What's the word, y'all? Yep, another blowout. Another blowout, yep, absolutely. But this one's similar to the one from yesterday. There's a lot to be said about these blowouts, a lot to be said about the games. I didn't upload yesterday. Some of y'all know that I'm a new father and with the recent events in Texas, my heart was extremely heavy. It was hard for me to watch Warriors vs. Mavs and be thinking about content, taking notes about this game when there are so many bigger things to be talked about, so many different things to be thought about. And though thoughts and prayers literally mean nothing, those families are in my heart and I cannot imagine what they are going through. I don't even know what else to say. But today, we got another game. Let's talk about it. Hopefully this 15 minutes of content or whatever it is, is enough to keep our mind off of the things that are tragically going on in our world. At the halftime of this game, it was ugly. Hold on, actually, why am I acting like I don't got the stats? Let me get these stats for this ugly halftime. At halftime, the score was 37 to 42, right? And they put a graphic up on the screen. Of the 2022 NBA Conference Finals, this is the first game that's within five points of halftime. We are four games into the Eastern Conference and five games into here, and we finally got one that's relatively close. The Miami Heat, they were up by five points and they were shooting at 37% from the field, 19% from three. The Boston Celtics shooting at 38% from the field, 25% from three. It was ugly. 10 turnovers for the Boston Celtics and it felt like every single one of them from Jalen Brown, he had a dramatic change. I don't know what the hell happened at halftime. He must have went up to Twitter because he was getting dogged on Twitter for that first half performance. It came out in the second half, it was dominant. Fourth quarter was dominant, but this game was extremely ugly. And I was like, even though it was ugly, it was close. And if it was ugly to this extent, you know we might have a game where in the last two minutes somebody turned up and boom, it's a close game. We think it about last possessions. We're playing a foul game. We haven't seen intentionally fouling at all throughout the Conference Finals because no game has been close. When the first half we kind of got that. And I was like, yeah, the comments was flooding on my mentions, yeah, Kenny must be happy. And things changed. Things absolutely changed once he got to that second half. And you know what's crazy? Before this series, the Miami Heat in the third quarter were dominating teams. The first couple rounds of the playoffs, when you got to that third quarter, the Miami Heat turned it up. Even if you look at game number one, right? Game number one of the series, the Miami Heat absolutely turned it up in the third quarters. But since then, the third quarters have been the third quarters for them, ladies and gentlemen. They have not been able to do anything. And obviously, you know that they're star players and you cannot convince me otherwise. Jimmy Butler is like extremely, extremely injured right now. But he's trying to play through it like a lot of other people. Cal Lara was trying to play through and we go talk. We have a whole segment about Cal Lara because Jesus Christ, how? How do we get to this point, Kyle? How? Second half comes around. And the one thing that was the advantage for the Miami Heat in the first round was one of the reasons why they were up by five points at halftimes because they were getting a ton of second chance points. In the second half, they still did an amazing job on the offensive glass, but it felt like the second chance points weren't necessarily there. You also got Jason Tatum having a great performance. Now I know he didn't shoot the ball very well, but the playmaker for Jason Tatum in this game was one of the best I've seen from him. Second half, Jaylen Brown was dramatically different than first half Jaylen Brown. And then Derek White, last game, I think the first quarter, a lot of the damage that Derek White did last game was all in the fourth quarter other than the great defense and stuff. And this game, boom, another great performance. He's getting the Fred Van Vleet boost. If you don't remember, Fred Van Vleet had a child and then he turned it to an all NBA player basically during the playoff run. That's what we're getting right now from Derek White. So if you want to have an amazing playoff run, you have to breed at the right time. So once we get to May, you're on fire because that's what we got from Derek White. So you got Derek White doing his thing. Basically he filled the Marcus Smart role in last game. And now he did Marcus Smart things while Marcus Smart was still also there. Luckily, I mean, not luckily. Well, unfortunately for the Celtics, Marcus Smart didn't have a great shooting performance, but he had to clutch three down a stretch. But I'm pretty confident saying that this series is over. Maybe this is an all-time jinx and it might be. You know what I'm saying? I'm not rooting for either any other team, but there was like sometimes you watch a game, especially a playoff game, right? And the losing team loses, right? And you're like, okay, I see this is something we could take away and we can expand on. The next game is gonna be solid. There is like close to nothing redeemable about the performance we got from the Miami Heat. They ended with 80 points. They had 79 in the other game earlier in this playoffs. They ended with 80 total points. It got Joel and Bede as a bystander tweeting about how they need a