Good afternoon everyone. My name is Renee Campion and I am the commissioner of the Office of Labor Relations. We are here today for another important labor announcement. We've reached historic agreements with our largest unions, our smallest unions, and so many in between. Today we are proud to announce another tentative agreement with the important people who lead our schools, the important people who serve our city's children. So without further delay, I'll now turn it over to our mayor, Eric Adams. Thank you so much, Renee, and just really, Commissioner Campion has been an amazing, fair negotiator for our men and women that's part of our municipal workforce, the professionals who do the job every day, but also for our taxpayers. And it's interesting because they're both part of the same groups. Our teachers, our principals, our police officers, our firefighters are all part of the tax base as they want to make sure that this city remains affordable for them and ensure that they're able to provide for their children and families. So we're really excited today to be standing here with my good friend Henry Rubio, the president of the CSA, as well as the chancellor. And Henry says something that really resonated with me, that we have been able to settle close to 90% of our contracts because of the speed of trust. To have a blue collar mayor that is selling in contract with blue collar and professional workers, that trust allows us to move at a rate that we have not witnessed in the past. After only a few months, we've been able to settle some of the most difficult negotiations that have historically taken long periods of times and years to settle. For example, the 13 year period of settling the ferry boat operators and the New York City Police Department. We've come to an amazing agreement today. I'm proud to announce the city of New York has reached a tentative five plus year contract agreement with the council of supervisors and administrators. These are the men and women who look after our children and young people and ensure that we could continue to have a professional environment for our children to be educated for the future that lies ahead of them. The contract provides a substantial wage increase for the people who support and safeguard our children. And it secures a fair deal for our taxpayers as well. This agreement will cover approximately 6,400 municipal employees and include annual raises, employee retention payments and a ratification bonus. All of which will help retain talent, which is crucial in this environment, and keep our public schools strong. Under the leadership of Chancellor Banks, it has been clear that we want a strong professional leadership and our principals and other school administrators play a major role in accomplishing this task. This contract also includes a provision to ensure principal and administrators play an integral role in the design and implementation of our virtual learning program. First of its kind in the nation of a school system of this size, and both school-based and non-school-based employees will be able to work flexible schedules in line with the work flexibility provision in the DC-37 contract. This is a great deal for Rokas, it's a great deal for the taxpayers of our city, and it would not have happened. I'm clear on this without a clear and focused leadership that's coming from the CSA, our President Henry Rubio. I cannot thank Henry enough. He came in with an open mind. He built on the long years of trust and not only the formal partnership, but the informal communication as he moved towards having our school system continue to evolve to be a first-class school system. And we cannot thank our champion of labor relation, Renee Campion. We need to put a C in that name because you're just a champion. You know, he's tough, tough, tough decision. She has been a real keeper of taxpayers' dollars and also a keeper of my vision and dream as a Rokan-class mayor. How do we ensure Rokan-class New Yorkers benefit from the prosperity of this city? And the President Rubio made it clear. He stated that Rokan with Chancellor Banks has allowed him to give the assurance and the understanding that we are going to be fair. And what we ask of our leaders and our teachers and the employees of the New York City Public School, but we're also going to make sure we are fair with them. And it is that trust that allowed us to get here today. And today's agreement brings a total city workforce under contract to approximately 88%. 88% in just a short period of time we've been able to solve these contracts and the concerns. We started out as this far apart. Not only came together, but we're now united as we move our city and our workforce forward. I want to thank the hard work of our educators. Educators mean so much to me. The nurturing, the consultation, the support that my educators throughout my personal journey has shown me and how educators have shown so many of our children in the city. Many of these children coming through an environment with so much brokenness, concerns, and despair. And the safe place for them we've always stated is in our school system and that is materializing every day. Great job done by all. Great partnership. Let's continue to move our city forward. Thank you, Mr. President. So now I'm going to give you some specifics of the agreement. So the term of the agreement, as the mayor said, is five years, two months, and one day. It starts January 29th, 23, and it expires on March 29th, 2028. There are five series of general wage increases. On January 29th, 23, a 3% wage increase. On June 29th, 2024, a 3% wage increase that's compounded. January 29th, 25, another 3% wage increase. January 29th, 26, 3.25% wage increase compounded also. And January 29th, 2027, a 3.50% wage increase. For a total of 16.77%. The additional items that were funded in this contract were all funded internally through contract extension and other internal mechanisms. There's a ratification bonus of $3,000 upon ratification. There is an annual retention payment that the mayor mentioned that starts December 29th, 2023, a $400 payment. On December 29th, 24, a $700 payment. All pensionable. December 29th, 25, a $1,000 payment pensionable again. December 29th, 2026, $1,033. And December 29th, 2027, $1,069. And each payment after that will go up with collective bargaining. In addition to those items, there's an enhanced salary schedule for elementary and junior high school principals to close the gap between those levels as well as the high school principals. That was funded by the city's internal equity provision. In addition to those items, there are improvements to the existing school size differential. For the first time in 20 years, we've increased the school size differential substantially. Principals in schools with larger enrollment and city-wide special ed with greater numbers of classes will have substantial increases in this existing school size differential in addition to their salary and differentials. For those schools with enrollees, 700 to 899 students. The current amount of $1,000 is going to $2,250. In schools 900 to 1,199 students, the current amount is $1,750. That is going to $3,500. In schools from 1,200 to 1,999 students, the current differential of 2,500 is going to $5,000. And in all schools, over 2,000 enrollees, the differential goes from $3,250 to $6,750. This was all funded internally through the contract. I'll just highlight that as the mayor said, there are 6,400 city employees in the CSA. Approximately, for the most part, there are 1,700 principals, 3,500 assistant principals, and over 1,000 education administrators. There is also an increase to a differential for principals in charge of summer school that currently was 2,500. That is also increasing more than double to $5,620. As the mayor mentioned, part of this agreement is the inclusion of virtual learning and remote work in the CSA contract. Supervisors play a vital role in the ground-baking virtual learning program, and there are now going to be flexible work arrangements for school-based and non-school-based employees, and I'll defer that to the Chancellor when he comes up next. As far as the budget, the cost is $500 million through FY 27, already funded in the existing budget, and fully funded in the financial plan. As the mayor said, we are at nearly 88% of our city employees, and I look forward to negotiating with those other 12% of our city employees. I'm doing it day after day after day, as I've said, 24-7, which is how we go until we reach 100% of our city employees. So now, to hear about how this tentative agreement will impact the Department of Education, I'm happy to introduce our Chancellor, David Banks. Thank you. Thank you. Thank you, Mr. Mayor. Thank you, Renee. You know, we started out today, and it was raining heavily, but it's shining now. The sun is shining now. This is a great day. It really is, and I could not be happier today than to be the Chancellor at a time where we have settled a very, very, very fair agreement with the CSA, particularly with my friend, Henry Rubio, who serves as the President.  