Ie ddeg. Fyddwn ni'n waith i fy Skylfe likio. Rwy'n fawr. Rwy'n fawr. Rwy'n fawr, rwy'n fawr, am gyfaniaeth i'r sgweithio cyfnodol. And so, hopefully you will cover both of the kind of deep tech piece which will really be an advert for the TensorFlow Meetup group, and all the way to AutoML. So, thank you for the introduction. I'm Martin Andrews. I've got a kind of background in machine learning startups for Nantes. from New York to Singapore in 2013. 2014 I lived in Singapore under the radar doing machine learning, deep learning, NLP for fun, and robots and drones. I converted myself from a finance guy to AI guy. I proved that in 2015 when I got serious working for a local company in Singapore and we've been doing natural language products there. Along the way I've become this GDEML, which is great, I guess, free labour for Google. I do the TensorFlow and Deep Learning Meetup here. I've written a couple of papers as a gentleman scientist and I've been running a developer course with my co-organiser for the Meetup Group. And with him we have also built a little company, so this is Red Dragon AI. We're a Google partner for doing kind of deep learning consulting prototypes. We partnered with SG Innovate to do educational training and we're also very interested in this kind of conversational AI, natural language voices or natural voices and knowledge bases. So that's about me. So I've done the Who Am I. I'm going to talk a bit about the TensorFlow ecosystem and how it's evolved. I can do a little demo for TensorFlow with Keras. I can talk about AutoML and then wrap up. But just so I know who you are now, so I've done the Who Am I, how many people have done machine learning anything before? OK, so how many people are at a company where they wish they had done machine learning of some kind? You wish you could be machine learners? No one cares. OK, I might as well go home. OK, so in that case you must have either come for pizza or dancing. How many people came for the pizza? Ah, so the rest of you must be, this is a kind of a new example from the deep learning field for dancing. So this guy, this guy can dance. These people cannot dance except if you use the power of deep learning. This is kind of interesting. So whereas the task from machine learning is to say good dancer, originally bad dancers, this is where the kind of things that people are now beginning to be able to do with deep learning techniques.  OK, bad dancing, better. It's surprising how limber the guy on the right has got. OK, so let me go on with the TensorFlow stuff now. So how many people have seen this picture before? This is from last year. OK, very few people willing to put up their hands. So basically this is how TensorFlow is a diagram which has grown from when TensorFlow was originally launched. On top of the TensorFlow, which is basically a means to get models built into something which you can execute. Basically, this can be accessed via C++ Java or Go, but the primary thing is a Python front end. And on top of that, there's various other libraries of layers of different operations. Keras is kind of an important way of doing machine learning as a high level API. And also Google have been pressing on to make estimators to make it so people can use like a psychic learn kind of interface. But also how can you deploy it? You can deploy it onto your CPU, onto your GPU, on Android and iOS devices. And also using this thing called XLA, which is like a compiler for this stuff. So basically this is grown and grown and people have kind of thought that they would make this diagram last forever. But from what I saw from when I put this together last night, people have given up with this diagram because too much has been happening. So one of the things which has been happening in particular to this middle layer, this TensorFlow main layer, is there are now more options for that layer. So you've got TensorFlow, which is kind of the original version. We've also got TensorFlow Lite, which can be deployed on mobile devices or as part of these cloud functions or whatever. So this is kind of a stripped down, efficient version which doesn't have all the bells and whistles. There's also TensorFlow.js. Basically if you can train a model in your big GPU setup and then deploy that into any browser. So this is a model which should execute on people's browser and give them machine learning functionality. You can do real time video overlays and all sorts of crazy stuff using JavaScript just in their browser. So they're a while times ahead for that because the JavaScript people is kind of a different animal from the Python guy. So that's kind of exciting. There's also a thing called eager mode which is coming along and the TensorFlow teams are excited about eager mode and maybe I should talk a little about why this is interesting. So the way in which TensorFlow works is you kind of describe your model to the TensorFlow and then TensorFlow goes away and crunches it up in some way and then it executes it. This is very different from if you're just writing a standard program where you tell it the steps you want to do. If you tell it the steps you want to do, you get an error as soon as you tell it the wrong thing. Here you would take your whole recipe for a real TensorFlow, you would take your whole recipe, compile it and then from the compile code you would say you made an error and the error messages are diabolical. Because you don't actually, there's no actual necessary correspondence between what it's giving you an error message about and the actual line of code you told it because it may be hidden away in the wrapping up. One thing which has come along last year is something called PyTorch which is another framework for building neural network models. And that works very much like a Numpy, like a standard Python library but using your GPU. And what has got Google concerned is that a lot of researchers are using PyTorch to build papers with. Partly because it's so much easier to debug if you make an error it just tells you straight away. So this is where they've now built eager mode which is a new mode for TensorFlow where instead of taking your graph doing all these operations and then finally letting you know at the end of the day how it's working, it tells you immediately. And so this is something which is suddenly as the talk about eager mode has ramped up and this is essentially PyTorch, not that Google would want me to say it, but it's kind of PyTorch mode for TensorFlow and it makes TensorFlow kind of very nice to use but it's got this huge ecosystem behind it of all this other nice stuff which PyTorch was never really designed for. PyTorch was much more of a prototypy kind of thing. So another thing that has just come along which doesn't really fit in the diagram too well is ingesting data using tf.datas. This is APIs where you tell it where my data is, it's a CSV file, it's all these images, you ingest it, so you pull it off the disk or whatever, you fill it around and then you can train models using it. And what people would normally do before is you'd have a loop in Python pulling this stuff little by little into the GPU, but the tf.datas that they are arranging it so that it can be all handled within this TensorFlow graph. And the benefit of doing that is you can organise it so that it works in an asynchronous manner so it's much more efficient, your CPU being kept busy while the GPU is doing the training work. But the bigger benefit, which Google didn't really, hasn't been so upfront about, is the whole thing with TPUs. Now TPUs are extremely fast pieces of silicon that Google has made because Nvidia licensing, Nvidia licensing, all these other situations, and also Nvidia chips, the GPUs which people have been using, have also got a lot of silicon devoted to graphics. For the machine learners we don't care about the graphics bit, we care about matrix operations. So TPUs, basically Google has rolled their own silicon and they make it into massive arrays of these things. These are pods, so this is a pod of TPUs, but it's a TPU is not a card you can put in a machine. In particular it's not a card you're going to put in a machine even next to your virtual machine at Google, in the Google Cloud. The Google Cloud virtual machines are in one place, the pods are in another place. So the benefit of having this TF data ingestion going on in the graph is that they can then transmit the graph into the pod and have it executed there. So your actual VM, if you've got a TPU attached, can be a tiny VM. As long as you can transmit what you need to do across the network. So once you've got this network over there, what about the disk? Where's the data come from? Wouldn't it be better if I just had the data locally? But the thing which amazed me is even if you have it on a normal local machine you'd have an SSD with all your data on to pump it into your GPU and everything will be happy. The problem with TPUs is that they're so fast SSD will not keep up with them so that you'll be starving your TPU for data even with local SSDs. But the neat thing in a Google data centre is that if you store your data in buckets, basically the data will be streaming into the TPU pod from lots of different directions simultaneously and the actual network transmission speed of this will overall be higher than SSD just because you've got multiple parallel routes into your pod. And so this is kind of crazy, it's a crazy thing where the kind of the old idea of having network accessible storage is a you know a slow problem. Actually Google has levered this with their data centres. I digress. Making it easier, okay, here's a key point. So Google has made also making this machine learning thing easier first by these cloud vision APIs. Basically these are pre-built models where you can ask okay what kind of dog is this or what kind what of these hundred thousand classes is this from or you know what landmarks are these. But there's also this new product which is kind of heavily pushed at the cloud next which was this AutoML. Basically this allows you to build on an automatic or allows Google to build automatically from your data um good models. So I'm going to talk about that a little bit. So let's go back to the kind of the raw stuff the the real deal um in the cloud. Another thing which Google has come along with is this Google collab. How many people have heard of this thing? Some people? Okay this is a super thing which people should everywhere should be using. If you use Jupiter and you want to do machine learning basically it allows you to use a free GPU from Google um it's a preemptible one in that they could be taken away from you at any time and the machine could die died any time and it's it will definitely die after 12 hours but you can then just have another one back okay.  