What's going on everyone? Sam here with late sports cards. Ripping 10 box Tuesdays, 10 box Nauara, maculet basketball mixer number 156, random team style. There's a look again at your random results. As always, you can find those on the website under the results tab, as well as the link that's in chat. So good luck everybody.  I'm not ripping anything, that's wrong. Start with 2020-21, Donress optic retail. Bunch of inserts and such in here. Nick Richards, my house Zion Williamson. Josh Green, purple bam out of bio for the heat. Desmond Bain, rookie. T-minus Steph Curry, I think a Coro. T-minus Stephan Curry, again. Janet McDaniels, rookie, nice one there for the T-wolves. Hollow, Yannis, my house. Nico Mannion, purple is Gary Payton Express Lane, Sonic. Lamello, rookie, nice one there, horns. Express Lane, Devin Booker. Cole Anthony, purple Luke Dort. I think it was me something big out of that iMac. 2020 iMac, I think it was me something big. Mark my words. Oh, great. Hollow, Zion. Keir Lewis Jr., splash, other hero. Splash-O, yeah, it's Splash-O. Yeah, Splash-O. Forgot it, Splash-O in this year. Manuel, quickly, rookie. Purple, Steven Adams. Lever, Tillman.  No, Jalen Smith. Sticks. Sticks. Nickname, Sticks. He's like mad skinny. Really, like very, very skinny. Like would not like, not that skinny. That's like, that's like next level, Lonnie Walker. But like skinny enough to where if you saw him on the street, you'd be like, there's no shot that guy's in the NBA. I'll see if I can pull up a picture. Paul Millsap, my guy. Let's see, Jalen Smith. He's actually kind of jacked now. That was good to see, though. Wow, he's kind of jacked. Never mind, all right, I take it back. I'm sorry, Jalen. Okay, skinny in college, skinny in college. There you go. Exactly, I got proof to say, bro. Right, I mean, it's still beat me up if like, if we got into a fight, but like, there's Tyrus Maxi for the Sixers. Purple rated rookie, nice one there. Purple's and these are actually pretty solid. They do sell fairly well. And you know, two roots, that's very. Oh, be toppin', ain't no stoppin'. It's a little bit. Saw Donnie do it, so I'm like, ah, I'm gonna do it now, too. I have a positive influence on him. All right, we've got a 2022, 23 prism draft hobby now. Kind of a positive influence. Again, any auto of a guy who doesn't have any NBA experience or is still in college will be random to all at the end of the break. We're just gonna do them in order that we pull them if that is the case on any of these. Might not be. And OBS is invented, and it reconnected. Silver Shea, Alfonzo Plummer on the Red Auto 2.199, Alfonzo Plummer from Illinois, Alfonzo Plummer. Okay, let's see. T-t-t-t-t-t-t-t-t-t-t-t-t-t-t-t-t-t-t-t-t. Where are you basketball? My real GM. There he is, real GM. I'm undrafted. My real GM or my GM? Real GM. Oh, I would say I'm wrong. Not a temp camp. G League. They did play in the G League for, I'm assuming the Capital City Go-Go, I think that is. Believe that's Washington. Come on, dude, really? Oh no, no, no, no, no, no, no, no. He's one of the weird ones. He played in the G League in Mexico, which is different from the actual... See, if you go to G League, unless that is Capital City Go-Go, which it could be. Yeah, no, that's Mexico City Capitanes, which I don't believe is... Affiliated. I don't think it is affiliated. Mexico City... I don't remember. Currently a member of the NBA G League. Yeah, so they don't have a typical... They don't have an actual affiliate, so that'll be a random, all that for a random. Palo Bancario, Hypers. There's Orlando Robinson to 75 on a purple. Jordan Walsh. We will also do a random at the end for all non-autographed college cards. That includes any numbered, any base, any refractors. We'll all go to one spot. Blue, O'Shaig Bajie, nice one there for the jazz. To 149, congrats to Utah. O'Shaig Bajie. Got Wendell Moore Jr. Dude, that's kind of a squad for Mexico City. Oh, I wish Vaughn was here, dude. There's Kyrie Irving to 149. Prospect, Snake Skin, Dylan Mitchell. That'll be part of the non-autowed college cards. Bajie is... He is jazz. Okay. Yes. I was gonna say Pacers. Neymar, I think you might be thinking of. Maybe. Chris Likes, Penmanship. Chris Likes. Scalabissier, just so you guys know, Scalabissier, Gary Clark, Kenneth Fareed, Alfonso McKinney, and Jaleel Okafor all currently play on the Mexico City Capitanis. Which is kind of wild. Undrafted, Miami, the, I see, random. Yeah, that'll be a random. Chris Likes, random to all. Of the second random we do, Clay Thompson. Hyper. Jammerant, Purple Ice, to 149. Jalen Williams. Harry Eason, Jason Tate. Last pack, ooh. Prism draft hobby, Lake Wesley, Kenneth Lofton Jr., Blue Ice Otter, nice one there, Memphis. 25 of 75, Louisiana Tech legend, Kenneth Lofton Jr. Kenneth Lofton Jr. Max Christie. Russell Westbrook, Blue Wave, to 249. I'd say he's Memphis. Yes, Chris Likes, yes sir. All right, Prism draft choice now. Yes, Prism draft, 22, 23, Prism draft choice. See something big, man. John Montero, Tyler Hero, Bradley Beale. Silver Deer and Fox, Silver Kyrie. We've got a Justin Lewis, that is 42 of 88. Justin Lewis, I'm not sure on him. I think he's somewhere, but Lewis from Marquette. Yes, Marquette, he has bulls. He has two-way contract with the bulls. So there you go, Chicago. Got to aim Mark Williams. And red, Scope, Bradley Beale to 88 for the Wizards, nice. That'll do it with Prism draft. We are on to 21, 22, Mosaic, fast break. 21, 22, Mosaic, fast break. Looks like Jay Nardy. Pink, Kobe White to 20, nice one there. 14 to 20 for the bulls. Cade, Rookie, Fram's Rookie, and a Dwayne Wave, Jillian Green, National Pride. Disco, Hawaii, finals MVP, Kuala Lumpur, Alfred Shengyu, Corey Kisper. What should I get? I guess it depends on the time of the week. We'll actually get to eat. Demontus Sabonis, Blue, Scotty Barnes, nice one there. 285, Jay Nardy Green, Rookie. Skull, Walla, Dave Alvace, Pambos, Pambos. Oh, God, yeah, 2 a.m. in the morning. What's wrong with that? Purple, Nikola Vusevich to 50, Jay Nardy Suggs, Rookie. Ron and Joellen B. What do you want, Krabby Patty? I would do anything for a Krabby Patty right now. What's up, Larla? Disco, Deer and Fox, that is Otto, Robert Horry, Big Shop Bob, going to Lakers, nice one there. You got to tell LA. Yeah, that's worth a decent amount for sure, for show, for show. The Inaq box, Chris Duarte, Trey Mann, Cade, base Rookie. Disco, Evan Mobley, debut, Scotty Barnes, Rookie.  Off the charts. It's this company called Tate's. It's kind of like hard to explain it. Cade Cunningham, Cam Thomas, Corey Kispert. Is it like a business or is it just a- Yeah, no, it's like a legit company. Like, they sell it in Target and stuff, like Walmart. Okay, yeah, that's what I was gonna say. Tate's Cookies, I'll show you what they like. They're like super thin. This, right here. Oh, dude, they're so good. So good. If you see them, I'd give them a try. Are you a big chocolate chip cookie guy or no? Yeah, I like chocolate chip cookies. Yeah, and it's definitely, I'd say it's worth it. Disco, Tyrese Halliburton, Davion Mitchell, Joshua Grimo, Kai Jones, Tate's Cookies are good. I usually just rock with the chocolate chip ones. I'm a big chocolate chip guy. Mosaic, Yudah Watanabe, Disco, Brandon Ingram, Usmame Garuba, The Goofy Garuber, Reeve, Sandro, Mamakalisha Billy. Disco, Trey Murphy, Rookie. Disco, Variation, Cam Thomas, nice one there. The Nets. Aaron Wiggins, Bones Highland, Jomerant. All right, Mosaic fast break off the board. 22, 23, Consenders now. Again guys, we have a bit behind going to the next one. We only had 35 minutes on this one actually. No, 25 minutes on this. Yeah, we only had 25 minutes on this. Well, you're gonna take the cookies. I know, I guess so. Yeah, you got it, Eric. Thank you, buddy. That thing is awesome. That's so cool. Those champ tags are fantastic, man. Wendelmore Jr. There's variation is Mark Williams. Nice one there for the Hornets. There might be like one or two more would be my guess, Eric. We don't have a ton left unless we restock on it, which we might. We definitely might, but Hornets, yeah.  Quantity 500. All right, let's go, dude. I've never seen that before.  