There are going to be times when you have to say yes and no, you've got to push and pull. But the fact that we were able to settle on an agreement without being so disagreeable means a lot to me. It means a lot to the Mayor. We were able to work well together, because at the end of the day, I was a school principal. And it's critically important to me that we have an agreement with principals and assistant principals where they feel that they are appreciated and respected. Because I'm very clear, the work that we have set out with our agenda cannot get done without the leaders in these schools who have to deliver to make it happen each and every day. This contract says we see you, we appreciate you, we respect you, and we need you. And so as the Mayor made the announcement in the Grand Hall or the Grand Ballroom here at the hotel, the place just exploded with energy. Because those were the leaders in the field saying thank you. Thank you to the Mayor. Thank you to Renee. Thank you to their President. And I want to say thank you as well to my team. I want to say thank you to Kim Warwood and her team. I want to say thank you so much to Dan Weisberg, our first Deputy Chancellor and his team, to Dr. Danica Rucks and her team. This is a team effort that gets us to a place like this. It doesn't happen overnight, but it happens because good people come to the table with the best of intentions. And I couldn't be happier for the leaders of our school system. And I think some of the innovative things that we've got in this contract are going to serve to really inspire our school leaders to even greater heights. And we're going to be on the cutting edge of leadership all across this nation. So thrilled to be here. Can't wait to hear President Rubio's position on this and how he feels about it. But Henry, we did it, man. Good stuff. I will be brief because I believe the Mayor, the Chancellor, and our commissioner has said it all. We are excited. As the Chancellor said, we've got 1,500 school leaders in a ballroom next door. And when this Mayor made that announcement, the crowd erupted. Because it was clear that they understood that the Mayor understands what it means to be a leader. And the sacrifices that they in and out our members make to make sure that we protect the city's children. And we make sure every single day that we're making progress. And in those conversations, Mayor, I never have to explain it to him. And I think really the best decision he's made is to select David Banks as our Chancellor. My brother here gets it. He was a principal. He was a CSA member right here in New York City. The largest school system in the country. And so he gets it. And at the table, there wasn't a lot of explaining. Because parties at the table really understood what our school leaders needed. And if we can take care of them, trust me, they're going to take care of our city and our children. Because we wake up every single day with no other desire than to serve our city, serve our parents, serve our students, and make sure that our parents are proud of us, that we send your kids home safe every single day, and we give them the best quality education we can. And let me tell you, our Chancellor has a bold and bright plan for this city. And I love that he's partnering with CSA to create that plan. And we're only seeing the beginning. There's so much more to come. And I look forward to working with the Chancellor and the Mayor on unfolding everything that's coming out. We got to understand this is a huge system. This isn't a rowboat. This is a cruise ship. And it takes time. But he's got all the right people in the right places. And I want to thank my team that was at the table. Rosemary Sinclair, Dale Kelly, General Counsel David Grandwither, but also David, your team and Renee, Donica, Kim, Dan Weisberg, you guys were all amazing. You got it. They're great people. I am blessed to be the leader of CSA at this time with this Mayor, this Chancellor, this Commissioner, and the incredible team you have. I'm blessed. So on behalf of our 6,500 members, Mayor, thank you so much for what you've done. When I called him, he made this contract happen, and we're excited to get to work on Monday. God bless and thank you so much.


Welcome back to Tennis Talk, my name's Cam Williams and with Indian Wells starting in a week's time We have some big names that are pulled out of this event. Some massive names are pulled out over the last couple of hours So let's go to the withdrawal list starting with the women's withdrawal list from this tournament Starting with the women's list for the tournament We have a few names pulling out of this one and some Grand Slam champions Alexandra over she's pulled out party of course has pulled out the Australian Open finalist Brady She's also pulled out Sophia Kennins pulled out Serena Williams has also pulled out Conta and Osaka has also pulled out of this one heading over to the men's side now And there are so many names that are pulled out of this one over the last couple of hours some big names are pulled out So we've started with better name. He's pulled out public Shadi Kwebos Chorich, but Novak Djokovic the world number one He is not going to be playing Indian Wells this year Which makes it four out of four for Masters 1000s events in North America that he will not be playing Remember Miami opened at the start of the year. He didn't play that and he didn't play before the US Open either So he's not playing Indian Wells Federer is not playing Goffin, Umbeer, Nick Kirios Rafa Nadal of course with injury. He's not playing Raunich, team, and Stan Vebrinka is also added to the list of Grand Slam champions that won't be playing at Indian Wells There you have it Indian Wells It is not going to be the fifth Grand Slam this year because we haven't got the big names that are supposed to be playing Let me know down in the comments below Who are you most shocked about not being in this tournament because not only on the men's side, but the women's side We're also missing the world number one with Barty not playing and of course Osaka as well So let me know down in the comments below who are you most shocked about not seeing at this event? So Indian Wells it starts in a couple of days time, but we're gonna have some massive names not playing


Hello everybody! In this video, I'm going to show you how to create a bootable USB drive for Windows using surprisingly an Android smartphone. Problems with booting an operating system may appear out of the blue, and usually it happens at exactly the wrong time. To make things even worse, a recovery disk of bootable USB drive might be missing just on this special day. But wait, isn't that a kind of a portable computer you're carrying around in your pocket? With an Android smartphone, you can easily create a bootable drive and restore the operating system of your computer. So if your only PC or laptop won't boot, and you decided to restore or install its operating system, this video should be extremely useful. In a minute, I will show you in detail how to create a bootable USB drive or memory card for Windows using a portable Android device. So let's begin. Hello friends! If you need to recover deleted data, view or restore removed browse in history, Hetman software products will help you. Follow the link in the description, download the necessary program for free, install it, and analyze the disk. The utility will show you the data you can recover, so you will be able to view it or get it back. In our channel and blog, you will find solutions to any problem, from installing an operating system or configuring it to fixing possible bugs and errors or optimizing mobile gadgets. Our specialists will answer any questions you ask in your comments under the videos or articles. And before we start, let's find out what we need. First of all, a USB drive or memory card of sufficient capacity. It will have to be formatted, so if there is any important data, save it elsewhere. If you are going to use a USB drive, you will also need an on-the-go, also known as OTG, adapter to connect it to the smartphone. Before the process begins, make sure your phone's battery is fully charged, as creating a bootable drive is quite a long and power-consuming process. Finally, you need an ISO image of the operating system. You can download it from the Microsoft official website. It is necessary to have that image in the memory of your phone. So open any browser from your Android device and search for a page where you can download an image of the operating system. I strongly recommend using images from the official website only. Select version, confirm it, select language, and confirm your choice again. After you select the image bit-ness 32-bit or 64-bit, the download will start. Wait until the entire image file is downloaded. As soon as it's ready, start creating the bootable drive. In order to write an ISO image of the operating system to the storage device, you need to download a special app – ISO to USB – to your phone. It's a very simple tool, and it doesn't require you to have root permissions. Unfortunately, its description doesn't state clearly what particular image file times it supports, but user reviews suggest it can work successfully with Windows, Ubuntu, and other Linux distributors. Alright, now connect the USB drive to your Android device and start the app. In the app window, tap on Pick button next to the line Pick USB pendrive and select your USB device. For the line Pick ISO file, also tap on the Pick button and specify the path to the ISO image file you have just downloaded. By default, it should be located in the Downloads folder. Since the drive you are using is not empty yet, check this box to have it formatted. Finally, tap Start and wait until the bootable USB drive is created. It takes quite a long time. When the drive is ready, you will see this notification – USB device removed. That's all. The bootable drive is ready to use, and you can insert it into one of the USB ports on your computer to boot from this drive. Visit our channel for a detailed video tutorial about installing an operating system, and you can find the link in the description below this video. If you connect the freshly created bootable drive to a computer with a Windows operating system already up and running, it will tell you the drive is faulty and suggests to fix it. Please don't. In fact, the USB drive is alright, and your computer won't have any problems with booting from it or installing the operating system. The reason why Windows responds so critically to this USB drive is that the Android app formats it in a way that seems unusual to the operating system, though it does use the proper FAT file system. You can see that the drive contains all the necessary components. Everything works properly, and installation begins when the computer boots from the drive. And that is all for now. Hopefully, this video was useful, remember to click the Like button and subscribe to our channel. Hit the bell button to receive notifications and never miss new videos. I leave you comments to ask questions. Thank you for watching. Good luck.


