At Oracle OpenWorld 2012, this is SiliconANGLE.com's theCUBE. theCUBE is our flagship program. We go out to the events and extract the signal from the noise. We go to all the events we possibly can and where the action is, we are there. SiliconANGLE and Wikibon are on the ground providing great coverage, blanket coverage and breaking analysis. I'm John Furrier, the founder of SiliconANGLE.com and I'm joined with my co-host. I'm Dave Vellante at wikibon.org. It's our pleasure to introduce you to Lee Doyle, first-time CUBE guest, a friend of mine, worked with Lee many, many years. Lee, welcome to theCUBE. Thanks, Dave. Great to see you. Yes, so Lee and I, we work together. God, I think we started in the same week at IDC back in the early 1980s and... Is it that long ago? We don't look that old. So Lee, you know a lot of people in this business. You've seen some major changes over the years and the hot thing now is software-defined networking. So help us squint through that. Is it, you know, what's real? What's hype? What does it all mean? That's a great question. It is the hottest thing going on in networking these days, for sure, and it is real, and it's going to be a change in how networks are architected and how agile they are and how they're managed and how you architect for the cloud. So what does it all mean, software-defined networking? How do you define that? So it's really the ability to abstract the intelligence and separate some of the hardware from the software or the control from the data plane is the classic definition. But a more real definition to me is having open APIs or having an open ecosystem around the network as opposed to simply being this closed black box where you optimize software for ASIC performance. So networking is an interesting industry from the standpoint of Cisco has dominated for such a long time. I don't know what its market share is. It still is. Well over half, right? It's two thirds, you know? So is this trend, the software-defined networking trend, is it disruptive to Cisco? Is it going to allow others to gain share or is it more evolutionary and Cisco will just continue to dominate in your view? I can go a little bit in both but it is potentially disruptive because it does offer a new way of doing things and those who are more nimble and who can move faster and the incumbents always tend to move a little slower in these markets. So what's your take on Oracle Open World? I mean, is this a show you've gone to frequently and Oracle's not really a networking company to say but so what brings you here and what's your angle on Oracle? Well I've traditionally worked with Oracle on the telecom business and they sell a lot of servers and other things to the telecom industry but also networking's a big part of the cloud story these days and so I expect Oracle to embrace software-defined networking at least in part. Well that's their MO. They tend to go after the trends that kind of percolate and kind of cross the chasm and then put their blanket around it and bring it in, wrap it into the fold and vertically integrate that Larry to get his data center future. It's no secret in the industry for folks who have been in the industry long as us and you've been covering the sector. And Larry wants his own data center. He's always dreamed of being HP. He's had HP envy for years back in the day when HP actually was envious in terms of like the Bill and Dave days but now with HP struggling he's obviously got Mark Hurd on the team. He could get there. I mean he's got the package. So how do you as an analyst look at that and say okay we got to get some networking in there but hey Larry you got a multi-vendor situation going on. It's been going on for the history of the tech business. This is not a pure play consumer clean sheet of paper.  That always comes around in every cycle. It always comes back, interoperability. What's your take on that and how does Oracle need to deal with that? Yeah it's an interesting question. I mean there's clearly the ability to buy a company. There's lots of startups and VMware has taken its plunge with 1.2 billion with the CERA. There's the partnership route and there's also open source or an open community around open flow so there's a way to play it all of those different ways for Oracle depends on what they want to do. What about the confusion, the market? I'll see the Nacera deal pops everyone up and say hey you know something going on here. This is important, yeah. Wow I mean big step up from valuation when they just did their last venture round now that's a billion dollars and so everyone's kind of high-fiving each other in Silicon Valley but it puts everyone in a notice. So at VMworld when we had theCUBE there we noticed an air of a cold war. We called it the Cuban Missile Crisis of Cloud where Cisco steps back. They got this weird fresh release with VMware. Got Juniper kind of going wait a minute we did Junos and you got open flow startups. So you got this like balancing kind of a re-tweaking of everyone kind of a reset. Right. Explain that to folks out there. One is that real or is that just more a facade of the dynamic? And what are the big postures of the big vendors? What are they doing at post Nacera acquisition? What are they thinking? Right, well I see three categories of players. So there's the startups. There's Nacera, now VMware. There's Big Switch, there's M-Brain. There's a lot of other guys announced and unannounced in the valley. So there's a ton of money there. There's the networking guys and they all have a transitional story, buy our stuff and we'll take you to the SDN vision that includes Cisco, that includes Juniper, that includes Brocade. And then you have the IT players which include VMware but also HP, IBM, Dell and others who need to have a full Oracle perhaps who need to have a full stack. So if you software define data center or however you'd like to define the next generation cloud build networking has to be part of it. Yeah, we gave you a little peek of our software led infrastructure position we're coming out with because that's undefined. So software defined data center really means nothing right now. Which is good, that's a marketing dream. So marketing guys out there scramble and put stakes in the ground but the geeks call it network virtualization. The market calls it software defined networking. So connect the dots. Where is those next journey? What happens between network virtualization, software defined networks and then the destination of software defined data center. Is it the same thing, is there tweaks to the story? Well SDN is really about helping the network keep up with the migration and the tremendous innovation that's happened in servers for sure and then storage to maybe a slightly slower extent. So you need to provision virtual machines, you know to move them, you need to do so securely and you need to do so in a very high performance way. So that's what software defined networking is primarily about. It's about public cloud builds, then private clouds and then we can talk about campus and branch and WAN and all sorts of other things when we get there. So I wonder if you could put your practitioner hat on for a second and talk to the users in our audience. What does all this change mean to the user organizations out there? What should they be thinking? Right, well you know if you're looking at the cloud holistically, let's say it's private cloud, moving to a more agile data center, right? You've got a number of different parts in that. In your networking part, you need to look at what are the APIs, what are the ecosystems, what are the tools and some of the software that you can use to leverage software defined networking. Because networking without tools without something to leverage it, it's just more networks. You need to look at management and provisioning. Ideally you want to reduce OpEx. The manual configuring of the network is just very time consuming and very costly in people terms. Security's a big issue. Also from a legacy standpoint, everything is you have to migrate from. Unless you're starting over, unless you're blown up or going completely green field, you start with switches and routers and go from there. And the plumbing's not going away. So you want to have a high performing, resilient network architecture as well. Lee, what's your take on the whole service provider trend? It's interesting, you go to most of these events and certainly at VMworld, you heard a lot of focus on service providers. We were at EMCworld, we were at SAP Sapphire. There's a real discourse from the host vendor to really try to court these big service providers. You haven't heard a lot here from Oracle. Now maybe they're selling technology in there, but certainly not from a partner standpoint. They're not hearing, oh, we're going to partner up with the service providers and deliver clouds and they're going to buy our technology. What's your, two questions here. What's your take on the whole service provider trend? Is it disruptive? Is it new form of distribution channel? Is it just the evolution of the services business? And what's your take on Oracle's angle with service providers? Well, I think it depends on how you define service provider, right? Being a networking and telecom guy, when I think of service provider, I think of AT&T or Verizon. But you could also think of Google and Amazon and Rackspace and a whole lot of other folks. So clearly there's a whole lot of servers going and storage going into these organizations. So they are a new channel. So they're a buyer. Yeah, a huge buyer, right? And the traditional telecom companies really need to change their business model and are changing their business model to be full scale cloud providers, especially the tier one folks. Oh, we were talking off camera about Dell a little bit. They've really stepped it up in a number of enterprises, certainly storage. They've always sort of been in the server space with the x86 service, but also networking. They've made some acquisitions there. What's your angle on Dell's networking strategy? Well, networking and security. So they bought a number of companies in that space and they're trying to put that together with their server and storage to really go to traditionally mid tier enterprise, right? Is the Dell story. So they're trying to package that all together and leverage that into a consistent integrated offer. Do you consider them a major player in the space that you follow, Dell? I think that their player in the networking area, are they going to go after the big public cloud? I don't know. Lee, talk about HP because we're bullish on HP. We've been critical of HP. I have been in and taken some heat for that with HP, but not the audience, because I love HP. I've been working there nine years myself. If folks know that, I've been very transparent about that. But they're networking group solid. I mean, they've had a great legacy in networking, going back to the ether switch days back in the 90s. And then they bought three coms most recently. They just solid on networking. They've been shipping an open flow product. Bethany Mayer was on theCUBE talking about that.  