My name's Chris Sibley and I'm the lead researcher for the New Zealand Attitudes and Value Study at the University of Auckland. And this is Joseph Babilia from the School of Art History, Classics and Religious Studies down at Victoria University of Wellington. In this video, we're going to talk to you about feedback from one particular publication from the New Zealand Attitudes and Value Study. And this is one of the first big papers from the study looking at change in New Zealanders over time. So we're putting this video up on the website, mainly as a way to try and say thank you to all of the people who've taken part in the study. So thank you if you have taken part in the study and we really hope you continue to participate because the study is about to go into its fourth year of data collection. So it's really starting to get exciting with the different findings that we're discovering. What we're going to talk to you about today is looking at change in religion in the New Zealand population and particularly in relation to the horrific earthquake that's happened in Christchurch. So I'm going to pass over to Joe to talk about that now. Well thanks Chris and I'm just going to say how I got interested in this. So I study religion and Chris set up the NZADS to see how attitudes and values are changing over time. And in particular I was interested in how religion is changing over time. And one thing that came out of the data on the NZADS is just the magnitude of suffering and difficulty that people encountered in Christchurch after the earthquake. So that was a really tough thing. And so what we want to do is sort of think about what resources are available to people in the wake of a natural disaster of this sort of magnitude. So we're interested in the people of Christchurch but we're also interested in really contributing some sort of knowledge that would be helpful to people who might encounter a similar kind of difficulty. Now one thing that philosophers and theologians have debated about for centuries is just the role of religion in the wake of extremely traumatic experiences. Some people say that religion is a kind of comfort and it helps people to get through times of exceptional difficulty. And others say that religion is really sort of like provokes anxiety or increases anxiety or it's about getting people to do things which are not quite good for them. And it's really an open question whether or not being religious really brings any kind of comfort to people. And the second question is even though we might be drawn to religion at times of exceptional crisis, well does being religious help us in any kind of way? So these two questions are different so we could be attracted to religion at times of crisis but it could be something that really makes no difference. So as we think about the Christchurch earthquake we can begin to think in light of the responses that you people have given to us, we can begin to look at how religion changes in people both before and after the earthquake in Christchurch and elsewhere in New Zealand. We can begin to see what it is about religion if anything that helps people to move forward. So I'll hand over to Christy who's going to talk about our results. The data we're analysing here is from the first phase of the New Zealand attitudes and values study which was collected in 2009. So back before the Christchurch earthquakes occurred and we matched the people that completed that phase of the study with those who also participated again in 2011 last year. So after the Christchurch earthquakes had occurred and in total we had about 4,000 people who completed both of those two questionnaires two years apart. And the questions we were interested in looking at in those two surveys related to people's religious affiliation, so the exact wording of the question for those who took part in the study was do you identify or affiliate I believe with a religious or spiritual group? And people simply said yes or no to that and if they said yes we asked them what group specifically. And we also asked people just to give a simple rating on a scale from 1 to 10 of their satisfaction with their overall level of health. Was their health very good or was their health very poor or was their health somewhere in the middle? And by doing that we could look at changes in people's perception of their own health. So their subjective rating of their health over time and we could match whether that paralleled or coincided with any sort of change in their religious affiliation over time. Whether people had found God or become religious over time or whether people had always been religious over that period of time. Whether they had been non-religious consistently over that period of time. We also had another very important variable which is that we knew which region of the country people were in. So we knew whether people lived in the Christchurch region or we knew whether they lived in other regions of the country. We also knew from subsequent questionnaires whether people had been personally affected by the earthquakes because we asked them that too. And a fair few people of course all around the country said they had. So this allowed us to look at change over time and subjective ratings of health and how that would coincide with religion. So the first figure that I'm going to show you figure one shows levels of or percentages of the percentage of people who affiliated with different religious groups in 2009 and again in 2011. And as you can see we've presented two identical figures or near identical figures. The top one splits people up by region of the country. And here we're comparing the Canterbury region on the left with everywhere else in the country groups together on the right. And what you see is that people were more likely to convert to a religious group or to find faith if they lived in the Christchurch or Canterbury region relative to other regions of the country. So something different happened in Christchurch to religious affiliation. If you look at the lower half of that figure you see exactly the same pattern when you split people up by whether or not they said they were personally affected by the Christchurch earthquakes. Regardless of whether they themselves still lived in the Christchurch region or not. So of course these two overlap a lot. People in the Christchurch region were far more likely to say they were affected by the quakes. And these two measures are strong when you put them together because they tap slightly different aspects of this effect. And in both cases what you see is religious affiliation increased in the Canterbury region and for those affected by the earthquakes relative to the rest of the country. Their religion grew. Our second set of analyses looked at change in ratings of health. And what you can see in this figure are people's overall ratings of their satisfaction with their health in 2009 and then two years later in 2011. And this is split up for people who said they had been personally affected by the earthquakes versus those who had not. And it's also separated for people who were consistently religious. That is they said they were religious in 2009 and they were also religious in 2011. And as you can see in the left side of this figure for people who were religious and across that period of time whether or not they were affected by the earthquakes had no effect on their ratings of health. They were massively consistent. We don't see any change there for people who were religious. We also don't see any change as you can see in the middle figure for people who were non-religious. In fact their ratings of health are the same as people who were religious. We don't see change over time in their health ratings. They're very stable and it doesn't make any difference to their overall ratings of health as to depending on whether or not they were affected by the Christchurch earthquake. So this is very interesting because you would expect people's ratings of health satisfaction with their health their subjective wellbeing to have dropped following the earthquakes. Certainly I think a lot of people would have thought this and you would also expect or a lot of scholars have argued that religion may have these protective effects for health and wellbeing in some cases. What we see is a rather different pattern of results in New Zealand. And as you can see in the very right figure we do see a drop in people's ratings of health for one specific group of New Zealanders affected by the earthquake. And these are the people who lost their faith. These are the people who in 2009 said yes I am religious and by 2011 when we sampled them again said no I'm no longer religious or no I'm not religious at this point in time. And for people who had changed from being religious to being non-religious over this two year period from 2009 to 2011 for those people if they had been affected by the earthquakes we see this quite dramatic drop in their rating of their health. And this drop is statistically significant at the 5% level. Okay I'm going to pass back to Joe to talk about the practical side of this result. Well let me just recap some of the interest of these findings. And so remember our first question was the question of whether going through and enduring the agony of a horrible natural disaster would affect religious faith and in which direction. So although the Anglican cathedral was smashed by the earthquake although the Catholic cathedral was destroyed although a third of the properties in Christchurch were damaged beyond repair and although so many people lost their livelihoods and although nearly 200 people lost their lives we find that there is this increase in religious faith in this area. Now that is interesting because what we find in the country wide data and what's supported by census data over a 60 year period is just increasing declines in religious faith. So we find that across New Zealand people tend to be moving out of religious affiliation but in Christchurch we found a market shift in the opposite direction. So that was quite interesting. So the second finding remember our question was well what does having a religious faith do for us? And here we find something quite interesting. What we find is that having religious faith appears to have some sort of buffering effect so that if you had a religion before the earthquake and you had a religion after the earthquake your experience of subjective health will be similar. But what we also found and this is quite interesting in the context of New Zealand a place that has communities outside of churches is that if you were not religious before the earthquake your experience of subjective well-being would be similar after the earthquake. Now that suggests to us that there's something about these communities outside of churches which may be doing work in supporting people. This suggestion by the way is supported by this subjective reports that we find in the data. So when people are reporting about what it is that is supporting them within their communities they're talking about other people. And so we find this both in the church context and we find it outside of church. But what we do find and what I think is very important is we find this signature or signal of declining well-being among that group of people who lost their religion. So we're really interested in thinking about who it is we want to look after in the context of a terrible natural disaster. There's something about this group of people who have lost their religious affiliation during the time of that earthquake that suggests to us that they're experiencing market declines in subjective well-being. And I think to me one of the interests of this sort of study is it points to the importance of listening to people over a period of many years and attending to what it is that's happening to them in the context of an entire country. And seeing what sort of changes are associated both with improvements, protection, buffering, and here we find both church and secular communities are doing something like that. But we also want to attend to the signals of suffering as well. And I think that we do find it in that group and it raises a whole lot of questions about why that is so, questions which we hope to address in future studies. So I think I'll just close my bit for thanking Chris for inviting me on to this project and thanking you all for participating in it. I think you've really helped me to address questions which are important to me, but I think you've also helped others with a kind of knowledge that we were able to provide in this study. So thank you. And I'll just add my thanks to that. I hope you've found this recording interesting. And if you are a participant in the New Zealand Attitudes and Value Study, then a special thank you to everybody who's been taking part in the study. And I hope you'll continue to do so. And I hope I'll be able to put up many more videos of findings that we can't really imagine in terms of what we're going to see and change over time. So cheers. Thanks.


