All right, so we've got our last speaker coming up. And this is really a treat. So this is about the feet on the ground. This is about the people who are actually doing work and having tremendous impact. We're about to welcome Prince Emmanuel de Maraud from Varunga National Park. I'm gonna tell you a little bit about Prince Emmanuel. After training as an anthropologist, Dr. Emmanuel de Maraud worked for 20 years in support of Congo's national parks. He spent most of those years in the field supporting Congolese park rangers through the 12-year civil war that claimed the lives of over five million civilians and 140 of the rangers with whom he worked. In 2007, after witnessing the slaughter of nine mountain gorillas by armed militias and investigating the root causes of environmental destruction in eastern Congo, he decided to focus his efforts on overcoming the illegal trafficking of natural resources in Varunga National Park. As a result of these efforts, he was appointed as chief warden of Varunga National Park by the Congolese government in 2008. To overcome the various challenges in the post-conflict region, he helped to develop the Varunga Alliance, a Congolese initiative to create an alternative clean development strategy capable of eradicating poverty and bringing stability and peace to eastern Congo based on the sustainable use of water resources for energy and fisheries together with agriculture and tourism. It's a real treat. Please welcome Prince Emmanuel de Merode. Thank you. Yeah, okay, thanks. Good morning. It's an incredible privilege and I'm extremely grateful to be here. I think I, perhaps more than most, need to explain who I am. I'm a bit of an outlier with respect to a lot of the discussions that are going on today, but actually I'd like over the next few minutes to just explain how important the work that people like yourselves are doing to the future of a region that desperately needs change. And really the way I would like to do that is through the extraordinary team that I work with. And in that, to explain who I am, essentially what you would define as a middle-ranking civil servant in the Congolese public service. I work for the government of the Democratic Republic of Congo as a national park warden as the director of a national park that's called Varunga National Park. It's located in the east of the country in a region that has been affected by what is now recognized as the most tragic episode in human history in terms of human suffering since the Second World War, a war that's claimed the lives of over six million Congolese people, six million people who should not have died in that way. And every single one of those wars has started either in or immediately around this national park that we've been trying to manage now for over 20 years. And it's this extraordinary team that I work with whose story it is, I tell. But for me, at the beginning, growing up in East Africa, growing up in Kenya, I had this very different image. I had this image of this landscape in the center of Africa with these incredible mountains, some of the highest mountains on the African continent with glaciers on top, and that stretch down through tropical rainforests across these amazing savannas with some of the greatest abundance of large mammals of elephants, hippos, and buffalos, and then through to these extraordinary volcanoes, these permanently erupting volcanoes that give you a glimpse of the very center of the Earth. And between these extremes, some of the greatest diversity of wildlife on Earth, what essentially made it, what essentially makes it probably the greatest national park on Earth. And of course, that one species that drew me to Varunga 23 years ago, which is the mountain gorilla, this incredibly vulnerable species that's right on the edge of extinction. But when I arrived, I bought a motorbike in Kampala and drove across East Africa, and then across the border, across the great East African Rift Valley, and then down into this amazing landscape. What brought me there were the mountain gorillas and this amazing national park. But what really kept me there was this team of Congolese rangers. And very quickly what I realized is I became, over the years, a part of that team, was just the incredible sacrifice that they had gone through. Over 140 of them had died protecting this park, and yet for months and sometimes years on end, they hadn't received their salaries, they hadn't received any support from the outside world, and yet they were continuing this struggle because it was the work that they had always done that their parents had done and that grandparents had done. This is the oldest national park in Africa. It's the first to where conservation was born in Africa, and this tradition was continuing in spite of the incredible difficulties. And it was met with incredible success. The mountain gorillas were increasing. They doubled over the last 20 years until this terrible day in 2007 when we heard gunshots up in the forest and came across the beginning of what was, seemingly, the end of this species, the mountain gorillas. They were being exterminated by militias that were moving into the forest and killing the gorillas. And that really was the event that precipitated a series of decisions that ended up with a situation when I was asked by the government to take on this incredible privilege, which was to lead Varunga's rangers as their director. And so we continued from there. But what I didn't realize was that my work was not going to be about protecting mountain gorillas or wildlife or managing a national park. It was about managing a deeply, deeply troubled political situation. And within weeks of when I was appointed, we discovered that an army was being created, a rebel army. And very soon that erupted into the third of the great wars of Eastern Congo. These wars that killed literally millions of people. And of course, this was the view just outside my tent when I woke up in the morning. It was a war that came right up to the park headquarters. And within days, our staff were forced out on this terrible march towards the city of Goma and into these internally displaced camps. Suddenly I found myself from being a park director to being somebody who had to manage a refugee camp for the rangers and their families. And suddenly we had to re-adapt ourselves to a situation of crisis. And this continued, but I was surrounded by these very extraordinary people. And it's them really who led me forward, helped me with these ideas. And the first was the decision never to leave the park. And so we spoke to people. We spoke to the rebel leaders. We spoke to the government. And out of that came this agreement that the park would continue its work whatever the political situation. And we were able to continue the work. But what really struck us, what was a real problem was this struggle with respect to this national park. And there was this one photo, this terrible photo that was taken by a good friend of mine of this woman at the feet of one of our rangers. And it really captured for me the struggle that we were up against. So it's not a simple matter. This woman was trying to get into the park to obtain charcoal. And of course, the victim here is the woman. But it's not that simple. The woman is a victim of the armed militias who are trying to extract the resources. It's like a protection racket. And you've got this economic situation around the park where you have what is essentially what was once a colonial economy based entirely on the extractive industries that's gradually evolved into a conflict economy. And it's based around the extraction of charcoal in the forest, the destruction of the forest for charcoal, the destruction of the water resources for illegal fishing. And then more recently, this international corporation that came into the park for illegal oil. And this economy was essentially based on coercion and patronage and was breeding these armed groups. And what we found was this situation where we had 12 armed militias that were drawn to the park around these resources, fish on the lake, illegal fishing on the lake, charcoal in the forests, and then this illegal oil. And the three together were generating about $100 million a year for the armed groups. And we were completely disempowered in our ability to confront this.  For his efforts, he was illegally detained for 17 days and very, very badly tortured to the extent that we had to get him out of the country. And then Atama, this dear man who I worked with right from the beginning, 23 years ago, who moved up through the ranks and became a warden. One day when he was coming back to the park, he was shot in the back by militias and died of his injuries. And so we were losing all these amazing people who were helping us to protect the park. My day came on a warm day in April when I submitted the report on the investigation into the illegal oil to the state prosecutor, this investigation that we've been carrying out for four years into the illegal exploration for oil. I believed, as I still do now in my naivety, that there is something about the sanctity of the rule of law, what holds society together. And I submitted this report. As I was coming back, five men were waiting for me in the forest and they shot me in the chest and in the stomach and left me in the forest. It was two young Congolese farmers who saw this happen. And as I was alone in the forest, they came and picked me up and put me on a motorbike.  And really what that came to was this idea that we were just too small, too vulnerable, too weak to really confront these big problems that we were facing. And this idea of the Varunga Alliance, of drawing in other players based on certain values, values to do with upholding the needs and the rights of the most vulnerable in the poorest in society, of upholding the needs and the rights of future generations, the whole concept of sustainability. And of course of upholding the rule of law, the rules that hold our societies together. But we needed the resources. And those resources really focused around three key things, four if you include fishing. But the first is energy, this idea that you need to recreate a whole new industry, a whole new economy that is based around diversification of the economy and that's based around this idea that you've got to break the conflict economy that has been there for so long. And really that is, you know, one of the very great challenges. And it's not that it hasn't been tried. You know, the international community, the world has invested $90 billion in Eastern Congo since 2000 in trying to put an end to the war. It's the biggest UN mission in the world and it's failed. And the reason it's failed is because of the whole demobilization of the armed militias where we are, there are between 5,000 and 8,000 of them. It's not that much. It shouldn't be insurmountable. But the demobilization disarmament and reintegration process has failed because of the R in the acronym, the reintegration. And that has failed because there are no jobs in Eastern Congo. There's 70, 80% unemployment. And really the answer lies in the kinds of work that people like yourselves are doing of creating successful enterprise that can create sustainable livelihoods for those people. And so we looked at those resources that can provide that and there's the two untapped resources of Congo, water and the human resources. And what can we do with that to turn this economy around? The others, of course, are agribusiness and tourism, the traditional industry for a national park. And we invested massively in that. It's perhaps not massively by your standards, but for us it was a lot. We've now reached an investment, committed funds of about $92 million to try and get sustainable energy to these communities. And what we discovered is that in doing that, for every megawatt of electricity that you provide to conflict affected communities, you can create between 800 and 1,000 jobs. And the research that we did in the early days demonstrated that we could create between 100 and 120 megawatts. That's 80 to 100,000 jobs in the community around the park. And we're well on our way now. We've now got the committed funds for 50 megawatts, we're halfway there. But if you remember the figure, 5 to 8,000 militias, that's 5 to 8% of those jobs that we're in the process of creating. And so really that's the answer. And it lies right at the heart of the kinds of work that you are doing. One example is the soap factory that has invested in Eastern Congo because of the fact that there's electricity and because of the fact that the park is bringing a degree of stability to that region. The soap factory has created 400 jobs. It's a $5 million investment, 400 jobs in a conflict affected community, but it's also increased the revenue for 10,000 farmers and it's decreased the price of soap for 5 million Congolese consumers. And so really that's the answer to conflict. It's a much smaller investment than the $90 billion from the public sector and it really comes from the kinds of ideas that people like yourself are developing today. So really that's why I came, was just to thank you for these amazing ideas and for this amazing work that you're doing. Thank you very much. That's so kind. Thank you. Sorry, I used that more than I share of time. I'm glad you used it for time. Yeah, thanks. Thank you. I knew you'd be glad you stayed, but we're gonna let you go now. So the next session started at 11. I wanna let you know there is another plenary this evening from 5.30 to 6.30, so please join us back here for that. And next up we have a session that sort of continues the theme from the morning that'll start at 11 around impact investing sort of at this tipping point hitting the mainstream if you'd like to stick around. Thank you so much.