secondary star. And if you do not remember, this offseason they gave out some bags, y'all. They gave Jimmy Butler a huge guaranteed deal. I'm not looking at Jimmy Butler's performance so far. I'm thinking that his contract is now terrible because he's last three games. He said like 19 total points. Like I said, he's legitimately injured, but he's also 32 years old and he's guaranteed through 2026 where he's making $52 million. I'm not guaranteed. That is actually an option, a player option. Might as well be goddamn guaranteed because nobody's turning down a $52 million player option. So he's under contract, he's 36 years old. You extended BAM out of bio to the max and he has gone completely ghost. You know what bothers me about BAM out of bio, bro. Shout out to my boy Derek because Derek is the exact same way. You ever played sports with somebody that was so extremely talented that everybody around them knew that they could dominate other than them? That's how my boy Derek is. We were run fives and we know he could be the best player on the court but he is his own defender. That's how I feel like BAM out of bio has been this entire series. He had the nerve and the fourth quarter with his game was basically over to start taking shots. Well, I don't look at that final satellite and say to you in a good game, my boy.   And that's a detriment to the entire team. Then you got Cal Lauri. Another guy you could tell is dealing with injuries. He's laboring out there, but you gave Cal Lauri a three year deal where he's averaging around $28 million a year. He's already 36. You gave Duncan Robinson a $90 million contract and he couldn't basically see the floor until last game. There was a ton of money spent by the Miami Heat and yes they won the conference as far as being the number one seed but once you get to the playoffs you gotta need to know how to score in the half court. Especially with Tyler Hiro being out they just don't have that. Especially if Jimmy Butler can't give you 50, 70% of himself, they just don't have that. So you're looking at Jimmy Butler under contract till he's 36. Being able to buy you on the max contract for the next four years. Cal Lauri at 36 guaranteed to the next three years. Duncan Robinson's got a contract that goes as long as I don't even know how long. And then you also have to make a decision on Tyler Hiro. I would not be surprised if the Miami Heat looked dramatically different after the season. Pat Riley has given out a ton of big old contracts in recent years. Now the one thing about Pat Riley is that even if he's giving you that money he always finds a way to like if it is a bad contract get off of it one way or another, you know what I'm saying? So if Cal Lauri is washed or I don't know if he's washed or he's really laboring on his injury but still he's 36. Father time don't lose to nobody. You feel me? He's 36. You got him under contract till he's 39. Till he's 39. One thing about Pat Riley, he can maybe flip that and do whatever, whatever to get off that money. I don't really know. But they gave a lot of money to a team that looks like they're gonna be a team that can compete in the regular season. But once we get down to the nitty gritty conference finals, they're gonna struggle. And like we said in previous videos this has somewhat been a race to health, right? One player gets somewhat injured and if it happened to be your star player, it's raps. And your star player's injured right now. Now you gotta look at the other people. They gotta look with the enemy like in this game six can we put up a fight? Because these guys like they held their hand, bro. There was two, no there was one possession. We're Victor Lodivo had two wide open threes and he had all backboard on both. This team decided to go out there and miss 30 plus threes. I'm sorry. There's nothing redeemable about that. There's nothing redeemable about that, man. So I don't really know Cal Laurie gave them 24. I don't think he should be playing out there, bro. He's obviously not a hundred percent. And he's obviously has not been an impactful player other than the first quarter of the game that he came back. We got Bam out of bio going. And that's the game that Bam had 29 or whatever it was. He got Bam going in that very early on. But in this one, I mean the flop, come on, man. Can we have to talk about the flop and then go out there to give them zeros across the board? I'm sorry, he had a rebound. They get them zeros across the board other than this 5,000 three turnovers. That's an all time terrible performance. When your back court, your two back court players are over 15 from the field. How do you win that game? The bosses Celtics didn't play a great game either. They just made a little bit less mistakes or made a couple more shots than the Miami Heat. And yeah, like I said, there's not a ton of redeemable things about this. Is there a possibility that the Miami Heat win this series? Sure. But based on what I'm seeing with Jimmy Butler being out there and just basically walking around giving you four for 18, I'm sorry, bro. I mean, I don't, I don't know. The fans were leaving early because they knew, they knew.  And it was, it's not, it's not happening. So we're really preparing ourselves from the bosses Celtics go to stay warrior NBA finals, which is so very interesting. Two months ago, March 1st, 2022, I was called up by my people over it and bleach your port.  