Hi there, I'm Sandy Olnok and I'm excited that today is day one of the 24 Tags of Christmas. Each year I've been making 24 tags using six different videos, six different stamp sets, and some dies and fun things. And this year is no different and I'll give you more details on the giveaway at the end of the video. This first one is going to use some shaker pouches from MFT. Recently I did a video with these thin ones and then there's also these high profile ones. For a tag on a gift, the high profile tag ones are excellent. You can also use them for candies and things, but I'm not even going to use them as a shaker. I'm going to use them as a diorama. Do you remember doing dioramas when you were a kid? Maybe I don't even know if they do them anymore. But what you need is a ring to go around the outside and then a circle to go in the back of it. And I'll let you figure out your measurements. The biggest thing for this one, if you get the pouches that I will link in the description down below, the center circle has to be two inches because that's the width of it.  The background circle needs to be the same size as the outer circle of the ring. And then I'm going to do some kind of simplistic sort of coloring on this panel here. And you'll see why because there's a few things that we're going to do to it to create a layered diorama.  We used to do them in school and put all different kinds of scenes together. And that's one of the places where I really started learning about foreground and background and stuff because you had to put them in some sort of order to create some depth in your little display that you were making. We did them for science class. I think I did some for books that I was reading. I had to create dioramas to tell the story of the book, do a scene from a book, etc. Okay, back to the project. Now I've got my image colored like my little dog Vienna. And I drew a little a little kind of snow hill behind the dog. And then I'm using the other piece to create another snowy hill. It doesn't matter what kind of curve. But the cool thing about this is you don't have to fussy cut or die cut the whole thing.  And the idea is to create a little layered hill in the foreground in front of the little dog. And these are stamps from MFT, by the way. They always do a cute dog set every year for Christmas. And this one is no different. So then you need to figure out how to do the background on this diorama card. So I'm going to hold it in place. You could tape it if you wished. I kind of wished I had done that by the time I had gotten this far. But you want to create little shapes on the sides on that ring that match your snowy hillside. And if you do like three or four snowy hill sides in depth, which you certainly can, then you'll need to do that many of those little layers. The background in this is going to be just a green forest. And that means the whole top section can just be solid green. And you'll see how that is going to make a difference when we get to doing the background of the circular panel. But I had used a kind of more of a bluish color in addition to the the blue-green color. So I kind of had to adjust my colors so that it was going to work with the colors that were in the snowy hillside behind the dog. The background, this is really cool, just flood it with color. And it only needs to be that top section. So I don't have to color the whole thing. I can just color part of it and then throw my little doggy in there. And I'm going to need to glue the dog in there because it's going to fit together kind of like that except the ring is going to be on top. But you can see how the scene carries across all those pieces. The snow then you can add at whatever rate you want. The other ones that I've made in this little series of four tags have different amounts of snow. So you can look at them when we get to the pictures at the end and see which amount of snow. Do you like a lot of snow or a little snow? So now I've got some dimensional adhesive on it. And since I've placed it while the ring was in there, it's in the middle so that I can throw the pouch on top and then use some be creative tape. It's super sticky tape. I like it when I'm doing things that I want to stay together. And then just place it over top of this thing. I don't have any adhesive on the pouch at all. It's just going to be the adhesive from the ring sticking to the thing on the back is going to hold it together.  Now you can make a shaker out of this. You can put shaker stuff in there too. But I decided to just leave it as a diorama tag instead of being a shaker tag. And I thought it still came out absolutely adorable. Just tied some red ribbon on the top and held it on there with a little piece of twine. And these are some of the other dogs that are in the stamp set. Dog number five from this stamp set did not make it into a tag because he did not fit in the little circle. So he made it on a card instead. You can see that card which you will not want to miss. It's adorable over on my blog. And the reason I'm sending you over there is because when I do YouTube giveaways, I have lots of things people never claim because they never come back to see if they won. But on my blog, I can email you if you leave a comment there. So I'm going to be picking from blog comments. They're going to be 24 winners as always. So you want to go over to my blog and leave a comment. The link is in the doobly-doo down below as well as the link to the MFT products and all the fun stuff. And you can also pin things from the blog. You know you're going to want to do that anyway. So I will meet you over on the blog so that you can qualify to win. And then go out and make something beautiful. I will see you again tomorrow with video number two. Bye-bye.


extra-frustrials, adventurers, travelers of the multiverse, stowaways, rapscallions all. My name is Kim, this is Dustmoots in Velikor, and today I get to tell you about my most recent binge read, The Invisible Library Saga by Genevieve Cogman. I'm gonna come clean. It took me a long time to get into the first one. It took me a while to realize that Genevieve Cogman's brain can turn on a dime, and you cannot miss anything, like mid-paragraphed, like mid-claws. You can all of a sudden have a complete plot shift or a hint dropped that you absolutely need to make sense of the puzzle that is each one of these books. High stakes, constant twists and turns, an incredible adventure in every box. Above all, I love Irene. She is a complex, multi-faceted woman with clear purpose and a clear head. Though sometimes her loyalties lead her to not investigate things that merit investigation, but I'm getting ahead of myself. Let's start big picture. The Invisible Library is a transdimensional organization that collects unique books from different alternate worlds in order to stabilize the existence of those alternate worlds. Different worlds, different circumstance, different influences on the author, all leading to different, altered, longer, shorter, or even additional books. Mary Shelley's hand written manuscript for the sequel to Frankenstein? Done. Shakespeare's clearly stolen adaptation of Escalus? Done. Each book strengthens ties between its alternate and the library, like backing up hard drive. The more data stored outside the alternate, the less likely that world is to simply unravel. Which does happen, especially to worlds too close to either end of the spectrum. Chaos? Order. Excessive chaotic energy will manifest itself into dramatic storybook tropes. The Fae, vampires, werewolves, humans, will find themselves woven into stories that will fly dramatically out of hand. The Fae rule chaotic worlds by making themselves more powerful, by defining themselves in dramatic archetypes and building sympathetic storylines throughout their lives. Arch-hemesies, blood feuds, bizarre assassination attempts, all these things and more can be found in your local world ruled by chaos. Excessive orderly energy manifests in dictatorial rule and military oppression. The dragons rule orderly worlds, encouraging scientific and technological developments in those worlds. Dragons are more hands-off rulers. Worlds under their supervision barely know that dragons exist. They are also bound by a strict hierarchy and code of conduct. Not a lot is known about dragons and they like it that way. Due to their antithetical natures and to literally no one's surprise, the Fae and dragonkind are almost constantly on the brink of war. The invisible library trains its librarians in book acquisition. It is a task which often requires situation management, lockpicking, disguises, negotiation, an impressive on-the-go problem-solving ability, as well as the language, which lessened enlightened people might call magic. But librarians know the words whole power and that is the tenet intrinsic to the language. Our heroine is Irene, who as I have mentioned, I love. Her favorite tactic is to go utterly unnoticed. Given adequate time and the necessary tools, she is a chameleon. She can become exactly whoever she claims to be at the moment. She's a sign of apprentice Kai, who claims a partially criminal upbringing, but can't seem to hide his ease in high society. And to round out the party Peregrine Vale, a world-class detective on par with the great detective, and he becomes an ally and friend throughout the journey. A myriad of other characters fly in and out of the storm, but these three are the eye of the hurricane. I love. I love the idea of librarians as spies, secret agents, con artists. Genevieve Cogman has created a multitude of worlds that are very true to themselves. The first book gives you steampunk themes, detective stories, vampires, and murder most foul. The sequel, Masked City, dives deep into chaos and takes you to a fey Venice where every day is a masquerade and enemies come disguised as friends and as shadows. The burning page sees the library itself in danger against its oldest enemy, and the lost plot pits dragon against dragon set in prohibition New York with mobsters and speakeasies and ancient Chinese literature. Most of all, I love the bond between Irene and Kai. Throughout the series they become less student and teacher and more partners, teammates. Irene worries that she's not teaching him the things he needs to know as a potential future librarian because she does the quick thinking and he's her extra pair of hands. But he is learning and one of the things she's taught him is to rely on their individual skill sets. In the last book we also get to see him make a plan and stands Irene, and if that doesn't show the problem solving skills he's absorbed by osmosis, I don't know what will. There is also just a tiny bit of romance in these books and I really love how it's done. How can I tell you about this without spoilers? I'm just, I'm just gonna tell you about it. Okay, so in the first book a character called Bradamant is introduced. She was Irene's mentor back in the day. Irene doesn't look kindly on the time they spent together and it influences the way she teaches her own student. Since everyone is an adult before they even get recruited to the library, everyone's over the age of consent. But Irene is adamant that she will not go to bed with her student. It's a power and balance that she feels very strongly about. So even though Kai is very charming about it and there wouldn't be anything out of the ordinary if she did, she doesn't accept his advances. But despite this, throughout the four books even as they careen through adventures that take your breath away, you are still watching them fall in love very slowly. At least that's where I'm hoping this is going. It will be a love based on mutual respect and shared intellect, communication, respect for boundaries, and the unbending faith that comes with knowing someone will come through for you. So I'm in love with the Invisible Library series by Genevieve Cogman. Given the pattern of publications, I think we can expect the fifth book sometime around December of 2018, but no release date has been announced. They are fast-paced, heart-pounding, occasionally terrifying, occasionally hilarious, and just ludicrously intricate. If that sounds like something you'd be into, you should probably check them out.  I'm moving my upload dates to Mondays because book releases happen on Tuesdays and that gives me a whole week to read a new release, write film, edit a video, and then upload it on Mondays just in time for the new set of releases to happen on Tuesdays. Boom, I'm a genius. Everybody, everybody, genuflect. All right, I'm feeling, I'm feeling the love. And, abiento.


