So that's everything in the vector 2 type. The vector 3 type of course has an extra z component It has a third float and so we have these two more static properties forward and back. Forward is the unit vector pointing down the z-axis in the positive direction and back is the unit vector pointing in the negative direction down the z-axis. Then vector 3 has several methods which vector 2 doesn't and I'm not sure why that's the case because I can imagine scenarios where you'd want to do these things on vector 2s as well. Though the fact that vector 2 doesn't have these methods isn't really a big hindrance because we can always just take our vector 2 values and get their vector 3 equivalent where it's the same values except of course the z component is zero and then use these methods to get our results and just convert back to a vector 2. So it's not much of a hindrance that vector 2 doesn't have these but it is still just a little strange. I'm not sure what the rationale for that was. Anyway, so the first of these ortho normalize has two overloads, one taking two vectors and one taking three and all these vector arguments are passed by reference. Ortho normalize doesn't return anything. It's modifying the vectors that you pass in and here looking at the first overload but it does to a the first argument is it simply normalizes it but then the second vector it wants to make that a unit vector that is perpendicular to a and of course given a vector there is a full 360 degrees of possible vectors that are perpendicular to it And so the question is well, which unit vector does B become? Well, what it does is it takes whatever the input vector B is and between A and B that forms a plane and you can imagine B being swung on that plane until it is perpendicular with A and that's what ortho normalize does. Given two vectors, we want to tweak them until they're unit vectors that are perpendicular to each other. A doesn't move, B is adjusted to be perpendicular to A. The second overload does the same thing but then for C the third vector argument ortho normalize will mutate this into a unit vector that's perpendicular to both A and B. So this is like adding in a third perpendicular axis adding in a unit vector that's perpendicular to both of those while there's two possible choices and Of course those two vectors go in opposite directions So the question is well, which of those two possibilities will ortho normalize choose and that is determined by for whatever your input vector is vectors A and B form a plane and C is either pointing to one side of that plane or the other and that determines which of the two unit vectors perpendicular to A and B that C will become and Actually, also this is the case where C is already on the plane of A and B So I don't know how it breaks the tie in that case That has to break it in one way or the other. So the algorithm probably just takes one of the two Next we have the project method and here you should think of the vector B as being more like a ray So it's describing a direction But imagine it not really having a magnitude it just goes off into infinity and The vector that project returns is the point along that ray that ray of B Which a is closest to a describes a point and somewhere along the ray of B. There's a point which a is closest to Another way to think of it is again imagine B is more like a ray going off into infinity But it forms the right triangle with a and project will return for that right triangle It returns the length of the vector along B that forms that right triangle It returns a so-called adjacent of that right triangle And then we have project on plane which does something very similar Except instead of projecting a onto the ray described by B. It projects a onto the line perpendicular to B So it's returning the point on that line perpendicular to B, which a is closest to The formula for this is actually very simple You simply project a onto B and subtract the result of that from a and that gets us the result of project on plane Here's a little illustration of the project method We have our vectors a and B and we're projecting a onto B and then we're going to draw a is green B is yellow and the projection vector will make black Come over here play the game Maximize this so here's our vector a in green our vector B in yellow And if we project a on to B There's a right triangle formed between them and when you form the right triangle between them This is where the so-called adjacent of that right triangle ends or another way to think of it the closest point on the line Described by B the closest point to vector a this point over here is this one right here with a black line ends And actually I really shouldn't have said that we project on to a ray of B It's always a line because as we can see here if we're to change a and make this Negative to come back here So now I imagine B is a line extending off in both directions into infinity And so the closest point here of a on that that line of B is right here In fact, just be clear It really doesn't matter what the magnitude of B is if we normalize this or just here I'm just going to divide it by some random number to make it much smaller if we do that come back Wait for the code to recompile doesn't change the projection whatsoever So the length of the B vector we're projecting up to its magnitude does not matter Now instead of project Let's use project on plane drawing a screen B is yellow and the projection vector as black. Let's see what that gets us Here's our a vector. Here's B And again the magnitude of B really doesn't matter all it's used for in this case is to describe a line running Perpendicular to it so it'd be a line running this way and what we want to find is the point along that line Which a is closest to and as you can see that's right here Though another way to think of it is that we're forming a right triangle And so the vector we get back for the method is actually the adjacent of that right triangle It's just in this case. It's a right triangle formed between a and the line perpendicular to B rather than B itself So that's both project and project on plane and just understand I've demonstrated them in two dimensions on the XY plane, but we can use any three-dimensional vectors So here, let me just quickly demonstrate that I will give this a z-core of four And we come back wait for the update for the code to recompile and well It doesn't seem to have changed But that's just because of our orthographic perspective if I switch out to perspective mode you can see here that now that vector is Going back into the z-distance and so what the method does is that for the plane formed by a and B It finds the line perpendicular to B on that plane and that is what is projecting a onto Next on vector 3 we have slurp and slurp unclamped These are much like lurp and lurp unclamped which we saw in vector 2 vector 3 has those as well But in addition it has these two slurp and slurp unclamped and the difference is that here We are spherically linearly interpolating rather than just linearly interpolating and what that means is that we are interpolating a position in between a and B we're going from a towards B in this case 40% of the way But instead of going directly It's as if we're moving along the surface of a sphere hence the name spherically linearly interpolating Except because a and B aren't necessarily the same magnitude I think it might be more accurate to think of this as a method that linearly interpolates the angle of movement From a to B rather than the absolute position of a and B. It's probably just easier to illustrate this in code So here we have our vectors a and B and we're going to draw the first one as a green line the second as a red And then we're going to draw three slurps from a to B first 25% of the way from a to B then 50% and then 75% And we'll make those blue and then we'll do the same thing with lurp But make those magenta and because both of these vectors just for simplicity of demonstration I made them both on the XY plane they both have Z components of zero When we go back to unity, I'm gonna want to fix my axis. So we're looking at