Well, hi there, I'm Sandy Olnok, and today I'm going to talk about expanding the mixing palette. And I'm going to start by using Prussian Blue as a guinea pig and testing it with a whole bunch of watercolors to see what it works with. When I've bought a new tube of watercolor, I have not done the testing that I would like to have done with it. So what I'm going to be doing this year is taking practice paintings like that one, turning it over on the back and doing some testing. The preliminary color testing I'm going to do regardless of what other testing I do, because this preliminary one is with the Daniel Smith Essential Set, which has a warm red, a warm yellow, and a warm blue in it. And it's going to tell me a lot about what I want to test with later. And we'll talk about that more later on in this video. And I only need to test it with two colors, so I've made an X if you're testing with three colors, then you can make an asterisk or something or just make blobs and test each one of the colors in this set because it's going to tell me whether I want to explore more of the warm yellows because I like the kind of green that makes. Do I want to explore warm reds because they're going to lean more toward a neutral? Am I looking for a purple color? Do I want to look for all of the cooler reds that I have and what kinds of purples that they're going to make? And all those decisions can be started by testing just a basic warm and a cool with the color that you're testing. Be sure to tip or bend your paper while you're doing this as well and see if you can make the colors blend in different ways and what's going to happen when they do. Now, before I get to the testing, I want to talk about my insane paint collection.  No one needs this.  When I first got into watercolor, I didn't have any idea what I was doing. And Daniel Smith, wonderful, wonderful people there, since they heard that I was going to get started on this, they hooked me up with a bunch of these, not all of these because I have bought an insane amount myself, but they hooked me up with a bunch of the five mill tubes and that's what a lot of these are. And I put them in pallets at the time in orders that I thought made sense. I wasn't thinking I was going to add more colors.  I didn't have enough space in the place where I started putting the reds. So they morphed over into with being stored with the grays, et cetera, and it was ridiculous. So I haven't used all of those because it was such a mess. So I recently went through and cleaned everything out. The palette I was just digging on is my main palette that I've used for about a year.  It was really bad. But I went through all of these. I pulled out all of the colors. I put them in order so that now my yellows will be with my yellows and my reds will be with my reds. I took off the old key sheets. I made new ones. This is my new main palette and I've committed that I'm going to keep my main palette, my main palette for crafting for two years so that you don't feel like you have to go out and buy all the colors all the time. So this is the one, same colors as it has been, just a different palette, a fancy Italian one this time. So now let's talk about color testing, which is what you're here for. And I have tried to find a pretty way to do color testing. Usually I just do blobs. But since I wanted to now systematically use all these colors I have in all of these pans, it's ridiculous to have them and not play around with them. I've invented this little thing I call the beetle. And I put a big blob of wet paint down, keep it wet so that you don't end up trying to mix with dry paint by the time you're done with the exercise. But I'm just going through a bunch of the different colors that I have. I'm not testing everything. But I know that there's certain colors that I want to test because I love them. The nickel, azo, the azo and the Indian yellow all have similar properties. So I wanted to see if any of them does something different.  And then I'm going to move into my cool yellows. Each one of them is going to act different, mix a different green. The colors, the pigments, sometimes the yellow moves into the blue. And sometimes the blue moves into the yellow. All of those properties are interesting to know about. And unless you paint them out yourself, it's hard to know what they'll be. Now you're not going to have this ridiculous number of colors. I'm just going to say that. Nobody needs that. Don't even take this video as a statement that you should have it. However, whatever colors you do have, test them. I'm going to go through it and I'm going to test the same way with my palette. I want to see how that Prussian blue works with all the colors I already use all the time. You can test them with other brands. Test them in a bunch of different ways. But if you do this, it's going to teach you a lot about how the colors move and what they mix to be when you put them together. But make sure you work quickly. A tip I have, I ended up talking to myself while I was filming this so that when I was finished, I could go back to the audio of that and write down which colors they were. So I spoke out loud to myself that, yes, I'm going to use Opera Pink. And now I know that this first one that I was using was Opera Pink. And when it was all dried, I could mark that on my sheet because it wouldn't do any good to do all this testing and not have any idea what the colors were. Well, you're not going to likely be filming a video while you're making a beetle or whatever blobs of color.  And it's just for your own edification. Comment to yourself on what color you used and ooh, I liked what it did there. Ooh, look at how that color moved into that other color. Oh, I hated that one. And that's going to give you notes that you can put down on your test sheet later when it's dry. But you want to do this quickly while it's wet and doing it by audio, just talking to yourself while you're doing it, is going to help to keep those notes handy without having to try to stop and write things down because the paint on the right hand side is going to dry by the time you get finished with writing down everything for the colors on the left hand side. Another tip for you in doing this kind of testing is to do a giant blob like this in whatever shape you make it, because then you only have to rinse the brush in between each of your reds. Otherwise, you'd have to make a blue blob, rinse it, make a red blob, rinse it, etc. You could alternatively have one brush that you have ready for your blues and not rinse it at all and then just change out the reds in the brush that you're using for the color you're testing it with. But in general, anyone who knows me knows that I'm not big on swatching for swatching sake. So what I'm going to be doing with these tests is starting to paint with them. And I want to try to do some paintings where I test to see what does a certain combination look like when I paint a building with it or paint a tree with it in context, instead of just in a blob on a sheet of paper like this, because this doesn't tell me much about how it's going to perform on an actual painting. If you'd like to see how all these colors dried and which color is which, because I have them all labeled, they're over on my blog, so you can go check that out at SandyAllKnock.com. Link is in the doobly-doo down below. And if you would like to do a much simpler way of playing with colors and testing them out and also getting a card made at the same time, then go over to Ellen Hutts since channel where I have a video over there today. And that's about it for me. I will see you soon with more videos using Prussian Blue and testing out some of these colors in reality. Take care. Thanks. Bye-bye.


Hi, I'm Cindy Kang, the curator at the Barnes Foundation, and welcome to Barnes Takeout. We are in the main gallery, and we're going to look at a painting that is hung rather high up. So it's a great opportunity to look at it a little bit more closely. This is Sera's Posas from 1888. So let's zoom in. It's a pretty enigmatic painting that shows three artist models in Sera's Paris studio, and you can see in the background on the left another painting by Sera called The Sunday Afternoon at Le Conjotte that is taking up one whole wall in his studio. So this is a complex picture with a painting and a painting, and Sera called this painting a toil de lutte, which can translate to battle canvas or a canvas of combat. So he painted this work in reaction to criticism that he got of Le Conjotte when he showed Le Conjotte in 1886 at the last Impressionist exhibition. The critics said of Le Conjotte that this technique of pointillism that Sera was using was fine for landscapes, but it was not good for figures. So pointillism was used a very small, fine dots and dashes of pure color that when placed next to each other would create an optical mixture in the eye of the viewer. That was supposed to be more vibrant than if the color was mixed on the palette and applied to the canvas. So let's zoom in on a little section just to see what that looks like. So if you look at her arm there and the shadow of the skin, you can see that Sera has placed many dots of blue next to orange and an orangey red that shades into lighter colors, even yellow and green. And what happens is that it reads as a shadow on the skin, but keeps the vibrancy of the color. So this style or this technique of painting was based on the latest advances in optics and color theory. So Sera was very much paying attention to the scientists of his day because he wanted to ground his painting practice in science, and this was a direct reaction to the perceived subjectivity and spontaneity of Impressionism. It was Sera's friend and art critic, Félix Fénéon, who coined the term neo-Impressionism to describe what Sera was doing and what Sera and his followers were doing. But Sera himself called his approach to painting Chromo-Luminarism because of his great attention to color and light. So this painting, the Poiseuse, was a manifesto of neo-Impressionism. What he's doing here is tackling the very ancient subject of the female nude. And so he's throwing down the gauntlet. He displays this work in the 1888 Salon des Andependons, and this is a very deliberate repost to this criticism that pointillism cannot be used for the figure. So he says, fine, you think that I'm going to paint three nudes. This is a reference to the classical theme of the Three Graces, in which generally there were three nudes that were viewed from the front, the side, and the back. And Sera has very much quoted various figures from art history and ancient art. The figure on the right that you see with her leg crossed over is a reference to the ancient Greek figure of this binario, this figure that is removing a thorn from his foot. The figure that you see on the left is a reference to one of Angela's bathers from earlier in the 19th century. So this is a painting that is very much cognizant of the weight of tradition and art history. But what he does with this classical theme is turn it into a genre scene of modern life, into this scene of these skinny working class women getting undressed in his studio. Now the composition was very meticulously planned. There are studies, there's sketches for each figure. And to tell you how some things have changed, the sketch for the figure in the middle, she was originally posed so that those rectangular prints or paintings on the wall were right behind her and framing her head. But in the final painting, of course, you can see that Sera has put her smack dab in the middle so that she is kind of this vertical element joining the two sides of the wall together, right? She's right in the corner there. He also changed other details like in previous sketches, the couch that the model on the left sitting on was blue and now it's red. He made the model on the right, her stockings, this amazing bright green. The model was originally wearing black stockings. So you see he's playing with different color harmonies and different color contrasts. So this painting is not about spontaneity and even in the way the figures are so stable and hieratic, you could say, and static, some critics kind of read them as