Okay, good afternoon everybody. So I thought we are done with clustering and classification and we want to start with neural networks. But I want to cover one more topic in clustering and classification beyond neural networks before we move actually to the foundation of neural networks and talk about perceptron and regression and things like that. SOM, mainly because we talked about clusters, so the question of validity will arise which is a sort of Turing test. So how do I know if I apply K-means? And K-means does its magic of distances? Or SOM does the same thing in a different way? How do I know that the groups that they find are really good? How do I know that? How do I test it? So basically cluster validation is a big part of machine learning for any type of technique so you get a group of clusters and the question is, are they good enough? We usually don't ask this question for classifiers like SVM because they gave us the guarantee that, wow, I'm giving you the optimal result, nobody can do any better. Okay, so this is not for the superwise one because the superwise one, if there are doing their job, they match what the supervisor has said this should be the output. So if they can guarantee that they converge, they already passed the test. But clustering techniques are a bit different. So the main question is, how do we know the clusters are valid? How do we know the clusters are valid? How do we know it's not a bogged-in algorithm? How do I know that these are really the best clusters I can get? Or at least, good enough? Well, at least give me something. So if you cannot tell me they are valid, can I know somehow they are good enough for me to work with them? So yes, K-means has been around for 50 years and SOM for 40 years, but my data may be complicated. I may not have enough data. My data may be noisy. I may have missing points in my table. So there could be many, many problems. How do I know that the clustering technique is working properly? So what is desirable from our perspective? Generally we want high interclass separation. So the distance between companies that collapse tomorrow and the companies that will be very successful tomorrow should be really large. And the difference between the class of dogs and class of cats when I'm doing image distribution should be really large. They should not be close to each other. If things are close to each other I can mistake them for each other. So high interclass separation. We want clusters to be way apart from each other. That's desirable. That's a general thing to ask for. And of course we want a high interclass homogeneity. We want within the class to not be many different stuff. So if I have a class of dogs they should be very similar to each other. If they are not similar to each other then my attributes are not good. My features are not good. So if I have a group of successful companies they should be very similar to each other. If I have a group of people who survived chemotherapy they must have similar attributes. Why? That's the structure we are looking for. If there is no such a structure what are we doing? So what we want is high interclass separation and high interclass homogeneity. If we get this this is sort of objective two-ring test. If you give me this because don't forget every time that you run chemies or SOM you get different results. Why? It will not be gigantically different but this group maybe here the next time is there and so on. So how do I know which one is the best? So we need then to define a sort of index of validity index of validity that uses two things. I don't want to write down maybe I write here. So first I want to use some sort of sum of squares within cluster and we will call it SSW. Sum of squares within the cluster. So I want to figure out within the cluster my stuff should be similar to each other. If I calculate square distances when we say square we mean distance. Distance is our business. So sum of squares means sum of square distances which means Euclidean distance. So people don't mention it because we are used to it. You say sum of squares it means Euclidean distance. If I calculate the distance between all dogs in my class it should not be a big thing. So the sum of squares within the class is one measure SSW and maybe we also create something like sum of squares between clusters between clusters or SSB. In the statistics people know that you have been using it for quite some time. You can apply it when you have a conventional things for grouping stuff or you have you can apply it when you are using machine learning it doesn't matter. The validation is the same. It doesn't matter who grouped the stuff together. The way that we validate them is the same thing. So for example now we want to define it. So let's say SSW is simply you build a sum of the differences X sub i minus C pi squared and i goes from 1 to n and n you have n data points and X sub i is your data instance basically is the current measurement and Cp sub i is your class prototype or center or mean class prototype for the i for the data instance X i for the i data instance X sub i. So I'm basically building the difference between all data points that I have with corresponding centers. So which is if this is my this is a cluster this is my Cpi and then I have many many data points in this cluster. So what I'm doing is I'm building just everything from the center from the prototype. So I'm adding them up I'm building the sum of the squares within the cluster. So how different is every dog from the most prototypical dog. So of course if the cluster center is not a really good center which means the clustering did not do the job and things will be messed up. So this tells us because you need a point of reference you want to build how homogenous is the group the group has to be homogenous. This is not the place for diversity here we don't want diversity yeah no diversity we want homogenous everybody is the same ideally but if everybody is the same everybody will converge toward the center and will collapse and you have one big egoistic megalomaniac in the center well we don't want that either. You want some data a little bit of diversity because we have some instances of dogs. So this is our SSW which is our sum of squares within the cluster and this has to be small so SSW has to be small if the clustering techniques is not up to us assuming somebody knows the perfect location for the mean for the prototype of each cluster for every class assuming somebody knows that it's fixed yeah but can you find it that's the point can you find that perfect point and you can you can calculate the distances you cannot get below a certain level that's the nature of the data but can you get that. So then we have the SSB the sum of scores between the clusters that we go the sum of n sub i times the Euclidean distance between the sub a C sub i and the average of averages and you go from i equal 1 to m so you have m clusters we have n sub i which is the number of elements in the cluster number of elements in cluster C sub i of course the current the current class mean or prototype or center or centroid and x bar as the mean of the means mean of means so every cluster has a mean you build the mean of all clusters you get the mean of means so now I'm looking at the sum of the scores between clusters so I want to see how how separate the clusters are now I have something like this so I have cluster a I have cluster B I have cluster C which is very close to A and I have let's say cluster D here well with a B and D I'm okay but perhaps see I don't know it's too close to a so how can we do this if I measure this I get I get it I get a number that gives me some idea about how close are a B and C and D and we want them to be way apart so ideally perfect clustering is you have m clusters you get the Milky Way Galaxy and you put them there on the outer circumference of the Milky Way Galaxy and each one of them you have densely packed elements wishful thinking not going to happen but what can you can you get in that direction can you go in that direction well this to SSW and SSB or part of ANOVA or the statistical analysis so which is analysis of variance as anybody as anybody used ANOVA it was right in Excel it can you saw one somebody has used it people use it all the time hospitals finance people they use it all the time so it's embedded also in tools like Excel any other financial spreadsheet program it is it's not something new we have been doing this for 40 50 60 years so but again so suddenly they become more significant because I have autonomous clustering and I don't know whether it's doing the job or not so and then I go back to the old guy and say guys what did you do back then so back then we had just fixed many many examples we have in the statistics come from biology because biology is where you can get a lot of data the data is fixed is the nature of things how many flowers I have how many leaves have this type of tree and so on so you put that in and you do your analysis you classify them sometimes even manually now we can use the same wisdom for for for machine learning so there are others so this SSW and SSP are quite common in the statistics are not new people have been using them all the time so what about any other clustering cluster validity measures validity measures well there are many for example we have Kalinsky dash arabas index so these are basically just formulas that I'm throwing at you maybe one or two of them so ch index which is SSB over m-1 over SSW over n-m so now we are playing with the two fundamental and this is very common in computer science and statistics we establish really basic stuff like mean and standard deviation and variance and we start playing with them okay what happens if I put this here and I put this here and I multiply this with then you have to justify it you have to investigate the property of that formula that you are putting forward so now we want to put it in one equation so that within the class and into a class we want to put it in one equation this is one way of doing it so why you are doing it this way well I run experiment I get it gave me good good numbers other one is the hardigan index so H which is the index is SSW for m over SSW of m plus 1 minus 1 times n minus m minus 1 now we are getting created okay what about so m was the number of clusters what about we look at it so what was the separation that you gave me for m and what was the separation that we get me for m plus 1 if I cluster you remember for k means we have to determine the number of clusters k equal 5 okay what about k equal 6 so one of the things that we can do we can use the SSW and SSP to get some sense how many clusters make sense in my case for my application one of the ways that we can do it we can play with this we can we can or even sometimes you see also the other version of this which is log of base 2 of SSW over SSP over SSW yeah so there are there are different things we can do that and you see the pattern that we tend to combine both measures again because we wanted those two desirable functions in one we want within the class everything compact dense similar to each other and between the class we want as much as distance the classes should be clusters should be very different from each other then there is also maybe one of the most extensive one is the dawn's index which which tries to define things differently so the dawn's index says you get the minimum over all m for index i equals 0 to m and then you get the minimum of j equal j plus 1 to m and you do that for all distances between c i and c j c sub i and c sub j and do you divide that now we are getting really intensive with calculation the maximum if you go from k equal 1 to m of the diameter of c sub k and what are they those d and diameter so the distance and diameter so the distance between c sub i and c sub j is the minimum of the Euclidean distance between x and x prime when x comes from c sub i and x prime