the XY plane orthographic projection Expand this and then hit play Okay, so green. This is the a vector The red vector is B and then if we linearly interpolate between the position of a and B This is 25% of the way. This is 50% of the way. This is 75% of the way But when we spherically linearly interpolate This vector here is a vector where the full angle between a and B. This is 25% of that angle This is 50% of an angle and this is 75% of that angle And it turns out that well halfway our lurp and a slurp are going to have the same direction vectors But that's not the case at other points along the way from a to B And understand the magnitude of our slurp vectors the blue vectors here That is determined by linearly interpolating between the magnitude of a and B But in this case, I made their magnitudes the same So the magnitude isn't changing in this case. It really looks like a spherical linear interpolation It's as if we're moving along the the surface of a sphere or rather in two dimensions along the surface of a circle But if I come back here, I'm gonna make this shorter same direction just shorter divided everything by two Wait for the recompile and then okay. Yeah So the linear interpolation part that that makes sense. It's just the points again 25% 50% and 75% of the way from a to B But now our slurp vectors The angles haven't changed their directions are the same but the magnitude of these vectors this one is 25% in between the magnitude of a and B This is 50% in between the magnitude of a and B and this is 75% of the magnitude from a to B and just to illustrate again that This can work in three dimensions here I'll give this a z component of seven save that code go back here wait for the recompile and Now we actually get different results and that's actually apparent in the orthographic projection But here I'll switch to perspective view come here and rotate. So the way to think of it is that A and B form a plane and for slurp. We are linearly interpolating the angle between a and B So this first vector over here the angle between it and a is 25% of the angle between B and a and Again, the magnitude is linearly interpolated to be 25% of the way from a to B I believe in this case a would be a longer vector. Let me make that even more apparent I'll make this a big number and we'll just move over a little bit Wait for the recompile and yeah So now a is definitely bigger than B And so when we linearly interpolate 25% of the way from a to B The magnitude is actually being shrunk as we get closer to B As for slurp unclamped in that case our percentage value is not clamped into the range of zero to one So we can have like 1.25 1.5 1.75 etc And we'll draw these in black and let's see what that looks like here and There so this is 125% of the way from a to B. This is a hundred fifty hundred seventy five hundred two hundred percent 225 250 and notice in this case because a is longer than B The magnitude is getting shorter and shorter and in fact eventually our unclamped slurp vectors are going to go negative In fact, I can illustrate this if here. I'll just make this z component larger So now vector a is a significantly larger magnitude than B come back here wait for the code to be compile and okay Well, here's 1.25. Here's 1.5 And here's you expect the angle to be right here and it is but now our magnitude has gone negative So it's now going off into the other direction So just watch out for that. It can be a little confusing We have another method rotate towards which is like slurp But here the third argument is not a percentage of travel from a to B It's rather the amount we want to travel in terms of absolute radiance So we're still finding a vector that is swung from a to B But we're specifying in radiance the actual angular distance We want to go and in the event this angle is larger than the angle between a and B Well, what we get back is a vector with the same direction as B. It won't overshoot So in a sense the the rotation the movement is clamped by B itself And then this fourth argument is specifying how much we want the magnitude to change Like say here imagine a has a magnitude of 5 and B has a magnitude of 10 Well, the vector returned by rotate towards should have a magnitude closer to be how much closer Well two units closer So the returned vectors magnitude would be 7 in that case though if the difference in magnitude between a and B Were less than this distance the returned vector would just have the magnitude of B itself So it's possible we specify values large enough for both these arguments where the vector we get back is just equal to B And someone unfortunately the documentation says something about linearly interpolating the magnitude But in all my tests I didn't see that behavior at all It seems to always return a vector where this is the change in magnitude This is the delta magnitude, but it's capped at the magnitude of B So we're never going to overshoot B As far as I can tell that's the real behavior and there's no actual linear interpolation between the magnitude And so whatever the angle it is we specify that has no bearing on the magnitude of the return vector Only the last argument has an effect on that Lastly we have the cross method which gets us the cross product of two vectors And this is the one method that doesn't apply to vector twos It's something that doesn't make sense in two dimensions Because what it returns effectively is a vector that is perpendicular to both A and B And the way you compute that is that well for the x component That's found with these operations which note don't involve the x components of either A or B And then when you find the y components it's these operations which note don't involve the y components of A or B And then to find the z component similar deal except again it doesn't involve the z components of either A or B Anyway, we won't go through the proof here But this gets us back a vector which is perpendicular to both A and B And the question is well, which way does it go? Given two vectors the third vector that's perpendicular to them goes in one of two directions And so which way does it face? Well the way this is computed effectively gives us the the cross product according to the left-handed rule So if you hold up your left hand and form an L with your thumb And index finger and stick out your middle finger away from them It's like your hand is representing three perpendicular axes Well, your thumb would be the A vector your index finger would be B And your middle finger is the cross product If you made the same gesture with your right hand Where A is your thumb and B is your index finger your middle finger would in the sense be oriented in the opposite direction So that's the distinction between left hand and right hand systems So when we have three axes we talk about left-handed systems and right-handed systems Anyway, so that explains the direction, but what about the magnitude? Well, it turns out the magnitude of the cross product again We won't prove this but it turns out to be equal to the magnitude of A and B multiplied together With also the sign of the angle between them And like with the dot product this equation can come in useful in certain circumstances. We won't go into here Let's though just see a demonstration of getting the cross product Here we have A and B. I'm drawing A is green B is red And I'm competing the cross product manually And then drawing that as a yellow line But then I'm also competing the cross product using the built-in method and we should see the same result But I'm drawing that as a black line on top of our yellow line, but normalized so it should be much shorter Otherwise we wouldn't see the yellow line at all And lastly here I'm getting the cross product of this time B and A. I've reversed the order Because cross product unlike dot product is not commutative the order matters And what that changes is it flips the direction of the cross product vector The left hand rule still applies form three perpendicular axes in your left hand with your thumb index finger and middle finger But in this case now B is your thumb A is your index finger And so now the cross product is going in the other way than if we got the cross product of A and B So let's see this in unity There so we have our two original vectors A is green B is red Yellows that are cross product with the full magnitude But then if we normalize it we get this little black line And the cayenne line is the cross product of B and A also normalized And notice it's just the inverse direction from the cross product of A and B