So it is basically at work we've been keeping these going continuously for several months right. Just you know you you leave a job running overnight come back the next day it's all on your drive the machine died but it doesn't really matter you can then revive it continue training. So you get a free a free k80 um Google very nice of them. But the the reason they've got all these these k80s lying about is because the TPUs have taken over all the workload that these old GPUs did so now they've got these for free. They also built some kind this is for dollars now so Google's built deep learning VM images so these are all pre-installed with the latest Nvidia drivers tensor flows compiled to use this even better than normal um they also have the same thing for PyTorch which is a very interesting move so um hats off to them. Okay so now I will do a quick demo of TensorFlow and Keras but since no one here cares about machine learning I'm not sure why I'm going to do that but one thing says that this is part of a repo which I have so I've been running this kind of deep learning workshops for quite a while we've been running these um I have a repo with tons of examples in um but also now we're just converting over to running these things in colab essentially last year we ran a developer course where half the class had a GPU and half the class had a really tough time okay now everyone can have a GPU it may not be a great GPU um but just running this directly on colab from github um makes for a great experience so this is this is my Jupy this is a Jupyter network running directly on here because colab is not trustable in front of 200 people so okay so what I want the the the kind of the use case here and this is kind of a common use case that people will have is they've got a bunch of images that they want to classify and so this is a classification problem I would love to be able to show you the everyone can dance but this is not that's not for today so um and the way in which people do this typically is instead of building a network from scratch and training it on just your images they will take a prebuilt network and then kind of switch out what it's looking for so I'll go through this so so within a thing called Keras which is a higher level API on top of TensorFlow there is a model zoo and basically there are a whole bunch of models where if this is the number of parameters these these are really big models these are small models and this is the kind of the precision to be expected then you can kind of have this efficient frontier of of smaller and better models will be up in this corner and so all of these are available these all come from different waves of research as time has progressed so I'm going to take a model called nasnet which has a particular picture here so hopefully it's just going to load the model load the model of course it may decide not to load oh if it's not going to load it's a problem so the issue with them with an external model zoo is I have to call the model zoo come on okay I will pragmatically redo this okay I'm not sure I can trust this to work at all okay I will have to plug ahead let me talk a little bit then about this nasnet thing so rather I could show you this is this is a demo essentially I could I could show you online so it is available online so let me just talk a little bit about this nasnet and the reason that's nasnet kind of thing is interesting is because this is the basis on which some of this auto ml stuff is done so in previously what has happened with with all these architectures is that somebody or particularly like these big and bad ones originally somebody built these by hand using kind of old technology and there's a very laborious process simly that it then went on to inception you know reception one two and three and then there's these resnets all of this being built by graduate students essentially and so this is known as graduate student descent in that they would build good models and basically you'd use human labour to build these things and there's a limit to that and a limit to the scalability of that so what google decided to do was instead of using people to do this they would make people build models that build models so this is where this this is the net nasnet stands for network architecture search so basically they made the network architecture search into a game and so the game is played by coming up with a network architecture seeing what I can score an image net then then coming back and trying again but because I know what my essentially I come up with a structure I come up with a score I can use that as kind of a playing go I can use that as a do to five kind of thing I can learn how to build these network architectures so what then happens is basically it then builds basically the game has structured to build me a cell from which I can build an image net recognizer so this is the cell which has been built by a machine learning to build things and so this is where essentially this it will now become impossible for humans to compete in this game in that because the machines do this so efficiently and can be told all sorts of constraints that I want this as small as possible I want this to fill fit in this degree of latency extremely difficult for humans to play that game but computers can do it ultra well so this this whole thing has got more and more efficient these nasnet models on the other hand have just been released so you can download one of these things when the network through and just get these nasnet mobile weights directly and then use it so basically I'll get a single prediction from this thing I here's an image I read the image I show it and then I get a single prediction from this nasnet model and so here is a this is an image net prediction demo it's just book so um so this is coming back this is the image net is a standard image competition which has recently been abandoned just because machines have surpassed human accuracy and so now we essentially need something more more difficult to do um so this is it is basically a thousand categories of things including it clearly tabby tiger cat Egyptian cat Persian cat and links okay and this is pitch of tabby cat is fairly or fairly sure of that and basically I've got a a directory full of images here and so here's another one it thinks this one is Simon's cat not a bad idea this one I'm not a I'm not sure what it is anyway standard poodle maybe it image net the thousand to us is don't cover um like snowy white owl okay so it has no idea what this is so I mean sometimes it would say like golf ball or in any way that it just does not know right so it's but it would be relatively reasonably certain about the wrong answer okay and then this this one dingo could be um or maybe that may be in anyway so image net also has a very large number of dogs that large number of dog breeds in there for for no apparent reason um but these are also very difficult to for humans to tell apart so part of the way in which image net beats humans is by being good at dogs which is not anyways not is but it may make horrible errors because it has the image net has very few humans in there image net is not not good at human distinctions at all it may have person as being a class right um but it doesn't have it also has gorilla as a class and that's been a problem for google so um so there's a question of data and dignity as well okay so basically here here is the model which is loaded um this is a neural network with many many layers of these things so this is a very very long network basically designed by computer rather than human so what I'm going to do now is I'm going to take this model and do a trick so this is the the if I can I view the head up so basically what I'm going to do is I'm going to take this is the image net network and I can take and this is my input image and then I do some kind of black box mystery work with this nasnet and at the end I'll get back like probabilities for each of the thousand classes or not even probabilities something before probabilities okay now that I there would then do an operation called a softmax which would then give me a kind of probabilities and the biggest one is my choice okay so the biggest output in this layer is no tabicat or whatever it is but it will kind of have like answers for all of the other classes they'll just be very small answers hopefully what I want to do with this this repurpose network though is I'm going to strip off this last answer because I want to use it on my own classes I don't want to use the image net classes it may be that I want to classify for instance cars okay so what I'm going to do is take off this last piece and instead use these kind of pre outputs essentially basically this is a bunch of apart from this one all of the rest of it is kind of degrees of mistake so I'm basically I'm taking the the way in which it's made mistakes and trying to use that to classify cars or whatever to my own thing I'm going to use a model called an SVM it's very standard kind of your boss would understand this model thing okay so one of the nice reasons for wanting to do this transfer learning this is what this is called like this is that you can say well this dense black box complex cnn I didn't train you myself Google trained it using all of their resources this is a Google standard model okay and SVM brackets boss you've seen this before you understand SVM just fine right so in combination of these two it's got some industry tested this this this um here's a model which works and in in some ways that's much better than saying well I built this from scratch and I think it's pretty good right so um so this is this is the game we're going to play basically I'll I'll pull in some functions I've got these separated so these are cars I've got these separated into two directories one of which is called classic cars and the island calling modern cars and then I'm going to walk through this directory and then essentially convert this into a model image and then get these kind of these things called logits which is from this layer and I'm just going to build a like a list of all of these so I'm going to take logits for all these things so basically here's here's my training set and it's kind of a small training set I've got 10 images of each of these kind of cars right so here's here's some classic cars like these nice cars and then here's some modern cars so these are Exotica and so that is my training set now normally uh one of these classifiers require hundreds of thousands of images right if you're going to build this using from the ground up but because I've Google has already built