Challenging ourselves is an important part of life. The desire to experience something new and to conquer new grounds may often lead us to taking on new projects and endeavors. But while things may seem exciting at first, many new endeavors end up being more challenging than we expected. While everything may seem to be going well at first, soon we hit a plateau. Nothing seems to be moving and that leads us to question. If things are not working out, maybe it's a sign I shouldn't do it all together. Many do not know that as Seth Godin explains in his book The Dip, experiencing a sense of no progress at the beginning stages of new projects is more normal than you think. The gist of it is to understand how the dip works, how to overcome it, and in some occasions when not to waste your time. We already have the best singers in the world, the best computers, other creations, services, and products. When we have so many already existing options to choose from, we become extremely picky. It is increasingly difficult to reach a place where you or your product would become worthy of people's attention. Logically thinking, why would anyone choose your product or service when there are already trusted and approved alternatives? Hence, the dip. In one way, the dip is the phenomenon of being average, the opposite to outstanding. Whenever we start something new, it is natural that with lack of experience and gathered knowledge, we will have a difficult time standing out, especially with all the alternatives around. That is why in most cases, after beginning a new endeavor, we experience the usually long lasting stagnation.  And honestly, why should they? This may make the situation feel hopeless. For quite a while, everything you do doesn't seem to be working, but there is a light at the end of the tunnel. It takes time for you to gather experience and develop the necessary skills to stand out. While we may think that our new idea is the best, nobody will rush at you at first. You will need to grind, improve, develop, improve yourself. First, you do that to a very few people. As you, your service, or product gets better and better, more and more people start to notice you.  Enough people start to recommend you to each other, and you start to feel progress again. But this process takes time and a lot of effort. That's why it may feel so demotivating when it's happening. But there is a reward at the end. If you pursue long enough, develop the necessary skills or level of quality to stand out, and build the required trust, you become the go-to or the best yourself. While others still struggle in the dip, you've already passed it and reaped the fruits of your long-lasting effort. Few people actually overcome the dip. That's why whoever passes it, we admire them. It's no coincidence that outstanding individuals or products are rare. Few are willing to experience the long-lasting discomfort of being in the dip. But if you do, your story changes altogether. Contrary to how many people think, overcoming the dip is not based only on hard work and effort.  It's not enough to work hard if you are doing the wrong thing or the same thing over and over again. Overcoming the dip depends not only on working hard, but also on adapting, learning from your mistakes, and changing with circumstances. Trying new things and strategies until you discover something that works. Something new, something that stands out. One way or another, almost every situation of the dip will require time as well. As author Jeffrey A. Moore explains in his book Crossing the Chasm, at the beginning of offering a new service or product, most people will be skeptical. Very few people are interested to try out new things and they rightfully have little trust towards you. There are so many new products or performers that pop up each day. It would be simply too much to invest and trust in them all. In fact, few of them will make it through the dip. It makes sense to care less about them, since your investment in many of these people or projects may not prove to be worth it in the long run. Instead, why not rely on something that's already trusted? This way of thinking is very common. The majority of people wait until others test and approve something. The majority wait for someone else to tell them that it works. The majority wait to see if the person or product will really go all the way. That takes time. And of course, effort too. Effort in finding the first people willing to try and vouch for whatever you offer. Effort improving that you, your service or product, are worth the investment. And effort and time until everyone has an opportunity to see that you are serious, that you are here for the long run. As you can see, there are quite a few elements that are required to overcome the dip. But there is also one more thing important to mention. The dip that you are facing is not always one worth overcoming. The option of quitting certain endeavors is important to consider too. There are many aspects that can help in deciding whether what you are pursuing is worth sticking to or quitting. I explore them in more detail in another video called Why Winners Quit All the Time. But to offer a few suggestions in this video, you can start out by asking yourself, if I stick to this endeavor and push through, will the rewards be great enough? And is this endeavor important enough for me to go through the uncomfortable and long lasting experience of being in the dip? In summary, almost anything that is worthwhile doing includes the dip. It's almost like the natural selection of today's modern world in weeding out those who are not ready to work or innovate enough in order to offer something new and worthwhile. But if you believe that what you've chosen to pursue is worth the effort, knowing that the dip is inevitable and understanding how it works can make all the difference. Possessing this knowledge, you will be much less prone to panicking and second guessing yourself, instead asking, is it a matter of more time, effort, or adaptation? If I'm willing to go all the way and stay comfortable in the face of uncomfort. One last thing, overcoming the dip on your own may be a challenging experience. It's always great to have someone qualified to help you out on your journey. This is where we can join forces. I recently finished the first stage of my interpersonal coaching training and I'm open to my first clients. As an interpersonal coach, I'm trained to offer powerful questions and insights, which lead the client towards greater self-awareness, discovery of solutions, change, and action. As coach, it's not my job to give you answers, but instead my role is to facilitate a process where you discover them on your own. As we all know, everyone is better at things they figured out themselves. If you're interested to learn more about taking a journey of coaching together with me, in the description and at the end of this video, you will find a link to my website where you can learn more about it and contact me. This was Rokas, and as always, I wish you to work hard, smart, and purposefully.


Let's style this brown suede bomber. I'm thinking less casual, more sophisticated, so we're going to go monochromatic. For pants, the stone cotton pair, and then up top, a tan cashmere turtleneck. I'm actually going to tuck the sweater in. I think it looks clean and helps elevate the look. Now I know I'm going to wear suede shoes, so I'll add this suede belt, but I'm not sure whether to go with these chuckas or these penny loafers. Here are the chuckas, kind of rugged, and here are the penny loafers, a little more elegant, so I'm going to go with these. On my wrist is the Vacheron Constantin Traditionale, and finally adding the suede bomber to complete the look. One tip for dressing monochromatically is to make sure you're using different shades of whatever color you're wearing. So here I've got light, medium, and dark in the jacket and the shoes. This ties everything together and helps make the entire outfit look more cohesive.