comes from c sub j so you take pair of data from two different clusters and you find the minimum which is in that class c that I just drew was very close to class a they will be members in those classes that get really close to each other there are members of neighboring clusters that are very close to each other yes sorry again yes this one sorry i thank you i plus one thanks so we have to find so what this does if this is one class and this is another class close to it so there is one item here and one item here and they are very close I want to get sense of that how many cases you have like this because this could be confusing cases that you misclassify stuff so if you create groups with many situations like this that two first the big problem is that two clusters are close but maybe you cannot do anything about it maybe not but maybe who knows maybe the cluster shape should be actually something like this and maybe these two are belonging to the same class so we don't know so I can only know if I compare it with other stuff I have and the diameter the diameter of ck for any given class you find the maximum of the Euclidean distance of x minus x prime when x and x prime belong to the same class c sub k well okay and you're doing that over everything so a lot of calculations a lot of distance measurements so now this is the built-in validation that we talked about at the beginning when said look you take the data you break it down in two parts test training and testing but inside training you still take a small part of it for validation because after the training is done you have to give the solution to the world you cannot change it anymore so whatever you can do do it within the training session to make sure what you have is good so we will do a lot of calculation distance distance measurements are not cheap Euclidean distance in high dimensions when you have 500 features wow it it will it will consume everything we have what we do it this is during the validation as part of the training this is not when we have given the software to the user the user does not see this this is happening in my lab in my R&D department so we do this sort of stuff this is quality control for when I am working in a new company and I want to make sure what I put forward is good enough so I do this make sure that classified I'm deploying this is my first big project after six months in a company doing some serious stuff I want to put my best foot forward I checked out it takes two days to calculate so be it and then I can document everything I said we did this okay there is also much simpler than this and many people work with that we simply call it WB index is up to you and WB index as a function of M which is number of clusters is M times SSW divided by SSB another example that everybody cooks with water you can play with it is SSW and SSB because that's the quantification of a real phenomenon there is no escape from the real phenomenon so I have to go back and say okay measure this measure that now divide it now add it now multiply it so and of course this should be ideally low ideally very low the sum of squared within the cluster should be ideally very very small very small and this should be ideally very large so this fraction should be a small number even times the number of clusters should be a small number so if I have two alternatives I go with the one that gives me a smaller value so I validate my my clustering technique past the Turing test with SSW and SSB okay now I can use it this is the best I can do there's no there's no other way of doing it okay there is cluster validation is a big field within machine learning anything we cover anything I cover here it in within 10 minutes you have to take it times a thousand it is a subfield you just a scratch the surface a cluster validation you do SSW SSB good to know because if you start and you want to go deeper you start here and say oh my god there's other so many other stuff but this is a good place to start so then we have some confidence but we have other problems too we have so many problems we made a big assumption we made a big assumption so the assumption is that X sub i belonging to C sub k and X sub i does not belong to C sub k sorry C sub j for all j not equal k this is the assumption that we just made for the classification on cluster what does that assumption telling us what am I saying with that if there isn't data instance that belongs to a class and that but that data instance does not belong to any other class which means the class membership is true or false is zero or one that's what they're saying what that was the reason we could pull off the SVM magic because we assume is binary is yes and no positive negative well this is hard or dual or crisp many people call it different things clustering so the membership of CI sorry the membership of X sub i belongs to zero or one so that's a set right so or other way which means the membership the mu of X sub i to any class to any class so I can write a k or j it doesn't matter so the membership of X sub i to C sub k or C sub j or to any other class is not in the closed interval 0 and 1 so is not 0.2 is not 0.75 is 0 or 1 that's a big assumption to make that's a gigantic assumption to make for the data that is noisy that is hyperdimensional that has outliers okay so what do we do with this what the problem is the problem is much deeper than that the problem is that AI is supposed to deal with imperfect information imperfect information so okay what is imperfection so if it was perfect you wouldn't need AI so but if the data is imperfect what does that mean so imperfect means hard tough okay so imperfection means either you are dealing with uncertainty or you are dealing with vagueness very different things what is let's let's like any a natural event like raining so is raining an uncertain event or is a vague event while it depends what does it depend on so if I have my event horizon so before something happens and after something happens so I have before and after before it rains after it rains before the market crashes after the market crashed yes this one this one this means the membership of any data point you have is either 0 or 1 and it is not a number between 0 and 1 so it's a hard clustering or crisp true or false black or white this one this mu mu is the Greek word for membership we use mu as so mu of something is membership of something okay so before it rains after it rains so before rain after rain so if you ask me tomorrow tomorrow will it rain I look weather network and tell you with a probability of 60% it will rain so this is uncertain with so with 56% who say who can say that with 50% likelihood it will rain tomorrow the probability that an airplane in which we are flying fatally crashes and I die is one roughly one in 35 million passenger flights we have per year one in 35 million wow that's safe but if you're sitting in that one airplane that will crash you will curse the hell out of probability theory because it doesn't help you that I'm sitting in the one that is crashing so probability probability is responsible before things happen when they are uncertain when things are uncertain we use probability theory very powerful too since over 250 years has brought us a lot of stuff has brought us to the moon if the moon landing was not a hoax I don't know if it was was fantastic so so but now it's tomorrow and we see that is raining now is tomorrow is raining there is no uncertainty we can watch out the window is raining so what is the problem there oh we do not agree is it a heavy rain is it a drizzle is it a light rain is it a heavy rain so what is it so is it a drizzle light rain heavy rain what is it what type of rain is it so the problem that you're dealing with you want to classify the rain with respect to its intensity there is no there is no uncertainty about that so here you cannot use probability theory after things happen after things happen we use fuzzy logic generally so because things become a matter of degree so what is the likelihood that the next person that enters this room from that door has a red t-shirt okay we wait and wait and wait somebody enters the room he has a red t-shirt well then we do not agree what type of red is it is it a dark red is it the light red is it vermilion what type of red is it we need that information we want to classify we want to subclassify when I am looking at it there is no uncertainty anymore I see it is red but I cannot agree what type of red so the imperfect information that AI tries to solve is most of the time one of these two sometimes we are in good luck and is a combination of those so either we are dealing with uncertainty we don't know what will happen we try to guess we try to predict we try to estimate but most of the time we know it's right in front of us we cannot agree what is it what is the intensity of it two different theories for two different things so let me go a little bit because I want to give you I want to give you another example for clustering and classification and that's what you are doing is to remove this assumption I don't like this assumption and I want to I want to talk about another clustering technique that is one of the simple and really good capable clustering technique but for that let's talk a little bit not too much a bit of set theory and if it is boring for you so just look at your cell phones without set theory this wouldn't be here so we have this because of contour the logician who came up with a with the foundation of Boolean set theory and now we have everything so our entire computer system is based on 0 and 1 which is the set theoretical framework so in set theory we work with a universe of discourse which has some instances in it universe of discourse is a fancy name for the set of all your variables if you're talking about temperatures measured on planet Earth what is the universe of discourse minus 60 to plus 60 what is it so everything so you have to unit universe of discourse contains everything if your variable is temperature okay can I go from minus 70 to 70 who has who has experience plus 70 minus 70 to 70 I'm fine that's my universe of discourse it contains everything for that variable then we have a set a which is a subset of x a is a subset of x you are doing high school stuff but okay it's fun how do we how do we how do we write a set well I can just write a set and say set a is the set of ABC so I can list its elements that's the way that we show and a set I can write a set and say the set a is the sum of all x's such that x belongs to n actual numbers so I can list the property of the elements in my set or I can say the set is a such that the characteristic function of a for every element is either one if x belongs to a and is zero if x does not belong to a so this is called the characteristic function characteristic function of a so every set has a characteristic function that tells you I didn't write mu I wrote f because this characteristic function is a function I would rather use f okay so I can't like there aren't many other ways so these are the most common ways that you give a set to somebody else what is the set of pleasant temperatures I know 18 19 20 21 22 23 24 25 maybe 26 so that's the set of pleasant you just list them or you said the set of old temperatures whereas the temperatures is larger than 18 and smaller than 24 so you give the property or the set of temperatures such that if the elements is less than 18 or greater than 26 is 0 and if it is between is one so we can play with sets in many different ways why is that important what this is our business this is what we do because a could be a set or could be an event so this guy works with events this guy works with sets they have different businesses some people don't get the difference is still I don't know after after 300 years of this and 60 years of this we still don't get the difference but if we do we can come up with some nice stuff for machine