So, in the previous lecture we were talking about this projection matrix right. So, we saw that if you have the subspace U contained inside this inner product space V and if you suppose that there is an idea of a best possible approximation of a vector in V of any vector in V onto a subspace of itself that is U then this best possible vector can be obtained through this map which we showed was a linear map which we showed was a projection map and we have seen that this projection therefore takes an object V and maps it to V hat which is the best approach of V in U right. So, of course this is a mapping from V to U right. If I remember correctly we have also shown that the image of this projection map is the entirety of U right. So, in fact without loss of generality if you pick out any object in U, U is its own pre-image right. So, if you try to project an object that is already sitting inside it and get its best possible approximation within the same subspace even commonsense says that it must be the object itself right. So, that is what this essentially means and we have also proved it through our idea of best approximation where we had this relation in terms of the inner product between the error and any vector in the subspace yeah and that inner product needed to vanish. So, we showed that this is indeed true the next object of interest is the kernel of this projection map and we are going to claim that this is nothing but the orthogonal complement of U right and why should this be true. So, again it is again the same old trick how do we show that two subspaces are equal containment either way. So, suppose V belongs to the kernel of P which essentially means that PV is going to vanish right. But what exactly is this PV? PV by this very definition is nothing, but the best possible approximation of V inside the subspace U right. So, therefore, V minus PV it is inner product with any object inside what? If I take the inner product of V minus PV what is this? This is the error. So, it is inner product with any vector inside U must identically vanish right. But what do we know PV is already 0. So, this is true for all U contained in U, but whether I write this PV or not does not matter because I know by my supposition that this is already in the kernel of P. So, this is 0. So, this essentially means that the inner product of V with every object inside the subspace U is 0. Then what does that mean? Where should be V come from? V is exactly going to fit the bill for a member inside the orthogonal complement of U.  That means V must belong to the orthogonal complement of U. So, based on this I started with the assumption that V belongs to kernel of P and now I observe that this implies that V belongs to the orthogonal complement of U. This part till here is it clear. So, now if you plug in PV is equal to 0 here this means that V is inner product with U is 0 rest of it does not change. So, that means V is a vector which if I take the inner product with which I if I take its inner product with any vector inside the subspace U it vanishes. So, that means, V must belong to the orthogonal complement because the orthogonal complement is precisely that vector space which contains all those vectors which are orthogonal to vectors in a given subspace U. That is the definition of something belonging to U perp or U use orthogonal complements. Therefore V must belong to the orthogonal complement of U. If that is so that it means that based on this I can claim that the kernel of P must be contained inside the orthogonal complement of U but I have to show equality. So, I will have to show the other side as well right. So, now on the other hand suppose V now belongs to the orthogonal complement of U alright. So, what can I say about PV then? Where does this belong to? Of course the job of PV is to project anything on to U. So, PV belongs to so this is naturally, naturally true yeah because the image of P is U itself. So, this is job to project it to U right. Now then think about this object V minus PV what can you say about this object? What is this object? By definition of being the best approximation of V inside U that is PV, PV is supposed to be because that is how it maps. PV is the best possible approximation of V inside U. Therefore, this V minus PV is what? It is the error and where is the error supposed to belong to? Sorry U perp the orthogonal complement of U. So, now this is equal to 0 for all U implying V minus PV belongs to the orthogonal complement of U okay. V belongs to the orthogonal complement of U, V minus PV belongs to the orthogonal complement of U what does that mean? V minus V minus PV where should it belong to? This is an object inside the orthogonal complement of U by my basic premise. This of course by the very nature of the projection map which maps to a best possible approximation that means thereby making this the error. So, the error must belong to the orthogonal complement. So, both of these objects belong to the orthogonal complement. Therefore, there is some odd difference whichever way you call it also belongs to the orthogonal complement of U which means that look PV now belongs to the orthogonal complement of U. So, I have two things here. This is true naturally yeah is there any doubt about this? Projection map always maps it to you. So, anything when acted upon by the projection map will live inside you.  What does this mean? So, therefore, its inner product with itself will be what?  Therefore, we erase this and carve out this space PV is inner product with itself consider without loss of generality the first PV to come from U is dual identity right double identity. So, you can consider it to either come from U consider the other to come from U perp matter of taste really which one you consider to come from where this is 0. But this means that the norm of PV is equal to 0. But if the norm of PV is 0 when can that be true? Yeah, it can only be true if PV is equal to 0 implying let me carve out a little more space implying that V belongs to kernel of P. So, when V starts out with the premise that it belongs to the orthogonal complement of U perp it ends up being in the kernel of P. So, from this we can infer that of course I can erase this part now this is now old. So, therefore, V belongs we have already written that. So, that means U perp must be contained inside kernel P earlier we had this now we have this combining these two together we get U perp is equal to kernel of P yeah savvy right ok. So, now what I leave to you as an exercise to try is that assume that P is this projection map from V to U. So, by now you know the definition of the projection map it is basically the best approximation of any vector in V inside U. So, this is how P is defined. So, for P defined in this manner this is a projection let me just state it upfront. So, you have to verify that identity minus P is a projection map from V to the orthogonal complement of U yeah. What do you have to show you have to show that it is linear as we have done with the projection what are the properties we have seen four properties essentially. The fact that a projection map orthogonal projection map is linear the fact that what it is idempotent we have seen it the other day because of course, objects in it on the subspace themselves get mapped to the same object. So, it is idempotent what about the third property we have seen that the image is the entirety of the subspace to which it projects and the kernel is nothing, but the orthogonal complement right. So, verify that this is also going to be an orthogonal projection. So, just try to check that this is. So, you can take V to be finite dimensional for your verification right. So, just try this out as an exercise and that will solidify your concept and let you realize where you stand in so far as the concepts are concerned ok. Any doubts about this derivation that we have done so far today yeah. Because P V is the best possible approximation of V inside U. So, by the previous days development by the in the preceding lecture we have already seen that if P V is supposed to be the best possible approximation it means that this is the error and then the error vector remember that example we gave of this child running after this balloon that is flying high the closest that the child can get is right below the balloon which is when the vector joining the child from the balloon is perpendicular to flat earth. So, it is the same idea this is that vector joining yeah. So, this P V is where the child is standing the V is where the balloon is. So, V minus P V is the vector joining the balloon to the child and that is orthogonal to any vector inside on the on the flat earth which is where the child can go to any position can that can be assumed by the child is a vector on flat earth. So, this is it. So, this must be true we have proved this also in the previous lecture. So, we have proved that this is true and that is what we are just using here right ok. So, now we will immediately see an application of this idea of orthogonal projection although we will not necessarily use this important result about the inner product. We will approach it quite directly and eventually show that we are going to land up with the projection matrix in fact an orthogonal projection. The problem is one that is very close to our hearts because this is the theme that has connected the first half of our course it has to do with the solution of systems of equations of the form Ax is equal to b ok. So, let us get back to that again from these abstract vector spaces we are going to talk about very tangible vector spaces and their subspaces. So, let us write again Ax is equal to b suppose A is real matrix m cross n b is an m tuple x is of course an n tuple this is the unknown. Let us further assume that m is greater than n what do we call such systems over determined the A matrix is tall there are more equations than the number of unknowns which is something that you most often encounter when you perform experiments you want to determine some unknown parameters some unknown coefficients you obviously do not perform the experiment as many times as the number of variables are there. Simplest case being verification of Ohm's law or to obtain the resistance of course the resistance is going to be just one parameter v is equal to r i right. So, when you perform the experiment this is your i or this is your v is going to be a straight line. So, what you could have done is you could have just performed the experiment once yeah with some current and check the voltage and the slope would have given you the resistance, but we do not do that do we we actually go ahead and carry out this experiment multiple number of times yeah and then due to noise and other things we end up getting several different data points and you do not actually go and fit this data like this to pass it through every point we do not do that because from the physics we know that this is going to be a linear relationship we draw the best possible line the best approximation what I am going to tell you is that this idea is very similar except for the fact that now you do not have just one unknown coefficient which is the resistance in this case you can have multiple unknown parameters, but think about it like you perform the experiment n number of times to obtain n different parameters makes sense right. So, that gives it some sort of a physical outlook or interpretation. So, over determined is mostly what you do in experiments unless your experiments are very costly to set up and you want to perform fewer experiments and number of variables and then do the best job that you can in which case of course you have to be very precise in that case you normally do not expect too much noise or disturbance and you know parametric uncertainties and all everything is very precise it is one way or the other you have to pay the penalty somehow anyway. So, let us suppose that this is the case where it is over determined also suppose we will talk about situations where some of these assumptions are removed later on, but for now suppose A has full column rank which is to say that the rank of A is n clear with this being the situation of course it is not trivial now right it is not trivial as to how you can obtain. In fact, you do not even know if a solution will exist if you take the row deduced echelon form of this