So first of all, thank you very much for having me here today. I'm very excited to be here at Defcon in the adversary village. And I hope you like this presentation, which is called New Generation of Peace. We are going to discuss a little bit about this piece and why is there a new generation. My name is Carlos Palop. I work as senior security engineer at Merrill. I have some certifications. I play CDF analysis stuff. So if you want to know something else about me, just check my LinkedIn. And you can also contact me via Twitter or even email for the more traditional stuff. So in this talk, we are going to talk a little bit about the Peace Suite, what it is, why is it useful, why is there a new generation. Then I'm going to very briefly introduce Hactress and how can it be useful in combination with the Peace Suite. And then we are going to see some demos about Limpies, Mac Peace, and Wimpies. At the end, we are going to very briefly see what is a both piece, the last being edited just one or two months ago. And then I want to talk a little bit about the to-do in order to indicate the community how you can help to the Peace Suite if you like it. So Sweet Peace. Peace is the name piece. It comes from Privileged Escalation Awesome Script Suite. Basically, this suite is a combination of scripts that will allow you to enumerate the most common hosts. And I'm talking about Windows, Linux, Unix in general, and even Mac, in order to find easy ways to escalate privilege. So before the Peace Suite, there were already a few scripts that performed these actions. But I like them, but I didn't feel very comfortable with them because there were a lot of data that was mostly useless, and I didn't want to lose my time reading this data and figuring out if you can use it in any way or not. And because they didn't have enough text, or at least they didn't have the text I wanted to enumerate. So that's the reason I started this suite. So in Peace, we are going to find very comprehensive scripts for enumerate hosts and see how hard they are and how you can escalate privilege. You are not going to find endless data lists. So you are going to know where you need to focus in order to find these vulnerabilities you are looking for. They have more text than any other tool. At least I tried this, and this is because I mostly had probably one new check per week. So that's a very, very cool way to update the scripts. As we have said before, the piece can be executed for Linux, MacOS, mostly any Unix flavor, and also Windows, which is great. Anything is the only privilege escalation enumeration tool that can be executed in this amount of different operative systems. But I think the fact that most people love about the piece is that the output is colored. This means that you are going to find, for example, the color red, where something is suspicious, or the color green, where something is well configured. And this is very useful in order to know where you need to focus or where you should focus in order to try to find vulnerabilities. Last of the characteristics is the monetization. We will talk about this later, because actually you cannot use any monetization at the moment, but my idea with these piece new generation scripts is that you are going to be able to execute the piece in a host as frequently as you want and be able to compare different results in order to see how good you are doing hardening your systems. As I have said, this is not available yet, but I hope this will be soon. Well, the more help the community gives me, the sooner this will be prepared. But before going into depth with this, I want to show you what is hacktree, because this is going to be highly useful when using these tools. So I'm going to open this link. And you can see that hacktree is basically a book with a lot of cool tricks of hacking, but now I want us to focus on the previous escalation checklist for Linux and Windows. And in a few weeks, I hope I will create one specifically for Mac, where you can basically see some checklist of things that you should search on each computer in order to try to find vulnerabilities and improve the security or exploit the vulnerabilities, depending if you are in a routine or in a routine. Also, hacktree is pretty useful, because when you execute these tools, you are going to see broadly some links to some parts of the book. And this is because if you don't understand the tech that the script is performing, you can access this URL and you are going to have the theory about the tech, what, why it's being performed, and what you should check for, and how can you exploit a vulnerability phone in that section, if any. You have it for Linux, for macOS in the future, I'm just starting, and for Windows. And to give you a very brief example, for example, let's say that you find some vulnerability which is related to access tokens, and you don't know what access tokens in a Windows environment, you can just come here and have a description about what is the access token, how to enumerate them, how can they be in abuse, and everything. Basically, this is pretty useful to know why are the P's performing the techs they are doing, how can you exploit them if you find any vulnerability. Also, you have here the URL. This is free for everyone. You can just access the book and use it as it is. So let's continue. Now we are going to start with the demos. We are going, first we are going to perform our Linux with Limpies, and then our demo with MacPies. So I have, here I have a pretty vulnerable and very outdated Debian machine that we are going to, where we are going to execute Limpies just to see the vulnerabilities that it is finding. So I have already accessed this machine via SSH. So I'm logged it. I'm on my, I'm the user-user. And I'm in this very outdated Debian machine. I have also already uploaded Limpies. So we are just going to execute it. First of all, take a look at the options because Limpies and with P's have several options that may be useful. For example, in Limpies, you can find the dash A. This means to perform all techs. And this is because there are some techs that are very, very slow or that are very noisy that by default they aren't executed. But if you are playing a CTF or you don't care about being noisy or about the time, I completely recommend you to execute the same test what more techs are going to be executed. So you have the superfax option. And you also have some options that will allow you to perform network region just using Limpies, which is kind of cool because just with Limpies you will be able to enumerate the machine, but also to enumerate the network if you don't want to upload any other tools. So let's execute Limpies. We are just going to run it without dash A. We were just going to run Limpies in a normal version. So here we can see that Limpies start with a very, very, very beautiful banner. Here we can find the version, we can find the legend. This means what the legend indicates, what the color means, which is kind of awesome because here we can see that the red yellow indicates a 90%, 95% chance of previous installation vector, red means that we'll just take a look at it. And green basically means well configure things or common things that you really shouldn't care about because this was found in other machines. We have some basic information, some information about the tools that are available to enumerate the network. And then we start enumerating the system. So here we can find system information. We can see just in red that the kernel is pretty real. The pseudo version is also a little bit old, so probably they are vulnerable. We can see a little more information about the systems. If you're in for environment, you may be able to find some passwords here. We enumerate also some Linux protections, like is a Linux enabled, is ASLR, is this a virtual machine? Actually this is, so we have here just some information about the container, if this was a container, but we are an inside a container, so nothing interesting here. Devices, available software, it's good to know if you have some compiler available to compile possible kernel exploits. Then we start taking a look to processes that may be vulnerable to scalability less, binary process permissions, ground jobs, and here we can find the first highly probable privilege escalation vector that Limpis has found. So for example here, this user is able to write in a path that is being used in a Chrome, that is then being executed by route without indicating the path. So for example, if we create a file called override.sh with a river cell in this folder, this is going to be executed by route, and we are going to obtain a river cell executed by route. Here we can see that Limpis enumerates, enumerating more information. Also network is always important to know where you are in a network, and with other networks, new networks, you can access and enumerate them. Here we have just an enumerated local network, and we are even checking if we can sniff traffic using gcp.com.  More information about the users, software information. This is pretty interesting, and it's one of the main themes of Limpis and Wimpis, and it's one of the main new topics in the new generation scripts. So we are going to talk about them later, but basically here we are just looking for sensitive files that, for files that maybe contain sensitive informations related to some specific software. For example, some Tomcat configuration files that may have passwords inside of it. You can see that we are looking for a bunch of them, actually. We'll talk about this later. So we are going to continue till the last section, which is interesting files. These files, the files that are here are here just because they have some interesting fact that will make Limpis to enumerate them. For example, here we can find all the QID files. Some of them has been vulnerable in the history, so we have here some information about in which systems this QID binaries are vulnerable, and also the QID binaries that aren't known, but Limpis are going to be executed in order to perform a few checks to see if we can abuse these files to execute arbitrary commands on Scali privilege. Same thing for SDID files. Again, if you don't know what these files are, you can follow this link and you will find all the information. Taking misconfigurations. Well, we are taking more misconfigurations. We can write a few files and folders here that we can abuse to Scali privilege, so this is very cool. With all the other files, so basically here you are going to see that well Limpis is looking for a lot of information that maybe give you our sensitive information or the power to Scali privilege. Always take a look to everything because you can find something interesting. So this is Limpis. Now we are going to continue with a Mac piece demo, but there is something important that you need to know. First of all, there is no Mac piece script because Mac piece is actually inside of Limpis. If you execute Limpis inside a Mac host, Mac piece is going to be executed. And I have created this script this way because the code that both scripts, both flavors of the script serves is like 90%. So almost every part of the code is served. So I just generate some specific parts for the Mac version in order to run these parts in a Mac computer instead of their regular Limpis. But as you can see here, just execute Limpis in a Mac host system and the Mac piece version will be automatically executed. So this is very cool because you only need to know how one script works in order to execute it in Linux, in Mac host calls and potentially any flavor of Unix. So my current host is a Mac. So I have already executed the Limpis version in my Mac. Actually, I can execute it from file, but I have executed it from memory. I have just downloaded from GitHub and piped into a SHL. So this way the script is never going to touch the disk. Yeah, here we can see that. I have already executed because as my host have a lot of more files than a virtual machine this could take instead of taking just one minute, this could take around five minutes to minutes and I don't want to keep you waiting. So here we can see that the banner is much more ugly. I really definitely needs to improve that, but well, it's not all my priorities. Again, here we can see some basic information. We can see some system info and we can mostly see the same information from Limpis in Mac piece. The difference is like underneath, there are different binaries being executed to obtain the same information, but it's pretty cool. So because you just need to execute one, it is going to be intelligent enough to distinguish between Mac or Linux and just the correct version is going to be executed well. As I have said, you are going to find most of the same information as before. So we can pass this output. You can just test it in your own. Okay, okay. So Limpis, we can start. Also, there are all their more stealthy ways to execute Limpis.  