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  • What to Look For in an SEO Handbook

    An SEO handbook is a good investment for any website publisher. Organic search engine optimization will help your website get higher rankings and increased traffic. That should help increase your profits.

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  • Business Website and Importance of SEO

    The most important objective of search engine optimization is help a website come up on the search results of search engines. Today more and more business owners are using the web as a path to reach the customers globally.

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  • Basics of SEO

    SEO stands for "search engine optimization." It is the process of promoting a website to such an extent that it starts appearing on the search engine results page (SERPS).

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  • Organic SEO Marketing Defined

    Without SEO you wouldn't be able to locate any of the millions of pages online. Search Engine Optimization (SEO) is the process that must be used to direct traffic to a website; it is really what fuels the Internet. SEO connects everyone who is looking for anything with the pages that they need to see.

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  • SEO Consultant - 1

    Most every business has a website these days and their goal is the same as the bricks and mortar store - to make money. In order to achieve this goal, one needs traffic, optimization and website usability.

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  • Migrating to Amazon AWS etc: What key statistics/questions should be analyzed and asked?

    - by cerd
    I searched SOverflow pretty extensively for something similar to this set of questions. BACKGROUND: We are a growing 'big(ish)' data chemical data company that are outgrowing our lab and our dedicated production workhorses. Make no mistake, we need to do some serious query optimization. Our data (It comes from a certain govt. agency so the schema and lack of indexing is atrocious). So yes, I know, AWS or EC2 is not a silver bullet in the face of spending time to maybe rework your queries/code entirely 'out of the box'. With that said I would appreciate any input on the following questions: We produce on CentOS and lab on Ubuntu LTS which I prefer especially with their growing cloud / AWS integration. If we are mysql centric, and our biggest problem is these big cartesian products that produce slow queries, should we roll out what we know after more optimization with respect to Ubuntu/mySQL with the added Amazon horsepower? Or is there some merit to the NoSQL and other technologies they offer? What are the key metrics I need to gather from apache and mysql other than like: Disk I/O operations, Data up/down avgs and trends and special high usage periods/scenarios? I've reviewed AWS/EC2 fine print, but want 2nd opinions. What other services aside from the basic web/database have proven valuable to you? I know nothing of Hadoop or many other technologies they offer, echoing my prev. question, do you sometimes find it worth it (Initially having it be a gamble aside from basic homework) to dive/break into a whole new environment and try to/or end up finding a way of more efficiently producing your data/site product? Anything I should watch out for in projecting costs, or any other general advice when working with AWS folks from anyone else where your company is very niche and very very technical (Scientifically - or anybody for that matter)? Thanks very much for your input - I think this thread could be valuable to others as well.

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  • Weighted random selection using Walker's Alias Method (c# implementation)

    - by Chuck Norris
    I was looking for this algorithm (algorithm which will randomly select from a list of elements where each element has different probability of being picked (weight) ) and found only python and c implementations, after I did a C# one, a bit different (but I think simpler) I thought I should share it, and ask your opinion ? this is it: using System; using System.Collections.Generic; using System.Linq; namespace ChuckNorris { class Program { static void Main(string[] args) { var oo = new Dictionary<string, int> { {"A",7}, {"B",1}, {"C",9}, {"D",8}, {"E",11}, }; var rnd = new Random(); var pick = rnd.Next(oo.Values.Sum()); var sum = 0; var res = ""; foreach (var o in oo) { sum += o.Value; if(sum >= pick) { res = o.Key; break; } } Console.WriteLine("result is "+ res); } } } if anyone can remake it in f# please post your code

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  • NLP - Word Alignment

    - by mgj
    Hi..:) I am looking for word alignment tools and algorithms, I am dealing with bilingual English - Hindi text, Currently I am working on DTW(Dynamic Time Warping) algorithm, CLA(Competitive Linking Algorithm) , NATool, Giza++. Could you please suggest me any other alogrithm/tool which is language independent which could achieve Statistical word alignment for parallel English Hindi Corpora and its Evaluation, some tools languages are best for certain languages.. Could one please tell me how true is that and if so could you please give me an example what would suite better for Asian languages like Hindi and what shouldn't I use for such languages. I have heard a bit about uplug word aligner.. could one tell me if I could use it as a tool for my purpose. Thank you.. :)

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  • Searching a large list of words in another large list

    - by Christian
    I have a list of 1,000,000 strings with a maximum length of 256 with protein names. Every string has an associated ID. I have another list of 4,000,000,000 strings with a maximum length of 256 with words out of articles and every word has an ID. I want to find all matches between the list of protein names and the list of words of the articles. Which algorithm should I use? Should I use some prebuild API? It would be good if the algorithm runs on a normal PC without special hardware.