very stiff and very wooden. There's something almost lifeless about them. And what's really interesting is the way Sera contrasts that with the comedy of clothing below them. The accessories below them seem even more alive than the figures themselves. Let's look. I love, for example, this glove, it is so creepy. It really looks like it's crawling out of the painting and in like this claw that's about to grab you. And then there are the shoes, these fun shoes that are also kind of walking out of the painting. They're very lively. These two hats here, these are fun. They are this dark hat and this light hat that are facing each other at this very jaunty angle. They are like in conversation with each other in dialogue. And this is all very deliberate. You can see that all these accessories, the parasol, the ribbons, the clothes, they are all painted with these dynamic lines, which are very much in contrast to the very stable, calm lines of the models themselves. So Sera actually attached emotional associations with lines. And so these kinds of dynamic diagonals were supposed to be more joyous and versus calm, stable verticals and horizontals were about tranquility. So there is a sense that he is attaching emotion to line and color also. So these accessories point back to the fact that this is a scene of modern life. These are not three graces, but they're three working women, modern kind of urban Parisian women. So this is a scene of work. And this brings us to the title of the painting, Poses. It's a pretty idiosyncratic title. It's translated into English as models. But Poses is a really strange word because generally in French, if you want to say the word model, you would say model. Poses has different connotations. In the masculine form of the French word poseur, that can be used to describe a manual laborer, like someone who is laying down brick or laying down your parquet floor or something like that. But in the female version of the word poseuse, it also connotes someone who is striking a pose, someone who is putting on airs, someone who is pretending to be something that they are not. So what Sarah is doing with this title, Poses, is pointing to the artificiality of the painting and the artificiality of these scenes of modern life, that these are constructed scenes, including the grand draught, this great painting that was of Parisian urban leisure. By placing the models in front of the grand draught, he's setting up this contrast here. There's the contrast of the clothed and the unclothed, the interior and the exterior, the idea of real versus fictive space. So one detail that I love to illustrate that point is you see this dog in the corner there on the left. That dog is part of the painting, but the dog looks like it's leaping and about to grab that parasol handle. The parasol handle is part of the quote, unquote, real space of Sarah's studio. So he's really playing these little visual jokes and thinking about these contrasts. And the fact that he is posing the models in front of the painting also, he's inviting this implication that these are the same models that posed for the grand draught, especially that central figure there. You get the sense that there is a rhyming or a parallel between her and that main figure in the grand draught holding the parasol. So Sarah actually identified as a worker, the way these models were also workers. When a collector inquired about buying this painting and wanted to know how much it was, Sarah replied that he would charge the equivalent of a year's work at a model's day rate. That's pretty interesting. And this idea of labor is actually echoed by the technique, the materials that Sarah used to paint this painting. The painting is apparently done on gesso made with plaster. This, well, for one thing, it gave Sarah a very white surface to work on, which was really important for this pointillist approach to painting of applying pure color. This is as opposed to in old master painting, you would start with a ground of a kind of mid-tone, a brown tone, so that you could work up to the highlights and then work down to the darks. But Sarah wanted to start from a pure white surface. But the other thing about this plaster gesso surface is that it is a very hard surface to work on. It made the painting a very slow-going process, and it gave the sense that painting was a form of manual labor, you know, going back to this idea of the posers, this manual laborer. So I guess what I am trying to say about this painting is that it is as much as it is grappling with tradition and history, as much as it is a highly intellectual statement that has inspired many fascinating interpretations that I can't go into in this short video. It is also grounded in the idea of handcraft and in the reality of Sarah's world, you know, the day-to-day of regular people's lives. So I think of it as much as a tribute to art and history as it is to these regular working women who made these pictures possible. So that wraps up today's Barnes Takeout. I hope you enjoy another episode, and thanks for watching. Subscribe and make sure your post notifications are on to get daily servings of art. Thanks for watching and for your support of the Barnes Foundation.


Hello everybody! This video will show you how to create an Oracle VirtualBox virtual machine and how to recover data from a virtual disk of such machine after a failure or when the virtual machine can't start. In order to create a virtual machine, install and run Oracle VirtualBox. Click the New button in the toolbar at the top of the window. In the Wizard window, enter the name of the new virtual machine. The operating system type that you are going to install – in our case, that is Microsoft Windows, version Windows 10 64-bit. Please note that you can install onto your virtual machine not only Windows but also other operating systems including Linux. Next, assign the memory size you'd like to give this virtual machine. It is recommended to have at least 2GB. I allocate 4GB because my computer has 8GB of memory installed. To make sure the virtual machine is not going to be too hard for your PC to handle, I recommend setting its memory size at no more than 50% of the actual memory available. Next, select Settings for the hard disk of the virtual machine. You can create a new one or connect an existing one. I select Create a new virtual hard disk. Create. In the next window, select the virtual hard disk type. By the way, there is also an expert mode button, clicking on which you can access more hard disk settings. Now let's get back to virtual disk types. A user can choose the disk format for the machine being created. By default, it is VDI. If you don't need to use the disk with other software virtualization products, you can leave the setting as it is. But if you expect to use the disk with other virtual machines, please remember that VDI is a disk format compatible with Oracle VirtualBox. VMDK is compatible with WinWare Workstation, VHD VHDX with Microsoft Hyper-V, HDD is a Parallels disk format, QED and QCOW are the formats for QEMU. In this window, you can also set the size of the disk you are creating. The size can be anything you like, but it's better to have at least 30 gigabytes and make sure that your hard disk still has enough space after you create the virtual machine. That is because your virtual machine may become larger in the course of work. You can also choose a format dynamically allocated or fixed size. Dynamically allocated option is more efficient in terms of saving hard disk space, as such virtual disk will grow when more data is saved there. Next. The virtual machine is created, and we can see it in the list on the left. All of its settings are shown on the right. Run the virtual machine by clicking on Start, shown as a green arrow in the toolbar. As our virtual machine has no operating system installed, we have to show the path to the installation disk. By default, it is the CD-DVD drive, in our case disk D. However, by clicking on this icon, a folder with a green arrow, you can change the path and select an ISO image or a USB drive instead. Watch another video on our channel to see how to create a bootable USB drive to install Windows. Find the link in the description. I specify the path to the ISO image I have prepared beforehand, next and after that Windows installation starts. Installing Windows on an Oracle Virtual Box virtual machine is similar to making a clean installation on a desktop or laptop computer. That's why I won't go into details. Just watch another video by our channel to see how to make a clean installation of Windows. You will find the link in the description. After the operating system is installed, you will have another, that is, virtual operating system that works inside the operating system of your computer. All files of this operating system, both system files and user files, are stored on the computer's hard disk. By default, this folder is named virtualboxvms and can be found here. See users username virtualboxvms. Note, you can change the location of the virtual machines folder in the program settings if necessary. To do it, go to File -> Preferences -> General and specify the folder you want to place virtual machines in. The main files of an Oracle VM Virtual Box virtual machine are as follows. Vbox. A settings file describing the virtual machine and its settings. And actually the virtual machine disk image file, VDI or VMDK, VHD, VHDX, HDD, QED, QCOW, depending on the format of the virtual disk that you have selected during the installation. While the virtual machine is running, the program can create additional files of folders if necessary. Logs. The folder containing virtual machine log files. And snapshots. The folder containing virtual machine snapshot files. That is images of the virtual machine condition. In this video, I want to show you one trick just in case if for some reason your virtual machine is no longer working and there are important files in its disks. I will show you how to recover them. Although Oracle Virtual Box is a virtual machine, real data can be saved there. As we have already said, all files stored in virtual machine disks are located in VDI files of the virtual disk, in our case. Hetman Partition Recovery, the program for hard disk data recovery, has the function of mounting virtual disks and recovering data from such disks. You can download Hetman Partition Recovery following the link in this description. In order to get access to virtual machine files, run Hetman Partition Recovery and mount the virtual machine disk. If there are several of them, you can mount them all at once or one by one. To mount a virtual disk with the help of Hetman Partition Recovery, click on Mount Disk in the Quick Access menu of the program. As a result, the window of choosing a virtual disk will open. Go to the folder containing the virtual machine and choose the necessary virtual disk file. As you do that, the section Mounted Disks containing the list of mounted virtual disks will appear in the window, where all disks found by the application are shown. If you mount several disks, you will see the entire disk list there. Scan the disk with the application by clicking on the disk in the Disk Manager. After the analysis, the application will show the directory tree of the scanned disk. Select and recover the necessary files to a convenient location in your main operating system. By the way, you can use this method to move files from a virtual machine disk to the main operating system. The methods we have described for recovering data over Norco VM Virtual Box Virtual Machine are good for virtual machine regardless of their virtual disk format. VDI, VMDK, VHD, VHDX, HDD, QED, QCOW and they will be absolutely similar for all of these virtual disk formats. In this video, we have used the VDI virtual disk format as an example. I also recommend watching our previous videos on VMware Workstation and Microsoft Hyper V virtual machines. You can find the links in the description of this video. That is all for now. If you liked this video, hit the Like button below and subscribe to our channel to see more. We'll be glad to answer any questions in comments. Thank you for watching and good luck.