Hi everyone, it's MJ and welcome to this course on foundational mathematics for actuaries. And I thought what better way to start this video than to give an analogy which my very own lecture gave to me with regards to mathematics and actuarial science. And he said that mathematics for an actuary is like fitness for a soccer player. It's not the only thing you need but you can't play a full match without it. So many people sometimes think that actuarial science is only about maths and that's not true. You definitely need maths if you want to be an actuary but there's a whole bunch of other things that you need to learn as well. Now what I'm going to be doing is building this course based on the syllabus set by the International Lectorial Association with regards to foundational mathematical topics. So I kind of took their syllabus online and created the course based on that. So let's look at the syllabus in a higher level and then we're going to go into a little bit more detail for each of these topics. So we have functions and sets. We've got differentiation, integration, sequence and series, differential equations which are probably the hardest one. Then we have real and complex numbers, matrices and systems of linear equations, vectors and vector spaces and inner product spaces and finally probability. Now let's like I say have a little bit of a deep dive in each of these topics. So when it comes to functions and sets and this kind of makes up 10% of the syllabus, here we need to define a function, apply functional concepts such as domain, co-domain, image, limit, inverse. We need to know what asymptotes and turning points are and how to find them. We also need to know set terminology as well as concepts and we need to calculate the roots of equations and evaluate integrals. Then if we come to differentiation which kind of makes up 10% of the course as well, here we need to find the derivative of a function as a limit. We need to look at the derivative of a function from first principles. We need to be familiar with the chain rule, partial derivatives, derivatives for various functions such as power, trigonomic, exponential, logarithmic, hyperbolic, guys these names are quite a little bit difficult to try and roll them all of the tongue but you also want to look at extreme points of a function of two variables and this one's probably also a little bit complicated and difficult to pronounce but we're going to try and use Lorraine multipliers for constraint problems and that's probably that's where you want to get to with differentiation is that one and we will of course do a couple of examples in the course. With regards to integration we need to evaluate integrals and this can be both definite and indefinite integrals. We're going to be looking at the substitution technique as well as integration by parts technique and then we'll also look at how to evaluate double and other multiple integrals. We'll also look at how to calculate the areas and volumes of geometric shapes and evaluate multiple integrals and interchange the order of integration and apply various techniques such as the trapezium rule and the Simpsons rule. Then when it comes to sequences and series also again about 10% you can see the syllabus is quite evenly spread. We've got the Taylor and Maclaurin expansions for functions of one and two variables very very important when you do mathematical statistics especially around moment generating functions. They are quite tricky though. I'm not gonna like Taylor and Maclaurin expansions. I even struggle with them a little bit but we also need to define sequences and series. We need to look at concepts such as boundedness, convergence, limits, monotocity as well as looking at formulae for the sums of arithmetic and geometric progressions and techniques to determine the convergence or boundedness. Then we move on to differential equations where we need to solve first order differential equations. This can either be separable, linear or homogeneous and as well we want to solve first order differential equation models for various applications such as when we've given conditions or when we need to find the values of parameters. Then real and complex numbers only makes up 2% and we just need to carry out arithmetic with complex numbers isn't too difficult and then moving I guess to a big chunk which is 20% is matrices and systems of linear equations. Here we're going to be doing matrix operations such as addition, scalar multiplication, matrix multiplication and transposition. We also need to calculate the determinants of a matrix. We're going to need to know how to use Kramer's rule to solve a system of linear equations as well as Gaussian elimination to find the rank of a matrix, inverter matrix, solve systems of linear equations as well as looking at eigenvalues and eigenvectors and we use them to compute the characteristic polynomial of the matrix. We also need to determine whether a matrix is diagonalizable and we need to find a diagonalizing matrix. Then when it comes to vectors, vector spaces and inner product spaces this is 5%. Here we need to do vector operations which is addition, scalar multiplication, vector product, scalar triple product, vector spaces, inner product space and orthogonality. Like I said the pronunciation of some of these things is a bit tricky but your probability 25% and this of course has expanded greatly in mathematical statistics. So some basic concepts, set functions, sample space and event, the basic axioms of probability, we know how to calculate probabilities, how to use the addition rule and union of two events, conditional probability, Bayes theorem and correlation versus independence. And the thing is your mathematical journey with regards to actuarial science is not over yet. So even after you've gone through this course and you've gone through all of these topics it's not the end because once you've done these you're then ready for the actuarial subjects and these are like mathematical statistics, financial mathematics, stochastic calculus and there's many other topics as well but you need to know these foundational topics before you can attempt those. And let's maybe end with a nice little quote which says in maths the art of asking questions is more valuable than solving problems and that's from Gagore Cantor and we're going to be starting set theory which is Gagore Cantor's little baby and we're going to be talking all about it and how it is the foundation of mathematics. So if you're still watching this video it means you're coming from YouTube and you would have realized that this is the very first video for my foundational maths for Actuaries course which is on Udemy. So make sure you go to Udemy and check it out. It still is in the early days so I'm still constructing it. I think that's why my rating is only 4.1 because we got a whole bunch of students just through Udemy's natural search engine and they're probably like penalizing me for not having everything completed yet but at the moment we have got set theory and functions which is some nice pre-calculus.  So we basically have all of the calculus. I still need to film and record the linear algebra. The videos or the presentations have been done. I actually find time to do the recording and then I will upload it onto Udemy as soon as that is done. What I'm also doing is turning it into a maths study aid. I don't want to say textbook because I've taken a very informal approach to trying to explain everything rather than having theorems and diagrams and trying to be too accurate that it becomes a little bit confusing like you sometimes get in a traditional textbook and this book is also as you can see there it's an early pre-release edition. It's not completed yet and you can see still very much early days but this is me releasing the video now. I know these videos you know stay on YouTube for a long time so if you're watching this like a month after it's released then everything's probably already out there which means you can head to Udemy. I'll put the links in the description below to watch the video series. You can go to Amazon Kindle again I'll put a link there if you want to rather just read and it's at the moment it's 50 pages. Once it's finished I can't see it being more than 60-65 so it's straight to the point. It's a very very thin maths textbook thing that we're covering so many things but like I say I want to get straight to the point and very mindful of your time and then final thing to just say is that if you're part of the Actuarial Society then contact them because you'll be able to get all of these videos for free. So part of the Actuarial Society, the Academy is all the students will get to watch these videos for free through them but if you are coming from outside the Actuarial Society you can still access them either through Udemy or like I say if you want to read it through Amazon Kindle. As always thanks so much for watching and I'll see you guys soon. Cheers!