learning okay let's go back back to the past to the high school and talk about van diagrams so this is my universe of discourse and I have a set a and I have a set B so and whatever is in between is the intersection of A and B so this is a intersected with B and of course this is a subset of X all of them are subset of X okay when I classify I don't want any intersection between classes just just a little bit indication that why this is relevant to us then we have again the universe of discourse X I have my set a and my set B and if I'm talking about everything so this is a union with B which is again a subset equal of X so a unified with B is all elements that belong to A and they belong to B okay whatever and we have X and we have A and you ask me what is if this is a what is not a so this is not a so not a is negation of a so somehow so we can say a bar or negation of a so everything that is not a now in the van diagram nobody told us in high school we made that big assumption that things are crisp true or false so you are either old or young I don't like that logic especially being 53 so I still want to have one my food and in the young domain but according to the Boolean logic I'm old you are about 50 you're old well I want to be young to 20% or something so not a is important for us because if these elements belong to this set to this class to this group with what certainty can I say they do not belong to the other class okay we have bigger problems right when you are dealing with a are you don't know you may be fighting you may be fighting giants and you have no idea that you are doing this so we have some logical laws we have this laws since antiquity there are more than 2,000 years old when you talk about this laws is about Aristotle and Plato and many people be before them that we don't know and many people after them so one is the law of non-contradiction so which means what which means a intersected with not a is empty the set of rainy day and not rainy day has nothing in common the set of successful companies and bankrupt companies has nothing in common the set of young people and old people has nothing in common if old is the negation of young the law of non-contradiction if we wouldn't do this we wouldn't get digital technology this is not an exaggeration without the law of non-contradiction we could not come up with the circuits transistor nothing we don't have we are still running after if you violate this we are hoping that quantum computers help us good luck with that I'm not gonna experience maybe you hopefully you will experience second logical law the law of excluded middle which means a union with not a is the universe of discourse the set of rainy days and not rainy days it's the set of old days the group of young people and old people is the group of everybody there is no middle there's nothing else okay whatever makes you happy if you violate this people will get upset some people violated this in the 60s so and they are called fuzzy sets fuzzy sets violated this and not only they violated this they violated this in the worst possible time it was early 60 the beginning of digital boom it was so proud of digital technology and somebody called and calls his technology fuzzy what this guy has his fuzzy headed so a fuzzy set a is the set of a pair of X and mu a of X such that X of course comes from the universe of discourse and mu a of X the membership belongs to the closed interval 0 and 1 blasphemy this was blasphemy because this will violate this to we can't do this or sometimes we just write a is the fuzzy set a is the integral over the universe of discourse mu a of X over X which means we just have an order we don't really build the integral we just we just show them the concatenation of all everything every element comes with its membership we will see what that means okay let me give you an simple example now we are the down the rabbit hole so let's let's have a simple example really simple the universe of discourse is 1 2 3 7 so this is my universe of discourse seven numbers so then we want to have we want to define a being the set of neighbors of four neighbors of four so I want to define a as the set of neighbors of four the classical Boolean crisp binary definition is a straightforward is 3 4 5 the neighbors are four are for itself to the left is 3 to the right is 5 do you have a problem with that you want to be more generous you can define a bigger circle like SOM and we can give you also 2 and 6 but you have to keep it consistent but either you are a neighbor of four or you are not a neighbor of four make up your mind because this has to count now if I define a as a fuzzy set basically you get this 1 2 3 4 5 6 7 so I wrote them in the form of this integral I wrote the elements here and then I have to say what is their membership and I copy the Boolean guy 1 1 1 so this guy does not need to give me the membership value because everything that is listed here the membership is 100% everything that is not listed the membership is 0 he has an easy life but this guy every time has to list everybody and say okay but you know what this guy also is sort of a neighbor of four and this guy is also sort of a neighbor and this guy is also neighbor and this guy is also neighbor but not just 100% 70% 50% 60% so a fuzzy set will list all elements and will also list the membership that's why we say this is an ordered pair the element and its membership value clearly for this kindergarten example I can see that is much easier to work with the binary technology for this simple example look how bigger the fuzzy guy is the fuzzy guy need GPU and CPU and APU and DPU and any other technology that has not been invented yet so what is that this membership so membership is what is it a similarity membership is intensity this similarity is not a subject of uncertainty why you say I'm not sure is similar or not what why can you put a number on it yeah well okay then can I look at it as probability yes you could probability so that could be the cause of few confusion that you have a number between 0 and 1 and people say well that's probability why it depends was it before the event or after the event numbers between 0 and 1 you just normalize who cares what you call it probability likelihood intensity similarity fuzziness vagueness ambiguity but if you call it probability you will set a different tools to solve the problem and if you call it fuzziness you will see the very different you will use a very different set of tools so one of them may not be able to get the solution depending on what the problem is membership is approximation what at the latest here I know that this is AI business because AI is function approximation logic is a big part still nothing has changed nothing has changed you're still doing weak AI logic is a big part of AI and of course membership can be compatibility and many other things that I'm not writing so membership can quantify many different things intensity similarity approximation probability likelihood fuzziness vagueness crispness you name it so there are many many things okay this was high school this is valid for the Boolean logic that is conforming to this so Aristotle sees this he's the happy guy because there is the decision boundaries now we are talking classification the decision boundaries are clear right there is a this there is an exact boundary between things and not things the boundary between a and not a is well-defined of course that's Boolean logic true or false young or old so what's the point now how can I come up with a fuzzy version for this I cannot draw this for fuzzy systems because then I need Photoshop to put a gradient here in the center is absolutely black and as you go away it becomes brighter and brighter and brighter and when I get here so here is black and when I get here it becomes white so is it is a circle of gradient color but we cannot work with that that's not serious science that's Photoshop okay let's come up with something scientific we cannot visualize fuzzy sets what can be visualized the membership function because the membership functions will do the Boolean logic is 011 we don't need to visualize it is there is right in the listing so I don't need to I can visualize the set but visualization of the of the characteristic function does not make sense so if this is my universe of discourse and then I have a membership function for my set a so me of a is a membership function for the fuzzy set a what is the fuzzy set a the set of old man the set of pleasant temperature the set of successful companies the set of people who smoke and do not get cancer whatever set is it is that we are interested and then I have of course this goes from 0 to 1 here goes from min to max and we don't know what that is and I have this which is the mu of B so is a characteristic is a membership function for the set B so now I can show a intersected with B through the intersection of their membership values so this would not mean anything in van der Grams because van der Grams is for dual logic so if I now if I have again mu of a and mu of B and I build the union so this is then a unified with B and negation if this is mu of a so what is the mu of not a the mu of not a is defined by 1 minus mu of a why is that well the Boolean clear if you are not a member you are not zero you are one and so on so if my membership to the young people is 0.2 what is the membership of being the member of not young people 0.8 the remaining part so which means if I come here so this is mu of not a and at the latest here somebody should screen a not a is not empty blasphemy a and not a intersect no cell phone for us no no digital technology this is not an exaggeration worse if this is mu of a and this is mu of not a so one minus mu of a and I build the union a union of with not a is not the universe of discourse because this part is missing this is not part of a unified with not a why is that why if you were dealing with binary Boolean logic so a would be this is binary right 0 1 what would be not a not a would be this so this is a this is not a what have a and not a in common nothing what will happen if this type of concept come into classification and clustering what they are there we just don't see them so does it mean we have to revise some of the stuff maybe at least we need additional tools look in some cases our binary logic works perfectly we don't need to be worried about but in some cases maybe you should issue membership cars that you say you are member of this library to a degree of 65% of gold and platinum and silver and the rest of the nonsense that's a degree that's Lucas Yavitch because the first one who said guys we cannot do with true and false we need something in between at least we need to say I don't know 55th yes the overlap will disappear if you become binary gap between a and not a no if you are working with normalized fuzzy subsets no okay so what does that mean that means again so for fuzzy logic says no no which is fresh where does this guy come from he came from Berkeley but okay he passed away in 2017 he was 93 years old and in one of the conferences I asked him how did he came up with this idea it doesn't happen that frequently that you made one of the colleagues who has introduced a whole different theory I asked him I was I was a student I was enthusiastic and said sometimes indiscreet so I said how do you came up with this idea and he said it was in the some it was the winter of 1960 and he was sitting in John of Kennedy Airport was waiting for his flight to Los Angeles and there was a snowstorm and flights were delayed and he goes to the information that's and ask the lady there what about my flight and the nice lady says don't worry you're you don't have a long delay he goes back and sits looking at the display and as he had a mixed background with math and engineering he tries to come up with his in his mind the set of old flies that do not have a long delay and he immediately realizes