So check this out because it's going to be very cool for you to learn how you can execute Limpis probably just using Netcat and Kool or even without that, using bus pipes. So yeah, that's all for Limpis. Okay. So let's continue with WIMPIS. WIMPIS is obviously the, well the Windows version of the script. Okay. So here I have a Windows virtual machine where we are going to execute WIMPIS. Obviously, WIMPIS is using a completely different script that Limpis. Actually, there are two different projects for WIMPIS. One is the BATS version and the other one is the XC version. The BATS version is less maintained than the X version and is mostly created for all Windows machines. So the most maintained version of WIMPIS is going to be the X one and is the one that you recommend you to execute if you can. Obviously, there are some requirements like the .NET version, but mostly you are going to, in our days, Windows, you are going to be able to execute it so I definitely recommend you to execute this version. So here also we have a quick start and we also have a few ways to execute a WIMPIS from memory or WIMPIS or execute WIMPIS while doing some kind of stealthy things in order to avoid antivirus to detect that we are executing the binary. I recommend you to take a look at it because it is pretty interesting. WIMPIS also have some interesting parameters. For example, WIMPIS allows you to execute WIMPIS and this is very cool because if you find in a Windows, the Windows subsystem for Linux, you can execute WIMPIS because it is a BAS script. So if you just indicates to WIMPIS, the URL where it can find WIMPIS, it is going to download and execute it from memory, I think. Also, you don't even need to host your version of WIMPIS, you can just indicate the URL for WIMPIS inside GitHub. Oh, well, you have also more health information, basic information. Where are my colors? When you execute WIMPIS without doing anything in a new Windows host, you are not going to see any color. You need to execute this first in order to indicate the registry that, hey, I want you to interpret the colors that are going to be displayed. Just run this, you don't need to be root or anything, the colors will magically appear. Well, you need to start another power cell. Okay, so I have already run WIMPIS in this host. We can see that we have another very beautiful banner. We have some information about the creators. And we start with LIMPIS, we start seeing some system information. We have integrated Watson inside WIMPIS, so this is pretty cool because it will just run with WIMPIS. Here we can find that we have, we are numerating the hot fixes that has been applied to this virtual machine. In this case, information about environment variables, information about audit, web, labs, WG digest. Again, if you don't know what, for example, if you don't know what is WG digest, you can just access this URL inside hot tricks and you will learn what is this and why is this important. Same LSA protection, protection of guards. So the main difference between WIMPIS and LIMPIS apart from the obvious one is that you are not going to find these red, yellow colors in WIMPIS that you will find them in LIMPIS and this is because maybe in WIMPIS is more complicated to be so sure that something is going to give you a privileged escalation path, so I haven't implemented those yet, but I may do that in the future. Well, as you can see in WIMPIS is enumerating a lot of things, interesting events. Now we have some user informations. In red, you can find interesting things for attackers. So here you can find the user administrator, home site folder, something interesting about WIMPIS is that it is taking every path that appears. So for example, if this path was reachable by our current user, this will appear on red and we'll tell you, hey, you can write this binary, maybe you can escalate privilege because this is being executed as you can see here. Obviously you cannot escalate privilege over writing WIMPIS because this is a binary of the user we are using, but maybe if this was being ruined by another user which is an administrator and you can write the binary, you may be able to escalate privilege. Same for services information, actually here you can see an example of what I have been talking about and is that this service, this binary of this service is reachable by everyone. So here you have a privileged escalation path. Also WIMPIS is taking for no quotes and space in the beam path of the services. So you may be able to abuse this misconfiguration also to escalate privilege. More information about applications, how to run, same. You may find some places that you can write and your binary is going to be executed with higher privilege. So that's pretty awesome. And I want to show you a few more things. Network information again is very important to know where you are inside an internal network if you're inside an internal network or any way which other networks you can access that you weren't able to access before. And also it's important to check the ports. I don't have well, UDP, the ports that are listening just in localhosts because maybe these services are vulnerable and you can, well, escalate privilege of using those. We found some, well, the NLM has of our current user I think, user, yeah. We found the unattended file. This may have credentials of the administrator user. And this is pretty awesome because you can see here this section, the fine analysis section and that we are also looking for a lot of files that may be storing sensitive information. So before the piece new generation, every time I wanted to search for a file that may be containing sensitive information, I needed to add a specific tech to LIMPIS and then a specific tech to LIMPIS. Which was pretty awful because, well, it was kind of hard and well, it took several minutes. With the new generation scripts, we have this build list inside this build list folder. We have this sensitive file gemel where you can find all the sensitive potential files that can storage sensitive information. This is pretty awesome because for example, file figure is specifying to search the folder with the name file figure and inside this folder to search the file called site manager dot dot XML. And if it is found, print in red, all the lines that contain some of those reg access. So this is pretty awesome because LIMPIS and WIMPIS are automatically created using this gemel. So both of them are going to search all these files and if they found them while executing, they are going to bring them to you. This is very awesome because now, in order to just add a new tech, if I discover a new file that may be containing sensitive information, I can just add it to this gemel and the new LIMPIS and WIMPIS are going to automatically be built and are going to be searching for the new sensitive file. So this is very easy to maintain and also is very easy for the community to help me adding new files that may be storing sensitive information. Actually, you have here a few examples well explained. So if you want to just contribute to this script because you know about this file that may be containing credentials that aren't included yet, just take a look to the example, include the new gemel, create a pull request to master and the new versions are going to be automatically built. So it's just awesome. As I have said before, WIMPIS has another flavor, which is the BATS one, which is meant for all machines. Actually, the syntax is a little bit more complicated because BATS is not very flexible. Anyway, if you need to use it at any point, you can find it here and also take a look to this explanation of how to understand the permissions because you will need it in order to find the paths to escalate privilege because here you are not going to find colors or because BATS is not very, very, very flexible. So we are ending this presentation. We are going to continue with BATS PIS. I created this like one or two months ago, I think. This is a very, very simple script. This is basically monitoring new CVS and the ones that are related to privilege escalation are going to be indicated in this group of telegram and in this group we also discuss about HAC3's PIS and latest news in cybersecurity. So it's free for everyone. Do you feel free to join it? And also you can find BOT PIS in GitHub. You can find it here. And actually, this bot allows you to create your own bot in order to monitor the CVS you are interested in. So basically you can modify this Jamel and set your own keywords and then put your slab web hood or your telegram token. And the bot will send you all the new CVS that are this color that contains the keywords that you have specified here. In this case, we are just looking for things related to privilege escalation. Yeah, things related to privilege escalation or docker container escape. Okay, so yeah, feel free to join the group if you please. Finally, about the to-do. So we have the, we now have in this new generation too, we have the capability of create GA zones from the raw output and you can find the script to generate these GA zones in the parcel folder. So you can basically execute the piece parser, give the path to the output of one piece script and generate the JSON. I'm looking forward to someone that from that JSON can generate a beautiful report PDF HTML reports. That will be awesome. Also, I want to develop web piece which is going to be the, well, the centralized agent that will allow you to automatically execute lean piece or wind piece and compare the results and even add new features. So I'm really looking for someone with experience in front end or back end. So if you want to help me develop in web piece in order to allow to perform this constant monetization, just contact me. And finally, obviously, wind piece and lean piece are very big scripts, but can be bigger. So if you know about new techs or if you want to help update in the list that they are using, just contact me because well, this is a huge project that needs a few help. The help of everybody that can, any help that you can bring me. So I hope you have enjoyed this talk. Thank you very much. I hope you are enjoying DevCon and now I will be in the Discord channel if you have any questions. Thank you for your time again.