What's the word, y'all? The NBA regular season is close to over, man. It's so weird to say. I'm so excited for a playoff basketball, but it's so weird to say that this season is almost over. I feel like it flew past, man. Just feels like just yesterday the Bulls were good. And a lot of things have changed. A lot of things have changed. And once we get to this part of the season where it's like 10 games left of the NBA season, I started to take my NBA award ballots so very seriously. I'm not a dude that has an official vote because while with this random YouTube, I have one, but I deal with it as if I did have a vote because I understand the stakes involved in all of these things, right? And I just dream of one day I get a call from Adam Silver and he gives me an official ballot and I can send him back a resume of my NBA award winners from every single season, whether it be in video form or written form. And this is the reason why this is my MVP and this is the reason why this guy's second. This is my sixth minute of the year because there are some people, let's be honest, there are some people that have a war or votes that don't watch the game as bad a basketball as closely as me and you. There are some people that don't take it as seriously as me and you. Like every single year you can look at the ballot and be like, why this person get a vote? Who voted for this? I'm not that dude. You know what I'm saying? I think about things. I go into statistics. I really take this stuff seriously.  And this year might be harder than the last because I feel like we're like two months left for the season. Yocates had wrapped up the MVP award last season. This year, I'm still flip flopping, y'all. I don't know who it is. I think it's still a three-man race, but Honest is probably a two-man race between the two centers up top, whether it be Yocates or Embiid. Every single award right now is up for grabs. I'm not up for grabs, but like I haven't made my definite answers. Except for six-minute a year. Spoiler alert, it's Tyler Hero. But like, rookie of the year, you can make a conversation for Cade, Mobley, and Scotty Barnes. Defensive play, don't even get me started on defensive play of the year. There's like seven people on my ballot. Nobody jumps off the page from me right now. So it's just so very difficult. And I woke up this morning and there was a lot of conversation, whether it be on my timeline or my mentions, about Devon Booker and the MVP Candidate C. And I'm here to talk about that. Or one of the things we're talking about. This is a hot spot. Just thoughts, you know how this channel works. So I think the first time I heard somebody in a national media or people on the timeline talking about Devon Booker as an MVP candidate was just two days ago. A lot of things have ramped up, or three days ago, a lot of things have ramped up with MVP conversations around Devon Booker in the last 24 to 48 hours because he had the big game against the Minnesota Timberwolves and he just had 49 last night. But a couple days ago, Isaiah Thomas, and I mean Zeke, not sure King, but NBA Hall of Fame Isaiah Thomas said, let it be known that at Devon Booker should be an MVP conversations. Best record lead and score has always been a part of the hashtag criteria. And then Devon Booker quoted that as said, appreciated IT, but the criteria change is dependent on players' names. And why I don't completely disagree with Devon Booker here. I don't think it has a lot to do with the names. I don't think there is a national vendetta against Devon Booker or Slas the Phoenix Suns, but I do think the criteria change from year to year. There are some years where it is the most dominant Slas best player on the greatest team, and some years it's about the person having the best statistical season. And what I am realizing is, the years that it is the best player on the best teams, it usually coincide with nobody having a historically great whether it be advanced stat season or counting stat season, for example. So I've referenced the Russell Westbrook MVP season a ton on this channel because, A, it was one of the best seasons for fans. It was just a crazy, crazy year. And B, I think that this really fits what I'm trying to say. So Russell Westbrook became the first player since Oscar Robertson to average a triple double. It was a 30 point triple double where his team exceeded he won MVP, even though his team was a 60. Well, the best team in the league was the Warriors who won 67 games this season. And Steph Curry was still averaging 25 points per game, six and a half assists and about five rebounds per, but he ended up six in MVP voting. So the Warriors won 67 games this season, y'all. Think about that, 67 and 15. And their best player ended up six at MVP voting. The criteria changed because Russell Westbrook was doing something we had never seen in the last 40, 50 years of basketball. This also is a bit of a voters for T because they were just 73 and nine last season. And he had been a unanimous MVP the year before that. And then it left a bad taste in our mouth when they ended up losing their finals. But either way, he was still putting up MVP caliber numbers on the best team in the league and ended up six. Also, Kevin Durant was there. Forgot to mention that. And then we have the year after that. And this was the James Harden MVP award. And guess what? James Harden was the best player on the best team. And the reason why he did this because everybody beneath him whether it be LeBron, Anthony Davis, Damian Liller, Russell Westbrook, everybody that got a vote, none of them were having a crazy historical season. They were still having good seasons, very great seasons, but none of them were breaking all-time records. None of them over the advanced stats had at the best season in NBA history. So James Harden still putting up really good stats. Basically, the similar stats to last season ended up number two and his team being better and his team being the best in the league, he won the MVP award.  He was having a crazy historical season individually and they still were the number one seed. So he fit the both, both standards that you get. Then we get to last year where Yolkic and Yolkic just had, I think it was the highest PR season of all time. And guess what? This season he is better than he was last year. So the criteria, as you can see, has changed. Sometimes it is the best player on the best teams and sometimes it is somebody having a historically great season. It's unfortunate for Devin Booker that he fell into a season where Yolkic and indeed are both having historical seasons one way or another. I mean, Devin Booker in the last 25 games he's averaging 30 points per game, five rebounds, six assists, 50% for the field, 36% for three, 88% for free throw line and they are 21 and four in those 25 games and they are nine and three without Chris Paul. His resume is great, but it's still not gonna be compared to those other top three dudes. I think it's okay to say that Devin Booker is having one of the best seasons in the league without saying that he should be top three in the MVP. One thing I do hate about the NBA and this might just be a basketball reference thing is that his season might get lost in the grand scheme of things. This is Stephen Curry's basketball reference, by the way. You can see Stephen Curry is great. You can see that he has his two MVPs. He's got his all his accolades up there. But one thing that I wish was on basketball reference I guess in the NBA thing is the same thing we get on baseball reference. Baseball reference don't care, bro. A-Rod back in 2004 was 14th in MVP voting and that's on his award list. I think there are gonna be so many NBA seasons that are lost to time because it's hard to put into perspective how dominant or how important that MVP award was. Let's say MVP 28, and I wouldn't go this far. I wouldn't go this far. But I would love to see on basketball reference or just in NBA in general, they're like, hey, Devin Booker finished fifth in MVP voting this season. And you can see that in 10 years to say that, oh, Devin Booker was really that man this season because I feel like context is definitely lost as we get further away from something or an event happening. They really get A-Rod 28 in MVP award here. Jesus Christ, that's actually insane. The basketball has one of the least amount of awards amongst the major sports, right? And baseball, you have basically a split between the conferences. I guess football also has a split between the conference and I've been hearing a lot of people tried to have the conversation of the NBA doing that because the NBA is becoming such a talented league. I mean, it always has been a talented league, but more talented than ever before that it feels like we're neglecting some great players because they can't get these awards. We're in baseball and football. You can divide it into different leagues. You know, overall, I wouldn't even consider myself an NBA traditionalist. I would say I'm an NBA progressive. I like the idea of changing rules every once in a while to see how it works. But I don't really like the idea of splitting it down the middle and say that this is our Eastern Conference MVP, our Western Conference MVP because I think it just loses value just a little bit because we will have those years where like, let's say, Rookie of the Year, for example, the top three Rookie of the Year candidates this year are all in the Eastern Conference. We're gonna have Josh Giddy when Rookie of the Year, which is fine. I guess it will be fine if he was a Western Conference Rookie of the Year, but that ain't really nothing compared with the top three are actually doing an Eastern Conference. Another argument I guess is that basketball is a 15-man sport on the roster with football as 50-something people and the baseball was at 20-something people. So there's a lot more candidates for these awards when basketball, there's only 30-point guards to get a start and there's only 30 centers to get a start. So I don't like the idea of splitting it up, but I keep hearing people have that conversation so that Joelle and Bede and Yolkets both get recognized as the best players in the league, the best season in the league, but I'm okay with somebody has to come in second. You know what I'm saying? We don't have to give everybody a participation. It's okay to have somebody come in second. Maybe Joelle Bede comes in second for a second in a row that fuels him next year. He's undisputed at the NBA MVP or maybe he jumps the, he jumps Yolkets this season. Like, oh, last year him coming in second was the reason he ended up first this year. He used that in motivation. I'm okay with people coming in second, y'all. It has to happen. I treat this channel like my main channel now. 2K is such a terrible game. I was going, I'm gonna keep it PG. It's such a terrible game that I just don't have fun recording. I do it to pay the bills, you feel me? But this of course is what I'm more passionate about. So I'm gonna try my hardest to treat this as a main channel since then. I'm gonna try to upload way more often. Some videos are gonna be random rambles like today and some videos gonna be recaps or some videos gonna be lists and stuff like that. I just enjoy talking the game of basketball more than being on the sticks and making trades for people. I think I made my first, I made my first rebuilding video on like 2K14 and we're in 2K22 and nothing has really changed. You know what I'm saying? I've grown older, my interests have changed and my interest is basketball in general. It's unfortunate that 2K has a monopoly on the gaming section of basketball because I feel like there should be some people competing to beat them, but it won't happen. So let me know in the comment section what you think, what in your opinion, I'm gonna have a question of the day at the end of all these videos, if I remember. What is the hardest award for you to determine? Is it Rikki of the Year, Defense Player of the Year, MVP, maybe you think somebody is in conversation with Tyler Harrow for some reason? Cause matter of fact, if you think, you bugging if that's the case though, ain't nobody even mess with Tyler Harrow. You feel me? We can be arguing about two through five, but nah. I think this year I'm gonna treat it as in baseball where like instead of giving you all my top three candidates of every award, I'm going 10 deep. Yeah. Yeah, so be looking out for the official Kenny for Real awards ceremony. We gonna get some awards that aren't in the NBA, just the Kenny for Real All-Star type thing. Kenny for Real, MVP's. Well, I gotta think about that. All right, we got a lot to do.