what is it going to look like how many non-zero rows can you have at most n. So, a bunch of those rows m minus n precisely will be all zeros and if you perform the same operations on B and if any one of those last m minus n elements in B turns out to be non-zero you will end up with the conclusion that there is no solution recall very beginning of this lecture of this course we had discussed this possibility right that if you do not have this compatibility that is when the rank of this augmented object AB the rank of this and the rank of A alone if these do not match then you will have no solution you know this already maybe from your earlier matrix theory course or whatever preliminary courses you might have done and we have also visited this result right at the beginning of this course maybe first few lectures and we have told you why this must be so so hopefully you have a better understanding of why this must be true right. So, the same thing here so you might not even have a solution but the point is if you perform an experiment you cannot just say oh you know it is either the perfect data set or nothing at all yeah does not make sense that way right. So, we will still have to do with the best possible approximation that we can get you might also say hey there is another solution if they do if the data do not fit then discard that round of experiment but then that defeats the purpose of performing multiple experiments it means essentially you are choosing just n of those arbitrary runs of the experiment for n n parameters to be evaluated again it is a waste but we would not want to do that. So, this curve fitting thing and all of this you see these are very important even in the modern day there is there is a serious problem called over fitting and there will be a problem that you will encounter you probably already seen if you have this is problem of interpolation and once this lecture is done if you attempt that problem you will see something very interesting I will not reveal to you yet what it is but the question should also make it apparent what it is that I am trying to hint towards it has something to do with this over fitting that is the hint anyway. So, let us focus on this picture shall we. So, what do we have here we are basically saying that this is the A matrix whose columns can be written as a 1 a 2 dot dot dot till a n each of these are m tuples of numbers right. Now, what we are asking for is an x such that x 1 times a 1 plus x 2 times a 2 plus dot dot dot x n times a n is equal to b in other words what you are asking for is some sort of excess that leads to this equality that is this is only going to be possible if b belongs to the span of the columns that is if b belongs to column span a we have a solution, but that is the boring case that is anyone can see that what if it does not if b does not belong to the column span of a what do we do yeah sorry we approximate. So, approximate what to what what is it that we are going to do yes best possible. So, best possible of what just flesh it out for me because it is a very tangible problem right project b to the column space of A yeah and then check out. So, what is that projection map it is not very straightforward now is it I mean to say something is there up in the air we can get something it is all well and good, but what is this projection map after all how is I mean is there a straight forward way to predict though this is a projection map and on top of that this see the way we have defined it it satisfies those four properties you cannot just think this through like Tesla did apparently with his induction machine yeah you have to really work this through. So, let us not take this approach of the inner product here let us just think about the basics what is the basic assumption here that if there is this I mean for want of a better demonstration let me just take again a 2D and a 3D case. So, suppose this is b and apparently this b hat is probably the best projection yeah when this vector is the normal to this plane, but that also means that any other potential b because we have seen that right that this best approximation is also unique. So, that we can call it the best approximation not just a best approximation the best approximation whenever it exists is unique. So, if you take another say b 1 b 2 b 3 b 4 all of these will entail a larger error the minimum error is in fact this. So, what we are essentially looking for is can we get x hat in R n yeah such that a x hat minus what is it a x hat is living where in the column span right anytime you take a post multiply this a by a vector all you are doing is taking a linear combinations of the columns of a. So, what we are doing is this by letting this x hat be arbitrary. So, this is of course arbitrary so it is like my search space this is now like an optimization problem is how we are going to formulate it because we have seen that this is going to be the lowest value of the error among all possible vectors that you can pick out in the column span of a right. So, what are we going to say that this error between a x and b this fellow's norm is minimized I will presuppose a bit of knowledge about multivariable calculus here one of the primary things being that when you are when you have a multivariable function like this it is a multivariable function you agree because there are x 1 each x hat has n variables and this is not a optimization over a single variable. So, what is the first order condition for optimality for a function such as this the gradient of that function has to vanish right. So, what is the gradient of a function of scalar function would look like it becomes a vector right. So, the gradient of a function becomes a vector what is going to be the second order condition I wonder this is where things start getting a little more interesting the second order is something that we call a Hessian or a Hessian and that is a matrix because now you have a vector and you are going to operate on this another derivative if I may call it that and that results in a matrix. So, you start with the scalar function take its derivative with respect to a vector that is a gradient it is itself a vector. So, again there is no unanimity about whether the gradient should be a column or a row most often it is taken as a column vector, but in some textbooks you might also encounter a situation where it is dealt with as a row vector because quite often the gradients dot product or inner product is taken with some other column vector. So, prima facie they just define it as a row vector, but let us not get caught up in that they are ultimately the same objects this function that you have you differentiate this with respect to the first variable that is the first of the n tuples second variable that is the second n of the n tuple and so on. So, you differentiate this partial derivatives of this function with respect to each of the variables and you stack them up together to get a vector that is the gradient. So, the first order condition for optimality is that the gradient must vanish and the second order condition is just like I am not going to the details of why because that will again take us too far a field. We just know there is that just like you have the second order condition in case of a single variable what is it double the first derivative must vanish and the second derivative must be positive for minima negative for maxima right. So, in case of matrices we have something called a positive definite matrix which by the way as an upshot of or Fourier into this we will also be in a position to define what a positive definite matrix is. So, just know that the gradient has to be 0 and at that point the Hessian must be a positive definite matrix. So, that is the condition. So, we will now investigate next how we can solve this problem using these two ideas right.


All right, good afternoon everyone, 3 p.m. and it's my pleasure to introduce Uku-San. Thank you. Hello everyone, welcome to my, by the way, can you hear my voice in the back side? It's okay, great. Welcome to my talk named Generating Personalized Worklist with Natural Language Processing by Analyzing Tweets. Let me introduce myself first. I'm Uku-San. I'm usually doing researches and writing tools which are about offensive side of security. I'm currently working for Tier Security. You can find detailed information on my website and you can follow me on Twitter if you are interested in. In this presentation, I will start by talking about password guessing attacks. Then I will explain why reducing the word this size is crucial for password guessing. After that, we will see how can we generate word lists based on target's personal topics. Finally, I will demonstrate the Rodeolo tool which does that job. Passwords are our main security mechanism for the digital account since the beginning of the Internet. Because of that, passwords are main targets for the attackers. Of course, there are lots of different ways to gather a target's password. For example, the attacker can prepare a phishing website to trick targets into entering their credentials to a rogue website. Or an attacker can conduct a password guessing attack through brute forcing. Password guessing attacks are usually described in two main categories. They are offline and online attacks. Offline password guessing attacks are usually conducted against captured hash or encryption keys. For example, for hash cracking, attacker calculates some password hash and compares with the target hash. There are two variables which affects the success other than the password complexity. They are hardware resources and the type of phishing algorithm. More hardware resources are providing us speed, therefore increases the chance of success. The other thing is, of course, phishing algorithm. For example, cracking an MD5 phish will be faster than cracking a Bcrypt one. However, online password guessing attack is something different. In online password guessing attack, attacker sends username password combination to a service like HTTP, SSH, et cetera, and tries to identify the correct combination by checking the response from those services. There are lots of different variables which affects the chance of success. For example, our connection speed, server bandwidth, also server can block our IP address or log target's account, et cetera. There are lots of counter-measurements. Online attacks and hardware resources has no positive correlation, therefore it's much, much harder than offline attacks. Most of the web applications have password complexity rules where users have to use at least one number, upper-level characters and, of course, special character. Therefore, reducing the brute force size, brute force pool to an acceptable size is very important for the attackers. Instead of brute forcing all combinations, actually we can make some smarter choices. For example, we can try the most common passwords. If it doesn't work, we can make some smarter combinations. To reduce the combination pool, Haschke team created a technique called mask attack. The main idea is people are choosing their passwords with similar patterns. They are not pure gibberish data. Therefore, we can define a pattern which is called mask and we can do brute force with its boundaries. Let's say our target password is Yulia1984. With the brute force approach, we need to brute force all nine characters with all of our char sets. The formula is 9 over 62, which is a very, very big number. For the mask attack, since we define the pattern, we don't need to brute force everything. For example, the first characters can be only upper-letter. There are 29 choices. The total amount of combinations are way smaller than the pure brute force technique. It's around 200 billions. But of course, it's still too much for the online attacks. We usually can't send 200 billions requests to web application and check its response. But of course, pure brute force attack and mask attacks are not the only way for password guessing. There's also science fiction method based on smart guessing. For example, on Sherlock's Hand of Baskerville episode, Sherlock Holmes was checking the personal stuff of the target and we're guessing the password in just one shot. So we can talk about the third method now. It seems very unrealistic, but in theory, it's possible to find Yulia1984 password in less than