I am new to SageMaker. I have a large csv dataset which I would like labelled:
sentence_id
sentence
pre_agreed_label
148392
A sentence
0
383294
Another sentence
1
For each sentence, I would like a) a yes/no binary classification in response to a question, and b) on a scale of 1-3, how obvious the classification was. I need the sentence id to map to other parts of the dataset, and will use the pre-agreed labels to assess accuracy.
I have identified SageMaker GroundTruth labelling jobs as a possible way to do this. Is this the best way? In trying to set it up I have run into a few problems.
The first problem is I can't find a way to display only the sentence column to the labellers, hiding the sentence_id and pre_agreed_labels.
The second is that there is either single labelling or multi labelling, but I would like a way to have two sets of single-selection labels:
Select one for binary classification:
Yes
No
Select one for difficulty of classification:
Easy
Medium
Hard
It seems as though this can be done using custom HTML, but I don't know how to do this - the template it gives you doesn't even render
Finally, having not used mechanical turk before, are there ways of ensuring people take the work seriously and don't just select random answers? I can see there's an option to have x number of people answer the same question, but is there also a way to put in an obvious question to which we already have a 'pre_agreed_label' every nth question, and kick people off the task if they get it wrong? There also appears to be a maximum of $1.20 per task which seems odd.
I have two questions
the first one is:
I have large data come from the server I saved it in a list , the customer can filter this data by 7 filters and two by text watcher this thing caused filtering operation to slow it takes 4 seconds in each time
I tried to put the filter keywords like(length or width ...) in one if and (&&) between them
but it didn't give me a result, also I tried to replace the textwatcher by spinner but it's not
useful.
I'm using one (for loop)
So the question: how can I use multi filter for list contain up to 2000 row with mini or zero slow?
the second is:
I saved from 2 to 8 pictures in the server in string form
the question is when I get these pictures from the server how can I show them in high quality?
when I show them I can see the pixels and this is not good for the customer
I don't want these pictures to take large space in the server and at the same time I want it in good quality when I restore them to display
I'm using Android/ Java
Thank you
The answer on my first quistion is if you want using filter (like when you are using online clothes shop and you want to filter it by less price ) you should use the hash map, not ordinary list it will be faster
The answer on my second question is: if you want to save store images in a database you should save it as a link, not a string or any other datatype
I have a worksheet that has become very complex. On it, there is a sheet in which a user will paste data about once every other day. The data will always be in the same format, and is provided to us in an exact way only. Once pasted in, I need a way for a very average user of excel to be able to press a button (or key combo, or whatever) and excel will run a series of about 8-10 regex find and replaces. All of these will be on column A of the data. Once those are all run, a simple formula would be run on every cell C2 and below in column C. Those columns should be reduced by 80% - =C2*.8
This should all be done with minimal user input if possible.
Would anybody much more versed in regex or excel know a better direction for me to look for a proper start? What resources would be recommended to best accomplish this?
If you're multiplying by some factor, then regexp substitution will be overkill. Excel is very good at multiplying an array of numbers by 0.8.
Search for "Excel paste factor" and you'll get an easy explanation, such as this one.
I might record a macro for your less-experienced users and hope that the previous user pasted the numbers in with absolute perfection.
I'm currently writing a Bag of visual words-based image retrieval system which is similar to the Vector Space Model in text retrieval. Under this framework, each image is represented by a vector (or sometimes also called histogram in the literature). Basically each number in the vector counts the number of times each "visual word" occur in that image. If 2 images have vectors which are "close" together, this means they have many image features in common and are hence similar.
I'm basically trying to create the inverted file index for a set of such vectors. I want something that can scale from thousands (during trial stage) to hundred of thousands or million+ images so a home made data structure hack will not work.
I've looked at Lucene but apparently it only indexes text (correct me if I'm wrong) whereas in my case I want it to index numbers (i.e. the vectors themselves). I've seen cases where people convert the vector to a text document in the following way:
<3, 6, ..., 5> --> "w1 w2... wn". Basically any component that is non-zero is replaced by a textual word "w[n]" where n is the index of that number. This "document" is then passed to Lucene to index.
The problem with using this method is that the text representation for the vector does not encode how frequently the particular "word" occur so the ranking of the retrieved images would not be good.
Does anyone know of a mature indexing API that can handle vectors or perhaps suggest a different encoding scheme for my vectors so that I can continue to use Lucene? I've also looked at Lucene for Image Retrieval (LIRE) project and have tried the demo that came with it but the number of exceptions that were generated when I ran that demo makes me unsure about using it.
As for language of API, I'm open to C++ or Java.
Thanks in advance for any replies.
You can try GRire which is a Java library that implements the Bag of Visual Words model. It is my project and I am currently working on implementing an inverted index.
This is to be done in C++ or C....
I know we can read the MP3s' meta data, but that information can be changed by anyone, can't it?
So is there a way to analyze a file's contents and compare it against another file and determine if it is in fact the same song?
edit
Lots of interesting things coming out that I hadn't thought of. Not at all a good idea to attempt this.
It's possible, but very hard.
Even the same original recording may well be encoded differently by different MP3 encoders or the same encoder with different settings... leading to different results when the MP3 is then decoded. You'd need to work out an aural model to "understand" how big the differences are, and make a judgement.