Hi everybody, it's Sean. I forgot to do a giveaway in the Panini Impeccable Soccer Hobby 1 box break. Pick your team number six, which was the three cereal box randomizer. I forgot to give that away. The details were, as it states in the new one that I just loaded. You just had to have two or more teams, so obviously this is the brand new one, the third box from that case that's available. Pick your team number seven, but two teams equals one entry, four teams, three teams, six teams equals three entries, et cetera. Top name gets all three blasters, so that's the same instructions for this one. So what I'm going to do is I'm going to copy the customer list over to a separate spreadsheet. And Eric, thanks for getting in, but no entry for you. Matthew with two, you've got one. Michael with two, you've got one. Nicholas with five, you've got two. And Will with two, you've got one. So there you go. So five entries, four there, obviously the random number block teams are not part of it. But pop over here and remember it's top name gets all three blaster boxes. Dice roll, three and a six, nine times. Ninth and final time, Matthew disbarred on top, nine times on the randomizer, nine times on the dice. Matthew on top, you get yourself those three blaster boxes. There you go guys, that was the randomizer for the three box cereal box blaster randomizers for impenetrable soccer hobby, one box break, pick a team number six from jaspyscasebreaks.com. The fresh last box from that case, pick a team number seven is in the store now, jaspyscasebreaks.com. Thanks for hanging out. See you next time.


We study ecosystems, not real ones, digital ones, underneath our digital cities. So let's be more specific because that's very abstract, right? So this is Rudy from Hamburg, a little local guy. Rudy wants to cycle more, it's good for the climate and himself, but he eats a good used bike. So he goes to eBay, as we all do, and Googles or eBay is for a good used bike. And then he clicks on accept, right? And accept is something we do so much in digital world. If you click on accept, you're part of the digital ecosystem of eBay. This is a big ecosystem. By clicking on accept, you agree to basically share your surfing behaviors with 900 legal entities around the world. It's China, it's the US, it's the EU, it's everywhere, global surveillance capitalism. Now back to Hamburg and our city. Let's think mobility of the future. And just think of the choices we have. So we have Rudy again. Rudy can do bike sharing, car sharing, transit. He can do scooter sharing, all just a fingertip away, but all very much location-based apps. So when you engage with these services, you immediately expose your locations to a big ecosystem of companies. 38 and just these nine examples, Google and Facebook just there in the background. I mean, don't get me wrong. I mean, it's a great opportunity. There are great services that we can use, but we need to think about what it does to us in terms of the behavior we are exposing. Don't get me wrong, I like this data sharing idea. The integration we get through all these services, the design designers can do. We're actually looking at the observable behavior of users, the decisions we can take in cities by basing decision on data. But if you share your fingerprint, a lot of data taken together gives you a pretty unique profile and anonymous is not anonymous in the end. So let's think about the cities you want. Primarily, cities were about freedom, but now urbanites are the best surveyed people on the planet. I don't think this is my view of the city of the future. So what can we do to fix this? What can we do to work against cities of surveillance? First and foremost, I think we need ecosystem awareness and management. That's something we actually study across several universities here. Can we actually see how much exposed we are to this big ecosystems of data sharing? Can we make it transparent? Can we make it explicit for people to agree to? And can we also enable developers of new services to see what they're doing? Sometimes exposure is just a click away. Secondly, I think we need much better regulation on location-based data. I'm really surprised. This is a vital data stream that shows everything about you, where you live, eat, pray, everything that's in the location. Why isn't there better and enforceable standards for how we deal with location data? And third, I think we need to think hard about information governance in these areas. Who do we want to regulate our data? Is it just us, the me? Well, we can do that, but it's tedious. This is the big platform where it's very convenient but a little bit carnivorous if you think surveillance capitalism. Is it the stay? Well, that's what be not my idea. I think we need to think about this idea of urban data cooperatives, sharing data, making it accountable to the membership. We have a great history of cooperatives in this country. They look a bit antiquated at the moment, but I think they paved a path for many villagers in the position of powerlessness to become part of agribusiness and banking. Why can't we copy that model to the digital world? That's something you're interested in. Come to the workshop tomorrow for cities about privacy and engage for a city set, a functional, but also set us free. Thank you very much.