Christian Coloco, variation auto. Nice one there on card for the Raptors. Probably took the auto. No, no, that was the, there was an auto in here. Luckily drew holiday cracked ice to 25. We got both, thank God. We got both of them, thank God. I would have been livid if that's what we got in place of the auto. Just for giggles, do you think it'd be funny that the both signed it and ran it off? Jesus Christ, 22, 23 for some battery. We'll get that mag for you. Get that mag for you. Oh, wow, that was kind of a low out. Yeah, it was. Where's Vaughn? Yeah, Vaughn's gonna be walking back in here. Red to 100, Chris Weber. Miles Bridges, or McCall Bridges. Disco, PJ Tucker. Disco, Anthony Davis. Rodman, Palo Rookie. Mag of History. Oop, bronze, Dougie McBuckets to 27 of 20. Got Dyson Daniels, fast break, Rookie Auto for the Palakins. There you go, Michael. Let's go Frosty. Basin base, Christian Brown Rookie. Kevin Garnett. Disco, Ant-Man. Dyson Daniels and AJ Griffin. Disco, Jordan Poole. Disco, Lamello. Trace McGrady, Jabari Wong. Red, Lexi Pogoshevsky to 100. Palo, Loco Lottery, Disco. Jang, Joshua Knox. Purple, RJ Hampton to 75. There's Yannis Widescreen, Disco. Shade-on, Sharp Roof. Disco, Kendall Brown. Rookie, Silver, Demar DeRozan. And kind of Lawton, Jim. Tyries Halliburton, blue to 150. Pacers, Devin Booker. Keldon Johnson and Walker Kessler Rookie, very nice. Disco, Carmelo. Disco variation, Dyson Daniels. Nice one again there, Frosty and Shep Rookie. All right, Prism-Hobby now. You guys do apologize, we'll be a little bit behind going in the next one. I guess I didn't expect that you'd only have this much on 10 bucks, but Contender's pretty easy to deal with. And the mixer's not gonna take that one. Was that a Mojo? Mojo, James Harden Fearless. Nice one there. 225. Wave, Pascal Siagun. Hyper, Jaren Jackson, Jr. Like PTSD. All right, it really is. That's about three. Yeah. Red Wave, Isaiah Roby. Kauai Laird. Jalen Durran, Hyper. Blue Ice, Jalen Brown in 125. The Seas, Steph Kerr. Silver or Dwight Palin? No, single prism. Silver, anything in that pack, interesting. There you go. There's a lot of interesting packs today. Silver, Darius Garland, I know. One of whatever that thing was. Mojo Harden. Stop saying the word. Silver, Jason Tatum. Wackalari, Shade-on-Sharp. Josh Minott, Silver Rookie. Hercene, J90. Mighty Python, Holy Grail. Part of it, yes, but not the entire thing. Auto, Marcus Smart. Nice one there for the Celtics. It's got a win. Yeah, Kera Slivert. O'Shaig Vajie, Kendall Brown, and Kevin Brown. Do you see the nice-to-say knee part? I don't think so. I don't think so. This is a bunch of people that say this random word, and it goes to be like, I don't know. Sorry, Smet. Anyway, cool concept is, at some point in the movie, they're having a conversation, some guys says a certain word, they're like, ah, don't say that word, they're like, trying to figure it out. So ridiculous, Mr. Mitch, Jalen Dern. Every time they say the word, it, they like, freak out. I stopped saying the word. Red Wave, Corey Kispert. Silver, Jeff Green. Ty Ty Washington. That's a great movie, though. Oh, I know, I know it's a very good one. Alondes Williams for the Nets, Sensational Signatures. Hyper, Buddy Heald. Marjombo Champ. Yeah, I saw the one-on-one Drew Gopold. Jaden Hardy. Saw the one-on-one Drew Gopold. That's pretty sweet, congrats to the owner. It'd be interesting to see what they do. All right, we're on to OptiCobby now, 22-23 OptiCobby. We'll do 2020-2021 iMac and then we'll finish with Noir. Nice getting close to being over. We got a lot more people that need to start talking about the Drew. I know. Or to bring up the Drew. Or to bring up the Drew Jones, yeah. We're not even halfway to the over-under. I know. Silver, Harrison Barnes, I love it, thanks. All right, that's fine. Silver, at least, Dominator's got some fishing for it now. Yeah, I don't wanna be, I don't wanna be at wrong, Sam. Raining three, Steph. Drew Jones, Super Frac, they're one-on-one auto from Bowman. Blue, bam, out of bio to 49 for the heat. $250,000 balance. Is that much of it? Was this that really about me? Yeah. Oh, I had to do that part. Yep, one, two, three, left, four, five, six, right. We'll go for the rest of the box.  So it's gonna go left. Go left. On any of those, unless they're one-on-ones or auto. Jabari Smith rookie, Lam Green to 149, that is Jaden McDaniels, and Anthony Simons. Yeah, not here, somewhere. It was just like a one-box personal, which is nuts. Chet Holmgren rookie. Lighted out job, there it's basically. And maybe it's called the Well of Cruel. Hollow, Nikolay Jovic, James Harden. Cam Reddish Hollow. Orange, C.J. McCollum to 199, and Nemhart. There's a blue to 49, P.J. Tucker. What if I end up ever doing a break in the first box that it opens and has a super practice? He's gotta freak out. Walker Kessler, Hollow Dewey, you gotta freak out. Oh yeah, I got that part. Not to your level, bro. Right, 40 of 99, Evan Fournier. Dominator's auto for the Knicks. Oh, would've been nuts. Ooh, gold. Is Josh Giddy, five of 10. Nice, second year gold for the Thunder. There you go, Isaac. Nice one there, Blake. Hollow, Jimmy Butler, Dominator's of the movie. Jalen Williams. Red Siddiq Bad to 99.  You just got jingled. Send that card to your friends so they jingle them as well. You only got jingled there. Yep. All right, we've got... Shame. I know, if the Giddy was a rookie, it'd be massive. 2020-21, I'mac. I jingled Forrest last night, too. Oh, no, I heard. Oh yeah, that's right, I forgot you were here. I was here.  I just said to myself, I'm glad it's not me. Glad it's not me, right? I like Forrest like that hard. To 99 is Chamal Murray. Ooh, nice one here. There's gonna be 81 of 99, Steph Curry. This is gonna be a random to these two teams at the end. It's kinda nasty. Seven of 10, Lamello Ball and James Wiseman on the dual patch between the Hornets and the Warriors. Nice one there, Charlotte in Golden State. Rookie Otto Sam Merrill. Two of 10, Initiation Ink for the Bucks. That's Bucks. We've got 13 of 50, Jared Allen. Nice one there for the Cavaliers. Yeah, Noir is sweet, dude. Noir is pretty sweet. Ah, you can match this next one. 14 of 50, going to the Pistons. Three colored RPA, Sadiq Bay. There you go, Detroit. Chad B.A., Chad B.A. Congrats, buddy. 14 of 50 on the Sadiq. That's one of those ones that's probably close. Like it's an RPA at Vimec, one of those kind of situations. All right, 20, 21, 22, I'm Noir. Yeah, I know what you mean, Frosty. It's just tough because of the borders. That's what makes it so tough. You're starting off with a banger. It's kind of a banger for sure. It's gonna be a Russell Westbrook, 3 of 25 feature length. That's one there on the frame. Got a Darius Garland, that is 46 of 99, as well as a Carl Anthony Towns, 88 of 99 base. Oh, let me get that, I'll need that patch for random. I just don't wanna forget. There's a Moses Moody, 11 of 99, rookie Jersey guard. The Warriors. Next up for the Jazz Jared Butler, 30 of 99. Next up is DeMontis Sabonis, 32 of 75 newsreels for the Pacers, still a Pacer in this game, Warren. Congrats, Indiana. First auto, that's gonna be going to the Clippers, 41 of 99, Marcus Johnson. Very cool. There you go, Frosty, nice one here. That is gonna be 19 of 99, RPA. Trey Murphy, the third for the Pelicans. We've got, nice one here, is gonna be at eight of 99. Larry Johnson, wow dude. For the Hornets on the shadow signatures, it's very clean. This team killed it, dude, 21 of 49. Showtime signatures, Cade Cunningham. Rookie Auto, congrats to the Pistons, Chad B.A. There you go, buddy. Again, a little bit of weight on the corner. We'll send the empty box and wrapper, just in case, cause that's massive, if you wanna get that replaced. That's a massive card. I love the Showtime signatures. We'll get that magged. We grab the box and wrapper really quick for ya. All right, I got a couple of randoms to do here. There you go, my good sir. We've got the Alfonso Plumber Auto, the Chris Likes Auto, and then the non-autographed college cards, as well as the Lamello and Wiseman. The Lamello and the Wiseman. All right guys, gonna start off with the college cards. Way this is gonna work, gonna roll the dice. Whatever team is on top, after six times gets the plumber. The second team will get the Chris Likes, and the third team will get all college cards that are non-auto. Do you might wanna just make a note of that on the- You wanna write it on the back tag. Perfect. Just do all college cards, yeah. Okay, cool. Good luck guys, we've got one. Two, three, four, five, and six. All right, so Lakers are gonna get plumber. The Mavericks are gonna get Likes. And then- Mavericks, yes. Yup, Dallas. And then all non-autoed college cards will go to the Celtics. The Celtics. All right, gonna roll the dice on the Lamello and Wiseman duel as well. Good luck to the Hornets and the Warriors. It's Hornets Warriors. Gonna go seven times on that. One, two, three, four, five, six, and seven. One of the Warriors. Warriors gonna get the Lamello. So the Mitchell was all non-MBA team. Yeah, all non-autographed college cards. So any of the rated prospects and a guy who's not in the NBA. All right, let's do a recap guys. Pretty solid mixer overall for sure. We've got a gold Josh Giddy. Jared Butler, Demontus Sabonis, Moses Moody, Steph Curry, game youth. Autos now, we've got Marcus Johnson, Larry Johnson, Trey Murphy, Jared Allen, Sam Merrill, Evan Fornier, Alandes Williams, Marcus Smart, Dyson Daniels, Christian Coloco, Mark Williams, Robert Ori, Justin Lewis, Kenneth Wapden, Oshai Agbaji. Magged cards, we have the Siddique Bay, 14 of 50, three-colored RPA, and this Siddique Cave Conningham Showtime Signatures, 21 of 49. That'll do it for the break guys. Thanks again for the fill. Next up, we've got Contender's PYTES.