What's up everybody Jason here for Jasper's case breaks.com. This was our last filler guys We're gonna give away the last four teams team random spots for prison picketing number one That'll officially sell out the break guys with this 2021 prestige football hangar box break number 43 And we're giving away six spots in this filler here So we're using dice roller here randomized the 28 or 26 customer names Yeah, 26 customer names the top six get a spot in this filler to get an additional chance to winning a spot Then we'll do the randomizer for the names and teams and then at the end We'll get the list of names that bought in again Randomize them with the winners of the giveaway here and then top four get a spot in the team random So let's roll the dice and we got ourselves a two to three five times so top six I believe get a spot in this break one two three four and five five times two three five Sean Carl Maxwell Carl Carl and Alan Murdoch. So there you go. So two and a three five times top six Congrats guys So you guys want an extra spot in this break to increase your chances of winning a team random spot just for buying in guys so let's put a character sign there and Then let's copy and paste this list for later over here. Just we have that ready already All right, so five times down here one two three four five shawmatic down to Carl five times here one two three four So again, stick to what you got guys. No trades. All right guys. Yes. So here we go. Skip this is all base up until right here this space All right guys, so switch things down and here we go roll Two or three and a two five times again and top four get into the team random guys. Good luck five times one two three four five Boom five times Top four guys. Good luck So sad times from five Alan Murdoch down to Adam cuppermen. Thanks for giving it a shot But happy times now for top four Carl Scott last spot mojo Shawmatic and Carl two out of the four they go So boom congratulations guys. So let's paste your names right here Perfect coming up next guys is the team random Jasper's case breaks.com


Hi to everyone, and thanks for coming. There are two things. The first one is that we are going to, well, Amleto here, is going to explain an app for your mobile. It's in beta phase, and he will explain better than me. But he asked us if we could use it and try, because they are starting, they have this initiative, and they are starting, they work with different tabs. And we thought that it's good to support local initiatives, and we thought that it might be a good thing. So we are going to try for some time, and I think that it makes a good appinta. And if you feel so, we will send information on how to download it and install and so on. And I think that any comment you can send him an email saying, oh, this is not working or whatever. But OK, that's the thing we're going to try. So first, Amleto, he's going to make a small introduction about the app, and then Carlos Castillo is going to make the presentation about crisis situation using big data. And he will introduce himself better than me, but he's the director of data mining at the Urecad, which is a research center here in Catalonia. And he's done many things and many interesting things. So I hope you enjoyed the talk. Thank you. Thank you, Leish. We appreciate it, what you're doing for us. So I'll talk a little bit about Flutter. It's going to be quick. But how many of you have been to Meetups before? How many of you have met a Meetup more than five people? How many of you have actually left a Meetup thinking that actually they missed out on meeting great people and they didn't know who they were? OK, good. That's Flutter. We're building a company. It's a platform. It's not just an app. But what we are trying to do is actually allowing communities to come together. And I actually go to quite a few Meetups, professionally based Meetups, things about business. And many times I leave over a conference too. And I think, OK, actually, I'm missing probably a lot of the interesting people I could meet. I don't know who they are. I just generally talk to the people that are next to me sitting next to me or drinking a beer next to me. And I think it's a shame. And the other thing is that generally between Meetups there's a lot of knowledge that could be shared. But actually, it's impossible if you don't know everybody that is in the Meetup. And for example, the Meetup forum or the comments doesn't work. So what we are trying to do is try to build a platform where professionals that share same passions could actually interact and also meet each other. And then the idea is also to expand that and allow different communities in different places to share the same sort of topic to meet each other, virtually and hopefully physically. So this is the beta version that it's not beta version. You'll see it works extremely well. But basically, there are a couple of functions. One, it's members geolocation. So for example, if you download the app, you can see everybody that is here. And you can see their profiles and then connect with them. And the idea is that way you could actually meet people that might be useful for you to work with or might be interesting to meet and to have a discussion with. But then if you see point number two, that's point where you'll find the professional communities. So now we have a few professional communities in Flutter. You will find the machine learning, Barcelona, BCN in there. And the idea is, you'll see later, but you can share things. You can share doubts, questions, everything you want, articles and everything you want. And then if somebody's posting, that gives access to a professional, as to a profile, a clickable profile. So you can see who that person is, the LinkedIn profile, and decide if you want to meet them or not. One other feature is actually the members feedback.  So the idea is that if somebody's spamming and making advertisements, you can download, download, download, and that will send flags to us and will remove that person from the group. That is the idea. It's pretty harsh, but it's necessary. And the other thing is you can put, if somebody you see it's posting good things, the idea is to upvote them. And then what we want to do is basically a weekly digester and a monthly digest for the best posts, the ones that get more upvotes, get sent out and shared with the community and other communities too. There's quite a few more functionalities, like chats, you can see in there, but the most important ones are these ones. The first one is around me. So there's a functionality in it to see whoever is around you and decide what you want to do. Now in the app we have about 100 people and there are actually some very interesting profiles. So the first one, you know it. It's Alish. The second one, she's a data scientist that we met and she has an amazing background. So once you have the open, I suggest you to go in and look for her. And then the third one, she is a guy that actually built a company in San Francisco, sold it to Apple, worked for Apple two years and now he's here in Barcelona and he has an extreme wealth of knowledge about everything that is going on in the valley, if you're interested. The second part that is interesting is my feed. So for example, there I shared an article that I found extremely interesting about deep learning. There's a lot of debate on deep learning and AI, but the idea is that you can share anything. If you want to share an article, ask questions and the community is the thing for the community to learn. Our objective is for people to grow in their passion and topics by interacting with other professionals. So that's a whole objective of Flutter. So I mean, a few things. There's definitely direct communication that generally you miss between meetups. There's continuous interaction that you can have with other people that participate in this community. You can establish new connection, but then the idea is to grow the community because if it becomes a kind of an ongoing thing, more interesting people will come in because they know of this great community and more interesting will become for all of you too. So as I said, grow the sense of community. The whole objective of it is to create quality interactions and that's what we're trying to do and there's no cost and it's free for everybody. We are not looking for money at this point at all. So I mean, this is Flutter. If you want to download it, I actually posted two things in the comments of the meetup. One are the Wi-Fi details, so you can connect to the Wi-Fi, you can get a good connection and then you need to go to flutter.ine, like fluttering will be. And then basically you put your email address and we send you the invitation out and you can download it, yeah. Okay, thank you. Another thing, we are not a very large crowd, so don't be shy and everything that you want to ask, please ask Carlos and it's quite informal, so don't be shy and ask. No, it's okay. Okay, good afternoon. So yes, as Alex said, we can make this more conversational and interact me and I have a ton of slides and it doesn't matter if we see only a few of them and then we discuss more and learn more. So this talk is not about machine learning, it's about applied computing and where there are some elements that are from machine learning you will see. It's more like how you can apply some very simple techniques to a very interesting problem. So I work on crisis data and disasters data, so I always look for emergency exits. This room has four, two in the back, two in the front and one bicycle here. So if there is a fire, you can jump over the bicycle and then exit through the emergency exits. Gonna be fun. I study the information retrieval and I work on a lot of things that are not really much interest to you right now, but I think what's really interesting is that most of this work I did while I was in Qatar. Qatar is the tiny peninsula attached to a larger peninsula, which is the Arab Peninsula. It's an Islamic state. It has the largest GDP per capita in the world, far exceeding the one of many European countries. It is very secretive about their demographics, so that's why the question mark are there, like 1.5 maybe, blue color workers and then about 500K white color workers, 300K citizens. They have a big, they put a lot of importance in education and science and this here was at the end of 2015, mid-2015, the social computing team there and this was the best they had in Qatar. It's a desert actually, so there is very little to do and the best you can do is you stand in the desert and look at the sea. This is the skyline of Doha, which is the only city that is there in Qatar. It's a very interesting place and most of this work was funded by them. Now, bringing us back to the topic of the talk, if you remember what happened in March this year, there were these explosions in the Brussels airport and then there are many things and as you, as I mentioned in the talk in DataBeers, it's interesting to see what the police asked to the people who were in Brussels. So the police asked them to use social media, not to use the phone, they asked them to avoid streaming when it was possible and they asked them to avoid sharing real-time information about police actions, okay, three things. Now this being the internet, of course, people will do exactly the opposite, so you will see a lot of information in real-time. This is unavoidable and this happened like a few hours after the attacks. I took these screenshots and you can see almost 3,000 words in the Wikipedia article. Now if you look at the Wikipedia article for the Orlando shootings, there is a lot of information there, there are lots of references, many YouTube videos, Facebook pages, images, photos, a posting in Reddit with 17,000 comments and so on. There is a lot of data here. Now the talk is about disasters. I'm gonna tell you more about the domain. I think it's interesting to look a bit more in the domain and then you can think of what are your ideas in which, the ways in which maybe you work and contribute to this domain, about social media, about computing and so on.  