Okay. So I'm Katrina Hayes. I currently work for a company called Mandova Network Services. We help support ISPs. So we provide the back end services for ISPs that otherwise don't have the same sort of resources as Time Warner and ANT and those companies. So why logging? It gives your code a temporal dimension which as the internet is coming of age we've got like 10 years worth of stuff that's built up. And as you're sort of going through these processes, much like baking, I don't know if you can tell that's a picture of a pie, and it's a pie that's built up by layers. So the idea is that you sort of have this like crumble nut layer on the bottom. But then when you go ahead and add the next layer on top of it, you can't see the nuts anymore. So you're going to get people that are walking through. Now if the pie is originally like, it could be strawberry or rhubarb. It could be a memorandum or it could be turtle. You're going to end up, if the label is just there and it just says pie, nobody's going to know that there's nuts in it until somebody has an allergic reaction. So that's why you start adding logging because if your original system wasn't documented very well, you aren't going to know that there's nuts in there until somebody goes up and crashes. So by logging, if you weren't standing there next to the pie and watched somebody fall over, you weren't going to know that until afterwards. So some general things to this talk. Like I said, as I was looking at it, I was looking at these two particular case studies of bugs. As I was going through it, I realized that both of these types of bugs are particularly in studios because they build up over years. You don't really know that they're initially there and they aren't initially there. The two general types of bugs that have come up. The first one sort of builds out of this process. You start with a very simple system 10 years ago. Over the years, you're sort of adding all these different things and somebody comes in and is like, oh, I'd like to be able to send things by email instead of mail. So you have to go in and add this extra system. And as you add all these extra things, you get different inputs and stuff and nobody actually knows how the entire system works anymore. The other type of bug that I was encountering while we're doing this cooking, each person comes in and is like, I'm going to build my own type of pie. And so you just end up throwing all these recipes at them and forgetting where everything went in the first place. So the first type of bug, that color's on again.  So the first type of bug was exemplified by, this was on a site where you could navigate. You just had some sort of static content and you navigated from page to page. Over the course of the years, different people came in with different requirements. You're sort of like, okay, this particular course needs to navigate in this order and this different person needs to go through these slides in a different order. And so you end up, instead of just saying like, okay, if the person clicks the next button, you go to the next page, you have all these different roles that get in with getting layered in. So what we ended up having to do with this one is that every time someone clicked on those buttons, we had to go in and add a logging step. We had somebody describe it as an open stage of mass visualization, right? So each person that's going through the site now has their own unique place that they're supposed to be going to.  Okay, so you sort of add things to your logs as you can. And then you can track people like, you can look at what we were initially looking at. See what we can see? Well, this isn't actually a data set, but if you track different things, you can see like, okay, here's how the person is going through the site. You get the different timestamps and we have the different user that it is. And so if you're going through all of these different things, you can get, it's when they were initially setting up, they weren't going to want to click. You weren't going to log every time someone clicks on it because they're just going to the same page, right? But now that we've got all of this storage space, we can start adding data sets like this. So you can watch the person as they actually go through the site in real time and you know what page they're at now. And so for this bug, it saved it because we were able to see where the person is supposed to be going. So if they got stuck in a little loop that just kept sending me back to the same page or something, we would see that happening to just kick them over to the next page. Now that we have this log with all this information in, we also start to save some other bugs that we didn't even realize were bugs in the first place. So we have the networking timestamp issues that people are calling like, hey, you know, I'm supposed to have, I've been on the site for half an hour, but I didn't always say I've been on your site for 10 minutes. And we can look at it and be like, okay, well that's because one time this time seems to be showing up on your computer through JavaScript and the timestamp that's on our servers is saying, you know, Sam, think that's great. We also started tracking some stuff. Like if somebody called in and they were still running Internet Explorer 6, we would say, oh, that's why this isn't working on your site. Instead of just trying to hear