Okay, continuing with our discussion on blocking, this is a rarely used concept but has lot of practical value. So it is not possible for you to carry out repeats on a single specimen but you do it on different ones. For example if you are trying to look at the effect of different fertilizers in the plot of land. So you put one plot of land and then you put different fertilizers in that plot and monitor the growth of the crops. Obviously if you are talking of repeats then you have to wait until the first crop is ready before you put the fertilizers the second time. But if you want to repeats in parallel then you have to put different fertilizers in a second field and the second field is different from the first field or it may be different from the first field. So the first field and second field are called as blocks. So the experiments are conducted on different blocks and we have to account for the blocking influence also. And if you account for the blocking influence you lose on the degrees of freedom and what happens is that is a negative point but on the other hand the sensitivity of your tests will increase when you do blocking. So in blocking we try to answer the question how to account for the difference between the specimens on which the treatments were carried out. So what we are doing is we are carrying out different tests on a particular specimen. Now that specimen is used up then we do the same tests on the second specimen. But the first specimen is different from the second specimen or it may be different it may be identical but usually it will be different from the first specimen. So the first specimen is a block, the second specimen is also a block. So here we are accounting for the variability due to the two different specimens. So we are blocking out the effect of the type of specimen on which the tests were carried out. So there is some loss of information because of blocking. Actually the blocking helps to increase the sensitivity of the experiment to differences between the levels of study variables okay. So please look at the ANOVA table given for blocking and see the degrees of freedom eaten up by the blocking and on the other hand how the test became more sensitive due to the blocking effect. Next we move on to factorial design. Here we are talking about not a single factor but we are going to talk about more than one factor. So it can be multiple factors and those factors may be set at two levels. In factorial designs of level 2 we can go for any number of factors 3, 4, 5 but each factor will be set at only two levels, one lower level and the other a higher level. So we call it as minus 1 setting and plus 1 setting. What are the advantages of factorial design? It helps to analyze and interpret your results in a scientific manner. You can carry out the response surface methodology and qualitative and quantitative factors may be analyzed together. For example you can have temperature, pressure and the type of catalyst carried out in your analysis. Temperature pressure will be having continuous range of values whereas the type of catalyst may be catalyst A, catalyst B and so on. So it is a discontinuous variable or a qualitative variable but using design of experiments and factorial design you can account for all the 3 factors simultaneously. And this factorial design is compulsory for industrial competitiveness and the advantages of factorial design are many fold. The design is orthogonal as the different effects and their interactions contribute to the sum of squares independently. So when you have an orthogonal design each factor contributes to the response in its own way. So the variability in the response is contributed independently by the different factors. And factorial design of experiments enable us to extract required information from the experiments even the phase of distractions created from unpreventable random variations. So there are going to be random variations throughout the course of our experiments. Despite the distractions from that the design of experiments especially the factorial design will help us to find the or identify the main effects and their interactions. One more important thing especially in this tree is design of experiments help us to extract rich informative content from data using limited number of experiments. Another important concept in factorial design is the interaction between factors. Factors really meant by interaction between factors. What will happen is the role of factor A will depend upon the level of factor B. At one level of factor B, A may behave in one manner and at another level of factor B, A may behave in another manner in which case the two factors are said to interact. The two factors that are not independent of one another are said to interact when factors interact the change in response to the change in one factor depends on the level of the other factors. So if the change in level of the first factor causes a certain change in output response at one level of the second factor and identical change in the first factor level at the second level of the second factor will produce a markedly different output response. So for this you please look at the example I had given on cricket scores from a batsman depending on whether he had taken tea or beer before coming out to play. Now if you look at the typical analysis of variance table in factorial design of experiments you have a source of variability due to A treatments and B treatments and then you have the interaction between A and B. Then you have the contribution from the error and so you have sum of squares of factor A, sum of squares of factor B, sum of squares of factor AB, sum of squares of error and again you have the degrees of freedom A-1 for A and B-1 for B and interaction has A-1 into B-1 degrees of freedom and error has AB into N-1. We calculate the mean squares as usual by dividing the sum of squares by the degrees of freedom, the respective degrees of freedom and so this is what you have. So to find the f0 for A we find the mean square A by mean square error for f0 for B we find the mean square B by mean square error for finding out the f0 for interaction between A and B we take mean square interaction and divide it with the mean square error. So when you have these 3 f values you compare it with the f alpha numerator and denominator degrees of freedom. So the numerator degree of freedom would be corresponding to the different factors and denominator degrees of freedom would be corresponding to the error degrees of freedom. If the computed value of f0 for the different factors and the interaction are higher than f alpha numerator, denominator degrees of freedom then those f values are lying in the rejection region. So the f alpha numerator and denominator degrees of freedom would define the critical f value and if that critical f value is exceeded by one or more of these 3 statistics then those particular factors are set to lie in the rejection region and we can reject those appropriate hypothesis. The hypothesis here would have been the treatment A is having no effect at all, mu A is equal to mu or tau A the effect of factor A is equal to 0. So tau A is equal to 0. Similarly for factor B we say that mu B is equal to mu the overall average or mu B is equal to mu plus tau B the null hypothesis says that tau B is equal to 0. Similarly for the interaction if your f statistic lies in the rejection region you reject the null hypothesis and say that factor A is important or factor B is important or factor AB the interaction between AB is important depending upon whether the f value is lying in the rejection region or not. Then we move on to multiple regression where we have the experimental response in terms of beta 0 plus beta 1 x1 plus beta 2 x2 plus random experimental error. So the response y is not only strictly determined by factors x1 and x2 but also by random error component. So beta 1 and beta 2 are called as partial regression coefficient 1 and partial regression coefficient 2 respectively. So this is a very interesting diagram here we plot the response versus x and we find the response is scattered in the 2 dimensional plane and we try to fit a line which passes the best possible manner through the points. We try to balance the line through these points there can be more than 3 such points. What is of importance is the regression