Hello, this is Gerrit Leonhardt. Welcome to another edition of Meeting of the Minds. Today I have with me from Sydney, Australia, Ross Dawson, futurist, author, strategist and a good friend. So, Ross, you have written a book on crowdsourcing. What is it called again? Getting results from crowds. Okay. I have it in my Kindle queue and I read some of it, but I saw your slideshows on this and a lot of people talk about crowdsourcing. So, how do you find this? How do you actually define this and what could it mean on a global scale? My simple definition of crowdsourcing is tapping the minds of many. So, it's not about physical labor, it's about our minds or sometimes our money. But what crowds is about getting many, many people to be involved and engaged. So, it's not just finding one person or a couple of people. It's about how do we get value from many. And this is, I think, transforming almost every aspect of our world. It's transforming organizations. So, now because you can access crowds of insights and energy around the world, organizations now need to go outside to crowds. Those organizations that only look at their internal resources are enormously disadvantaged compared with those organizations that go outside. We are seeing that governments can look to crowdsourcing not just to be able to get a more representative democracy, but also to help them get many people to assist them in achieving their objectives of supporting a greater society, of making the society being the one which creates value. We're seeing that crowds are transforming media, where many people are creating media and helping us to filter and sort through what is the most interesting and relevant media and content. And we're having crowds being able to really shape opportunity. So, I think one of the marvelous examples I love is called VizWiz, where blind people can use their camera on their phone. They can take a photograph and then say, you know, of the things I'm looking at in my cupboard, which one is the can of baked beans. That message goes out around the world. People get that and we immediately send a message back, which gets spoken by the camera. So, we are getting many people lending their eyes to blind people. There's many ways in which we have collectively been able to provide value for organizations, for governments, to those who are disadvantaged. So, I think this is absolutely transformative. This is a new landscape. It's interesting, you know, a lot of people would... And when I speak about this with my clients, a lot of people argue that the crowd doesn't know much. You know, there is no wisdom of the crowd. And I don't know what you take us on this, but this is obviously a key question. Are they actually doing anything good?  I mean, that's what people are saying. I'm not saying it was. But so, do we have wisdom of the crowd or do we not? It depends on the situation. I think it's very important to say that crowds are not relevant for everything. And in fact, insight of individuals or sometimes small groups will be better than crowds in some situations. So, we have to be very careful. When do we use crowds? When do we not use crowds? And there's issues around intellectual property and confidentiality, around understanding enough context. So, for example, if you're trying to create a new strategy for your organization, it's not going to be very easy just to ask the world, the whole world, all right, what should our strategy be? But if you ask the people inside your organization, it's probably going to be a lot better insights from that than if you just ask the, you know, the head of strategy. Does it diminish the odd, the expert ship of the top-level people who've studied for 50 years to know this? Does it diminish them when there's somebody coming who was 16 years old, like this guy, Andrakha, who's inventing the tricorder? So, you know, the doctors are saying, now, this, I'm not happy with this because I'm the expert. Well, absolutely. I think that those who think, look at their own expertise are sometimes threatened or challenged. I mean, back to the broader point, you know, irrespective of crowds, doctors, many doctors today are unhappy that their patients have access to medical data and can search and find out more about their own disease before coming in. And this is a form of crowdsourcing as well. We have access to a lot of information. But, again, I would prefer to go to an expert doctor than I would to a crowd to get input about my disease. That's probably a good example. I'm not sure about that. I think that, of course, one of the central issues of crowdsourcing is the question of who owns what and why should I, and who's authorized and, like, you know, education moving into the cloud.  And does it matter? So, this is the question that I would have for you there. When does it matter and when does it not matter? I don't think there's a recipe there. No, I think it depends, of course, on situations. That example of education, I think, is a really good one because, first of all, there's a lot of content being created. We also need to make sure that we're accessing content while they're actually saying the right things. So, part of it is we get a crowd that gets reputation measures saying, yes, this is indeed quality education and you can trust that. Part of the next thing is if we get that education is how do we get the certification? As in to say, well, actually, you have that sufficient knowledge and what we can start to do is get crowds, this peer assessment. Peers can actually assess the people who they're studying with just as well as a professor can when you add up all of their opinions. So, we can get some probably some very accurate certification. Yes, you are at a university level or you're a Harvard university level even if you haven't necessarily gone there and a professor signed off on that and that's something which employers can look to and the way in which a crowd assessment can in fact be as equivalent to a expert official assessment. Well, isn't in the end I think that both of those ideas can miserably fail. You know, the old system, like a botanical, they had mistakes in it and Wikipedia came and wiped them out pretty much but Wikipedia, for example, my own entry was deleted on Wikipedia by somebody who hated what I said about the music business, I think. So, they told me I wasn't important, whatever that means, enough to be listed on Wikipedia, which I don't, I could care less. But, I mean, this clearly made me think about whether the crowdsourcing that happens on Wikipedia is actually good or not because if they can kick me out just because somebody doesn't like me, is that better than a botanica? There's many flaws in Wikipedia and I think that people have measured the best articles on Wikipedia compared with Encyclopedia Britannica, for example, and they're fine. If you go further and further down the long tail, it's more and more serious information. It's less and less reliable and I think we do need to understand that. But it comes back to saying crowds can be fantastic for some things but we do need to be very, you know, careful about that and understand the application. But one of the other massive applications of crowds is crowdfunding and this is the new ways in which capital can be allocated to where it can get the most benefit. And we've seen that, you know, many massive campaigns on Kickstarter and other platforms around the world and these are ways in which, you know, where money, people are voting with their wallets and being able to say, this is where I want to put money and getting fantastic things to happen which never would have happened otherwise. Yes, I mean, of course in Switzerland we live in this very interesting system where we are doing a lot with the crowd. And some, all kinds of issues, big and small. So we have a crowd, but, you know, we only have about, I think, 25% of people actually participating in these smaller elections because the crowd does not have the time to review the issue. So one of the key questions I have about crowdsourcing is this, you know, do we need a really unauthorized consumer? You know, if we have 7 billion people on the internet, will we get 7 billion of them involved in crowdfunding and contributing and, you know, or is it still going to just be that 1%? It certainly won't be everybody and will be a small proportion of crowdfunding but if you look at the size of the global capital markets you don't need much to be able to fill in some of the gaps for where things aren't happening today. And again, I think moving forward is what I describe as the crowd mechanisms. So for example, if many people have an opinion well, which opinion should you listen to more than others? These are the things which we can get evolve over time. So, you know, fun way I think is the idea of crowds and crowdsourcing is transformative but we still have a long way to go not least to be able to work out, you know, answer some of the questions you have around what works and what doesn't. So what should the company do now? Let's say in 5 years we've got 5 billion people on the internet and if you're a bank or you're an insurance company or a government, you know, how should you start looking at crowdsourcing as being part of your future? I think the first thing is to be able to say what is it that we should be doing inside our organizations? What can we look to outside? And there's 5 domains right now which I think are really important for organizations to look at. One is in terms of marketing. Very simply, in terms of how do we get great marketing content? How do we get customers engaged in what we are doing? Creating new products, for example. Another one is in terms of innovation. So throughout the innovation process to get ideas from many people very insightful people around the world about that. To be able to change business processes so the business processes can be allocated to whoever can do that most effectively around the world being able to build new business models to think around how can we build our business model so it is not just about us creating value but how the crowd participates in that business model and also for large organizations for their internal crowds getting, tapping the energy and insights and intuition of the people inside their organizations who can do more than just their job role but indeed contribute to many other things that the organization is doing. In a way so that's really sort of a summary of all points in being open essentially because crowdsourcing involves being open and taking the rest of being open. This is one of the things I think that for companies that are looking at this they have to be able to open up and take more of a risk in this process of finding these things because they want to be faster and you can't really have both you can't be closed and be fast or not take a risk and so this is sort of the natural trade-off and this is where, this of course gets difficult because when you decide to open then you also decide to take the rest. It is challenging but that's going to be the difference between the organizations succeed and those that get left behind. So one of the most visible projects is being the e-pebble watch which is a watch which is something that sits on your wrist I think we're going to have to stop calling them watches now which is linked to your mobile phone and gives you important things like social media updates and the weather and the latest information all the things and it's synchronized with your mobile phone. They didn't get any venture capital support they couldn't get a venture capitalist who would say this is a good idea we're going to invest in it. So they went to the crowd got more than ten million dollars in investment from the crowd and they've created a product which is transformative. So this concludes today's episode of Meeting of the Minds. Thanks very much to Ross Dawson for being part of this today. If you want to know more about the show you can go to meetingoftheminds.tv We're also taking questions and inputs for the next show. Just use the Twitter hashtag meetingoftheminds and we'll be responding and trying to work your comments into our next show. Thanks very much for joining us.