you can't do this because you don't know what long means I said this idea didn't let me go and I went home and sat down and wrote something and I said the boundaries sometimes are fuzzy so I called it fuzzy said and after two years he sent the paper to many journals they reject him of course and like SVM like bad propagation like evolutionary algorithm if you have good idea it will be rejected definitely so and he get desperate after the fifth submission fifths rejection he goes to Bellman Richard Bellman dynamic programming one of our pioneers in computer technology and says Richard what should I do said you are editor of some journals yourself you say yeah I'm editor of that journal information science information control I said send it to that journal they know you cannot reject you say but what I put them do you want to publish or not yeah okay he send it to them they say as a god damn it he's on the editor board so we cannot reject him so we got fuzzy logic so okay how do we measure fuzziness so if that's the case so let's be clear fuzziness is not a good thing like uncertainty is not a good thing probability theory is to remove uncertainty fuzzy logic is there to remove fuzziness so a gamma is defined as fuzziness being two times over the number of supporting points of your function the sum over all I you take the minimum of the mu of a of x sub I and 1 minus mu of a x of I so for every point you take the minimum between the membership and its negation you sum them up times two divided by all number of supporting points because these functions are of course discrete so one two three four five and so on so I can I have n supporting points why times two why times two where the come the magical number two wasn't this also somewhere in SVM but this has nothing to do with that too but two is a is a funny number so why two here what is the range of minimum of mu and one minus mu yeah so minimum is zero maximum is one if this is zero this is one if this is one this is zero if this becomes 0.5 this is 0.5 so the maximum number that comes out here is 0.5 times two is one normalized minimum of zero and one is zero minimum of one and zero is zero minimum of 0.5 0.5 is 0.5 so that minimum goes between zero and 0.5 so this never goes this is 0.5 it cannot go above that this is in the definition 50 50 glass half empty or half full this is a logical dilemma this is a real thing that too that too has something to do with the glass half empty or half full so normalize it and then you get a number between zero and one which means zero one this is my gamma the fuzziness I can call it index of fuzziness and here I go between zero and one and then I have my 0.5 Mr. Lokozievic sometimes we got to say I don't know and this index has a shape like this so the fuzziness becomes maximum when the membership is 0.5 of course sure if your glass is 20% full you can still say it's almost empty if your glass is 80% full you can say it's almost full come on take it pay the full price but if it is 50 50 you really can say you have to flip the coin this is from logical perspective the point of maximum uncertainty nobody can make a decision are there such cases in clustering and classification that objects are sitting right on the decision boundary are there such things if yes then nothing can help us so gamma is max for mu of x being 0.5 we don't want 50 50 so the entire fuzzy logic is to try to push you either in this direction or in that direction this are the regions that we like because I can easily make a decision right don't get close to 50 50 because then I cannot easily make a decision well nobody can make a decision okay so based on all this can you give me another clustering technique that deals with vagueness gives up the big assumption about things are true or false black and void young and young and old we had k means k means it's fantastic but maybe sometimes k means cannot do a good job if the vagueness is the dominant characteristic of my data set how do I know that I don't so I run k means I get some results with my SSW and SSP I run something else get some results run some statistical analysis I go with this one I say oh okay the nature of data was rather vague not uncertain good scientists make that distinction okay so what is that around 70 actually well well early 80 which was not even a decade after came is was introduced somebody else introduced the fuzzy version of came means so and he called it fuzzy see means fuzzy see means to date one of the best clustering techniques we have so and I go I jump right into it I want to just give you the sort of pseudocode so first step of fuzzy see means you initialize why you initialize for any clustering technique so the not you need the number of clusters and so no difference to came means you still need to tell the algorithm how many clusters are you looking for then we have a fuzzy fire fuzzy fire M so that's a specific parameter for the fuzziness site of things that we call M call fuzzy fire and then we have membership function membership function membership function mu in in came means we didn't have a membership function because everything was true or false either you are a member or you are not a member so and I only list you if you are a member so now we need a degree of membership if you want to do the concept of fuzziness second step we find an update we find cluster centers cluster centers so C sub i is the sum of k equal 1 to n number of instances of mu sub i k raised to power of M which is my fuzzy fire times X sub k so basically a modified version of membership times the data set so now before we had this but this was either zero or one so I didn't know to do this I would just add the ones who were there what is the M about why M how much like vagueness do you have how difficult is the problem you can play with that that's a parameter so we like nowadays we leak hyper parameter this is not a hyper parameter this is a high pole parameter because it's just one and then of course we'd go the sum of k equal 1 to n mu sub i k to M just divide by same thing to normalize so the center of classes is defined as a weighted version of the k means so I'm not just building the average I'm building the weighted average but the weights are memberships that I defined somehow okay well how do you define the memberships what do you think what is it that we have to measure anything distance there's nothing else so so step three of FCM by the way we call it FCM step three is update memberships updates what it has to be a learning procedure involved otherwise it's not AI memberships so where does that come from if this is the first step well like everything else the first we initialize it I didn't write it here so well I did so you you initialize the membership values how randomly assign some numbers to them in step two and three and four we will adjust them and make them more meaningful so the membership mu of sub i k is equal now this is one of those handcrafted stuff that you have to sit down and say why did this guy do it this way one over the sum of j equal 1 to M number of clusters of the ratio of two distances d of sub i k divided by d sub j k raised to the power of 2 over M minus 1 and M was my parameter faze fire so I'm saying the ratio of the distance inverse of that is my membership and of course for all of them you will get a number between zero and one you will not get zero or one the nature of the equation but how how this how do we come up with this equation why there is no philosophy behind it you sit down and put your intuition and everything that you know the domain knowledge try to put it in an equation try to justify it and then hopefully it works and then you come with some fancy adjustment of it and again it has to work if it works you say well that's my equation and everybody buys it that's my equation this was the phd of somebody this was the phd thesis 43 pages so and then step number four again we need a stopping criteria stopping stopping criterion so how do I stop you build the difference or the Euclidean distance between your current partition and the previous partition so you current minus you before and this is your fuzzy partition what is that well every element then in your data set has M membership values so if you grab if you grab X X X will be represented by let's say if M is four classes X will be 0.1 0.2 0.6 what come next 0.1 so if I have four numbers this is for one X put them together you get your partition so every then every element every measurement of that big file that I get for training columns being features rows being measurements if I am if I'm looking for four classes I have four numbers so this is this is for FCM if I look and of course if again if you put them all together for all measurements you get your fuzzy partition is a matrix and then you compare this matrix with the before matrix is it did things change substantially if not then stop so I converged so if I if I do this for if I do this for K means I will get 0 0 1 0 there is no partition per se very different thing they suit they do the same stuff interestingly there are applications if you have to apply K means and you apply FCM you get more less the same result of course because he comes from that or he comes from that door but this is the same room the same data of course you get the same results if you have a reasonable approach so so that was an example there are more example there are other examples for clustering but if I when I start to do something I start with K means FCM is also available our fly FCM do some SSW SSP applications take a look at the numbers which one is better continue with that there is no way to say ahead of time so just an idea and hopefully we all we have done justice to the concept of clustering and classification so next lecture we continue with regression and jump into normal networks see you later


Students, we are discussing about mergers and acquisitions, and that we are discussing in the international context. Merger and acquisition can take place between two companies which are operating in the same country as well, but we are discussing it from the perspective of international management. So international merger or acquisition will take place between companies which are operating in two different countries. So when companies are operating in two different countries, that means that people from different countries will be coming to work in a foreign country, and who are those people? Those people are expatriates. So for example, if there is a merger between a company from UK and a company from Pakistan and in Pakistan, some of the people from UK will be coming to work in Pakistan as expatriates. So we want to know what is the role of these expatriates in the management of the merger and acquisition, and how do they affect the merger and acquisition? What is the importance of these people? First of all, these expatriates are representatives of the merging or acquiring company. So they are representatives of the image of the company. For example, if McDonald has come to Pakistan, people who are coming from McDonald USA to Pakistan, they will be the face of McDonald's for people who do not know in Pakistan what McDonald is. So the way they carry themselves, the way they act, the way they merge, and the way they integrate and collaborate with the local people that is going to represent what McDonald's is. They don't know what McDonald's is like in USA. The people in Pakistan will be able to see what expatriates McDonald's will come and present their image. So they are representatives of the company. They are face of the company for the people who belong to the local market. Then these people are responsible for knowledge transfer. Whatever happens in the company, in the headquarters, in the rest of the company, in other parts of the world, that becomes insignificant. What these people carry to the local market, what these people carry to that particular country, that is going