A person who wins 10,000 people every month to God, let's say, brings 10,000 people to Christianity. It will take them 60,000 years to bring the whole world to God. So that's a lot.  So 10,000 people every month. Let's face it. Let's pushing it. In our church, we focus one. If we do one a month, we probably need to leave eternity here before we see 6 billion. But people are multiplying. And so 10,000 people a month, that's a lot. 60,000, we only had, was it, 2,000 years since the birth of Jesus? 60,000, that's a long way to go. But the statistic says that if a person brings two people every month, somebody say, I can do it. And teaches them to do the same will win the whole world in 30 months. So that's what Jesus is saying.  It means teach the people to do the same. That's what apostle Paul says to Timothy. He says, whatever I tell you, tell to other people, make sure you tell to people who can tell to other people that they can tell to other people and that they can tell to other people. So it gets to some people in Washington state in Pasco in 2014. And I'm so glad the link wasn't broken. Our goal is not to just, we win as many people, but the people we bring to Christ that they immediately begin to understand the mission under life to help others come to Jesus Christ, to become those fathers in Jesus' name. Amen. Guys, God doesn't want us to be Elijah's. Elijah, Elijah was the one that died and he had anointing in his bones. You know what that means? That means anointing was never passed on to next generation. He died in his bones.  What would have happened if that anointing wouldn't stay in his bones but went on to someone else? Not only one person would be come back to life. Hundreds and thousands. Why was the anointing that was double on Elijah, stayed in Elijah? Because Elijah was anointed, Superman. But Elijah lacked the Father's heart. Because when his servant made a mistake, he put leprosy on him. Now, fathers don't do that. Prophets do that. Fathers don't do that. Jesus was greater than Elijah. And all of his disciples, some did corny things, some really foolish things. And Jesus rebuked them. Jesus encouraged them.  John, leprosy. This guy, dead. Drop dead right now. We don't see Jesus ever cursing his disciples, though they made mistakes. Jesus on the opposite, he came back from the dead and he saw them returning to their old life. And what did he say to them? He says, come on, Peter, I got breakfast for you. He didn't say, come on, Peter, I cannot wait to put leprosy on you. Who betraying me. He didn't say, come on, Peter, I cannot wait to just send you to hell for what you did. And have you joined your friend Judas over there? He didn't do that. Because see, a real mentor is not someone who can just pull the potential out of people but who can deal with people's mess. If you cannot deal with people's mess, you cannot be a father. If you cannot deal with people's mistakes, with people's shortcomings, with people's weaknesses, that is the part that requires on our end to do the part of bringing people to Jesus Christ. Come somebody say amen. Fruit that remains, revival that remains, is we're going to bring people to Jesus. And most importantly, we are going to disciple people. Our vision in the church is evangelism. Our strategy is discipleship.


My name is Ken. I'm the Enterprise Learning Architect at SimCorp, and in this I'm going to try to tell you a little bit about how we adapted and changed our learning and development organization to fit our new agile setup. This is supposed to be a speed talk, so I have 20 minutes, but I need to give you just some quick background on sort of the circumstances for all of this. Can everyone hear me? Sorry? I don't know. Can we turn up the volume a little? No, okay. I'll speak louder then. Okay. Just a quick show of hands. How many of you have ever heard about SimCorp? Couple of people. All right, that's sort of to be expected. So I just need to tell you really quick we are. Basically, we have one product. It's called SimCorp Dimension. It's a product that's targeted for the investment management and asset management industry. So basically if you're a professional working with an investment, that's that's what our product is all about. We developed on this product for more than 40 years. So it would be accurate to say that we are not exactly a startup anymore. We were founded in 1971. Annually, we plow sort of roughly 20% of our revenue back into R&D. We have 1600 employees worldwide. And of those 1600, around 650 are working on the actual product, developing the product. We have offices around the globe. Basically all the major financial centers were Scandinavia. We were started in Denmark. So we have offices in Sweden and Norway, Scandinavia, but other it's Paris, New York. Basically where you have big financial centers. We have more than 16,000 end users and we have around 180 clients worldwide. So those are seven sort of relatable numbers. And you can ask, well, OK, you've been in business for 40 years and you only have 180 clients. That's four and a half client per year. What are you doing? So let me give you just one big number to sort of round the introduction of assets under management by SIMCorp clients, so 20 trillion US dollars. So that's trillion with a T. That's basically what dimension is used for. It is used to manage stupendous amounts of money. This is a number that is so large that it's hard to sort of relate to it. So just to give you something, when Sweden today, the gross domestic product, so the value of all the goods and services produced in Sweden, a country of 10 million people, a very productive country, was half a trillion. That's 40 times less. The GDP for the entire world was 80 trillion. And since it is an astronomical number, the estimated number of stars in the visible universe is 100 trillion stars. So it is a very large number and that's what dimension is all about. It is managing this kind of money. There you go. So every path to agile is different. Our path was, we chose SAFE, the Scaled Agile Framework. Anyone heard of that? Familiar with it? A couple of people. Okay. It is basically a framework that allows you to scale agile from team level over portfolio to the entire enterprise. This is doing some strange things. We introduced this in 2016 and then we spent pretty much a year doing nothing else but train the organization in how to be, how to work in this new setup. So the entire organization, learning and development in particular, was completely focused on this. We did nothing else but run this training program. We had over 200 courses. We had eight full-time agile coaches working on this that was basically everything. We had no bandwidth for anything else. So that's one thing that led us to change our organization. The second was that in order for us to do this, we changed our organization. So we had a very nice, normal, very understandable line structure in the past. We had this falls under our CTO. So this is basically his area. I'm sure you're familiar with this kind of structure. Our learning, we had also a very conservative, traditional approach. We had three different academies that was targeted at this. Software engineering, product management, project management. Each of the academies had an executive sponsor. It had a principal, which was basically a program manager. And then there was a steering committee that ran everything else. Once we introduced SAFE, then all of this disappeared. So I didn't even have a structure anymore. These are the new roles that SAFE worked with. So they were rolled out. Everything was focused on rallying around the agile teams, because that's where the value is created. And now we have an organization that looks pretty much like this. So a little more complicated than a nice, normal line. The important thing to note here is that there is a complete separation between execution, that's what we have over here, and people development, which we have over here. The teams are self-managing, self-organizing. So a completely new structure. So since we had this kind of blank piece of paper, we might as well think how can we change the organizational set of around learning and development to actually fit what we're doing since we don't have anything else and since we've been on hold for basically a year doing nothing but agile. How should the new organization look if we had a blank piece of paper, which we