three shots. We just need to have some Sherlock Holmes skills. Let's assume that target is posted at Tivit and we are a Sherlock Holmes candidate. We can make following deductions. Target's outer name is Yulia and the target loves her so much since he or she Tivit's about her. And the target's favorite author is George Orwell, whose most popular book is 1984. So combine them together. The answer is Yulia1984. Is this that simple? We will come back to this later. So according to some researchers, which are conducted by Carnegie Mellon University, most of the people are choosing words from their hobbies, sports, movies, et cetera for their passwords. This means that most of the user passwords are contains meaningful words and they are related with the password owner. So in theory, we can become a Sherlock Holmes on password guessing. We can actually prove that people are mostly using meaningful words for their passwords. When we analyze leaked MySpace and Ashley Medicine passwords lists and generate most used masks, we can see that almost 95% of those passwords are formed by sequential alphabetic characters. So there is a high problem that these are meaningful words. Let's try to prove that they are actually contains meaningful words. So what is a meaningful word? We can say that a letter sequence is a meaningful English word if it's listed in an English lexicon. For those who are not familiar with the NLP context, lexicon means that the complete set of meaningful units in a language. We used Stanford's wordnet lexicon for this job. Our analysts showed that almost 40% of those word lists are included in wordnet lexicon. Hence, they are meaningful English words. Now we need to apply post tagging, which means part of speech tagging to these words to understand what kind of words they are. Post tagging is a process to find a words class. They are eight different part of speeches in English language. For example, I learned them in English lessons. For example, for those who are not familiar with those, for example, there are nouns like table chair. There are verbs like eating, going, et cetera. So we analyze those words with the help of Python's NLTK library. And results show that most of these words are singular noun. So let's recap what kind of facts that we have so far. First, our analyzes show that people are using meaningful words for their passwords. And the second, from the research conducted by various of universities, we know that passwords are mostly based on personal topics. So Sherlock Holmes' method is legit in theory. But can it be done in practice? What Sherlock Holmes did was analyzing personal topics of the target. Then he combined them in his mind and came up with a candidate password. But how can we do it in real life? To achieve this, we need information about the target and algorithm which extracts good password candidates from that information. We need a data source just like Sherlock Holmes had. We needed source where we can find hobbies and other interest areas of the targets. And actually, we all know that kind of source. It's Twitter, of course. In Twitter, people tend to write posts about their hobbies and other interest area mostly. Since there's a character limitation for each tweet, users need to write things more focused. And this makes things easier for us because we don't need to deal with large gibberish texts. So let's use Twitter as a data source and try to build our personalized wordless generation algorithm. First of all, we need to gather tweets from target via Twitter's API. Then we need to get rid of unnecessary data. But how do we know if a word is necessary or unnecessary? Since we're trying to find personalized things, we can remove stop words since everybody are using them. For example, we can remove things like I, my, she, et cetera from the tweets. Secondly, as you recall, our research showed that people are mostly using nouns for their passwords. We can remove any verbs. For example, we remove suggest words from the tweets. As you can remember from the previous slides, leaked words were mostly formed by nouns. So we can apply post-tagging to the rest of the words to detect most used nouns and proper nouns. For this tweet, nouns are doubter and outer. Proper nouns are George Orwell and Yulia. Sometimes users are combining two meaningful words for their passwords. But of course, they are not like two random words. They have a kind of semantic similarity. We also need to combine similar words. We used WordNet's pet similarity algorithm for detecting semantic similarity of the words, which are extracted from the tweets. Pet similarity algorithm gives us a score between 0 and 1, and we are combining two words if their score is greater than 0.12. In the example shown in the slide, we will combine cat-tag and flamethrower with each other. Researchers have also found that some of the most used semantic teams in past passwords are locations and years. For this syrup, we will send the extracted nouns to Wikipedia and parts related years and cities from them. In this example tweet, we sent George Orwell to Wikipedia and it's returned us words like London, 1984, et cetera. So the last step is combining all of our data. From the example tweet, we got George Orwell word, we sent it to Wikipedia, and it returned us 1984 words. Beyond that, we also had Yulia as a proper noun. So when we combine all of our data, we will have the correct password Yulia in 1984 in somewhere. So instead of millions of combinations, we could correct this password in less than 20 or less steps. So it's just like Sherlock Holmes did. So to automatize all processes, I coded a tool named Rodeola. It's written in Python and most based on NLTK library. It follows the algorithm that I described in the previous section. With a given Twitter handle, it can automatically compile a personalized word list with elements such as nouns, proper nouns, cities, years related to them. Currently, it only supports English language, but I will finish the Turkish and German support soon. You can use Rodeola in three different modes. In the base mode, Rodeola takes a Twitter handle as an argument and generates personalized word lists without any fancy stuff. For example, when you give Elon Musk username, it will generate passwords like Tesla, car, SpaceX, boring machine 2018, et cetera. In the regex mode, a user can generate additional strings with the provided regex. These generated strings will be appended prefix or suffix to the words. For this mode, Rodeola takes a regex value as an argument. Regex value defines the string placement. With the regex shown in the slide, it will generate passwords like Tesla root 01, Tesla root 02, and it goes like this. In the mask mode, user can provide Hashcat style mask values for the world this generation. So in this example, we used mask in which first character is upper letter, second one is lower letter, and the third one is upper letter again. If you don't have any Twitter API or you want to use another data source, you can bring your own data. Rodeola provides you two different options. In the first one, you can provide a text file to Rodeola which contains lots of harvested texts. In the second one, you can provide a URL list and Rodeola harvests text from these URL automatically and will build the personalized word list right away. You can download and try the tool from our GitHub page by yourself. Okay, so demo time. To make this demo, I will get a Twitter handle from the audience volunteer and we'll pass it to Rodeola and we will check its results altogether. So is there anyone who are actively using Twitter in English and willing to share the username with me? Yes, sir, but let me open up my terminal first. Okay, I'm all yours. A, B, A, V, E, E, K, yep. D, E, E, S, this one. Okay, let's see what we will have got. Now it's downloads to bits from that Twitter handle. Probably it will download like 2,500 because it's Twitter's API limitation. I hope I have a internet connection right now. Mm-hmm. Weird. All right. DevCon Wi-Fi is not working. Are there anyone who are using DevCon Wi-Fi right now? Is it not working? Sorry? The Wi-Fi? Yeah. Weird. Actually, it was working for like three days and it just stopped in the middle of the day and it was not working. It was not working. It was working for like three days and it just stopped in my presentation. Anyway, but I want... It can stop me, you know. I will use my hotspot. Sorry? DevCon open. No, it's rogue Wi-Fi, right? All right. Come on. Yep. Now we have a working Wi-Fi. Come on, man. No, internet? Yeah. Great. I hope we can find your password and you have lots of tweets, sir. Sorry? Actually, it can't do that at this moment. It just downloads like 2,000 tweets. Actually, it will complete in a second, probably. Yeah, good idea. Actually, it downloads faster but because of my internet connection it's a little bit slow. Now, okay. Now it analyzes the downloaded tweets and we'll get like most used nouns, proper nouns and we will see them in a second. Okay. So, most used nouns are... Wow. Giants. Let's hope... Let's go giants. So, is there like a sport team or something? Yeah. Yeah. United. Manchester United. NFL. So, does this make sense to you? Yes. Great. Now... Okay. Now it sent those words to Wikipedia and parsed related locations and years from them and let's see what kind of passwords we have. So, I don't know. Actually, now I don't know the reason but it couldn't parse related years it should have probably because of my connection or something. So, sir, is your password listed in this word list? Could you please take a look again? No? All right. So, of course, it will be a miracle to create a password with this method to a DEF CON attendee. All right. So, let's turn back to the slide again. All right. Anyway, I can stop the mirror. Anyway, so as a conclusion, since people tend to use words from their hobbies, movies, sports, et cetera, for their passwords, users should not be able... should not use these words for their passwords since we can create some kind of accurate word list with given data. Beyond that, any actor that has much more data about that person will have an ability to create more accurate word lists for the target people. So, people should avoid using those kind of e-times in their passwords and should use password manager with random passwords. So, that's all I have today. Thank you for coming for listening. So, are there any questions? Yes, please, sir. What kind of list? Yeah, yeah, of course. They are leaked MySpace and HL Medicine word lists. So, when you analyze them with the pack tool, you can find it on GitHub. You will see the same statistics. So, yeah, please. Yeah, currently you can't do that, but in future I will do that. Because since it's some kind of experimental project, I didn't want to limit, you know, password length. Yeah, please. Yeah. To be honest, zero. Yeah, of course. I couldn't crack any, like, for example, my friends are using mostly, you know, complex passwords, et cetera, and I couldn't crack any of these passwords. But when you try it on your, for example, mother or, you know, some older person or some not ungeek person, it may work. So, hmm, but I need to know the real identity of the target. So, I need to get, like, their hobbies, their something and, like, cross-check with all of them. But if only I know the correct real identity of the MySpace word lists, probably your method will be work. But actually it can be done in HL Medicine word lists. Probably their real identity is already revealed. Maybe I can check them, yeah. Please. Good idea, actually, I haven't talked about that. Yeah, good idea. So, there's, as far as I can see, there's no question. Yeah, there's a question. I used Python's NLTK library mostly. Sorry? I only know Python support. Probably it only has Python support. So, wasn't that your answer? Only Python. Only English. Yeah, actually, I think it's kind of supports French or Spanish, but they are not strong as English one. So, you need, if you are working on a different language, you need to find some local implementation for the NLP stuff. Actually, each language has own website. So, for example, for Turkish language, I can download some items from university websites, but I didn't research about other language. But there is no single place that you can download everything. Okay, so that's all. Thank you.