line represents the true value and the experiments are showing deviations from the true value because of random fluctuations. So that distribution of the fluctuations from the true regression value is described by a normal distribution centered around the true line value and the variance of this distribution is sigma squared. The sigma squared is also called as the error variance because of the random fluctuations or random errors only the data points are deviating from this straight line. So the mean of these normal distributions correspond to the regression line value y is equal to beta 0 plus beta 1 x but there are deviations from these because of random error contributions and when we do the experiments next time we may get the data point lying somewhere here or it may be lying somewhere here because it is a random phenomena. We can also do multiple regression model especially with linear algebra in a very swift manner. So we describe a general multiple regression model as y is equal to beta 0 plus beta 1 x 1 plus beta 2 x 2 plus so on to beta k x k plus epsilon and this has k regressor variables. This is a multiple regression model with k regressor variables. So they are also called as factors or regressor variables x 1, x 2 so on to x k. The parameters beta 0, beta 1, beta 2 so on to beta k are called as partial regression coefficients. Now we can have a matrix approach to multiple linear regression. So if there are k regressor variables x 1, x 2 so on to x k and with n observations here the index i represents the run number. You can have n runs performed. So xi 1, xi 2, xi 3 and xi k are the x values corresponding to the ith run for factors 1, 2, 3 so on to k and the model is given by beta 0 plus beta 1 xi 1 plus beta 2 xi 2 plus so on to beta k xi k plus epsilon i or epsilon i is the random error component and usually the number of experimental settings should be greater than the number of regression parameters. So this may be represented in matrix notation as y is equal to x beta plus epsilon. So you have the column vector here y is equal to y 1, y 2 so on to yn, x is equal to 1, x 1, x 1, 2, x 1, 3 so on to x 1 k. This perhaps may be main factor A, main factor B, x 1, 3 may have been interaction between the 2 factors and so on. You can even have quadratic terms like x 1, 1 squared or x 2, 2 squared and so on. And so you have for n experimental settings and then you have the beta column vector comprising of beta 0, beta 1 so on to beta k. Epsilon is the column vector corresponding to the random error component epsilon 1, epsilon 2 so on to epsilon n. So when we define the matrix approach to multiple regression y is equal to x beta plus epsilon using these coefficients in the following model will help us to predict the response of the various values of x i. So we are knocking off the error component because this is a prediction. We say that y hat is equal to beta 0 hat plus beta hat 1, x 1 plus beta hat 2, x 2 plus so on to beta hat k, x k. Note that we are having k regression parameters beta hat 1, beta hat 2 so on to beta hat k. Beta hat 0 is the intercept in the multi-dimensional space. So when we want to find the least squares estimators for beta we can solve this equation for beta hat x prime x inverse x prime y gives you beta hat. So next we go on to the variance covariance matrix which is a very important one and we want to look at the variances of the estimated parameters. If the variances are small then those parameters are been estimated quite precisely. So you have the x prime x inverse matrix multiplied by sigma squared and this is what you have here. The sum of squares of the error is given by y prime y minus beta hat prime x prime y. There is a typo let me just correct it. So we have sum of squares of error as y prime y minus beta hat prime x prime y that may be represented as y prime y minus sigma equals 1 to n yi whole squared divided by n minus beta hat prime x prime y minus sigma is equal to 1 to n yi whole squared by n. The sum of squares of error is written as sum of squares of total minus sum of squares of regression. So this term here represents the regression sum of squares. Here we are knocking off sigma i equals 1 to n yi squared by n to correspond to the sum of squares given by the intercept beta hat not. So we again have the analysis of variance table source of variation due to regression and errors or residual sum of squares of error. So you have k and n minus p degrees of freedom and total sum of squares is having a degree of freedom of n minus 1. And again we find the mean square regression to mean square error. So we get sum of squares of regression by k which is mean square regression. Sum of squares of error is given by is divided by n minus p degrees of freedom to give mean square error. The ratio of mean square regression to mean square error will give you the appropriate f0 value which you can test to see whether this f0 value is lying in the rejection region or in the acceptance region. Then we also talk about r squared which is sum of squares of regression by total sum of squares. This represents the proportion of the total variability accounted or explained by the linear regression model. Here an adjusted r squared we have mean square of the error and model total sum of squares are used. And here we penalize the model for having too many parameters r squared adjusted is equal to 1 minus sum of squares of error by n minus p divided by sum of squares of total by n minus 1. Here we are using the mean square of the error and the model sum of squares. So here we are dividing by n minus p when the number of parameters increases then n minus p will decrease 1 by n minus p will increase. So this term will increase and the r squared value will go down. So the suggested procedure is keep adding more parameters to your model until the r squared adjusted starts to decrease. So it starts to penalize the model for having too many parameters. So if the adjusted r squared value also increases along with the r squared upon adding of the parameter then that particular parameter you have added to the model is making a effective contribution. So now how to analyze for lack of fit. So what we do is we take the residual sum of squares. What is the residual sum of squares? It is the residual is defined as the balance or leftover when you subtract the experimental response with the model prediction. So the balance is called as the residue and the residual sum of squares are obtained by summing the square of these residues. So the residual sum of square is split into lack of fit sum of squares and the pure error sum of squares. So the degrees of freedom of residual sum of squares is nr and that is split into lack of fit sum of squares and pure error sum of squares. Pure error sum of squares is having degrees of freedom ne and lack of fit sum of squares is having degrees of freedom nr minus ne. And when we divide the lack of fit sum of squares by nr-ne, we get mean square lack of fit. When we divide pure error sum of squares with ne, we get mean square pure error. Now what we do is we compare the mean square lack of fit with mean square pure error using nr-ne numerator degrees of freedom and ne denominator degrees of freedom. If the F test says that these two are comparable and it does not lie in the rejection region, then we can say that the model does not have any lack of fit because the lack of fit sum of squares are comparable to the pure error sum of squares, there is no further incentive to develop the model further. But on the other hand, if the mean square lack of fit is considerably higher from the mean square pure error, then the F statistic would be lying in the rejection region, then you have to conclude that there is sufficient scope for model expansion and the lack of fit is significant. So you have to consider the addition of more terms in your equation. How do you get the pure error? The pure error is obtained by carrying out genuine repeats in your experimental runs. You fix all the factors at certain value and then repeat at the same value more than once. Then you choose some other set of values for the factors, repeat the experiments more than once at such factor settings. Like this, if you do, you will be able to get genuine repeats which will help you to find the pure error sum of squares and then you will get the mean square pure error. So this is a lack of fit test is very important in linear regression analysis and it helps you to stop at a particular stage of model development. So the next important concept we discussed is about orthogonal designs. We touched upon the advantages of orthogonal designs a few slides back and if the two columns of the design matrix are orthogonal, it implies that the levels of these two factors are linearly independent. The