Have the floor, my guy. Talk about anything you want. And I wanted to talk about a team that I saw as a sleeper team. And at this point, remember March 1st, 2022, the Boston Celtics are 30, 37 and 27. They're in the midst of a legendary run. They are the sixth seed currently as I'm recording this video. In this video, I'm talking about how great the defense, how historic a defense had been over the last month before that and how I believe they have a chance to make a, I think I said Atlanta Hawks type run where they started off very poorly and found themselves in the conference finals. Boom, I make that video, I'll put it out there and I'm like, okay, I'm excited. You know what I'm saying? I got Candace Parker, I got Adam Letko, I got Dwayne Wade. All these people about to watch my two minute segment and they're gonna have some comments. My two minute segment runs, boom. And then Adam Letko looks to the people. He looks to Shaq, he looks to Candace. He looks to Dwayne Wade to say, how do y'all feel about that? Dwayne Wade said, what did he say? He said, I don't believe in that or I don't know about that. And here we are, here we are. One game away from being in the NBA finals. I'm not a guy that's perfect on his takes, for sure. But there's one thing I got there right and I got dismissed on national TV. Love Wade, you know what I'm saying?  And I, hopefully I get another chance to talk to him about this. But it was completely, not even just about Wade, it was completely dismissed. My two minute segment went and they decided to talk about something completely different. I ain't feel slighted in any way, but I was like, the ball's just gonna make some noise, whether they believe so or not.  One game away. And even in my video, my two minute segment, I said, I don't know if they gonna make an NBA finals but I think they can make the conference finals. Even I, even I didn't have the ceiling exactly where it should have been. But here they are, you know? I mean, I could go back and watch this video every once in a while. I'm going to look at, actually, let's go look at the comments. Let's go look at the comments. Congrats Kenny, this was completely right. Look at Kenny, so inspirational. Here after Celtic swept the nets. Yep, he was right. A month and a half and he was completely right. Yeah, I mean, somebody said at NBA TNT, more Kenny Beecham please. That would be dope. Hopefully for next season, I know TNT Tuesdays don't really exist anymore for the rest of this season. Hopefully next season I get like a weekly, so give me a weekly segment to just talk about random nerd NBA stuff and I can do that. And I'm not gonna be right 100% of the time for sure. But I might get some things right and I can gloat about months later like I'm doing right now. So here we are, Boston versus the Warriors. That could be a good series, bro. That could be a really, really good series. So I'm excited about it. Even though it's not actually in stone just yet, I'm excited about it. And I appreciate them giving us a relatively solid game enough for us to talk about because today's video, if it wasn't going to be talked about this, I really was gonna deep dive into the all NBA voting and we might do that tomorrow or the day after that or whatever.  Somebody voted Desmond Bain all NBA thirds, hey, listen. I'm a Desmond Bain believer. Before this season started on our podcast, I said, I believe he's about to take the next step and he absolutely did. I had him as my winner of the most approved player over his teammate, John Moran.   He also had McHale Bridges on his own ballot. And I don't know who the person is. I don't wanna count, I guess, outs anybody, but come on, bro. Jaylen Rose went on to national TV and he owned up to his mistake. He had Kyrie Irving as an all NBA player this season. Bro missed 50 games this year, you know what I'm saying? And then like half of the games he wasn't even like super great. You feel me? I just, I agree with Tatum when he says that these awards, these all NBA things should not be completely attached to the dollar amount that players can make because some people don't take it seriously. You had Desmond Bain on an all NBA team, you didn't take it seriously.  I think my all NBA team was identical, identical to the one that actually won. I took that very seriously and I gave you adequate reasons. And if you didn't agree with my points, I gave you reasons why this player is here, this player is here. You don't have to agree, but I thoroughly thought these things out. There's nothing you can say to me that Desmond Bain deserved to be an all NBA player over. Matter of fact, I'm gonna make a whole video about these things, but I'm about to bring it up right now because I gotta remember who he left off his ballot because it was, no joke, I think it was LeBron was one of them. And if you're arguing is a LeBron to make the playoffs, I'm like, okay, then you also left somebody else. I'm about to go bring up the, I'll be back. I gotta do it. This voters third team, this is their third team, Kevin Durant, Desmond Bain, Steph Curry, McKell Bridges, Carthony Towns. He also had McKell Bridges as an all NBA player. Oh, LeBron did not make his all NBA team. Wait a minute. Oh my God, wait a minute. Let me go through this again. Bro, are you serious right now? Hold on, I cannot make this up. He did not have, so Jama Rant didn't make his all NBA team. They make this so hard to read. Okay, so looking at all NBA, who did not make your list? So Jama Rant did not make his all NBA team, but Desmond Bain did hot, hot. All right, we're gonna make a whole video talking about this. That's all I gotta say today. We'll see exactly how the hell things go and I'll talk to y'all soon.