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  • Hashing words to numbers with respect to definition

    - by thornate
    As part of a larger project, I need to read in text and represent each word as a number. For example, if the program reads in "Every good boy deserves fruit", then I would get a table that converts 'every' to '1742', 'good' to '977513', etc. Now, obviously I can just use a hashing algorithm to get these numbers. However, it would be more useful if words with similar meanings had numerical values close to each other, so that 'good' becomes '6827' and 'great' becomes '6835', etc. As another option, instead of a simple integer representing each number, it would be even better to have a vector made up of multiple numbers, eg (lexical_category, tense, classification, specific_word) where lexical_category is noun/verb/adjective/etc, tense is future/past/present, classification defines a wide set of general topics and specific_word is much the same as described in the previous paragraph. Does any such an algorithm exist? If not, can you give me any tips on how to get started on developing one myself? I code in C++.

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  • unzip strings in javascript

    - by sopppas
    anyone knows a simple JS library implementing the UNZIP algorithm? No disk-file access, only zip and unzip a string of values. there are ActiveX, using WinZIP and other client dependent software for ZIP, written in JS. but no pure algorithm implementation, is it really difficult or non-functional? i would use it for displaying KMZ files in a HTML page with the GMap object (google maps). The KMZ file is just a zipped KML file. I want to unzip a KMZ file and feed the KML to GMap.

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  • How to best design a date/geographic proximity query on GAE?

    - by Dane
    Hi all, I'm building a directory for finding athletic tournaments on GAE with web2py and a Flex front end. The user selects a location, a radius, and a maximum date from a set of choices. I have a basic version of this query implemented, but it's inefficient and slow. One way I know I can improve it is by condensing the many individual queries I'm using to assemble the objects into bulk queries. I just learned that was possible. But I'm also thinking about a more extensive redesign that utilizes memcache. The main problem is that I can't query the datastore by location because GAE won't allow multiple numerical comparison statements (<,<=,=,) in one query. I'm already using one for date, and I'd need TWO to check both latitude and longitude, so it's a no go. Currently, my algorithm looks like this: 1.) Query by date and select 2.) Use destination function from geopy's distance module to find the max and min latitude and longitudes for supplied distance 3.) Loop through results and remove all with lat/lng outside max/min 4.) Loop through again and use distance function to check exact distance, because step 2 will include some areas outside the radius. Remove results outside supplied distance (is this 2/3/4 combination inefficent?) 5.) Assemble many-to-many lists and attach to objects (this is where I need to switch to bulk operations) 6.) Return to client Here's my plan for using memcache.. let me know if I'm way out in left field on this as I have no prior experience with memcache or server caching in general. -Keep a list in the cache filled with "geo objects" that represent all my data. These have five properties: latitude, longitude, event_id, event_type (in anticipation of expanding beyond tournaments), and start_date. This list will be sorted by date. -Also keep a dict of pointers in the cache which represent the start and end indices in the cache for all the date ranges my app uses (next week, 2 weeks, month, 3 months, 6 months, year, 2 years). -Have a scheduled task that updates the pointers daily at 12am. -Add new inserts to the cache as well as the datastore; update pointers. Using this design, the algorithm would now look like: 1.) Use pointers to slice off appropriate chunk of list based on supplied date. 2-4.) Same as above algorithm, except with geo objects 5.) Use bulk operation to select full tournaments using remaining geo objects' event_ids 6.) Assemble many-to-manys 7.) Return to client Thoughts on this approach? Many thanks for reading and any advice you can give. -Dane

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  • OpenGL FrameBuffer Objects weird behavior