important implication of linear independence is that the roles of the two variables on the process response are being assessed independent of each other. When you are having factorial design, they are orthogonal based designs and when you have factor A factor B, you can see that A factor is treated independent of the B factor. The contribution brought in by the A factor is independent of whether you are considering the B factor or not. Then we talked about AB interaction. How the experimental response goes from one value to another value upon changing A from lower level to upper level depends upon whether B was at a lower level or B was at a higher level. Then the two factors A and B are said to interact but we are talking about orthogonal designs and we say that A effect is found independent of the B effect. So when you have AB interaction, the AB interaction effect is also found independent of the A effect and the B effect. So when you develop a model and you do not consider the interaction term between A and B, the factor A would still have a particular value. Let us say the effect brought in by factor A is 20. If you consider interaction between A and B also, the effect of A would be still 20. So it does not matter whether AB is present in the model or not. The effect of A is computed to be the same. Similarly, the effect of B may be 10. The effect of B is 10 independent of whether A is present in the model or AB is present in the model. Suppose you have a full fledged model and you are considering AB and AB, then the factor B would have an effect of let us say 10 units. If you do not have factor A and factor AB, B would still have 10 units. So this is very important. The best way to understand this is to actually do a problem. Develop a model for the orthogonal case. Find the effects. First you have only A. Then you have B. Then you have AB. Then you will find that A effect, B effect and AB effect are found independent of each other. And in our model development, we have to be careful. Suppose we are having only 4 runs corresponding to a 2 power 2 factorial design. So we are having only 4 runs. This is the column vector of once. This is the column vector corresponding to x1, corresponding to x2 and x1, x2. So you are having all these so 4 independent runs and so you can estimate 4 independent parameters beta hat0, beta hat1, beta hat2, beta hat12. However, if you want to estimate more parameters from your experimental design, for example you want to consider the quadratic terms also in your design, like putting beta hat1, 1 and beta hat22. You will find that the beta hat11 corresponding to x1 squared and beta hat22 corresponding to x2 squared. x1 squared is having all once and x2 squared is also having all once. And if you look at the x matrix, the ones are corresponding with the column vector of once. The columns are not linearly independent anymore. You can see that there are linearly dependent columns. There are 3 columns which are not linearly independent and that would lead to all kind of difficulties in your estimation of the parameters. So it is important that you restrain yourself depending upon the size of the run. You do not try to fit too many parameters in your model. Do not go for the greedy model. Look at number of parameters you want to estimate and number of experimental runs available. You would think that number of experimental runs and number of parameters can be the same. Then it is no longer a regression analysis but a procedure for solving A equations in A unknowns. So you will get a exact fit to the experimental data. Normally we conduct experiments in such a way that we conduct a large number of experimental runs and then we estimate only a few parameters. So that we have sufficient scope for accounting for experimental error and also for lack of fit tests. In certain experimental design strategies, we require center runs. You can do factorial design and you can repeat the experiments at factorial points, factorial design points but that may probably lead to large number of repetition of the runs and they may be also expensive. So rather than doing that, you may want to carry out experiments at the center of your experimental design space. So the repeats are only conducted at the center of the experimental design space which is midway from all the experimental settings. So that center runs are very important because you are able to get an idea about the pure error and it is an important augmentation to the factorial design and it does not contribute to the linear effects and the interaction terms and it helps to see qualitatively whether curvature is there or not. Another important parameter which people do not really understand or use in their design of experiments is the scaled prediction variance. There is a bit of linear algebra associated with it and that may be the reason why people do not really appreciate and utilize it. It is associated with the prediction nature of your model. So your model is going to predict certain values in the experimental design space and how good and what is the quality of the model predictions. If the model predictions have wide variance associated with them, then those predictions cannot be really relied upon. So we want to have a controlled scaled prediction variance for which we have to focus on the experimental design. For finding out the scaled prediction variance, we do not have to conduct the experiments as such. We have to choose upon the suitable experimental design strategy and by looking at the x matrix, we calculate x prime x and then we calculate x prime x inverse and then we identify a certain set of coordinates in that experimental design space and then we compute the scaled prediction variance. And once we have the scaled prediction variance, we look at different points in the domain and see whether the scaled prediction variance is kept under check in most of these points. If there are certain points in the experimental design space where the scaled prediction variance shoots up, then that particular design is not to be recommended. So you can have different definitions for the prediction variances. In the first definition, you have sigma squared and xm prime x prime x inverse xm into sigma squared. Xm is the coordinate point expanded into the model space. Please look at the appropriate slide for this definition. Then you also have the unscaled prediction variance where you divide the prediction variance by sigma squared and you get xm prime x prime x inverse xm. And then you also have the scaled prediction variance where you multiply the unscaled prediction variance by the size of the run. You can artificially make your scaled prediction variance as small as possible by increasing the number of runs. If you want to compare different designs, then you have to put them on a common basis and to do that you multiply by n which is the size of the run. And the scaled prediction variance which is a very important parameter in statistical design comparisons, we have spv is equal to n xm prime x prime x inverse xm. Coming to this next slide, we have what is called as the estimated prediction variance. Please note that we do not know the value of sigma squared, the error variance. In such cases, we try to replace the sigma squared with the suitable error estimate and that would be the mean square of the residuals. The residual sum of squares is divided by degrees of freedom for the residual sum of squares. If you have n exponential points and then you have p parameters including the intercept beta hat not beta hat 1 so on. So you have p parameters, note that p is equal to k plus 1 where k is the number of regression coefficients okay. So you have n minus p as the degrees of freedom for the residual sum of squares. So you can divide the residual sum of squares by n minus p to get the mean square error and that can be used instead of sigma squared. We call it as sigma hat squared to denote that it is an estimated one. And once you get the estimated prediction variance, we can find the square root to get the standard error. We talk a lot about second order models. You look at many research papers. They also talk about second order models. Why should there be so much fuss about second order models? Because the experimental design space may no longer be planar or have only simple interactions. It may also be characterized by peaks and or valleys and second order models are required to estimate this response and enable the identification of an optimal solution if any. So second order models are of the form y is equal to beta not plus i equals 1 to k beta i xi plus sigma i sigma j beta ij xi xj plus sigma i equals 1 to k beta i xi squared plus epsilon. So you are accounting for the main effects. You are accounting for the binary interactions and you are accounting for the quadratic terms.  