We are coming back towards corporate governance and we had a very interesting discussion earlier in part one and as a comparison of corporate governance codes of different countries and this is going to be the second part. We basically were looking at four different countries, we were looking at Bangladesh, we were looking at Sri Lanka, we were looking at India and we were looking at Pakistan. So let us move a little bit forward and look at how they tend to be similar or they tend to be different in the context of the audit committee, the remuneration committee and the nomination committee. So when we look at the size of the audit committee then ladies and gentlemen, in Bangladesh, in India and in Pakistan minimum of three members while in Sri Lanka, we are talking about only two members. The chairman of the audit committee in Bangladesh and India, it should be an independent director while in Sri Lanka it has to be a non-executive director and again in Pakistan it is preferably an independent director. Non-executive or independent directors under the audit committee, well in Bangladesh at least one independent director, in India two third members should be independent directors. So in Sri Lanka there should be the majority while in Pakistan all non-executive directors and one independent director would be formulating the audit committee and the audit committee is the most important committee in the context of corporate governance. Reporting framework of the audit committee, well in all four countries in the reporting framework of the audit committee is to the board of directors it to be Bangladesh, India, Sri Lanka or Pakistan. So again, a lot of similarities. We see the frequency of meeting of the audit committee and time gap between the two meetings, well in Bangladesh it is not covered. In India at least four times and not having a gap of more than four months between two meetings. In Sri Lanka it is not specifically covered and in Pakistan at least once every quarter of the financial year. So again we see a little bit of disparity and independence in the time frame of these different meetings. The quorum of the audit committee meeting, well in Bangladesh at least one independent member. In India either two members or one third of the members of the audit committee whichever is greater but the minimum of two independent members should be over there and it is not specifically covered in Pakistan and Sri Lanka. So therefore in those areas in which it is not specifically covered I think that Pakistan has a lot to learn from our other SAAK countries so that things can not be ambiguous, there cannot be any gray areas and things should be much more specific to ensure better compliance and better implementation of the guidelines and code of governance. We look at the size of the remuneration committee, well that is not covered in Bangladesh, at least three directors in India, two directors in Sri Lanka and again three directors in Pakistan. In the remuneration committee, non-executive and independent directors, well not covered in Bangladesh, all of them should be non-executive directors and at least half should be independent, well in Sri Lanka we see exclusively non-executive directors while in Pakistan a majority of non-executive directors and preferably an independent director should be a part of the remuneration committee which is extremely important. Now the chairman of the remuneration committee is not covered in Bangladesh but in India should be an independent director in Sri Lanka an independent non-executive director and in Pakistan can be any member of the remuneration committee except the chief executive officer. Now when we look at the size of the nomination committee which is the third committee, it is not covered in Bangladesh and Pakistan but in India at least three members and in Sri Lanka two of them. When we are talking about the chairman or chairperson of the nomination committee then not covered in Bangladesh and Pakistan but in India should be an independent director and in Sri Lanka should be a non-executive director, non-executive or independent directors under the nomination committee not covered in Pakistan and Bangladesh, in Sri Lanka it should be the majority and in India all of whom non-executive directors and at least half of them should be independent. So again what we see is that actually in Pakistan what we see is that the nomination committee is not a requirement and therefore we see that it is not covered and similarly also in Bangladesh but because it is a requirement in India and Sri Lanka therefore we see that the stipulations are properly formulated. So again we looked at two committees, three committees actually the audit committee, the remuneration committee and the nomination committee and the differences between Bangladesh, India, Sri Lanka and Pakistan. Thank you so much.


Alright, welcome everyone and thanks for coming to our session on the, on AMP, the audio visual metadata platform. My name is John Dunn. I'm assistant dean for library technologies at Indiana University Bloomington and am a project of service project director for the audio visual metadata platform or amp project for the past several years. And I'm Sean Averkamp. I'm a senior consultant with AVP and I'm a project manager on the amp for this phase as well as a subject matter. So we were just recalling that we had planned to come to San Diego two years ago for CNI to give a presentation on amp. So it's it's great to to be able to actually finally do that in person. With all of you, we have given some updates in the past kind of an original conception of the project and then some of the work that we had done during the second phase. But here we're going to talk about a few things. We're