Abstract coronavirus disease 2019, COVID-19, caused by severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, is an ongoing global health emergency. Here we highlight nine most important research questions concerning virus transmission, asymptomatic and pre-symptomatic virus shedding, diagnosis, treatment, vaccine development, origin of virus and viral pathogenesis. This article was authored by Kit Sen Yuan, Xi, Wei Yi, Xin Yi Feng, and others.


Hi, everyone. So this talk is about the secure computation in the correlated on-demand model. And more specifically, it deals with the long quest for secure computation with low communication. So what's secure computation? You have some number of parties. Here, I will focus on the case of two parties for simplicity, but everything I say can be extended to more parties. And they would like to jointly compute some public function F on their joint private inputs. And the protocol should be correct, meaning that all the parties should get to learn the output of the function. And it should be secure in the sense that no party should learn anything more from the protocol than the output of the function. So if you don't care about security, and if you want to compute a function, then it's easy to see that Alice and Bob only need to exchange our inputs, meaning that the communication required to insecurely compute a function is just the size of the inputs multiplied by the number of parties. And in contrast, the first protocols for secure computation, the one by Yao in 1986 and the GMW protocol in 1987, had a communication complexity which is proportional to the circuit size of the function. So a very important, a very fundamental question in secure computation has been since the introduction of the first secure computation protocol to understand whether this is inherent, whether securely computing a function does inherently require much more computation than insecurely computing a function. Now, since the breakthrough work of Gentry in 2009, we know that under some suitable computational assumptions, or some variants of the learning with our assumption, it's actually possible to securely compute any function with optimal communication, input size plus output size plus some polynomial in the security parameter. And it will also learn more recently that it's possible to securely compute a large class of function with a communication which is sublinear in the size of the circuit and there's a decisional defilement assumption. So under computational assumption that are widely believed to hold, this is more or less a solved problem. So in this talk, I'm going to revisit a bit this question. In the context of secure computation in the correlated randomness model, what is a correlated randomness model? In this model, you have a trusted dealer that before the start of the protocol will generate and distribute correlated random coins that are independent of the inputs to the protocol and give them to the parties. Then the parties can use these correlated random coins in the actual secure computation protocol. So why do we care about this model? There are two main reasons. First, as was shown by Beaver in 1991, such a allowing a trusted setup that distributes correlated random coins to the parties allows for information-certically secure computation in the online phase. And this is in stark contrast with standard secure computation without a setup, which obviously requires computational hardness assumptions. So the second reason why we care so much about the correlated randomness model is that it turns out that it leads to extremely efficient, like concretely efficient, secure computation protocols. And in fact, almost all modern secure computation protocols that aim at being concretely efficient that you can see nowadays do work in this model. Like they work in two phase. In some preprocessing phase, the parties will jointly generate some correlated random coins. And then in the online phase, the party will consume those random coins to securely evaluate the function. And this secure evaluation will not only be information-certically secure, it's also extremely fast. Yet, all known protocols that we know of in the correlated randomness model for general functions require a communication which is proportional to the circuit size of the function. So a question is to understand whether this is inherent. And actually, it was proven by Damgar Detal in 2016 that this is indeed inherent for a large class of protocols in the correlated randomness model that were called gate by gate protocols, which captured, at the time, all known protocols in the correlated randomness model. So in this work, I'm going to revisit a bit this question and show that we can somehow hope to get the best of both worlds. Meaning, simultaneously, the sublinear communication guarantees that we could only achieve as of today in the case of computationally secure computation. But at the same time, having information-certically security and good concrete efficiency with only known cryptographic operation in the online phase. More precisely, I show the following. For any layered Boolean circuit, so a Boolean circuit is layered if you can partition its nodes in layers so that any edge only goes from one layer to the next layer. For any layered Boolean circuit of size s, there is a secure computation protocol with all the properties you can want, like no hones majority, any number of parties, adaptive security, security against malicious adversaries, that can securely evaluate this circuit using total communication proportional to s over log log s. Arguably, that's only slightly sublinear. But the real takeoff of this result is that there is no circuit-sized barrier. It's possible to go below this barrier. This result can be extended to the interesting case of arithmetic circuits using kind of the same techniques, but it requires a few additional ideas, as well as the case of function-independent preprocessing. And since the constants are very small, as a constant here is one up to lower the terms, it also actually leads to concrete efficiency improvement for some existing secure computation protocols. And in this talk to simplify, I will focus on the case of two parties and semi-honest security. The extension to the marginal case is not that hard. OK, so how does that work? Our starting point is a well-known protocol for sharing true-stable correlations. These protocols allow to securely evaluate any function in the correlated run-on-ness model with information-seerotic security when the function is represented by its true-stable. So we take some function f and we represent the function f by its true-stable m, so the function evaluated at all possible points. And our trusted dealer will first pick a random shift, r, and he will shift the true-stable r by this offset, as the true-stable m, sorry, by this offset. And at the same time, our trusted dealer will share this offset r in two parts. Here the addition will denote a bitwise operation, so bitwise xr. So we have the shifted true-stable m prime, and then the trusted dealer will additionally share this shifted true-stable in two parts, m prime 0 and m prime 1. And then each of the parties will receive a share of the offset and a share of the shifted true-stable. Now, using this preprocessed correlated random coins, securely evaluating the function f turns out to be extremely easy, because the parties can simply exchange their inputs, x0 and x1, masked by their share of the offset r. So u0 and u1 form shares now of x plus r. So by simply now doing a lookup search in their share of the true-stable, they will recover what you can easily check to be additive shares of f of x. So this means that to securely evaluate the function f, the parties only have to exchange their masked input. So the protocol has optimal communication, 2m. However, the storage requirement to securely evaluate this function is proportional to the size of the trust table representing the function. So it's exponential in the input size, and that's quite bad. So the natural ID, if we want to get sublinear communication in the correlated randomness model, is to start from this protocol and ask whether it's possible to make this storage polynomial instead of exponential. Turns out that it's unlikely to be easy, because it was proven in 2013 that if we could achieve a polynomial storage for securely evaluating any function, then that would imply a breakthrough result for some long-standing open problem related to the study of information theoretic private information retrieval. So I'm not trying to do any breakthrough result here. Instead, let's try to work around this issue by doing something extremely simple. And the extremely simple thing will be just a simple modification of this true-stable correlation protocol. The core lemma that I'm going to prove, and which is a very, very simple lemma, is the following. Take any C local function. What's a C local function? It's a function such that any output bit depends on at most C input bits. So you can see that the function as being concatenation of functions f1 to fm, each of them being applied to a size C subsets of the input bits. Then there is a protocol that securely allows to evaluate to obtain shares of the function with perfect security in the correlated randomness model, whereas the communication is, again, optimal, bigger of n, the input size. But where now the storage requirement is m times 2 to the c plus n? Compared to the previous protocol, what we've done is just shifting the exponential cost of the storage requirement from the input size to the locality parameter. So how do we do it? It's pretty simple. Intuitively, since a function only operates on subsets of size C of the inputs, instead of looking at the big true-stable of the entire function, you can look at the list of true-stables for each of the output bits of the function, for each of the partial functions. Because each of these partial functions is a function on inputs of size C. So each of this true-stable only has size 2 to the c. So what we can do is just apply the previous protocol in parallel for each of the output bits. And then the trust earlier will generate those shares and those shifts. Now we get a protocol where the storage is just m times 2 to the c up to some additive thing. But now the communication has increased because for securely doing all those protocols in parallel, we need now 2c bits of communication for each of the m output bits. And this is too big, actually. That's more than the big of m communication that I claimed that we could have. So the next idea is that we can go back to this communication proportional to m by not using independent shifts for all those m true-stable. Instead, the trusted dealer will only pick a single global offset r. And those r1 to rm will not be defined as independent offset, but as appropriate subsets of the bits of this single global offset, where the subsets are the subset s1 to sm corresponding to the subset of the bits that you need to take to compute the function. If you do so, then the parties now only need to exchange x0 plus r0 and x1 plus r1. And intuitively, applying the m protocols in parallel only requires to take some appropriate subset of the bits of those masked inputs. And the security is easy to prove. So with this variant of the protocol, we obtain essentially exactly the same communication as before. But the correlated randomness now is a bit different. And we can still prove the protocol to satisfy perfect security. So this is what I call the core lemma. As you could see, a very simple lemma. You can have optimal communication and, of course, exponential in the locality parameter of the function. Let's see why this is useful for the question at hand. So as I told you, the result that we prove shows that we can securely compute with sublinear communication any layered Boolean circuit. So let's look at the layered Boolean circuit. The nodes can be partitioned into layers. The edges go from one layer to the next layer. And we will partition this function into chunks. Each chunk will contain k consecutive layers. And we will look at some selected node on the last layer of a chunk. And Cs is selected node, like the value computed on this node, as a function of