From around the globe, it's theCUBE with digital coverage of MIT Chief Data Officer and Information Quality Symposium brought to you by SiliconANGLE Media. Hi everybody, this is Dave Vellante and welcome back to theCUBE's coverage of the MIT CDOIQ 2020 event. Of course, it's gone virtual. We wish we were all together in Cambridge. They were going to move into a new building this year for years. They've done this event at the tank center, moving into a new facility, but unfortunately it's going to have to wait at least a year, we'll see. But we got a great guest, nonetheless, Doug Laney is here. He's a business value strategist, a best-selling author, an analyst, a consultant, and a longtime CUBE friend. Doug, great to see you again. Thanks so much for coming on. Dave, great to be with you again as well. So I got to ask you, I mean, you have been an advocate for obviously measuring the value of data, the CDO role. I don't take this the wrong way, but I feel like the last 150 days have done more to sort of accelerate people's attention on the importance of data and the value of data, than all the great work that you've done. What do you think? It's always great when organizations actually take advantage of some of these concepts of data value. You may be speaking specifically about the situation with the United Airlines and American Airlines, where they have basically collateralized their customer loyalty data, their customer loyalty programs, to the tunes of several billion dollars each. And one of the things that's very interesting about that is that the third-party valuations of their customer loyalty data resulted in numbers that were larger than the companies themselves. So basically the value of their data, which is as we've discussed previously, off balance sheet, is more valuable than the market cap of those companies themselves, which is just incredibly fascinating. Well, and of course all you have to do is look to the Trillionaires Club, and now of course Apple pushing two trillion to really see sort of the value that the market places on data. But the other thing is of course COVID. I mean, everybody talks about the COVID acceleration. How have you seen it impact the awareness of the importance of data, whether it applies to business resiliency or even new monetization models? I mean, if you're not digital, you can't do business and digital is all about data. I think the major challenge that most organizations are seeing from kind of a data and analytics perspective due to COVID is that their traditional trend-based forecast models are broken. If you're a company that's only forecasting based on your own historical data and not taking into consideration or even identifying what are the leading indicators of your business, then COVID and the economic shutdown or slowdown have entirely broken those models. So it's raised the awareness of companies to say, hey, how can we predict our business now? We can't do it based on our own historical data. We need to look externally at what are those, external maybe global indicators or other kinds of markets that precede our own forecast, our own activity. And so the conversion from trend-based forecast models to what we call driver-based forecast models isn't easy for a lot of organizations to do. And one of the more difficult parts is identifying what are those external data factors from suppliers, from customers, from partners, from competitors, from complimentary products and services that are leading indicators of your business, and then recasting those models and executing on them. And that's a great point. If you think about COVID and how it's changed things, I mean, everything's changed, right? The ideal customer profile has changed. Your value proposition to those customers has completely changed. You got to rethink that. And of course, it's very hard to predict even when this thing eventually comes back some kind of hybrid mode. You used to be selling to people in an office environment. That's obviously changed. There's a lot that's permanent there. And data is potentially at least the forward indicator, the canary and the coal mine. Right. It also is the product and service. So not only can it help you and improve your forecasting models, but it can become a product or a service that you're offering. I mean, look at us right now. We would generally be face-to-face in person to person, but we're using video technology to transfer this content. And then one of the things that it took me a while to realize, but a couple of months after the COVID shutdown, it occurred to me that even as a consulting organization, CUSERDA focuses on North America, but the reality is that every consultancy is now a global consultancy because we're all doing business remotely. So there are no particular, or certain, there are no particular or real strong localization issues for doing consulting today. So we talked a lot over the years about the role of the CDO, how it's evolved, how it's changed. Of course, the early, the kind of pre-titled days it was coming out of a data quality world. And that's still vital. Of course, as we heard today from the keynote, it's much more public, much more exposed, different public data sources, but the role has certainly evolved initially into regulated industries like financial health care and government, but now many, many more organizations have a CDO. My understanding is that you're giving a talk in the business case for the CDO. Help us understand that. Yeah, so one of the things that we've been doing here for the last couple of years is running an ongoing study of how organizations are kind of impacted by the role of the CDO. And really it's more of kind of a correlation and looking at what are some of the qualities of organizations that have a CDO or don't have a CDO. So some of the things we found is that organizations with a CDO nearly twice as often mention the importance of data and analytics in their annual report. Organizations with a C-level CDO, meaning a true executive are four times more often to be likely to be using data to transform the business. And when we're talking about using data and advanced analytics, we found that organizations with a C-I-O, not a CDO responsible for their data assets are only half as likely to be doing advanced analytics in any way. So there are a number of interesting things that we found about companies that have a CDO and how they operate a bit differently. You know, I want to ask you about that. You mentioned the C-I-O and we've seen, we're increasingly seeing, you know, lines of reporting and peer reporting alter shift. The sands are shifting a little bit. We've seen, you know, in the early days, the CDO and still predominantly, I think, is sort of an independent organization. We've seen a few cases and increasingly number where they're reporting into the C-I-O. We've seen the same thing, by the way, with the chief information security officer, which used to be considered the Fox watching the henhouse. So we're seeing those shifts. We've also seen the CDO become, you know, more aligned with a technical role and sometimes even emerging out of that technical role. Yeah, I think the, I don't know. What I've seen more is that the CDOs are emerging from the business. Companies are realizing that data is a business asset. It's not an IT asset. There was a time when data was tightly coupled with applications and technologies, but today, you know, data is very easily decoupled from those applications and usable in a wider variety of contexts. And for that reason, you know, as data gets recognized as a business, not an IT asset, you want somebody from the business responsible for overseeing that asset. Yes, a lot of CDOs still report to the C-I-O, but increasingly more CDOs are, you're seeing, and I think you'll see some other surveys from other organizations this week where the CDOs are more frequently reporting up to the CEO level, meaning they're true, they are true executives. You know, I long advocated for the bifurcation of the IT organization into separate I and T organizations. Again, there's no reason other than for historical purposes to keep the data and technology sides of the organizations, you know, so intertwined. Well, it certainly makes sense that the Chief Data Officer would have an affinity with the lines of business, and you're seeing a lot of organizations really trying to streamline their data pipeline, their data life cycles, bring that together, you know, infuse intelligence into that, but also have a system, take a systems view, and really have the business be intimately involved, if not even own, you know, the data. You see a lot of emphasis on self-serve. What are you seeing in terms of that sort of data pipeline or the data life cycle, if you will, that used to be kind of, you know, wonky, hardcore techies, but now really involving a lot more constituents. Yeah, well, the data life cycle used to be so much short. When we look at the data life cycles, they're longer and they're more kind of data networks than a life cycle or a fly chain. And the reason is that companies are finding alternative uses for their data, not just using it for a single operational purpose or perhaps a reporting purpose, but finding that there are new value streams that can be generated from data. There are value streams that can be generated internally. There are a variety of value streams that can be generated externally. So we work with companies to identify what are those variety of value streams and then kind of test their feasibility. Are they ethically feasible? Are they legally feasible? Are they economically feasible? Can they scale? Do you have the technology capabilities? And so we'll run through a process of assessing the ideas that are generated. And the bottom line is that companies are realizing that data is an asset. It needs to be not just measured as one and managed as one, but also monetized as an asset. And as we talked about previously, data has these unique qualities that it can be used over and over again and it can be generate more data when you use it and it can be used simultaneously for multiple purposes. So companies like you mentioned like Apple and others have built business models based on these unique qualities of data, but I think it's really incumbent upon any organization today to do so as well. Well, when you observe those companies that we talk about all the time, the sort of data is at the center of their organization and they maybe put people around that data. That's got to be one of the challenge for many of the incumbents. If it talks about the data silos, the different standards, different data quality, that's got to be a fairly major blocker for people becoming a quote unquote data-driven organization. It is because some organizations were developed as people driven or product driven or brand driven or other things to try to convert to becoming data driven takes a high degree of data literacy or fluency. And I think there'll be a lot of talk about that this week. I'll certainly mention it as well. And so getting the organization to become data fluent and appreciate data as an asset and understand its possibilities and the art of the possible with data, it's a long road. And yeah, so the culture change that goes along with it is really difficult. Listen, we're working with a 150 year old consumer brand right now that wants to become more data driven and they're very product driven. And we hear the CIO say, we want people to understand that