    - by Ben Jones
    My algorithm is this: Render the scene to a FBO with shadow mapping from multiple locations Render the scene to the screen with shadow mapping ...black magic that I still have to imlement... Combine the samples from step 1 with the image from step 2 I'm trying to debug steps 1 and 2 and am coming across STRANGE behavior. My algorithm for each shadow mapped pass is: render the scene to a FBO connected to a depth array texture from the POV of each light render the scene from the viewpoint and use vertex/frag shaders to compare the depths When I run my algorithm this way: render from point to FBO render from point to screen glutSwapBuffers() The normal vectors in the screen pass appear to be incorrect (inverted possibly). I'm pretty sure that's the issue because my diffuse lighting calculation is incorrect, but the material colors are correct, and the shadows appear in the correct places. So, it seems like the only thing that could be the culprit is the normals. However if I do render from point to FBO render from point to Screen glutSwapBuffers() //wrong here render from point to Screen glutSwapBuffers() the second pass is correct. I assume there's a problem with my framebuffer calls. Can anyone see what the problem is from the log below? Its from a bugle trace grepped for 'buffer' with a few edits to make it a little more clear. Thanks! [INFO] trace.call: glGenFramebuffersEXT(1, 0xdfeb90 - { 1 }) [INFO] trace.call: glGenFramebuffersEXT(1, 0xdfebac - { 2 }) [INFO] trace.call: glBindFramebufferEXT(GL_FRAMEBUFFER, 1) [INFO] trace.call: glDrawBuffer(GL_NONE) [INFO] trace.call: glReadBuffer(GL_NONE) [INFO] trace.call: glBindFramebufferEXT(GL_FRAMEBUFFER, 0) //start render to FBO [INFO] trace.call: glBindFramebufferEXT(GL_FRAMEBUFFER, 2) [INFO] trace.call: glReadBuffer(GL_NONE) [INFO] trace.call: glFramebufferTexture2DEXT(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0, GL_TEXTURE_2D, 2, 0) [INFO] trace.call: glFramebufferTexture2DEXT(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT, GL_TEXTURE_2D, 3, 0) [INFO] trace.call: glDrawBuffer(GL_COLOR_ATTACHMENT0) //bind to the FBO attached to a depth tex array for shadows [INFO] trace.call: glBindFramebufferEXT(GL_FRAMEBUFFER, 1) [INFO] trace.call: glFramebufferTextureLayerARB(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT, 1, 0, 0) [INFO] trace.call: glClear(GL_DEPTH_BUFFER_BIT) //draw geometry //bind to the FBO I want the shadow mapped image rendered to [INFO] trace.call: glBindFramebufferEXT(GL_FRAMEBUFFER, 2) [INFO] trace.call: glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) //draw geometry //draw to screen pass //again shadow mapping FBO [INFO] trace.call: glBindFramebufferEXT(GL_FRAMEBUFFER, 1) [INFO] trace.call: glFramebufferTextureLayerARB(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT, 1, 0, 0) [INFO] trace.call: glClear(GL_DEPTH_BUFFER_BIT) //draw geometry //bind to the screen [INFO] trace.call: glBindFramebufferEXT(GL_FRAMEBUFFER, 0) [INFO] trace.call: glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) //finished, swap buffers [INFO] trace.call: glXSwapBuffers(0xd5fc10, 0x05800002) //INCORRECT OUTPUT //second try at render to screen: [INFO] trace.call: glBindFramebufferEXT(GL_FRAMEBUFFER, 1) [INFO] trace.call: glFramebufferTextureLayerARB(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT, 1, 0, 0) [INFO] trace.call: glClear(GL_DEPTH_BUFFER_BIT) //draw geometry [INFO] trace.call: glBindFramebufferEXT(GL_FRAMEBUFFER, 0) [INFO] trace.call: glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) draw geometry [INFO] trace.call: glXSwapBuffers(0xd5fc10, 0x05800002) //correct output

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  • Compute bounding quad of a sphere with vertex shader

    - by Ben Jones
    I'm trying to implement an algorithm from a graphics paper and part of the algorithm is rendering spheres of known radius to a buffer. They say that they render the spheres by computing the location and size in a vertex shader and then doing appropriate shading in a fragment shader. Any guesses as to how they actually did this? The position and radius are known in world coordinates and the projection is perspective. Does that mean that the sphere will be projected as a circle? Thanks!

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  • Face recognition Library

    - by Janusz
    I'm looking for a free face recognition library for a university project. I'm not looking for face detection. I'm looking for actual recognition. That means finding images that contain specified faces or libraries that calculate distances between specific faces. I'm using OpenCV for detecting the faces and a rough Eigenfaces Algorithm for the recognition now. But I thought there should be something out there with a better performance then a self written Eigenfaces Algorithm. I don't talk about speed as performance I'm looking for a library with better results as an simple Eigenfaces approach I took a look at faint but it seems the library is not very reusable for my own applications. I'm happy with a library in Python, Java, C++, C or something like that. The best thing would be if it can be run on a Windowsmachine

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  • Explain BFS and DFS in terms of backtracking

    - by HH
    Wikipedia about DFS Depth-first search (DFS) is an algorithm for traversing or searching a tree, tree structure, or graph. One starts at the root (selecting some node as the root in the graph case) and explores as far as possible along each branch before backtracking. So is BFS? "an algorithm that choose a starting node, checks all nodes -- backtracks --, chooses the shortest path, chose neighbour nodes -- backtracks --, chose the shortest path -- finally finds the optimal path because of traversing each path due to continuos backtracking. Regex, find's pruning -- backtracking? The term backtracking confuseses due to its variety of use. UNIX find's pruning an SO-user explained with backtracking. Regex Buddy uses the term "catastrophic backtracking" if you do not limit the scope of your Regexes. It seems to be too wide umbrella-term. So: how do you define "Backtracking" GRAPH-theoretically? what is "backtracking" in BFS and DFS?

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  • Made an interview mistake. Should I try to correct after the fact?

    - by AT Developer
    Ever been in a situation where you were in an interview, and realized immediately afterwards (after the nervousness wore off) that you did something wrong? I had a phone interview today. I was asked an n-ary tree problem, and coded an algorithm that used a space overhead, then a different algorithm with no space overhead. However, my solution was inefficient, since I traversed the tree top-down rather than bottom-up. The interviewer said I did a good job, but I'm still wondering if he noticed and marked down for my choice of implementation. Should I follow up with an email correcting myself, or just let it and avoid making things worse?

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