were lucky we had. So we wanted to create something that was durable, something that was scalable, and something that was agile. The main principles were that we needed to create a strong, sustainable link between the business and learning so that there wasn't a gap between that learning was over here and the business was running over here. It had to be in sync. We want a complete transparency around everything that had to do with learning. People should know exactly what was going on. It didn't matter what kind, where you were in the organization. We wanted to create as much transparency also around budgets as possible. The key part here is we wanted to engage, involve, support, and empower the development managers. So this was a new role that was introduced by SAFE. The development managers sort of simplifying this a little, but their key job is to develop people, to make sure that the people we have grow in the roles that they have, that they become better for want of a better world. And then we needed to ensure that the business critical behaviors and skills that we had actually got the bandwidth that we needed. That was a challenge we had in the past. How, without adding headcounts to an L and D organization, how can we actually create enough bandwidth to support all the stuff that we're doing? And finally, obviously, we wanted to create a fit between the new ITL organization and everything else we were doing that had to do with learning and development performance management. So blank piece of paper. We had our new organization. We had the ITL teams. The teams were picked up by the art structure. That's a SAFE concept. Don't worry so much about that. We had a new leadership structure. And we had the development managers who were organized around the geographies that we were located and so mainly this part of the organization is in Denmark and it is in Kiev and the Ukraine. Then we have small satellites in Germany and UK as well. So a development manager works with the people in the country that they're in. We also had a strategy farm. The development managers, along with the enterprise leadership team, get together. They define a strategy, a business strategy, and that's basically the starting point for what we were doing. So we work with behavioral competencies and we work with something we call strategic skills. So basically something that's important enough to take to an enterprise level. And we wanted to ensure that that was business driven. So as soon as we have a business strategy, we look at that, we look at our behavioral competencies and we say, okay, what do we need in order for us to do, to execute on the strategy? These are just examples and the same, oh, sorry, and you can sort of sum that up into what we would define as our agile mindset. The same with strategic skills, coaching and facilitation, safe know-how, databases, architecture, C-sharp development. All of this is something that we could derive from our business strategy. Once we had that, we would create a learning strategy and say, okay, this is what we want to do. This is how we can support this as best as possible. And then the really big thing was that since we changed the organization, we now had an opportunity to centralize the budget. So instead of having learning and development budgets that basically rested within each of our organizational entities, be it a group, a unit, a division, we said we have one budget for learning and development. And we took a two-pronged approach to that. We have a discretionary budget. That discretionary budget is money that we allocate directly to the development managers because they have stuff that is not offered on a corporate level. And then we have the enterprise level and then we have the enterprise activities. So we have, for instance, a C-sharp course that everyone has to take. It makes sense that we have that over here, but there might be a specific conference, a specific training event that would be covered by the discretionary budget. Organizing all of this, we have the learning architect. It sort of sits in the middle of everything and tries to coordinate. And then we pulled the rest of the organization into this. So we pulled our development managers into this. We pulled our agile coaches into this. And we pulled product management into this because those are the three major stakeholders that we have in this. And then we looked at what we defined based on strategy over here and said, okay, it makes sense if you take charge of this, you become learning owners for this area that we call the agile mindset. And you might join up. So here we have a development manager working closely with an agile coach. So they take responsibility for that and they become learning owners for that area. And I'm sure you get the gist here. All of these are then sliced out and they're allocated a learning owner or team of learning owners who then run with it. The discretionary spending, as I said, that goes basically just into the development managers. They're free to use that for whatever they want to use it for the only thing that's important for us is that we sort of track it so that we can get inspiration if you send someone on a course. And it's a great course. It's important that the rest of the organization knows about it. But other than that, it is discretionary spending. It's also important and it sort of feeds into this centralized budget is at some point we would want to have the teams themselves have a budget for this. So that there wasn't a buffer in that you had a development manager who was actually allocating it. But the teams themselves would have the budget and they could spend it on whatever they wanted. All of this sort of feeds into a normal budget process would mean that you would sit down at, sorry, in Q4 usually. And then you would make a list of line items and saying this is what I would like to be doing for the next year. And you would try to guess, what am I gonna be doing in Q4 next year? It never works that way. But nevertheless, that's the budget process. So that's how we did it. But since we could now sort of change everything, that's what we did. We said, instead of having a fixed process, we say, okay, we have a reasonable guess as to how much money we're gonna need. Safe runs in program increments. In SimCorp, we run three-month program increments. Why not do the same with learning and say we're running learning increments? So every three months, we do a learning increment.  Now we can certainly say, okay, we already know that we're gonna need something in Q4. But other than that, we try to keep as much flexibility in our budget as possible. We don't commit to things that we just think we have. We make the decision based on what the actual need is. We have a support structure for all of this. We have learning management systems, all these kinds of things. And we have performance management processes that are being revamped to fit agile. But the key essence in this is that instead of trying to guess, which was what we had to do in the past, what are we gonna be doing a year out? We took just the natural consequences of that and said, no, we're gonna chop this up into three-month increments. That also meant that when our development organization hits into or starts a program increment, because we're timing this, they have a full picture of what kind of learning is gonna be available for that increment. And they can adjust their capacity based on knowing that we have this course running, we have that course activity. Okay, that means that our capacity is gonna be instead of 100%, it's gonna be 80%. And every basically ladder rinse repeat. The strategy process repeats itself every year and we start this process all over again. Yes, it's basically just a time duration. So it's a three-month period. So an increment is just, it comes from safe where you say you have a program increment of three months. So we say, okay, a program increment is how much we're gonna produce on the product. A learning increment is how much are we gonna produce in learning and we just slice that into a three-month period. It might make a little more sense because in order for us to manage all of this, we said, okay, we'll use just a traditional Kanban approach. So every three months, everyone who's involved in learning meet, we have the Kanban bought up and then we decide what are we gonna take from the backlog and move into