the value computed on the last layer of the previous chunk, together with the input nodes of the same chunk. Let's ignore them for now. So intuitively, the obvious observation here is that since a graph representing the circuit as in degree 2, then the selected node can only have 2 to the k ancestors if you go k layers above. So afraid in terms of locality, the function that maps the values on the last layer of a chunk to the values on the last layer of the next chunk is a 2 to the k local function with w inputs, where w is the width of our Boolean circuit. So we have a local function. We go back to our call Emma. And so using the call Emma directly, since this fi is a 2 to the k local function, we know that we can securely compute shares of fi given shares of the input with communication proportional to w, this width parameter, and storage, which is only exponential in the locality parameter. So the storage will be proportional to 2 to the 2 to the k. What follows is quite easy. We will divide the input. We will let the parties share the input between them. And then they will divide the function to evaluate into d over k chunks. Each chunk, as I said, containing k consecutive layers. D is the depth of the function. And we call each of this function f1 to f d over k. For each last layer, for all the values of the last layer of the chunk, the parties will use a protocol given by the call Emma to securely compute with optimal communication shares of the value on the last layer of the next chunk. That means that we obtain now a protocol whose total communication is the width times d over k. So if you adjust things right, w times d, that will just be your circuit size. I'm assuming here, for simplicity, that the circuit will deal with is a rectangle. But you don't need any such structural requirement. You will end up for arbitrary shape of the circuit with a protocol whose total communication is s over k. And the storage requirement of the protocol now is w times 2 to the 2 to the k multiplied by d over k, which is just essentially s times 2 to the 2 to the k divided by k. And as you can guess, you obtain the claimed result by setting k to be log log s. So that shows that you can securely evaluate any layered Boolean circuit with a polynomial amount of storage and total communication proportional to s over log log s. This can be extended to malicious security. That's quite simple. You can also extend it to the arithmetic circuit. Here, you cannot use truth table correlations anymore because a truth table for an arithmetic function over an exponentially large field will be way too big. But if you represent instead the chunks of your function by multivariate polynomials, you can essentially derive the same kind of approach and obtain a similar result, communication s over log log s number of field elements. There are some open questions, of course, that comes from this work. First, now, where's the real buyer? So we've achieved s over log log s. That will be not very satisfying if s over log log s was a real communication barrier that we cannot hope to break. So where is the real barrier? Is it possible to get sublinear communication and still linear computation? Because a protocol I gave here has a computation, which is quadratic in the size of the circuit. And it can be reduced to slightly sublinear in the size of the circuit. But linear is not something we can achieve here. So can we achieve linear computation and sublinear communication? And is it possible to remove this layered circuit requirement and extend the result to all circuits? And that's all. Thank you for your attention.


All right, I'm lying.  I took the mailbox out of the ground, but we got a tree And the tree and the boat So we've got a tree down here Came down out of here. Anyway, there's a tree Tree limb down. Look how close it came to the boat.  I don't know if they can see any trees moving.  I'm worried about this one right here That's where the tree limbs are coming from Hopefully it goes That way and not this way Turn this back There's all kinds of stuff going on over here just a bunch of little limbs so Anyway out here We're doing it guys. We're doing this where you ain't with me. There she is behind me here Not good, huh babe in the boat Yeah That limb might have totaled that far but might not have but it would have fucked the boat up For the truck that was put the truck where it is.  It's getting better here guys We're getting about 59 hour gusts Sustained probably about 30 ish. I haven't measured it yet, but it's something like that so alright, we're gonna get off here and We'll get back with you as it goes on man.  We're riding it out, baby Yeah, baby. Come on. Give me some love.   Bye guys I gotta see it. Oh Oh, yeah a comment on here.  I forgot to hit stop. Yeah. Bye again


And so we have Chris Pearson, who's a Vice President for Strategic Development at General Atomics, who's going to discuss kind of what some of the themes that we just heard on the stage, what does that mean for industry? What is the Air Force of the future? How does industry adapt to that? Mr. Pearson is a retired Air Force Colonel with multiple deployments in Iraq and Bosnia and Afghanistan, distinguished flying cross, bronze star, multiple degrees in computer science and logistics, and ideal person to think us through some of the big themes and what that means for industry going forward. Thank you, Peter. It's an honor. Good morning, everyone. I've been given the opportunity to comment on how industry can support the Air Force of the future. This is not an easy task, and frankly it's a little presumptuous on my part to try. I'm not an architect to the future force, and I'm certainly not a spokesman for the entire defense base. But I can share my thoughts and give you my perspective as someone who spent 25 years wearing Air Force Blue and who spent the last nine years in industry. As a national security posture shifts from a focus on non-state, transnational threats towards a worldview of great power competition, military and industry must partner to deter aggression and, if necessary, defeat adversaries that threaten the security of the United States and our allies. Since 9-11, and arguably since Desert Storm, the U.S. military has been engaged in a decades-long campaign of counterinsurgency and counterterrorism. The Air Force has dominated its adversaries with air and space superiority, practically omniscient, persistent situational awareness, precision attack with impunity, globe-spanning mobility, and robust, trusted command control and communications networks. Industry was the innovation engine that provided the tools and technologies that enabled the Air Force to maintain this advantage. Even as enemies such as Al Qaeda, the Taliban, al-Shabaab, Islamic State have metastasized and evolved, industry will continue to support the fight against violent extremism because although these threats are diminished, keeping them at bay will require eternal vigilance. The world has never seen such powerful Air Force as that of the United States Air Force today, and there has never been an aerospace and defense industry as technologically superior, as innovative, or as robust as the American aerospace and defense industry of today. But our adversaries are gaining ground and have closed the gap in critical areas. The United States military and the industrial base supporting the armed forces reap the benefits of technological superiority and freedom of action in the coin-CT fight, but the permissive operating environment of the past three decades resulted in an atrophy of capabilities and diminished readiness necessary to deter or defeat our great adversaries or great power adversaries. My own early years in the Air Force, my own personal experience during the Cold War, preparing for the ultimate contested fight against the Soviet Union. My first operational experience was Desert Storm, where the territory and airspace of Kuwait and Iraq were both contested, at least at the beginning of the conflict. In both cases, my fellow airmen and I were confident that we would prevail because we had a qualitative advantage such as stealth, precision weapons, electronic warfare, perhaps most importantly, we had superior planning, superior training, exercises that kept us ready in the highest level of readiness the world had ever seen. As we re-engineer, reorganize, re-equip, and re-train for great power competition, there will be a sense of going back to the future. What's different today is that geography is not the only contested domain. Conflicts may no longer be primarily about natural resources, lines of communication, or disputed borders. Today, space, cyber, the electromagnetic spectrum, and information itself are just as contested as air, land, and sea, and arguably more so. I would also argue that time is contested, both in the sense of precision timing and decision space on the battlefield, as well as long-term time horizons to subvert or whittle away an adversary's advantage. Because of the multi-domain nature of modern conflict, the US Air Force and industry must think differently about the future force and how it will be employed. There'll be a need for highly-survival weapon systems that support the core missions of the Air Force. There will also be the need for affordable weapon systems to create a balanced, high-low-force structure mix. Otherwise, maintaining the peace will be unaffordable. We must also think of conflict in terms of information assurance, supply chain vulnerabilities, industrial espionage, dual-use technologies, social media, and other non-conventional attack surfaces. To circle back to the question posed at the beginning, how should industry adjust to the Air Force of the future? My short answer is industry must lead innovation. Our most vexing challenges must be met head-on with boldness to try new paradigms. This will require resources to incentivize risk tolerance on both industry and government, or from both industry and government, creative problem solving, and partnerships based on trust and confidence. We can't and shouldn't divest legacy platforms overnight, but we also can't continue investing in the old, the new old. Our national security and the security of future generations demands the new, new. Prototyping and experimentation are how the force will grow and evolve. I want to close with some thoughts on where I believe the Air Force will benefit and where industry must adjust to a new paradigm of prototyping and experimentation. These are foundational, fundamental capabilities that will require rapid innovation, more rapid than our current acquisition system permits. Some examples are assured precision navigation and timing. Admiral Richardson mentioned the importance of PNT, but we need to figure out how to operate without satellites in highly contested electromagnetic environments. Assured communications. Think of 5G for the combat cloud with resiliency and reliability that's robust and can survive threats. Global precision attack and the targeting capabilities that enable it. There are many modes and methods and we can't rely on only a few. Have to consider hypersonics, swarming unmanned aircraft, intelligent weapons, and directed energy. Persistent battle space awareness and shared battle space awareness. We've been spoiled by years of unfettered access and now we need to deliver the same capabilities in contested spaces. When we talk about shared battle space awareness, it's not just a common operating picture, it's shared knowledge. Artificial intelligence, many comments on that today, but this is the ability to recognize patterns and discover knowledge underserved by the human operator. When we get there, it will be a game changer. The commercial sector is leading the way and we need to somehow leverage this. Electronic warfare is another important area. Software defined radios and other technologies are changing the nature of electronic warfare. Whoever dominates the EM spectrum will dominate the battle. Finally, cyber security and offensive cyber. We need to think of terms of cyber from the front lines all the way to the last link of the supply chain. Thank you for my attention or thank you for your attention. Short time is up. I'll have you take any questions during the break.