And one important second order design is the central composite design where you have a regular factorial design and it is augmented by axial points. I am showing it for design involving only 2 factors so that I can represent it on a 2 dimensional diagram. So you have the regular factorial points and then you have the center points which help you to find the pure error and also tell qualitatively whether the curvature effects are there. In addition to the center and factorial points you also have the axial points and these axial points are important augmentations for the central composite design. So the role played by center points in the central composite design are it helps in the detection of second order or curvature effects beta 1 1 plus beta 2 2 but not in their individual estimation. The number of center points decides the distribution of scaled prediction variance in the region of interest. So what I am trying to say is the prediction capability of your model may also depend upon the number of center points considered. The axial terms help in the contribution help in the estimation rather of the individual pure quadratic effects and if the axial points were not present only the sum of the quadratic term significance beta 1 1 plus beta 2 2 could have been gauged using the center points. And the axial points also do not help in the estimation of the interaction effects and that is obtained from the factorial points. The center points and the axial points contribute to the flexibility of the central composite design. An important alternative to the CCD is the BBD. An alternative to the central composite design is the box Benken design. It is a creative approach to planned experimentation and involves relatively smaller number of runs. It involves a balanced incomplete block design. So for 3 factors you have the box Benken design. Here you have a regular 2 power 2 factorial for factory AB and C is kept at the center point. In the next phase you leave B at the center point and then construct a 2 power 2 factorial for A and C. For the next phase you consider B and C and then you have A at the center. After you have exhausted all the 3 combinations you then have a set of center points defined here. So when you have large number of factors let us say 6 factors instead of going for a 2 power 2 design for a certain subset of factors you go for a 2 power 3 design. So first block or first phase you consider x2, x3 and x5. Here you constructed 2 power 3 design involving these factors. Then after doing out the 8 settings corresponding to x1, x2 and x4 you go to x2, x3 and x4 carry out the 2 power 3 design. In such a case all the remaining factors would be at the center values. Similarly you go around taking 3 factors at a time and construct all your 2 power 3 designs out of these factors. So the important thing to notice you are considering 3 factors out of the 6 at any given time. So those 3 factors would constitute a 2 power 3 factorial design whereas the remaining factors would be at the center values. Finally after exhausting all the combinations you come to the center runs where all the factors are kept at the center values. And once we know how to do CCD and VBD we can then do the response surface methodology where the objective is to find the optimum value. So the current level of operation may be very far away from the optimum and we cannot afford to wander around in the n dimensional space wasting resources manpower and time. So we need a structured and we need a well thought out procedure to quickly progress towards the optimal solution. For this we use the method of steepest ascent. So I am demonstrating this method for 2 variables. Here you are having x1 and x2 and this is responses are obtained from a 2 power 2 factorial design and they are showing no interactions. The contours are not curved but they are linear and to proceed along the direction of steepest ascent you go in the direction perpendicular as shown here perpendicular to the contour lines. These are response lines we are going in the direction perpendicular to them. So once we keep doing experiments, please remember that we cannot use the develop model to identify the values or outcomes along the direction of steepest ascent but we actually do experiments out of this design space and we keep doing the experiments until we reach a stage where we find the values passing through an optimum. Here we construct a central composite design around this optimal point and then evaluate all the parameters in the model and we also see whether the optimum is a minimum or a maximum or a saddle point. So the central composite design around the optimal point is shown for example here. Here you have the factorial points, here you have the axial points finally the repeats. So now you can fit a second order model as given by the following equation and once you have identified all the model parameters then you have to identify the stationary point. You have to first locate where the stationary point is. The stationary point is where the dou y hat by dou xk where xk can be x1, x2 depending on the number of independent factors you have considered that all the partial derivatives should be set to 0. So from the identified model equation you can set all the partial derivatives with respect to the x values to be 0. Solve the resulting set of perhaps nonlinear algebraic equations and find the set of stationary conditions. There is another way of doing it that is from the matrix method. Here you identify what is called as a small b matrix and the capital B matrix. What is the small b matrix and the capital B matrix? The small b matrix is the column vector of all the main factor parameters and then you have the capital B matrix whose structure is interesting. Here along the diagonal we have the coefficients for the quadratic terms and along the off diagonal we have one half of the interaction term. So this matrix is symmetric for example beta the 1, 2 location will be equal to the 2, 1 location. So here also you have beta hat 1, 2 by 2 and in 1, 2 or 2, 1 location also you will have beta hat 1, 2 by 2. So this B matrix is a symmetric matrix, Bij is equal to Bji that is the matrix being symmetric. In such a case to account for the interaction effect twice we are dividing it by 2. On the other hand the diagonal terms are all the quadratic coefficients beta hat 1, 1, beta hat 2, 2 and beta hat k, k. So once you have the capital B and the small b matrices evaluating the location the stationary point is quite straight forward. This is obtained by the solution to the equation B plus 2 Bx is equal to 0. Remember here we are dealing with matrices. So once you solve the above equation we get the stationary point coordinates as minus half B inverse of B. So the predicted value of y at the stationary point is y hat of s is equal to beta hat naught plus half of xs prime B where xs is given by this equation. So you can look at the response surface and it can see for this particular example it passes through a minimum. So we have a minimum solution here. To be in order to identify whether the identified stationary point is maximum or minimum or a saddle point we have to check the eigenvalues. If all the eigenvalues are positive then the obtained solution the stationary point corresponds to a minimum. And if all the eigenvalues are negative the identified stationary point is a maximum. And if you have some eigenvalues positive and some eigenvalues negative then it corresponds to a saddle point and not a true optimal location. So the CCD and BBD runs are augmented with center runs. They are very popular among the practitioners and the statistical design experiments is evaluated based on factors such as good fit to the data. It should allow for lack of fit and it should allow for sequential construction of models of increasing order or complexity. And you should have enough repeats at the center to have an estimate of the pure error and it should be robust to the presence of outliers in the data. And it should be cost effective that means it should involve less number of runs and it provides us good prediction of scaled prediction variance. So this completes our discussion on experimental design strategies. We have covered quite a lot of ground that should be enough for researchers who are planning to do design experiments. We have covered sufficient theory as well as done significant number of problems. I request you to go through them and solve as many problems on your own and then also become familiar with statistical software like Minitab, design expert and so on. So I wish you all the best in your application of the statistics principles we learnt for your experimental program. Thanks for your attention.