doing and what are we gonna start executing on. So if you sort of put some, try to populate this, then there are five things that you can, that we start every learning increment by doing. The first thing is you look at the web limit and here we're not just looking at what is an individual team learning team able to do. Are they within their web limit? But we can also look at the entire enterprise and say, are we actually trying to roll out too much learning? That was a problem we had. Sometimes we would be offering courses and the business would come back to us and say, well, it doesn't work for us because we have this, that or the other happening in this particular increment. So you can't be running these courses right now. We need to sort of readjust that. Here we have complete visibility of everything we're doing, both in terms of what the teams are doing but also across the entire enterprise. So if we had two test related courses running, then the business could come back and say that's not really good for us because we have a lot of testing that needs to be done here. Can you move that back? Can you reschedule that to the next increment? The next one is we have Ideas Request New Knowledge as part of this particular Kanban board. This was a way to open up learning to the entire organization so that everyone could just pitch in with whatever ideas they had. This is something that we should be doing. So one thing was democratizing for one of a better word, the process around learning. But it's actually also a very interesting monitoring tool where we can see if we have emerging technology and we only have one thing in that, then a couple of red lights might go off and say, are we seriously not generating any new knowledge within this space? Is there nothing we want to share with the rest of the organization? The third is the budgets. I've already talked a little about that, but basically instead of trying to fund everything, we look at what's our backlog, what is important right now, and then we fund according to that. And it also means that we can look at, so just as an example, the agile mindset. If we have, this is full up and it's very, very expensive all of it, then I might need to have a conversation with the learning owners for that and say we can't spend our entire budget on this because money is a finite resource in all of this. And fourth is learning strategy. Instead of trying to know exactly what we're gonna be doing a year out, we basically revisit this every three months. Are we still on track? Are we still actually trying to do what the business needs for us? And the fifth is the done column where we instead of just having a done column, this, oh, sorry, to a large degree. So everything is the old traditional 70, 20, 10. I get back to it in one second. So basically done becomes the repository for all the different knowledge that we have. It's not just, as you say, it's not just courses, but it's everything we have. So we need to figure out how do we take the done column here and put that into the various different internet learning portals, everything we have. How do we then make sure that it's visible to everyone? Yeah, no, every quarter. No, I, yeah, yeah, the top line is fixed. The top line is fixed. Yeah, not necessarily that way, but so just as a round number, let's say I have a million euro to spend every year, that number is fixed. Finance is not gonna come and give me anything more than that, but how we distribute it across the different skills that can change every month. So we might go into a year saying we expect to be spending 25% of our budget on agile skills, but it turns out that we actually need to spend more on our technical skills. And those are decisions we make every corner. It also just, and the discretionary budget as well. So we start off by, we don't allocate everything, we do it in the increment way.  And then just to add just the two new roles that we've introduced, we introduced the role of the learning architect. Basically the learning architect facilitates the strategy process, the facilitate the budgeting process, they own the learning budget and the learning strategy. They facilitate the decentral budget distribution and allocation process. They coordinate capacity and coverage with our senior leadership team. They drive the execution, mainly through the Kanban process. They coach and they advise learning owners. This is a very important thing because what we've basically done is we've taken a lot of people, we brought them into our learning setup. Not all of them have a background within learning. Most of them have, are good, can get stuff done. They can go out, they can contract with a vendor, they can do whatever, but they might not have a theoretical approach to learning. So coaching and advising is a big part of it. Coordinating with different parts of the rest of the company. So we have a group learning, that's actually why I belong. We have a group learning function. How do we coordinate with that? How do we talk to our leadership academies making sure that they get the right input from this part of the business? And then they align with different global processes. Could be performance management. And again, they align best practice approaches and they define what best practice is within, within research and development. So if you look at the words here, facilitate, facilitate, facilitate, there's only, this role has a tremendous amount of influence and very little real power. And it's been designed exactly to do that. The only lever that the learning architect has is the budget. But you have to work very closely with the rest of the team to make sure that this gets done. But other than that, the learning architect is not intended to be someone who just makes decisions and say, okay, this is the way we're gonna be going. Because we wanted to create the new role which is, so it says development manager, but think of these as learning owners. They own whatever budget they get allocated. So if they come and ask for 100,000 euro and we allocate that to them, they own that money. There's no approval process. They don't have to come and check. They have to spend within their limits, but other than that, they own whatever money they have. They define and evolve a 70, 20 cent based curriculum. That's something that we try to drive, as you said, not just courses, but everything else. They obviously need to identify and work with different stakeholders within the organization. They drive the development of the learning material and the key here is they drive. They're not supposed to sit down. They're welcome to do it if they want, but they're not supposed to sit down and create learning material. They are prime movers. They start the process. They prepare and they lead the area that they have once we get to the Kanban process. That's a hugely important thing in this because if you try to do everything in here, then you don't have it. You're not passionate about anything. And I wanted to have people who were passionate about their area. I wanted someone who would step up and slam their fist on the table and say, you know what? This is the single most important thing that we do. Maybe we disagree, but we wanted that kind of passion into the system. They're sparring partners for the rest of the organization. So if we have a learning owner for C-Shop, it makes sense that other development managers come and talk to that person and get their input on how best to develop people who are working with this particular skill. And they facilitate the creation and execution of effective communities of practice within their area if needed. So if we don't have a COP within something that we think is important, they might try to establish one, but still it's a COP. They're not trying to run it. They're just trying to facilitate the creation of it. So basically, we have been doing this for around a year. I think we are on the right track. Right now we're working with 14 strategic skills that have been identified. We have 21 learning owners who are assigned and who are on the job. And we have complete transparency and visibility to a number of dashboards that we've created where everyone can go in and see everything that's going on. So in 20 minutes or less, that was the very quick introduction to how we are doing this in Simcoe. I'm happy to take any questions sort of offline once we get there. Thank you.