Thanks, Ebi. Yeah. And Karan, welcome to Dev Nation Day. So I've heard that you're playing with some automation in smart cities. So how does it work? Lots of automation. You guys are going to see lots of tools, lots I can already see on the chat. People are excited. So yes, I'm going to show you guys a tech soup that I'm calling it. So bear with me. Yeah, please. Yeah. Show us. Sure. Okay. Thanks, Edson. I'm going to share my screen. Give me a hand up once you see this, sharing screen number three. So what do you see? Do you see my preso presentation? Oh, it's a different screen. I do. Yeah. We're seeing one screen. Oh, fantastic. So hey, guys. I'm Karan and I'm going to show you so all the previous experts on this, on the keynote session, show you how you can seamlessly and in the simple way deploy your apps, your Doc scanner or doc detection apps, CAD detection apps and Pacman game shows. Today, I'm going to show you how you can move these apps onto the edge, right? Because we have billions of people to go to, right? So I'm going to show you how you can deploy, seamlessly deploy open shift and Kubernetes and move to edge and definitely get the data from the edge to the core, do some machine learning solution designing on top of open shift. So we'll start with some, as a standard software development practice, what's the business requirement, right? So let's start with the business use case, a business requirement. We need to build an app which or a solution, it's not a single app, it's a collection of apps. Build a tax stack that should help us reduce some congestion and we need to charge vehicles, some kind of fee as they drive into a city area. So we have chosen an area in London called as ultra low emission zone in which there's a special charge that the vehicle needs to pay because the authorities wants to protect or because of environmental reasons they want, not all vehicles should just enter into the city.  If the vehicle does not meets emission standards, we want to apply more charges so that the owner will not enter the city very frequently. And maybe a third use case would be, hey, I want to locate a wanted vehicle, right? So which the officials can use the system for. So that's the business, very, very high level business requirement. We got to walk together on this and touch upon how we can capture all these things. So that's the text I was talking about. I'm going to show you lots of tech, lots of tools, open source and Red Hat tools and lots of tools and each of them have their specific reason why they exist on the solution, right? We don't want to over engineer, but yeah, there is a special place for Kafka for Ceph, for Superset, for Grafana, right? For databases, obviously Kubernetes is the heart, the beating heart for all the solution. Ansible is going to be the magic through which we're going to create some tech soup on this. So yes, before we go into the Morse presentation mode, I'm going to go and show you, let's deploy. Let's deploy this thing right now and let's use Ansible to do it for us. While Ansible will do the heavy lifting in the background, I'm going to take you over to a few more generally like what I'm doing in this actual implementation. Meanwhile, the system will be on its toes. So what you guys are seeing here is I have my terminal opened up here. I've already logged in to my OpenShift CLI. On the other side, we have OpenShift console through which we're going to see how things operate. So step one, there is nothing on the system right now. It's a vanilla, yeah, not vanilla, but it's an empty OpenShift cluster and that's the first workload, the smart city workload we want to deploy on this cluster. So starting with, starting the basics, create a new project, right? New project, we'll see a new project called a smart city, you want to run this? Okay, we are into the project and the CLI is smart. It have already added me to the project. Next I'm going to run an Ansible Playbook, which I'm going to explain you in a few slides. So bear with me, this is going to do a lot of things in the background. This process will take close to 10 to 15 minutes. Meanwhile, we'll go and explore how the overall solution looks like. So we're going to run Ansible. So those of you who are familiar with Ansible, Ansible Playbook is the command to do it. I'm going to run it from local because I have the connections established on my local machine. And then this is the main master playbook we're going to run against. So I'm going to hit this and meanwhile this is running, let me pull up my project, smart city, the project is here and just go to my favorite section, workloads and parts. So we're going to see lots of stuff coming up, right? So coming back to my deck, back into presentation mode, system will take some time to set up. Let's understand. Let's understand meanwhile what is happening under the covers. So from the solution design point of view, okay, we have this city of London, ultra low emission zone. We have all these cameras installed across various toll locations in across the city and the use case is that each toll location that we're calling as an edge, right? Each edge location will recognize the vehicle, the passing vehicle and detect its license plate. And through machine learning models, we're going to grab the string that the model detects in real time from all these edge locations and append some metadata to it. What is the timestamp through which this image was captured? What is the geolack long so that we can do all sorts of amazing stuff once we have a lot of data into the system? But the data is being generated at the edge and there are multiple edge locations here. How we can move the data to the central data center, so the central system where we can do lots of compute and analytics, do some free calculation on the data that we have collected, apply some more business logic like, hey, apply some dirty vehicle, dirty charges to the vehicle and notify the officials in case if the system finds out a lost vehicle, right? So remember our use case that I've explained before? So that's the very high level design part one. In part two, great, we are live capturing data across from all the edge locations on to the central core location. We have done some business analytics in there, but wait a minute. We are capturing a lot of data. What should we do with this data? Well, we should retain the model because obviously machine learning, the model has to be updated as we learn about the data as we collect more data, right? So we need to store vehicle images, license plate images and license plate strings into the system, right? And then retrain the model. We have a model already on the edge. Retrain the model, improve its prediction accuracy and then deploy the same model or the new version of the model across to multiple edge locations. So OpenShift helps to do that and tools on top of OpenShift helps you to build a solution like this where you have multiple few hundreds to thousands of edge locations. You just need a system that provides the right tool to do this kind of stuff. So that's part two of the solution design, right? Deploying the model at the edge. Let's come back and understand how this things work, right? So this is more kind of a thousand foot view of the system. We have multiple edge locations running OpenShift environment. It could be fat edge. There could be a three node OpenShift cluster or maybe a very, very thin location where they just need a single node, a single OpenShift, right? That's really a thing right now.  