So they got some work on marketing. Like Bannex and others got to do some marketing on that. But for the most part, they're solid. They're part of that ESSN group now called Enterprise. So they've been executing converged infrastructure in a cool way before anyone else really is trying to go over there. What's their current situation right now? Are they just not getting the word out? Are they being smothered by the HP kind of reboot? And what's their prospect? Give us your take on HP at the moment. Sure, I'd love to. They're the number two networking vendor behind Cisco. So they've been growing in networking. So they're a solid player, have quite a broad product line and people respect their products. I think they do suffer a little bit of the HP hangover from the corporate side, but they've been busy getting the message out and putting together a data center story that leverages the network. They have a good story? They do, yeah. How about the servers? Do you follow the servers at all with that group? Not, you know, a more networking guy. Well, what about the converged infrastructure play? I mean, that obviously affects networking. It's kind of an evolutionary trend, but people are using that as a wedge, as a way to compete in the marketplace. Clearly Cisco getting into the server business looked like it was going to be disruptive, but it seems like it's more just an evolution now. Have you tracked that market and what's your take on it? Right, everyone's trying to put together server storage networking with the management wrapper and make it a lot easier. I think HP has done some really interesting things there, as has IBM, as has Cisco, so there's a lot going on. I don't think it's all been played out yet. Are IT organizations ready for that convergence? Are they organizationally aligned for that? They're thinking about it, but are they there yet? Probably not. We had Jay Sri, the CEO of Arista on theCUBE.  We love her, a big fan of her work, and also she's great on theCUBE. She gives great content on theCUBE, but Arista, they got funded. They got in the trenches when there wasn't a lot of funding, and it wasn't cool to be a networking player, and we've talked to Doug Gore-Lay many times. He's a friend, he's been at Ex-Sysco. It's hard to build a systems company, and I talked to the founder of, Pradeep, founder of Juniper. Doing the networking startup wasn't like it used to be. It's really hard, it cost a lot of money. So Arista did it when it wasn't sexy, and now SDN hits the table, and they're kind of clawing, hey, we're here! So, and they have a good product. I see their bosses in all the top data centers of the web-scale companies. What's your take on Arista? I'm relative to, one, their current situation, and then now the market's spinning with SDN. Well, I wanted to comment on your startup question. So software-defined networking, you need tens of millions of dollars, maybe even a couple million dollars to startup, as opposed to, you know, Arista and Juniper, they need hundreds of millions to build the A6, right? So order of magnitude, so there's going to be a lot more software. It's not a super angel deal. Right. This is not a micro VC, 500K, put up a website. Well, Arista has built some very high-performance networking, they've got a niche, especially at the high-end, and they're now starting to tell their software-defined networking story. And Jay Sherry's done a nice job of putting some of the comments I've seen there. So you talked about building A6 and some of the custom silicon. Yet, we are living in this world, everybody says it's going to commodity, software-defined implies commoditization. We had Jay Sherry on, one of the first things out of her mouth was software-defined, and she's very savvy, taking advantage of those trends. But what's your take on that? Are we moving toward a more, you know, a less custom A6 world, or is there a more of a demand for that type of capability because of the networking bottlenecks that we're seeing? I don't think software-defined networking doesn't presuppose commodity. It's an option, right? There's always going to be plumbing, and plumbing, you're always going to need high-performance plumbing, and high-performance plumbing is going to require A6. Now, that being said, where's the balance of intelligence? Where's the commoditization? Is there going to be more cuts? Is there going to be more off the shelf, you know, x86 in networking over time? Absolutely. How fast and where? That's a great question, and one that I've been looking at really closely. So we were talking about NYSERA and VMware before, and Cisco, and of course Cisco and EMC, John Chambers and Joe Tucci have this very tight relationship. VMware goes out and buys NYSERA, everybody says, oh, that's the end of the relationship, and of course they, in public, have, you know, great relationship. They're doing a lot of business together, but if you fast forward five, seven years, you can see where a company like VMware will gain more networking function into the stack, try to commoditize the network where possible, because they want to keep it as low cost as possible, but I guess your argument is there's still room for guys like Arista and Juniper for the highest performance applications and use cases to tuck in there, but what's your take on that whole trend? Right. Well, you still need the network to move the bits, right? It's what's controlling that operation and where's the value chain, where's the profit pools go, right? And is VMware and Cisco on a five-year collision? Absolutely. So let's talk about Oracle. So Larry Ellison said in the keynote, small little comment, of course we picked up on it because our name's Silicon Angle. He said, Silicon, we want to go with Silicon. So we all know the Apple move, right?  Right. Apple made these moves early on, buying guys, building chips, proprietary chips, purpose-built for specific hardware functions. They have Sun, Larry's got to have his own chips. That's in the future. So where would you see that fitting into them, obviously, to this day? Any perspective there? We're trying to tease that out because no one really picked up on that comment.  But it's still a very important part. It's now fully in there? Well, also, you don't hear a lot of talk about Spark. Is that because Oracle was a software company that bought them for a yard sale? Or Larry's got his own little hardware group? I'll let you draw your own conclusions there. I think I just said it. But Sun is a toy for Larry. He's building it. He's not blowing off Sun. He's fully putting it in there. Right. I mean, that's a core part of his strategy. Well, I think a really interesting aspect of this, and one of the things I'm here to learn about is how Oracle will take its engineer-assistant approach and really build that into the networking and telecom industry. Because that's always an industry that has had very highly specialized hardware and highly specialized software in an optimized black box type function. You don't just split it out. Right, and you're seeing some adoption of these converged infrastructure solutions within telecom, and in a way, Exadata was one of the first converged infrastructure solutions, although it's not a general purpose converged infrastructure solution. So, Oracle, if I understand it, really doesn't have a clean product play there yet. I'm sure they have a lot of different products that, you know, Sun Legacy products that they sell at its telecom, because Sun had a huge telecom business, but what do you think the right move for Oracle is? Should they go, for instance, to go compete against the VBlock, for example, or some of these other reference architectures out there like you're seeing with FlexPod at NetApp in the partnership with Cisco and VMware, or will Oracle, in your view, just take a typical Oracle different approach? Well, let me take a slightly different angle on that question, which is in a little notice acquisition, they bought a company called Go-Head. So Go-Head does HA middleware, which is SAF compliance, which is a technical way of talking about telecom-specific middleware. And so they're going to bundle that in with their engineered systems and really try to offer a platform for both service providers and network equipment companies to build their specific applications on top of that. And then we were talking off camera as well, Oracle bought Zygo and they made it a, they marketed it as a software-defined networking play. It's really an IO virtualization play, you know, a form of consolidation, virtualization.  And next thing you know, they announced the Oracle Public Cloud or the Oracle Private Cloud, and it's all red stack in there. It's a really different strategy than you see. It's very IBM-like of the 1980s, early 1990s, isn't it? Right, and they can clearly offer the full public cloud or private cloud stack to the telecom industry, and they clearly intend to. Right, how about mobile? We haven't talked anything about mobile, but have you been tracking mobile and its implications to the networking business and what's your take on that? Sure, you know, BYOD, the wireless LAN, and once you have a network that is wireless-led, as opposed to wired, now you have a different architecture and you have potential disruption to go to a more software-defined arena and to look at what your incumbent is, because the plumbing's still in the background, but you're not as concerned about the actual hardwire plug. BYOD, that's a real challenge for a lot of IT. It's a management necessity, but how you implement it in a secure manner and a high-performance networking solution is not intuitively obvious all the time. Well, yeah, you've got a lot of inertia with the legacy desktops and devices and processes, and all of a sudden you have to support these new devices, it's not trivial. And oh, by the way, you need to build an app store. I mean, we really have anywhere, anytime, any device, is upon us. We talked about it for a long time, now it's here. All right, Lee Doyle, well, listen, thanks very much for coming by theCUBE, it was always good to see you, good insights, and I'd love to have you back at some point and enjoy the rest of the show, and thanks for watching everybody. We'll be right back with our next guest right after this.


Someone was chatting to me recently about how overwhelmed they felt with all the advice and feedback they were getting. So many people saying so many things to them it was kind of doing their heading maybe you couldn't relate and I've been there too. My wisdom to them and that I follow my own life was this try to find the signal amongst the noise. Try to find the repetitive pattern that's in the feedback and advice. That's the same thing being said many ways so what's happening isn't isn't lots of things have been said to us it's one thing being said to us many different ways and I think to dial it all down and find the benefit of it all. You've got to tune in to the one common thing that's been said to us through all the things that have been said because that's where the growth is that's where the feedback and advice is valuable. So tune out the noise and try to tune in to the signal within the noise the signaling this is where you need to grow and change and work. Hope that helps. Cheers!