Hello and welcome to a screencast about finding the average value of a function. So we have a kind of definition here for us, but it says if f of is a continuous function on the interval from a to b, then it's average value. So that's what we're gonna be looking at today. On that interval from a to b is given by the formula 1 over b minus a times the integral from a to b of f of x dx. So this integral here is what we've been looking at recently, and that's the area under the curve. We first used Riemann sums to approximate this area, but then we also can use some geometry to figure out what this area is exactly. Okay, then this also consists of an interval. So this b minus a here is actually the length of our interval, okay? All right, so just to kind of get an idea of where these pieces come from. So that way you can see that. All right, so here's a function g of x. This should look very familiar from a previous screencast. So it's kind of a funky function, but we have some lines and then we also have a semi-circle here. So they're all pieces that we can actually figure out what the area is. So remember again, this average value function is defined to be the area under the curve. So we need to figure out what that value is first. So that's gonna be all of this area here, this negative area down here, and then this positive area over here under the circle. So in a previous screencast, you calculated that area. I'll just roll through really quickly what that answer was again. Let me get rid of all my scribbles so you can see things. Yeah, so if you already know what these answers are, that's fantastic. Go ahead and pause the screencast or fast forward if you want to, to figure out then what this area is. So we broke this integral up into three pieces, or no, I'm sorry, more than three pieces. One, two, three, four pieces. We're gonna go from zero to two of our function g of x dx. Then we're gonna go from two to three of our function. And then we're gonna do the integral from three to six of our function. And then we're gonna do the integral from six to eight of our function. And the good news is then this gives us the overall integral from zero to eight of our function, which is really what we want. Okay, so that's our one piece of our average value function that we need. Okay, so as you roll through some of these values, let's see. So we have from three to two, so we have a rectangle here and then a triangle here on top. So this gives us a value of five plus, let's see, here we've got a really skinny triangle that gives us a value of five halves. And then we have another triangle down here. Let's see the width of this base is three and the height is negative two. So that gives us an area of negative three. And then finally we have our semicircle, so that has a radius of one. If you were to split that diameter in half, so that would have an area of pi over two. Cuz it's half of the circle and the radius is one. Okay, so anyway, these are all of our different values here. So when we total these guys up, and I just realized this one is not correct, this is not five. I thought that seemed kind of small. Let's see, so we do three times two, which is six plus two times two. So that would be, that's eight, okay? I apologize for that. I had a five in the brain cuz I was doing probably the eight and the negative three in my head already. That's what I get for working ahead, right? Okay, so anyhow, when you go ahead and figure these values out, let's see if we've got some fractions in here that we can work with. So that's gonna give us five, which will be 10 over two. Plus five over two plus pi over two. So that's gonna give us a grand total of 15 plus pi all over two. Okay, so again, this was the area that was given to you in the previous screencast. But now we need to add to it the average value, okay? So then we're gonna need to multiply by one over the length of our integral. So this is our interval, this is our area. Sorry, I didn't get myself enough room here. So my average value is gonna be one over eight minus zero times my integral from zero to eight g of x dx. Which is gonna give me one eighth times this 15 plus pi over two. So putting these two fractions together, we multiply. So we're gonna get 15 plus pi all over 16. And that would be the average value then of this function. Okay, all right, the other graph you guys looked at then on a previous screencast was this function here h of x. So again, we wanna figure out the average value of this function on the interval from zero to six. So we're gonna break this again up into three integrals. So if we look at the integral from zero to six of, oops, our function is h this time, not g, sorry about that, h of x dx. That's gonna be the integral from zero to two, cuz that's a fairly nice shape of h of x dx. Plus the integral from two to four of h of x dx, cuz again, that's a nice shape. And then finally, the integral from four to six of our function h of x dx. Cuz again, that's a nice shape. Okay, so these nice shapes, so now we need to figure out the areas of them. So from zero to two, we have a rectangle, and that has a base of two and a height of three, so that gives us an area of six. From two to four, we have a triangle, and that has a base of two and a height of three, so that's gonna have an area of three. And then from four to six, we have a negative area because it's below the x axis, so that has a base of two and a height of negative three, so that's gonna have an area of negative three. So that's gonna give us a grand total of our area to be six, okay? But now we want the average value. So the average value of this function on the interval is one over six minus zero times the integral from zero to six of our function h of x dx. So that gives us one six times six, which gives us a grand total of one. All right, thank you for watching.


Welcome to the Tick Mill Update, I'm Kiana, Danielle, the founder of the Investeva movement. Last week was a rough week for the U.S. dollar as the latest U.S. business sentiment data knocked it down early in the week to the levels that it couldn't even recover from even after a very strong U.S. employment update. This week we have the U.S. Consumer Price Index and the FOMC rate decision that could impact the U.S. dollar. On the other side of the pond, the U.K. is holding its general elections on Thursday, December 12th, which could get the pound moving and shaking. Today, with that I'm looking at the pound dollar pair, which has been stuck at the 1.3155 resistance level that it hit last Thursday. Looking at it from a longer time frame, say the weekly chart, we have a brand new Ichimoku indication. The pair confirmed a break above the weekly Ichimoku cloud and it's also in the process of forming a double bottom bullish reversal chart pattern. Now, with the Ichimoku signal, we normally expect a temporary pullback after the bullish break that I talked about in my book Ichimoku Secrets. And this could create an optimal buying opportunity for longer term bulls. Of course, trading in the financial markets involves a risk of loss and you should only trade the money that you can afford to lose. If you liked this video, give it a thumbs up and subscribe to the Tick Mill YouTube channel. I'll get back to you with more updates tomorrow.


This is covering the spread part of the Fandall podcast network It is not just the first week of college football this weekend It is that we talked about that with Ed Feng yesterday, but we also got in match week number four in EPL We have got the playoff opener for the NASCAR Cup series in Darlington and we've got formula one back on the schedule for this week there out in Monza for the Italian Grand Prix It is a jam-packed sports weekend here to break down that with us and talks me P. L Is Austin Cass will talk to him pick his brain on the biggest match of this week arsenal against man United No, let's talk about hysteria bets at Fandall sportsbook Then I'll go through NASCAR on Darlington and formula one in Monza This is covering the spread right here on the Fandall podcast network and Fandall research My name is Jim Sonnis. I am a managing editor of digital media for Fandall research joined here as mentioned by Austin Cass You can find him on Twitter at Austin Cass. He is a senior editor for Fandall research Austin We are on to match week number four. How you doing today? I'm doing really well Yeah, hearing you rattle off all the sports stuff coming up just got me really excited for this weekend It got me like I know that like it goes in like waves for me for the NFL where I like I'm like, oh my gosh I'm like so excited. Oh my gosh. I'm overwhelmed the seasons are on the corner, but still excited But then when you add in like this is like the last quote-unquote free weekend until January or whatever it is But when it's offset by having all this stuff It's really hard for me to have like the the Sunday scaries Effectively of anything because like it's just kind of a loaded weekend him Yeah, it's I think this is like the best sports time of the year, especially once baseball playoffs keep going I really like the playoffs and I mean March madness is amazing for those couple days of the first weekend But this is such a great time. There's just so much going on There really is so we're gonna try to cover all the bases as mentioned We did talk about college football with Ed Feng yesterday week one preview is posted on covering the spread the Fandall YouTube page And over on Fandall TV plus if you want to watch Fandall TV plus you can watch on Amazon Amazon TV Apple TV or Roku devices You can also check is check it out by going to fandall.com Slash watch login with your Fandall ID Fandall username and password You can watch up and Adams live there along with all the Fandall podcasts for covering the spread Solo shot and NFL heat check one week from today as well Let's start things off here by talking about match week number four in the EPL Austin And the big match of the week is going to be on Sunday That is between Arsenal and Manchester United right now Arsenal minus 130 to win this game at the man United money line is plus 330 a draw is plus 290 When you look at this match Austin anything stand out to you as far as betting at Fandall Sportsbook Yeah, so I like Arsenal at that minus 130 money line for me. It's pretty simple I think Arsenal are a good bit better than man United There's a lot of numbers that would point to that and then once you factor in the home field advantage I think Arsenal at mine minus 130 makes a lot of sense It's very similar reasoning to what I went with with Newcastle last week against Liverpool. The result didn't go our way but by XG Newcastle really controlled that game. So it makes me feel good about the process So Arsenal's got an expected goal differential plus 3.3 according to FB ref Through three matches United are at plus 1.9 A year ago Arsenal finished with an expected goal differential That was 12.6 goals better than man United's and by XG Arsenal thrashed United when they hosted him last year 3.1 to 0.4 It wasn't just at Arsenal that united struggled united had a miserable way record against all the top sides last year in four matches against the other Four sides that finished in the top five Man United lost all four times and the aggregate score was 18 to five But only one goal defeat of the bunch was the match at Arsenal which finished three to two despite the superlopsided XG score Arsenal meanwhile had the third best home expected goal differential in the league last year and probably just played very well in their last match That was at home. They