the image net thing using hundreds of millions of images it's pretty good at images now all I need to do is figure out the difference between modern and classic cars so basically I can here is I'm taking the outputs which I've kind of stored for all of these and I'm building just an SVM over these these features and targets so this builds in this is a half second at most right so I can then go through this and and actually then find out the logic layer for new images which has never seen before and then use the SVM to classify those so here it here it say well this is a modern one this one is not a modern car this is a classic car modern one another modern one it thinks this is classic which is ridiculous it was a Prius another classic one so okay so basically you can see that we're getting some kind of accuracy which is surprising given that it doesn't know about modern classic cars apart from what it knows about dogs and flowers and other stuff right and so what one of the things here is that it's probably not doing what you think it's doing in as much as if I'm looking if I'm looking at the difference between this is a kind of caveat for machine learning in general if I'm looking at these cars and this you know kind of sleek lines and modern cuts and all this kind of thing whereas this has got kind of bulbous I don't know there are different things which I would associate with classic cars versus modern cars but it may well be that the what it's interested in is the shape of the wheels because the classic cars have ones which are much more like plates um which he knows about and he knows about plates of food right and the modern ones are much more like lotus flowers right so the actual lotus seed pods okay so the actual things which it's looking out for in these images may have nothing to do with cars at all or it may be that the modern cars are always parked on a certain kind of parking lot whereas the classic cars are in always in a different kind of context so you've got to be very careful about what it's looking for when you train these models but I digress so so this is basically so this is just 10 10 copies of each image produces a model which kind of works but you have to kind of also know what you're doing I mean you hopefully you can find a blog post which does this repurpose it jazz it up for your own good so that's that's kind of the that's and this is the kind of thing which we'll talk about at the TensorFlow Meetup and more so um but let's talk about AutoML so supposing you're not going to get into Keras and do that whole learning curve so this is for it's in kind of a new google thing they're super proud of it this is for people that have data they have money but don't want to invest in people sorry they don't have any specialists they want AI results okay because they they have an AI company and that's what they need so what AutoML will do for you is it will build you a usable model tuned to your data basically by playing that kind of nasonet game that I described before that they essentially use they figure out which of their models we most applicable to your kind of data and then let it cook right and once it's done that fully automatically you just add money because it can both train better and serve the model for you straight away so this is a picture for people who are not resources but more like overhead i management so basically you get your photo data set you put it through this magic thing then you get a rest api and they can also do this for um there's some translation stuff there's some natural language processing stuff and surely these kind of suites of models will come along and get better so i've got an interest of time or and do the demo of this it's pretty much what you see here so basically there's a data front end where you can drag and drop your folder of stuff into here and give it labels there's the train interface where you basically you pick your budget and then you press the train button okay now your budget includes one hour of free training but what it will say is it will do it's one hour of free training it'll tell you how many more hours it thinks it needs which you can get a pretty decent model after one hour of free um but it may ask for another 23 hours of 20 bucks an hour so you know that's that's for your boss inside maybe and there's also a nice evaluation interface so basically you can look at the your prediction accuracy all sorts of metrics how the training went and there's also a thing where you can say well here's a it's called a confusion matrix if i said it was a a rose or whatever you know it did it make a mistake what what are the classes is it getting confused by and having done that oh it's way too small okay um basically here's some python code you can just call this model and it will serve it to you and not just serve it to you give you back the answers same with um you can you know you can do this you can call it fire any rest api and so my guess is this this is what's definitely easy to interface and because it's google it will probably scale so this model will be usable repeatedly from any device and it was built fairly painlessly so if there's time i'm not sure there's time i don't want to i don't want to crush other speakers um but this is basically i all i would be showing you is i have another image and i can just show you that this model really works we sam trained one in new yw'r gwrs we can share the model i can put an image in it just works um but there's no time to to give you to show you the hour training process because that's ultimately really boring because all it is is google doing something in the data center so the advantages for this is it's kind of easy to use in theory um it may not be quite so easy to do the data ingestion because it's really kind of finicky about how it wants it how it wants you to upload these things you don't need to know any you don't you hardly need to know anything about machine learning you need to know about training and test sets and and what accuracy might mean and all this kind of thing and whether the model is any good um but basically everything's done for you they're training valuation metrics and you get an api immediately um disadvantages is not it only does classification at the moment um so you will not be able to make you dance right so this is this is an advantage of using red dragon ai is that we can make you dance whereas this will only make classified not good dancer we also get no no control over what's happening um you can't choose various splits you can't do this kind of things where um you might want to have control over this and the results if you've had no control you may also not understand what's happened um and you can't really poke around because it's kind of it presents you a finished product and it's 20 an hour in beta at the moment not sure how that goes up or down um clearly they want people to experiment with this um so conclusions i guess so if you need a model but you don't know TensorFlow this is a fantastic product and i think what google has done is they've looked at the number of people on earth or the number of businesses on earth and the realistic number of people they can educate and seen that this is a massive gap so better to get this in the hands of people just the the way to democratize it isn't to make it so that everyone can use it is to make or make everyone understand it for at least they could use it um even if you do know TensorFlow on the other hand it's good for a baseline um it you know you can put in your dataset to this it will come back with something it might be it might be really good on the other hand it might be a great baseline to show that you can beat google so not nice isn't it um but it probably wouldn't win a cattle competition okay if you've got experts building these things you'll probably be able to build it beat it just because the variety of models it will test will not be and the features it will understand will not be as good as um like specialists who really understand your business and if you start retraining this just because there's a habit it might get expensive so there's a wrap up okay so the TensorFlow ecosystem is exploding it's gone kind of way beyond its early stages and now where it kind of is probably too much is the problem so so there's a an embarrassment of choice right now um on the other hand where they said where they started out saying we would get to all these compiled models and all this good stuff it actually works now it will actually go on to your your phone it's available in the cloud it will scale it will do all of these things um and this AutoML is a allows for a much broader adoption of AI um but really it's just machine learning um the AI thing that's way down the road right now um so the stuff I have the the thing which took a while to load is all in repo on github if you like it please add a star that's my kind of kpi for this um and I'll do a few a few little ads in the two minutes I'm ready so we have this TensorFlow and deep learning Singapore meet up um the next one's on the fourth of September we'll be talking about explainability where if you've got a model is it doing what you think it's doing um there are a whole variety of techniques but people are kind of worried that this is um that's one of the issues with these black box methods right um all these slides are I'm not sure whether there's a all these slides will be available somehow I guess um what we try and do is we try and have a talk for people starting out talk from the bleeding edge like papers and some lightning talks if we can just anyone who's enthusiastic about what they're doing um I said this before we've run these deep learning developer courses of which I think all we're calling jump start is module number one um we ran this last year um it was gruelling I think for the participants but they got a lot out of it including good jobs I believe so um in each module we have kind of instruction and people have to do projects for themselves there's SG Singapore government funding for those people who qualify which has been citizens and PRs um and we really want that to happen because otherwise we'd be teaching a room full of foreigners um so want that to that's been actually one of the slow pieces in getting this rolling just to get all of these approvals through um we're using SG innovate to help us do that now because they're plugged in far better than someone who looks like me will be so that's good um in terms of the jump start thing this is a two two day course um the next one is on the 12th 13th of September um there if you go in through SG innovate down here for some reason it's under their talent talent developer talent development link um this is 600 sing um but there's also if you can understand it there's also discounts for for Singaporean so um but this is another two-day thing you get to play with real models from the very beginning um then there's one last thing is the the red dragon ai intern hunt so if you want the if you're in a position where it might be interesting to you and you want to do deep learning all day um we're very interesting people who have an intern period to cope with and I I know because I've done this with several people over the summer it's worked out super well they've come out with cool projects cool resume things and we get kind of people to plug away at these models which is kind of still necessary so um there we go thank you