we're a data company that just happens to produce this product. We're not a product company that generates data. And once we realize that and start behaving in that fashion then we'll be able to really win and thrive in our marketplace. So one of the key roles of a chief data officer is to understand how data affects the monetization of an organization. Obviously there are for-profit companies of your healthcare organization. It's saving lives, obviously being profitable as well or at least staying within the budget depending upon the structure of the organization. But a lot of people I think oftentimes misunderstand that it's like, okay, do I have to become a data broker? Am I selling data directly? But I think you kind of pointed out many times and you just did that data is unlike oil. That's why we don't like that data in the oil analogy because it's so much more valuable and can be used. It doesn't follow the laws of scarcity. But what are you finding just in terms of people's application of that notion of monetization, cutting costs, increasing revenue. What are you seeing in the field? What's that spectrum look like? So one of the things I've done over the years is compile a kind of a library of hundreds and hundreds of examples of how organizations are using data and analytics in innovative ways. And I have kind of a book in process that hopefully will be out this fall. Sharing a number of those inspirational examples. So that's the kind of thing that organizations need to understand is that there are a variety of great examples out there and they shouldn't just necessarily look to their own industry. There are inspirational examples from other industries as well. Many clients come to me and they ask, what are others in my industry doing? And my flippant response to that is, why do you want to be in second place or third place? Well, why not take an idea from another industry, perhaps a digital product company and apply that to your own business? But yeah, like you mentioned, there are a variety of ways to monetize data. It doesn't involve necessarily selling it. You can deliver analytics. You can report on it. You can use it internally to generate, improve business process performance. And as long as you're measuring how data is being applied and what its impact is, then you're in a position to claim that you're monetizing it. But if you're not measuring the impact of data on business processes or on customer relationships or partner supply relationships or anything else, then it's difficult to claim that you're monetizing it. But one of the more interesting ways that we've been working with organizations to monetize their data certainly in light of GDPR and the California Consumer Privacy Act where I can't sell you my data anymore, but we've identified ways to monetize your customer data in a couple of ways. One is to synthesize the data, create synthetic data sets that retain the original statistical kind of anomalies in the data or features of the data, but don't share actually any PII. But another interesting way that we've been working with organizations to monetize their data is what I call inverted data monetization, where again, I can't share my customer data with you, but I can share information about your products and services with my customers and take a referral fee or a commission based on that. So let's say I'm a hospital and I can't sell you my patient data, of course, due to a variety of regulations, but I know who my diabetes patients are and I can introduce them to your healthy meal plans, to your gym memberships, to your at home glucose monitoring kits. And again, take a kind of a referral fee or a cut of that action. So we're working with customers in the financial services firm industry and in the healthcare industry on just those kinds of examples. And we've identified tens, hundreds of millions of dollars of incremental value for organizations from their data that they, we're just kind of sitting on. Interesting, I don't call you a business value strategist at the top. Where do you see, where in the S curve do you see you're able to have the biggest impact? I mean, I doubt that you enter organizations where you say, oh, they've got it all figured out, they can't use my advice, but as well, sometimes in the early stages, you may not be able to have as big of an impact because there's not top down support or whatever. There's too much, you know, technical debt, et cetera. Where are you finding you can have the biggest impact though? Generally, we don't come in and run those kinds of data monetization or information innovation exercises unless there's some degree of executive support. I've never done that at a lower level, but certainly there are lower level, more kind of immediate and vocational opportunities for data to deliver value through simply analytics. One of the simple examples I give is I sold a home recently and when you put your house on the market, everybody comes out of the woodwork, the fly-by-night mortgage companies, the moving companies, the box companies, the painters, the landscapers all know you're moving because your data's in the US and the MLS directory. And it was interesting, the only company that didn't reach out to me was my own bank. And so they lost kind of the opportunity to introduce me to a mortgage, to retain me as a client, to introduce me to my new branch, print me new checks, move the stuff in my safe deposit box, all of that. They missed a simple opportunity. And I'm thinking, this doesn't require rocket science to figure out which of your customers are moving. The MLS database, or you can harvest it from Zillow or other sites, is basically public domain data.  Some of these solutions are pretty simple. So is that part of what you do come in with just like hardcore tactical ideas like that? You're also doing strategy.  I try to take more of a broader approach where we look at the data itself. And again, people have said, if you tortured enough, what would it tell us? It's going to take that angle. We look at examples of how other organizations have monetized data and think about how to apply those and adapt those ideas to the company's own business. We look at key business drivers internally and externally. We look at edge cases for their customers' businesses. We run through some hypothesis-generating activities. There are a variety of different kinds of activities that we do to generate ideas. And most of the time when we run these workshops, which last a week or two, we'll end up generating anywhere from 35 to 50 pretty solid ideas for generating new value streams from data. So when we talk about monetizing data, that's what we mean, generating new value streams. But like I said, then the next step is to go through kind of that feasibility assessment and determine which of these ideas you actually want to pursue. So you're, of course, a longtime industry watcher as well. As a former gardener analyst, you kind of have to be. My question is, if I think back, I've been around a while, if I think back at like the peak of Microsoft's prominence in the PC era, it was like 1990.  And then, of course, Linux comes along and a lot of open source changes and lo and behold, a whole new set of leaders emerges. And you kind of see the same thing today with the Trillionaires Club. And you feel like, wow, even COVID has been a tailwind for them. But you think about, okay, where could the disruption come to these large players that own huge clouds? They have all the data. Is data potentially a disruptor for these, what appear to be insurmountable odds against the newbies? There's always people coming up with new ways to leverage data or new sources of data to capture. So yeah, they're certainly not going to be around forever. But it's been really fascinating to see the transformation of some companies. Like, I think nobody really kind of exemplifies it more than IBM, where they emerged from, originally selling meat slicers, right? The Dayton meat slicer was their original product, right? And then they evolved into manual business machines and then electronic business machines. And then they dominated that, then they dominated the kind of the software early, mainframe software industry, then they dominated the PC industry, then they dominated the services industry to some degree. And so, they're starting to get into data. And I think following that kind of trajectory is something that really any organization should be looking at. When do you actually become a data company? Not just a product company or a service company or, you know, so. Yeah, yeah, I mean, we have, Interpol Bandari is one of our new guests here. He's the Chief Data Officer of IBM, you know him well. And he talks about the journey that he's undertaken to transform the company into a data company. I think a lot of people don't really realize what's actually going on behind the scenes. Whether it's, you know, financially oriented or revenue opportunities, but with, you know, one of the things he stressed to me in our interview was that on average, they're reducing the end to end cycle time from raw data to insights by 70%. That's on average. And that's just an enormous, you know, for a company that size, it's just enormous cost savings or revenue generating opportunity. Yeah, there's no doubt that the technology behind data pipelines is improving. And the process from moving data to those, from those pipelines directly into, you know, predictive or diagnostic or prescriptive output is a lot more accelerated than the early days of data warehousing. Is the skills barrier is acute?  Well, I think companies are figuring out a way around the skills shortage by doing things like self-service analytics and focusing on more kind of easy to use mainstream type, you know, AI or advanced analytics technologies. But there's still very much a need for data scientists and organizations and a difficulty in finding people that are true data scientists, you know, there's no real kind of certification. And so really anybody can call themselves a data scientist, but I think companies are getting good at interviewing and determining whether somebody's got the goods or not. But then there are other types of skills that we don't really focus on, like the data engineering skills. You're still a huge need for data engineering. You know, data doesn't self-organize. You know, you can, there are some augmented analytics technologies that will automatically generate analytic output, but there really aren't technologies that automatically kind of self-organize data. And so there's a huge need for data engineers. And then as we talked about, there's a large interest in external data and harvesting that and ingesting it and even identifying what external data is out there. So one of the emerging roles that we're seeing, if not the sexiest role of the 21st century is the role of the data curator, somebody who kind of acts as a librarian, identifying external data assets that are potentially valuable, testing them, evaluating them, negotiating, and then figuring out how to ingest that data. So I think that's a really important role for an organization to have. You know, most companies have an entire department that procures office supplies, but they don't have anybody who's procuring data supplies. And when you think about, you know, which is more valuable to an organization, how to not have somebody who's dedicated to identifying the world of external data assets that are out there. You know, there are 10 million