This model will then extract out the areas of the image where it detects a potential license plate. And then it will basically give this subset of the image to an OCR model, Optical Character Recognition Model, which will read the letters from the number plate, right? Pretty standard. And then once we have fancy of data onto the edge, what we'll use? We'll use Kafka. Kafka is the go-to technology to handle this kind of workload. So we'll use Kafka, Kafka producer, and then Kafka producer will produce some messages onto the local Kafka cluster running on the edge. But now here's the magic. We have lots of edge locations and we need to move the data. We need to build this loosely coupled system that should handle the network disruptions, right? So Kafka does it pretty well for us. All the events, all the metadata will be stored on the local Kafka edge clusters. And then it will asynchronously, it will move the data from edge to core using a technology or using a feature called as Kafka Mirror Maker, which will move the data from one edge or maybe thousands of edge locations onto the core central data point, which is Kafka again. So we are capturing the data onto the central Kafka cluster. And once we have the data onto the central Kafka subsystem, fantastic. We can do lots of things with it. For example, we can build our own Kafka consumers, maybe a Python Go or Quarkas apps that can read and tap into the Kafka topic and build some business logics, do some wanted vehicle notifications or else for long term preserving the data of Kafka. Because remember, this is all governmental data, right? We want to store data historically, correct? So we need some persistent layer that can move data that is coming to Kafka, move into an object storage because object storage is autonomous storage, correct? You just store it, it's simple to use and it is cheap, it's low cost. So we'll use something called a Secur, which can help Secur is by the way a Kafka consumer, which will tap into the Kafka topic, read the data from Kafka, store that onto object storage bucket. And here we are using self object storage, which is the choice. And next, once we have data onto object storage, we can use, we can build analytics and reporting, real time analytics and reporting using tools provided by OpenData Hub, like SuperSet, Apache SuperSet, OpenGrafana, Starbus. So Starbus is a very amazing technology. It can allow you to write and build distributed queries across heterogeneous data sources. Like you can write a SQL query that's very powerful, which can go and read data from a SQL database. And at the same time, you can join it using data that is living on the object storage as three interfaces. So this is the power of Secur, sorry, SuperSet that provides you to have this kind of environment, which is a very powerful engine. And then SuperSet is basically the dashboard and reporting part of it, which will show you in real time the reports. And Grafana obviously, to manage this system, we would need a cool developers like you, as well as some operations people who want to keep an eye on onto their dashboard. And they need some real time Dashboard and Grafana is like the best choice, at least in my opinion, because I love Grafana. So this is the overall solution that the Ansible automation under the covers is doing for us, right? So what Ansible is doing for us, let's understand. It's installing and setting up a database for us. It's installing and setting up Kafka clusters, because this is a demo right now. So Ansible is deploying an Kafka edge cluster as well as a Kafka core cluster on the same OpenShift environment that we have. And then Ansible is going to deploy multiple microservices. So one of them would be license plate recognition microservice, which includes using build config builds and then from Git source to images and deploying that into deployment config and launching the pod and creating the service and exposing it through the route. So Ansible is actually doing a lot of work. Similarly, like LPR, Ansible is setting up microservice for events, microservice for image server and microservice for load generator, because I don't have a real camera hanging around and looking at the street. But yeah, so we have a load generator for the sake of this demo. And then setting up Secur, which will move the data into the system. So this is all microservice setup that Ansible will do for us. And then setting up OpenData Hub, which is the collection of tools which will help us do analytics in real time. And then finally, the Grafana dashboard setup. So Ansible is really doing the heavy lifting here. While I'm speaking and talking to you right the moment, the system is doing the job for me. And for all the enthusiasts out there, what all modules I'm using to make this happen, it is just these eight modules I'm using to get my system up and running from zero to 100. So there's a community model called KITS, which basically uses OpenShift client, Python client library, client tool, I think library, which it will call up and it has full access to all sorts of OpenShift objects. So Ansible can just tap onto the KITS module and boom, you can just do whatever you want, you can do it with an OC client, right? Similarly, by the way, OC client does crud. It will do and change the objects. While KITS info, another module, it will go and read out all, basically all the get commands that you do, like OC get or kubectl get, KITS info will do the more or less the same stuff. We are using set effects because we have to manage the state and dynamically update the inventories and have some variables that we wanted to capture into the system. I'm running some commands because there are certain things which are still complex and I don't have a module for. I'm like Supercept, Supercept data updations. I'm using some commands for it. I'm using copy modules to copy images from my data store to S3 and then adding host to the inventory, S3 sync to move the data and a pause just for, you know, sake of completion, like I'm pausing it just so that system can get stable. So yeah, look at this. I mean, I'm just using the eight modules to do a lot of heavy lifting using Ansible. And Ansible is a really powerful tool to do this kind of automation for you. Okay, let's now quickly check how does Ansible KITS module or Ansible usage will look like. You will write something like this. Okay, KITS module, the state is present and I need to do multiple, I need to apply multiple ML files. Like, okay, create a secret, create a deployment config, create a service, create an image stream, create build config. So KITS module and Ansible will make sure you're applying these ML files like you do in your OC create or OC apply minus F commands, right? Add into the right namespace. So that's what KITS module will do. It's pretty simple to use. And then KITS info is doing the other way. It's basically getting the data from the cube API. Okay, tell me the secret and the name of the secret is this and register the value in a PostgreSQL register variable and then later on, you set facts to pull out secrets like database username passwords and name that you can later on use in your playbook. So this is how you will create these Ansible playbooks for your lovely Pacman apps. Okay, and all of the code is available onto the Git repository. It's open, go and look it out, check out, happy to get some PRs if you guys are interested at some, we'll share some links and credits to my friend, Chris Bloom, who is an awesome guy who helped us to with a lot of Ansible automation around that you wanna see. Okay, let me go in and get back to my system. Okay, so the system is still doing the job. You can see that Ansible, I'm gonna scroll up a little bit and show you. It's not very user friendly, but since I'm using this in a very narrow window. But yeah, look at this. Deploying databases, deploying Kafka clusters and deploying Kafka drop APIs. And then all sorts of stuff that I explained to you. So system is still doing the job, so bear with the system. Let me see, yeah. So yes, so I'm into all projects. Let me go to Smart City project. Smart City is here. And yes, let me see if I'm getting any failures. No, there's no failure at the moment. So things are good. So yes, Ansible is, so at this point Ansible is importing some