Since myself Alia Arsandane, assistant professor from Department of Electronics, Wailchen Institute of Technology, SolarPool. Today, we are going to see the topic acoustics and reverberations from the subject audio-video systems. So, what are the learning outcomes from this topic? At the end of this topic, student is able to define acoustics and reverberation. Also, he can calculate absorption coefficient reverberation time for efficient acoustic effect and design acoustics of auditorium and meeting hall. So, these are the learning outcomes. So, basically what is acoustic? So, acoustic is nothing but it is the basic branch of physics that deals with the study of wave propagations in different types of mediums which include gas, liquid and solids. So, whenever this study is made, the parameters which are considered for the acoustic or the wave propagation is vibrations, sounds, ultrasounds and infrasounds. So, it can also be defined as the qualities that determine the ability of an enclosure such as auditorium. So, here the ability of enclosure basically denotes the reflection of the sound waves in such a way that to produce a distinct hearing or a clear hearing. So, sound suffers reflection, refraction, diffraction and absorption. The reflected sound basically will be heard as a distinct echo. If the time gap between original wave and the reflected wave is greater than 60 milliseconds. So, whenever the echo is heard in some structure, in some building structures then you have to keep in mind that the time gap between the original wave which has been transmitted and the reflected wave from the surface the time gap is greater than 60 milliseconds. So, what is the role of acoustics? So, when we consider the acoustic branch then the role of acoustic branch is very much wider. So, here as you can see the acoustics plays a major role in the medical science. It also plays an important role in the electronics communication system. It also plays an important role in geology. In geology also it plays an important role in terms of architecture and chemistry. So, fundamentals of acoustics basically nowadays we are developing much more content on this. So as you can see here medicine is there, physiology is there, psychology. In communications we are dealing with speech music art and when we consider the geological conditions then we are dealing with the seismic graph of the earth atmospheric physics, seismic waves, sonar waves and ultrasonic waves. So, when we consider them in terms of chemistry then it is used for chemistry, mechanics and architecture. So, these are some of the branches in which acoustics plays a major role. So, why there is a need of acoustics? So, as we know that sound can be classified into three major categories. First one is infrasound where the frequency is less than 20 kilohertz in which or this particular frequency can be heard by the animals. The audible sound basically it has a range of 20 hertz to 20 kilohertz. This can be heard by the human beings and the ultrasound where the frequency range lies much more than 20 kilohertz. So this can be heard by the flying bats and some species of the birds. So here as you can see this can this will give you a basic explanation. So infrasound so when we consider the waves characteristics or the sound characteristics then you can find that infrasound is having less wavelength or it is kind of a lower frequency audible sound. It is having a characteristic which is in proportion that means the crest and trough are in proportion whereas ultrasound the characteristics of the sound or the sound wave which is represented for ultrasound is much more that means these basically denotes low frequency, medium frequency and higher frequencies. So what are reverberations? The prolongation of sound in a hall even though the source of the sound is cut off is called as reverberation. So whenever for this we can take an example of building or construction of a building which is having dome in which when you shout out a name then it is vibrated for seven to eight times. So you are shouting it for once and you are hearing the voice or the repeated name for about seven to eight times. So that particular example is nothing but the reverberation. So what exactly happens in reverberations? So reverberations are gradually fading or the traces of decay are faded that means whenever the reverberations occur there is a decaying of sound. That means at instant whenever we speak the sound gets higher and as it moves out or as the echo goes on increasing then the sound gets decayed and it gets faded and a point comes where it gets totally lost. The reverberations are calculated in terms of time and it is basically given as t is equal to 55.3 V upon C A where C is velocity of sound V is volume of room A is total absorption and reverberation time is defined as time taken for sound energy in a room to drop down for about ten to six times that is 60 dB below. So please understand that 60 dB means maximum so it drops down by 60 decibels. Some examples of the buildings and the reverberation periods. So when these are the constructions of certain buildings and these are having the reverberation time. So when you compare all these buildings and their respective reverberation time then you will find that the lecture hall is having less reverberation time whereas the church building is having the maximum reverberation time which is of about 2.5 seconds. So this will give you an exact idea about the buildings and how reverberation times are there for them. So reverberations can be explained by giving the below example a sound produced in a hall undergoes multiple reflections from the walls floors and ceiling before it becomes audible. So whenever the teacher is speaking the first person or the second person can hear the sound or the voice of the teacher directly but the person who is sitting in third, fourth and fifth row he can hear the sound of that teacher by the multiple reflections which are taking place from the flooring, the ceiling and from the side walls of the classroom and then they are receiving the voice of the teacher. So this is the best example. So it can be also defined reverberation can also be calculated by using Sabin's equation. So Sabin defined the reverberation time as the time taken by sound intensity to fall on one millionth of its original intensity after the source has stopped emitting the sound. So it is given by t is equal to 0.161 v summation 1 to n alpha n into s n where v is nothing but the velocity of the sound alpha and s is nothing but the intensities of the sound and alpha is the Sabin's. So sorry alpha is the total absorption of that particular sound. So further it can be above equation can be written in this particular manner. So Sabin's equation absorption refers to the process by which a material structure or object takes a sound energy when the sounds are encountered and opposed to the reflecting energy. The total absorption is given by alpha is equal to sorry a is equal to summation of alpha into s where alpha is absorption coefficient of the surface area. Factors affecting the acoustics of building first one is reverberation time. If a hall is to be acoustically satisfactory then it should have rewrite reverberation time. Reverberation time should be neither too long nor too short. The total the optimum value for reverberation time basically depends upon the purpose for which the hall is being used. Hence what are the remedies for this particular how we can overcome this particular factors. So reverberation time can be controlled by choosing right building material. So windows can be used or windows which are to be constructed they should be minimum and sometimes they should be kept open or closed to provide the particular or correct reverberation. Similarly the second factor which is affecting acoustic of a building is loudness. For proper hearing of sound sufficient loudness is required more use of absorption surface near the sound of near the source of the sound. Hence due to this less reflection will take place and hence it will decrease the loudness in sound. So how this can be reduced? This can be reduced by choosing reflection surface or the reflection material such that it should be very much nearer to the source of the sound. Over positioning of absorption material can increase the loudness in sound and for larger halls when we consider in these halls if there are more audience then at that particular time we can use two or more speakers which can make a difference in loudness. The third factor is focusing use of concave surfaces can concentrate the sound at a fixed focal point hence concentrations may sometimes lead to deficiency of reflected sound. So such kind of deficiencies are called as dead spots. If there are highly reflecting parallel surfaces in the hall the reflected and the directed sound sometimes get mixed up and it can form a standing wave pattern hence due to this standing wave pattern there will be no uniform distribution of sound in the hall. So how this can be reduced? It can be reduced by proiding sound focal length and removing the dead spots by eliminating curvilinear interiors by using highly absorption materials on these curvilinear or concave surfaces then a parabolic reflecting surface can be used nearer to the speaker which will help in directing a uniform reflected beam of sound in the hall. So these are the factors which are affecting acoustics of the building. So these are the references for you people for the further study. Thank you for watching the video.


The study used GIS and IS platforms to predict quantitative geothermal potential in the southwest section of the mid-spine belt of beautiful China by selecting six impact factors, establishing a certainty factor model, CF, and applying weights to information entropy certainty factor model, ICF, and evidence certainty factor model, ECF. The results show that ECF had high prediction accuracy and identified six main geothermal resource areas.