y dyfodol. Felly, rydw i'n dweud yn gweithio ar y dyfodol yma, ac yna'r ddweud yma. Felly, yna'n gwneud i'r ffost, yna'r ysgrifennu. Felly, rydw i'n dweud, Sean Mackie. Felly, yn ambrangodd yng Nghymru, ac mae'r rôl wedi'r ysgrifennu'r ysgrifennu yma, i ddweud y teimlo yma, oedd o'r oes i chi, a dweud yna'r dyfodol yma. Mae'n gweithio o hynny'n oed agor. Udo erioed yn cais i'n nhw'n troi. Yn ystod sut mae'n gyda'r bobl rhywunion oherwydd mae'r cyfrifiwn ff Mogh. Mae'n cyfrifiwn sydd e'n hi'r cyfrifiwn. Yn y gallwn i'r cobl, mae wedi cydnod i ddod i'r troi arno. Mae'n cael eu drwsgrifennidau tod сейчасon. Roedd yna, mae yn gweithio ynglyn ag ein rhaglen, ond bydd yna ymlaen o eu cyfrifiwn ffainian. Yna, mae'n gweithio i'r program. We actually called it Teranova, since we do see this as new land, new territory that we are entering in, and the purpose or the vision that we had was to really define the subsurface data foundation of the future to get more hands-on with the OSDU, see where it works well, see where it doesn't, and to then know what that means for our company going forward. In order to achieve that, we had several objectives, so five objectives here that we wanted to demonstrate. The first one was that we wanted to bring well-data, seismic data and documents into one central place and see if we can reduce the search time for our data managers and our G&G users, and to also create this environment for quality control and also start to embed some QC automation processes in there. We also wanted to show that we can improve the collaboration between our data managers, our G&G experts, and also see how different disciplines and their workflows can integrate with this platform. We wanted to enable faster and more efficient workflows, so we did a lot of testing on some of these new AI machine learning workflows on top of the OSDU and also tried to enable a better interaction between our data scientists and our subject matter experts. I would say last but not least, and this became a major focus towards the end of the project, is that all of this sounds nice and good, but you need to do this in a cost-effective way so that it's for us, it's not an investment, I would say, it's actually how can we use the OSDU to drive down costs in the company, and that's something we will touch on. So we have a hyper-cost focus at the moment in our company, so for us to get to this next deployment phase, the business case needs to be extremely solid and we'll try to show you some of the input we have to that business case. Looking over the past year, these were the work streams that we had in order to try to achieve those objectives. There were six work streams, many of which were run in parallel, and five of those six are now completed. To give a summary, maybe the first one there, the top right, this took up, I would say, the majority of the programme, especially the first six months, was dedicated to that work stream itself, so this was implementing the OSDU in our own Azure tenant and also utilising an enterprise data solution from SLB that can enable our data managers to bring data from three assets across three of our business units into the OSDU to visualise and to do the data management workflows. So I think a key component I want to mention here as a mid-sized company is, I guess we don't have the luxury of data engineers, developers and so forth. We're really dependent on more traditional data management workflows, so we needed a solution that our data managers can use to bring data into the OSDU and do those workflows. So that was a one key component of why SLB has been a major partner in our Terra Nova programme. Secondly and second work stream is, well, our most widely used subsurface application by far is Petrel and if we have an OSDU environment and it's not working with Petrel then it's also not really going to go anywhere for us. So we wanted to test how well Petrel can integrate to the OSDU, streaming seismic and also testing some of the new AI machine learning workflows that are available there. We also wanted to test some data science use cases. We have a data science team that gets use cases from the business and we wanted to see how well does that work on the OSDU. So instead of them getting their well data from various different silo databases, can they fetch it using the OSDU APIs, run their data analytics workflows and provide a bit of collaboration between the data science and the subject matter experts. And maybe just to summarize the last few, we also wanted to look at a couple of third party applications that we use. One was earth science analytics for machine learning for geoscience and the other was geologic for well planning. And we also demonstrated the connection of those two applications to our OSDU environment. So then we had Petrel, earth science analytics and geologic all kind of using the same data. And I just want to mention that one work stream we also focused on was change management. So we had a dedicated work stream on change management using the structure ADCAR model. And we found this vitally important because I think we had more than 40 users across the business involved in the testing and making sure you're getting the lessons learned from them, the feedback, the surveys, knowing where they are on the change curve. That's sometimes the biggest hurdle and we tend to focus on all the technical stuff. So we put a dedicated work stream here and I think that definitely benefited us a lot and we were able to at least make sure the user felt they were also part of the journey and their feedback was taken on board. So that's kind of a nutshell of what was done and I guess Max will also touch a bit later on some of the outcomes but maybe bridging over to Max, Max will then touch on what are kind of our challenges around data in the initial data. So apart from being fashion models, I think we also created a OSDU PowerPoint portfolio which you'll see in a second. But going back a bit to the why, why we've done this, we have a few pains that's me in the company and probably these will be similar to other companies or people here in the room. Yeah, we don't have a master database for subsurface data. We have different databases. We don't have a connection between our legal documents and the data. So yeah, we're working with Seismic but we don't know if we're still allowed to use it for example. Quality control is very time consuming and we don't have a best practice for it. Our archive data is basically not searchable because we don't have any tools running up on top of it. We have data in the cloud because we have some cloud applications as well but we have no control of this data that we push into the cloud. We have a diverse landscape. We've heard that before I think today that between business units there are different ways of storing your data in databases or not even a database which is a file structure. Where is the latest and latest grader? Is it in a project? Is it in a folder? Is it in a database? Which of the databases is it in? And we don't have standards, not even a simple naming convention. I guess yeah, every time you switch for example from a physical person you get a new naming convention. So those are the pains. And then I guess the solution in short is then OSDU. It's me again. And then with the solution we also of course have a value because you can bring something in but we need to show them what is the value of our solution. So the value. We have a centralized database so we can dissolve some of our all databases one point to go to now. We have the legal tagging so we adhere to the legal contracts that we have mitigating the risk of not being compliance to the rules and getting penalties. Automated QC. And I guess what's missing here maybe is also the metadata announcement. So we make sure we don't have any duplication in there. We add metadata to it but we also have an automated QC now and a scoring that we've never had before. OSDU can catalog so also our data that is in the archive system can be made searchable now. We have a standardized approach to storing and organizing or accessing our data whether it's cloud or on-prem because with the on-prem stuff or in the archive stuff we can make it searchable at least for the time being. We have a unified landscape so we're doing everything the same in every business unit still letting the business unit being the owner of their own data. Latest data so you have the golden record so you don't have to go through the different versions you know where it is. The last one I guess this is the whole story but you don't have to explain that too much what the idea behind it is. And we have standards so we can finally have a technology that works for data governance as well. Then next. Yep there we go. Then these are kind of the workflows that we've tested with OSDU in the middle. I'll start on the right top side. Kind of if you've been to London I present there as well on what we've done. So the enterprise data solution we've trained 15 data managers on it who did the ingestion, the visualization, the QCing of the data. Also testing is against the criteria that we had and basically concluded that we could use this tool as our future data application on top of the OSDU. And it's also supporting all of our most important data types apart maybe from one which is 2D seismic which will come early next year I think and that will also be the moment where we say we go for deployment and that was also our advice which Sean will go on a bit later. Then the second workflow that we've tested is then the Petrel PTS where we had 10 geoscientists working on the workflows between OSDU and Petrel, ingesting data, liberating data from Petrel into OSDU, but also consuming the data, but also we did some with Petrel PTS comes on AI or machine learning workflows that we've tested and in our business in Norway where we did most of the testing they came up with a efficiency gain of 50% for the workflows that we have in combination with OSDU and then Petrel PTS. Then the third one is data IQ which is kind of then the data science part where we had two use cases of which one was fetch data from OSDU, do some reprocessing on the well logs, some machine learning or AI on it and then push that back into OSDU and then consume it with Petrel. But of course data IQ is one of you can use other data science tools I guess on top of it as well. Then on the left side we have three other components of which two are cloud applications that we use at the moment and the third one is a Delphi native application called Opportunity Assessor where we yeah for data for air science analytics for example we push seismic into OSDU and then consume that with Petrel vice versa and also we push data from Petrel well data into OSDU and then geologic consume that to show for example these well pass and then with the logs on top of it and some interpretation like markers and I think I got everything on that one yeah then this is basically showing our journey the change journey I think change has also been named mentioned a couple of times today. I don't think I've ever worked on a project where emotion was such a big factor every day and people you're fighting against people with preconceived ideas biased views and I guess your main your main enemy is fear, fear of something new because we all like our old stuff if it works it works or if it's comfortable but also the fear of new good tools replacing potential jobs which is not the case but yeah there is that fear component but also fear of a tool that's maybe not 100% working despite actually covering all your critical workflows but you can use that feedback or you can use that fear as well to change it around if you use that well if you can explain it if you know what