So we're live at the Texas Legislature's hearing on redistricting in San Antonio and the room is packed with people from San Antonio who came here to make sure their voices were heard, as you can see, the state of Texas. Okay, so let's see if that audio is a little bit better now. Here at Texas Legislative Redistricting hearing at Fort San Antonio, three bus loads of people came from San Antonio to make sure their voices were heard. Initially, the folks of the state of Texas were pretty mom about these hearings. They didn't even want to have hearings in some of the urban areas, but there you go. I apologize so much that we're running out of seats. We just did not anticipate this level of interest in the program that competes around what those rooms would hopefully see. And so please accept our apology for that. Also, however, we do have an overflow room that's here to my left and the audience right and the sound while we brought tests to there. And then also out in the lobby, I understand it's being broadcast as well. Again, we'll start in just a minute, but if you do wish to testify tonight, there is an affidavit form that you have to fill out so that we can know you're here and that you have to wait. And it's almost four, so give us just a few minutes. Again, I want to apologize so much for the crowded condition, but we're so excited that we're trying to make the best we can. Thank you. This is full too. Thank you, Joe. So we're going to let the state take it away with another screen.


To tell us more, we have from Repsol, Elena Thomas and Emilio Martin, both senior data scientists at HubData and Analytics. Welcome to you both. Thank you, Nicolas. Hi Emilio, are you sharing your screen? Yeah, I'm going to start setting my screen, so let me know when you start seeing my presentation, please. Sure, I'll confirm it. Okay, still, we still can't see it? We can't see it. Everything is all set. Take it away, Emilio. Okay, thank you. Okay, so thank you very much for your introduction, Nicolas. As you say, my name is Emilio Martin. Today, together with my teammate Elena Thomas, we are going to talk about how we are managing corrosion detection in our refineries. Essentially, the talk is divided in two clear parts. The first one is related more about what is the mission of Repsol as a company, how we are performing our digital transformation program. The second part that I think is going to be more interesting, not only because I'm not going to be the speaker, but also because Elena is going to share a lot of technical details about the kind of neural network architecture that we are using to detect corrosion in our refineries. When we were preparing this talk last week, we tried to put on our audience shoes. The first question that came to our mind was why? Why a company like Repsol with more than 25,000 employees and with almost 100 years of history is investing so actively in this kind of technology? The answer to that question is pretty simple. Essentially, we are a multi-energistic company and our mission is to bring and design the energy of the future to our customer. Essentially, this is a simple statement that is a really difficult puzzle that we are trying to solve in our day-to-day. To be honest, I don't think that we have a definitive answer to that puzzle. We know some of the pieces. For example, one of the pieces on that puzzle is to provide clean energy. In this regard, last December, our company made public our commitment to become a net zero emission company by 250. I know that 250 could sound like 30 years time, so it's a lot of time. But this commitment comes from our CEO. Also, we have a set of milestones that we have to achieve. That means that we are working in our day-to-day actively to achieve that goal. I think that a good example of that is how we are using our technology and talent to try to figure out what is the answer to that question. It's the hackathon that we participated last summer. Essentially, the hackathon was arranged by Microsoft, and the end of the hackathon was to detect methane leaks using satellite images and, essentially, artificial intelligence. So, the main challenge here was that, as it's pretty common in our industry, there was a scarcity of data. Essentially, the team came up with an innovative solution that instead of trying to stick with a supervised learning approach, we came up with a simulation approach. On top of that simulator, we put an optimizer in order to, essentially, assess what is the probability for each of our assets to be responsible for causing the leaks. And, essentially, we won the hackathon and it was an international hackathon. And I'm sharing this piece of history that maybe we could provide further in the next big things, not only because the hackathon was pretty good, but also because I think that it is simplified pretty well how we perform things in the epsilon, how we are trying to solve this big puzzle that is to design the energy of the future. As I said, we don't know, we don't have yet definitive answer, but we know some pieces of that puzzle. And, essentially, some of those pieces are, for us, key ingredients are people, talent people, technology, and resources. And to say we do some figures about our digital transformation program, the first thing that maybe draw your attention is the scale of our digital transformation program. We referred to our digital transformation program as a kind of umbrella under we developed all kinds of digital initiatives. We have underway more than 200 digital initiatives in which are involved more than 1,200 people in a cross-company collaboration with a budget set only for the previous year, sorry, on 150 millions of euros with an estimated return by 2,022 of 1 billion. And, obviously, we can't accomplish that huge amount of project by only using internal resources, but so, therefore, we rely on some partner and supplier. And the last part that I think that is also quite important is the people and talent people. And under this digital program, we created almost 500 new roles and employees. And it also, and me, works for the data analytics hub, but there are other hubs like digital chain on each channel, robot, professional automation, cloud competence center, security, digital marketing, and hardware robotics. Essentially, in this picture, you can see our chain of value. So we have different portfolios. We have exploration and production, trading, refining, distribution, electricity and gas, consumption, and corporate. The project that we are going to share with you today is from the refining portfolio. And inside its portfolio, we have different strategy initiatives. In the case of refining, the first initiative that we have is flow lessons and always say that essentially it's aim, which is aim to improve the workforce and plan safety levels with tax automation and some real-time data assessment. Here also, safety is, I would say, our highest priority. Autonomous plan that is related to use data in order to optimize the parameters of our units and also of our plans in order to improve the process in an automatic manner. And improve the process could be like reduce the energy consumption or produce more product of certain quality. And to business planning that essentially in this kind of project, we use predictive analytics to optimize our decision and our decision range from design which kind of component you can buy or how to charge or recharge some vessel or what this kind of stuff. And then the project we are going to share today is framed in the strategic initiative that we call zero and effective failures that essentially is aimed to increase reliability and reduce maintenance cost and somehow extend the asset life cycle and maximize the production. All this, I mean, every single digital product that we develop under this strategic initiative is inside one internal product that we call asset health that essentially is some application in which we can deploy different models to asset the health of our assets. So focusing a little more in the topic of this talk that is corrosion, just as Nicola said at the beginning of the presentation, corrosion has a huge impact in our operation. So typically it means a couple of millions per year of impact and we can distinguish two types of corrosion, internal corrosion and external corrosion. In this graph you can see the number of intervention that we have made each year and in blue we have internal corrosion and in red we have external corrosion. Essentially internal corrosion represents 80% of our interventions and external corrosion only the remaining 20%. Here you have a snapshot of our application which is called asset health. Right now I'm going to share a video. Essentially the aim of this kind of project is not only the fact of corrosion in our pipe but also to provide a prioritized list to our technician in order to make inspection because as you can imagine with thousand of kilometer pipe what you have to what you want to have is just a prioritized list about in which part of the refinery you should inspect. So I'm going to put a video. So this is typically an image of one of our refinery and for those of you that have never been in our refinery, our refinery is a pretty big thing so maybe it has several kilometers, several square kilometers regarding the application. Essentially when one of our technicians logging, sorry one of the technicians logging he has a list, he has an agenda and he has a list of next situations and for each pipe he has the name of the asset, the line, the parameter who is having a higher impact in the asset health estimation and a set of nodes and also he has a set of assets located in their refinery and also ordered by the asset health evaluation that different models are providing for each asset. So if we click on a particular asset we can also see the evolution in the time about the data set, how it's evolving that health index and so on. So now I will pass you over to my colleague Helena who is going to provide more details about the models and the challenge that we face during this external corrosion project. Helena are you there? Can you hear me? Yes, can you hear