There's over a million yoro flashes in this game, and yet every yoro main I know still thinks and says that this is the most consistent and best one. Two and a half years later, and it's still getting reactions like this from people. But before I show you that, are you tired of losing in Valorant because you're solo queuing and getting teammates with zero comms? Yeah, me too. That's why it was perfect when Z League reached out to me. Z League is a huge all-in-one app for gamers. There's a feed where you'll see a ton of really cool gaming content and only gaming content.  Like I mentioned though, they have a really cool team finder called LFG. Just swipe through different profiles and invite people to play with you. But the best part about Z League is their new feature called Play Now. You can compete against others at similar skill levels and win real prizes. Yes, even cash prizes. You also can play for free and choose what and how much you want to win. If you win Z League coins, you can trade them in at their shop for things like controllers and headsets, or put them towards your next Play Now session. So use my link below to download the Z League app and you will get 500 coins just for signing up. Also, if you comment the word Yoru on my Z League post, I'll select a lucky winner to run a Play Now session with. I have like 5,000 coins on my account, so there's a lot of stuff we can win together. Don't forget to follow me on the app while you're there and enjoy the rest of the video. Raise the brush. One skills, one skills. Yeah, he's... Two skills, two skills, two skills right here. Alright, two. Flash them, my flash. Yes, he's there. They can't hear you, so they need... We have a lot, we have a lot, we have a lot. One enemy remaining. Yoru players, what? Yoru players? Yeah, gross. Nice, take one if you can. And if I trade too, I'm sure I do. He puts up an enemy. His bomb. I'm down, A. Nice kill. Screwshot me! By me, by me, by me, by me, by me, by me, by me. The main. Oh, he's on mount, he's on mount. He already dropped, he dropped already. Nice! Nice. He's already worthy, dude. Flash, don't get too careful. Alright, I'm gonna ult and find him. I have eyes still, by the way.  In default, he's teased in this corner right here. I'm gonna flash him. Nice job. Got him. Got him. Alright, like for sure. I'm gonna ult, I'm... Yeah, I'm ulting, I'm ulting, I'm ulting. I hit two, I hit two. Last one's on Jen, last one's on Jen.  It's time to jump. So he's gonna be back. I'll handle this. No, Omen's, Omen's stopped flat, Omen's stopped flat. Good kill. How is that? Chamber is nowhere to be found. He's on long, he's on long. Oh, what? How do we know she's gonna hurt? How do we know she's gonna hurt? Oh, for... One likes to flank, yeah, yeah, yeah, yeah. Yeah, yeah, yeah, yeah, yeah. Good shot. I'm gonna reload. I'm gonna, I'm gonna flash this day. Yeah, alright. He could be baiting, yeah. I have all of my retake. I don't mind playing retake, but we wanna win at the win hole. I'm gonna flash, I'm gonna flash this. You can go see, go see, go see. Yeah, I'm seeing that, I'm seeing that too. You're on three lane, you're on three lane. Nah, they're all gonna mid to be. To be tower still.  Grim is still back flat. He's gonna get hit.  He's out, oh my god. Get out of my... Armor left side, left side. He's, he's tethered, he's tethered, he's dead. He's like, he's killjoy, killjoy, viper, and hugger. Going back? Nice kill, nice. They grabbed the guy. Nice. I'm peeking off your turret. I'm dead, I'm dead, I'm dead. He's on site, on site, on site. Two more short, I mean. Do not take TP, do not take TP. Up short, out through. Wait, you're on trap 2, trap 2. I'm, I'm trap 2. What the f***? Wait, are we both stuck? I can't, I can't move. Wait, what? Wait, wait, we're both stuck, we're both stuck. Wait, you guys can push it. We literally can't move, we literally can't move. I can't move. Short, hotbox. Um, so bad!


Can you see my slide? Great, then I will get started. Hi, I'm Isaac Johnson. I'm a research scientist at the Wikimedia Foundation. I'll be presenting my paper, Language, Agnostic, Topic Classification for Wikipedia, which is work that I did alongside Martin Garlock and Diego Saez Trumper. We're also researchers in the Wikimedia Foundation research team. So let's start with the topic part for this project. What are topics for Wikipedia article? That is, how do we describe what any Wikipedia article is about using a very high level and relatively small vocabulary. So we can do things like understand trends and page views or edits or help people find relevant content to their interests. So let's start with an example. This is the English Wikipedia article for the Scarlet Baddest, an adorable little fish. How do we describe the topic of this article in a high level way? Well, luckily for us in English Wikipedia, there's an extensive ecosystem of what are known as wiki projects, groups of editors who work on content under a specific topic domain. And they go around tagging articles among other things that are relevant to their wiki project. So in this case, wiki project fishes has tagged the article for Scarlet Baddest, but fish is still a pretty specific topic and we'd like to go even higher level than that. Again, luckily for us, English Wikipedia has just this mapping that relates to these wiki projects, one or more of 64 different kind of high level topics. And this was an approach that was devised by a son and half occur in their 2018 CSCW paper, and we adopted here. So this is all well and good. But what about articles that haven't been tagged by English wiki projects or articles in other languages. And this is the problem we tackle in this paper. How to model Wikipedia article, such that you can predict its topics. And this isn't a new problem and there are many ways to approach it. So I'm going to walk you through a few. So to be more specific, how do you represent a given Wikipedia article such that you can input it into a machine learning model of some sort that can then tell you what topics it predicts pertaining to the article. And the most obvious approach, I think, perhaps is to use the text of the article.  They use a simple approach, quite effective. You could throw Bert or any much more complicated models over top to, but regardless, the text should carry plenty of signal regarding what an article is about, and every article has text. You could take an outlink based approach so just use the links in the article, as I'll explain shortly this is the approach we take here to ignore the text just extract the other articles that it links to. You may be throughout a bunch of data, but the links are nicely fixed for that vocabulary. And the really important part is that while you could use a language specific links so for instance represent the scarlet baddest article as linking to the English Wikipedia article for freshwater fish. You can also treat the links as pointing to wiki data items, the Q IDs you see in that third step. And this makes the model language agnostic and that in English Wikipedia article that links to freshwater fish is represented the exact same way in the model as a Spanish article that links to a Spanish language version of the article for freshwater fish, and so on. So the model can make predictions for language additions it is never seen, as long as the links are to known wiki data items. So they just did this in their 2020 paper and they showed it to be quite effective. And there are other approaches wiki database so ignore the Wikipedia article entirely and said look at articles associated wiki data item and the statements it contains use those for the modeling. We can keep doing this exercise for at least other a few other approaches. So you can see there are a lot of ways that one might and many have approached this problem. So how do we choose the outlinks based approach. Well, we did it based upon the following guiding principles. Our first principle was coverage, we wanted a model that would work for almost any Wikipedia article in any language, even if it was a fairly new article. And we're focusing on three approaches here outlinks text and wiki data, because they all do pretty well here, all articles have text, almost all have links, and almost all are linked to a wiki data item. So how many model parameters, how long to train the model, does it store comfortably in memory, things like that. Here the outlinks in the wiki days, wiki data based models do well. They use a relatively small vocabulary that's based on wiki data covers every language of Wikipedia. They don't require language parsing. This means you can build a single model for all languages. The first base approach requires individual pre processing models, probably word embeddings for each language, and this is far harder to scale feedback. Is there a clear path to improving the predictions for outlinks and text start argue yes, if there's topics that are missing, you add content to the article that is relevant wiki data kind of breaks this feedback loop though because the topic predictions are based on the wiki data item, not the individual article and so there's this kind of disconnect. And finally performance performance is generally the foremost principle that paper is used in declaring success with the model, we purposely put it last to emphasize the other considerations take precedence here. That said, all three models actually perform quite well outlinks and text a bit better than wiki data, but in the same area. Based upon this we chose a topic classification model based upon article outlinks other certainly room for improvement I think this model is unique in its ability to satisfy all these principles at once. Some quick specifics on how the outlinks model actually does. So coverage, we're close to 100% of articles on Wikipedia having at least one outlink so that's great. That's in simplicity. It takes about 10 minutes to train the model and we use the fast text Python library here. And it's less than a gigabyte for the entire model on disk. So that's including the 50 dimensional embeddings of which there's about 4 million for this model. Feedback, because the model so simple, we've already identified some areas of low recall ways that we might improve the data here. So that's good. And then performance. So in the standard kind of held out test set. We found that the text based approach or the link based approach was within within 1% of the text based approach for precision close to 90% recall close to 80% f one and average precision curve these are all micro statistics for macro statistics it was a little bit lower but still in the same area. And then we also gathered feedback from Wikipedia's on 10 examples of each topic and five different languages. So these are examples that we might not have had ground truth data for and here we beat the text based approach by 5 to 10%, depending on the language. So this shows that this link based model does transfer well across languages. Finally, a few thoughts on what's next. There's