the fears are and you can explain it to them in a better way or you can use the feedback provide it for example for the enterprise data solution tool um the feedback that we gave to SLE back they improved the tool we bring that back to our users kind of making a few more comfortable that okay we are listened to um we're part of this whole process that's kind of the change the change that you need to or the change topic that you need to keep in mind that you always keep them informed and involved um then there's also negative stakeholders which maybe only listen to 10% of your story and there's always 10% that's really bad spread it create fires we need to extinguish them um yeah that's basically what you do on a daily basis then on top of that we're kind of proving the technology you're also fighting all these emotions unfortunately within the company we're now in a phase of hard cost cutting so on top of that we also need to show a good cost picture um but I think at the end you know with a good motivation motivational team and a good change management I think we got there in the end and we even managed to get all the critical stakeholders in the closet of a project to recommend the deployment of OSDU which is IT um exploration reservoir management and also our managing directors from the business units they all said let's go for OSDU um let's push forward I think I got that yeah so maybe just to to highlight I'm sure many of you are probably going through some of this and yeah for us it was daily weekly and some stakeholders we thought yeah are we ever going to change their mind but at some point I can't put my finger on really why but the tipping point did happen and I would say our most skeptical and conservative stakeholders also changed their minds just through I guess also max demonstrating to them in super detail the workflows the visualization and then I guess it took many many times but it is it is possible and I guess we were thankful at the end as you said all the all the key stakeholders not only were positive but they also put forward a recommendation to deploy so I think that also is detriment to uh thankful for the change management support as well it helps along the way basically what it shows is that every issue you solve you think you're going up but then there's a massive tumble down start again and I slowly you get up to the top yeah thank you one more effect there oh yeah I forgot one sorry it's still me um managing directors they said let's go for deployment so we also showed them different deployment scenarios of course uh of which one is the most simple one I guess it's a single source of truth um so we replace some of our databases uh go for the OSDU um then with that establish this single source of truth with no third party application integration for the time being um then the second one is basically number one plus um so we have this single source of truth but we also connect our cloud applications to it um so we have a greater control of our data in the cloud um also empowering our data managers to use yeah these these applications better and more then the third one which is not something we want is a hybrid environment so you kind of leverage your current infrastructure and investments and connect the OSDU to the on-prem um application environment sorry I shouldn't have to drink in the Pepsi on stage um this comes with significant technical complexity high cost and and poor end-to-end user because you have to switch between um virtual machines probably and it's not going to help the the whole case then there is deployment scenario number four which is probably not really feasible at the moment so this will be our dream uh scenario um or deployment so this is kind of more strategic way of looking at it um where we go fully integrated so we host OSDU an application environment on our azure uh to reduce complexity enable full integration um like I said this is considered as a strategic target for the future then then there's also the fifth one uh that's a no deployment where we also showed some alternatives with a cost picture even um and where we then also utilize our traditional databases basically go forward the way we did um which then comes with some risks like no open access no modern search no legal tagging no automatic you're seeing um it will be harder to enable AI and machine learning workflows on your data because it's all spread around um so that's definitely something that we would not advise but these are our we are deployment scenarios and I think Sean is going to go more into our actual deployment scenario yeah sure yeah yeah so just to highlight I guess we had to show that was a request from our steering committee and manage directors in their business units what are the scenarios even no deployment so that it's fully transparent all the ways that the all the different scenarios that they could choose from and in the end the scenario that we proposed was to basically get started with a deployment scenario with the smallest step possible that is cost effective and that was scenario one and this is basically a deployment of the OSDU and the enterprise data solution for our business unit in mexico which is one of our major business units both operated and non-operated assets and they have some of the most fundamental data challenges that max mentioned so there is no master repository in mexico all their data is unconsolidated on the file system and it's from pre-legacy mergers and acquisitions of companies and they're crying out for us to really support them with the OSDU by creating a clean environment for data managers and for the G&G community that also comes with modern search and visualization that can support them in their daily business and part of that deployment scenario is we'll bring their well-related data into the OSDU and the associated documentation with that but we will likely keep the seismic data on premise just because most of their workflows are still in a non-premise environment and we don't want lots of transfer costs but we will catalog that seismic data and make sure that it's searchable in the OSDU and also that the file path is there so that's kind of the the first approach and then the second step is that we're going to use the OSDU to rationalize it's roughly 10 corporate databases for well and seismic related data that we have some of them are not even maintained anymore and we need to find alternatives and that rationalization alone is close to 2 million euros of savings which more than pays for OSDU deployment for our business unit Mexico so that was our deployment scenario proposal but our feedback also from our leadership is that well we don't just want to do this for Mexico now if we're going to do this we might as well consider what our global phase deployment will look like which I will also touch on but before I do I want to mention that in order to achieve a more global phase deployment you can't get away from having a cloud data and application strategy and that's one thing that we've had to push I guess that's mostly been pushed from the OSDU project and something we've been working on for the past year and we see that if you want to get to this fully integrated environment you need to archive your data in Azure you want your OSDU deployed in the same cloud environment and you also want your applications to be running in the same cloud environment even if they're not yet OSDU compatible just so that over time as they do at least you're set up in the right infrastructure to enable that and for that you need a common cloud and application strategy which is something we are finalised by the end of this year and it's also how we see our IT and digital coming closer together and this is where this cost saving now comes in that we see that this strategy can really enable us on the storage savings it can support our application rationalisation project that we have it can help us build a more right sized infrastructure and it also enables us to achieve these data driven aspirations so those four areas you see below those are projects global projects now being initiated within our company and we kind of put forward made it clear that you can't do this in isolation you need to do this under one common strategy and we now have kind of bayon that at least the OSDU is the way forward when it comes to our future data foundation so I think this is the last slide for myself and this is now really the final pieces that we are putting together for the deployment strategy so I think we have the all clear to go for for Mexico but this is now looking at it in a any broader context so the first thing that's being triggered is how do we prepare our data for the OSDU and there is now a global cleanup being triggered to de-duplicate data but probably most importantly is actually we want to shift most of our data into cheap archive storage because we have something like 70% on high performance machines and the vast majority of that's not been used in a year so we have major costs that we want to want to mitigate here and then we want to rationalise our databases and as again I said 2 million from that rationalisation alone and I would say multiple times that from the cleanup and moving data to cheaper storage and that's kind of the precursor of what we need to do to then implement the OSDU for business unit Mexico and we aim to do that by the end of next year as max said there's one key feature we're waiting for and that's the 2d ingestion from the enterprise data solution and we need this prep work done on the cleanup in order for us to do that so once that's in place and in parallel I guess our business unit Egypt and Germany are also doing a cleanup that we can then scale the OSDU and the seds environment there but what is the next step there is actually is then to test how does that our application environment look like in the clouds and to do some testing between that we can host our third applications in a new azure environment and then start to see how that works a bit more with OSDU not for all but at least for a few of our key applications that are the brunt of our work workflows and then from there it's kind of a global scale out so this is what we're piecing together now basically and the main value that we think we can deliver is related to rationalisation of applications quite significant storage savings and we're looking at this from a total cost of ownership perspective so what is the total cost of ownership today and how does that look when you configure this new environment and so far we see it's quite a positive business case especially in times where we have very severe cost cuts going on right now which makes a deployment extremely difficult but it's looking at least likely that we'll be going ahead with a deployment but the decision for that deployment is on the 24th of November so it's not confirmed yet but we already see positive feelings from the main stakeholders and we've been verifying them with the key business case topics up front so at least in 2024 the Mexico deployment we see is is highly likely and we'll come with it a cloud and application strategy to support and to orient our IT and digital initiatives so yeah that's where we are today and I was saying and our project is always do you what always do you you