Maybe you have watched it, but it's kind of what you can do with the social media. Now if you think of this, if you try to do the math of this thing, okay, can I receive actually a tweet before I get a shock waves from an earthquake? The answer is more or less yes, right? So you have some speed for the seismic waves, you have some speed for the tweets and one is much faster than the other so you can have like after 100 kilometers some messages about an earthquake overtaking the actual earthquake. Now I started to work on this topic in January 2010. I was in Barcelona at the moment and there was this big earthquake in Chile. This is in Concepción in the south of Chile and I called some of my colleagues at the University of Chile, it was my alma mater and I asked them if they were okay and if their house was okay and I also asked them if they wanted to do a paper out of what happened, right? And they said that something that was very interesting to them was whether social media and Twitter in particular contributed to the chaos that followed in some areas, the earthquake and to the feelings of unsafety of the people during the earthquake and so on. So there were many research questions there. We eventually ended up working in some of those questions. I will show you in the rest of the presentation. This research belongs to an area that you may or may not have been in contact with which is humanitarian computing which are all these applications of computing to humanitarian issues like analyzing and managing a crisis, seeing how mobile phones can be useful crowdsourcing, human computer interaction and NLP, et cetera. So no matter what field are you coming from maybe there is something for you to contribute to these areas. And of course in all the field of humanitarian computing topics, social media studies is the part in which this talk is mostly focused. So we have here all the ingredients for doing interesting applied research. The problem is of global significance because everywhere there are disasters. In Europe the most damaging disasters are floods. In other places are typhoons, hurricanes, earthquakes, drought and so on. The current way in which people make sense of all these thousands of comments and thousands of tweets and videos is mostly manual. So there is a lot of, there are attempts to try to make sense of what the public does during a crisis but they are mostly based on monitoring the Twitter feeds and Facebook pages and so on. So it's manual. So we can do it automatically. If you provide a better solution you have a public good. We have data sets. I will point you to some data sets. And there are also volunteer communities. And all that, there is a tiny detail that remains to be tested is whether what we can provide is relevant to practitioners. Of course it's not a tiny detail but it's a lingering question over many of the things. In some cases I would claim that we have actually made a difference. In other cases I would say it's not clear that we are providing anything better. Now this whole research program of trying to understand what the public says during a disaster in social media is not motivated by what is the best way of facing a disaster. It's not that we have emergency responders or humanitarian agencies telling us, okay, what do you think is the best that we can, the best way in which we can improve disaster response and we said, hey yeah, Facebook and Twitter are your best allies. I don't think that's the case. This research starts from the other side. It starts from, okay, there is a certain phenomenon. There are these thousands of messages, photos, videos. What can we do with them? There is certainly something we could do with them. Then you can say, well, it's very noisy. It comes from the internet. I don't know if it's true or false but this is the life we live. We navigate these questions all the time. It's mostly about whether there is this object which is like this activity can be mined in real time for something useful and this is the question that we are trying to answer. And many collaborators that I'm very thankful for people like UCRI, EPFL, Daily and Microsoft. Now, the point of this talk is disaster. So, I'm gonna define disasters. Disasters are disruptions of routines. A disaster is anything that changes the routines of people in the city. And hence, it's socially defined. What is a disaster in one place is not a disaster somewhere else. Here, in Beirut, there are three hours of electricity per day. And there is not a disaster that you have 21 hours without. In Barcelona, 21 minutes without electricity will be a significant disruption of routines and hence, could be called a disaster. Something interesting for trying to unfold like what is this data that one can work with is to understand that the people who study disasters, they tend to classify them according to several categories. One of those is whether this is a sudden onset disaster or something that progresses slowly, whether this is vocalizing in space, whether this is natural or induced by humans. And there is, of course, the observation that a lot of people say that there are no natural disasters. I mean, the river may have flooded, but you choose to build a city next to the river. So, it's not really entirely natural causes. Here are some examples. Most of what we know about disasters is heavily modulated by what we learn from movies because we are exposed to a few disasters during our lifetimes, but there are not really very genetic cases and we don't have enough data points to generalize well. So, we tend to, I mean, this is what the sociologists of Disasters tell us, is that we tend to reason about emergency situations and disasters based on what we see in movies. It's very influential. So, have you seen the movie on the left here? Anyone? You have seen? You saw it in the entire thing? Is it good? No, it's not. So, the movie is called Sharknado. And it's about, as the name suggests, a tornado that grabs sharks from the sea and throw them over a city. Is that right? I have seen only the good parts of the entire thing. Now, the right side is a real disaster. These are floats in Brazil. Of course, they look very differently, but I'm perhaps speaking a very particular data point. This is more interesting. So, the left frame is something that no movie director would ever film, most likely. I have never seen a scene like that in a movie. So, you have an airplane that is in flames and people are kind of walking very matter-of-factly with the bags. There was a lot of criticism against these people, right? Also, because the instructions say, leave your luggage behind and abandon the airplane and everybody is carrying their backpack and nobody's running or anything. And the right is from a movie. I don't know which movie, but it's more like a disaster. A more, you would say, this is a more faithful portrayal of what is a disaster, right? And I would claim, no, it's because we are used to see disasters in a certain way. This is relevant for what we can expect. Like, in real life, it's true that some people panic, but a lot of people don't panic. They gather information from sources that are familiar to them, the people who are around them, mass media, social media, phones, whatever they can grab, whatever we can grab when there is a disaster situation.  And a lot of people improvise these rescue operations like on the spot. So this mindset in which you say, well, the public is actually actively trying to get themselves out of trouble or solve the disaster, is a very peculiar mindset that is not the mindset of many emergency management organizations and governments and so on. So as a researcher, you have to first like to say, okay, I'm gonna kind of try to free myself from this view from the movies that people running circles, panicking. I'm gonna say, well, there are some people who panic, but there is a lot of people who are actually very capable of getting at themselves out of trouble. And the way in which they do it involves the usage of communications technology and information technologies and social media. These are some messages that are handpicked by researchers that from the crisis informatic area have studied this problem. These are handpicked because the majority of what you read in social media as more equally to the majority of the books, the majority of the music and the majority of the internet is crap. It's very little, there is very little information out there that is repetition and so on, but there are these very peculiar messages that are very informative because they contain something that you cannot get from other sources. You do not know, maybe you do not know exactly what's the height of the water in a place. Maybe this is one of the messages that broke the news when there were these attack in Norway. So the first news about this disaster were actually posted in social media. So then the question is, can we try to very quickly, as this thing happens, detect immediately that something is going on. Now the scale of this thing is actually large but not so large. So it's large but manageable. It's large for humans, but it's not large from, let's say, like a big data perspective. So the day Pope Francis was elected, the peak rate in Twitter was 2,000 messages per second.  So it's not really a ton of information, right? It's a small quantity of information. Now what happens is that it's coming very fast and in those 2,000 messages about the Pope elected, maybe there is some interesting information. Maybe somebody started shooting someone. Maybe somebody planted a bomb someone. Maybe there is a reaction that is very important that one need to hand pick from what's going on there. The same, now in typical disaster situations, you observe like hundreds, 500, 1,000 tweets per second and much more in videos, Instagram photos and many other sources. So I work in this group when I was working in the crisis informatics team until next year, we were trying to collaborate with the UN and with other agencies trying to answer like two sets of requirements that are very different and maybe they also have appeared in applications for data mining that you have built that use citizen data. Some people want to, the second point is the easiest. This is what we tend to focus on when we build these applications, not only for disaster, like in general when we do mining of public information, citizen information, there is a lot of focus on the second part, which is okay, I have like all these reports from a city, all this information. I want to find like two or three best restaurants. I want to find two or three places where people need water in this catastrophe. I want to find where the shooting is taking place and so on. So I want this actionable insights and there are some groups of people, for instance the police, emergency services, medical services that are deeply interested in this kind of requirement. They want to know this actionable insights and your job is to take this data stream and select those. But there is another part that is much less explored from a data perspective, which is to try to understand the big picture. So there are agencies that ask us like a very, in a very direct manner, okay, can you tell us like in five minutes after the typhoon passed through the capital of a country, can you tell us more or less how many people were injured or how many houses will need to be rebuilt or what is the amount of damages that the typhoon caused like based on these preliminary observations? The answer is no. I mean, not yet, but then the question is then what is the right mechanism to do something like that? So can you tell us from, in the case of other types of application, could you be able to tell from all the social media activity around Barcelona whether employment is gonna recover next year or not or whether there is a, whether house prices are going up or down? Like all these kind of big picture questions have received much less attention than the questions related to kind of actionable data points. Of course, you can build a big picture with the actionable data, with the data points, with the data points, but perhaps there is some way of modeling