data source into SuperSet. So at this point, it has done lots of setup already. We are into the SuperSet part of the equation. Meanwhile, while this is getting ready, I have another environment where I was pretty sure that this is gonna take a little bit of time. So I'm gonna switch my window. It's another environment which is completely set up. So I'm not cheating here, I guess. Or you guys can let me know if I'm cheating. So just to save time here. So this is how the end result will look like. You're gonna deploy lots of containers, lots of parts, lots of services. And Ansible is basically automating and taking doing the all heavy lifting here. So this is my developer view. If you're a fan of administration view, go to the right projects from here, Smart City, and then you will see, okay, great. I have lots of parts running into a system, I guess. Yeah, 36 parts, these are jobs. But yeah, 36 parts up and running at the moment right now, which is making happen this demo. So I'll open one of my route, which is the dashboard, Grafana dashboard for the birds of view of the city. So as I mentioned before, this is the city of London and all these black boxes here are a camera. And we have this generator, which is generating images or like simulating vehicles which are passing from the station. In real time, we are capturing the count of the vehicle. We are detecting the vehicle, last known vehicle from the system. We are detecting its license plate, which is the optical character recognition and the license plate recognition model of the vehicle. And who is the owner? So by the way, these names are just made up names. So that, yeah. And here's a nice graph which can tell you this is a rich data that we are collecting. Okay, please tell me out of the city, what are the top stations we have in the city, right? So, okay, so station one is very popular because there are so many vehicles passing from the system. So you know what, these are some business metrics and data that you can take up. I'm gonna real quick show you the CPU chart because I like this because it's a GIF and it's pretty awesome to see how this looks like. Okay, as I mentioned before, we have edge, open shift edge, open shift core, we have Kafka and inferencing happening on the edge and data flows from the edge via mirror maker onto the score Kafka cluster and via second data move to stuff, stuff object storage. And then later we use Starburst and PostgreSQL and other tools to do some dashboarding around it, which looks like this. So this is your, this is the view for your managers and your key stakeholders. This is a reporting view that they will see. Okay, so far we have done like 215,000 pounds of collection, toll fee collection and 100,000 pounds of pollution fees and 50 or 45,000 vehicles have passed from the city, right? Again, this is all generated data, similar data, however, this to give you an idea that what you can do with these tools once they all work in tandem. And then we have these nice panels in superset which you can use adjusted according to your business case and you can get very precise metrics about your business. Okay, station number 5201 is getting 22% of the total traffic of the city, which means we need to do something here, right? Maybe add one more lane, maybe build a super path here, right? And then again, so not very interesting, but anyways, what type of vehicles are more popular in the city? Okay, Nissan's are popular or Audi R8 are popular. We can debate on that. And some table panels that you can tell me, tell me my top consumers who needs to pay a lot of money to the government, right? Okay, so Suzanne King needs to pay like whatever, 15,000 pounds, oh, why? That's a lot of money to the government. But anyways, this is a fake data and you get an idea, right? This is the view for your managers that they can enjoy and OpenShift and tools and projects running on OpenShift can make it happen. And all the nifty Kafka lovers here, I've seen that Kafka, I love Kafka, Kafka is great. And so I'm using this Kafka, which is a nice, very nifty utility. It's an open source project that you can tap into. You'll deploy it, you have the code in my repo. So yes, it will help you to, you know, check out Kafka messages in real time, no need for Kafka cat, instead just plug into this. It's an open source project. When you open this LPR and then you will, hey, quickly show me the messages. So this is real time messages coming onto the LPR topic at the core data center, right? See this URL. So when I expand this, you know, I'm getting events like, okay, timestamp and event ID and vehicle license plate and detection is successful and which one is on which station we are doing the detection, you know, those kinds of data. And I think we also, we definitely have another deployment of this. Let me see where we are on my deployment. Okay, wonderful, fantastic. So this worked, at least demo God, God is with me. So Ansible is completed, it has done, it has took like 29, oh yeah, 15 minutes. It has taken 15 minutes to deploy the entire setup that we have shown. By the way, you guys, we have already gone through all these things that you wanna show. The only thing I'm gonna show you here is if I go to network and see routes, there are so many routes in here. ODH, ODH is a great, great piece of solution. I'm just using two components of ODF. That's why we don't have a lot of component, but check out ODF, ODF is pretty cool. And then I wanna show you just last thing, my, we have shown, I've shown you a core, right? I've shown you a core, this core Kafka cluster. I just wanna show you my edge Kafka cluster as well. So these are the cluster deployed at the edge. The topic name is LPR, license plate recognition. I'm gonna view messages and I'll do quick view. Okay, so the point here is that we are capturing the message at the edge. Why a mirror maker?  We are doing inferencing at the edge, detecting the vehicle, capturing the license plates in real time, putting that strings onto Kafka topic from the edge, moving the data to the core. And then once the data is in the core, we are doing analysis on top of the data, analysis like this, analysis like this. So this is what I have to show you guys coming back to my deck. This Ansible OpenShift and a lot of the tools makes this happen. Okay, so I think I am done with my prepared content. If there are any questions, I can take it up for you. I do not see any questions, but yeah, I'm hanging around in the chat. If you guys have any questions for me, anything that you believe I should explain more or give you some pointers to play around, I'm happy to do it. So that's it. Are you still live streaming? Yeah, thank you Karan for this amazing presentation.


An example of a custom simulation used in our courses would be our EBF 473 course in risk management in energy industries. And in this course students need to apply the efficient market hypothesis to a stock market simulation. Now as you can see from the screen here, students have five different periods that they can participate in this activity, and so students are provided with a variety of companies with which they can purchase stocks, and then they get a series of percentages here that help them inform their choices, the number of assets that they currently own, and the total value of those assets. And then students have the opportunity to apply the efficient market hypothesis to these various scenarios. And so the students can take no action, they can buy, or they can sell, and they can choose the number of stocks with which they want to do that. So we're going to do a test run here. We'll buy, and we'll just buy lots of stocks. And we'll see what happens. Students click the submit trades option. Oh, something didn't go well. I started out with $30.09 and I ended up in the hole by 36 and 47 cents. So let's see, let's scroll down and see what the results of our choices were. Okay, so Madoff Trading went bankrupt. And then students would have an opportunity to move on to the next try. And as you can see, the company that went bankrupt is now removed. Students are no longer able to use that as a choice.