an API and dumps of all the model prediction so I'm going to paste these links at the end but I encourage everyone to go kind of explore test it out. And then there's frame more types of language agnostic models using this framework or similar frameworks. There's always model improvements you can do things like imputing links that haven't been added yet which is something that party and West doing their work, as well as different model architectures things like graph based methods. And finally, and perhaps most important is question of addressing biases. So the precision is quite high, but we do see areas of low recall and we're looking into those and trying to understand what's going on there. So this is just a bigger question of whether this is the right taxonomy of topics. So I mentioned it's based upon English Wikipedia. But it's not clear that that actually applies to all the other languages, even if you can build a model that will make good predictions based upon that taxonomy. And with that, I will close my presentation if we have time we'll take questions otherwise we'll be happy to answer them in the chat and Martin Diego my co authors will be able to field questions as well later in the poster session. Thank you, Isaac. I think we have time for a question to turn is our Q&A manager. Do we have questions on the top. Yeah, there is a question from Akhil maybe Akhil if you want to ask directly the question for Hi, Isaac. Thanks for the nice talk. Just curious. Did you guys try using the pre trained embeddings available via fast text or even sentence from summers. There are multiple multilingual embeddings available. And how why do you still think they are resource inefficient if you have pre trained models and can be used for a downstream task. Yeah, it's a good question we did consider and I've done some work around this. Two reasons why we don't use kind of this text based approach. One is that the embeddings rarely are actually available for all 300 languages. And when they are languages that don't have that much content the embeddings tend to be relatively low quality. So there's that kind of piece of it. And then even if you do have these pre trained embeddings you're still training kind of a specific model for each language. And while you know 10 models not so bad as you get up to 300 models that ends up being a lot of models that you have to be kind of monitoring and training and and a lot of disk space so thanks thanks for the answer. Okay then there is another question. Maybe you want to ask directly or. Oh yeah for the feedback and for the quality check. You're researching text five languages from the English and Vietnamese and French is, but are there any criteria for that I mean, usually, is it based on the quality of the results or based on the number of the editors was etc. Yeah, for those languages that was based upon the availability of folks who could go you know wikipedia in those language editions who would go through the predictions and accurately say yes this topic applies or no this topic doesn't. We also do when we do the held out test set. We're using all English level ground truth that we do propagated across the languages so for instance for a subset articles and pretty much every language. We also do the held out test set that we can test against to and show that we get good performance still. Thanks for the answer. Thank you. Then there is a one last question from Tina about the other requirements, particularly memory wise. And with a small memory fingerprint. Yes, I, one of the reasons I use the fast text library so much as I found it to be really forgiving about a lot of things. It's designed to run fully on CPUs. It's very simple so trains very quickly. The whole model, like I said is under a gigabyte so if you have essentially gigabyte gigabyte and a half or so of Ram, you can store it all in memory and make predictions quickly. So we use it on the servers but I've used fast text on my laptop to great effect to. So I highly recommend it. It should run on full force. Yes. I think we can go to the next presentation. There is another question. I don't know if there is time. There is no much time. Okay, we can. Isaac, would you mind answering it in the chat. Yep. All right, we have a second oral presentation which is from. Yes. Are you here. Are you here great. Please share your screen you should have the permission to share the screen go ahead. Yes. Can you see my screen. Okay. I'm a PhD student and I'm studying University of South Florida. Today I am going to talk about opinion dynamics and group decision making process in Wikipedia content discussion. And this work was done by me and my supervisor, you can look at Shampaya. In each Wikipedia, every day, 1000 of articles are created, but not all of the articles can meet the guideline of the Wikipedia properly to monitoring the quality of the article there is a process called article for deletion in which editor and reviewer can have discussion and can recommend delete or keep for the article. And based on the consensus decision is made. So, today I will walk through our research questions such as are there in biasing factor that correlate with the voting patterns of the editors, and how, how do these biasing factors evolve over time and form among the editors. And also, can the estimated votes can be regarded as the predictive factor of the final outcomes. So, in prior work may field and black have proposed a predictive model of individual vote and outcome of the discussions and their model was based on the natural language processing, and they have used the AFD discussion log from 2005 to 2018. In our analysis we also have used the same data set. So our first question is, are there any biasing factors that can explain the voting patterns such as the degree of agreement or disagreement with her peers. At first we have measured the bias or the preferences of each individual editors by calculating the probability of keep and these queries between zero to one zero is the indicator of being full deletionist who often recommend delete for the article. One is the indicator of being full inclusionist. And then we feed Gaussian mixture model so that we can get the major group of the editor in prior work terribly and champagne have found two major groups who we chair deletionists and inclusionist. In our recent analysis we have found more than two groups major groups, and the four major groups are strong deletionist who are who have the score goes up to zero then moderate deletionist, then moderate inclusionist and the strong inclusionist. Then for capturing the agreement or disagreement level. At first we have created a bipartite network among the set of the editors and the set of the AFD discussions or the articles. So in this bipartite network, each age signifies the recommend they have casted for example keep or delete. And then from this bipartite network, we have built a signed network among the editors. Here, each age between the editors is the indicator of participating in the same discussion and the sign the positive sign is the indicator of a recall of recommending the same action and negative sign is a indicator that most of the cases that the record their recommendation is different. So, then we feed community detection model moving and from this model we have found four major groups who are based on the connections of the voters. Then we have analyzed K core, K core is the maximum sub network of a network such that each every node has degree at least K. So the higher degree core we have we have found the most central user. So, we can find the most active user or they have participated in the most popular article discussion. So, at this point we have found two types of faction, which are GMM faction based on the preference of the bias and the Louvain faction based on joining to the same discussion, and then we calculated the spearman correlation between the faction and the age information such as the weight and the weight. And we have found that GMM faction has the higher correlation. That means that individual level bias explains more the pattern of agreement among the editors. And we also have found that at the higher core, the higher correlation that means the core of the network is dominated by like minded editors. Then our second question is how do these preferences on content, content inclusion or deletion form among the editors and how do they evolve over time. For that, at first we have selected the most central editors from the sign network, and then we split the discussion they have participated in 10 equal sized beans, and then we compute the probability of keep from each bean. And then we obtain the preference trajectory designs. And again, we fit the Gaussian mixture module. In this case, we also have found qualitatively similar kind of a similar number of groups, such as strong inclusionist moderately inclusionist strong inclusionist and moderate deletionist. And we also found that the preference are relatively, relatively stable over time, but there is a substantial narrowing of the opening at the early period of the reviewers. This can be this is an evidence of social learning due to the imitation of the year reviewer. And also we have found that the deletionist are highly resistant to change their opinion. And the last question is, can the estimation of the votes of the editors be regarded as the predictive factor of the outcomes. So, at first, we, we used latent factor model, so that we can estimate the hidden, hidden factor of the editors and the articles Then we use the predicted or estimated ratings as features and use the logistic regression for building the predictive model for APT outcome. So, and as the features are the continuous value so we also introduce a threshold above which we assign positive one, which is the indicator of keep for otherwise we assign negative one which is the indicator of delete for. So, this is the result of our outcome predictive model architecture. So, at threshold minus point for we have found the highest accuracy which is 82%. And we also have found weighted precision and weighted a points code. And at the threshold minus point for we have found everything highest every performance metric highest at the right hand side, you can also see the RLC score. And we also got the highest RLC score at the threshold minus point for which is 82%. And in summary we can, we can say that we have found the voting pattern of the editors can be explained by the degree of agreement and disagreement of the peers. And also inclusionist are more open minded than the delusionist. And it is possible to predict or infer the outcome of the APT discussion simply from the knowledge of the composition of the APT group, such as the bias of the editors or bias of the article. And we also have found that active and experienced editors are from the early phase of the APT project. And though they are the cohorts from 2005 to 2008, and we have discussed in details in our paper, and you can check out our data