going to provide some basic background and context on the project for those of you who aren't familiar with it. We will talk about what we accomplished and learned in the last phase of the amp project and talk about the goals and work. For what we're currently doing and where we see it going in the future and I should say this is a project that has really been a partnership between Indiana University AVP. New York Public Library and in phases one and two the University of Texas at Austin and has been generally supported by a series of grants from the Mellon Foundation. So we thank them very much for making this work possible. So first we'll talk a bit about background and context. So amp is a project that's born out of a challenge that we were seeing at Indiana University, but that others who are dealing with large volumes of AV audio and video. And I lump motion picture film in that as well under under the rubric AV even if that's not technically correct but for purposes of this presentation, essentially time based media audio video film. And the fact that their AV collections and the interest in AV collections on the part of scholars and for use in teaching is growing exponentially. Libraries and archives are dealing with large numbers of AV materials and legacy formats that are becoming challenging to digitize or reformat but also cannot continually preserved in their current forms, either due to media degradation or the obsolescence of the equipment that can play these formats back or both. At the same time as I mentioned the expectations and demand for these collections is growing and user expectations for discoverability and access are growing based on people's experiences with video on the web in streaming platforms on YouTube on Vimeo, etc. And libraries are and archives are needing to step up the discoverability of collections to make them more usable and useful for teaching and for research. The challenge in that is that often there's not a lot of metadata, particularly for archival AV collections and in many cases, certainly at Indiana University units that hold a lot of AV materials have not necessarily even been able to view or listen to these materials. To describe them if they wanted to because of the issues of format degradation and obsolescence that I mentioned so it's hard to improve discoverability without without metadata. And at the same time, you know resources for traditional approaches to cataloging and archival description are limited compared to the volume of content we're having to deal with. So the opportunity that we're trying to address through amp and other related projects at Indiana University is to take advantage of the fact that one can take a mass digitization approach to AV in the way that say Google, you know, took for books and really do did take a digitize first approach and then work with the see how we can work with the digital files then to better enable description. Rights determination. And, and so on. And so we have had a project at IU that wrapped up or is actually still in the process of wrapping up called MDP I media digitization and preservation initiative, which has digitized about 300,000 plus pieces of audio video and motion picture film media. But the, the level of description and metadata that exists for these collections varies substantially across units so for example our music library has very rich metadata at least at the item level for a bunch of their material that we have by ethnographic collections. University archives collections that are much less rich in terms of the metadata that's available. And you know IU and there are a few other places that are taking this similar approach at this point to really taking an all in approach to digitization of AV collections that are encountering similar problem. And so we have this problem and then at the same time we have the opportunity of having these things in digital form and the fact that machine learning based tools and other automated tools to try to extract. Potentially, you know, meaning and information from from media files are emerging from multiple directions and potentially able to help with this problem and so that's really what we've tried to do in amp is to look at how can we leverage machine learning and other automated tools together in workflows with human expertise, flexibly, and in a way that can be configured to be appropriate to different kinds of collections the kinds of work you would do with a music, you know concert performance archive would be quite different than what you would do with oral history archive in terms of the kinds of processing that's needed and so we wanted to be able to build workflows that are appropriate for for the collections. So the vision of amp has been to create an open source software platform to really help with metadata creation and augmentation for AV collections that lets one build these workflows that combine automated and human steps. A little bit of vocabulary definition that's going to be important to the to the rest of the session here. MGM is a term we used to refer to metadata generation mechanisms. This can be any, any tool or step that creates some sort of metadata for for an item or a collection. It might be an automated tool such as speech to text. It might be a human or manual tool such as correcting the output of speech to text. And it might run in a local computing environment might run in supercomputing or high performance computing HPC environment or might be a cloud service. And so we wanted to amp as a system to be able to handle the integration of all of these different types of services. And