Hello and welcome to the session. This is Professor Farhad in which we would look at demand and supply shocks. Usually those topics are covered in macroeconomics. In this session, I will cover them from an essential of investments perspective. As always, I would like to remind you to connect with me on LinkedIn if you haven't done so. YouTube is where you would need to subscribe. I have 1900 plus accounting, auditing, tax, finance, as well as Excel tutorials. If you like my lectures, please like them and share them. That's very important. If they benefit you, it means they might benefit other people. On my website, farhadlectures.com, you will find additional resources to complement and supplement your accounting courses, CPA, CFA, CMA, or finance accounting courses. Let's first take a look at demand shocks. What is a demand shock? A demand shock is an event that affect the demand for goods and services in the economy. Let's look at some examples. One example could be a reduction in tax rate. What happened if the government lowered the tax rate? Businesses and individuals will keep more money. Technically, they're giving you, in a sense, they're giving you an immediate refund. Also, an increase in the money supply of the Federal Reserve, and this is what's happening now. The Federal Reserve is printing more money. That's trying to create a demand shock. Increase in foreign export. The most classic example of this is the economy post World War II, where we were the only producer and were exporting to the whole world. Therefore, we had a demand shocks for expert. A big increase in government spending will tend to stimulate, generally speaking, the economy and eventually increase GDP. This is what creates demand. Stimulus payments, like the coronavirus, what's going on right now? This is a demand shock. But that's what could happen. What comes with the demand shock is could, might increase interest rate by increasing the demand for borrowed funds by the government because the government is spending. They need to borrow money as well as businesses that might desire to borrow to finance new venture because there's new demand. They want to take advantage of the new demand. They need to borrow money. The government need to borrow money to stimulate the economy. There's a demand on the money interest rate might go up. This is not what's happening in actuality. It could also, as a result, it could increase the inflation rate if the demand for goods and services is raised. If you gave every older people money, what's happening now? Now, that amount of money is chased a limited amount of supplies. Eventually, when you don't have enough supply, prices will go up, which in turn, inflation will go up. So once the demand is raised to a level or beyond of total productive capacity of the economy where we don't have any more capacity to produce, prices will go up, inflation will go up. So simply put, demand shocks bring, generally speaking, interest rate up as well as inflation. It works in tandem with those two. Supply shocks, what are they? It's an event that influenced the production capacity and basically the cost of production. They're really characterized as aggregate output that's moving in the opposite direction of inflation and interest rate versus the demand. They go hand in hand in the same direction, supply shocks in the opposite direction. Let's see how, for example, if we're looking at changes in energy prices. And this happened in 2007-2008 right before the housing crisis. A big increase in the price of imported oil is inflationary because the cost of production rise and lead to an increase in prices of finished goods. I still remember 2007-2008 before the housing crisis, oil prices went up. As a result, the price of everything went up. As a result, some people could not afford to make their payments because their payment resetted, their payment went up. They had those resetted interest rate and this is what kind of, that's one trigger of the housing crisis 2007-2008. My experience in this is I still remember I was still in public accounting at that time. I used to, once a week, I used to visit a pizza place in downtown Bethlehem. This is what I used to work. And I still remember that note the pizza owner put because of the increase in gas prices, there was an increase in the cost of the cheese. As a result, he was increasing the cost of the pizza. It's still in my mind kind of how things connect. So the increase in inflation rates over the near term can lead to higher nominal interest rate. Of course, because remember, the nominal rate part of it is inflation. So if inflation goes up, inflation is added to the real rate. The real rate plus the inflation equal to the nominal rate, so the nominal rate will go up. Also, with raw material more expensive because again, the cost of production, the productive capacity of the economy is reduced. Why? Because the ability of the individual to purchase now is at higher prices. So here's what happened.  So it's not really a good combination. Therefore, GDP tend to fall when we have a supply shock. Other examples will be freezes, floods like the freezes of the oranges. What happened when we have that prices go up, droughts that might destroy large quantities of agricultural crops could also be the change in the educational level of an economy's workforce or change in the wage rate at which the labor force is willing to work. If the cost of labor goes up, then the cost of production goes up, just like the cost of oil. It's part of the input of the input of our economy. Just so I want to just kind of snip this story from this. This is around late March, early April. I don't remember. I don't have a date on it. I saved it on my phone. And basically, this is a classic negative supply and demand shock at the same time. McDonald's streaming its menu as long as the coronavirus pandemic persists. So this is basically a supply chain. They're not producing as more in order to deal with both. Failing customer traffic because there is not enough demand. There's a negative demand and staffing is labor negative supply. So this is negative supply and negative demand at the same time. And this is why this coronavirus, this pandemic is really very hurtful because it's hurting both supply and demand. Again, as of October, our economy is to a great degree. Many industries are benefiting the, the Amazons afterward, the technology, Apple, all these companies zoom Netflix. They're benefiting from this. They're benefiting from this. Other companies are not. Okay. So for example, McDonald is not doing as well. The restaurant chain said the move is an attempt to simplify its operation. Basically, the move is to increase earnings per share. Basically, cut down costs so you can survive. That's basically what it is. So how can we relate all of this to demand supply to what we do in this course, which is investments? Well, basically what you want to do is you want to identify the industries that most that that are most held or hurt from this supply and demand, whether that those shocks are positive or negative. For example, what happened if you forecast tightening of the money supply, you might to avoid the auto industry. Why? Because you're not going to have a lot of lending. If people can buy their cars auto industries will not be doing as well. They're also hurt by the increase in interest rate homes also are if interest rate goes up home prices go down. At the end of the day, everything that we talk about here, everything in economy, in my opinion, is an art rather than a science. So you have to take everything not with a grain of salt. You have to take everything with a context. And it's always you can bring two experts and they can give you two different opinion about the same data or the same perceived data, economic data. In the next session, we would look at the role of the Federal Reserve in the government in general federal government. As always, I would like to remind you to visit my website, Farhat lectures outcome for additional resources for this as well as other courses. Good luck. Study hard and most importantly.


the 2017 Total Solar Eclipse, presented by Science at NASA. On August 21, 2017, the shadow of the moon will sweep across the U.S. landscape, transforming day to twilight. In the surreal gloaming of an eclipse, the temperature drops, birds go silent, crickets begin to chirp, and blossoms start to close. As this scene plays out across a 70-mile-wide path of totality from coast to coast, a continent-spanning wave of instruments, ranging from homemade pinhole cameras and certified Eclipse safety glasses to the most sophisticated telescopes in operation today, will be trained on the eclipse across America. Just before totality, a point of glistening sunlight is visible, making the moon look like a diamond ring. Then, as that drop of sunlight shines through rugged valleys on the moon's limb, tiny points of light called Bailey's beads appear like a string of beads around the vanishing edge of the sun. But best of all are the pearly white streamers that emanate all around the dark disk at totality. Scientists eagerly anticipate this eclipse for the unique science opportunities it provides. Alex Young, NASA's Associate Director for Heliophysics at the Goddard Space Flight Center in Greenbelt, Maryland, says, this will be the first total eclipse to span the U.S. since 1918, and scientific instruments have come a long way. The event presents a perfect opportunity to shed light on phenomena such as solar flares and coronal mass ejections, or CMEs, billion-ton clouds of magnetized plasma ejected from the sun. To learn about their dynamics, researchers must study a region of the sun that reveals itself during an eclipse, the corona. Solar telescopes can create artificial eclipses using disks called coronagraphs, explains Young. Unfortunately, man-made coronagraphs also block the sun's inner corona, where CME eruptions originate. Fortunately, during a total eclipse of the sun, the moon makes a better coronagraph. At totality, the sun's inner corona appears in clear detail. Totality lasts mere minutes at any one location, but the CitizenCade experiment will be positioning 68 identical telescopes across the country in the path of totality. The combined network will capture 93 minutes of images and will show changes in the solar atmosphere. Matt Penn, astronomer at the National Solar Observatory in Tucson, Arizona, says, the telescopes will be operated by a variety of volunteers, ranging from middle school students to retired scientists and everyone in between. Meanwhile, NASA planes will photograph the sun, and a cadre of balloons will be floating near the edge of space to observe the eclipse. You can watch, too, live from the balloons on August 21st at eclipse.stream.live. The terrain and weather are quite varied across the U.S., and the moon's shadow will briefly change the climate in each locale, sometimes with drastic drops in temperature within seconds to minutes. Young reminds everyone to be safe. Solar filters, such as eclipse glasses, are necessary to look at the sun directly. If you're in the narrow path of totality, you can safely remove your solar filter when the moon completely covers the sun. However, when the moon moves off the center of the solar disk, even a little, the sun's blinding surface reappears, and you should once again use your solar filter. NASA has a whole web page devoted to safety tips. Eclipse2017.nasa.gov.slash.safety. Many people don't know that the moon's orbit is slowly spiraling outward due to tidal forces. In about 600 million years, the moon will appear too small in the sky to fully cover the sun. Every solar eclipse is counting down to the last one. Young says, we live in a fortunate time. For NASA's live coverage of the eclipse, go to www.nasa.gov.slash.eclipse.live. For total coverage of NASA science, visit science.nasa.gov.