repository to reconstruct our findings. And that's all. Thank you very much and I'm happy to answer your question. Thank you so much. So we don't have a question in the chat. Okay, so what we can do, while people think about questions, maybe you know this, this was an amazing presentation so maybe there is a little bit of thinking to do before I formulate the questions, we can maybe start the live in talks and you will answer the question in the chat Okay, yeah, sorry we are a little bit late with the workshop so I think if that works for you we're going to go to the to the lighting talk session. Okay, thank you so much. It's not thank you so much for monitoring the chat. All right. I believe you are the first speaker for our second night in talk session. So, please get ready. I'm going to share the screen. Yes. I think we will get there. Okay. So, this is you right this is your person. Yes, yes. Thank you. Thank you. Perfect. Perfect. All right. So remember you have three minutes each please. Let's keep it on time. Okay, so first, please. Okay first place the link in the chat. Okay, so my name is a lot of R.D. I'm a PhD student at the university under the guidance of Professor Lev Muchnik. In my research about public trends and content consumption I wanted to use the traffic data for Wikipedia. Wikipedia is sharing the traffic dumps but not in a way that is efficient to run big queries as it gives you the traffic per hour but not per subject. Online tools were lacking critical features. They were not well maintained and other major disadvantages. For example, they didn't allow to search the Wikipedia's entire traffic history. So I downloaded the entire dumps but using it required a lot of effort. So that many other researchers use the traffic dump again and again from scratch. To solve this we created Wikishark. You can go to the next slide. Thank you. Wikishark is an online tool that it's easy to use, is hourly updated, allows querying all of the traffic data back to 2008. It also enables searching and comparing unlimited titles, view charts and export the results as data files or image files. There is also a browser extension that let you see the traffic directly on Wikipedia. And we also developed a trend engine surfacing new trends and hot topics. In the next slide I will briefly explain how is the data stored. So after downloading all of the data dumps we discovered that over 90% of the titles had never more than 255 pages within an hour. Over 90% we saved that in an efficient binary way, which means only one byte per hour. And for the rest we used only three bytes per hour. Moreover the data was saved in a sequence and it allowed much less disk access request. The query to the whole data since 2008, which means about 13 years, takes only a few milliseconds. And this way it allows us to run big data queries on the page with data. Finally, if you are interested in collaborating with us on either data or analysis, we are happy to explore such opportunities. If you want to learn more, come to meet us. We are at room number four. Thank you very much. Thank you very much. Thank you. Yeah. Hi. So hello everyone. So the paper vision going to discuss talks about the reorganization of information and Wikipedia articles. In the next slide we can see that by reorganization of information, I mean how factoids are rearranged in an article and the level of actoid in this paper we have taken a sentences. So in each with each revision sentences gets edited new sentences are getting order inserted. They are reordered. So do they do it progressively I mean with each revision. Does it happen. Does it impact the semantic semantic meaning of that article positively or just some random thing happens in Wikipedia articles. So we wanted to evaluate this. And for this we collected a sample of Wikipedia articles using certified sampling, so that you can represent the actual class distribution of articles. So, yes, so we calculated. The semantic similarity between consecutive sentences, and then average semantic similarity of a revision and then we observed how it varies with each revision. So, in the observation section we can see that in the first observation we found out that the overall semantic similarity increases as we go with the subsequent revisions. In the Pearson correlation density, it was a positive value and does it implies that the overall semantic similarity within sentences or factoids here, the semantic similarity increases. And then the observation to be tried to see. So, we know that Wikipedia articles are divided in certain classes, starts to FHA good articles. So, so they are supposed to be of varying quality. And we wanted to check how the semantic similarity varies within these articles within these classes. So we observed that, yeah, according to our hypothesis, the average semantic similarity was increasing as we go with the higher quality or higher quality class of Wikipedia articles. So, yeah, so users are not randomly shuffling the sentences editing the sentences they are positively contributing in the development of the chart. So this is our observation for this. Thank you so much. Thank you. Now, you can hear me. Yes. Okay. Perfect. I will speak about the role of local content in Wikipedia. And especially how it relates to editor and reader engagement. So basically, what is local content. So local content is like the content which corresponds to the cultural context of a Wikipedia language edition like geographical places of events, particular figures, etc, which correspond to this language community. And this is about a quarter of all Wikipedia language edition, all of the content is a local content. Okay. And we, we now look at does this local content reflect the higher level of editor and reader engagement. This is the question we're looking at in our next slide. We see a nice figure for several language edition so on on the top the top bars, the dark green bar corresponds to the percentage of Wikipedia pages which are which are local content. And then we see that if you look at the page views which is the light green bar we already have a higher percentage of page views which are directed which these pages attract. And if you further look on the edits we have the orange bar which is edit by registered editors and the red bar is the edits by anonymous editors and we see that the further we go down there the more the highest proportion especially it's especially high for anonymous editors. Okay, so then in slide in the next slide. This leads us to the hypothesis or the conclusions that the local content is more engaging for readers, and especially for editors, and anonymous edit we see even a higher proportion of it so that somehow leads us to the hypothesis that these editors the editors interest in the local Wikipedia project is more likely to be ignited by this local content so this is actually might be a way to get new editors to Wikipedia, promoting this kind of content. And basically, instead of considering this type of a bias because every language edition favors of course this. It's a local content related to its language edition it should be seen as relevant and important and for both extending the diversity and the content of Wikipedia and attracting new editors. And if you are more want to see more we are in session for room for. Thank you. Hi. This is Shubham. I'm actually the second author for it. I will be taking a bit. Yeah. Yeah, absolutely. Actually, we all know right that Wikipedia is an open source and anyone from the public can create the account and read the article so obviously we all agree that there needs to be some rating mechanism right. The rating mechanism via which we can create the articles is some we the idea is to SDSM no article grades to each and every article right. So that's what the core idea is and I think it's already been taken care but the one of the prominent ways via which we were no grading the articles is the quality grades right is the manual interventions we are using some other other rating systems or everything people have tried to automate it they have tried some national language processing methods or some social networking methods. So our idea is very much based on the social network analysis we are using the social network properties to train the models and predict the quality grades of articles. Next slide can you please. Yeah. So what we have done is actually we have collected the for each and every note of the Wikipedia articles we have calculated these network properties over them. That is in degree out degree between a centrality cat centrality page rank clustering coefficient hub scores and shell number h index and the recent properties right which have been mentioned. So we have tried to plot the graphs on the seeing that how no and try to observe that know how these stars and articles are behaving depending upon these properties. Then we collected all the Wikipedia articles names and their corresponding quality grades must both these informations. In the block diagram you can see that we have much both this information to create the data set for ourselves right. So the data set what we are doing we are trying to train the if we try to train different machine learning models, the classification machine learning models to predict the qualities over these properties. And yeah, the best results we are getting with with the random forest, yeah. The random forest is giving us the best results as we can see in the confusion matrix and the difference course for the random forest when we trained it over this, this data set. And then we try to compare our results with it. There were the two types of the people's two types of researchers who have tried to perform the similar kind of classifications one was the multi classifications like they are trying to cover the whole spectrum. And the other was the people who were trying to do the binary kind of classification. We have compared our accuracy along with them. One thing like at the end that I would like to mention is that a few papers have some eliminated the a class or a class, they haven't expressed the reasons that why they are eliminating all this class. But we have tried to focus on all the whole spectrum of the seven classes. That's it. Thank you. Thank you so much. All right, hello everyone. My name is Sebastian and together with my two colleagues Florian lemmerich and Marco Strummeier we tackled inferring socio demographic attributes of Wikipedia editors by looking at several state of the art approaches and possible implications for editor privacy. Next slide please. This is the overview of our approach in total illustrated for a specific user and here we can see all the different parts of the standard profile page. For example, the profile text is located in the middle of the page below that we have the categories a user chose to associate himself with. And on the right we have the different user boxes. And as you can see editors tend to display lots of different information about themselves like in this