Another day no logo. Let's get it.  It's been kind of stagnant.  Give me a moment That engine dripped out for every season and wears it out at one time Hey, why can't I join Ronald's fixture game? I gotta do this every year bro. Y'all never thought maybe to patch this part Fix just go man. It's been 20 minutes trying to join people man.  I don't even want my logo Yeah, you were to cage in there brown nobody it'll like you wearing beats by doctor Dr. Pepper Do this why do you buy this bro? I feel like a didn't really bought this I don't know what you talking about Right. Oh, you was just talking about how you want to make Ronnie's pockets fat, right? No, I just buy VC and then leave it in my account. You know a fan of pay attention.    He just clicked eggs.  Oh green. All right, but so you trying out are you trying to get out? I will beat it out.  What's up a question is that what though it's an um And then he did not come back for defense.  He literally said agent will eat me before he gets logo. It's like that's not funny Ronnie Nothing you say is ever funny. Did you know that about yourself the community manager for a multi billion dollar companies fat shaming? Some of its biggest influencers think about that. Hashtag cancel Ronnie Cancel that mother man cancel him. What are we waiting for bro? He began mad when the girl come over, you know when the dogs be like they know somebody at the door to be running through the door That's that's that's agent bro. Oh my goodness. Hold on wait. Oh Like that as the Phantom is the best seventy six year overall. I've ever played with seventy six year overall 69 Well, one of them is a basket. I'm gonna tell you something. Actually, no, we'll keep that off Let's go Do that and then look wide open for the game. It's to the four What happened to the off-bought defense didn't mean to shoot Hey, I'm actually gonna try bro, so let's go yo You don't Why do you talk like that bro, man? Why do you take that long bro? All right. All right. We just need some team chemistry. I'll go first. Yeah, bro. Oh, right, bro. All right, you bro Good chemistry, bro. I'm all about physics, but let's get physical. I what's up with you, bro Agent agent be going to the gym and he swear to God, even he lost 30 pounds in one night. That'd be like Oh I'm not trying to lose Stop blowing open layups. Oh My god I know but that's that's what we're complaining about your bounce back No, they're bad Yo I'm not even like trying to be funny. The latency is so high. It is so There it is. I can't it's so hard to make a jump shot for this This milkshake be taking me back to my old days when I was still breastfeeding. That's how good this machine is, bro That's short of God, bro. Oh You're pulling like that. I'm getting the hang of the shot. That's all Man He got a Stop At that 13 only you know you could have passed But I did go ahead go ahead ring it in ring it in that's a bucket. I'm quitting after this game, bro I got a offensive rebounds if we lose brum done I'm quitting bro God No, I can't I can't bro. You're playing bro. I can't with you, bro What's my time?   I can't Circle and wide open. I'm looking at you, bro. You're now I'm done. Yo, yo, I can't I'm done All right, yeah, I got it. Yeah, I can't And I know you see me I'm wide open bro. They literally give me this side of the court just for me I can't look at this agent bro looking at moving while you set me screams Sometimes I don't want to use bro. No, you're trolling, bro We're getting wrapped though, right


First one, I am your host, Nav Green. Me and Error Jordan. Shout out to Prize Picks and Carlos Miller really coming together, you know, to do this show. It's a real good feeling. The budget went big enough for us to have a co-host, so we got Error Jordan right here. I got a LeBron story. What? Go ahead. So, um, this is way back, way back in the day, I'm probably like 10 or 11 or something like that, 22,000, 3, whatever. So, he's in Atlanta, me and a couple of friends we like, we know it's like, yo, that's LeBron. It's LeBron, like, yo, LeBron, LeBron, LeBron.  And he kept it moving. Hey, we got a story running these stories by Dom before y'all just tell them. I want the show to be authentic because I pray dumb, I hope you edit and cut that out. Them 28 seconds, I can't get back. I don't blame LeBron. LeBron gonna see this and say, I knew it was the reason they want the little niggas around me.  Yolkich, how you feel about Yolkich? Like, is he, if you say it, then I gotta start agreeing. Is he, is he him? He's him. I mean, the rules have changed though since I played. So like, offensively they have way more freedom. That's why you see a lot of, anybody's getting 50 points these days. You know, you'll see a person get 50 that you never heard of before. Until that moment and then you'll never hear about it again because they don't, they don't get to 50 no more. They're back to being a role player. So I would, I would love to see how you would have played in the era when you can kind of like grab and hold a little bit to see, see how successful. You let him go for 50 on you? No, no, no. Oh man, that way, I just know me. I have 65s. I don't do the type of pride though. Yeah. Yeah, I come from the south. I'm not, I'm not letting that.  Bruh. And this is what a lot of people don't realize that. D'Angelo Russo, I felt like he was due for one good game. Yeah. Prize picks had him at 10 points. I said, this game four. LeBron needs some people to step up. It's going to be D'Angelo Russo.  Never again, nigga. I don't follow him on Instagram and he don't follow me on Instagram. I went to his page and I blocked that motherfucker. I don't want no interaction with you ever, nigga.  Y'all don't remember he snitched on Nick Young? Yeah, never. He been a lame-ass nigga.  But sometimes somebody starts playing great basketball, you forget. But never again, I will forget. D'Angelo Russo, you was not in the garbage buster hall of fame. Once you get in that hall of fame, you can't get out.   And I will remember you that for life. So it's him and Sabonis in there now? Him and Sabonis, number two garbage buster.   Hey, what's up, man? It's your boy Nav Green. And you know what I came to talk about? Prize picks. It's available in over 30 states. It's no excuses. And if you don't stand them 30 states, move to them 30 states because you're missing out. Man, all it is, more or less. Your favorite players. It's easy. 50-50 chance, man. You can win it up to 25 times your money, whatever you put up. So if you put up 50, you win times 25. What's that? Man, you can win 12-50. If you put up 100, you could win up to 2,500. What makes it so good? If you deposit $100 using the broken play promo code, they're going to match up $100. So now you got more chances to win all the money you can win. What you waiting for, man? Prize picks. Go type in the code, broken play. Man, they got so many sports going on right now. You got college football. You got basketball. NBA just started back. You got NFL. Right now, pick it. Tool. Miami Dolphins. He going to get all the yards you need him to get. That's a lot. Man, what you waiting for? NBA rebounds. Who I like? Capella. I like Capella with them rebounds. With them assists, give me Trey Young. With points, give me Kevin Durant. I'm just throwing you some options if you want to win some money. Shout out to broken play. Use the broken play promo code.  Shout out to prize picks. Mike, you ain't never played sports. You could have been like a football player like... I got a cup of football. I got treated. Treated? In P-Weeks. It's never happened.  That dad of all shit? Bro, you know you're wrong.  Not your momma was getting down.  They be like, my momma's nothing. So I think he was the other one asleep. So it happens, you know what I mean? Why don't y'all turn into this? I was ready to talk about Pat Riley. We talking about Pat Riley. Nigga, if your coach only take you on.   Your momma's gonna be at A-30. I gotta go.  Momma fucking.  That's all I want you to do. I was because I was bad. I was real bad. What was this man had you at? Man, I played everything. I tried kicker. I couldn't do nothing, bro. Oh, my God. The fifth event was what I ended up at, but I never got a game. Once I was so bad, I got treated to the other team for nobody. He treated me to the other team. But it was so bad. That's what you got. You didn't get dragged in any of that. To the other block. You got to see it. I got to see it. I got to see it.  