data sets published by government organizations and NGOs. There are thousands and thousands of data brokers aggregating and sharing data. There's web content that can be harvested. There's data from your partners and suppliers. There's data from social media. So to not have somebody who's on top of all that is I think a gross, it demonstrates gross negligence by the organization. That is such an enlightening point, Doug. My last question is, I wonder how, if you can share with us how the pandemic has affected your business personally. I mean, as a consultant, you're on the road a lot, obviously not on the road so much. You're doing a lot of chalk talks, et cetera. How have you managed through this and how have you been able to maintain your efficacy with your clients? You know, most of our clients, given that they're kind of in the digital world a bit already, made the switch pretty quick. Some of them took a month or two, some things went on hold, but we're still seeing the same level of enthusiasm for data and doing things with data. In fact, some companies have kind of taken our advice that data could be their best defense in a crisis like this. It's affected our business and it's enabled us to do much more international work more easily than we used to. And I probably spent a lot less time on planes, so it gives me more time for kind of writing and speaking and actually doing consulting. So that's been nice as well. Yeah, there is that bonus, you know, obviously theCUBE is not doing physical events anymore, but hey, we've got two studios operating and Doug Laney, really appreciate your coming on. Always a great guest and sharing your insights and have a great MIT CDOIQ. Thanks, you too Dave, take care, cheers. Thanks Doug. And thank you everybody for watching. This is Dave Vellante for theCUBE, our continuous coverage of the MIT Chief Data Officer Conference MIT CDOIQ. We're back right after this short break.


Hello and welcome to this video on error detection using cyclic redundancy code check. Myself Mr. Vipul Kondekar from Valchin Institute of Technology, Sholapur. At the end of this session, students will be able to differentiate between the two error control schemes error detection and error correction. Students will be able to calculate the CRC code for the given data using generator polynomial and identify the presence of error whether in the data transmitted data if error is there or not can be identified with the help of CRC. These are the contents of this video presentation. We will talk about error control technique, error detection correction, CRC code what generator polynomial, division how we carried out at the time of encoding and how decoding is done to identify the error in the transmission. When you are talking about the error control basically when you are transmitting the data the data is going over channel. Channel itself is sometimes unreliable so when you want to make your communication reliable or unreliable channel you need to have some sort of error control techniques. Now this error control can be done in two different ways either you can go for error correction techniques this is also called as forward error correction or forward error control you transmit the data if error is detected then try to correct the error at the receiver side itself. So this technique is called as error correction where you detect the error as well as correct the error. And then another approach which is called as which is also called as reverse error control is error detection technique. Here only error is detected and once the error is detected then receiver will ask for a retransmission of the data to the sender so retransmission will take place. You are using error correction or detection ultimately what you do for that is suppose n number of bits you are sending as a code word so that n bits will be made up of actual m number of message bits to that r bits or redundant bits are getting added. So actual code word send will have message bits along with some redundant bits and then these redundant bits will give you ability of detecting the error or correcting the error. Now think when to use error correction technique and when to use error detection technique. Now we will go for CRC cyclic redundancies check technique for error detection. So here only error is detected d number of data bits suppose you want to send so what we do is we take the help of g as a generator polynomial so that generator polynomial has let us say r plus one number of bits then what we do is we use that r bits in such a that we concatenate the data bits along with the CRC code which is generated by using a modulo to division technique. Now this modulo to division is when you are doing in mod to operation your addition and subtraction is analogous to XOR operation itself with in the example we will see how this mod to division is carried out. So you will use generator polynomial and then data will get divided by that generator polynomial. Now receiver knows that generator polynomial so what receiver does receiver again divides that transmitted code which is generated by the same generator polynomial and if the reminder is nonzero then error is detected and if reminder is zero then there is no error in the transmission this is what conclusion you can make. This CRC is quite popular technique it is widely used in the practice for Ethernet or 802.11 Wi-Fi ATM as well. Now let us try to understand this CRC check how it is carried out if you have some data d so to that data I already at the transmitter side I append n number of bits now how many bits you will append it depends on if your divisor which will be decided by the generator polynomial has n plus one number of bits this number of zeros will be n means if your divisor has five bits here four bits you will append. Now data along with this appended zeros this whole thing is divided by the divisor and this divisor will be decided by the generator polynomial and whatever is the reminder of that that reminder is called as CRC code and that CRC code will be of n bits. Now what I can do is these n bits can be appended here so data previously all zeros were there but while sending what I do is data and instead of zeros this CRC bits will be appended here and at the receiver side what I do whatever is the received data I will use the same divisor very important thing is this divisor and this divisor has to be same and then I will check for the reminder and if reminder is zero then it means that there is no error in the transmission and if reminder is non-zero means the error has occurred in the transmission so that data has to be rejected and then this receiver will ask for retransmission to the sender. Now let us try to understand different generator polynomials available these are few standard generator polynomial examples so let us take this is one generator polynomial x raise to 7 x raise to 5 so then from this generator polynomial I can form a divisor and the divisor will be what wherever that x raise to 7 means 7 degree is available so there I write 1 so x raise to 0 is 1 fine so x raise to 1 is present I write 1 x raise to 3 and x raise to 4 are not present in this polynomial so at these locations I write 0 0 as well x raise to 6 is not available here I write 0 so based on this generator polynomial I can have binary equivalent of the divisor now what I do is these are some standard generator polynomials use if you are using a synchronous transmission ATM so CRC 8 is used as well as this CRC 16 is used for HDLC high data link layer control protocol as well same thing is used for Bluetooth also and for LANS Ethernet this CRC 32 is the generator polynomial used for the error control now let us try to understand how error is detected using CRC now assume that this is the data you want to say so data has four bits now I decide that the generator polynomial based on generator polynomial this is the divisor what I have so divisor has four bits so what I do is I append three zeros here then I carry out mod 2 division so when you are calling out mod 2 division you look at the most significant bit appearing here and if that bit is 1 I write quotient as 1 and then if quotient is 1 this whole divisor I will be writing here so this is 1 0 1 1 now actually in regular division you are carrying out here subtraction but if it is mod 2 division this subtraction is analogous to the XORing operation so this 1 XOR 1 will be 0 so like this I do that XORing operation I get here 0 1 0 and then as here I have four bits I take one bit from here this is the appended three appended zeros are there so now look at this here again most significant bit is 0 so the quotient part I write it as 0 and then if left most bit is 0 use 0 0 0 as a divisor so again I write 0 0 0 here now again I do mod operation here most significant bit is 1 so I write here 1 and then I write divisor as it is I do mod 2 subtraction I get this result finally I get this reminder reminder is 1 1 0 so now what I do is how I form code word is code word is formed by actual data bits appended with the reminder what you got here so this will be the data word which will be sending now assume that this data word is sent let us have two cases where the data word is sent which has no error and this is the same data word sent but what you find here is this bit is altered this bit is becoming 0 actually it was 1 so now what happens again what I do is I use the same generator polynomial for the receiver side again I carry out mod 2 division and then what I get I check for the reminder here now if I find that reminder is 0 0 0 it means that there is no error in the transmitted data but here if I look at this division here I know that this error has occurred but how the system will come to know error has occurred so system will carry out again mod 2 division at the side of receiver for decoding's purpose and then when that division is carried out what result you get is non-zero and when the result is non-zero you can conclude that data word has got error so that has to be discarded and the receiver will ask for retransmission of the data to the sender so this is how CRC will be doing the error detection that error control will be of forward error control where sorry that will be of reverse error control where the receiver will detect the error and once the error is detected receiver will ask for retransmission of the data and then it will be expected it is expected that when you are transmitting read when you are retransmitting the data there will not be any error so basically when you go for error detection technique you assume that the channel is reliable and because of some reason if error has occurred then when the data is retransmitted the probability of occurrence of the error in the retransmitted data is very less in that case we go for using error detection techniques so this is how the CRC code can be used for error control it can detect the occurrence of the error so these are the references used for this video presentation thank you


The management of state house Abuja would not hesitate to punish any staff who betrays the trust and confidence reposed in them in the course of the assignment. That is a strong statement from the permanent secretary of the state house, Tijani Umar, at the inauguration of the villa's anti-corruption and transparency unit. The unit was inaugurated by the independent corrupt practices and other related offences commission. Tijani is meant to check corruption at Nigeria's number one residence.