them describe it over the phone. Am I talking too fast? Am I going to be addressed a lot more by open source, I think. So this is what happens when each person comes in and says, like, I've found the solution. I'm going to throw out everything else that's come before me, except that I don't actually know how to get to the flower anymore. So this is too many cooks in the kitchen, right? Each time somebody comes in and decides, like, I'm just, I don't know what's going on, so I'm just going to throw everything out, you also still don't know why it was there in the first place. At the same time, if you aren't too careful about throwing out everything, you end up with these layers, like, oh yeah, where's this coming from? So in this particular bug, what was going on was that certain cases were getting turned out. It turns out that there was a previous layer that was adding a marker to those that nobody knew about anymore. So we had to, the way that we ended up tracing this bug was that we just had to sort of keep adding new logging points at each join that we could find. So at each point that we would get this data dump, and then it would point to another place or something that was going on. So we'd have to sort of keep adding new logging points at each join that we could find between the different coders. And eventually it just ended up being the first being a flag stuck to these cases that was causing them to get thrown out, and nobody knew where those flags were being stuck to them. In the first place, we just sort of like, so we go in and out at another point, and we're like, okay, erase that flag. And yeah, that's just sort of another layer of crux that's getting added, but see, I don't know why. The Internet today, it's a lot more complicated than it was 10 years ago. So nobody can understand everything anymore. So each time you come into a project, you know, don't just try to throw it away and write it off the scratch, which is kind of where that was. So I guess this is sort of where I was going with this. I mean, does everything anymore? Yes, we have these giant data acts and stuff that we can sort of toss everything into. As long as you can go back and find it, what you're looking for when you need it, you'll think about it. That's why I thought I was talking too fast. Add an additional login. You try and keep it all in a standard log format across all your systems, or is it sort of ad hoc? Like sort of throw in just whatever texturing makes sense. That's human readable. So I guess for a lot of these, before I was at Neo Nova, I was an independent contractor, so I was going into a bunch of different small businesses, and sort of, you know, okay, we're going to throw a code at it, and that'll fix everything, and then we're going to not have a code anymore, which is why you can't necessarily talk to people that came before you. Right. So do you have like tools? I mean, do you use tools to like, parts of those logs? You know, like when you come across a bug, going to the log file is trying to find out are you using like Elk stack or anything like that to like index the logs, or are you just going in with like wrapping? So it depended on the type of access that I had at the system. Then in some systems I didn't have real-level access, so I wouldn't be able to actually look at the log files themselves, which is why I would try to add some sort of like a big table or something that I could look at in access, even if I didn't have real-level access. So I guess I'm using logging in a sort of loose sense here. Gotcha. Okay. Thank you. Any other questions? Who has a question? Was it you? Oh, yeah. So if you're logging everything, how long would you retain these logs? Eventually you're probably going to have to purge. Yeah. So you probably don't need logs from 10 years ago. I guess it'll depend on the level of resources that you have. So the longer you can keep your log files, the more likely it is that you'll need to go back and look at them. I guess in one particular case we had people that were from, they hadn't realized that they'd been hacked three and a half months before or something, and our logging went to about three months before. So that's something I'm from. So I guess as long as you can keep backups you want to, at the same time I know that a lot of small businesses don't start restricted in the level of resources. So they have to store what you can. Yeah, and especially now that we have these giant data centers and stuff.


This paper reviews the current state of knowledge regarding COVID-19 as an occupational disease of medical professionals. Data from studies and reports indicate that medical professionals are most at risk for COVID-19 infection in their workplace. Additionally, the article discusses issues related to the epidemiological situation, in particular data on infections, deaths and quarantining of healthcare professionals, risk of infection at the place of work or provision of services, and arises questions related to the recognition of COVID-19 as an occupational disease. For this purpose, both common court decisions and doctrinal positions are presented. In this regard, the actions taken by the Ministry of Health and the Chief Sanitary Inspectorate were also indicated, especially the issued guidelines and recommendations. This article was authored by Yaroslav Pyotr Shimolevsky, Melanie Raksak, Marta Puschen, and others.