to matter, what knowledge they have and how they transfer that knowledge. If they don't transfer that knowledge, if they are not friendly enough, if they are not open enough, if they are not skilled enough to transfer the knowledge of the company which is coming from outside, then it is going to be problematic. So these people are responsible for knowledge transfer from the headquarters, from the main company to the local market. Then these people are drivers of change management because they carry the strategy, the vision and mission of the company which is coming from outside to the local market and they know what is the vision and mission of the company and they are the drivers of change management. And then these people are responsible for cross-cultural integration. So these are the people who are the face of the company which is coming from the foreign country and they are responsible for carrying out the cultural integration between the two cultures. So these are the ones who need to understand the culture of the local market. They are the ones who need to be proactive. They are the ones who need to be flexible and they are the ones who need to understand and evaluate how much flexibility to show which values to modify, which values to absorb, which values to transfer. So these are people who are responsible for this cross-cultural integration. This is not necessary that you are completely the local market or your acquired firm, you become like that, this is not what is required. So what is required is that you need to keep a balance between how much you can change and how much you have to retain your own values. So these are for cross-cultural integration. Now factors behind successful integration when we see research, we see different studies. So it has been found that the factors that lead to successful integration, those are number one managers, industry experience, so for example, if you are related to FMCG, then your industry experience of FMCG can be successful. Then your experience in similar projects, if you have, then it is possible that managers will be more successful. Then your level of inter-cultural competence, how much you are competent with inter-cultural integration. For mergers, prior experience with host country or entry mode is important, that is, if you have experienced that host country before, or if you have experienced that kind of entry mode, for example, if you have been subcontracting, or if you have been franchising, or if you have been franchising, or if you have been franchising with your franchising prior experience, then mergers, that is something which is an important factor for success, but not in acquisitions. Then another factor is local language skills. That is more important for eastern managers rather than the western managers. Because the most important factor in language is learning, and for learning, the direction of flow of learning is important. So, where learning is coming from, where knowledge is coming from, understanding and learning is more important. So, if you are getting books, and talks, and all the case studies in English, then it is important for you to learn English, rather than those people who are coming from outside, although they need to learn a few people who are coming from outside, they need local language. But what is important and important in this thing is the direction of flow of learning. If the flow of learning is west to east, then you will have to learn the English language, even though you are larger in number. So, you have seen that the whole of Pakistan and the whole of Bar-e-Saghi, they learned the English language. In the British rule, when the British came here and ruled, they were so few people, but they taught the whole subcontinent, the whole world the English language, because the flow of learning was coming from Europe to the rest of the world. So, that is one of the most important features of who's language. In the future, according to the running trends, it appears that the rest of the world is going to learn the Chinese language now. Anyway, so these are the factors which make an expatriate manager more successful.


What's going on? If you're not in a family welcome back to another video. It's your girl Shanice. If you guys are new to this channel, make sure you hit that subscribe button and join the family. Guys, today I am going to be pranking Isaiah by making him think that I am stealing money out of his wallet.  Guys, right now Isaiah is watching TV downstairs So I'm just going to make noise and hope that he comes upstairs so he can catch me in the act and we are going to get his reaction. So if you guys are ready for today's video, don't forget to smash that like button. Comment down below how you think he's going to react and let's get this prank started. I have all this stuff laid out on the bed so that way when he comes in, I'm going to pretend like I'm just going through everything and then when he comes in and I get caught, I'm just going to throw something or act suspicious. So guys, if you are ready for today's video, don't forget to smash that like button. Comment team Ginny's in the comment section below and let's wait for him to come in so we can get him. Babe, look what I can make the skeleton do. What? What are you doing? Tell me, what were you doing? What are you doing with my stuff? You were giving like a piggyback ride or something? What are you doing with my stuff? What are you talking about? You have all my stuff laid out. What are you doing? No, I don't. Yes, you are. Why are you trying to hide it? I already saw it. What are you trying to hide? This is my stuff. I already saw it. What are you trying to hide? Why are you trying to hide my stuff? Why are you getting so aggressive? Why are you going through my stuff? I'm not. This is my stuff. Were you just trying to take my money? No. Brother, why do you have all my money laid out? Stop trying to hide it. I already see it. What are you trying to hide? I'm not trying to hide it. You are clearly trying to hide all my stuff behind you. I saw my wallet and everything and I saw my license. What are you doing? That's not your license. That's my stuff. Like I just said, it's my wallet. All right, let me see the license then. Show me it's your license. No, you don't need to be my license. Show me it's your license. Show me what you were about to show me. Show me it's your license. No. Why do you have all my stuff laid out? I don't have your stuff laid out. Stop. Stop. Look, why do you have all my money out? This is my money. Look, and yet it's clearly my wallet and my license. That's not yours. Yes it is. No, it's not. What are you doing? Nothing. Look, give me that. You over here trying to take money for what? I'm not trying to take it from you.  You're not even trying to hide it right now. Why are you talking? Taking my money and stuffing it in your shirt. For what, brother? If you needed money, just let me know. Why are you going to steal it from me? I'm not stealing anything from you. I'm telling you, this is my stuff.  No, I didn't. Is it not stealing? That's not stealing. It's mine. So what are you doing? I'm giving myself all. Giving yourself back your money. You gave me this? No, that's mine. You didn't. No, this is my money, right? Why are you trying to take money from me? If you needed money, just ask me. You know I'd give it to you. I'm not. I was just looking in your wallet to try to find something. Looking in my wallet, taking stuff from it, and stuffing it down your shirt. That's what you were doing. Yes, you were. I just saw you. That was accusing me of stealing stuff.  What did you just put down your shirt? I didn't put nothing down my shirt. What did you just put down your shirt? I didn't put anything. That's my... Bro, this is... Look, my cards. Right? No. My cards. Why are you hiding? Why are you hiding it then? I'm not hiding anything. Let me see. No. This is your BMW card? No. Oh, okay. This is your AutoZone card? I didn't know you shopped at AutoZone. I didn't know that. I just... I just... I was trying to find... What did you leave in my wallet? Because as far as I know, there's nothing of yours in my wallet. You don't know a lot about that. Like, don't worry about it. There's nothing in my wallet that is to your concern. Yes, sir. I told you to find it. I put it in between some dollar bills and I was just trying to find it. Bro, you have $20 in mine. No, I don't. Why are you stealing $20? Why are you being...  I'm not stealing nothing.  Bro, I had $40 something in here. I'm missing a 20. I'm not stealing money. But I have money on my own. That is so expensive. Exactly. Why are you stealing money? If you have money on your own, why are you stealing from your boyfriend? How are you accusing your government of stealing from your boyfriend? I just saw you put it down in your shirt. What are you talking about? Alright, bro. You're literally making yourself... Again, no, I'm stealing stuff because all this stuff literally just it teleported out of my wallet. Because you didn't take all of this out of my wallet. You didn't take my money. I didn't. No, you didn't. No, you're proud of this. You just want to argue. You just want to argue. Yeah, I definitely want to argue. I was going to come in here with something cool that I wanted to show you. But instead, I walked in and I see my girlfriend taking money from my wallet. I'm not taking money. That's always something that a guy would love to see. I'm not taking anything. Okay, so what were you doing then? Let's hear it. I wasn't taking anything. Like I said, I was trying to find something between the guy and the girl. You're trying to find something, right? You're trying to find something. Yeah, make it make sense, bro. And you're going to say... Stop! You... That's not funny. You think it's funny to steal? I'm not stealing. What are you doing then? What's yours is mine. No, it's not. That's not how that works. Yes, sir. We are not married. That's not how that works. Give me my money, bro. No. Alright. That's all. So you couldn't ask me first? No, because it's my money. It's your money, okay? Yes. You couldn't ask me first.  No, you're just going to go through it. No, because you weren't going to take it. You're going to take all my money and then you're going to try to ask suspicious and hide from me as if I didn't catch you. You weren't going to take me. Alright, bro. So I had to steal it. Alright, bro. Babe! Babe! Give me a favor. Don't come near my stuff. Don't... I don't want you near my stuff. You actually owe me $23. It's actually $22. Don't touch my money, bro. Do what this? Look, Nene about to rob you. Look at him, look at him. What are you doing under there? Nene!  Look, look. Nene, you about to rob daddy? Nene, start keeping... Go get him. Go get him. You about to start keeping my stuff in the safe, bro. Yeah. You owe me a dollar still. No, I don't. That's literally all I have. No, bro, you owe me a dollar still. That's literally all that you have. You owe me a dollar for pain and suffering. Oh, pain and suffering. Please. No, bro, you're getting my blood pressure up. Can you save Team Genese? No, bro, I already had enough salt today. Save Team Genese. Where'd you put... I had to sell my nightstand, didn't I? I don't know. Where'd you have it? Bro, where's your wallet? No. Let's talk about that. No. All right, Infinite Fam, so that was clearly... What are you doing?  I'm doing the... I don't know. She about to get beat up. I don't know.  Yeah, be quiet. Anyway, that is it for today's video. Infinite Fam, I hope you... I hope you all enjoy. If you guys did, don't forget to smash that like button. Comment Team Genese in the comment section below. And it's time for today's post notification shout out. Was good. Today's post notification shout out goes out to... Cherise Williams, thank you so much for your love and support. Isaiah and I love you.  And with all that being said, we will catch you in the next banger. Bruh.