this directly. Okay, I'm gonna pause. Is there any, I haven't given you time for questions, but is there any questions so far? Comment, anything? Okay, I will move over there. So the typical thing that you do when you are facing, like okay, coming back to the disaster scenario, you have this stream of messages about the disaster and you want to do something with them, right? So the typical reflex of a data mining is to classify things, of course. Now, for classifying messages, you really need to be able to do some text mining. So how many of you work in NLP, Natural Language Processing? So anyone? The rest, a bit with text? One, great. So I think what's, what I would say is that most people that work in machine learning are not afraid of text, but I would say people who work in computing in general are afraid of text. People tend to work only with data that they can, that is kind of readily available, like people tend to be very comfortable working with a data table and now with nested columns. There's no problem, like some structure, time series, people are very comfortable working with that. People are deeply uncomfortable working with text, particularly computer scientists and computing engineers. They say, well, I'm not an NLP person, so, or I don't know how to process text. I mean, maybe not you, but I've seen a lot of people who are reacting this way when you tell them, okay, let's try to understand what people are saying in this city, what people are saying in this disaster. And the truth is that you don't need to know much to work with text. So there are many tools, there are many well-established methods, there are mature software libraries, there are libraries for doing anything, like dependency parses, identity extraction, linking. There are lots of tools that are readily available in case you want to understand text, which brings me to the Chinese Room analogy. Do you know it? Have you heard about this thing, Chinese Room, yeah? Okay, that's enough for me. I'm gonna skip it, but if you want to some interesting reading, take a look for this thing. It's really interesting and you get a better sense of why I'm telling you that you shouldn't be afraid of text. Other times, you do need to be afraid of text. So for instance, take this kind of message that is posted in social media. So try to parse it with your 11,000 million neurons. See what comes out of it. So to understand this message, first this is probably beyond the reach of a computing algorithm, beyond the reach of most people, unless you're familiar with Indian politics, because there are lots of context information here. So there is a China NC, it's a member of the parliament. RSS is not the party of China NC, but it's a party that has close ties to the party of China NC. And the photo on the left, which represents a party in Malaysia going to help in Nepal for the earthquake, is actually an old photo. It's a photo from two or three years ago. So in order to understand this, you need a lot of context, it cannot be done independently. So understanding social media, like trying to extract meaning from this content is very difficult. You are basically looking at a conversation. There are people who use fragmented language that is not grammatically correct, full of typos, abbreviations and lots of other things. And it's also conversational, so people answer to each other and then if you don't know the history of the conversation, it's difficult. So understanding this in general is not so easy. So for instance, this was after the attacks in Belgium. Both messages, you can understand them as, both messages show sympathy in very different ways, but they are both ways of sympathizing with Belgium, the Belgian people. Slang is easy to manage. And then, well, typically you start classifying this thing. So let me show you some example, like more domain-specific example, like what we did, like classifying these messages from social media. So these are the, maybe I can show you this for instance. So these colored bars are, each one of these bars represent a disaster. These are data samples from Twitter. And for instance, the left one is an earthquake in Costa Rica in 2012, floods in Manila in 2013, Typhoon Pablo, floods in Sardinia, an earthquake in Guatemala. There is the, the next to last is the derailing of that train in Galicia in 2013. You remember this happened in Galicia, and high-speed train was going even higher than necessary and it derailed. So the colors are types of messages. And something that we observed, like very early in this research, was that something that sociologists of disaster have also said that disasters are very different, but they have very common elements. And people tend to more or less worry about the same things. So they start first like, okay, things such as, okay, what is the main problem? Like trying to advise people on what to do, trying to warn them about the danger. Affected individuals is the yellow part, the blue part is in the infrastructure, like things that are damaged, green is donations, pink is sympathy, and the last part is useful information. It's from Twitter and it's done with a combination of crowdsourcing and classification, like automatic classification. Crowdsourcing, so we use some, we use a crowdsourcing platform like Mechanical Turk to collect, like Amazon Mechanical Turk, that is called Crowdflower. And these we use to collect labels and with those labels we train a classifier and when we're confident we start labeling data. It costs money, yeah. So doing this study costed some money. But it doesn't cost to you because the data is free, are free now. There is this website crisislegs.org that are like nine or 10 data sets by now. We have been collecting data sets about disasters. They are good for machine learning exercises. If you are into teaching it's also interesting for showing something. They're good for demos, like small demos on some useful topic, yeah. We are a picture but you don't know what to do with it. Right, so this, I mean it's interesting, what can we, so suppose you take a disaster and you say, well in this disaster there are lots of messages about donations and very few about infrastructure damage. Now we did several attempts at relating that with actual physical variables from the disaster, like for instance official reports of people dead injured and so on, houses damages. We never succeeded. So this was, we tried many ways of normalizing the data of interpreting like what should we count, like one tweet, one person to be normalized by the activity in this part of the world in the previous week when there was no disaster and so on. So we had some data set that was very appropriate for this which was a typhoon in the Philippines that affected several islands. And then we knew in each island what was the amount of damages, infrastructure damage and injured people and so on. And we wanted, and we had the Twitter data and there is some correlation of course but it's not really obvious how this variable respond to the actual magnitude of the disaster. We have the conjecture that this is concave. So if you have, if nothing happens then nobody tweets, like nobody posts tweets about disaster, like if something mild happens then people start getting more excited, like they write more about this thing that happened. Now if the thing is really serious then they start having less time or less inclination to post about this in social media. I mean it's a meteorite wipes the earth then there will be zero message in the extreme, right? And then maybe there is a sweet spot where the disaster is really important but it leaves you enough time and inclination to try to post some information. So weird relation, this is why this is difficult. No answer yet. So we also observed a progression of these things over time and this was across 26 disaster and now even if you think of the Orlando shooting well there is no infrastructure, I mean infrastructure damage is not the most important issue in other cases it is. But there is this progression where people say well, look out, there is a shooting, people should leave the post nightclub and then people start expressing their sympathy, support and then describing who was killed, who was injured and then trying to give other information is more specific and then the nations are volunteering sometimes in a framework of day, sometimes in a shorter framework. This is very, of course every disaster is different but there are some commonalities. You can extract information from these tweets, you can extract what's the important part of the message, what is the time, who is the person posting these messages. There are many things that one can do, track hashtags, URLs, and this is some example of information extraction here. These are the same, yes. Yeah. Yeah. Yeah. Yeah, it's true. We typically created like separate collections per language because many, all of these things are language specific. The, you can, I mean in some cases like when, when you need like a quick and dirty solution you can build like a classified that works across languages like in many cases here you have no, so far there is very little data to bootstrap like a classifier, like if there is something happening today in Barcelona, a bombing. Will we have a classifier for Catalan and Spanish and English that can classify that information? Maybe not that we need to bootstrap it with some training data from before or we need to, we have a classifier and we try to transfer it to the new setting. I mean it's very, you may, maybe you need to translate. It's complicated. So in general, I mean we were very pragmatic and we said okay, different collections per language but maybe one can do something better. It's a good point. And the same with information extraction is not only for classification. So another thing that we did was to try to identify people asking for something with people needing something and this was done independently by other groups also in Japan and they came up with a solution that was very similar to ours. So you basically try to locate this kind of, the important part of the tweet. Like you have a phrase, I mean the tweet is 140 characters but it has a part that is more important than the rest. The wind, it's about the nation of blood, it's about the closing of the bridge. So you try to identify like the central part which is the problem and then try to identify the central part which is the solution in some other message and then try to match these donations. So not only based on text, in the end we have very small precision but if you use text, you can do even worse. And so if you're able to kind of first try to identify the important parties, you can be better. Right, so the problem with the matching is that there are very few messages that involve quantities. So you don't know for instance, I suppose I say there is a shelter open in this street. I think of a hurricane, but there is a shelter open in this street and people say, where can find a shelter? And then you say, well, there is a shelter there. How many people accommodate that shelter? I don't know. People don't post this information. The same with food donations and so on. You don't know if the place is saturated or not. So it's tricky, yes. We didn't get into much complexity but saying, okay, this tweet is the solution to this, this message is the solution to this problem. This is the evaluation and this is the 21%. So in 21% of the cases, one in five, we were able to find a solution to the problem, which means you will have to scan five messages in order to find solution instead of 300 per minute or whatever it is. Yeah. Here we use something similar to what part of a speech taggers do. So they have this extractor learning models where the input is a binary vector, right? The input, in this case, think of the input is a, the input labels are a binary vector. This binary vector has a zero whenever this part is not the important part and a one whenever this part is the important part, right? It's annotated by experts. So you have this expert who annotated this vector in this way. And now you're learning, and now the features are a matrix that