beat Or they tied for him two to two but on XG it was 3.2 to 0.6 So we've got an Arsenal team that I think is better than Man United Arsenal plays really well at home and then the united side that struggles mightily on the road against good teams So it all adds up to me like an arsenal at that minus 130 Do you put more weight into performance against like top tier teams in a league where It can be a bit top heavy and where the gap between The top end teams and the bottom end teams is a bit larger So like in theory you look at you look at the table you look at, you know Futures markets for the EPL Man United actually grades out as being a decent team in that regard if you like rank them But it seems like there are big tiers and it doesn't seem like you view Man United as being As close to that top tier as you view Arsenal. Is that a proper way to sum things up there? Yeah, I'd say that's fair. Um, I think I don't know if it's last episode or maybe episode before Uh, I think we talked about how I like Man United to not make the top four So those top teams I have them near the bottom But your point about like the big matches when the other big sides play each other I rely pretty heavily on that sample because those matches will play out much differently for the top teams than other matches. So for example In those matches Man United are going to have a lot less of the ball have to play a lot more defense Probably try to hit teams on the counter, especially away from home So if they're really struggling in those types of matches that matters a lot to me when trying to predict How they're going to be in those matches going forward It's just a different kind of Skill then and a different kind of strength is required in those matches I think it does make a lot of sense to put more weight into those and that does lead austin To liking the arsenal money line currently minus 130 Over at fangal sportsbook But plenty of other matches on tap for this week Austin before we have EPL on a bi-week next week So when you look at the other matches for this week, where else do you see value at fangal sportsbook? So this is a little terrifying to say, but I like everton's money line. They're uh plus 145 It's the first match saturday morning at 7 30 a.m eastern time They're playing at sheffield united It's it's really scary to back everton right now They've lost three straight matches. They haven't scored a goal So far it feels like they're headed for another season where there'll be fighting off relegation And they probably have to be in a pretty fragile mental state right now given how the first three matches have gone for them So all that's obviously pretty scary But there are reasons to back them. I think odds odds makers agree because they've made everton's sun favorite here The biggest reason To be bullish about everton is that sheffield united have been even worse than everton Um, I think I've kind of dogged on them a few times here. I think they're they them or looting town Are probably going to be the worst team in the league this year Um So yeah, the blades are also winless. They haven't expected goal differential of minus 5.1, which is the second worst in the league They've mustered just 0.5 0.5 and 0.7 expected goals In attack through their three matches. They've conceded at least 1.5 hg in every match Uh, the other reason to not be super grossed out by everton is that they haven't been as bad as the results say Their expected goal differential is only minus 0.0 or sorry minus 0.8 So 4.3 goals better than sheffield united Sheffield united's xg numbers are a little skewed by having to face man city last time out But the blades aggregate xg tally versus knotting them for us from crystal palace Um Two sides, I would say are somewhere comparable to everton is one point xg scored in 3.3 xg allowed So yeah, like I said, we've got a pretty bad everton team But one that's not as terrible as the results say and sheffield united has been every bit as bad as their results say so If everton spirit isn't too crushed by their dreadful start I think they can take three points from this game and i'm into them at this money line, but I am a little worried that if if everton concede first that things might really snowball for them, but Overall, I do like him at this plus 145 number We do see situations like that in like if you're betting nfl Where a team just gets like demoralized and things like that But I think the the saving grace for you in this regard is There's gonna be a lot to play for for everton regardless if they're trying to avoid relegation and still pretty early in the year So I understand where you're coming from from the perspective of I don't want to be too wary of betting on a gross team and you know the stink of having zero goals so far May allow us to get them at a bit of a value and obviously again Like you said bookmakers are not Not dumb to that they know and they do make everton the same favor here at plus 145 but How much do you worry about the mental side of things at this point because From my perspective the season just started so there should be a long way to go Does that bother you knowing that it's been such a rough start? It definitely does bother me. Um everton are a really big historically and not a club that should be uh Fight and just sustain the division right they've had to do for two straight years They actually came into the last day last year needing a result to avoid relegation so I think It each time it feels like well this next year like it won't happen again And then it's like well it sure seems like it's gonna happen um But I think we see it a lot in the nfo where where you have a break like a week long break between games That momentum doesn't really carry over right and each each games is so monstrous. So That's what I'm telling myself at least when I'm back But uh Yeah, I just think there's a lot of things that point to everton being the better side and I like that plus 145 number Well, hopefully for us sheffield is just as crushed and we can uh ignore The narrative side of things and just bask in what xg is saying that is austin cast You can find him on twitter at austin cast find his work over at fan dual research by going to fan dual dot com Slash research austin no show next week because of the off week But looking forward to having you back on once again later on this fall to talk more epl Good luck to you this weekend. We'll talk to you again soon Sounds good. Thank you very much Alrighty, thank you once again to austin for breaking down the epl for us and slowly slowly getting me to understand Soccer a bit more than I did before we'll dive in and talk some NASCAR and formula one here in just one second at first get out your game day gear because college football is back And fan dual wants you to join in on the fun right now All customers can get a no sweat bet for week one just place a bet on any week one college football game You'll get bonus bets back if you don't win that our money line spreads totals and more just visit The fan dual sports book app and kick the college football season with Number or america's number one sports book must be 21 plus and president select states refund issued is non-referable bonus bets That expire seven days after a seat max refund is five dollars unless otherwise specified Restrictions apply c terms at sports book dot fan dual dot com fan dual is offering online sports Wadring in kansas's under an agreement with kansas star casino llc gambling problem call one hundred gambler or visit fan dual dot com slash rg in arizona 1 800 next step protects next step to five three three four two in connecticut one eight and eight seven eight nine seven seven seven seven Or visit ccpg.org such chat in indiana one eight hundred nine and with it in y oming in kansas one eight hundred five two two forty seven hundred or in kansas ks gambling health dot com Louisiana's one eight seven seven seven seven zero stop in mariland mv gambling health dot orc in y o West virginia one hundred gambler dot net in massachusetts hope is here gambling helpline ma.org Or call 800 327 50 50 or 24 seven support and in new york one eight seven seven eight hoping wire text hope and why Let's talk now about some nascar where they have the opening week of the playoffs this week It is in darlington and it's a bit of a tricky race to handicap because the past two or so months That's three months actually have been a lot of road courses a lot of pack tracks and not a lot of courses similar to darlington now darlington is unique to begin with but Haven't seen many Tracks like this recently we've seen some teams like rfk racing ford in general Getting a bit better recently So it's kind of hard to know fully what to expect and as a result My model has no drivers win odds above 10.12 percent I think that's where denny hamlin settles in at as a favorite in my model For this week's race and you see kyle arson martin trex jr. 5 to 1 if annual sports book obviously I won't get there The good thing is i don't have to avoid playoff drivers in order to find value in the outright market for this week I can still Get a driver with big motivation get a driver who has been fast on tracks like this on a good team And bet them for this weekend to me that leads me to tyler retic 13 to 1 at vandal sports book now I think it's worth mentioning that vandal is actually above market on tyler retic 13 to 1 a pretty short number relative to What you can get elsewhere But even at 13 to 1 I still show value on retic as of right now He is 8 to win for me versus 7.1 implied odds and The reason my model loves retic is because he is very good on tracks with a lot of tire fall off That is a key characteristic of garlandton because Very few tracks have as much tire fall off as this one does other tracks that are similar to that or fontana and homestead Couple of spots where retic has traditionally run very well Now garlandton is not a spot where he won in exceeding. He has not won in the cup series either, but He did have two podiums here last year when he was racing with richard children's racing I think 2311 is a bit better equipment than rcr is so equipment upgrade for him Where he finished in the podium twice last year also had two podiums here in the xfinity series back when he was a regular down there so retic in theory should be a good track fit here because of his experience and his quality on Tracks the lot of tire fall off and we've seen that Even if it hasn't resulted in a win just yet So retic to me. I think is a great bet for this weekend I also should value in his teammate bubba wallace if you could bet 2311 racing in general to win this week Which you can do at some spots. I showed value in bubba individually at 281 I just like retics upside more than I like wallaces So I feel good about 2311 in general getting back to a speed or a track where speed matters quite a bit a track where They both had good runs or you know are capable of having good runs So 13 to 1 on retic not the best number you can get But even if it is the best number you can get I do still show value there on retic to win for this week Other bets I like for this week are both top 10 bets on non-playoff drivers And I don't I don't mind betting an outright on a non-playoff driver actually had eric jones to win this race last year. So He was a non-playoff driver non-playoff drivers won each of the opening Three races in the first round of playoffs last year So I can bet on guys not in the playoffs and still feel pretty good about that If they're fast enough and alex bowman is driving for hendrick, which means to me He's probably