then ultimately deliver metadata that could be used in what we call target systems, which would be online discovery or access systems such as archival finding a discovery system, a library catalog, AV access platforms such as I use Avalon or AVP's Avery and so on. So we have had undertaken three phases of work so far the first phase back in 2017-2018 was a planning effort that really kind of developed and refine this initial concept of amp convened a workshop of number of experts to help inform the technical architecture and design of amp. And there's a white paper that came out of that. All of the documents that will reference here and a lot of the documentation are available on our wiki and we'll put up the address at the end again for that. And then from 2018 to 2021, which was extended a bit due to the pandemic, we really sought to build enough of this amp system to be able to test it start to test it with actual real collections, real librarians and archivists. To see is this idea that we have is this approach truly feasible. And there's a white paper report that came out of that that talks about that work and I'll go into a little more detail on on that here in a minute. And then currently we're on our third phase of amp that has been funded by by the Mellon Foundation working to develop additional components of the system to improve the usability of the system to work on packaging and deployment issues so that this could be used by others. And testing with a broader set of collections and Sean will talk about that current work on phase three in a minute. But before then I'll talk about what we accomplished what we learned in phase two over the past several years. And phase two was also referred to as amped the amp pilot development projects so we were we were amped to work on amp. So the main accomplishments of this last phase were to actually develop the initial version of the application. And I'll show a little bit of that here in a minute, but there's two main pieces to that there's a web based user interface. That's written in Java and in JavaScript that allows archivists and librarians to interact with the system to upload or load in content to run workflows against that content and so on. There's a workflow management and execution kind of back into that called Galaxy. I will show here in a minute that we adopted from the actually the computational genomics bioinformatics community that we use as kind of the workflow back end. And then Sean led this piece evaluating and implementing a variety of MGM these metadata generation mechanisms automated tools tools regarding human intervention and really developing a pretty substantial evaluation of rubric to to select those tools. And then we tested with 100 hours of audio and video from each of three collections to at Indiana University and one at New York public. So this is just a screenshot of the amp application as it currently stands which you're not going to be able to read a lot of that but this is a dashboard screen where one can see what files have been uploaded what the status of workflows are and actually access the output from individual workflow steps so this is something that's more useful in kind of sort of direct work with the system for debugging and understanding workflows and so on. Ultimately we would see this feeding into a discovery system or an access system. As I mentioned such as Avalon or Aviary or other platforms. This is a screenshot of the galaxy system so galaxies I mentioned comes out of the bioinformatics community and it was a tool designed to to allow researchers to create workflows that integrate multiple processing steps that might run in different computing environments to process genomics data but it's been it's been adopted by a number of other communities. In particular the computational linguistics community has an international kind of infrastructure for doing computational linguistics work that utilizes galaxy and we learned about galaxy from a project somewhat complimentary to amp at WGBH archives and Brandeis University in Boston called Clams which is applying computational linguistics approaches to helping to create metadata for AV resources and they had selected an adopted galaxy and we felt that adopting galaxy would make sense for us as well and would eventually allow us to share tools so essentially what galaxy lets you do is you can create wrappers in Python to integrate different tools and processing steps and then you can visually go in and chain these together into workflows that might might be appropriate for a particular need so again you probably can't really see this but we're starting with an input that might be audio or might be audio and video we extract audio from that. We run it in this case through Amazon Web Services transcribed to get text. There's a human transcript correction step and then that eventually outputs a web BTT file so this is a very simplified example. It doesn't get into a lot of the mgms that we integrated but it gives you a sense of what what galaxy can do. And this is just a screenshot of one of the what we call human mgms, which are those those steps that require human intervention. And we've worked to integrate other tools as much as possible so here we used open source to call the BBC transcript editor from from the BBC and and integrated that into amp so that can be used for for a transcript correction. And so this is a I won't go through all of this but this is a list. We'll share the slides can refer back to this later but a list of all of the mgms that we have implemented and integrated with amp in various categories