Now, the army of Vifa, his imperial majesty, Oba Adeyeh, Anita Ogunshido Janyard II, says, the Abbey state governor, Dr. Alex Oti, will leave a legacy in governance that will be difficult to march. The highly revered Ubal leader spoke when he visited the governor in his country home in Uvose, Ishalangua, South's local government area of the state. The army was in Abbey where he attended a civic reception in honor of the deputy speaker of the House of Representatives, Honorable Benjamin Kahlo, organized by his Bende community. The event also witnessed the unveiling of the peace in Southeast project, PSAP. An idea of Honorable Kahlo was part of the activities marked in the grand civic reception, thanksgiving and hope coming of the deputy speaker. For me, I'm here. To pick his brain, your Excellency, beyond winning, beyond celebration, been engaging one another, and I'm very convinced that it's the same Dr. Alex Oti of yesteryears, in fact, much, much better, a final wine to deliver, deliver dividends of democracy for the Abbey people. So you get notified about fresh news updates.


Communalization, Centralization and Corporalization slash Commercialization. These are the three essential aspects of the draft national document, policy document that I am concerned with. Thematic formal learning from even the age of three is something that would be disputed by many scholars and educationists. Almost moving it de facto from the concurrent to the union list. And this certainly will work against girls, Dalits, religious minorities, all sorts of private agencies to assess educational institutions, opens up a large space for pretty nefarious practices in the process of evaluation and assessment. The draft report of the committee to formulate national education policy chaired by Dr. Kasturi Rangan has been in the public domain for some time now. The present government of the center was very keen on releasing it very soon after coming back to office and initially they had given hardly a month for discussions and suggestions. Although the document was available only in English and Hindi and even subsequently the government has taken a position that they will not take the responsibility for translating it to all languages. Luckily democratic minded people have done it across the country to translate this into many languages. Now there are three essential features of the draft national education policy document which cause a great deal of concern. One, it seeks to further and comprehensively commercialize education, make it completely a commodity and what is more also corporate is it in that process. So commercialization and corporatization of education is a key feature of the draft document. Second important feature of the document which is also a matter of being concerned is that it is highly centralizing both in terms of the structures of authority it creates and in terms of its many recommendations on how education should be conceived regulated and implemented. So it is a highly centralizing document which does particular violence to the rights of the various states of the Indian Union whose people are often you know consisting of people speaking a given language with a particular historical background. So the rights of states and of linguistic nationalities are not being respected in this process. Third is the fact that the system of values that is held up in the document as exemplary all better a commitment to a Hindutva view of the world and the report therefore also has a bias that you can only call the bias of communalization. So these three aspects communalization, centralization and corporatization slash commercialization. These are the three essential aspects of the draft national document, policy document that I am concerned with. In the document you will find many good passages, many fine words, many fine formulations but when it comes to recommendations these are completely abandoned and we get a whole set of recommendations which is sometimes contradictory but overall they carry these three features that have already identified. One could of course talk about the positive recommendations of a certain kind for example when it says that the right to education should be extended to all children from the age of three years to 18 years as opposed to the present 16 to 14 but that's a very nominal kind of advance because in effect the emphasis that the document puts on early childhood care and education ECC for example has some problems it is not first of all backed up by any suggestions as to how this enormously increased expenditure would be funded especially if you want quality ECC in the country but equally importantly it is also the case that the pedagogical approach of the document which suggests fairly systematic formal learning from even the age of three is something that would be disputed by many scholars and educationists. When it comes to school education the document suggests that we should think in terms of school complexes where there will be one institution that offers classes 9, 10, 11, 12 education and then there will be a whole cluster of institutions in that campus which offer primary and elementary education and so on now this involves significant centralization of the access to schools and since this will not be viable for every hamlet or habitation we'll end up having complexes that will require younger students including three-year-olds to travel quite some distance to access education that is something that is going to work against access to even school education for those who are socially discriminated against or socially weaker in terms of access to opportunities this will be women, Dalits, scheduled tribes and religious minorities especially Muslims so again there is an element of centralization here that has to be seen for what it is also the approach to evaluation although there are fine words about pedagogy and about the need to move from root learning to critical learning and all that there is an enormous number of examinations being proposed for the children from class 3, class 5, class 8 and then semester examinations in classes 9 to 12 all of which are to a huge burden on the children and this very notion of evaluation through classroom exams defeats the notion of a critical open-ended educational system more can be said on this but basically what I find is that if the school education proposals are to be implemented in their ideal form it would require a great deal more resources than is presently being spent on school education in this country secondly the whole idea of a single model of school education across the country militates against the need to provide states of the union space to design their own school systems you must recall that education was in the state list until it was taken away from the state list and put in the concurrent list during the emergency period when country suffered the obligation of civil liberties the real task now is to restore education from the concurrent list to the state list instead what the recommendations of the draft document do in fact are to further empower the center to decide on all policy issues in education so that essentially you're almost moving it de facto from the concurrent to the union list this is something that is highly objectionable when it comes to college education or higher education the draft proposes three types of institutions those universities which primarily are research universities although teaching will be there in those universities as well and likewise universities which are primarily teaching institutions but there will be some research there as well and then finally stand alone colleges autonomous colleges which are degree awarding institutions now it also says that the size of each such higher education institution must be much larger than it is now and it recommends anywhere between five ten fifteen twenty thousand depending on the level you're talking about this means again a significant degree of centralization this means for example that children who finish higher secondary school class 12 would then have to move distances hundred hundred fifty kilometers stay in a hostel or some accommodation and study and this certainly will work against girls Dalits religious minorities from accessing higher education because many in these particular categories that I have just mentioned there will be financial impediments as well as social impediments for children to pursue higher education in this manner so again this centralization does not do any good moreover while the draft document talks about what ideal education should be like it also expresses itself against any serious regulation of these institutions they talk about how regulation must be light but tight whatever that means but de facto what it means is that you are going to allow these institutions to function as they please with very little regulation and that I think is something that is a matter of concern especially when this goes along with the de facto emphasis on privatization and commercialization of education so this is something that is a matter of great concern little is said about governing structures and processes in these