case that he was part of the military the time that has passed since he joined Wikipedia his gender is age and so on. And as we can see the profile text is used as input feature after applying some kind of embedding. And also the extracted socio demographic attributes are also uses labels and then both are given to different classifiers with the goal of predicting an editor's gender age, education and religion. Next slide please. And the prediction results we can see here are for our different models based on precision recall and F one score. So that bird outperforms TF IDF and doctor back for all socio demographic attributes and predictions for the attribute gender are more accurate than the predictions for the remainder of the attributes. And very important aspect deals with the implications for editor privacy because we should only utilize the acquired labels on an aggregated level to find general disparities in the Wikipedia editing community, which was our initial goal, is using this kind of information on an individual level, for example for personalized recommendation would you wrote editor privacy and would therefore most likely result in unethical applications. But the problem is that not disclosing personal attributes is not enough since as long as large enough sets of editors still decide to disclose some socio demographic attributes. So this would enable predictions for editors who might have might have explicitly decided against it. So, if someone is really concerned with the privacy set person should probably refrain from using the user page, or at least shouldn't use their user boxes to make automated processing and predictions more difficult. And now I'm handing over to Oscar I believe. Thank you. I don't see the chat so if there is. Oh, sorry, I was muted. Good. Please go ahead. Okay, sorry. Hello, all again. I'm Oscar and I'm presenting to you the research we carry out together with Lorenzo gatti and Kiriaki Calimeri. In this work, our aim is to model delivery moral foundations from text and access like please medium. We have a multi vehicle with which we express our beliefs and values. In this study, we operate as an allies morality via the moral foundation theory. Originally, the theory presents five foundations that have to pull our opposites by some virtue. The final model, this model, the creators of the theory propose a six dimension, the liberty dimension individuals that value strongly this moral value are consistently less concerned about individual level concerts, such as harm benevolence and altruism. There are also much less concerts with with group level moral issues for for instance a conformity loyalty tradition that are typically associated with conservative morality. The moral foundation theory is commonly used in computational science and why for the man for the main five dimensions that are lexical resources for the liberty dimension is still there is none. A million can you pass to the next slide. Here, our goal is to assess how people express the moral values of liberty through text. Since, since these faves the way to understand conflict conflicting social issues, such as vaccine hesitancy and subset subset 30 to conspire to conspiracy theories. Since according to the moral foundation theory device and virtue of the liberty scale relate to the conservative versus liberal political spectrum. We consider in the week we consider the Wikipedia pages and their conservative counterparts as a natural experiment. We collected all the conservative pages and we try to align them with the respective Wikipedia ones. 30,000 37,000 aligned pages, the figure shows the frequency distribution among the Wikipedia and conservative documents. At the top left, the blue dots, we found the terms most most commonly, most commonly using conservapedia, for example, Antifa or homosexual agenda in contracts at the bottom right we find the terms not using Wikipedia for example, affordable care. This is, this is in the red dots. Then we use a lexicon generation method, including the use of word embedding strain from our data set. Using this derived resource, we finally obtained an outdated vocabulary that aims to model the liberty dimension. This is a preliminary and novel work. Currently, we are working on refining the obtained dictionary to accurately model the liberty dimension. We are evaluating the quality of the end resource on value on various real life scenarios such as news and Twitter debates. Well, thank you very much for your attention. And if you have any questions, I will, I will be happy to take them. Also, I will be in session five. I think it carries a work on I think sorry if I can't work sorry. It's okay. Thanks. This is Chang-Wook Jung from Christ and IBS, South Korea. I'm presenting the study about information flow on COVID-19 of Wikipedia, which is part of the topic knowledge creation and diffusion. This research has been done by Christ, IBS, Post Ed, Sung-Shi University Wikimedia Foundation, and Max Planck Institute. Next slide. During the COVID-19 pandemic, misinformation killed people. Another incident was a rumor that methanol might cure the coronavirus. This misinformation spreads worldwide and homes more than 700 people just in Iran. How can you stop it? Misinformation can be blocked by the facts at the right time, right place from credible information sources such as Wikipedia. In this study, we analyzed the pattern of information generation and consumption by languages to provide the information needed in each language services. With the analysis, you can see the regional and cultural similarities and differences of the pattern in Wikipedia language services. We chose 11 target languages and collect COVID-19 related items in Wikipedia using Wikidata relationship. Then view counts and edit counts are collected and we categorize the items to see the topic shifts. Next slide, please. With the collected items, we ranked all 11 lists. Also, we made correlation metrics and clusters hierarchically based on the list to check the likeness between the languages. The clusters in first figure indicates similarity in reader's interests with view counts. The results reveal that European languages come together while East Asian languages make a cluster. Wikipedia items were categorized into four topics with biomedical information and reason, people and others. Such categorizations makes it easy to analyze how the pattern differs from other languages. The second figure shows the number of coronavirus pandemic related documents and the amount of access were examined by category. Although the number of items in the biomed category is just three. Coronavirus pandemic, coronavirus 19, and SARS COVID-19, the view counts is relatively high and indicating that there are high demand. In a nutshell, we found that information generation and consumption in Wikipedia are related to cultural and geographical connections. To establish the basis of the information provided strategies for the next outbreak or next pandemic, the information generation and consumption, speed and item network developments should be examined. We published our data set on ficture to promote more analysis from other researchers and also our data collecting method code is available on GitHub. I will post the links on the chat. Thanks. I will wait for the comments in room four. And next will be Karthik Madanagopa will present the marvelous research about knowledge gaps. Hello everyone. I'm Karthik Madanagopa from Texas A&M University. I'm going to talk about my current research with Dr. Kavali towards ongoing detection of linguistic bias on Wikipedia. First of all, what is neutral point of view? Next slide please. It is a Wikipedia guideline that expects all articles to be returned fairly proportionately and as far as possible without editorial bias. And yet knowingly or unknowingly, some form of subjective bias is injected into the subject to treatment of facts in Wikipedia articles. Let's take a look at these two example statements that are extracted from Wikipedia. The use of the highlighted words in these two statements introduces a subtle form of subjective bias that can influence the reader's perspective on those topics. Not all articles are reviewed for NPOV. Only articles that are tagged by readers or editors are disputed and NPOV board. Especially in case of current events like 2021 storming of United States Capitol. As the event unfolds, these articles were viewed by millions of viewers before it being revised by the NPOV board. It may be possible that some of the readers might get influenced by the subject to content present in those articles. There is a need to build a robust bias detection algorithm in case of these crowd source encyclopedias like Wikipedia. Various researchers in the past have built classification models that can detect bias statements as the writing styles of Wikipedia editors change over time. And some editors try to actually evade these kinds of bias detection models by using different language styles. These models struggle to detect more subtle and emerging forms of bias. This is mainly attributed to the fact that these models were solely built using Wikipedia data alone. The primary focus of my research is to build bias detection models that are more resilient that can self adapt over time. Such models should be robust to changes in editor behaviors and new subjective writing styles that has never seen before by the Wikipedia community. Next slide please. In our initial investigation, we built a series of bias detection models solely using Wikipedia based statements alone. And it kind of validated the fact that just by using Wikipedia alone, we won't be able to build a model that can have consistent performance over time. Even though the initial model had a better position of 77% its performance kind of degraded over time. To enrich our model performance we started exploring through cross-domain transfer learning approach by leveraging bias statements from various subject to rich domains such as political speeches and product reviews. We have observed some encouraging results like 89% accuracy with Roberta-based classifier and also across different topic areas like politics, language and literature-based articles. In order to extend our models performance in other topics, we have started working on generating more statements using generative adversarial networks. The generative data can be used to augment our current cross-domain bias detection approach to further improve its classification accuracy. Thanks for the opportunity. The next paper is about accessing the quality of health related Wikipedia articles by Luis Coato and Carla Lopez. Well, thank you, Kartik. So, I am Luis Coato, me and Carla Teixeira-Lopes. We did a research work about assessing the quality of health related articles in Wikipedia using generic and specific metrics. Next slide, please. There are many works about assessing the Wikipedia quality using metrics based on generic features that we used. We wanted to research if there are