It's so bad. This is how bad it was. The first game, I was excited. I'm gonna give them, I had a mouthpiece and it was in the wrapper. Another player, he ain't had one. The coach was like, damn, he's like, Mike. I thought I was going to the game. He said, give me that goddamn mouthpiece. You would have gotten your mouthpiece, Mike.  I want to know. Give me.  He thought it was your moment. He said, Mike.  Sam, come here.  I ain't coming to your show. You come to mine.   You already watched my show. Dead pools, goats. What are y'all going to do in between NBA season and the NFL season? Because I can't see now talking about baseball, LOL. Look, that's what I'm trying to tell you. People put limitations on people. Don't put me in a box and just say I'm NBA and NFL. Talk about what we going to do after NBA season. But since you said that nigga, you right. We're going to take a break to football start up. When college football start up, I'm back because I do not watch baseball. I like to go to the games, but we got some boxing coming up.  Hey, if y'all counted us out, bitch, you can count.   He got a big show.  We got to get him on this motherfucker.  This come from Shahtavia Scott.  Nigga, I don't know who you is, but Shahtavia Scott appreciate your baby daddy using the phone.  And that's how I know his old lady ain't cheap. Because she let you watch a whole hour and a half episode on her phone, nigga. Nigga, you trust her and she trusts you, nigga. You got to be a brave nigga not to go through the messages.  You good.  They don't even have the niggas think they know them. Do you know athletes?  Like the major ones.   No. He saw her face. What she mean?  That's right. No.  I mean, and that's cool. I mean, I'm sure he is. I'm sure he is a millionaire, but I'm not going to talk to every nigga that's a millionaire just because he a millionaire. You got to be playing. I feel like if I'm going to talk to somebody in any profession, I want my nigga to be the best at it. I want him to be one of the best at it. I don't want the bench, nigga. What are you degraded? Why are you not good enough to play?  What do you think you're degraded at? Everybody ain't going to start. What do you think you're degraded at? What do I think I'm the greatest at? I think I'm the greatest stand-up to me. I don't think nobody could roast against me. I don't think you can find. I don't think you can pay a nigga that can beat me roasting. So who you think you're better than what you think I can't pay him to be better than you at? I feel like just being myself, I feel like that's true. Hey, that's all we've got to do.  But no. I'm just me. I'm not doing other bitches.  You got to do something too. No, but God, seriously though, I feel like me and my friend have literally built a brand on being ourselves. Thanks. And so I feel like I'm just. That's on a professional level. What are you doing in your relationship? That's what we're talking about. He wasn't asking me about a relationship. I was asking you. It got to come back to the house some. She know what that nigga leaving the house to go do. You can't tell a nigga, no, me and my friend, we just being us. I didn't even know how to seduce me. Bitch, y'all smoking hookah. Y'all ain't being yourself. That's doing me, and I always come back with a check. Nigga, I would never call you de-law again, nigga. Nick names is for niggas who can hoop.  D'Angelo Bernard Russell.  We got Javonte. Javonte fighting this week. Him and who we fighting that? Ryan Garcia. Man, a lot of people are trying to make this closer than what it is. Javonte Tank Davis is going to beat the hell out of that man. A lot of people, and I got to argue with somebody the other day, he was like, you got to watch boxing.  I'm willing to bet I will save all my health. I will go bald. And Ryan Garcia beats Javonte Tank Davis. You, you're hearing it live. Like I'm so confident in him. Did you see when he was training with that big weight ball and throwing it back at the, uh, his trainer?  You can't train a man that you can't train? Bro, I saw him training. I thought it was like Creed Six or something. I'm like, he doing all this with Ryan Garcia. And that's because people been talking, saying that he could lose. He was like, aw, I got to start taking this serious. I'll be, I'll be bullshitting.  But if you, hey, I don't know what prize pitch gonna offer for that, but whatever they offer for that, I'm willing to bet. I'm willing to make my entry. Knockout before round seven, before round seven. Maybe I don't think they saw Tank last fight when he knocked the, knocked buddy through the ropes and they had memes. They were comparing it to that Michael Jordan. Martin Lawrence, my bad era, Mike. That's why we got era Mike up here.  It's no question to ask.  Man, as ugly as Zion is, I'm happy he gets to do it. As ugly as Zion is, bro.  I do judge. Family stop lying.     Hell no.  Oh, with the turtleneck? Outfit ugly, he ugly.   I don't know.  Tell you the truth, because he had to have a fall. He was damn too perfect as far as character-wise. I think that should be scripted sometimes. No, he wasn't perfect.   Like, he was like a kind of golden boy, you know? He had to have something wrong with him. It wasn't drugs. It wasn't guns, but it was buns, bruh, so. Goddamn. Hey, all things, bruh. I'm glad Backpage went around when that nigga Zion West got a lead. Zion West, he the tight. Nigga don't ran the train. He was like, man, let me see what it tastes like. Nigga don't run now. Nigga on that. You see that nigga, he in the push. Like, nigga, you tripping, son. The last one. Zion want to go last one. Yeah, you want to go last one. Y'all going to get him. You going to go and get him out of there. Goddamn, bruh. Go ahead, get him out of there.  I'm the headliner. I'm the headliner. You peaking at that nigga doing the 69. I think that really made it worse is all her tweets, all the porn stars tweets. She's still tweeting right now. She is. What she saying? She's still going. I'm going to say, read a few of her last tweets. Let's see what she's saying. In this part of the highlights. There ain't no more basketball to talk about, because damn, Joker. Yeah.  No, not at all. Who is? I think he got a good team around him. Probably the toughest white boy. Got to be the Bill Lambert. You got the way of... Oh, when he played with the Pistons. Yeah, Bill Lambert.  Bad boys. Who else? Toughest white boys. Hey, I think Larry Bird was overrated. No, Larry Bird went overrated. He played in the 80s. Ain't no way. He played in the 80s. The niggas snogged powder, bro, with the referee. Them niggas was tough back then. Every nigga... You said the referee was snogged powder? Yeah, bro.  Some type of way. I'm trying to tell you, bro. If it was statutory rape, some type of rape.  You can't beat them niggas in the 80s, bro. I'm telling you.  That's what you think, bro. Larry Bird dipped snuff to bulk, had bumps in his face. He didn't want to look like he had AIDS. Not magic. I'm trying to tell you. That's why they... He was a bad motherfucker, man. Larry Bird was a bad motherfucker, man. Magic didn't help. It was Larry Birdy. This ain't my man's servant, bro. You said who?  You probably just here for me today. I need these lambs. Legend, bro. That nigga said Larry Bird and one look like he had AIDS. He had HIV. They just made magic say he had it. It was Bird, my nigga. I'm telling you. The Five Stages of Grief with a b***h. First stage is denial.  It was literally my man, and I never once looked for clout from anyone I never posted when I was in NOLA. I was loyal to you, Zion. She's adding him on every tweet. And you got thoughts, preggers. Before you keep reading, Zion looked like he can't read. Bitch, he don't...  That ain't done. That ain't done. I... Read this to me. And the niggas around Zion, they gon' word it now. She just saying you a great player. Okay. He still loving him. He still got love for him. Whole facts. I mean, he said the delete... He said, like, can you delete the tweets and we'll talk about this? Like, that's what he... Yeah, cause he can't read them. And she... He said, can you delete that shit? They're talking. They're talking to me.  Tell my grief. Bruh, niggas don't know this, bruh.  All the dancing niggas get in the way. Yo, any nigga who tick...  Niggas, you got too much energy. You don't dance five straight songs. Bitch, I know I ain't gotta know when to fight you.  He said, no, let him keep stepping on my shoe.  Move out of here. Get out of his way. Let the nigga dance. You in his way. You in that dance. Let that nigga dance. This the dance floor. We're supposed to be back. I thought it was all in the issue. Ma'am, tell me, the niggas swear, like I tell you, musty.   