Let's hope it should be out there we are. Okay. Okay. Wonderful. Thanks very much Steve. Good afternoon everyone. I'm really quite honored and excited to be part of the conversations this afternoon. And I'm hoping that I might ease everyone back into the post post lunch break with probably something that's probably a little bit less technical than where most of you are. I wouldn't call myself a data scientist at all. But I sit in a really interesting space across a number of organizations involved in place naming. So I had a little bit of difficulty with that. With that mentor I must say Steve and I probably quite a few tick boxes in there on my part and I'll explain a little bit of that context in a moment. I think as it says up here I'm really I'm here today to just share some examples around place names and vocabulary use across Australia and some of the work that's happening in that space. But and this is going to come from I guess my perspective across having roles in a number of different organizations and they all have a bit of a bearing on the place names and vocabulary sort of overlap there. So before I jump into specifics for Australia though I wanted to provide a little bit of that context. I'm actually going to jump back a little bit into some some international context just to help you all understand a little bit not just where I'm coming from this afternoon. But also I guess the organizations and people involved in place naming in the vocab as well. So at the global level we start with a UN group of experts on geographical names and I thought the easiest way for me to explain what that group is about is to show you their vision which is that rather rather big mouthful of words that you've got on the screen there. Most probably relevant to the conversations this afternoon is the second half that I've put involved and it's really about having place names easily accessible for national and international use which of course this relates to consistent worldwide use of geographic names to foster communication and cooperation. So I think that fits really really nicely with the concepts of vocabaries and the work that we're trying to do. This group is probably only really a few years ago we started talking about linked open data. We very quickly acknowledged that it was sort of that next stage and a bit of a technical development for us and understanding that we needed to start to learn a little bit about what this meant. Most recently we hosted a webinar and we had some really interesting presenters from overseas. There's actually some from Norway, the Netherlands and Germany and actually demonstrating some really good working implementations of linked open data that we're using using place name vocabaries. The link is actually on the bottom of the screen though. So if you want to take a look at that please do. I think the preference though for our group is not to upskill ourselves into a whole data science space. We really want to encourage collaborations between national naming authorities and sort of linked data practitioners at that national and regional level. So thank you Steve and all the other organisers today for helping me in that little part. At an Australian level we don't really have a genuine, a single national names authority. We've actually got a couple that work together to do that job. So some of you might be familiar with the Australian New Zealand working group on place names. That's where the composite Gazetteer Australia comes from or the Geoscience Australia Gazetteer if any of you have used that and are familiar with that. It's also really, it's effectively a collection of all the state and territory and Commonwealth naming authority. So the government naming authorities across Australia and New Zealand and they come together to make decisions around what place names are going to be. So those official place names and they also make the most jurisdictional data sets. So it's a really a bit of a focus on that, that sort of decision making that authoritative kind of section of things. If we consider that working group on place names to be governmental or intergovernmental, the Australian National Place Names Survey which is the ANPS one is probably by comparison sits more in the academic space and the research space. It's a long running project to study and record the origin, the meaning and the motivation behind Australia's place names past and present. It was actually initiated by Australian Academy of Humanities quite a number of years ago but these days it actually sits as a not-for-profit. I think like a lot of things struggled a little bit with funding. So it's actually how now there's that place names Australia which probably no one has heard of even though it's got a really handy name for us is actually a not-for-profit. So that project and all that research in that space actually continues today with the support of a really small group of volunteer researchers, mostly retired academics which we're very grateful for. If anyone's used the TLC maps, the time laid culture map of Australia, the gazetteer that's behind that that's the data that comes from this group here. And finally down the bottom and as Megan mentioned my day job when I'm not down here having a chat to you guys or we're doing any of this other stuff is with the Queensland Government. So we're one of those principle government naming authorities that come together under that working group of place names and Queensland and the other states and territories as well work in various capacities with that Australian National Place Name Survey to share research across the sort of cultural aspects of names. So I think the purpose of trying to explain a little bit about I think is that it's to show that it's really quite an interconnected if not very well-known network of naming in Australia and that itself can make data of vocabulary creation and you reuse quite challenging. We've heard similar stories from a lot of people today. I think what's a little bit different about naming is that we're actually a very small community. So we may actually have a little bit of hope perhaps in getting on top of some of these things or they get too far away from us. Oh yeah. Yeah, I'll just give you that. Yeah. And we'll one second, we'll just switch. Okay. Is that okay about here coming through? Okay. Okay. Well, I'm sure someone will put something in the chat if it's not working for us. Yeah. Thanks very much. All right. So I'll jump now into some of the examples that I wanted to share and there are probably a lot that I could have talked about in this space, but I've chosen just three for you today. The first one I want to talk about place types and we've had a bit of a conversation briefly, I think in an earlier one about beacher types. So there's a little bit of an overlap there and a little bit of a space in there. When we talk about place names, it's a very, very broad term. We very rarely talk about it quite so broadly. We talk about things like hills and beaches and roads and towns and that type of thing. So our place names data is similar categorized based on the types of places and I should talk, I'm talking now really around that context of what the states and territories in Australia are doing and the data that then comes together to actually create a national gazetteer because you can see by the map there, we've got a whole bunch of states and territories. We've got some wet area. We've got external territories and we've also got Antarctic territory down there as well. Each one of them has a different data set. So it's a little bit of a challenge to try and create a national product not only for our own purposes but for anyone else to use for anything at all. The problem, we had a bit of a problem with this in that actually mashing all of that data together was a manual process. So there was a person sitting in Geoscience Australia, if you're lucky then, who had to source all of that data from the various jurisdictional databases, to do some kind of interpretation and join it all together somehow to create a product. And it was taking about two years to create a national data set and that was sort of cycling through all the time. So we had 10 data sets, of course it means we had 10 different sets of data types, sorry, place types. They weren't all completely wrong. We had had a national model for some time, a national vocabulary. It's just over the years that it sort of diverged a little bit. It was pretty similar, but it just not identical. That evolved a little bit. And of course there was a whole lot of variability interpretation because you had humans on the data creation end, sometimes putting place names into certain categories and then you had this person sitting in Geoscience Australia trying to mash them all together and sometimes putting them into different ones again. So it's a little bit challenging. This all came to a head. We knew it wasn't great, but it kind of kept going as is usually the case, the people who were creating the data and creating the problem weren't the ones trying to join it. So things kind of kept going along. And really what happened one day is the person who was sitting in Geoscience Australia doing all this work retired and his team, when they went to sort of distribute all that work, went, no, we're not doing this anymore. And basically, so to the states and territories, you guys got to put your heads together and come up with a vocabulary and some similar types so that we can just automatically do this. So that was our solution. We basically decided we're going to agree to a national vocabulary. Everyone was going to supply data using that. And we had this bit of an idea that there was probably a fair bit of overlap with topographic data and existing feature types. There was a whole range of different, different vocabs within that discipline and within government already. And we kind of mashed all those together to try and create a more modern one that worked for us without taking a huge deviation from our existing previous national model. So off we set. Very, very low tech approach here, very different to probably some of the things that you used to. We literally did some whiteboarding. We had a bit of some good ideas and then we sat around a laptop and created a bit of an Excel spreadsheet. So this is as simple as it needs to be and from a subject matter expertise within the discipline, we don't have any data, you know, the big data skills that many of you guys do. So this is where we got to. And it was actually quite reasonably effective. What we ended up with in that green column there is what ultimately became the place types and you can see they each had definitions. We didn't know anything about linked data. We didn't know anything about vocabularies. By pure chance though, we did a few helpful things through this journey. The green column there used to be two to four letter codes in the past. We had hundreds and hundreds of things. And so we decided this time around, we're going to do away with codes. There wasn't limitations in data storage like there was in the past. And it was hard for us to remember and look up extra code lists and things like that. I think you talked about some of that stuff before. So we got rid of those and decided to use whole words that a regular human probably had a pretty good, you know, handle on what they might actually be. We also created about, we created three different tiers. So you can see we've got on the file left, we've got different groups and categories and then all the features or the place types belong to those. Our thinking there was that, that was a little bit of just human logic. And we're also looking at ways to be able to provide different types of grouped products for people. So if they wanted to look at, you know, all the undersea features, which a particular organization might want to do, it was all in there together. And we also actually reuse some existing vocabularies. So for example, I mentioned the undersea features there. We didn't go down the path of trying to define all sorts of different place types for undersea. We just have fully adopted, there's an international standing committee on undersea feature names. So we just fully adopted their vocabulary and slotted it into our spreadsheet there. So I've learnt since then that some of those things are probably, were probably pretty helpful. It worked for our purposes. So the spreadsheet was agreed to, we came up with all of that.  