institutions in the draft document but whatever it does have to say is worrying enough it it does not see any role for democratic representation and participation of students teachers and non-teaching staff in these higher education institutions or in the school complexes so what we get is a kind of a free shit to the management to run these institutions as they please and this can be extremely harmful for any democratic discussion and participation in the education system and certainly this idea of a critical education would be next to impossible to implement if institutions are controlled by a caughtry of management which also have a national interest in the institution as well as the higher education and governance structures are concerned the report really retrograde features or that it visualizes at the national level an apex body for higher education that will be chaired by the prime minister the the Rastri Shiksha Ayog it's called and even the education minister will only be a vice chairperson or whatever and it will be packed with ministers and bureaucrats of the education department some representation for the states by turn and maybe a handful of so-called educationists or experts appointed of course by the ruling government this is an extremely worrying understanding of how education should be regulated and this is such a powerful body so almost all decisions on education need clearance from this body secondly it talks about a national higher education regulatory authority which will have the power to ratify or approve higher education institutions now currently universities are created only by acts of the legislature or of the parliament I mean there are deemed to be universities in the private sector which are which are only deemed to be universities but now this document says that the national higher education authority which will be appointed by the center again in highly centralized manner will have the power to license all higher education institutions again a very very unhealthy tendency to centralize education and also take away parliamentary and legislative accountability from these institutions there are many such features for example the when it comes to assessment the document suggests that institutions can be evaluated or assessed by any agency provided that agency is nominated to do so by the national assessment and accreditation committee again a centralized body which will be determined whose composition will be determined by the government of India so the idea that NAC can then authorize all sorts of private agencies to assess educational institutions opens up a large space for pretty nefarious practices in the process of evaluation and assessment I think this is something that should also be viewed with serious concern then going into this question of the key question of financial allocation because the recommendations here and there talk about world-class institutions although it's not clear what that means because one would think that any curriculum at any level in India should be one that relates primarily to the needs of our people of course knowledge is very fundamental in universal and certainly that should be understood and practiced and implemented but what is the world-class education institution what is the point of reference is it and then you know interestingly enough the document vacillates between citing contemporary global institutions of repute like the American universities and on the other hand talking about the greatness of Dalanda or Takshasila of which we very little form evidence in terms of how universal they were in character how easy was it to population sections to access it we do know that for most of the history of the Indian the territory that is now we call Indian Union education was not accessible to the majority of the population formal education was a preserve of a very small proportion of the population so even Randa and Takshasila would reflect the informities of such an exclusive society in any event the higher education sector recommendations or of great concern because they are undemocratic in character they they talk about a four-year undergraduate degree in liberal arts education despite the fact that the four-year degree has been shown to have many flaws in the past when du Delhi University implemented it for example and there's no learning from the ground experiences in this process so one gets a feeling that the personnel of the committee which drafted this policy document may not have had much hands-on experience in running institutional institutions of education at any level so I think I think these are matters of concern finally the there is nothing in the document that tells us where the money is to come from for implementing suppose you take it face value the really ideal descriptions of the system which of course make for good reading then the question of financing such an ideal system should be addressed and in fact what the report does is to dodge that question by either sometimes suggesting that the government of course it does suggest that the governments that the expenditure on education would rise to six percent of GDP as suggested long back by the Kothari Commission in the 60s but the catch is Kothari Commission was talking about six percent of GDP as public expenditure on education not all expenditure so here there is no an ambiguous commitment to six percent of GDP as public expenditure on education by all levels of government and moreover although this is formally stated at the end of the day when the question of financing is raised it is said that oh you know that we found by private philanthropic this is not our mandate we have provided our next year and which is our some guidelines are there but basically we expect that the funds can be obtained by government partly but also by resort to private philanthropy now this is actually laughable in the context of a country like India share size of the education budget this is a very non-serious suggestion that has come from a committee we should have known better at the last point I want to make is the communal aspect of the report because when I say that we are not talking only about Hindutva we are talking about corporate Hindutva because there is a whole emphasis on privatization commercialization and so on but the report has an interesting discussion on the constitution and its relevance in the in discussing value education it the usual you know sermons are presented on values and their importance in education but ironically the report when it drafts it especially after saying constitutional values doesn't have the word secularism or federalism anywhere in the discussion instead we are told about jataka tales panchayatana tales and we're also told from time to time that you know that the Indian tradition has many many valuable stories of the victory of dharma or adharma and other such absolutely patent nonsense we shouldn't find a place in the report of this kind and then there is this whole you know bit about how it's a fair system not even taking into account the enormous exclusion of the features of the Indian social system caste gender tribe these things don't enter at all into the understanding of the very flawed nature of the Indian societies over long periods and therefore you know the the invocation of is sometimes it gets very dangerous because example on legal issues that legal judgments take into account the prevailing community you know held values which is absolutely dangerous which means that you don't intercast marriages will be frowned upon and people who different intercast marriages can be open to serious difficulties so this is very disappointing from a committee of people who should have known better to kind of take up one strand of the much more complex and multilayered cultural heritage of the territory that we now call India and to present that as a soul and desirable cultural heritage notwithstanding is many flaws is something that certainly suggests to us that the document is inclined heavily towards hindutva in terms of philosophy and towards corporate culture and corporate values in terms of the largest set of recommendations that it makes so in my view this is a draft document which it is pointless to modify here and there the document must be rejected lock stock and barrel one needs much more time to discuss the serious issues that are being raised here and this one month in English in Hindi is just not okay we need a much wider country wide discussion with the draft being made available in all the national languages underline national because I heard one of our ministers talk about all local languages in another contest in Rajisabha and it came to the postal entrance examinations but let me repeat here that the languages listed in the aid schedule are all national languages in the English maybe official languages but Tamil Malayalam Kannada all these are national all these are national languages so the document must be made available in all national languages must be widely discussed from the grassroots and then we have enough time to take a call on it not there's no need to rush it and turn it into a in inflexible piece of legislation