Cause you know, you can't be musty allegations.    All that nigga stink.  And don't walk Bob and make it fast. Cause he finna be like, what's up? Like, yeah, I don't want to know. Yeah, I'm crazy, man. Talking about you ever, like when you said you fought like briefly, was it a bad loss though? Oh, yeah.  Oh, yeah. I thought you were gonna say something. Nah, I'm gonna give it one thousand, cause people was there. People was there. Oh my God. Nigga only one. Mike, you lying. Y'all boo was there at the time? Nah, she wasn't at that one. Oh, Lord. Now the one, so I got it, I'll tell you, I'll tell the story. I got into the fight because I found out I didn't know how to fight for real. It was his brother's name. I ain't gonna say his name. I told you earlier his name. But I thought I could fight because I used to knock people out all the time. You know what I'm saying? But the reason why is because my mama, she raised me like, don't let nobody hit you first.  My mama always say the first hit might be the last. That's why. That is facts. So I never, you know what I'm saying? So I knew, isn't my, I'm finna hit them up for you. And I would hit them and I was, you know, I was heavy handed. They go to sleep. So I thought I could fight. So this dude, we had a block party.  You know, I was, I was that nigga. What your nickname was? I don't know. I don't want to say it. I'm about to cry. What are you going to make a new relapse? Hell yeah.  Nigga, I'm going to make my channel. Nigga just start swinging off the floor. All right nigga. It was slim low. I got, I got this. I had to erase it. Slim low. Slim low. I was skinny, but I was. Oh, all right. All right. So low goes.  What are we going to do? They're going to slim low. One little time and fair place. Where Eddie Winglow don't mess with the wrong guy. Mom, it was slim low. He's a couple. Yeah, no, no, no, no. And Eddie's looking like this. He got a little jacket on. Shit. Now Carl got to talk to slim low. Why I tell you all that, man. I'm sorry. Okay. So slim low. Yeah, I'm sorry. I'm sorry. Bad deal. What you did, slim low? So I thought I could fight, right? So it was a dude. I didn't know he's a professional boxer. And he was my neighbor.  He was, I was in the wrong 100%. I was the bamming the door at night.   Like, what you want to do? To the house, yeah. So anyway, I said, do that.  Like, leave them alone. They're trying them or something. They're trying them or something. What? But you know, you don't even feel like that.  We kicking and he lame.  And me and my guy, we was them niggas, you know what I'm saying?   So he came out, he was mad about the night before. And he came out and confronted me from there, everybody. And he talked like, he talked very proper.  I said, and what, my nigga? What you gonna do?  And he was like, let's see how that works out for you. So I was like, man, hold my beard. Man, I give up on myself. Did he say pal? It's giving pal. It was a pal. Okay, somebody say pal run. He didn't say pal, but the energy was definitely pal-ish. Okay. So his guy came out of his apartment and was like, yo, hold on, y'all put the gloves on, fight like men. So my head, see, I'm young and I'm not thinking why he got boxing gloves on the house. I ain't thinking that. Because what? I'm thinking, oh, this is gonna be another story of Mike knocking them up for God. And there's the girls out here, we can buy some girls from the club. I was like, oh, this is gonna be great. Yeah. And he was smaller than me. You know, I was like, hey, I'm gonna take it easy and just pop it at the end. So I'm putting my gloves on. I'm like doing the teeth thing. You know, I don't know what I'm doing. I look over and he take his shirt off. And I'm like, nigga, where did the muscles come from? He was so cut.  You know, bubble, six pack, the grrrr. He had the grrrr. Not the grrrr. And he got the grrrr. I was like, yeah. But I had to keep myself going. Because I was like, no, I weigh more than him. There's no way that, that don't mean nothing. It's just muscles. You know what I mean? Oh my God. And then, and one thing about a train fighter, they go to a different zone, like an actor. You know, they go to a different zone. They by the fight.  I look in his eyes and then they looked at me, bro. I'd never had nobody in their life to this. They look at me like this. Like that man wanted me, man. He wanted to fight. He was looking, he could, he was gone. It was like- Like you fell into his trap. Thank you. I've been waiting. I was hoping somebody was like, come on, Mike. Don't do it. It's like, you know what I'm talking about. It's a detainee, please. Mike, it's a detainee. I was like, I ain't man. Slim low, chill, man. It's not a little bit like this. All right, man. So I get out there. We got a glove on. And then his stance was different.  Oh my.  You know what I'm saying? And my stance was, all I had was the right punch. So he wasn't standing still.  I'm like, that's a nigga. I'm gonna let me hit him. And then they could throw in jazz and weave it. And then that nigga punched me in that body, but regularly people don't punch in the body. Yeah. You just think to go to the face.  The body, we all watch the boxing and then body shots. Like you ever play football or whatever. Hit the ground, you're back in a win. You lose the win. Yeah. And you can't breathe. That happened. He hit me so hard. I was like, ugh. And I was trying to get away from him. Oh my God. And I was starting to look cool, but you can't look cool when you can't breathe. You were scaring all the holes. I'm like, you know, right now they was terrifying. No, no, no. They said, bitch, slim low.   This nigga boxing, not slim low.  So he lost your, lost your win to your bro.  That's the one you ever see somebody get knocked on. If you see it coming, naturally your body can. Brace it. You can brace. You can roll it. Yeah.  Like a car accident. Man, I still don't remember. I don't know what Hannah was to this day. I heard someone go, oh, Lord. And the crowd said, oh. I'm like, somebody got dumped on. I'm looking for the basketball rim like this. Who had it? Oh my God. And my eyes, when you get knocked out, the side of your eyes closed from the side. It don't come to. So it's almost like, bro, I know you're not lying.  You ever like swim? You ever be like with this like cloudy? No, nigga. No.  I'm like, I ain't no hat. I was on the side. And then I already hit you at. You still don't know. I just heard it. It was like, boom. I didn't feel it to this day. I didn't see it or feel it. So then my boy was like this coming to me like, and slow motion like, bro, that's enough. So then I try to act like, you know, I couldn't go out for everybody. I said, man, take these gloves. We're going to fight for real. He was like, boy. Boy? And we hit you like that on the glove. Without the gloves. You ain't going to make it. So then, you know, I faked I wanted to fight.  She is my man. He looked out. He held me back. Slim, chill. I'm like, let me go.  I know Mike. Come on. Oh, God. Oh, my God. Oh, my God. Hey, that was cloudy. Go ahead. It was cloudy. You just in the room. Yeah, no. We got in the house and I was just in the house. And I'm waving somebody to be like, it wasn't that bad, dawg. You know, and nobody said that. It was so quiet in the house.   He said, man, that didn't even know something, man. Like, that wasn't regular. So I felt, after that, we got cool. I bet you this. What's up, bro? Big bro. Big homer, I just came to check on you, big bro. Number love, big bro. I'm that type person. Teach me. Hey, sign my gloves, big bro. They say it's the coach to blame. They put him as a low like. I think so. You think so? Yeah.     Put the camera on yourself, nigga.     Now it look like I'm copying. Phones in this motherfucker. No phones, no beepers, no goddamn computers. Nobody need a computer but Marcel. Nigga, you don't do stats. Put all your electronics up, nigga. And work that camera. Got my young nigga on your team. Oh, bitch, you don't start at one now. I can make $200,000.  So if Zion, if you want to do that, live your life. That nigga from South Carolina.  That's what I said. Ugly nigga from South Carolina. You better get it in while you can.  God, nigga, as soon as you can't dump no more?     I'll change the expensive.  Michelle Obama, Simone Biles.   All the ugly women I ever heard.  But that woman ain't no good turn to mama.  Oh, out of two, out of flow, out of fuck, Florida to Georgia. Ugly nigga from South Carolina. Get it in, man. You got a mission out here, bro.  I ain't seen that before. We ain't got to read no more. I ain't seen that before.