specific features of Wikipedia that would be used to assess the quality of health articles and which of them are the most important. We also wanted to propose specific metrics based on these features and finally we wanted to know if these metrics are better to those existing generic features for the health domain. To achieve these objectives, we used the methodology represented in this diagram. So, we began by exploring the health related content features in articles from health and medicine areas. Then we collected the top 1,000 most-viewed health articles, at least provided by the Wikipedia Project Medicine. After that, we analyzed the quality of generic features proposed by Svilje and the specific features we proposed on assessing the collected articles. From that, we proposed health-specific metrics that I will talk about soon. In the end, we analyzed the quality of the proposed metrics, comparing to the generic metrics proposed by Svilje. The results are present in this slide. So, as health-specific features, we proposed those present in the first table of the slide, which are the number of external links with a reported source, the number of sections in the article that belong to the list of recommended sections in the Wikiproject Medicine guidelines. Another proposed feature is the presence or not of the article in the healthcare translation task force list. The next feature is the share of additions made by the Wikiproject Medicine admins. Next, we proposed the number of health-related templates present in the articles, such as that one in the second figure of the slide. We also proposed the number of medical code classifications present in the templates, as represented also in the second figure. The next feature is the number of health-related info boxes as represented in the first figure. And finally, we proposed the number of images included in the info boxes. We adapted the generic metrics of Svilje, adding or replacing the proposed features, creating the specific metrics shown in the second table of the slide, which are health authority, health completeness, health informativeness, and health consistency. The respective formulas are shown on the slide. And as you can see in the table, we achieved good results with the proposed features and metrics. The most relevant features were the number of recruited links, the number of recommended sections, and the articles translated by the task force. We improved all the generic metrics, although we get the best results in the authority and marginal improvements in consistency metrics. With that, I finish my presentation. I'll be available later in shot room 3. And next, we will have the presentation of Bhuvana. Thank you. Thank you. This is Bhuvana Meenakshi and I'll be presenting the research study on Virginia Gender Gap in English Wikimedia Communities. And this particular research study was done with association with the Center for Internet and Society and the Access to Knowledge program team. This study was basically to document and analyze the gender bias in the Indian language Wikimedia Communities. So next slide, please. Thanks. In this, we analyzed the previous research that's done globally and also the research is focused on Indian language communities itself that have been done in the past. And in a 2018 survey from the Wikimedia page on gender bias states that there have been 90% of the contributors across various versions of the Wikipedia. And among which the 90% each constitutes of male participants and 8.8% each of female and 1% as non-binary gender who have been editing and actively participating in Wikipedia project specifically. And other studies since 2011 mostly focused on English Wikipedia having a percentage of female editors as up to 20% each. And this gave us a thought about understanding how much of the percentage of the female contributors exist in the Indian language communities. And especially across various Wikimedia projects and also to basically understand how the gender bias has been perceived in the local communities. And therefore, we brought this study into three thematic areas, which is mainly about online participation that includes content created by women, content about women and their online engagement with communities. And also understanding offline participation by women across various Indian language communities and the strategies to remove barriers to sustain participation of women contributors and mapping the diversity of Wikimedia projects. And how women are involved with sustaining the participation across the projects. So in this study we had 15 interviewees from 13.


Nanotechnology is a key advanced technology that has the potential to lead qualitative and quantitative production of healthier, safer, and high-quality functional foods through the use of nanomaterials, which has superior shelf life, prevent contamination, and produce enhanced food quality. The review describes current trends and future perspectives in the food sector, including applications that enhance food bioavailability, taste, texture, and consistency achieved through modification of particle size, possible cluster formation, and surface charge of food nanomaterials. Additionally, the review discusses the use of nanodeliverimediated nitrocyticals, synergistic action of nanomaterials in food protection, and the application of nanosensors in smart food packaging for monitoring the quality of stored foods. This article was authored by Shivraj Hari Ramnayal, Venkadasamy Bhaskar, Divya Selvaraj, and others. We are article.tv. Links in the description below.


perovskite solar cells, PSEs, have been hailed as one of the most promising next generation photovoltaic technologies due to their rapid increase in power conversion efficiency. However, the instability of perovskite layers has been identified as a major obstacle to their widespread adoption. This review paper examines the chemical reactions of perovskite layers under different environmental conditions, including moisture, oxygen, light, and with charge transfer materials and metal electrodes. Strategies for mitigating these reactions, such as the use of buffer layers and additive engineering, are discussed. Finally, conclusions and future directions for the field are presented. This article was authored by Jing Zhuang, Jijing Wang, and Feng Nian.


Hello, everybody. Welcome to real life podcast right here on SBM Buzz. Kami supo host Miss Kithingi and this is where all feelings are represented. Joining me on set today is... Ian official. Rich girl is Jackson. Many other gracious host. Now today's topic of discussion is about co-parenting and the question is where do you draw the line when it comes to co-parenting? Sijuso rizenun igani zen yu Miss Kiaport co-parenting. Ian webutu anzena wewe as a man on set today. Well, I think there's something called, I want us to revisit, something called history. And I'll put it in a scenario of maybe a phone gadget. If I want to sell you my phone, I must clear all history. The same thing when you've decided, we have reached a point of co-parenting. We have first of all to clear the history of we were once together. Now we start thinking of us as parents. Standard procedure, standard. Drop the child if maybe nimama na kanae or maybe if it's the dad who lives with the kid. Drop the child on Friday and make sure the child is back home by Sunday. For what? No, as a quenda maybe class the following day. There should be minimal conversations between you both parties. If you're not discussing about the child, I don't see what we're discussing about. Not unless we have other things that have tied us, that require the both of us to discuss apart from the kid. Maybe we have property together. Maybe we are trying to sell property together. That's where me and you we have to come keep in touch. But zaidia hapo siku wambia mzazi, adropum toto piya na kujana mgozagi. You get my point. Ya. You get my point. That's when you start finding, oh we have the second kid. You know those scenarios? Ya. I'm your baby daddy, but now we have the second child. And when you are leaving, you will not be able to communicate. So I believe, first of all it is to cut on communication. Let us talk only when necessary. That's what I believe. Hey, I feel for me, I agree with him. So apondi onatu kakuja, for example details. For example, I have a baby daddy. Blockiana kila mahali, we cut communications like that.  You have to you have to keep it open on at least one social media platform. And that's what I say, let's pick what's up. I can block your Instagram and all that and everything. But now that will not hinder me from posting my things. Even Ruto being the president, Rila still lives in Kenya. But there are things and points whereby you are forced to collaborate because of all these things. Kama ni cbc, ntoto na faena kumaklazumu tu miwa kwa what's up. There's no I'm working up to go to GPO kukolektu kumakiamtoto, simply because kumab Blockiana. Kwewa unamka subu, una henda, una nilza uti mi postbox na kupatia, 00100, una henda una ni mailia, kumakiamtoto. That's a lot of nonsense. You get my point. Or number two, the people who do this, they introduce a mediator, like a lawyer. All right. Kama ni ntoto kukuja kwangu, ntoto na pelo koko akili, wakili na nmleta kwangu. Norisha ntoto koko akili, wakili na pelo kama toto. That's how you charge everything. If you want to pass even the 10 years information. Ryan alisa hawa Raba, nandiki ya wakili. Raba ya Ryan, nae wakili na nandiki ya mama Ryan. Raba ya Ryan, nae wakili na nandiki ya mama Ryan. That is how people have...  So you see at that point now, there is no interaction with you and the mother. If there is nila important, I have paid school fees, you know. Or I bought the outfits, get them to do somewhere. Just normal information. There is no place and trees of Ryan. A man zaku crawl, wa, wa, wa, wa. He able to make a video. Is of you too. That is where now you get to bring back all these emotions that were here before. Una pata na kwa parenting is not possible. I feel like I have so much to say, bachani yandze apa. Yandze amwanz. Mi yandze amwanz to nandze amwanz. I feel like if both of you, mule kachi name kargri, this is not working. Lets coparent, you might not need our lawyers, rina, nina, nina, nina. Me niza co-parent kamabisti. The only reason amfeku comeunikit sana, is if one party has moved on and is seeing another person. So to respect their relationship, you expect tia nabib mama kila tia ma na kutumia video, zarayan na na crawl. Om wa kea rayan kila. Euro nila tu respect. But if both of you are not seeing anyone na maia chana ukweli, you can just be friends nama co-parent uizuri. Na feelings az ita ruudi.  