I'm reminded too of something that I found much more helpful than resolutions for the future is like unity in the burning bowl ceremonies. Anybody ever gone through like a burning bowl ceremony on this night on the 31st of December? How beautiful, what an opposite to using the time to make resolutions for the future it's just taking all your grievances all your resentments all your struggles all your future aspirations all your goals and throwing them all in the bowl and burning. To me that's very much more like the Buddha empty the mind of everything good bad right wrong everything you think you want for your life just put it in the bowl and have a willingness to just turn it over and let it burn and then it's brand new like I I just don't I don't have goals for any day that's fun I like to wake up with big eyes like childlike wonder eyes you know of wow kind of like that scene at the end of 51st dates with what's true Barrymore is is that's the final scene of the movie she's she's up there he's taken her away she doesn't have this memory but he's taken her way to Alaska I think and she's opens up the curtains she goes you see her face it's like a little girl face childlike wonder like whoa she's waking up and looking out on this scene up in Alaska imagine just waking up that way every day you know talk about a reboot you just don't have a clue you don't have a goal you know other than maybe peace of mind you know which is what you're practicing that's your practice of peace of God is my one goal the aim of all my living here the end I seek my purpose my function and my life while I abide where I'm not at home if you're gonna have a goal that's a goal to have the peace of God and is that practical yes and so what if your life as you knew it starts to dissolve if it starts to break apart like Humpty Dumpty if it starts to fade away it starts to dissolve it's all part of like a retranslation and isn't it great that you can just give yourself over to it like you let the retranslation begin oh so and so died oh so so disappeared from my life oh this and this and this after a while people start leaving your life they're good the disappearing it's a disappearing actor they're going going going the most consistent face that I've had in my life over the last ten years is my three-legged cats face that's the one face out of the last ten years isn't that wonderful everyone in this world is trying to get continuity in terms of relationships or this and this and this and I'm happy to let the chips fall where they may let the great retranslation begin if I do not know my own best interest then suck it to me if you know my best interest in every single circumstance and I do not then show me not show me the money show me the vision show me the love you know show me the way and and when you kind of wake up and you start to feel that okay I want this that's the prayer of your heart then things can start to shift and change a lot I've even loved working with people who get you know they go through their whole life kind of compromising and they compromise compromise compromise in fact they'd compromise so much that they don't think they're compromising anymore even though their whole life is a one big compromise and then they get diagnosed with a terminal illness and then they think oh oh it's almost over for me times almost run out and then they start to question then they start to really live because they feel like they've only got a little time left so they're gonna not gonna waste it and they take off like a rocket they go on the spiritual advancement like they've never gone before all right maybe it's a little bit of fear motivation I think the time's running out but still the Holy Spirit uses contrast you know and and they start to take off and they start to really live like if I've only got five months to live oh what am I gonna do today because say well you can do this forget that I've only got five months to live shut up shut up over there enough of this enough I said I've only got five months left and they really go for it and they take off like a rocket did anybody ever see what was the movie that Robert Redford's daughter did what they call the red we saw it it's the one where she's diagnosed with this this terminal illness and she no it's right yeah the guitar it's the guitar it's a red guitar it's the guitar if anybody of you have not seen the guitar you need to it's Robert Redford's daughter made this movie and the woman in the movie the lead character she's diagnosed with this terminal illness and and she she gets fired I think she gets canned and she's diagnosed with the terminal illness and she goes through such a major transformation she's she's sure she's gonna die she's got fired so she just goes and she gets this loft apartment and she's got her credit cards her she's been working at the shop and he goes like that she goes she buys the best she wears everything she gives herself everything that she always wanted even when she was a little girl she wanted this little red guitar like an electric guitar and she wanted to learn and she learns how to play it and she goes through such she buys and buys and takes she gives herself everything that she wanted to talk about getting over the unworthiness issue of I don't deserve it's charges charges charges charges and and then by the end of the movie she totally goes into remission it was all the guilt and all the unworthiness of thinking she didn't deserve it that was the sickness and then she's shocked where she's got it just great credit card but isn't it worth it to know that you're worthy besides the world's gonna disappear anyway right yes it's all it's all for that experience of worthiness you know so that's a great movie yeah if any of you haven't seen it the guitar there's also that one with Queen Latifah she's so good in that isn't I love Queen Latifah yeah she gets misdiagnosed it's a the scenario yeah it was a misdiagnosis I forget how it happened where she's got this terminal illness so she's got this nest egg so she decides she's just gonna spend it all and she goes away and to this fancy hotel and she's like give me the best of everything it's a what's the best thing on the menu well there's this give me one of everything you know and so everybody thinks that she's some very wealthy person who just is living the great life and and because she's on her you know at the which she thinks it's the end of her life she doesn't hold anything back she tells it like it is and so everybody is attracted to her because she's free she's allowing herself to be free and and then you know it's it's as you might imagine but it's beautiful it's a powerful fun story yeah and some of you I think Tom Hanks says the worst movie ever made but actually metaphysically I think it's the best movie ever made he just doesn't know his own best interest Joe versus the volcano and it because again he's diagnosed with terminal illness a brain cloud and it's you know this brain cloud and so he goes and he swear he thinks he's gonna die but he's got this dead-end job is making Vaseline petroleum rectum petroleum in this they mean it's the darkest-looking job and the fluorescent lights you know in his face and he's twitching and he's just he's got the most dead and they they make it up Kathleen Kennedy Spielberg they make up the worst job in the history of the universe and he's got it and then he's diagnosed with a brain cloud and then he starts to feel like I'm gonna live and he turns his whole life around Meg Ryan plays like three different characters in the movie and and he goes through all these transformations that's the one I was mentioning with the big moon that he sees you know and thanking God thank you for my life but it's it's these are the kind of movies that are in the movie watchers guide to enlightenment that the Holy Spirit gave me because they're all about seeing that the world doesn't hold anything that you want and even when you think you've got the worst life ever then he gets the the diagnosis of a brain cloud and he's told he's gonna die very quick so then this guy shows up this it's Meg Ryan's character's father who says he's got to throw himself into a volcano because he used to be a fireman and save people and everything and so he goes on this great adventure and it's all about him letting go of everything that he believes is valuable and taking this journey and he does the guy gives him a credit card so he can go bye bye bye you know the best luggage the best everything you know it's you know we're on to it now these three movies we just talked about the guitar and this Queen Lativa movie and Joe versus the volcano they're all the same story that you've just been fooling yourself and thinking that your life was what you thought it was and and you can have a miracle and break out of it and the most wondrous miraculous experience is awaiting you when you just drop the old and the ego is going to chirp and say oh don't you do it don't do it because it's saying that what the status quo is in your life is is all you've got it's all you really know the other is a big question mark the big unknown and and what you seem to know in this world what you seem to perceive is all that you've got it's just not the truth it's just not the truth we're ready to really live truly live a vibrant life a guided life a sourced life we have to be sourced we have a source so we have to be sourced but the ego is saying no you can make up your own way you've got a band of time between birth and death to spend however you want it's your currency so eat drink and be merry because one day you still die like you better cash in all your chips and get as much scraps from the world as you can little scraps of pain and pleasure you know to try to just accumulate as many scraps as you can because you're going to die anyway and that whole thing is a lie it's just not it's not worth it it's not worth it that's not living we're like riding the wave of of a lot of devotion and and I think it's great with the community experience you can really feel it's a tangible palpable kind of experience you can feel the energy of it and and that's the ultimate of the collaboration that's why we're collaborating you know it's because the way is easy the way is clear you know before it was like the way is straight the way is narrow and from a time perspective it can seem like a very straight and narrow path you know like oh boy how can I live that life but but now we're on the wave and we've we've got all this love and all these mighty companions around us symbolizing that you can just kind of relax like that and be carried just carried into shore that's what they tell you when you're out kind of caught in a current you know don't don't try to fight the current and when you're even in a car that starts spinning you know just gently turn the wheel in you know and and relax don't you know because people try to do the the things when they're fighting against the current or turning the wheel you know to try to get out of a spin it just makes it worse because there's that efforting


Parthenon, Athens, Greece, what in the world does that have to do with Islam and the cultural dirt?  And they wanted the signs of God, of buttressing this deem before the Day of Judgment. And the overthrow of Sultan Abdul Hamid is fitment of Ahlas. The Prophet told us about that, not good, not good at all. And Sultan Abdul Hamid was a great man, he was a great man. And he cared about us, he cared about the Rohingya, he cared about the Palestinians. Okay, so that was a big loss, but the Ottomans ruled Greece for centuries. And while they ruled Greece, Greece was 50% mostly. The base of the Ottoman Empire was where? Where was their demographic base? And my wife is now here, I'm happy to see her back there. She always says, why do you ask questions that nobody knows the answer to?  But the base of the empire was Bulgaria. Not, what is today Turkey? Bulgaria was 80%. What is today Turkey? Anatolia was 50%. And Anatolia was basically just like Greece. Got all kinds of Greeks speaking Christians there. They spoke a slightly different type of Greek than the Greeks of Athens and so forth. They were Greeks, ancient Greeks. They had Armenian Christians as well. Okay, and Hungary was 30% Muslim.  When the Ottomans come into Athens, first of all, when they conquered Constantinople, they adopt the Byzantine church. They protect the Byzantine church. They cleaned up the Byzantine church and the Byzantines loved, I'm sorry. You know, the Greek Orthodox church. You know, and the Greek Orthodox church loved them. Don't believe anything to the contrary because that's not true. They won the love of the Orthodox church. And the Orthodox church stood by them faithfully until the end. And of course, now you see that's not the case with Serbia and so forth, but those are all later on. That's nationalism later on. But when they come into Greece, they took the Parthenon. Do you think that offended the Greek Orthodox church? No. Because the Greek Orthodox church had an attitude towards the pagan Greek past that made them not interested in that at all. So they got their churches. They don't want them. So the Ottomans said, we will take it. And during the whole Ottoman period, the Parthenon was completely intact. It was blown up by Greek nationalists in the War of Independence in the 19th century. So you see it now in ruins, but during the Ottoman period it was like that.  Now, what do you think about that?  Because like, are you saying to the Greeks that we really admire you? That, you know, Toimbi says the most beautiful building in the earth is this one, the Parthenon and the Blue Mosque. So it was like, we will honor the Parthenon. We didn't break any statues. We covered them. Sometimes we plastered over them. So you wouldn't see anything offensive there. But that was our Salat al-Jawam. And that's the cultural imperative.