Let's jump over to our man Teddy keg's dad folks. We talked to Teddy every Wednesday at 40 past the hour. You can check out Teddy's Tiger Forex report folks. He puts out a new issue every Monday morning. He has updates throughout the week. When warranted, when you sign up for Teddy service folks, you can access it under the newsletter tab at TFNN. You can subscribe for $97 a month and folks basically that's 29 days that you get to try out this newsletter. It comes with a 30-day money back guarantee. If you don't like it, just let us know. You get a refund, you get the newsletter for 29 days. You gain access to his webinar, Forex strategies and fundamentals, what's behind the Tiger Forex report newsletter. And like we always say, man, it's an especially interesting time to put it lightly in the forex market. Teddy keg's dad. Good morning. Good morning, Tommy. First, I want to extend my condolences to David White's family and to you guys on your loss recently. I appreciate it, man. It's been around for almost decades, which is remarkable. Teddy and a young guy and it's with us all for a little while to put it lightly and I appreciate it, man. It's boy life sometimes, man. I struggle with the words, but I appreciate it, man. We didn't talk to you last Wednesday. I had some technical issues. I think, man, we haven't talked to you in a couple of weeks. It's been quite a couple of weeks in this market, Teddy. We talked to you on a very important day. I love that we talked to you on Wednesdays, man, because usually we got some good action going on. Where do you want to kick things off in this market, Teddy? Well, it's Fed Day. In the next couple of hours, it's going to be a ho-hum trade. I wouldn't have any big expectations until later, but we have what a storm we have brewing. You have unemployment, excuse me, inflation running in the UK that blew out expectations at over 10%, which is something that we've been talking about for a while, that their numbers are not getting better. They're only getting worse. That's going to have a big impact. We had the pound that spiked high today. I'm surprised that they're not selling off much more than they have, but I think a lot of that might be because it's Fed Day. Now we're coming into that window where until 2 o'clock, our time, excuse me, 1.15, Chicago time, we're not going to know anything. Now here comes the big question is, what are they going to do? Well, I would say they can't end their resolve. The way we were looking at before this banking crisis started two weeks ago was that we went from having them supposedly stopping raising to possibly cutting at the end of the year to having now all of a sudden half and three-quarter point raises for the rest of the year. I think that you're probably going to see today as a quarter point raise. I would be really shocked if they didn't do anything because that sends really mixed signals. The reality is that they have to keep with their program just for them to, I think, even show faith in the banking industry as it is. That's a whole other ball of wax. When you look at how this started, SVB, what's the irony of it all is one of the top guys, the Lehman Brothers, is one of the top guys there. This is a do-over. It also shows that the compliance at the highest part of the banking level is atrocious in this country. The fact that banks have zero reserves when they should be functioning on at least 10% in the fact that they were allowed to run at that and then the way they had. What bank wasn't liquidating their bond portfolios a year ago when they knew the Fed was going to be raising rates for a year? We've had the most transparent Fed in US history or at least in my lifetime. How could you make that mistake?  That's why I love talking to you, Max. I agree. I'm not sure if you heard Kevin Hink saying it's just to not hedge that, man. There's all this rhetoric and I feel like it gets played out in not even the financial media, man, like the mainstream media and that's a bad term in my mind. I'm talking about NBC Nightly News where you just get the headlines and yeah, it's like, oh, rates have risen and the banks are in trouble.   Of course, she has some fixed income, you know, but why would you buy in 1% yield long duration, man? Even as a retiree, I'm like, what if you need that money, let alone being a bank and doing it with $100 billion and not hedging your risk? It's like, come on, man. Exactly. Exactly. And I agree. It's like, I was trying to do these gymnastics and last night I finally got to it where I said, he can't pause, man, because there's no way that this banking crisis alone, folks, is enough conviction to squash inflation. And maybe it helps, okay? And maybe it does, but you just can't make that leap, I think, in the chairman's role with where we are, because we haven't gotten a lot of inflation data really since he spoke two weeks ago. We've gotten the banking crisis, but the inflation data has to come, man, before I think he can make that leap, which is, man, oh, man, we're going to get to find out. Now, where do you see... I think that this is really actually going to impact the interest rates for a while too, because think about this, if banks are all functioning on zero, they have no reserves, that means they can't borrow money from the Fed when they issue loans. How are they going to be lending to people in a mortgage market where rates are going higher and you're already having an issue getting them financed? But this is going to affect the dollar too. So as long as they stay on the path that we... Let's remove the banking crisis and go back to our theory that we were on before for the past couple of months, if they remain on the hiking basis, I think the dollar's going to stay firm. I wouldn't say it's very bullish. Now, we've been talking about divergence in the markets already for a while, and you can see it's very representative where the dollar index right now, a lot of the lift, it's where it's fallen back recently, was because of the rally in the Euro and the pound. If you look at a lot of the other currencies, they're very sideways. I mean, you look at the US dollar, Canada, even though it made some new weekly and monthly highs recently, it's still in a very big, wide range trade for the past six, seven months. That's the perspective I think people really need to start to look at. And I think we're going to still see this over the next couple of months because now we have so many variables globally, financially, it's almost like, how are you going to establish a true trend? So I think the long-term trends that are in place are going to stay somewhat together. The intermediate term, you're going to see, I think, more of a directionless trade, and you're going to have a lot of short-term moves, but in short lived as well. So I think that people have to be very cognizant of that. I'm not saying volatility is going down, but you can have high volatility in the market. It really doesn't go anywhere. And that's something I think we have to really pay attention to, especially when you look at a lot of the commodity markets and stuff like that as well, because, but people don't realize this. Right now the Fed is buying back these assets from the banks. And the irony is they're not even buying at the market price. They're buying at the prices that these banks bought them at. So that's a huge loss. So that's also a big liquidity factor. It's going to start being, you're adding lots of liquidity in one direction, but it's really, really going to draw out liquidity because these guys are going to be under the gun now. Do you think that they're going to be able to just be speculating and entering the markets and the derivatives markets like they've been doing? Absolutely not. They didn't hedge themselves what they were supposed to do. We know that they were definitely double leveraged on certain things where they were actually gambling. And that's going to be, I think, for volume in a lot of markets, it's going to start to tighten up in a big way. Yeah, it would have been pretty interesting to see some of the CEOs of these big banks watching. You think we're watching Chairman Powell's press conferences with interest folks. Nobody had any idea how important these banks were watching probably those press conferences, hoping for a pause or a pivot that they just never got, man, and they just didn't hedge. And great point about the market. Just look at the SAP from where we were. Pretty interesting, Teddy. We're basically right back to where we were when Chairman Powell spoke 15 days ago at about 4,040, 4,080. Meanwhile, we were approaching almost 3,800 in this market. So yeah, right back to where we are, and you're talking about 5% swings. Can you hang with us, Teddy, for a few minutes? Sure. Okay, we'll finish up the program, folks. We get a few more things to talk about. I want to talk about yields, interest rates as we go forward. We'll be right back with Teddy. Stay tuned, folks. Welcome back, folks. We're talking to our man, Teddy Kegstad. Please check out the Tiger 4x report, folks. I've got a chart of the 10-year up here, Teddy, but I just want to talk a little bit about the general idea of where you think maybe rates may go. I know you look at the 30-year very well in the Tiger 4x report. Everybody's interested in rates. Quite the moves we've had recently. If the Fed goes 25, I know things can change a lot, but where do you see interest rates going from here? Do you see that number rising? Does the banking crisis put a halt on it, no matter what the Fed is doing in the interim, or where do you see? Quite the volatile market, to say the least. Sure. Okay. Well, the rally that we've seen started two weeks ago, and it started when the SVB first came to light. The bonds really rallied off. That swing low was locked pretty hard. Since then, it's gone nothing but higher and higher and higher. You had a $12 rally in the course of only a week and a half. Why? This was not because of any Fed number or anything like that. This is when I remember when it first came out, that weekend, people were like, oh, here's another bank. I'm like, this is not just one bank. This is a system wide. And they're like, why can't you just be that presumptuous on that? I'm like, the bonds made a move like that. This is big money. The big money knows what's going on. You don't have a bond move like we've had like that. That being said, even if the Fed doesn't do anything today, I think you're going to see yields going up because this has been an over-exacerbated rally. It was flight to quality. Now, things are calming down. Unless things get worse, that's the reality. Either things are going to get worse, and we're going to be like, oh, my God, this is such a huge problem, which they're going to cover this up and try and keep this subdued as much as they can. If that's the case, well, then the market has to go. Even if they don't raise rates, the pricing should be lower, meaning yields should be higher. And if they raise a quarter point or more, well, then that low from a couple of weeks ago, I mean, look out, baby, we're taking that thing out because the expectations will be that the Fed is still going to keep doing that. Yeah, we get to find out at 2 o'clock, man. Quite an interesting market. Teddy, I appreciate it. And that's good for the dollar too. Sorry, real quickly. No, I like it, man. Good for the dollar as well. Listen, I appreciate the time as always, man. It doesn't get much busier than Fed Day. Please, folks, right now, head on over to the front page of TFNN. Hit the newsletter tab. Try out the Tiger Forex report. 30 days of money back guarantee. It's a great time to do it. Teddy, man, I appreciate it. You have a great day. Thanks, Tommy. I'll talk to you next Wednesday, brother. Sounds great. Folks, stay tuned. We got our man Basil Chapman coming up next. It's going to be a great day in the market, folks. It's Fed Day. Don't go away. Thanks so much. Have a great day, everybody.