me? I was talking. Yes Elena we can hear you. Okay nice, I'm sorry. Okay so why did we choose external corrosion for our presentation? Okay we think that it's a more complex problem, we have developed our own external corrosion tool while in the market there were already commercial available solutions for internal corrosion so this thing we consider it more innovative. Why is this a challenge? The challenge is to look for correlations between the visual and the thermal images and try to find insulation breakdowns of our pipes. The reason it is challenging is that we want to do that in an uninvasive way, we don't want to do it to check the defect manually every time we have because to check the defect manually means that we have to remove the insulation and we would like to remove the insulation only if the operator is quite sure that there is a corrosion problem underneath. So this is what we aim to and the way we have started to deal with is to build a machine learning model to automatically detect these defects by using visual and thermal images. Here in these images you can see four different kinds of defect. The image on the left shows some defects of raptors in the following image shows some complete detachment of steel on a pipe. The third image shows why and this detachment of the steel is is only partial and the fourth image shows another kind of defects that are very popular which are defects that occur within the joint of the pipes. So this is our objective and the difficulties we find are different and several. Here in the image you can see some part of the rack of the refinery in La Coruña that we have used to start with this project which is the starting of if it's as sexual it will be followed by another phases of the project. But for this part of the project we are limited to this data from one rack in this refinery. Another difficulty is that the ground truth is very difficult to know. It's not only needed that the operators could check the ground truth in the images. The operator has to go to the real site and check if the defect is a real defect or not. So it's not a ground truth very easily to detect even for a human and it is combined with the fact that the corrosion defects are sometimes located in in parts of the rack that are most more difficult to be reached. So it's difficult to go there and check and then if we detect accurately X, Y and Z coordinates which is another challenge, detect three-dimensional coordinates from a two-dimensional image then our work will be more useful to the operator in order to make its impact. So these are the kind of images that we are going to use. I already showed the visual and these are the thermal images. We see that the available data is the scars as I previously mentioned for this part of the project. We have less than 1,000 images if we include both thermal images and visual models and we have built two models, the thermal model which will distinguish between two classes defect or non-defect and the visual model which will distinguish between the four kinds of defects. The thermal model is not always useful because there are some defects that may be not sweet of or on depending on the weather conditions and on the operating conditions. If their pipe are not wet you may have a defect but you may may be not detecting this defect in the thermal model. So this is the reason behind combining both. Okay just for those of you who don't have it very fresh, this is a summary of the analytical challenge of the field of computer vision in the previous years which almost has to do with the feature extraction part. In the past it was a very hard task to to be made but in the more recent years in the last 15 to 10 years in the field of deep learning a lot of progress that has improved the computer vision challenges because now they can build features in an automatically way. The reason behind this you can see in the upper right left of the slide you can see this is a convolution operation. We could see like if our images would be represented by a matrix made up of numbers and what is a convolution is a mathematical operation which will get another matrix and put this matrix across the original image and will produce another matrix or another image. The purpose in the image on the upper and the bottom left we can see what would be the what is the the normal architecture of the deep learning used for computer vision tasks. Normally it's based on these kinds of convolutional layers and the way that it layer operates is the one that you can see described on the image. The first layers will get features of the image that are low level for example we could detect the borders we could detect difference in the lights as we go and move forwards to the right in the layers we could be appreciating more complex features for example we could extract circles geometric like rectangles and then more even more complex things so that the objective here is to generalize the image that we have on the left which is a car into something that would say this is something large with four wheels so that we could apply it you know in the last layer of our architecture for classification tasks this has been it's a it's a has been everywhere in the scientific community there are several problems that you can approach and solve using these tools in our case we are going to use object detection because we want to detect the defects and image classification once we have the the defects detected we have to classify into one two three or the fourth label as I've said in the last 10 years starting from 2012 especially we can see in the upper part of the slide that the top one accuracy has increased greatly from 50 percent to almost 90 percent here you can see several architectures famous architectures in the field like alex net inception and the thing and the way these architectures has improved the results mainly deal with the number of parameters that we are including in the model we can see that the radius of every circle has to do with this million of parameters and that it's it's a very huge we observe almost the end in 2019 the effort of the scientific community by changing the in the accuracy which is called efficient nets that we have used in in this in our model but how can we make use of all these state-of-the-art studies of the scientific community the answer is transfer learning transfer learning is a change of paradigm apart from traditional machine learning in the past we used one data set to learn one task and another different data set to learn another task with transfer learning paradigm we are able to make take advantage on one data set that has been trained for learning task one and combine this learning with our smaller data set to in order to make our system learn task two so with less images you get a less computing time and less computing expenses you let you get a good performance on task two in order to see this is more in detail we can see here how how could we transfer transfer these parameters in a more technical way in the upper part we can see how we train the system one in order to complete the task one which is how to classify between elephant, wild snake and terrier labels we can see the architecture which is made up normally of several convolutional layers and at the end of it some fully connected layers and we make past the training images through it this is what we call the pre-trained model now we were interested in transferring some of the parameters of this network that it has been costly to to to get and the transfer parameters that we normally freeze are the parameters that are encoded in the in the part of the first layers well or the not the last layers of the architecture which has to do normally with embeddings and with feature extractions so in this way now we are going to pass through our training images so that for example for training what we say a window we could make take advantage of the thing that architecture one has already learned in previous tasks for example learn how to identify rectangles so this is the idea the main idea behind behind this paradigm in episode we are using it in many projects and many projects having to do with computer vision and many other projects that has to do with natural language processing there is some beneath it is that in that way we will save computing time we will save expenditures and we also will reduce our carbon footprint that as my colleague Emilio mentioned at the beginning it's one of our purposes for 2015 at zero emissions commitment so apart from that the other advantage of transfer learning and as I've already mentioned is that in many domains not only in this case of this project training data is scarce at the end all our companies under the digitalization program but to make it a real data-driven company with the size that it is we have to to start little by little and not we can we don't have always all the available data that we that we would wish so yes here why did I introduce the problem on transfer learning because this is the base the base are for our visual and thermal models are pre-trained models from using the architecture of efficient net and res net for the thermal which has been trained on the co-coded data set and for the framework we have used the tensor to flow object detection API because of its performance and popularity and afterwards we get our own model which is able to receive these kind of images and render output that coordinates x y set of the defect the class of the defect and the confidence score of the defect was a performance of our model okay so what metric did we choose to measure our performance since our data set is is unbalanced we cannot choose the accuracy and we have to go to metrics such as precision or recall I will remind you briefly what precision means precision is the true positives out of the positives that I say they are and recall is the positives that I say out of the real positives that we can find in the sample for our business unit the important metric here most important is recall because they don't want to miss defects in their refineries and if you see and take a look to the results on