The head of the UN's nuclear power watchdog warned on Saturday that the situation around the Russian-held Zaparizia nuclear station has become potentially dangerous as Moscow installed officials began evacuating people from nearby areas. Rafael Grossi, director general of the International Atomic Energy Agency, called for measures to ensure the safe operation of Europe's largest nuclear plant as evacuations were underway in the nearby town of Enerhodar. The general situation in the area near the Zaparizia nuclear power plant is becoming increasingly unpredictable and potentially dangerous, Grossi said on the agency's website. Grossi said that while the operating staff of the plant remain at the site, the conditions for the personnel and their families are increasingly tense. The Russian-installed governor of the Moscow-controlled part of Ukraine's Zaparizia region said on Friday that he had ordered the evacuation of villages close to the front line as shelling had intensified in the area in recent days. Grossi has repeatedly warned of the dangers of military operations around the plant.


In 1976, when Aravind started, 1.5% of India's 650 million people were blind, 80% due to cataract. This translated to about 8 million people being blind due to cataract. This required a high volume approach to make a dent. Aravind, when it started, had its mission of eliminating needless blindness and for it to achieve it, the high volume approach was a necessity and not an option. Effective service delivery in any organization requires a strong and equal focus both on demand and supply. In a high volume operation, it mandates that there is a steady and a large flow of patients into the system and matching processes which deal with that volume that's coming in in an efficient manner. On the demand front, Aravind's focus was on those affected by cataract but were not accessing care and they constituted a larger proportion. The approach included awareness creation, diagnosis, service delivery and follow-up. This took the form of outreach screening camps organized by local communities. At these camps, comprehensive examinations were carried out, glasses and medicines were prescribed and those requiring cataract surgery were transported immediately to one of the base hospitals. Further investigations were then carried out and patients were operated the following day and transported back the day after. After five weeks, Aravind team went back to the campsite for follow-up. In recent years, Aravind has been setting up a network of vision centers to provide primary eye care and currently they are in over 50 locations. Through outreach and vision centers, Aravind admits and does about 100,000 cataract surgeries each year. Additionally, about 100,000 come directly to the paying section and 50,000 to the subsidized section of the hospital taking the total to 250,000 cataract surgeries being done annually. This requires Aravind to perform 750 to 1500 surgeries each day. This is achieved through detailed daily micro-planning to ensure adequate staffing, supplies and equipment including sterile surgical instruments. Each surgeon operates on two tables supported by two scrub nurses and a circulating nurse. Thus minimizing the wait for patients or equipment. This process combined with a highly motivated workforce has ensured high output. Such high volume is enabled when quality is integrated with it. Higher productivity is possible by eliminating unnecessary remedial work and quality has got to be consistent since services are offered day after day. This means that robust systems have to be in place and quality cannot be left to chance. At Aravind, quality systems are built on the foundation of standardized protocols, good medical records and more importantly an organizational DNA that fosters a culture of continuous improvement. There are close to 400 doctors in the system with about half of them in training. Thus it becomes vital for everyone to follow a common protocol in patient treatment. Standardization is enforced using simple techniques like checklist that are integral part of the medical records. This allows complications and outcomes to be more directly attributed to processes. The scheduling of patients matches the complexity of the case to the surgeon skills and experience. Trainees and junior doctors handling the simple ones and the more skilled surgeons handling the challenging cases. A robust outcome and complications monitoring system captures data on every surgery. Every day, a sample of patients give feedback on their experience and satisfaction through a structured questionnaire or focus group discussions. This provides a better insight into ward drive satisfaction and areas needing improvement. Staff members collect and record data relating to quality. In addition, they also do the analysis and review. This results not only in continuous quality improvements but more importantly in strengthening quality consciousness and patient centricity across the hospital. The business model of any organization is largely governed by its purpose and philosophy. Aravind's purpose of eliminating needless blindness resulted in policies such as no one will be turned away for want of money. Reaching out proactively those who are not accessing care. Equity that is the core aspects of care around safety outcome and maintaining patient dignity are the same regardless of the patient's paying capacity. The hospital has to be self-reliant in providing patient care so these formed the non-negotiable policies. Financial sustainability is essentially maintained by a simple formula of keeping the cost to be less than the revenues. Aravind chose its income to come predominantly from patient services to ensure sustainability. Reflecting the paying capabilities, the Aravind pricing covered the full range from market rates for the paying patients to a negative price for the rural poor. Negative price means that Aravind spent money on patients in the community to enable access by transporting them. Zero was also a legitimate price point. The pricing structure was transparent and easy to comprehend which built patient trust. The patients coming to the hospital could self-select to be seen at the paying or free facility. Ambience and facilities like air conditioning was what the paying patients paid for. Some same clinical quality was ensured by rotating the staff between the two facilities. The focus on equity also gave birth to aural lab which made available good quality eye oils at low cost. On the cost front they were kept to a minimum by focusing on productivity, eliminating waste and efficiently managing bottlenecks. This was also helped by viewing costs from the patient's perspective. They were reduced through leveraging scale, completing the entire care cycle including surgery in a single visit and rationalizing the number of follow-up visits. This approach helped Aravind's financials to become robust, not only covering the cost of free care and subsidized care but also financing the building of new hospitals and purchase of equipment. This comprehensive approach of focusing on demand, supply side in terms of building capacity, quality and ensuring financial viability has helped to provide cataract services consistently and sustainably in large volumes.