going to have that speed and his top 10 odds are plus 190 I think that is a good amount longer than it should be I have bowman at 39 to finish inside the top 10 his implied odds here at 35 percent and bowman Has struggled coming off of his back injury But it does seem like he's been getting a bit better overall of late He's had better runs and it lines up well where we go to a slick track and bowman has traditionally run Pretty well on those and on concrete. I feel like even though there's not a lot of tire fall off on concrete tracks there is some similarities where Um, you got to search for the right groove stuff like that So I do tend to care a bit about concrete when we go to places like darlington and bowman does tend to run pretty well there now darlington specifically has not been a fantastic track for bowman To finish runner up here back in 2020 the first race off the covet break He had a sixth place finish. I think in the final race that year as well He was 10th in the second race last year. So it has hasn't been his best track But also has not been awful But I think that what we get here is a driver in very good equipment Who has shown strength on slick tracks in the past and you're getting a plus 190 for a top 10 I think that's good enough value to bet him. So alex bowman top 10 at plus 190 A quality bet based on my numbers for sunday's race The other top 10 that is also on a non playoff driver. That's eric elmerola at plus 750 and My model is not high in elmerola So I didn't expect to go into this week betting him in any market because I wouldn't really want to in general, but The market's a little bit lower than I am. I have elmerola's top 10 odds at 17.9 percent His implied odds are 11.8 percent Stuart house racing has been awful on tracks like this this year where they've had just no speed on The bigger tracks chase brisco has been a back marker effectively on this track type But elmerola at least has been somewhat competitive He had a 15th place average running position in the first going to race finished 13th in kansas 16th in vegas and both of those tracks have Medium tire fall off whereas going to is a big-time tire fall track They had the richman race recently and elmerola ran really well there Now it's a very different track because it runs a different rules package So you don't want to over weigh that in the model, but it is good to see him Have a good run at a track with a lot of tire degradation So I don't want to be here I don't want to bet on a driver for shr to finish top 10 At a track like this because they've been so awful on this track type this year all the resources all the focus for that team Will be on kevin harvick the lone playoff driver there, but I think 750 is too long. So i'm going to take uh, Eric elmerola top 10 plus 750 in addition to bowman at plus 750 and then add on the reddick outright at 13 to 1 Let's talk now about some formula one for this week as well. They're out in manza for the italian grand prix and The best value on the board earlier on this week has dried up a bit, but there is still value there When I ran my sims at the first part of the week The best bet that I saw was carlo signs finished top six, which was plus 115 at the time now Posted my sims over at on vandal research and since that time We've seen signs shortened to minus 110 for a top six I do still show value there Signs for me to finish top six is 55.5 percent his implied odds at minus 110 are 52.4 percent So I believe you can get even money in some places still So I would at least shop around so you can get that on signs if you can get even money I would take it honestly at minus 110 I don't think it's a bad bet either because I do still show a decent amount of value there I would also say for signs I've got value in him to finish inside the top 10 at minus 300 and a podium at five to one The podium bet specifically the implied odds at five to one are 16.7 percent I have signs at 22.3 percent That might be a bit high But I think this is a better track fit for ferrari than what we've seen here recently because it's a high speed track There is extra pressure on them this week because It is their home race and pressure on ferrari is probably not a good thing given the way Things have gone for them since the start of basically Ever since the michael schumacher era so like pressure is probably not a great thing for ferrari, but I think it's a good track fit for them So I think it's at least a consideration to go with the signs podium at five to one I like the top 10 bet at minus 300. I have signs at 82.1 percent for a top 10 implied odds there are 75 I also do show value on his teammate charle claire finished top 10 and that is minus 370 now for Fandall is specifically you cannot parlay top 10 bets. Um, so that's not an option But la claire minus 370 signs minus 300 both values for me based on what my numbers say I've got la claire 83 percent with his implied odds at 79 percent So I think the ferrari is in general kind of under value for this week now not to beat red bull not I'm not crazy But to get a podium maybe for signs get a top 10 for both those guys and to get a top six For signs as well. I think we can see good value there. So if you're betting formula one for this week I feel like the best route as frightening as it may be bad vibes abound I think it does make sense to be high on ferrari for their home grand prix in monza That's all that we have here for today on covering the spread But we'll be back once again tomorrow by talking to pitching ninja talking about his favorite strikeout props for friday And also full nfl slate coming up next week our new schedule will debut there looking forward to that So make sure you're subscribed to covering the spread wherever you get your podcasts. You can also check us out On the fandal youtube page and over on fandal tv plus as well Thank you once again to austin cas for swinging by talking epl with us You can find him on twitter at austin cas find his work over on fandal research I am on twitter at jim sonnis. You can also follow fandal research at fandal research I want to thank you all for tuning in for today Good luck to with your bets across college football for tonight Reminded to check out our podcast with ed from yesterday and we'll talk to you once again on friday This has been covering the spread right here on the fan dual podcast network


Please give a very warm applause to Stefan Burschka So I'm expected to speak now, right? I didn't expect so many people actually, so it's a great honor for me to be at the Kau's computer club conference because it's my first time I never expected to be here actually and I'm actually not a hacker so my formation is physics and I deal with traffic data and I also deal with troubleshooting large infrastructures large infrastructures is something like 10,000 20,000 pieces of equipment router switches any operating systems humans in it so pretty complicated stuff and when something goes wrong and They don't find the problem. You need to figure out where something happens and Then when you in that business you start developing Tools for you one week of open source here fertilizer hint hint and Then when I was asked to have this talk here. I said, okay The people are not minors the people are hackers really smart people. So I will talk about listening to packets So packets are actually something like that what you see here So I have my laser pointer here. I can kill you so this here and What you need is an ear who knows Snoop Nobody knows. Oh great. You know on a spark five or spark one They took this feature out I guess but there was a there was a command line switch Which routed the traffic to the amplifier and then to the loudspeaker and you could listen to traffic And actually you could your ear is amazing. It does a foyer transfer. Does anybody know that it's cool cool stuff And you you're in you are Actually in nerd against noise and when something happens whoop your brain detects it so the idea is actually to do that with IP traffic as well and As everybody everybody can do that. I guess if I play a tune After a few seconds, you know, what kind of tune that is Some people are so good. They identify the singer So you actually can do that with IP traffic as well So when I get that idea There's this poor guy Benoit do Pusky he was a student master student, so I worked with him on that and Actually he got for his thesis. He got a science prize at the PFL and now he does his PhD in Belfast So unfortunately, he's not here today so But smart guy So the contents Who what why I will tell you who we are what we are doing and why we are doing it I give you a short introduction to traffic mining who wants more just comes up to me and I teach you then Engineering approach, so we will need some engineering stuff in that something like transfer functions stuff and then Traffic mining signal analysis, but I will keep it simple There's some math in it, but I will explain it different. I'm I'm a private pilot I fly gliders a love flying glider, so I will explain it with airplanes And then I will give you some results and then you can ask me questions so this is actually the group some people are actually here and Yeah, the guy with glasses is clear who that is and we really stick together before we came to this company here We worked at a great operator really big one and We had some of these Looney projects there and I tried to educate young guys, especially here, which are in far beyond To actually go for the moon To do impossible things and you won't believe it these guys can do it and Then the people are 20 or 21 and then they're actually at at the university right? so I educate my own guys and There's a question mark. That's a hint. We are looking for people So we also working with universities So sometimes I pull people in and I do bootcamp So the people come to me and I make them suffer for one week or two weeks and then they can do these things So if somebody wants to do bootcamp come to us so what? Network troubleshooting you need three two things one is to discover the network a lot of people don't know What the network what is actually in there what it's doing? So we did something Like Nino that's Nina. It's out of discovery tranquilizer if you are dealing with one terror 10 terror 20 terror of traffic you need something that digests that and and transfers that into a flow or Into that right and then we do something like operational pictures and I had this problem Looking at 11 or 20 dimensions Who can understand here four dimensions? I mean visually. Oh, yeah. Okay. How do you do that? Okay, then six six spatial seven 20, okay There's Tom Tom rules smart guy and he's working with us. He's actually from University of Marlborough and These are maps so what you see here is something like one terror of traffic 10,000 IP addresses in it and 11 dimensions and you can identify regions now 10 regions and you can see it. It's projected What two dimensions? So and you can here identify an anomaly So that's actually the map we want to use. It's does anybody know co-owned maps Okay, that's a total different story Needs another talk. So if I give a workshop or something I will explain that to you then automatic protocol learning So you give me traffic we reverse engineer the state machine of the traffic Automatically until you to feel master student did that also really smart guy and This here that's the next generation protection Something like evolving an immune system and as I heard somebody will do a lightning talk about that Sometimes so if you're interested go there so It's Christmas you see packets visiting around so One direction and here the direction coming back So what's in it? Can you tell me? How do you guess that? No, I don't know I blindfold you. Okay, you're blindfolded. We make it easy. I take packets Me packets and I shift them over the floor right shift them and If they are heavy or not They have the same velocity What is in there? No, you cannot privacy and your bribe you're blindfolded or it's in the tunnel So I want you I tell you there are some animals in it elephants Tigers so don't open it you get eaten then Butterflies flies whatever how do you figure it out anybody an idea? You are blindfolded Timing What timing I shift them over the floor and sound yes smart woman sound But what is sound sound? How does the sound is created? Friction. Yes, very smart woman. So Friction so actually we convert Yeah, we have some thermal energy in it, but and we listen to it So what we can guess it actually is actually the material of the box for instance So when you do some physics you figure out They have you The early they stop So you can hear the time you know Could you figure out whether there are elephants whether they are green or yellow elephants in it? Can you do that? But you know are there any green or yellow elephants?  