Moj je Pjord Dus infection in to tomatoes about active defencing practice I go quiet rapidly through this life because I've got a lot of them so how keep it shus on and okay, let's start. So basically, shorthy about me, I'm a security consultant trust with small labs. Basically I enjoy security and among other things but so naredilskih kratkov. Zelo, kako je vse izgleda, da včešljeste izgleda na pravno pravno proprimovite, zelo, da je izgleda, da tega da začnevajte, da je tega zprimovati, da je zdaj, da bi naredil na pristom proprimovati, da je začnil, da se je ješljenje, da je začnil na pravno pravno pravno pravno pravno, zelo, da je začnila, da pa je začnila, da je začnila, da bi je začnila, system, in priživamo nekaj informacij, da je tudi nekaj poslednji. Četko je izglednje, da imamo nekaj nekaj nekaj atakov za softver, da imamo na internet. To je softver, da je vzglednje za skanje in izglednje svoje system. V svoj delu zelo je da je počet, če je počet na nekaj početnah, katero je bilo počet, počet nekaj počet. Tko zelo je, tko je počet na rekonesenju. Zelo vse rekonesenje je počet. Zelo sem održetilo na mnoj nabunu, z battlesh, because as we know is the most popular tool. So basically it's quite possible that somebody will be using to scan your system. Here we have a typical example where somebody is trying to scan the system and he gets a lot of information. And it is not exactly what you would like to share with our offenders. Basically, this information can be used as as in other steps to carry out some more sophisticated attacks. So, I thought, what would be the worst case scenario for a person scanning or trying to get a view of your running services on your system. So basically, what if, for example, all of the ports were open, and what if on every port, there was actually a valid, or it appears to be valid, a valid service listening. And your attacker has to basically as usual get a view of all running valid services on your remote system. So basically, I wrote this tool, which is proof of concept and still work in progress that basically implements that idea. So when you want to get a full view of the remote system, go through all of the ports, try to get all of the services identified, your attacker will need a lot of patience, because as I've seen, basically, as I've tested, all of the ports will be open, he will have to send about 120 megabytes of data, and scan will take approximately... Stop talking. So we have a tradition at DEF CON. First time speakers need to do a shot on stage. Let's give them a round of applause for getting selected. Cheers everyone, thanks for that. Now we have to see if you can pick up where he left off in the technical talk. So, you guys judge how well he does. I'm from Poland, come on, it's just one shot. So, coming back here, so basically, our attackers get nice juicy output, 65,000 or more valid services identified by NMAP. Of course, I focused on NMAP, but basically, it can be any other ports camera, but since it's so popular, so why not that too. If you go through the listing, you can see different services, like telnet, there's even a bug door if you can see. So basically, among that, there's some, probably your service running, which is valid and could be possibly exploited. But yeah, try to find it, it's not so easy, I guess. And somewhere in the, when the attackers go through your service scan, they can find a hidden message. So basically, you can put any ask yard there. Also, the all identification results are a bit strange. For example, you can see that the real operating system is actually Linux 3.2. Here you have like, unsure results plus, you know, Unix, Windows, Linux, Solaris, yeah, you don't know what it is. Additionally, which is actually the part, the second part of the presentation, you can also control certain fields, which can help you with the exploitation of a particular software. So yeah, NMAP, there are similar results, all of the ports are open, some of them are identified. Yeah, so what are the conclusions? Basically, the style scans are no longer helpful with this technique, because if all of the ports are open, then basically you can make a connection. If there is an open port, then there is a service running, all of them are open. So yeah, all side identification is a bit more challenging. Yeah, it also forces your attackers to generate huge amount of traffic, so basically you can easily detect them, or easier. It's easier. Yeah, for service probes, and of course it adds some frustration to your vendors. Some might say that it's a security by obscurity, but as far as if only it works, you know, it's the point. I don't know if you can see the fish there, but it's there. Yeah, so, but I'm sure that also we are thinking like, okay, fine, but I'm sure I can find some kind of bypass. So for that, there's also the way I was thinking, so basically answering maybe some of your questions. There's no trivial way to detect false signatures, apart from using some kind of protocol probes. IP fragmentation and other network activation techniques will not work, because it goes through the kernel to the user space program that I've written, so basically you can use fragmentation for any layer that you want. It will anyhow be assembled at the end. The only thing that will work is actually the full-connect PCB loss, but it's not a mistake in the ID. It's just that every software is actually vulnerable to this. I've made some tests. You can always try to mitigate this by using some of the stool parameters, or just try to use IP tables with traffic shaper. Also, if you have any ID issues for the bypass, send them to the mailing list. I'll try to fix the software, or I don't know, implement your ID. Yeah, just shortly about the port. It's a user space software. It doesn't require any root privileges, no kernel modules, just binds to one port per instance, and then later just configures through IP tables by redirecting some of the ports that you want to, and then spoof, to local host. Yeah, okay. Let's go to the good part, which is a practical exploitation of your offenders toolbox. I don't know if you have noticed maybe the output here is not very clear, but with Nmap you can control certain fields, like for example the version fields, host fields. That gives you basically a nice attack vector possibilities. So it went to the internet, looked for Google, we've googled for some software that could be exploited with that. And basically the first example is, okay, it's still anonymous because the outer haven't responded to me. Basically if you set up on port spoof, a particle payload, like on any port, and somebody will use Nmap to scan your system, then generate a report, and basically you are able to inject some of the javascript coded into his browser, let's say context, when he'll be browsing the report on his computer. There's actually a nice thing about it because for example if he launches a Safari and goes through the results, and basically same origin policy doesn't apply for file ure handlers, this actually my friend Mikael Auro told me this one. So there's a simple exploitation vector for this one, like port 17, you can have one of them. The next example is like non-Nmap, so we don't stick to Nmap all the time, it's just a proof of concept. You can basically exploit for example the McAfee super scan, it was fixed a few days ago, but basically if anyone would scan your system with this particle tool, and later generate a report, then you would be able also to inject javascript code into his browser context. Later you can for example use B for any other tool to do some post-exploitation, it really depends on you what we are going to do. Yeah, this is actually a real exploit from the internet. So I don't know if you can see the exact vulnerable line, but it's here. Basically we control the content of the storage file, which actually is retrieved from one of the ports. So what happens here is if we set up a payload, for example Huemi on port 80, which is actually the, which port that exploit will connect, well, if somebody will launch the exploit against your system, he will get an additional context, which is root, so basically you are able to execute or to do aus command injection in somebody's shell, if somebody is launching for example an exploit against your system, it's nice about this. You can also create for example a weaponized version of this payload, but I won't go through all of the details here because I mean for example, if you want to exploit this particular line that you have a evaluation of the file content, and basically you have to go around some issues like you cannot use spaces, you can't use apostrof, so basically this should be in the conference materials if you want to use it later, but the result is that basically if you set up such payload on one of your ports, yeah, next time when somebody will launch the exploit against your system, he won't only get Huemi output, but you'll be able to, for example, download his whole root directory. Another example is taken from the auto-pwn script, which is nice because auto-pwn scripts go usually for all of the ports, they try to exploit all of the possibilities, so basically if you have different payloads on every port, some of them might hit that particle vulnerability and you will be able to exploit your attackers too. In this case, we have again, and this is a real line of code, I don't know if you see the vulnerability, it's rather pretty obvious, yeah, and again, what's the surprise? Huemi will work, which will result in oscom interjection again. What you can do with this and what are the conclusions for the current state of the security tools because from what I've seen on the internet in different tools, different scanning software, most of them, not all, but most of them are exploitable with simple payloads, like for example, Huemi or any other escaping sequences, especially auto-pwn tools used by script kitties or I don't know who, but yeah, if they launch the type of script against your system, then basically you can also try to create an, I named it an aggressive honey pod because you can create different payloads for every pod with different escaping sequences, then it's up to you which command you will inject. And if you want to find, for example, more vulnerable software, just go to Google, use your Google Juts skills, the ones that I found is actually a top of a mountain, ice mountain, I mean many, many scripts are vulnerable, you can use just your imagination while creating some payloads. So, in this case, I'm sure you'll find something. Yeah, in the end, I wanted to show you a nice proof of concept demo for NMAP official NSC script, which again proves the concept, it's not nothing against the tool itself. Okay, let's... Can you see it? Yeah, in front here, you can see it. Yeah, okay, then I'll tell you. So, basically, first screen, you might not see, we set up a port spoof tool along with a metropeter. Second one, we scanned the remote system, we want to check actually what's on port 80. We can see that there is an Apache HTTB, IBM Lotus Domino in the old version that's exploitable. So, basically, what we can do. So, yeah, here is a reverse handler on a metasploid. This is the latest NMAP version, 6.25. So, if you have that, it's still vulnerable. And this is the exact HTTB Domino and password script, which basically will result in a remote arbitrary file plot. So, if you launch that against, for example, the system running port spoof, you'll be able to upload an arbitrary file, overwrite any file that's accessible with NMAP privileges. In this case, I have written the script itself. So, next time, because someone might think that it's strange that there are some strange results in the NMAP output. So, next time somebody will launch that particular script with the same parameters. Yeah, he will get points. He will get a remote reverse matter per ter. I know the quality is a bit low, but if you want to just go to the main website, you can view it online. I'll change, I'll upload it in a second. Sorry for this, I thought it would be visible. At the end, yep. So, yeah, thank you for your time and for coming. Hope you enjoyed it.