Geoscience Australia built some new infrastructure. They put a cloud-based repository with a GUI front-end so that the state governments could load their own data. They built some scripts in FME behind the scenes that joined all those data sets together based on the feature types or the place types that were created. And then they built this new portal which is what you can see on the screen there. And you can see our place types and that sort of three-tier structure there is working through in the filter as well. So we were happy with that. That got our national data back up again and people could use that again. We didn't really know much about this, as I said already. But meanwhile, there was a lot happening around loci and Australian Government Link Data Working Group and of course Geoscience Australia people and I think there's a bunch of them online at the moment. We're working on this as well and they went, you beauty. We've got a national data set and a national vocabulary for place names which is quite fundamental to a lot of the other stuff that we need to do. So they went off and did their thing and we've now got our humble little spreadsheet that we've all agreed to at state and territory level sitting up there in the research vocabs Australia. So very low tech but I would say reasonably successful sort of situation that we ended up with there. So that's great. We've now got this data there. The second example I want to go into is well, what's next? So we've now got this, what is really still a very simplistic data set we don't have any status types. So these are things like is the name official or unofficial, is it local, is it indigenous? Is it a dual name? So all these terms that you're probably familiar with none of those were able to be sort of be able to classify at this point. We also obviously only had current data in there. So there's a lot of people wanted to wanted to know what the historical data was. Then we start thinking about what about proposed names and all sorts of things. All this information sits in the state and territory data sets. So it's there, but it was just a case that it wasn't able to be able to easily get up into this into this sort of space. So we had a similar problem starts to look very, very familiar. So we had our 10 data sets again. We've got all these 10 different types and things like that. So very similar situation to what we had before. And what was different here was a little bit interesting and this is generally actually no currency type. So there was this sort of blended bit of status sort of stuff. And I've given in that sort of mess on the screen there. These are just screenshots from four different examples of the status types from some of the state and territory data sets. So you can see there's a whole mismatch of approaches. You have to have a fairly good handle on how those jurisdictions work, what their legislation is. How their data has been managed and all of that sort of stuff to really start to make a little bit of sense of that. We haven't broached this yet. It is the next step in actually being able to create a useful national product for Australia in naming. But this is just something that I've sort of started to have a bit of a play around with. So the solution for this one. Is it the same as what we did in place types? Do we just get together again and go down that same path? Look, it could be a little bit simpler, simple, similar, but I hope not. I think a few years down the track, we know a lot more now about fair vocabularies. We've got a whole room and a whole cyberspace full of experts that can probably help us approach this in a, perhaps a smarter way than what we have done before and actually combine the subject matter knowledge from our group and our people around some of those real specifics and actually bring that forward a little bit more. I'm probably running a little bit short on time. So I'm just going to wrap up really finely. We're just one final example. And this one was important. I wanted to show you because it sort of flips things around and puts things on completely the other side of things. Because a lot of the problems we have at the moment is we've got multiple datasets with multiple different types of vocabularies. Here we've got something that we don't really have a lot of data for yet, but we arguably have a vocabulary. So what you're looking at there is basically a taxonomy that starts to classify the motivations of naming. Now, this is not really so much into the government space, but into that national place name survey space. And this is the core. This is bread and butter. Their work is really understanding the motivations behind different names. And if you've been paying attention to the world, this is a really interesting space for a lot of people at the moment. People are starting to ask, well, how many names are named after people? And of those, are they of good standing? So if you're sticking to things about things that are happening in like Black Lives Matters movements, gender equality, inclusion, diversity, all that type of thing, people are asking a lot of questions around this sort of stuff. Government authorities are starting to tag their data or put different categories in their data around how to record this. So they've had to put tick boxes if a place has been named after a woman. So they can start to do gender equality statistics and things like that. So we are at the beginning of that proliferation, yet we have researchers from this group here that have actually started already to do some of this work. So this is just an example I wanted to show. And I guess these are the questions that I put to groups like this. Are there vocabularies like this that are already out there that we can start to actually publish in a fair way which might help prevent proliferation of this sort of these kind of things and different vocabularies in the future? So just a whole bunch of food for thought. I won't go into that because I think I'm probably a little bit short on time and I want to let Megan sort of move forward. But I could probably fill a whole bunch of this. I think the thing that keeps coming back to me time and time again is the importance of connecting people and finding the right people in the organisations to connect together. This is not a data issue. It's not purely a technical issue. It's really a lot about people in that space. A lot of the work we've been doing has been about building vocabs and nontologies or models around data sets. And those data sets are often really simplistic. They don't really represent the real world. So I think there's scope for us to start to shift some of this thinking into actually modelling real world concepts or things like that and actually start to shift away from that and not actually have people continue to reuse the same old data that actually doesn't quite do the job. From my perspective, I think there's a lot of non-data benefits for subject matter experts. Some of the stuff I'm talking about here and some of these terms that need to be defined for data also exist in legislation. They define who has the power to make certain decisions and things like that. So there's a lot of value, I think, for us to start talking about outside of that space as well. There's obviously all the maintenance issues. I won't go into that. I think this group is probably well aware of all the challenges around that. But I guess my final question coming from the perspective that I do is how can builders of fair vocabs, which is a lot of you here, help guide how often some of these unfair vocabs or people like myself and the organisations that I represent do our work in a way that doesn't proliferate the same issues all the time at the end of the journey in that sense. So I'm just connecting that. So I might leave it there, Megan. And we'll go from there. Thanks.


Hey there Bruins! Today your friends at the UCLA Library are here to help you understand and create the ever-mysterious literature review. By the end of this video you'll be able to define a literature review, describe your roles the author of a lit review, and match the type of light review to your research needs. Let's say we're trying to figure out how to make human life on Mars possible. Before we can start building rockets though we have to figure out what people already know about Mars, humans, and the potential for human life in outer space. The engineers on campus wonder how do we sustainably design and build rocket ships that can move large groups of people to space? Campus environmental scientists ask what about the possible environmental impacts on Mars that human occupation would bring? Social science communities inquire what is at stake in international relations with regards to space travel and Mars colonization? Health scientists ask what are the health implications of all that time and space? And humanities students wonder what are the moral implications of colonizing another planet? A comprehensive literature review is exactly what each of these campus groups need. Sometimes you may only want to know about a selective narrow aspect of a topic but each discipline in this example needs to map out everything they can, what is currently known, and what is not known yet about living on Mars before collectively attempting a voyage. Each group can start by gathering information written and published by reliable sources, scholarly articles, data sets, books, and more. The engineers dive deep into the web of science and social science students head to ProQuest. All of the groups consult their subject librarians. It's a lot of information though, merely knowing what each source says will not be enough. How do you make sense of it all? Fortunately, creating a literature review involves more than writing summaries. Some articles are related and build off of each other and some articles contradict each other. Some books seem to have outdated methods and some data sets are questionable. As the author of a literature review, you have to take bits and pieces, figure out relationships and contexts, and start putting the bigger picture together. What did this article contribute to our knowledge? How did they get their information? And what does it mean? Why might some articles disagree with each other? As you ask and answer these questions, you start to piece together the lay of the land. The connections you make and the conclusions you draw spark an understanding much deeper than just assembling summaries. As the author of a literature review, you're a cartographer, mapping out everything that's known about your research question. And along the way, you'll probably make some surprising discoveries about gaps in the collective knowledge. What do you do with your literature review now that you finished it? In our example, the different disciplines on campus would use their completed literature reviews to inform the next stages of their research. It can be a standalone document detailing the current state of a field of research. You get to share not only where people have been before you and how they got there, but also where people need to go next. Or it can also be part of a bigger piece of writing to put research and arguments into a broader context. The review can demonstrate the need for your individual research as part of a thesis or capstone project. Looking for help with the next steps? Drop by one of our many library locations or the writing center to set up a consultation.