When we are together in thoughts, we understand we left each other, we have our own different lives now, but we have a child, and this is the only thing that connects us, so I should not come in between your life, kama ule move on, ule move on, sife kuskevi baya? The problem is, most of the times, one party kwa gime move on, ina kwa gana two groups, ina kwa gana? So this is the problem? I can also remember that there are some points whereby co-parenting is not easy, because you find it is forced, and it is forced by law. Past me and you, we had a thing, then Ryan came in, then mi nasema huyu sim toto wangu nasi o wangu. So what happens, we go through the DNA test, okim toto ni wako, now we have to co-parent. But I'm not there physically, I'm there maybe financially. What is my job? Place school fees, sending money for bla bla bla, only the required things, but I'll never be present. You're not there emotionally available for your child? I'm not there physically, I'm not there emotionally, I'm only there financially. You get my point? Ya. Let's not look at the aspect of, we can make it easy because mi and you, we are communicating, it's not about communication. Sam is forced and that's why you find co-parenting becomes harder at times, not even because of emotions, but because others are forced. Even the foundation of that relationship was at zero? Yes, there are other co-parenting situations that are forced, that if I am asked today, do you want to continue? I'd say no, but I know the trouble that it would get me into. You get my point? You know the trouble that it would get me into? I have a question, so there was this couple that were having an issue, so kuna dem na kuna chali. So dem is in a previous relationship where she has a child. So now unfortunately the baby daddy died. So the husband was having an issue with the wife going to the baby daddy's burial. But remember, he was having an issue with the wife going to the baby daddy's burial. So you see, it's a couple yes, but now the baby daddy is having an issue with the wife. So what do you think is the problem? I think there are some things that require us to be mature 100%. And for that lady, she should start now noticing some narcissistic behaviour from the man. Because in all honesty, you can't be in a position whereby you are fighting with a dead man. He doesn't want to look bad to his friends. Number two, there is something called closing the gap or I'm seeking closure 100%. That once I've already done this and I know burial will have 24, 3 weeks, you're not going to sleep there. If I'm going for this burial for 2 or 3 hours, you have already closed that chapter. There's no other time I'll hear about babanani, babanani, babanani, babanani. As a matter of fact, it would be maturity times 2 if I accompany you to the burial for the essence of the kid. That's also part of maturity because you want to show me, myself, I don't have any bad blood between that guy and myself. And at the end of the day, you'd never wish death even on your exes, whatever. So for the lady, she should start noticing at least some narcissistic behaviour. Because that's being toxic competition because you can't be competing with a dead man. For example, on that same story, now that we're talking about pettiness and being mature, there was this instance of Akote when she got married to the last Mzungu guy and they went to visit the kids. And the husband at the time was there with her, you know, and they went to visit one of the baby daddies. And we saw a video that the two baby daddy, oh, the husband and the baby daddy were together. And I heard some men saying, would you be in such a situation if you were the husband at the time? Let me tell you, you see, and also for women, this now something we have similar between us men and you women.  Then when you see me with another person, you are ribbon much happier. You get, I don't know if you guys have come across that. But say, if you see me with a baby daddy with another woman, you'll be like, thank God, at least yes, at least I'm not in the picture. There's no way I'm appearing. Ha kuna mambo mingi sasa. Atlema temal kwa nashinda nani pigiya, pigiya sana, sana. There's some joy that comes in when you see your person or your previous person has moved on and it comes in time. Well, of course, and that's what I'm saying, it's all about maturity because the biggest consumer of all your behaviors is your kids. Your kids are the ones who are consuming the most content that you're doing.  You see the way we make ugali, the more you start it, the more you end up in a farm. But at the first process, you can be able to introduce new content like there are people who cook ugali with butter, with all those things. At that point where you're starting, that's when you can be able to introduce. But when you start molding it becomes difficult. And that's the same way for a kid. If you show the kid that in this world you can have somebody, you love them, you break up, then still be in good terms, kama tulia chana chawzuri, it is possible. And advocate for that actually. And advocate for co-parents to collaborate actually with the other parents. And I'll tell you why as we wrap up. There are these social events that happen in kids. Birthday, circumcision. Especially maybe on circumcision. And on circumcision we find there are some cultures that require the dad to be there. Please if you have a baby daddy, don't limit him to such events only because you are married. He has been there co-parenting, he has been doing everything. So in case to such a point whereby, hey, I need this kid to go get circumcised. Let your current and your baby daddy sit down have an agreement. We have a kid, I have accepted him as mine. I have been raising him for the last 10 years. You for yourself, you are the father and you have also been providing for him for the last 10 years. What should we do? Should you allow me to take him for circumcision or will you be the one who will take him? Let parents be given chance to co-parent easily even if the other partner has somebody in him. That's what I would say. As I wind up also, let me ask Kondanya this question. For example, you have a baby daddy who has gotten married. And now you want to communicate to your baby daddy about your child. But the other wife has decided that you will not talk to your baby daddy again. Uta kunongia direct na epesiki tumua ina tumua. Direct kutuka kwa baby mama wo uta yoongia nabuana. Jule de mama sema zi. Would you accept to talk to their wife instead of your baby daddy? I wouldn't mind talking to their wife but that is a very bitter woman. You know whether you like it or not. We will not say that you have a baby. We will not say that you have a mother. We feel like this whole situation of baby mama and baby daddy, the moment someone has given you a child, you have this person's child. This person is a connection that you can't even explain. You will still love them unless now you have those two dramas. It's love because I give birth to your child. Respect me mama. For each to work, we just agree. Yes, we have a child. We still love each other maybe, maybe not. But let's do it. Let me love you from a distance.  But if you get married and your wife says nini nini, as long as you're providing and you spend time with your child, But better let me ask you this here. On this scenario, and I don't want to know men to tell us, but say I'm a baby daddy to you, then you get married to somebody else and you have your well-off family that is doing good and all that and everything. Am I still required to provide financially back to my kid? For your child, yes. Now the conflict comes in ego. That other man starts feeling like kweni huyu babae, huyu anadani sinapesa. There's a lot of quarrel when it comes to you have moved on, you already have a new guy, you guys are doing good, you're in your fifth year, you're doing perfect, you have other kids actually. Now this is a family of like four, three kids and this now other kids. Now, me as a man, I'm sending in money, but that's where the problem really comes in. The ego games and the right games. From the other end, he's like, huyu kweni anadani mimi sinapesa. And from my end, I'm like, huyu kweni anadani, nona hua toto anaba yuave tuza Christmas, jaskia kia kia ungele wa toto ake. So me I'll go buy my kid. And number two being, if I'm going to visit my kid and you have already your family that is set up, am I required to come with gifts worth all those kids or should I only concentrate on my kid? Also I feel like I don't see why if let's say I was your baby mama and I've moved on, I have a man and all that. If you're coming to visit your child, I feel like they should hang out with you in your own space. Because now this is my space and we have the head of the house in this house. So this is my space and our child lives in this house.  If there are these social activities also I feel like we should hold them in neutral places unless it is really necessary to hold it at home and you are comfortable as the father of this child then we can hold it in a neutral place. That's the thinking for me. Nafil tu ma pensina emotions, emotions na emotions whether jealousy, whether ego, reason of it to worry about co-parenting but also like you said, it narrows down to why did it lead to co-parenting. Because it's a child's stand, it's a child's life, it's a child's need. If somebody just wants a kid because like let me tell you, I know of a guy who has a baby mama and the baby mama also has several kids but not from the same guy. Luckily or unluckily, she lives by herself. So umse, I can tell you that it's a big fast. Hidkari, things specifically for his kid. Pase food, best food, best toys, love of life, suicide is confirmed to be number 4 while you are at number 3 and number 4 pekiaki. Then when he starts questioning the guy is like, this is my kid, this son of my kid. As a matter of fact, if he wants to have a birthday, it costs him around maybe 300-400 or even 500. Then the rest of the kids, now the siblings, they are like, what about your birthday, what about your devil. So the mum is like, please, as much as you are coming to see your kid, narrows down to how you treat him because the rest are not feeling good, then he's like, you don't care about the rest?  But remember again, baby, if this kid comes to my space, he'll go back with all those things, not that I'm here, he'll go back with those things. Sasa, lakini sasa, kama kwa na pese, kutit muntutua kote na usim, it's a tricky... because now these two others are not your responsibility. But then again, kids don't reason like that. They don't. How are you supposed to tone down because you have your baby mama has other kids, in how you treat your child? What do you guys think? They should at least tell us, palitini, how, at what extent my baby mama has a kid, at what extent should I not go? Juna skia Christmas, me want my kid. I take my kid, we go with him to SA, to Bali, you know, they come back, so kids cannot reason it together. So at what extent, should this co-parenting... Alright guys, please help us solve that dilemma down on the comments section. Tell us, what has your co-parenting, what style has worked for you? Is it because you have a reasonable, ex-partner, what is it? Twambia paliwa kwa comments section, piya labda, but as idea, twambia paliwa kwa.