So, when is it okay to call yourself a service designer?   This is one of the questions that I've been getting over and over. I have a pretty clear take on when it's okay to call yourself a service designer. Now, first of all, I've talked a lot in the past about that it makes more sense to talk about service design teams rather than a service designer. But I totally get the need for people to identify themselves with the craft of service design. For instance, if you're looking for a job and you want to apply to a service design position, then calling yourself a service designer or a service designer practitioner makes a lot of sense. So, the big obstacle is that if you don't have a formal service design training, you haven't done a master in service design, or you're maybe just transitioning from a field like UX or design research into service design, then there's always that feeling like, is it okay to call myself a service designer? I don't have the experience. I don't have the credentials. People will find out that I'm a fraud, the imposter syndrome. That's basically it. So, the honest truth is, I don't have a master's degree in service design either. When we started back in 2006 with service design, there was no master's degree. We had to actually find out what it was. Now, fast forwarding to today, there's a lot of opportunity for education, training, so forth, so forth. But I think all those things don't really matter and don't give you sort of the credibility to call yourself a service designer. For me, there's one thing that really matters and that is intent. So, if you are designing for services or designing services intentionally, if you're following best practices and you're really trying to do service design and from that moment on, in my vocabulary, you're a service designer. So intent and following best practices and really trying to design for services is for me the thing that matters most. Now, we need to be very clear that this doesn't tell anything about whether you're a good service designer, a bad service designer, a professional service designer or an amateur. That's not the point here. The point here is when can you call yourself a service designer? And again, it's about, for me, it's all about intent. Another metaphor could be like, when can you call yourself a rider or maybe an athlete? It starts with really understanding and doing things with intent. What do you think? What is your criteria? And when would people be able to call themselves a service designer? Leave a comment on this video and I'm really curious on your take.


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So now let's take a look at the equation for the magnetic force on a current carrying wire. I'm going to start off by looking at the equation we had for the force on a charged particle moving through a magnetic field. And if we wanted to find just the magnitude of the force, it was equal to the magnitude of the charge, the magnitude of the velocity, the magnitude of the magnetic field, and the sign of the angle in between the velocity and the magnetic field. In order to start working this equation, we're going to start by taking a recognition here of this speed. And this speed is really the distance traveled over a certain amount of time. So for a single charge particle that's moving along a straight section of wire, we can replace that velocity by the distance traveled and the time. Now, for right now, I'm going to go ahead and drop all of my magnitude signs. And the reason I'm dropping that is just to try and keep everything a little bit neater as I'm doing the algebra. So I'm going to take this equation here where I've substituted in for the velocity for the distance over the time and start doing some algebra on that particular equation. So here's my equation again. And I'm going to take a look at this quantity here of the q times d over t. Algebraically, that's exactly equal to pulling the q inside those parentheses, the q and d and the numerator and the t and the denominator. Now I'm going to take the fraction here and I'm just going to rearrange it just a little bit. So I recognize that that's the charge divided by the time multiplied by the distance. So q over t times d is exactly the same as qd over t. So I'm going to take this equation which is just rearranging my original equation and think about it a little bit differently. So I'm going to start by looking at this quantity q over t. And a recognition that q over t physically is the current that's flowing. And if I've got a wire, the distance is really just the length of the wire. So I don't have a single charge moving over a distance in a particular amount of time. I've got a set of charges moving at a particular rate through a length of wire. By making these substitutions, my new equation tells me that the force is equal to the current times the length times the magnetic field strength times the angle in between the wire and the magnetic field. It's important to remember though, originally I had dropped all of those magnitude signs just to make the algebra easier. So far all I have found is the magnitude of the force. I can think of current as being how much current I've got, the length of the wire as being just a scalar length, the magnetic field, and my sign of my angle between it. So I expect all of these to be positive quantities and that's going to give me my magnitude of the magnetic force. It's really easy to start getting things mixed up when you're talking about the magnitude of the magnetic force. But just try and keep your word straight. It's easy to get tongue-tied over that. Now because that's just the magnitude of the magnetic force, if I want to get the direction, there comes my right-hand rule again. We're going to do a separate little video clip to explain that. But what if I did want to get my direction and I didn't want to have to use the right-hand rule? Or maybe I'm in a situation where they don't tell me the angle directly. Well just like we had our force on just a charged particle, this is where we end up getting our cross-product form. So rather than using the magnitude form, I can start to think about the sine theta as the angle in between my two vectors. Well obviously the magnetic field is a vector and so that's going to be one side of our cross-product. But I've got this i and this l and I just said that i is just how much charge and l is just the length of the wire. So how am I going to represent one of those as a vector? There's two different ways to think about it. One is to think about the current as having a flow direction. So it's not just how much current is flowing but also which direction the current is flowing in. And so you could represent the current as a vector quantity. More commonly though, we think about the current as being how much current is flowing and recognize that in a wire it is forced to flow along the length of the wire. So I can represent the geometry of the wire as a vector quantity, which direction is the wire pointing and that gives me a way to get my vector for my length. Regardless of which one of those you choose, we then have the vector force. It's not just the magnitude but the i, j, k vector components for which direction is the force in. So in all, we've got two different equations we can use. One if we only care about the magnitude of the force and particularly if we're given the direction between the wire and the magnetic field as the angle. And the other one if we're trying to find the full-blown vector force especially if we're given the length and the magnetic field as vectors.