Hi everyone, Joe for jaspyscasebrakes.com with a one-pack cello break of 2019, 2020, Panini Donneress Optic Basketball. This is number eight. We gotta do one more. I know I said otherwise earlier, I apologize about that. We gotta do one more pack for reals this time to unlock that flawless. So it's on the site now, let's make it happen, jaspyscasebrakes.com. At the end of this particular break, we're gonna do the pack break itself. The whole point of this pack break is to see who wins spots right here. Big thanks to these folks for getting into the action. And there's all the teams right there. Perhaps if you don't happen to win a spot, maybe you can try just one more time to help fill up that other break too. Maybe the second time could be at the charm. Let's roll it. So let's do the break first. Six times for each list. And six. Six in final time, we got Anthony. Down to Fuad. Six times for the teams. And six in final time. After six times, we've got the Knicks. Down to the maps. All right, so there's the first half of the list right here. Gonna go Chris Rowe, Pelicans. There's the second half of this list right here. Grizzlies, Kevin Menendig, I think. Really for this one pack break. Those are really what we're looking for. Let's order those alphabetically by team. TWC, trade window closed. And just like we do with break credit, we'll re-randomize that list right there. Once again, top seven will be in flawless. Serial number, break number two. How do we like the other two last dance episodes? The Rodman stuff was solid. What I loved about the Rodman stuff was I think obviously he's a character for sure. But what I love was just how hard he worked at basketball. All right, we gotta give him that. As weird as his off the court stuff is. On the court, Savant. I loved how he was in that interview chair and he was just like, if the ball gets shot by specific individuals, like the ball spins off the hands of Larry Bird like this, off magic like this. And depending on where it hits the rim, he knows where it's gonna go. And we got a little more fill in the second episode. So a lot of good stuff there. Can't wait for the next two episodes. We got Pink Darius Basily, 18 out of 25. Seja McCollum, Bias Harris. All right, so none of the big rookies here. Maybe here. We got Mike Connelly, Blue Velocity, Dame. And a nice Kobe White. There you go, James Teal with the bulls. Speaking of the bulls, gets this Blue Velocity. These aren't numbered, but exclusive to this cello pack set, if I'm not mistaken. All right, now, again, the whole point of this is to see who's gonna win those spots in the break. Once again, we gotta do one more of these. I apologize to people I let astray earlier today. And it's top seven out of the 30. It's not bad. And how much is that? That break is $150, so $150 value right there. So good luck to Kurt, all the way down to Steve Locke and everybody in between. Fresh list, new dice roll, top seven after seven. Seven and seven. And seventh and final time after seven. Big thanks again for those of you who gave this a shot. If you're down here on this side of the list, apologies, but we do have one more cello pack. Maybe you can help there, try just one more time. And congrats to Kurt, Steve Locke, Kevin, Fuad, Tony, Anthony, and John. You are the winners.  Thanks for watching, everybody. I'm Joe, I'll see you for the next one. Bye-bye.


Bismillahirrahmanirrahim. Salamu alaykum wa rahmatullahi wa barakatuh. Brothers and sisters, this is Imam Tahrir and we're reaching out to you. I hope and pray all of you are doing well, insha'Allah. You know we have a saying in the West. We say, dress to impress. It reminds me, there's a hadith of the Prophet sallallahu alayhi wa sallam, where he says, Inna Allaha jameelun wa yuhibbu al-jamal. Allah is beautiful and he loves beauty. There's a story that's narrated in the hadith traditions that once there were certain companions of the Prophet, peace be upon him, sallallahu alayhi wa sallam, who were traveling. And the Prophet sallallahu alayhi wa sallam specifically instructed them and said, you know, fix your saddles. And he said, be distinct that when someone sees you, they, you know, they're in awe. They recognize you. You're impressive. And there's a verse in the Qur'an also. Allah says, Allah subhanahu wa ta'ala talks about the donkey, the horse, and he says that you ride it and it's also for your beauty. The point I'm trying to make is that beauty, physical beauty is a very, very important part of our tradition. And part of that, as the scholars of Islam have reminded us, is to dress well, to dress to impress, but also dress modestly, right? Modestly means that we dress in a way like, you know, what does, what does modesty mean in Islam? Modesty means that we dress in a way where the curves of our body for both men and women are not shown, are not visible. And so to dress loosely, but dressing loosely and dressing simply as sometimes people understand it doesn't mean to dress dirty or to dress down, dress well. And so I just want to take a moment to remind myself and everyone else that as Muslims were required to dress modestly, being mindful of what we wear, especially in the house of Allah subhanahu wa ta'ala, you know, there's a dress code for everywhere that we go. There's a certain type of clothes you wear at a wedding, certain type of clothes that you may wear at work, certain type of clothes that you wear in a certain place. Well, we're coming to the house of Allah subhanahu wa ta'ala. So we dress modestly, but then we go above and beyond when we come to the house of God, house of Allah. And also as Muslims dress well, dress nicely, dress up, look nice, smell nice, be nice. So that's really all I wanted to say to everyone that, you know, dressing well and dressing modestly is a very important part of our tradition. And we should be mindful of it at all times, but especially when we come to the house of Allah subhanahu wa ta'ala. May you all be well. Jazakumullahu khayran. Salamu alaykum wa rahmatullahi wa barakatuh.