And she says help me out and he tells her okay, I will But this is what we need to do. I want you to notice what the prophet does not do. He doesn't write her a check. I Want you to notice what the prophet doesn't do. He doesn't cause the bills to evaporate or her deptors to die. I Want you to notice what the prophet doesn't do. He doesn't call the king and says could you fix this matter? Could you help you guys a welfare problem for crying out loud? She's a widow help her out He doesn't do that even though he could What he does is he asks her a question.   That's great. I Remember a pastor Vladimir Muntian from Ukraine. He said when he got married at the age of 17 and his wife left him because she was not a believer and He was not a believer because at that time he was sitting in jail and he came out of jail He got saved and his wife completely left him. It's been years and so now he's praying every day God give me a wife and he put a description, you know the figure the the eyes that the hair everything He just told God this is this is the kind of wife I want to see and make sure she's not worse than the one that I married before He keeps praying and praying and praying. I mean he had different people agree with him in prayer and God told him to go get his old wife back. He says God. No, give me, you know new one and He got the old one back She got saved and this is what he said He says I realized that God gave me a new marriage with the same spouse God is able to give you a miracle with what you already have Maybe you look at that and you say ah that that no But God when he touches it that becomes something beautiful. Can somebody say amen So that's what blind people when God heals blind people. He doesn't give them new eyeballs. He just Touches the eyeballs. They already have when God touches lame people He doesn't give them new legs. He touches the legs that they have many times God's anointing will touch what we have and What we have sometimes is enough with God to make a difference in our life We must understand God created the world out of nothing, but everything else in the world. He created out of something He took dirt to make man And then he took man to make a woman and then he took men and a woman to make a child God always uses something to make something else and many times we come to God and we say God Give me something something brand new out of the sky shipped by UPS and God says what do you have in your house? You said no, I don't have what it takes and God says if I touch it you will have what it takes Jesus took five loaves and two fish It's not enough to feed a multitude Jesus says watch when it lands in my hands It will feed the multitude and you will have enough for the next three weeks to take it to work to eat When God touches what I have What I have will become enough to meet my dreams and to fix my problems in Jesus name can somebody say amen Therefore when you come to God you must not limit God in Expecting always to make something new but in expecting him to touch what I already have and to bless what I already have So that will make something great and glorify his name


Hello and welcome back to the craft box. My name is Brent and today We are on a very special episode of the completionist crafter series episode number 48 also known as the Christmas episode Hi, welcome. We are back where we all started at Edora. We are finishing her off today. That sounds really wrong Don't mean it like that. We are finishing off all of the things that she can possibly give us With the final music scrolls. We're crafting all three of them today. So extra long episode But yeah, Carol of the bells deck the halls and jingle bells These can only be crafted during December or the Christmas in July event So basically whenever it's a yuletide season Theoretically Those are the only times you can craft them in the game But yeah, as always on screen all the things you'll need all three are all combined into one because basically they're the same thing Same requirements. So yeah, but the needs are just all all there as well.  Which is this bad boy here So yeah, Carol of the bells will start with that yuletoy I have only just fish that way I have like three energy. I got mine from grizzelheim and the first area north guard area. They are myth I will just show you that they are They are myth they're what ranked two or something. They're not very hard. They're not very big so many fish their Rank one. So yeah, not not difficult. They're also in the commons the Oasis and North guard and Jade palace then myth. So just win a myth in most of those places and they'll be the only myth that fish there So yeah, Carol of the bells five life shield just got that from Augustin five onyx really easy and cat tails. I mean we've been using a lot of cat tails recently So super duper easy that was the first one The next one is deck the halls, which is the mistletoe angler, which is another fish can be found in all of the same places. I got mine also From the From the first area in Zafaria, I can't remember what that's called But yeah, that that's where I got mine from it is I I'm assuming it is a rank one fish as well Where are the heck is it? Mistletoe angler a rank one life fish So yeah, really easy just win a life Don't do that in Zafaria though because they're our life school the fish and they are trolley as Frick I call about five of them to catch one of these and I managed to get two in the end But anyway, deck the halls miss shields go to Augustin again Amethyst reagent vendors deep mushroom You can just find them around But yeah, that's that one done and then jingle bells is the yul toy again So just where you got the first one from Fire shield Augustin again five sapphire just any reagent been to embers would that really really easy There is a video on that as well in the reagent hunter series Which is up in the top right corner with the eye you can click that and it'll take you straight there But anyway, there we go. Those are those three crafted. We are gonna quickly head over to my this house here This house here, which is my evergreen Um forest bundle house and you can already hear that one of them is already playing and that is um I don't actually know that might not be one that we Need that is actually the yul toy jingle, but um if you wanted to hear Um the others let me just turn up the music real fast and we will listen to the Carol of the bells So yeah, that's carol of the bells quite a quite a popular one. Um, we also have deck the halls theme Which isn't too bad And then we have jingle bells as well Which goes like this And then yul toy jingle sounds pretty much identical Um, so yeah jingle bells and yul toy jingle. I I'm pretty sure they are exactly the same But um, yeah, that is how you craft those. Um, so yeah, thank you for watching this very special episode of Completionist crafter. We will be returning uh next week or well on Monday. Um with another episode where we will be crafting another robe from Aquila, we've got what like three more to go. I think um, and then we'll be moving elsewhere Haven't decided where we'll be going yet, but um after every single tier of here So a robe hat and but it's just because aqua is so big we will be taking a short break and going somewhere else So if you have any suggestions of where you'd like me to go next Um, please let me know and we'll craft all the stuff there and then we'll move back to aquila But anyway, thank you for watching. Oh, look at that. You're on so cute again left like for fox because fox is fox is great Um, but anyway, thank you for watching room service like and subscribe before you subscribe press the little bell So you know exactly when I post another video and remember to craft outside the box


Okay, hello to the small audience this morning. I'm Tony Ristovsky and this is Kirill Utsinonovsky from Macedonian Wikipedia. This presentation is about our movement strategy proposal that we have implemented during past year in Macedonia working with underrepresented communities in our movement. It was not on, okay. So with which communities we worked during past year about this project, it was regarding two small Wikipedia communities, Romanian and Romani communities. And when we speak about Romani communities, we have this project that was conducted with Balkan Romani language. And also in the project, we have worked also with the users of sign language, specifically with the Macedonian sign language community. About why we picked these communities and we started working with them because they have like a small population in our country, less than 9,000 people. And as we started to work with them, we faced immediately like a challenge because not that all of them are like capable to write in that language because these languages are not like taught in the schools. And they learn only at home or in their families or like training that is provided by some NGOs. And they know how to speak but the written form is mostly it is not standardized or it is with very differentiation between groups in the country. Also we have a lot of problems with them working because it has like a real challenge to find written materials along, not just in hard copy but also especially in the digital form. And in the first phase of our project, we focused mostly on this challenge to write, to find literature about and materials on these languages. And because in the project it was included also Macedonian sign language, we are doing the research phase that we, I think we speak like a minute for now, but it was, we need to find like a solution to how these users to be included in the project. And that's why we experiment with new methods of their like engagement. And with this group, with the language, with the Macedonian sign language users, we created our videos instead of like them to write articles in the project. The project timeline was last year, it was conducted completely during the second half of 2022. And only like January this year we have evaluation phase when we concluded our research project implementation, and Kiro will speak how we learned the 10 lessons from the project. In the research phase that we highly recommended about this project to, like when you work with two unknown communities to start, this first to start research phase, we have conducted a server and interviews, and the interviews were much more useful than server, because we learned a lot from the real native speakers from these communities. And in the interviews phase especially we speak with community organizers from these communities, and like organizers of the NGOs, so like these interviews were very helpful for us. Also it was distributed thanks to our partners, external partners from these communities, from the Romanian and Balkan Romani community in the country. And we end up with more than 20 or 300 users that fill our server, which was like a good number when you take the amount that it is very small populations of just 9,000 people in the country who can speak the language. In the implementation phase like every other project, and also because these participants were with the basic Wikipedia skills, we started like working with them like usual, with basic tools like workshops, and after that it was followed with editing contests on their wikis. And also with ATSA I mentioned with the Macedonian Sign Language community, we worked with them like on a special seminar, and training how they can learn about Wikimedia Commons, how can they upload videos on that project, and it was also, it was a recording of these videos during the session. Working with Macedonian Sign Language, it was like working with the people with problems with hearing, and lots of, they cannot hear what you speak, and it was an unusual event for me, like for lecture, because in this situation I can speak to you and you can understand me, it goes smoothly, but in that case we worked with interpreter, and it was unusual for me because you should wait a little bit, so she can take over from you, and you need to wait for them to understand how she understands me, and it was because in terms of Wikipedia, sometimes we use our own terminology, so for her it was also the challenge to understand how she can interpret some of the terminology that we use on Wikipedia, but overall that session went quite good, we filmed, we recorded over, I think, more 15 or 20 videos in one day, and the users were very content with the recordings, and it was a happy event for them. About evaluation phase, my colleague Kiril will talk much more about these 10 lessons that we learned during the project. Okay, thank you. So far Tony talked about what was the project and how we implemented it, and now I'm talking more about what we learned from the project because it's also very important, it's not only to tell the story, but also to tell the learning from the story and the main result. So the first lesson that we learned from this project is that communities require thorough research in order to get more information and to gain better familiarity with them, and with their own distinct expectations. As Tony mentioned, we shaped our research with two primary tools. The first one was to launch a survey, and the second one to organize interviews with experts in the languages. This ought not to be the main way to go. Every community willing to work with such underrepresented groups in different countries may have a different approach on how to shape the research phase. I know that there are many affiliates in the movement who have done this in different ways, and my main recommendation would be to get a better sense and to experiment with many different things that you should make work. The second lesson is that every community is different with specific needs and expectations. In this project we included three of them. Two of them were pure language communities, while the third one was a community for sign language. This means that from the beginning there are two different ways that we should go through. The first one was to deal with people who can speak and write in the language. Even though this