Then there's the matter of different recordings. If I sing "Once in Royal David's City" and Aled Jones sings it, are those the same song? What if there are two different versions of a song where one has slightly modified lyrics? The key could be different, it could be in a different vocal range - all kinds of things.
How different can two songs be but still count as "the same song"? Once you've decided that, then there's the small matter of implementing it ;)
If I really had to do this, my first attempt would be to take a Fourier transform of both songs and compare the histograms. You can use FFTW (http://www.fftw.org/) to take the Fourier transform, and then compare the histograms by summing the squares of the differences at each frequency. If the resultant sum is greater than some threshold (which you must determine by experimentation) then the songs are deemed to be different, otherwise they are the same.
No. Not SO simple.
You can check they contain the same encoded data, BUT:
Could be a different bitrate
Could be the same song, just a 1/100ths of a second off
In both cases the bytes would not match.
Basically, if a solution looks too simple to be true, it often is.
If you mean "same song" in the iTunes sense of "same recording", it would be possible to compares two audio files, but not by byte-by-byte comparison of an encoded file since even for the same format there are variables such as data rate and compression that are selected at time of encoding.
Also each encoding of the same recording may include different lead-in/lead-out timings, different amplitude and equalisation, and may have come from differing original sources (vinyl, CD, original master etc.). So you need a comparison method that takes all these variables into account, and even then you will end up with a 'likelihood' of a match rather than a definitive match.
If you genuinely mean "same song", i.e. any recording by any artist of the same composition and lyrics, then you are unlikely to get a high statistical correlation in most cases since pitch, tempo, range, instrumental arrangement will be very different.
In the "same recording" scenario, relatively simple signal processing and statistical techniques could be applied, in the "same song" scenario, AI techniques would need to be deployed, and even then the results I suspect would be poor.
If you want to compare MP3 files that originated from the same MP3, but have tagged with metadata differently, it would be straight forward to just compare the actual audio data. Since it originated from the same MP3 encoding, you should be able to do a byte by byte comparison. You would have to compare all byte. It should be sufficient to sample just a few to get a unique key that would be statistically almost impossible to find in another song.
If the files have been produced by different encoders, you would have to extract some "fuzzy" feature keys from the data and compare those keys. In a hurry I would probably construct an algorithm like this:
Decode audio to pulse-code modulation (wave) in a standard bit rate.
Find a fixed number of feature starting points using some dynamic location algorithm. For example find top 10 highest wave peaks ordered from beginning of wave or simply spread evenly across the wave (it would be a good idea to fix the first and last position dynamically though, since different encodings might not start and end at exactly the same point). An improvement would be to select feature points at positions in the wave that are not likely to be too repetitive.
Extract a set of one-dimensional feature key scalars from the feature points. For example, for each feature normalize the following n-sample values and count the number of zero-crossings, peak to average ratio, mean zero-crossing distance, signal-energy. The goal is to extract robust features that are relatively unique, while still characteristic even if some noise and distortion is added to the signal. This can obviously be improved almost infinitely.
Compare the extracted feature keys of the two files using some accuracy measurement (f.eks. 9 out of 10 feature extractions must match at least 99% on 4 out of 5 of their extracted feature keys).
The benefit of a feature extraction approach is that you can build a database of features for all your mp3-files and for a single file ask the question: What other media files have exactly or almost exactly the same feature as this one. The feature lookup could be implemented very efficiently with R*-trees or similar, which could be used to give you a fast distance measurement between the n-dimensional feature sets.
The above technique is essentially a variant of what is used in image search algorithms such as SIFT, which is probably the base of such application as Photosynth and Google Goggles. In image searching you filter the image for good candidate points for relatively unique features (such as corners of shapes), then you normalize the area around that feature to get normalized color, intensity, scale and direction of features. Finally you extract the features and search an n-dimensional database of features of other images and verify that found features in other images are geometrically positioned in the same pattern as in your search image. The technique for searching audio would be the same, only simpler, since audio is one dimensional.
Use the open source EchoPrint library to create a signature of the two audio files, and compare them with each other.
The library is very easy to use, and has clear examples on how to create the signatures.
http://echoprint.me/
You can even query their database with the signature and find matching song metadata (such as title, artist, etc).
I think the Fast Fourier-Transform (FFT) approach hinted by jstanley is pretty good for most use cases; in particular, it works for verifying that the two are the same release/ same recording by the same artist/ same bitrate / audio quality.
To be more explicit, sox and spek (via command line and GUI, respectively) can do this pretty painlessly.
Spek is pretty foolproof -- just open the software and point it to the two audio files in question.
sox can generate spectograms (FFTs) from the command line line so:
sox "$file" -n spectrogram -o "$outfile".
The result from either are two images; if they look basically identical, then for almost all intents and purposes, the two songs will be equivalent.
For example, I wanted to test if these two files:
Soundtrack to an imaginary film mixtape 2011.mp3
DJRUM - Sountrack to an imaginary film mixtape 2011 (for mary-anne hobbs).mp3
were the same. diff reported a difference in the binary files (perhaps due to metadata differences or minor encoding differences), but a quick glance at their spectrograms resolved it: