, Scope, and Mechanism
Displaying Reputation
Using Reputation: The Good, The Bad, and the Ugly
Application Integration, Testing, and Tuning
Case Study: Yahoo! Answers Community Content Moderation
Here you can buy it: http://www.amazon.com/Building-Reputation-Systems-Randy-Farmer/dp/059615979X