seems like one group, at the end it appeared that the needs of the Romanian-speaking and Romanian-speaking communities were quite different. For example, even though both communities had problems with materials in their languages, at the end it appeared that the Romanian-speaking communities even had problems that they could not publish books in their language, even by the NGOs because the government required to have specialists in the language so that a material could be published. This was something that we learned during the interview and primarily this community wanted to gain some advocacy efforts to the government in order to make it easier for them to publish books. This reflected with the overall result of the project because the Romanian-speaking community was not that interested in writing articles on the Romanian Wikipedia but rather to boost some reforms and changes in the society. The third lesson is that fluent speakers of such languages with very small communities are difficult to find and it's even more difficult and challenging to inspire them to edit Wikipedia. I have to say that it was a very big success that we attracted a dozen of people who speak the Romanian language fluently to try and edit Wikipedia and this even resulted in an even bigger success that we had this year because the Romanian-speaking community continued to be active and they participated in the Sea Spring Writing Contest in which they wrote articles about the other participating Sea Communities and we are lucky that we have still contact with them and we hope that the Romanian Wikipedia would continue to grow in the future. Next, as we already mentioned, the availability of reference books is pretty scarce and it's very difficult to find knowledge bases and works that could be used as references across the Wikipedia articles. In most of the cases, the people used works in either Macedonian or English because in these languages there was a lot about these communities and this was really a barrier because usually people tend to prefer their own language to document some cultural things, traditions and other things that are related to their community but in our case we were restricted to what was written in Macedonian and English so that they can use while writing the articles. Next, this is something that we specifically had in mind with the Romanian-speaking community because at the beginning they told us that they would like to work on promoting their culture in other languages but not specifically in Romania. They firstly wanted to have a better coverage in English, in Macedonian and in the other world languages instead of writing in the Romanian language. The main reason for this is that they knew that there are many mistakes and inaccuracies in the English Wikipedia about their community and they even pointed out some prejudices in a couple of articles and their wish was not fully covered by the goal of this project to promote their Wikipedia and this is perhaps one of the main reasons why they didn't participate actively in writing articles on the Romanian Wikipedia. Then we also learned that organizing hybrid events is the right way to go when working with these communities. This is mostly because the geographic distribution of these people is too wide. It's impossible to gather people from different parts of the country at one place and organize in-person events like workshops and editor-tons. This was really helpful because we also had a Zoom link. We allowed people to participate on an online platform and also learn how to edit Wikipedia and write articles by following it virtually. This was in particular helpful with the Romanian and Romanian-speaking communities but not that much with the Macedonian Sign Language community because we had to work in a different format with them. Okay, so the lessons learned so far were mostly about the language communities that were involved and the ones coming after them, beginning from this one, are mostly about the work with the Sign Language community. So the first and most important thing is to find a language interpreter, a sign language interpreter who can facilitate the communication between us and the users of the sign language. At the beginning we were lucky to find a language interpreter who worked at the Cabinet of the President. She was really a high professional and she was even aware about the state of the sign language community in Macedonia and she also provided a lot of useful information in the research phase. Then a major learning was that the sign language users are not proficient users of the written language. So even though the Macedonian Sign Language is based on the spoken Macedonian language, the fact that these people have never heard how the Macedonian language is spoken means that they could not develop proficiency in writing it and even the level of comprehension of the written text was lower than, for example, for ordinary Macedonian speakers. It means that practically the Macedonian language for these people is like a foreign language and that's why they prefer to have recorded materials in the sign language so that they can easily get the meaning of the content in the articles. Then, as we already mentioned, working with these kind of communities requires experimenting and introducing innovations. The main innovation that we introduced in this project was that we recorded the gesticulations in the Macedonian Sign Language, which interpreted the content from the Wikipedia projects. All these materials that were produced are available in Wikipedia Commons and have been used across articles in the Macedonian Wikipedia. And then the last lesson learned is the famous quote by Pierre de Coubertin, the founder of the modern Olympic Games. L'importance participe, which means the important thing is to participate. We really didn't want to have very high expectations when we started working with the project, so it's impossible to predict how many articles will be created, how many people will get involved as we mentioned. We were working with very small communities and the most important thing was to get them involved in the Wikipedia movement in some way. And at the end, we also would like to mention something about the follow-up of this project. So, given that we had different experiences and different levels of success in implementing it with the three different communities, we decided to set different goals for the future. For example, regarding the Romanian-speaking community, we wanted to further integrate them into the C region, into the C network, and to provide them with the newly established C hub. And we managed to get a lot of success here because the Romanian-speaking community participated in the C Spring Writing Contest, and we also expect to have a delegate from this community at the upcoming C meeting next month. When it goes to the Romanian-speaking community, these activities were not that successful. And first of all, we decided to go with more promotional activities on other Wikipedia's in order to write accurate content on the English Wikipedia, on the Wikipedia's of other world languages, and on the Macedonian Wikipedia, because this is what the members of this community indicated is very important in the research phase. And at the end, when it goes with working with the Macedonian Sign Language community, the members of this community requested to record videos documenting illegal and economic terms. So, this is a completely new project which intends to increase their literacy in legal and financial terms that these people need to know, because every day, as we learn from the language interpreter who participated in the interview, every day these people are subject to manipulations when they go to a bank, when they visit a public institution, because when people perceive that they use the sign language, they try to exploit this difficulty and manipulate them so that they lose money or they can't do the job that they went for. So, the idea was to identify two lists of legal and economic terms to better interpret them. For example, with regards to the economic list, we identified some simple words like money, loan, deposit, interest rate, because these are terms that people don't fully comprehend, but need to know when they want to do their everyday activities. If you have a question, now is your time. Please go ahead. Hello, I want to ask what was more important for these communities to write articles? Articles only about their own culture, to inform their own people about the culture, or were interesting to write articles about general knowledge, to give them the possibility to learn about physics, for example, in their own language. And the second question, how to prevent own research in this kind of small communities, especially when writing about their own culture? They are only using references from scientific papers, mostly in other languages, or do they are using their own knowledge, which is not written down? Thanks for the questions. For the first one, we prepared a list of articles that we proposed for the participants in the workshops and editons, so that they could choose and edit. We mostly focused on topics related to these communities, because that was something which was requested during the research phase, even though we also encouraged people to write articles on any other topics, and this is exactly what happened during the editons. Not all of the participants wrote from the list that we proposed. There are also people who wrote articles about different topics, in geography, in culture, in religion. And when it goes to the Romanian-speaking community, I have to mention the fact that they participate in CE Spring, means that they focused on writing articles about anything else as well, because participants in CE Spring could not write articles about their own community. On the second question, as I previously mentioned, it was difficult to find reference works in their own languages, because there are not a lot of works available. They chiefly used materials in English and Macedonian, and we mentored them during the programme that they did not change the factographic accuracy of the articles. So we're not speakers of either of these languages. We cannot know if they did not use own research in these articles, but we trust them that it's not in their interest to do it. A comment and a question. Thank you so much for this. It's incredibly interesting and also really important, the findings that you're sharing. So if you haven't already, I hope that you would consider at least writing a Diff blog post so others can also take advantage of the lessons that you've shared. My question is, and I'm sorry I came in late, so perhaps you answered it. You had a slide that mentioned it's also difficult to motivate members of small language communities to participate in the projects. Can you explain the why behind that, why it was difficult to motivate them and what you think some of the blockers were? When it comes to, it is already published on Diff, that I can say, about motivation. We have a different motivation in different communities. For example, the Romanian community, maybe this is a small history, but the Romanian community in our country is most well established. And they're working in education now, in banks, institutions. They are working class. They are doing their job, and the poverty rate is much less, maybe even non-existing. So in that terms, in this community, we don't face lack of motivation, but other rights in the Romanian community, and it is not that it's only a case in our country, but in the whole Europe, they have a pretty much high percentage of poverty rates, and they even have a computer, like they don't know how to use computers in the first place. So in that community, we face lack of motivation, because they expect to earn money, not to volunteer to something, and maybe that was the problem that they don't want to engage in this phase of the project. To put it simply, the main reason is that when you work with small communities, for example, counting only 9,000 people, then if you apply the rate of participation in any other community, it's very low. It's very difficult to find someone to take part, and as Tony mentioned, there are differences between the social status of these people. The Romanian-speaking community is well-off. We had the Prime Minister, there are a lot of CEOs of many well-known companies who are Romanian, and they have different views of the world, and usually don't want to participate in such things. On the other hand, when working with the Romanian-speaking communities, these people are in a working class, and they don't have a lot of ways to use their leisure time to do such activities. Many of these people even don't have electronic devices that they can use to contribute to the Wikimedia project, so there was a completely different set of reasons to participate. Okay, last question, 30 seconds left. Thanks. I think you were mostly talking about Wikipedia in your project. Did you consider other Wikimedia projects, such as, for example, Wikimedia Commons, which might be an easier way for some people to interact with Wikiprojects? Yes, we trained, especially the Macedonian, the users of the Macedonian Sign Language will train how to upload images to Wikimedia Commons. But we also taught the participants in the workshops in Romanian and Romanian language the same thing. We also considered training them to edit Wikidata, but we figured out it's more advanced than Wikipedia and Wikimedia Commons. About lexems, but it was late to found out and to include it in the project, but it will be nice to have it. Okay, that's it.


Hi beautiful, you know we're all obsessed with this mask.  But did you know if you leave this mask on for five minutes versus 10 minutes, it does make quite a difference. If you have more of that yellowy, orangey, brassy hair, leave it on for a good 10. If you just need a little bit of toning benefits, just leave it on for a good five minutes or and look at the difference of the five minutes versus the 10 minutes.