They're great So you can assume There are no green and yellow elephants in it right this is what you do you have priors and then you can guess right What else? You can look at me. I'm an old guy. You have really sensitive ears. I have to lift the box You hear the cracking of my bones The cracking of my bones right the more they crack the louder cracked the heavier the box If I tell you there are elephants and there are hamsters in it From the cracking you could tell There's an elephant there's a hamster, but then I'm sinister I put more hamsters in it And then you're done, but can I fill a box like that with a lot of hamsters? And they have to wait with the elephant as small elephant baby elephant So you get the concept. Okay, we can go all drink a beer or do you want to hear more? okay, good, so Driving money What do we do there is listen what you did here listen see understand and find invariants. I Will show you what they are This is the application here, but the important thing is here So you have down here an IP stack with all its fault and stuff here operating systems They're all flawless right and here applications and they're flawless as well and we are invincible We are flawless as well and organizations are flawless as well That's right No, good, so What we have here we listen down here and we guess what's wrong here Whether there's malware around whether something is misconfigured We listen and tell you what kind of a play application that is or what kind of user So you have something like an SSH connection Whether it's you or him Whether you issued a kill command or he issued a remove command And you can even guess something about the organization, but just looking at one packet Not more that's actually the beginning of boot camp So what is possible what I will not talk today is about as is a command guessing or what you can do in IP tunnels It's earlier work. It's published some of it if somebody is interested he can come to me I Will I will guide him to that we will talk about listening to IP packets in a flow and And it's encrypted and now the question was the origin actually was a problem from the feds so and You know whether you can catch pedophiles when they are communicating encrypted So people said it's impossible to do that. So when I said, okay, it's impossible. Let's try it so what you do You take the packets if he had a time and you have a feature here, which is a packet length and Which is in the header IP had everybody knows the IP header, right? What's the first byte when I say for seven hex Then not everybody knows IP so you have to do boot camp so right so and We try to produce an ear Or something like that or use our brain to interpret that and To answer a question so If you start listening to this here so Yeah, and You end up here When you're done because that's encrypted so if you try now to decrypt it you can do that I'm not the cryptologist people make can't do that. Maybe or maybe not and here's your four five So it's IP version IP version four and has no option All right, that's there here in the blue range There's some information and here you cannot do anything in the end if you start there You end like that Yeah, it's too much effort as well. Is there a simple simpler method hmm so Actually said there's no real theory about it How to do that and what kind of features are relevant? So when you look at traffic you can look at the IP header If you have TCP traffic, you can look at the TCP traffic header UDP header. There's a lot of information around There's TTL. There's IP ID, right sequence numbers when you have look in the TCP header But are they relevant? Are they relevant? Yeah, you're sitting in the front row. Sorry. I was take the guys in the front row here next and then I take the second row Are they relevant? Maybe we don't know that was a minor, right? We don't know so we have to figure out so there are two ways to figure out that so you take a lot of data So let's say you ask the question One class second class third class and then you put put that on a machine some artificial intelligence and then You get something like a paper and the paper award or so, but that doesn't mean you understood the problem So we are interested in understanding the problem. So I Thought That's a good picture. So when you have something encrypted Everybody knows here what the tunnel is imagine trains Trains just going in the tunnel and the question I ask like with the packets what kind of train Where does it go that's easy how many passengers what kind of passengers humans elephants hamsters whatsoever What is color of the t-shirt who sent that packet and the only thing what you can do is listening outside the tunnel and These train tracks in the old days. They had they had this gap for temple temporal reasons Material expansion and then you hear these to them to them.  Yeah, and when something is heavy, it's noisier So that's the only thing what you can do. It's like blind being blindfolded and only listening So when you when you listen to that and we just say the sound is proportional to the force So it's not the exact theory what I'm delivering here. It's something like a motivation So we look at the force on the force is this differential of the impulse and I guess everybody knows that everybody wants to school, right? so we differentiate that and We get here the impulse is mass times velocity So we differentiate that plus we differentiate that and the packets On the internet we assume they have all the same velocity Right, they visit around with the same velocity. So This is gone. That's nice. So we have this term So what we do and now if there's a mathematician here, please look away because what I'm doing I Just expand with the packet and packets. They are smooth, right? Okay, they are smooth really smooth and you know expand and you can differentiate them and you get two terms So what is that? The packet per time. Do you know that? packets per time packet rate Time and here Mars per packet So what is Mars? I thought so Mars Is something like the length and This is the packet fire rate. That's actually the interdistance the machine gun your IP stacks fires out the packets So cool. So by thinking we found out two features So let's look at them and then we said, okay, so everybody does HDP DNS all this kind of stuff and at that time Skype Undetectable Skype really hard problem and everybody knows Apollo project John F. Kennedy. We do things because they are hard, right? So we thought we looked at Google talk zip. That was too easy. So we took Skype poor Benoit so then There are many undocumented codecs and there was the silk at that time was there was undocumented interesting and so The others they have a pause sending packets so you could easily detect when the speaker has a pause That's so easy, right? We don't do that. We like Skype which constantly fires that that's really a Really difficult problem and the Skype guys are really cool really love what they did I mean, they are gorgeous these guys beautiful minds and Then we said hmm if we know what kind of sentence we have Could we detect which Skype conversation has this? Sentence in there. What we do now is what I threatened I picked one or two and we do now an exercise feature extraction. What do you see? I'll ask him What do you see? Yeah, good. Do you have any question? Anybody having a question? Ah, what's the green and what are the blue ones? What's the correct question? Yeah, very very good. Okay, so the green ones Denote the communication between between two peers the blue ones are out to super notes Okay, very good. No, you have information Next What is what do you see here?  Oh Way flat Well We have to talk you understand wavelets See you're in trouble The timescale. Yeah. Yeah, but you know, that's just a resolution. That's to confuse you Okay, go ahead. Ah, so okay. Good. I do that. Yeah Next Hey, hey, listen, you I just wait here. Yeah The regularity at the end he says so you mean this year or you mean this year Yeah, cool cool guy. Yes, that's the first thing what comes to mind here is that and you see this is above zero When you look at the other applications They have packet length with zero so the first Skype detector of the very first version now They are smarter was minimum packet length equals three And Skype was impossible that's this and that's the pink This is very old Skype, right? And here are supernotes and Here, what is that and what is that here? There's sinister Why does the packet length go down? Codding at up the filters so you actually could see That this is Codding training Trying to figure out how can you optimize the packet sending and receiving And that was me That was Dominic a student at that time. He had windows and linux at that at that time You could just buy the packet length being used for the communication say which operating system that is even if it's UDP TCP is easy, right? But even if it's UDP so now this is no different And this is somebody who stalks slow here to listen you see a lot of Movements here, and that's the guy who talks a lot Yes, that's me so And then when I saw that I thought oh, maybe there's movement. Maybe we can do that Right, so we identified now features and you guys did now a mining approach by looking at it Not by using some some elaborate method By looking at the data and that's very important to look at the data and to understand the data very important So the first thing what you need is a hypothesis So engineers around Lift your hands electrical engineers. Yes. Good. So a transfer function the existence of a transfer function of the audio input that comes from you from the microphone and The observed observed IP packet length. So we said There exists a transfer function but transfer functions are linear, but we said what the heck we tried out and then Output the output what we measure is predictable and then given the output the input can be estimated That's our strong hypothesis. Okay Then We have parameters which influence so that's about understanding what's going on there So we have basic signals coming on so they have amplitude They have a frequency. There's will be noise Right, you can do assumptions about it and their silence because there are people who actually stop Sometimes talking I don't but there are people normally so for names. They're for names There are people who do language. They they do a study