Kalpana Sachin Khure transformed herself into a successful entrepreneur after she completed training offered by Mandesi through the Girls Inspire program. Kalpana is 25 years old. She lives in Lonand, western India, with her husband, their two children and her husband's parents. Life was hard for Kalpana and her family. Lonand is mainly an agricultural area and the local economy was in Islam. Kalpana's husband worked hard but he only worked four days a week and his income was low. Kalpana wanted to work to help out but there were very few job opportunities, especially for a woman. Things took a turn for the better when one of Kalpana's friends told someone who worked for Mandesi about Kalpana and how much she wanted to work. Kalpana was enrolled in the Digital Literacy program run by Mandesi as part of the Girls Inspire project. One of the things she learned about in the program was how to start a business without having access to large sums of money. Kalpana certainly did not have much money to invest but thanks to her training, she now had confidence and knowledge. She drew on that knowledge to open a business selling ladoos and salted peanuts. Her business thrived and her husband began to help her when he was not working at his own job. Kalpana did not simply sit back and enjoy her success though. She had bigger plans. She took out a loan through Mandesi and used the money to invest further in her business. Her confident, relaxed smile is the smile of a woman who has made the most of the opportunities offered by Mandesi's training program and in so doing taken charge of her future.


Hello everyone, my name is Ziad Damak, and today I'm going to present the project Effects of the Russian-Ukrainian War on the Editorial Activity of the Ukrainian, Russian and English Wikipedia. As we all know, Wikipedia is a leading resource of information that plays an important role in the spread of news, events and social movements. And during the time of process, this volunteer-based model could be influenced and which lead to potential discrepancy among the users. So mainly this research focuses on studying the behavior of such entities during the Russian-Ukrainian War between 2020-2022, and we're going to perform an in-depth analysis of the data and in order to gain insights into the dynamics of the platform and the communities. Now we move on to our research questions. The first one would be, how does the activity change for editors and viewers during the war? So we start from the pre-process to the set, which is extracted from the Wikipedia media Wiki history dumps from the period of January 2020 till June 2022. Then we were going to perform a difference in difference analysis in order to get an overall insight of the English, Russian and Ukrainian Wikipedia. And this methodology is replicated from the previous work of Rupert Sturantov in 2021 on the COVID-19 pandemic. The second research question would be, how does the collaboration between editors change? So first we start by pre-processing our data. So we extracted it from the Wikipedia API, which contains articles and revisions and comments. And our data would be divided into three main groups, which are the related articles to the main page of the war, the K-top viewed articles and K-random articles. Afterwards we're going to perform a reverse analysis in order to investigate potential edit words. Now we move on to our experiment result for the first research question and specifically for the edit volume. So we can see here that there is a significant decrease in activity near the date of the invasion in the Russian and Ukrainian Wikipedia. For the Ukrainian Wikipedia, the level ultimately surpasses the pre-invasion activity. This observed recovery in activity for both of the Russian and Ukrainian Wikipedia indicates that there is a resilience of the communities contributing to Wikipedia. For the English Wikipedia, we didn't see any significant change. Now we move on to our second experiment, which is about the contributed information. We can see here that there is an increase in both of the Ukrainian and Russian Wikipedia's in terms of contributed information symbiotes, which is also confirmed by the difference in difference analysis. So this addition of large volume of information is due to numerous events and changes appearing during the war. Also when comparing the contributed information metric to the edit volume metric, we can see that the Russian and Ukrainian edits remains consistent with 2021 and 2020, but there is a significant increase in terms of contributed information in these languages. The same thing for the English Wikipedia, we didn't see any significant change. Now regarding the revert rate, we can see that there is a spike in the revert rate for both of the languages and specifically for the Russian Wikipedia, which indicates a disagreement and a lack of consensus in the early stages of the war and specifically in the first 15 days. Now our next experiment is the reverse analysis in the context of the related articles. We found here that the Russian Wikipedia exhibited a spike in terms of revert rate and this high volume of revert rate for this language indicates that the political context is really complex and it may indicate that there is a high level of disagreement among the editing community leading to polarized editors. Now for our main findings first, we have found that there is a short term decrease in the number of edits and a smaller decrease in the aggregated contributed information, other than that we found that the Wikipedia editor communities are overall resilient in the times of the war and also there are some potential indications of polarized community inside the Russian Wikipedia and finally we have investigated the number of newcomers also, the readership data and finally we have explored some existing analysis approaches in our project. Thank you everyone and feel free to ask any question.