speech to text named entity recognition. Video OCR segmentation human correction a few others and we made a deliberate effort to try to incorporate both open source and at least one open source and one commercial cloud tool in each category to be able to offer options and to be able to compare those. And the selection of these mgm tools for integration was really based on evaluation criteria that included a number of different considerations accuracy of the tool whether it could support you know a variety of input formats output formats that we would need the amount of processing time and computational resources that was required the ethical and societal social impact considerations around tool and we'll talk about that a bit more in a minute cost support burden. The ability only having an API abilities to integrate it and looking at whether it was a case where we could use a pre trained tool or whether we actually needed to do machine learning training in order to accomplish particular goal. So some of the things we have learned in this last phase of work. You know one is that this choice to work with these proprietary tools led to both benefits and challenges so some of the challenges we encountered with good commercial cloud machine learning kind of pre trained tools that are out there from Amazon Microsoft IBM. Google and others is is the degree sometimes of in unpredictability from run to run because these tools are being improved and changed over time. Certain undocumented behaviors such as filtering out particular words in one case that was unexpected and undocumented. And just not having visibility necessarily into what is happening kind of being a related issue. That getting into kind of the ethical and privacy concerns that were discussed a bit in the opening panel of this conference. One big difference we saw across the vendors of these services is how they approached the both the documentation and contract terms around what they can do with your data. So if you upload data to be processed if you upload the upload data to augment training. Will they can they retain that for purposes of product improvement and use that for other things. Or what do they explicitly not retain that is that an opt in or an opt out sort of setting Amazon, for example is very much opt out you have to tell them don't keep my data where they will. Whereas IBM and Google are the opposite. At least with the tools we were using and Microsoft to be honest was very hard to figure out and I ended up in an email exchange that. Eventually I was talking to someone in the general counsel's office at Microsoft trying to interpret their terms of use and what you know whether what they really you know. What they really meant in terms of this so there are these challenges and we can talk more about that in the q amp a but. Of course these companies are investing huge amounts of money right into training and developing these tools so for some cases they work really well so there's this balance here of benefit and. And some of these challenges another area we dealt with is that we had a variety of content that range into music as well and. The state of the art for tools dealing with music much less well developed than those that deal with more textual sorts of content and so that's led us to focus on more spoken word content for this next phase. Of work. There's this decision point as I mentioned about when we use existing tools and models versus training new models so we chose for example to train our own model for applause detection to help segment. Concerts music concerts because the existing models we tried weren't working well for our data. Facial recognition as well we we chose to do training of models for very limited use cases like recognize this specific well known person in the set of video as opposed to more. General application of facial recognition or using. Vendor tools because of some of those ethical considerations and we can get into that in the q amp a bit. And some issues with technical implementation. I can talk more about that if you're interested and then librarian and archivist engagement you know we were really pleased to see that. That archivists and librarians special collections librarians are very excited about the potential of the amp. But that are you in my pl but then starting to think about how does this could practically implemented in workflows. What can I do with the output beyond just. What can I put in a mark record or any ad is a more challenging you know conversation because they haven't worked with these kinds of things and Sean will get into. More about how we're engaging them in this next phase and so to transition to that. Talking about our current work on amp, which is funded by a new grant from melon that runs through the end of this year we've really focused on. Number of different areas to take amp to something that could be used more practically in production, both at IU and elsewhere and Sean's going to talk a bit more about them. So as john said we're doing a lot of work this phase on improving the user experience and for this round we're partnering with managers of collections from underrepresented cultures and populations. So we can broaden our understanding of concerns around influence implementation and machine learning tools. And so we can surface some new issues with the existing implemented mgs. We also wanted to work with partners who represent a range of different sizes of collections and use a variety of different systems for description and access. So we're working with a diverse group of partner units of different sizes at IU and at NY. So