I'd rather help the community if I can keep doing that than I think I'm doing my part. I just joined to be a firefighter, just because that's what everybody else was doing. It's been a really cool thing to do so I joined to be a volunteer firefighter with my local brigade. Quite nice to be in communities and to see that as a hub, well respected and we provide help to the community. Really inclusive, we are really encouraging, we've brought people in for all walks of life and we feel like there's something for everyone here.


This study examined the prevalence of early initiation of breastfeeding and its associated factors in rural areas of Sindh, Pakistan. The results showed that 20.4% of mothers initiated breastfeeding within one hour of giving birth. Factors such as adequate knowledge, place of delivery, gestational age of baby, and maternal age were associated with early initiation of breastfeeding. These findings suggest that providing counseling and health education to mothers before, during, and after pregnancy can help motivate them to initiate breastfeeding early and continue exclusive breastfeeding for six months. This article was authored by Rahil Barkat, Asan Jawani, Anum Rahim, and others.


My name is Martin Schweitzer. I'm currently seconded to ANS and I'm from my substantive position is at the Bureau of Meteorology where I work in the climate section and I'm responsible for the climate database. So I work extensively with data and particularly with climate data. The moderators today will be Andrew White, Frankie Stevens and Keith Russell. So thanks very much to them. So last week we heard about FAIR and that it stood for findable, accessible, interoperable and reusable. Today we're going to look at the last two, the interoperable and the reusable. So what is FAIR about? This paragraph that I've quoted here comes from the preamble to the FAIR principles. And it talks about the Grand Challenges of Data Intensive Science is to facilitate knowledge discovery by assisting humans and machines. So it's both about humans and machines in their discovery of, so it's about discovery, access, integration and analysis of task-appropriate scientific data. So what it comes down to is it's a set of principles about, from my perspective, increasing the value of scientific data and in particular of research data. And so they go on to say, yeah, we describe FAIR as a set of guiding principles to make data findable, accessible, interoperable and usable. I'm going to start off with a little story. So I listened to podcasts and this was purely coincidence, but I happened to be listening to one called Revisionist History by Malcolm Gladwell. And this was about two weeks ago while I was busy preparing these slides. It was called the Basement Tabs. The top is a link to the podcast and just below it, there's a link to a transcript of the podcast. And while I was listening to it, I started thinking, wow, this is exactly about FAIR. So I'm not going to play you the whole 30 minute podcast, obviously, but I've extracted some of the vital bits. So it was about a doctor back in the 60s who was very interested in diet and who posited that cholesterol caused heart problems. And that by eating saturated fats, it pushed up people's cholesterol and therefore they didn't live as long as people who lived on a diet of non-saturated fats. So there were two ways in which one can do these studies and one is to observe the population and ask them what they eat, et cetera. But the second method is a controlled study. In other words, find two groups of people, give one group diet A, give the other group diet B. But it's very difficult giving people a particular diet unless you've got control over them. So what this doctor did, friends, is he went to a number of mental hospitals around Minnesota and signed them up to do this trial. So basically there were six hospitals at one nursing home. He had more than 9,000 research subjects. The trial, putting people on different diets, ran for five years from 60 to 73. And then there was a long follow-up period to observe their health afterwards. It was incredibly meticulous about the way he ran the study. They give an example that at the time there was a law that if people served margarine, it had to be in the shape of a triangle. And because this was meant to be a double blind study, he had the law changed so that the margarine would look the same as the butter. So this ran and they talk about it, but ultimately the results of his study weren't particularly convincing. However, 20 years later, another researcher called Ramston had a hypothesis that people who are eating vegetable oils, and particularly a lot of vegetable oils, are ingesting a lot of linoleic acid. And this apparently in very small amounts is good for us. However, in large amounts, it causes inflammation. So he felt that by substituting vegetable fats for animal fats, people were causing the linoleic acid in the body to go up and therefore it was actually affecting the heart. And this was a reason possibly why the results weren't as expected when people had gone off these saturated animal fats. However, in order to do a controlled study, as it says here, he needed a lot of subjects, but what was he going to do? Somehow convince the government to give him hundreds of millions of dollars for the study and then find thousands of people willing to turn their doubts upside down. Then he realizes, wait, those studies have already been done. So this goes back to the son of the original researcher, Richard Fans and Malcolm Gladwell. And so this person said, and this is going back about 20 years, universities make a decision to throw things out that they think are just taking up space and nobody's going to do anything with them. So what happened was it turned out his father didn't like that idea. And so he kept all his research notes, all his data, et cetera, in his basement. And towards the end it says they go back, then look in the basement for this data. And finally, and sure enough, there they were on magnetic tapes in a mould green box in the back of the funds basement. I think it's an incredible use case that talks about research data and about its reuse. What it doesn't then go into, which I'd be interested in is how easy it was to get that data off those magnetic tapes. But that's another story. So just continuing this recap on fair, some things that fairies not. So fair is not a standard. Fair does not say if you don't follow these rules, it's not fair. It's a set of principles or guidelines that says these are the best practices. Try to strive towards these goals. Fair is not equal to RDF, a resource description framework, linked data or the semantic web. Although it makes use of these things and posits the use, it's not equivalent to using these things. Fair is not just about humans being able to find access reformat and finally use data. A very important part of fair is that it's about machines being able to also access this data, but machines being able to operate on the data that's being used. And finally, fair is not equal to open. Fair acknowledges that there are sometimes restrictions on data. So it doesn't say that data has to be open. Of course, with open data, it does make reuse a lot more easy, a lot more simple and given a citation at the bottom. So the first one we're going to look at is interoperable. And the preamble to interoperable. To be interoperable, the data will need to use community agreed formats, language and vocabularies, the vocabularies being the pertinent one. The metadata will also need to use a community agreed standards and vocabularies and contain links to related information using identifiers. And last week we saw about things like Perl's Permanent URLs and DOI's Digital Object Identifiers. And it's a very strong thing throughout fair that all the data that we're using is somehow linkable that we can get to it that we have this permanent links. And the other thing is that it applies both to the data and to the metadata. So interoperable has three principles. One, I1, metadata, so data or metadata, use a formal, accessible, shared and broadly applicable language for knowledge representation. Two, that metadata use vocabularies that follow fair principles. And three, that metadata include qualified references to other metadata. So we'll work through each of these in turn. The first one, metadata use a formal, accessible and shared language. The first thing we're going to look at is ontologies and I have a definition here of an ontology. And ontology is a formal explicit specification of a shared conceptualization. And it goes on in this to explain exactly what each of those terms means. So by formal we mean machine readable or formally specified. Explicit specification means that it explicitly defines concept, concepts, relations, attributes and constraints. And I'll give an example in the next slide. Shared means that it is accepted by group. So it's not good enough if I come up with my own ontology and think, okay, I've got a ontology for my genetic data. I'm just simply going to use that. It needs to be accepted by group. And finally, conceptualization is an abstract model of a phenomenon. So there are a lot of ontologies out there. And I think this source I was looking at had at least a dozen for biological type data. But I've taken for gene ontology consortium, also known as GO, the sequence ontology, the generic model organism project and the ontology for biomedical investigation. And what I did is I went to a GO gene ontology and I pre-read insulin regulation and it came up with this term. So basically, the ontology is a set of terms and a set of relationships for that term that give us a well-defined context for a particular term. So it has this accession number through which we can always access it. The name is regulation of insulin secretion. It comes from the ontology biological process. In this particular case, they don't have synonyms or alternate IDs and there's a number of links. So this is one way of looking at the term in GO. There's a second method and it's a graphical method showing it as part of the graph. So at the bottom, we've got regulation of insulin secretion and now we've got these links. So this yellow link means regulates. So regulation of insulin secretion is a regulation of insulin secretion. It's also ISA, so regulation of insulin secretion is part of protein regulation and it's also part of regulation of peptide hormone secretion or in other terms. Or in other words, a more specific term for each of those or a more specific concept for each of those. And we can see how we can actually climb up this tree either looking at regulatory frameworks or just the more general terms for different concepts. Another type of vocabulary we can use is what is called a controlled vocabulary. A controlled vocabulary is slightly narrower than the ontology and it reflects the terminology used by a particular community or particular domain. And ANS has a