has in every of these positions in the text have a series of features, right? One feature can be the word itself. Another feature can see, well, this is capitalized, completely capitalized. Another feature can say, this is a word of length two and so on. So you have for every word, instead of having a feature vector, you have a feature matrix that has for every position in the text several characteristics of the word at that position. And now your labels instead of being a scalar is a vector. And now your learning method is mapping from the matrix of features to this vector of labels. So this is how you train this thing. These are used, the particular learning scheme is a conditional random field, which is a model that is like a chain. Like every state depends on the, every label depends on the label to the right and to the left of that label. So it represents the dependencies on that way. So that is how it works. Now, that thing is the same way in which a part of a speech target annotates that this thing is the verb in a phrase. So the same way in which a part of a speech target recognizes that this is a verb is the same method that we use to recognize that this is the important part in a phrase. Actually, we didn't even, we just changed the training date. We didn't even implement anything. Here for language, yeah. Yeah. So this is here, yeah, it's a good question, right? So for instance, okay, so this will reflect typical. So Joplin was a tornado. In Isuri, I think, in the US in 2012 and Sandy was the hurricane in 2013. So if you train on data annotated for the Joplin tornado and you test on data annotated for the hurricane, Sandy, you get a recall of 11% and a precision of 78%. If you train on Joplin plus a bit of Sandy and you test on Sandy, then your numbers increase. So this is sensitive to whether your training set corresponds to, has information that is current about the current disaster instead of just historical information. Of course, this is kind of the crappiest domain adaptation method you can use instead of trying to change the model. We just add a bit of training data examples from the second setting. So we have like, this is all practical, right? In practice, we had like a stack classifier. So the first level is, okay, is this a tweet that is relevant for the disaster or not? And that is the most generalisable model that we found because across the disaster, across these 26 or whatever training data we had, there is a fairly kind of consistent informative tweet. It is a tweet that is long. It contains places and locations. It is well-capitalized. It doesn't end with a smiley. Like it has like a certain shape of what's well-written. So you start with that and you filter a lot of things and then you start digging into, okay, what category this is. Okay, this is a problem tweet. It's a tweet describing a problem, like on which type of problem. There in your person or damage infrastructure and utility. Okay, and then you go to that branch and in that branch, you start identifying, okay, what's the root of this problem? It's all practical or things that are irrelevant. There is a lot of irrelevant information. I mean, yeah, tomorrow there can be an alien invasion and people will still be tweeting about like lots of other things. It doesn't go away with a disaster. This is very weird. You have these heavy disasters and people tweeting about like insane things. Okay, so it's 8 p.m. I'm gonna go until 8.20 maybe. Is it okay? 8.15 and then. So disasters have a very important geographical component and this is a visualization that was done by the people in Facebook. I show it in the data beers but now I can discuss a bit more detail. So these are the people who use the safety check. These are the friends of the people who use the safety check. Safety check is something that pops up in Twitter when you are in a disaster, in Facebook when you're in the disaster area. And then these people in yellow are gonna appear in a moment. These are people sending donations to Nepal and money and people all over the world sending money to Nepal. From Japan, you see Australia, North America, Brazil, Europe, lots of money flowing into this area. Now, just as people can contribute money, there are lots of people, and this is what makes this setting really interesting, is that there are volunteers. There are people who are willing to provide these annotations. In the case of research, we use a mixture of paid annotations like through Mechanical Turk or Crowdflower plus volunteered annotations. So the volunteers' annotations are much more, are much larger data sets. Volunteers are motivated, digital volunteering with respect to disasters has started with the work of the people in Ushahidi, which is a company in Kenya, that they have a platform for community-powered mapping. And this was picked up by this guy, Patrick Mayer, he's one of the pioneers in this space, and he is very fond of maps. People have been producing these community-powered maps for a long time. These are more modern versions, like where is Sika, where is a pond of water, then for Syria, there have been maps running from a long time. These are some of the maps that we produce in Qatar. This is for Taifun Pablo in the Philippines. And these are maps produced, these are other types of maps, right? These are graphs, they also map things. Now, the maps I show are powered by people, these are powered by algorithms. So in this case, this is researched by a group of people who studied floats in Germany. The top part are the water levels, and the bottom part is a kind of a heat map of tweets saying something related to floats. And if you are really generous, you will see a relationship between these two things. Now interestingly, here you can also see the biases. So this city is in Mandelburg, I think, and it is the closest city to the river that was flooding. So it's kind of, you have, I mean, the physical phenomenon is you have a river that is that river, Elbe, Elbe River, the river Elbe that goes in that trajectory, and it goes through a part of Germany that is not as inhabit as the rest. I mean, there are no huge cities in the Elbe River except for Mandelburg, which is this city. So in a sense, like the people bias, I mean, people are located there, so they kind of bias the reports towards themselves, right? You kind of see the flood where there is people. If there is no people, you don't see much of the flood. This is similarly done for Dengue. If you look at the top, these are official reports from hospitals, like weeks after, with a delay of weeks, and at the bottom, you have tweets. Of course, there are also biases here. So if you take a map of Brazil in terms of population density, maybe you produce a similar map and you make us believe that this is about Dengue. So it's a lot of, I mean, these are kind of very preliminary, I find them very preliminary results because they're still very heavily biased. This is a map of earthquakes in Italy. Airquakes are a very interesting case because you may think, you may believe that given that there are the sensors, you can actually tell very fairly well what's the magnitude of an earthquake in a place. But the damage the earthquake does does not depend entirely on the waves. It depends on hyper-local characteristics. It depends on what is the quality of this ground. It depends on the quality of the foundations of the building, the year of construction. Like it depends on so many things that are hyper-local, that you don't have, even with a very dense network of sensors, you don't know where exactly are the houses that you need to inspect or there will be damage. But people know, people can see cracks on the wall and so on and report them. And this is what this group of Italians did. I work for many years in, for a few years in this tool called AIDER. AIDER is a mixture of machine learning and crowdsourcing. It's actually two platforms that communicate with each other. On one hand, you have kind of a machine learning call which is nothing more than WECA, wrapped in several layers of things to be able to auto-configure and to be used by a non-expert. So you have a kind of a person who is interested in the disasters go here, enter some keywords, start tracking some tweets about the disaster. And then we also instantiate at the same time a micromapping task on the other platform that communicates with the machine learning platform. So you get examples that need to be labeled that are thrown to the crowdsourcing platform. And from the crowdsourcing platform, we receive labeled examples and we continue iterating this behind the scenes until we're satisfied with the quality of the classification. Now, what's interesting is that this is very easy to use. Like you don't need to, there is no, you don't need to select the algorithms or anything or the models or whatever you want to do. You just create your collection and we on the background create a crowdsourcing task and you invite volunteers to the crowdsourcing task and this kind of self-sustains. We do have a simple version of active learning. So whenever we classify a tweet and we have low confidence, those are priorities to send them to the crowdsourcing platform. So basically we are choosing tweets that are close to the decision margin. So I don't know. So the classification accuracy is decent often like you get like a set of goods. You can get very quickly a separation of the messages that are about the disaster from messages that are not like, and you can set up your own things. This kind of works. I mean, we were very careful to kind of choose like models that maybe are not perfect for one particular situation, but that they generalize well across several situations. We need in the order of a few hundred messages per category to be able to create a good classifier. And that was well within the reach of the volunteers. So I think like Typhoon Pablo is word news and we get maybe 1,000 volunteers and some of them produce like 1,000 labels. So I mean, we get a lot of, I mean, it's of course it's very heavily skewed, but you get some people who do a lot of work. Yeah, that's a problem. I'm still worried about and concerned about. I wouldn't say working on because I don't have like even a good path to solution. But the biases are a big problem. But biases are a big problem in all of social media research. There are ways like, there are ways of overcoming bias. We have, we gave a tutorial like about a month ago on biases in social media research. And most of the answers that we were suggesting to this problem were related to doing a careful design that is similar to an observational study design, like a natural experiment design. So you have things such as, okay, you have these two cities in Brazil and they are about the same size and they are about the same population. And one of them is reporting this amount of tweets and the other is reporting this amount of tweets. Then I have a data point that says, well, maybe this city has more dengue than the other because their underlying characteristics are similar. And if you generalize that, you can have like a propensity score. You can have, okay, according to what I know, according to everything I know about these cities, the probability of observing a tweet about dengue in these cities this much, in the other cities this much. And now I look at, I stratified the cities by this probability and in each bucket I compare cities that are equally likely to tweet about dengue or write about dengue. So there are some designs that one can try to do to try to remove bias. But it's tricky because in the end this is kind of an, you are not controlling anything. You're just observing what's going on and then you cannot tell people, okay, now please start, please go outside and see if there is a hurricane. Like people will self-select and I mean you have all the possible biases. Yeah. So this is one, I mean in the screen there is one, one, I mean the one comparison that we can do. But we did compare with, for instance, in this same case like Philippines, like Damach in each island and so on. And there is some relationship, but it's not enough to be able to