the thermal images we can see that the precision and recall are quite high we have a precision of 95 percent and recall of 70 percent however for the business unit this is not enough because as I mentioned previously not always are the thermal defects on switch on so we have to have a model that also performs good on the visual images here our performance is lower however we are trying to improve it by returning the model with more training data but this machine learning model that I have presented it's only a small part within our whole pipeline I want to emphasize here all the pipeline that we have developed in this project the first part in green is the manual process which has to do with a modeling three-dimensionally the space that we are studying and the second part which is automatically which includes the machine learning model and the link in between the 2d images and the 3d georeference of the defects and the web that we have built in order for the to do the manual model a photogrammetry and the photogrammetry to summarize is to uh convert the space that we are studying into a three-dimensional model how do we do this so here on the right we have an example in order to get a 3d example a 3d object of this house I would have to take 360 degrees images at three different heights low medium and high and then on the sketch on the left you can see how if I have these kind of images with these kind of um perspectives I could geometrically find the edges of the three-dimensional object this is the first step we did at the beginning of the project the second step was but the first step the photogrammetry it's give it give it it renders you a three-dimensional model but is a relative model we don't have it with a really absolute coordinates for that we have uh developed an object model based on a laser scanning of the same area and based on the label assets that the business unit provided us in this way we are modeling this three-dimensionally and we are going to be able to connect these things with the photogrammetry so the objective here that we have uh read is to convert the 2d image 2d images into 3d points and so ones that I have detect at detection in one 2d image I can relate it with this x y and set coordinate and the name of the asset which is great this is the thing that really helps the business unit and what's more here which is very advanced apart from this is the fact that we have built these visual and thermal cubes three-dimensional cubes so that we are able to group the defects for example we may have one defect that is being corresponded by 10 images taken for different from different perspectives so when we present these images to the to the operator maybe in this image you can see the defect from one or two and not from the from eight of the 10 images from the 10 of from the 10 images then this is the last part of the project which has to do with the model validation retaining and prioritization of areas here on the bottom right we can see what the operator would see in the website you can see that the blue dots are the defects that we find so the operator can click on any of these defects and prioritize and see the pictures that the model is choosing that that this corresponding to the to a defect or another in this way the operator can choose the area of the refinery where he where he has to move in order to assess that the defect is or it's not true or maybe he can see that there are four defects in the same area so he can decide he or she can decide to remove the insulation which is a hard part so this is one part of the web the other is that we are giving here a tool for the analyst so that in case that he can relabel the image as true or false defect so that it will help us with the model validation and retainer in the future and here are the key improvements that we have found out during the length of the project there are mainly three one related to image acquisition in this case it is very important to take very good pictures i mean good i mean need no very no difference in in sunny or clarity not shadows these kind of things that will help a lot of machine learning computer vision algorithm then these thermal images and visual must overlap in these 360 degrees when we do it and then another choice that we took when we started was to use radiation image instead of temperature image because temperature image depends a lot on the kind of day and it's difficult to be repeated in the same temperature images for the same asset then the second was labeling improvement we found several difficulties in the first weeks of the project only one person was devoted to label the thermal image another one was devoted to label the visual images so that the labelings were always done in the same way we had to remove one class in visual model because we didn't have many examples of that and we have to label some defects and in the in the field of data augmentation what it was to take different parts of the pictures zooming in zooming out and rotated to assess for the image variety variability and the most important thing is the retraining during the length of the project of this part of the project we have lasted for three months we have retrained twice during these three months and now we are retraining another and we have tried to build capabilities in our website so that in the future this retraining which will be much more easy and that the available labeled data will increase so I think with this we finish our presentation I would like to thank you for the opportunity you gave us to present our work and I hope you enjoyed our presentation and we are pleased to answer any questions that you may have thank you Emilio Elena thank you so much for for that very interesting presentation so we do have some questions for you let's dive straight in Ruben asks why defect no defect only one class in the thermal limit there is one class in the because there was only all the defects were switching on there's no difference you only see if something is being more wet or not you cannot assess the kind of defect good if there is corrosion you will see that the insulation properties of the pipe is different so or is it different or not sure another question google elmore because of the extremely limited availability of data did you try some one shot or few shot learning algorithms not yet not yet because in this part of the project sadly we devoted less time for them to the machine learning model than for all the thing that goes around the ones of the most difficult part here was have a reliable way of linking the effect to the positioning to the care positioning so we devoted more time on that and for the one shot didn't try we only tried the normal transfer learning with the images available and the unlimited okay question right did you use the pre-trained network for the classification or used it for feature extraction if you used it for extraction which classifier did you finally use for your classification and a follow-up does your model include both classification and continuous regression to arrive at its output i don't know if i understood the whole complete question but i will try to answer i mean we use the feature extraction layers not the classification layers the classification layers were our own layers and it was a classification layer not regression layer all right hopefully that answers your question Kenneth if not you'll have to get in touch um yeah how does the how does the model pick up cui cui is the part of internal corrosion isn't it this was a statistical model based on the flow properties okay i can't help you with that okay yeah he tried to contact through twitter so we yeah we can follow up that question on twitter that would be the best way for some of these questions are very specific is there any process to detect the quality of the images previous the machine learning model question from alejandra the process to detect the quality of the images has been carried out manually we don't have an automatically way of saying this is not valid this is valid because all our images go go through the training part uh of course at the beginning steps we saw that our performance of the was not good and we take a step backwards and say okay the images are not being okay but we don't have an automatically way of removing that uh for the image so all the images at the moment are manually taken there's no automation of the image capture no yes now there's automation but he means i think the question means for the for the quality all the images are taken okay it doesn't matter any quality yeah can we come back we have we have tried to we have tried to teach how to yes well the things that i have mentioned to tell the business units but in the case that in some part they do things badly we are not testing that automatically right final question what about the from the human resources point of view the technicians that previously you would have required to walk these kilometers thousands of kilometers and inspect all these pipes manually are they being retrained or are they being replaced what are some of the hr consequences of what we're doing here well i think that then i mean the technology is just a tool and and essentially um they are not going to be replaced of course the thing is that the we are just providing a tool to prioritize the world because most of the time when you talk with the technician they what they say we do is that essentially they don't know where to inspect i mean they have so many possibilities and so many kilometers to inspect that um just being given a tool to that give that give they are really decision is they i mean they are pretty happy with that tool so it's not um we are not trying to replace our technician because you know it takes a lot of time to to create a good technician great well that's that's great anyway okay well let's see it just remains for me to thank you both very much indeed we're almost out of time so maybe some of these more specific questions will find a way through the networking section to address them directly to you and once again thanks very much indeed to both of you thank you thank you