about languages They are for names. There are words and their sentences. They exist. That's actually the prior There are no yellow and green elephants. So we already know something That's very important. The next is Everybody uses Skype who uses Skype? see everybody so and Then to make things easy at the beginning we only use UDP communication only communication between two peers because It's otherwise so damn difficult enough. So we Just started what I say simple and then English Everybody speaks English. I speak English everybody Okay, so that's a basic lab setup. You have two laptops or you have computers. You have operating systems on them and they exchange packets So you have different speakers. It's not only the same speaker So when we have a woman's voice or male voice How does influence? How does this influence the packet length? We don't know we have to do the experiment And there was a nice guy who was actually into voice recognition I had a huge database with different speakers and different sentences in it a humongous database So we did the experiment So We know something about Skype. There's something about Skype to read so we know there's cryptography in it We know there's a network layer. We know there's sound cards. We know There's something different speakers and we know but there's software in there So these are things we know so we could use them Against these guys again with the green and yellow elephants And then the transfer function that means You put something in like a step and you here you have your black box and you Just see something coming out. Does anybody know what kind of filter that is? High pass or low pass? Thank you very much. Yes, you know, we thought okay. That's so easy. Let's try that Okay, let's try that.  Oh, we did with it a lot I mean we did everything what engineers tried to try to do and when you expect doing a frequency sweep you get this Oh damn, so there's something really complicated in that something that changes per frequency optimizes So you could now start looking at the codec That's actually a smart thing because they cannot change what the codec does they can change their program But they never can change the codec they use and you have certain requirements for a codec to Actually receiving packets. Otherwise, he can't do his adaptive filtering and predicting business anymore But this year. Yeah, it's a bit rising. But oh it's going like that So hmm, we did other experiments and figured out in the end That's guy business That's our model You know every scientist needs a model. So we said, okay, here come the trains and Here's the codec and he tries to unload something in the train and they have different speeds When you have different speeds here here you have a higher speed here of a lower speed Then something of that goes in there and then something in there and the next one is empty and so on and that somehow Explained this behavior and then we were very content and drank a lot of beer and thing but it doesn't really solve the problem, right? But it helps that's our understanding. I know it's wrong, but you the cow is round all animals around the first development it's so Now, okay, we do serious business. We don't do round cows anymore. We go now serious restore Understood the engineering approach Assuming the system is linear and we can do fit do everything what engineers do in order to understand That didn't work so much so hmm So, okay, we need something more with more juice. So it needs to be learned It needs to be learned. So the task is again You listen to this here These are the packet lengths and You know your phonemes in it everybody use words everybody uses sentences So we somehow need to find a mapping Okay We need to produce invariants and invariant is something that does not change when you change The environment so when you have different speakers when you have a different skype version When you use something else as an encryption, it does not change right It's something like when when I say animal You come up.  Sorry Animal if you have to characterize an animal and It's human. What's the first different? You're blind. No, you're not blindfolded. You can look at it, but you may not listen Yeah, you could Easier think simple. How about looking at the legs?  So yeah, but you know, it's a probability. So it's a good feature And not if you live Many million years something like 65 or 100 million years ago, but we don't so we have to produce something like that so We looked at the phone name we had a phone name database So this is pleasure, right and that's the signal. Yeah So we have 44 for my names in order to prove that we have to prove this a mapping a homomorphism, yeah that This is here valid and That's a tremendous lot of work, but we did it and we figured out We didn't find anything except For the signal length and silence So signal to silence so people will think it's trivial But estimating when you have silence because it changes. It's not trivial. I said, okay, let's use that then word construction when you When you have to take a very simple approach and we like to do a simple approach at the beginning is not easy as well then noise silence Estimation when you can estimate that is silent and that's a signal if You don't do that You're done. So you have to estimate silence and the longer the sequence The better the results so the longer somebody talks like me the better the results if somebody talks only two or three words Very difficult So we decided sentence detection. We do simple things Sentence detection here. You have a sentence Yeah, it's a really silly sentence like the frog prevents them from arriving on time anybody flying with planes here So this happens. So that's actually the sentence So we think we saw same sentence similar output not same similar So we have a chance With some statistical means and that's very good news Here we have different sentences Yeah, I put a bomb in the train.  So you see it here. So you can see with the naked eye there are differences and What when you look at that what what do you see is there something you recognize a feature in both signals Bit louder Yes, absolutely So by looking by looking at it you have to look at the data and you see similarities you see here and here Good and what you see here is actually they have different lengths. Hmm. How do you deal with different lengths?  So how do you do that? Yeah, there's something like hidden mark of models. So we thought, well, okay, but you know, well too complicated Let's do something Easier dynamic time whooping and that actually takes one signal and the signal signal and now Maps and onto each other in time. So you see here one point correlates to two points and tries to find an optimal path Expands one signal compresses one signal and tries to figure out an optimal path and then gives out a number and says these signals are so Comparable. So here we see two signals And what you see here this blue means very very Similar actually the same and the red areas. So if you compare this here to that or this here to that So this year to anywhere to this similar. It's dissimilar. So it's red and What you do is actually you compare two values and Do something like an Euclidean Difference so between two vectors. You have two vectors and here is the angle and when the angle is like that It's different when the angle is zero. It's almost the same right So With two signals, which are very different So we have two sentences very different. You see are all red and that would be the optimal path, but No way, they're different. So we have a means actually to figure out which signal Signals are the same and which signals are not so if we know the sentence we could create a model and Then we could compare it to all Skype conversations and then figure out Where is a similar signal that should have this meaning but now there's a problem. What's the problem? Thank you very much Absolutely different speakers. So what you do with that?  So we actually we figure it out We can do in the average 66 percent that doesn't sound much But actually if you can do that, you're pretty you're pretty good and when you we had 83 correct guesses under certain assumptions, right so But different speakers. Hmm, how do you deal with that? Engineering Kalman filter does anybody knows I love Kalman filters because it's from the 60s. I'm an old guy Right, they're really cool. Yeah, so they actually are able You can measure something even it has a control input like in like an airplane and then you can estimate What's the next expected sample? So this is can average From many speakers it can produce a model of a signal which is correct for all speakers And it works repetitively and these are all complicated matrixes and stuff, but you can do an assumption and Then you make all these things constant and then it's pretty easy So if somebody has questions when somebody has questions you can come to me But now I promise you I explain your Kalman filters. So I do it. We do it with airplanes hmm, so Imagine you're somebody right a guy in the old in the old days and you want to track a plane and you have Here Bob and Alice and you guys, you know, you're a cryptolo just guys, you know, there's Bob and Alice and You get from them Points points in time and space these points and you know, they are noisy. They are not reliable both are not reliable and You get from them data and you have to trust them or not trust them You have to decide whether I trust them and these are these two independent speakers And now you have to estimate the next position of next reliable position of the plane Without knowing anything if you have a theory about it how a plane flies it helps, but if you don't know anything It's actually our task We have from independent speakers unreliable information Which is noisy where we do not know how this signal is actually produced Yeah, by the IP stack by everything. What's in there, right? It's the same situation. This is what Kalman filters do You get unreliable information and You produce the next good estimate like an average. It's not correct, but you know, it's a good guess Not everything is correct in here, but I guess That's what it's does So we have here an example exactly from a tutorial from somebody else here we estimate We estimate this constant line and here you see the Kalman filter here We have all these unreliable points and now this thing if you tune the parameters like and right and It learns it learns that and you can feed that all kinds of crap, but it will learn to follow that line Hmm. So if this line is changing what happens then Here we are here we have now many speakers these different colors and the Kalman filter is the blue Yeah, and you see here the change the speakers the signals change and this common filter learns Oh, yeah, this is crap. I don't trust it. I just go through It adapts its average and when something really drastically changed and everybody agrees it follows as well This is how you produce a model of independent speakers That's a problem Did anybody see that did you see that I? Had time and now I have packet number here. Hey, come on You don't you have to listen or did you do drink too much beer yesterday? You have to you have to see that's packet number. What's wrong here? Yeah, and Yeah, yeah, it's a difference in speaking speaking speech What what you mean? What's what's wrong here? Different. Yeah, but you know packet number packet one. When does packet one and packet two come? What did I say about packet interdistance? Yes One thing but I could take care of it. I have more fits, but what else? Oh Sorry, the sequence