say. Yeah, so there is some, I mean it's clear that people in social media start speaking about an earthquake when there is an earthquake. I mean this is clear. Now, beyond that, whether if the earthquake is stronger, how much more they will speak about the earthquake, that relationship is not so clear. It's not at all and the hurricane is same. And also it's, yeah, it's messy, it's complicated. So what we can show is like this type of graph will say, okay, this is the hurricane path. Those are the tweets, the red are photos of things that were damaged. Well, most of the things that were damages were, most of the people who posted photos of things that were damaged in the Philippines were in the path of the hurricane, except for that guy in the bottom that ruined our visualization. But the rest were all kind in the path. I mean, this also requires, it's a lot of, I mean it's a lot of empirical things like try, observe different situations and see what's, what happened. Okay, let me show you the last video and then go to the conclusions and then we close, we wrap up. So maybe this needs a small introduction. So the, I think then the end goal to me, I mean one possible end goal is to be able to create systems that do real-time data mining using crowdsourcing, using people, right? So you'd system for participatory mining of streams. Let me give you one example of that thing. So this is a UAV in Banatu, a Phantom DJ. This is like, I think it's like 600 Euro machine that has four propellers and it can fly for 15 minutes. And what, this is a simulation in the sense that this video was shown to a group of 50 volunteers online and they were asked to click whenever they see something, whenever they see infrastructure damage, right? And then the system, what it's doing is just aggregating, for instance that guy there thought this role is damaged and the other 49 said no, this role is like that. So they didn't click on it. And the other, this long guy said, well, this is damage, maybe not really, like it's not so damaged as for instance on the right, like those, for instance, you see there, there is a rooftop that is missing, let's see a moment. So the drone is looking in the wrong direction, but now it's coming back. So you see there is a rooftop missing and then there is another one here on the right. So look at, here I think what's interesting are the possibilities, right? So here, this is a prerecorded video, but the possibility is okay, you can do a map of a crisis, like in the time it takes to fly the drone and then land it. Now extrapolating these two other situations, I believe there is a lot of potential to create real time data mining systems that use a mixture of algorithms and people and maybe you don't need a lot of people, maybe you need just a few operators and like five, six people who sit there with you and watch this whatever it is, like this news, this football game, this disaster or this map of a city or this transit maps or whatever. And with the help of algorithms help you make sense in real time of what's going on and generate an output that is valuable, right? So that's the kind of vision, like have a system for data mining that can involve people and algorithms and work in real time. Maybe you can build something like that. Maybe not, who knows. So is labeling all a crowd can do? What about validating? What about selecting features? What about generating hypotheses over the data or detecting biases and discrimination, suggesting an interpretation and so on? There are lots of things that people, humans can do and we could use them to try to understand the data. Okay, I will not speak much about this. Let me finish with the conclusions. So this research has good, bad and ugly aspects. The good part is that it's interdisciplinary, so you learn a lot and it's really great. The experience of talking to people that are passionate about their own domain because given that you don't know anything, you learn a lot. I mean, this is the best part of the learning curve is when you're at the beginning, you don't know anything. So every time you speak with them, you end up thinking, oh, this is really cool. And of course it's stuff that they have been doing for 50 years or so. The bad thing is that this particular domain has a lot of difficulties because these organizations are very old fashioned and they are difficult to engage with and they risk their kind of the organization is risking their life in every disaster because they are very easy to criticize. So they are exposed to criticism. So all they do since they want is to start covering their backs. This is very important because otherwise they don't survive. And you cannot have an emergency administrator that it changes every time there is an emergency. And the ugly thing is that of course you have like two competing things. You want to do your research and they want 24-7 support and so on. This work, I mean, most of what we do in data mining is here. We have the data and we have computers, so we do it. There is a lot of, this space is smaller but every now and then one can have like a good idea or a good partner and try to find something useful to do with this data. But most of what we do is here, right? There is no shame in that, in recognizing that as long as we understand that we are trying to progress over to this path. I wrote a book on this thing. It's called Big Crisis Data. If you are interested, you can search for Big Crisis Data and take a look at the table of contents and see if you like it and if you have any other comment you can write to me or whatever, that's it. Thank you. Yeah. I will filter it as a noise. For me. Yeah, so any, if there is any comment, question? I understand that if you think like for different purposes you prefer one thing or other thing. I'm trying to think it, for different purposes you analyze differently. And since there is a lot of subjectivity it's like, can you use the, in theory the Bayesian point of view in machine learning is that you take the most of subjectivity to make good predictions. So somehow it seems that in your case that would make really sense because you start from human subjectivity that you, is Bayesian machine learning more useful for you than in other applications? Or it's just something like, we sometimes do it, we don't use it. Do you know what I mean? Is it, in theory, it would be like a good application for Bayesian subjectivity? Yeah, I would say, I will agree with the fact that we are trying to, we are trying, essentially we're trying to model what the human would say about this thing. This is a human, but it would be a human that has access to any information that this particular label doesn't have. So I don't know. Because I mean it's partially true. It's true the part that we were essentially concerned with reproducing faithfully what the human will say. But it's also true that that person is deciding Bayesian information that is not available to them. So what about, so your blackmailing needs to find relevant tweets and to categorize them according to all the, but what about the implicit kind of searching that there are in any social network? In future you expect that relevant information has more requests, that the hashtags contain a lot of semantic information. So, we have used that kind of, yeah, that's a good point. So we have used some, for instance, the propagation of the tweet. We have used for determining whether people are believing what they are tweeting. Whether these propagates as a credible tweet or whether these propagates as something that people are hesitant about. But also we have some negative results. So for instance, just the amount of retweet that something has is not so much related to whether it's true or false. And we have very false tweets that end up being retweeted a lot and very true things that end up being retweeted a little. So it helps for some, it is information that needs to be used with care and it's not only the quantity, it's about the propagation tree, maybe it's about the number of people involved, whether people change and so on. So there is, yeah, with that conversation, how that conversation evolves, yeah. So there are cultural differences. Yes, we don't place that information in the models. But there are other people who have, yeah, there are people who have observed like heavy cultural differences in response of these things. So for instance, there is a very sharp division. There is a study that between Japan and Pakistan, users in Pakistan trust in more social media than the government and usually in Japan trust in more the government than social media, for instance. There is also linguistic variability even in the contents, right? So you may think that from the Philippines, if you look at all the tweets in English and all the tweets in Tagalog, they will have like a similar composition but it's not the case. They are written for different audiences. So the tweets in English are written to a foreign audience and they are kind of expressing how serious the situation is and try to inform others and so on. And the tweets in Tagalog are more interpersonal. They're about, okay, how do you know something about my casting or this local singer or football star or whatever like so. Even within the same culture, the things that you say in one language and the things you say in another can be different. Yes. So the volunteer groups, some volunteer groups tend to avoid human conflict because it endangers their own people and also they see that there is much more deception. There are much more many people trying to make you believe what is not true. So human conflict is kind of, we do some of it and some of it is human conflict but the volunteer group are hesitant to enter this space plus you have much more disinformation. Like wrong information purposefully injected into the data. While in a natural disaster, there are whenever you see somebody saying something that is false, most often it's just that they think that this is true and they are wrong. Like they post about a tsunami warning because they think, well, maybe it's true and if I don't post it, it's bad but if I post it, maybe nothing happens. So it's a, yeah, human conflict is kind of a different, I mean, it has some seminal techniques but it has extra complications. So things that we have, yeah, it's the same kind of people. There also there is some data protection thing to do. For instance, in the case of Syrian, maps of Syria, what people do is that they delay the map. They have a public map that is kind of two days old. And then a private map that is current. And in the public map, you don't show all the information. Just because there are factions in conflict so you don't want to get them alert about somebody else. Also there are cases where people try to retaliate against those who are posting information. So it's a kind of tricky, yeah, you need to kind of protect the data that people is complicated. Yeah. And how do you get the people to use the public services? We typically, we are using the public, the streaming API, the filtering API, but we had to talk to Twitter a few times. They have a governments and NGO division, not division, person. They have a person who interacts with governments and NGOs and that person authorizes some extra, to be more lenient with someone because they believe this is not a commercial enterprise, for instance. Bounding box and in crisis legs, there is a vocabulary. There's 380 words that are very repetitive use in English when there is a disaster situation. So we will use, for instance, sometimes a combination of words. In crisis legs, there is also a paper on this thing, like trying to mix with a bounding box, you get a lot of recall, but you get very little precision. And with the hashtags of the event, you get very high precision and very low recall.  So it's called, in information review, it's called information filtering program. You have a stream of data and you have to identify which are the queues that you're gonna do against this data to separate one part from another. It's a problem with itself. It's an interesting problem with itself. Good. Okay, thanks for coming. Great.