vocabulary service and the diagram on the right is a visual representation of the ANS vocabulary service. So the second principle was that metadata use vocabularies that follow fair principles. So what this is telling us is that our data and our metadata should be fair but also the vocabularies that we use for our data and metadata should be fair. Meaning, of course, that they should be findable, accessible, interoperable and reusable. And the important ones there are actually findable, accessible and interoperable. In other words, things like machine readable that they available easily etc. And the third one was that metadata include qualified references to other metadata. And so it's nice having a reference to other data. But a qualified reference is a reference that expresses its intent or relationship. So in the insulin example, we saw that X is a regulator of Y which is a lot more informative than X is simply associated with Y or X is a kind of Y or C or so Y. And in this case, all datasets need to be properly cited. So earlier when we saw those boxes under the code, the gene ontology, each one of those is also a term that appears in the gene ontology. And we can look at each one of those and we can look at their graph. So once again, there's a little use case that came up in the last couple of weeks with the work colleague. And I think this gave quite a nice idea of interoperable. What you see and what my colleague presented me here come across this data, which actually is from the CIA World Book. On the left hand side, we've got the names of countries. And on the right hand side, we've got exports. So what this is saying is that Chile exports 28.6% of Chile's exports are to China. For Croatia, 13.5% of their exports go to Italy, 12% to Slovenia, et cetera. And he thought it could be rather nice to plot this on a map with arrows going from one country to another. So from China, we'd have a thick arrow going to the US, a similar sort of arrow going to Hong Kong, et cetera. So the first thing about this data is it was a web page. There was no obvious way to download the data in a format that was machine readable. But there was a second problem. So copying the web page and putting it into a format that we could use took maybe half an hour, 20 minutes. But there was a second bigger problem. And that was, well, we've got these countries on the left, China. This is Democratic Republic of the Congo. This is the separate country, Republic of the Congo. But we don't know their length. Well, it wasn't too hard to find a web page that had the names of each country and the geocenter, the lat-long of the middle of that country. So this was great. So all we have to do is combine these two datasets. However, we go back here. What we see is Congo Democratic Republic. In this one, there's nothing like that. It's clearly Congo DRC. But we had to edit the data by hand to make it the same. The same with Congo Republic of the, well, in this one it's Congo and then in brackets, Republic. There's a more subtle thing. And I'll see if... Cote d'Ivoire or Avery Coast, if you look carefully at that one and then look at this one. I don't know if people have noticed, but in this one there's no accent on the O and in this one we do have an accent. So a machine doesn't recognize those two names as being the same. However, on the left-hand side here, we've got country codes and these are the international country codes. Also known as ISO 3116, the International Standards Organization has created two letter codes for every country. They've also created three letter country codes. So the first thing we did was to go back to this original set and for all these countries replace the name of the country with the country code. We also had to do the same thing on the right-hand side. And as I said, putting it into a format that was reusable for a machine took about half an hour. Replacing the country names with country codes took a lot longer than that. If the original authors had used a standard vocabulary, in other words, use the ISO 3116, we would not have had that problem. Okay. So the second thing I mentioned was that data was available as a web page. So accessing data is also very important to both accessibility and interoperability. So one of the best ways nowadays to use the use of APIs or what I prefer to call web APIs, application program interfaces and there's a number of APIs you can draw to put them into two. The first one is a standard web service. So a good example of that is the OGC or Open and they publish standards saying, okay, we have particular services. An example of one of these is the web mapping service WMS. And this is exactly how you will send a request and this is exactly how the data will be returned. A second one, a broad category of the non-standard web services. So I decide that I've got some data. It's about, let's say, the birds that I've observed in an around Melbourne. I'd like to be able to present that through an API. So my data will present the latter name, the common name, the data observed, the bird and the lat-long. So there's no other service offering that particular thing. So that's an on-standard web service. It can be documented and there's ways of documenting it, which we won't go to, but there's a documentation standard called OpenAPI if people are interested. The next method is by file download. In other words, my data is available as a file. Just download it. And once again, there are different ways we can download files. The first way is files that use an open standard and things like PNGs, CSVs, NetCDF or all examples of files using open standards. The second one is files using proprietary formats. And this is where it gets a little bit gray because some companies have published the format, but the licensing is a little bit murky. However, if one thinks of, for example, old Word documents that was using a proprietary format, if you did decide to write something that paused that file that looked at the information inside, you were pretty much on your own. Microsoft preserved the right to change the format at any time and not have to inform you. They made no commitment to staying true to the format. And then the third way that data can be accessed is by special arrangement. In other words, we don't give you any way to download this data. However, if you're interested in this data, send us an email or phone us up and we may send you a tape. We may send you a piece of paper. We may send you a file in a standard format. So in general, this goes from the most fair, APIs in particular using standard web services to the least fair where you have to find the data and then contact the company and then ask them for the data. And it may not be in any particular open format. Okay, we're going to get on to reusable. And this comes from an ANS document and reusable is about making data available so that it maintains its initial richness. For example, it should not be diminished for the purpose of explaining the findings in one particular publication. It needs a clear machine readable license and provenance and the key words there are license and provenance. And so we'll look at the three principles of reusable data. Firstly, metadata are released with a clear and accessible data usage license. And we'll get to that in a minute. Metadata are associated with their provenance. So we know where and how that data came about. And finally, metadata meet domain relevant community standards. So full license, some people assume, okay, if the data's got no license, anybody can use it. In Australia, this is not the case. In Australia, if there's no license on your data, then this regarded as all rights reserved. In other words, that is copyright. You can't use it unless the data explicitly says you can use it. A very good, if you're trying to make your data open and you don't want to get involved in all sorts of legal issues, etc. The group known as the organization Creative Commons has done a lot of work in order to make licensing data or the open licensing of data as simple as possible. So they've created a number of legal documents, a number of licenses to as fast possible allow people to find a license that suits them and that will be suitable for their purposes. And we'll come to a bit of those in a moment. So the first slide I've got simply shows what components are needed to apply Creative Commons license. And so this is an example of the Creative Commons of the work that's under Creative Commons. The work is a photograph of a birthday cake celebrating their 10th birthday. So it has a title, it has an author, it has a license and very importantly, this license is linked to the actual license and it's also linked to a machine-readable version of the license. It also has a source, so the title is linked to the source. And here's an example that I just copied from one of the examples. This is the MIT OpenCore square and I copied the license from the bottom. So what the CC tells us is that it's a Creative Commons license. This symbol is the attribution. So it says basically this is license about Creative Commons. If you use it, you must give the author attribution. In other words, show the source of it. It can only be used for non-commercial use and if you share it. So you can also modify it or make derivative work, but if you publish or share that derivative work, it must be done. SA means share alike. The conditions under which you share it can be no more restrictive than these conditions. In other words, you can't put extra restrictions on. So you'll say text version of what I've just said. So share means you're allowed to copy and redistribute the material in any medium or format. Adapt says you may remix, transform and build upon the material for any purpose, even commercially. The attribution is you must give appropriate credit. And share alike is that if you remix, transform or build upon the material, you must distribute your contribution under the same license as the original and this goes a bit further. There can be no additional restrictions. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. And yes, a very nice diagram, which, so as I mentioned, there's seven, one, two, three, four, five, six, seven different licenses. At the bottom, we see the most restrictive and at the top, the most permissive license. So if it's got across through the dollar, it means it can only be used for non-commercial use. These ones said that if you release it, you've got to share it like. All of these say you need to give the owner attribution or the original author attribution. And at the top one, we've got that it's equivalent to putting it in public domain, which is essentially saying there absolutely no restrictions on this work. Anybody is free to use it. It's in the public domain. So the next topic we're going to look at is provenance. The term comes from things like the art world where provenance on a painting meant if you buy a painting and spend a few million, you want to know that it's really the original. And so when paintings are bought and sold, they keep a track of each person who's owned that painting so that you can go all the way back to the original artist. And with data, if we're using some data, we want to make quite sure that the data we're using, we want to understand where it comes from, how it was derived. I was looking at some data a couple of days ago. It was how much money the top, let's say 50 companies, how much tax the top 50 companies in Australia were paying. So for each company, it had their annual earnings and their tax. One of the problems with this data was there was no reference back to how this data had been collected, who collected it. So it makes it very hard. Is it collected by the tax office? Is it collected by the companies themselves, like from their annual reports? Is it collected by some third party who has got some sort of agenda? Unless we know that it's very hard to make a decision. So this diagram is a model that I'm familiar with. This is very roughly how we get forecast from the Bureau of Meteorology in. So if we have a forecast that today it's going to rain and there's going to be gale force winds, that forecast comes from a model. AWS is our automatic weather stations. So they collect it every minute. There's 600 of them around Australia. Each of those is quality controlled and is fed into the model. There's also manual observations. Each one of those is quality controlled and fed into the model. And then yesterday's run of the model will be fed into today's run of the model. So the model gets fed back into the model. Now, for us, it's very important that if we make a forecast that we know where every piece of data went. So what kind of provenance are we interested in? Well, we can go back to the automatic weather stations and the kinds of things that we're interested in. We've got a station that's measuring, let's say temperature. What kind of thermometer is being used? It makes a difference even if the difference is a hundredth of a degree. What kind of rain gauge? Certain rain gauges have a bias if there's very high volumes of rain. Some rain gauges may have a bias if the wind is blowing while it's raining. So in strong winds, they may slightly underread or overread. When was that equipment lost calibrated? Have there been any systemic errors with any of these? When were the batteries lost replaced? Because sometimes as the battery starts running low in an automatic weather station, there could be spikes in some of the data. When it comes to the quality control, there are questions like who was the person who did the quality control? If a value was flagged as being suspect because they never change values, they just say if some value, let's say we've got a temperature of 10 degrees and a minute later the temperature is 12 degrees, that value may be flagged as being suspect because temperature very rarely rises to two degrees in one minute. What determination was made? When it goes into the model, questions are, well, which version of the model are we running? Which software? Which version of the operating system is this model running on? So that finally when we get to the forecast, we can always work backwards and try to recreate all the conditions that gave rise to that forecast. It also allows us, for example, if we see a lot of systemic errors in the quality control for a particular station or for a particular dataset to say, okay, is there any commonality with the person who was making these changes, who flagged these as like worrisome? Is there anything about the equipment? Are there any commonalities with the equipment? Is it a particular type of an anometer that keeps failing, etc.? So yes, an example from one of the files that we produced, this file is available openly, part of the provenance record that's actually inside each one of these files. So this is a daily temperature file and so it says on what date there was a conversion. It was converted to the next CDF format, a credit format, quite common in meteorology. This is the name of the script that converted it. This is the name of the file that the raw data came from. This was the intermediate file that was created and over here these were the parameters that we used with the intermediate file. This was the configuration, the general configuration file and this was the specific configuration file that we used. And then the provenance continues. This was the data created down to pretty much beyond the millisecond. This is the version of the software that we were using and this is the version of this particular piece of software that was being used. This was the version of the next CDF that we're using, etc. This one's interesting. This is where the keywords come from and this goes back to that previous issue where we said that the vocabulary should follow fair principles. The keywords that we use in our data go back to what's called the GCMD, Global Change Master Directory which is administered by NASA and is available at this URL. Once again, another story about provenance and this time it was a blog that I came across in the last week and as I started reading through the blog, the blog was understandably about provenance. It was about limits of artificial intelligence as you can see from the title and I'll quickly run through it because I started reading through this and thinking, wow, this is all about provenance and then the author had intervention provenance. So what the story was about, the author's wife was pregnant, he was a statistician and so they went for a scan, an ultrasound scan and there was a geneticist at the scan and the geneticist said, look, there's some white dots around the heart and these are often symptomatic of Down syndrome and typically what we do under these conditions is we refer people for an amniocentesis and if there's an amniocentesis, I forget the exact figure but I think it's something like this, a 1 in 300 chance that the fetus can be killed by the process, by the intervention but an amniocentesis is the only way to get a more positive reading of whether there's Down syndrome. So the author, obviously being a statistician and also being aware of these issues was a bit reluctant so he started having a look at trying to get more information and in particular got some information about the ultrasound machine that they were using and one of the things that he found was that this was a newer machine and it was a much higher resolution machine than the previous machine and he surmised that maybe what was happening was that it just happened to be white noise because of the algorithms for rendering it and he spoke to the geneticist and she said, yes, they had noticed an upturn in the number of cases that did come positive for Down syndrome since getting the new machines so where the story ended was that they decided not to have the amniocentesis and they had a healthy baby. However, I think the important thing is if people had simply been collecting this data but they hadn't been collecting the type of machine or the operator or a whole lot of other provenance they may not have actually been able to work back and actually found that these machines or the algorithms etc. were giving false positives. So once again, with any kind of data and particularly research data, provenance becomes hugely important. So I think we've got a few minutes left so to end on a lighter note we will see Neil Armstrong and Albert Einstein discussing data management. Hello Neil Armstrong, how are you today? Albert, good to see you. I am not good. NASA wants me to create a data management plan for my next moon mission but they'd rather watch cartoons. But Neil, data management plans are not difficult to make and they are important. More important than watching cartoons? Yes. More important than eating macaroni and cheese? Yes. More important than posting pictures of my cat on Facebook? No. But thinking about how to manage your data now will help you later on. Okay Albert, tell me how data management will help me later on. Well, my friend, it is quite simple. Data management plans help researchers manage, share and preserve their data. Good data management practices ensure the data is well organized and well documented so that it can be shared with others. Why would someone else want to see my data? They may be curious about a detail of your experiment or they might want to use it in one of their projects. Many funding agencies now require that you have a plan for sharing your data before they will give you money. I like money. I use it to buy macaroni and cheese and cat toys and a rocket ship to get to the moon. Managing your data well adds value and makes it easier to keep and share with others. If other researchers find your data useful it enhances your scholarly reputation. That sounds good. Then NASA will give me more money and I can buy another rocket ship. Yes, exactly. Just remember, you need to think about data management early in the research process. There are important things you need to prepare for like capturing accurate documentation and storing your data in a safe place. Okay Albert, I will go and start working on my data management plan now. Check out the DMP tool at dmptool.org. It will help you with your data management plan. That's great. I need all the help I can get. But before you go, let's dance. Okay, so that's all I'm going to say. I've prepared a little quiz so people can test the knowledge of INR. It's at this. I don't know if people want to spend five minutes now. I'll hand it over to Andy. Okay, we might go straight into questions. Yes, let's see if there's any questions and then people can do the quiz if we've got time. I'm guessing what Gareth is asking about is if you present your data with a share or like license being sort of you can do things with it. You can create derivative works, but whatever you create has got to be got to be released under the same license. So you're not allowed to create something that's derivative, but then keep that to yourself and not share it. The expectation is that whatever you create as a derivative piece of work will be shared under at least that same share or like license. Now that's straightforward if there's you're using one piece of work and that you're using that as a basis and you're creating a derivative work and then you say, okay, it was originally licensed under a CC by share or like. That means the derivative work I'm creating will also have to be released under a CC by share or like license. This also happens if you're actually bringing together two pieces of work, two different pieces of two different data sets with different share or like licenses. One is CC by share or like the other CC non-commercial share or like, then you mingle the two, you create a new derivative work, then it would be licensed under CC by non-commercial share or like. So there's like the more limited one of the two. Is that sort of help? Okay. So I hope thank you. Yes, let's answer the question.