might be wrong Yeah, but there's information in the packets. I could take care of it. I So you mean the difference in time? Yes, absolutely. So this packet could come a second later. Okay. We know that in skype Wouldn't happen right something like 20 30 milliseconds or so, but here we use the packet number not the time and you know, I did this Calculation there and it says time difference and I use the packet number. Why did it work? Thank you very much. Somebody is listening here Okay, so that's why it works and that's about science. You have to be honest if oh, sorry It I said it beforehand that skype has something like a constant packet rate 20 30 milliseconds So that's why it worked with the packet number and it makes things easier So you assume these packets coming in constant times And that can be wrong because there are signals who do not do that and With skype it works, but if you use time Right, and if you use more Pre-processing right invest more work You go higher than 83 percent and you go an average higher than 66 percent So that's an important issue So mitigation techniques, it's not perfect solution So there if somebody wants to do an encrypted communication, he always has a trade-off between performance and Computational power. This is what these skype guys did and they did amazing work I mean, it's really cool Then yeah, you can do padding you can give me all packets have the same size Absolutely, and you can influence the IP stack. You can do queuing. They come with the same rate, but be careful with padding We found a function in an IP sack tunnel And we produce the function which uses the padding against you And then we can do the job as well. So do it right with the padding Or you could add random payload as well Random packets randomize it right, but it's computational expensive but mitigation for you guys easy so conclusions We could under specific ideal Situation as in the lab we could reach 83 percent more elaborate effort. You can go higher Speaker independent methods Kalman filter and there are people who are afraid of Kalman filters and they come hidden Markov models And somebody asked me why didn't you use hidden Markov models?  I'm sorry. So then mitigation techniques as I said for you guys relatively easy and there was a paper in 2011 and They did that and they produced reasonable results and now here we come And here I come to my final slide So don't be afraid. Yeah, I stopped talking none What you see here there are signals and here you see a gap so if you take the packet you do something wrong You distort the signal Hmm, and that's actually looks like so what you do. Do you put zeros in here? Can you do that? And how do you sample this signal? There's something like Shen who knows certain theory? Yeah, very good. Very good. So So who do you do that and there are metallologies And then we come up with something like that that sounds that looks really silly But that's something like an average which tries to estimate How much of this signal is Relevant for me and to produce something like a cut-off frequency and then I have this signal and this signal I can sample with the twice the frequency and then I can do my signal processing on it and then I'm honest I don't use packets Yeah, I try to do the thing right because they are a lot. This is for instance an HTTP connection a complicated one and If you want to do Some mining and some guessing with signals on that you have to do that job right right so What I tried to convey It's actually this this is he's unfortunately good all the good guys are dead I mean Einstein is that Carl Sargent is that I feel really bad now. So these you have these Science is a way of thinking and this is what I try to teach. It's not applying Methodologies, it's not knowing everything about II. It's not about hacking Somewhere it's about seeing it's about understanding No, and it's not about knowledge. It's thinking what needs to be adopted and the Open source tool we deliver which actually does the preprocessing for that as well, which is very useful for network guys I today I overloaded the version 0 5 7 so on source forge if somebody is Interested giving us feedback because I see a lot of downloads since one and a half years But nobody talks to us and said, okay, that's crap or this is crap. So If you mind Yeah, this me or just try it out and you see here this night and eat and all these ants really cute It's actually here in the back So we might produce t-shirts sometime So if you test it or if you use it you get a t-shirt, maybe right? So that's it. This is my email address Was a pleasure go Ask me questions Questions no questions. I don't believe you I Mean if somebody wants to know more just come to me ask anything We have a question back here wait a moment. So somebody has a question Hi, I just wanted to know what your job is at Ruark, which is a Swiss defense company Yeah, I have I have to kill you if I tell you No, it's it's aeronautical in space and I'm a pilot and I always wanted to work for an Aeronautical and space company, but one of the job is this traffic money and Producing tools and you know, we even give something open source. Is that cool? Yeah, I mean it was developed at the company before but they told us we can give it open source And we give it get back to the companies we give it back to the community That's it, but now you're a target any more questions Hi I really don't know how Skype works, but do you think I know? If the interpretation use CBC I don't understand how you can see something or hurt something with that Okay, there's a guy for bootcamp. So Okay, what I try to convey is I do not decrypt. I just take the packet length, right? So you send packets. No not you Skype sends packets and I take the packet length as a signal and that's it And I just used It as a metaphor to listening to that. It's a signal. It's like I'm talking to you You listen to the signal and now we are building tools Which do the same process? It's not like that. I'm listening on the wire No, really. Yeah, I understand that but you are like trying to see patterns on something that Chair That must be like random So it isn't the idea is not the content you speak of the content. I do not look in the content So you can see patterns on encrypted data No on the packet length So not and the content think about the trains, you know, you don't know what's in the trains but from the Was in the tunnel you can estimate if the train is fully loaded or empty for example, and that is what he uses Okay, thank you. Okay otherwise Come next one now everybody's leaving. Can you guess the sex of the speaker? Yes You can even tell you what kind of under we are the speakers. Wearing no kidding It's hello another question now. What would you do to make Skype more secure now that you know? Thank you. Yeah, I know but let's say if I would play an audio book in the background Would with it more or less keep me secure? No on an on a sound level Very basic sound level is really high. Yes I Mean I said under ideal condition and the ideal condition is you speak Nobody else is speaking That's it. So what your ear and brain is doing, right? This is what we cannot do but that's actually an interesting question. Thank you very much. It's very easy No, let's let's rotate audio books in the background or let's let's put the audio books inside afterwards Absolutely, absolutely, and you could also say the same audio books are a private key for two people to communicate Yes, yeah, that that's another thing so you can add it to that. Thank you. I Mean we just thought something simple, right? More questions Did you ever tried a real learner like who's capable of time series? Yes? Yes, we didn't do that because we wanted to show You can do it even by looking at a very simple if you use these You get higher precision and recall. Absolutely last question Sorry, how you deal with no native speaker better doing mistakes on building the sentences Okay, these are these assumptions. I should have said that that's my fault Everyone who has more questions can meet Stefan outside of the hall and talk a little bit to him. Okay. Thank you


identifying rural, peri-urban and urban areas. For the identification of rural, peri-urban and urban areas in Pakistan, we can opt different types of the methodologies. For example, according to Qadir, Chimet and Vakas, they developed an agglomeration index to categorize the areas under the subheading of rural, peri-urban and urban areas. And for this categorization of the areas, they basically used their indicators and these indicators belongs to population size of a city, population density and travel time to a major city. According to them, an urban area is an area where population density is greater than 150 people per square kilometer and this area is located within 1 hour travel time from a city of population of 500,000 people. According to them, peri-urban areas are those areas that are located between 1 and 3 hours travel time from a city of at least a population of 500,000 people and here they did not consider the population density of people living in that area. Then, when you define rural area, an area that is located more than 3 hours of the travel time from a city of at least 500,000 people and here they did not consider the population density. So, they basically categorized an area, peri-urban area if travel distance is 1 to 3 hours from a major city and if this travel time is more than 3 hours, they categorized this area as a rural area. If we talk about the characteristics of rural area, these rural areas have low population density, they are using the land for agricultural purposes and there is limited infrastructure and settlements are scattered in the rural areas and rural areas are usually surrounded by nature. But when you define urban areas, you can see there is high population density, there is commercial and residential land used and extensive infrastructure and at the same time there are economic and cultural sector in urban areas. But when we define peri-urban areas, these are basically transitional zone that lie between urban and rural areas and here with the passage of time there is increase in population density and there is diverse land use. It is also used for agriculture, for residential purposes as well as for commercial purposes and here infrastructure is emerging with the passage of time and this is a mix of rural and urban settlements. But when we discuss all the references of Pakistan, which are our statistics, those statistics mostly categorized our areas under the sub-adding of rural and urban category. For example, here we are in a table, if we talk about percentage of population for population of 10 years and above and the labour force by gender in year 2020, we can observe interesting effects. If we look at rural areas, your population is relatively, greater portion of population is living in rural areas as compared to urban areas. Then if we look at all the references of Pakistan, we can observe that your female labour force that is more employed in rural areas as compared to urban areas. You have rural areas where almost 13.95% of your female labour force that is employed and you have total female that is 49.88%. But when you look at these statistics in the sub-adding of unemployed, we can observe that for rural areas, female unemployment is less than the female unemployment in urban areas. The main reason for this is that you have rural areas where female are involved in the activities that are carried out in the agriculture production. They are involved in forming activities to produce the agricultural output.