based on the progress and feedback from our previous phase we're focusing the space on workflow creation so that's allowing users to create and edit their own amp workflows. Currently you can only do that through the galaxy back end. We're working on batch upload of files, improve navigation of collections, and also using intermediary files as inputs for mgs independently from the predefined workflows, such as using a transcript output from speech to text as input for entity recognition. We're also improving and expanding our human mgm editing tools like john showed that the transcript editor. We are adding video to the apple on timeline tool which we've been using for human editing names entity recognition results. And we're at adapting it for use with other tools like audio segmentation and face recognition. So we're also improving the open source BBC transcript editor which is what we we based our transcript editor on it has needed significant customization to integrate that into amp. We also learned from our users that the human mgm tools need to be available independently from the amp workflows. So currently, to use the editing tools you need to set them up as part of a workflow and connect them to a JIRA instance but we realize that like with the intermediary files, we needed amp to have more flexibility for users to be able to process files through mgms incrementally, rather than making users plan out a full pipeline. So to engage our collection managers we've been using a number of different methods system demos hands on training discussions at all team meetings and targeted discussions around topics. And we'll be conducting focus groups and some more formal user testing as we develop new features. And as we have more to show users whether that be completed features or wireframes. And we've gotten some great feedback so far. So we, we, for example, we just led some focus discussions with two groups of collection managers on file upload and data exports better understand the needs for both smaller and larger size organizations for manual or automated upload to the system, as well as for exports of mgm outputs and trying to find out what the most useful date data formats would be for both humans and machines. So from that conversation we learned of a variety of different use cases for getting content into amp for processing at different points of the acquisition to access lifecycle. So archivists may want to process files in amp at the start of an acquisition to use the tools for streamlining accessioning decisions. Archivists catalogers or metadata librarians might want to use amp tools during description to add valuable metadata that they wouldn't normally have time to create. And after items have been described right staff might want to use amp to help them make rights determinations to provide the appropriate levels of access. And finally, even after access for the many media items that have been minimally processed or described in finding aid say researchers might request access or further information that could require some on demand uses of amp. So we found where a file might be at a given point in this pipeline, what system it's currently sitting on, what identifiers have been assigned to it, and what permissions are required to access it each of these cases might present special needs for getting files into amp. So this will require that we make sure our data model and our API's can accommodate the necessary flexibilities in who might be initiating uploads where the files are coming from, and how they should be identified in amp. To export, we've learned that some of the formats that we've already created are already a big hit like these contact sheets that we're generating for shot detection face recognition and video OCR, which our collection managers think will be useful, not only for archival processing and description, but also for making available to researchers so they can preview content that can't be accessible remotely, or it could just be an efficient way for them to quickly scrub through content. We also heard a need for automated exported data rather than just on demand download for hypothetical workloads or workflows that involve pushing large volumes of files through amp and having the MGM outputs accessible from a repository location by staff at any point in the archival processing or description pipeline. Now that our collection managers are finally starting to work with their own content in the system, we've been getting some great feedback on bugs or unexpected output from MGM, so I'll just show you a couple of examples here. So for example, with the open source shot detection tool, Pi Scene Detect, one of our collecting units discovered the MGM was generating excessive numbers of shots on some areas of video without content. So with speech to text transcription, they found that Amazon Transcribe MGM was making some strange transcriptions during areas of silence so you can see here where it seems to be finding the words okay, okay, thank you, okay, which maybe that's outside the range of human hearing. I don't know. And both of these examples represent cases that might be uncommon with the content that the models were trained on, but as we all know these are very common cases with archival content to have these gaps in content on media. So surfacing as many of these types of issues as possible will help us with tailoring workflows, creating workarounds, or at least documenting these known risks for future users so that we can improve the output before we release amp. I also wanted the other big additions to amp in this phase. So, in phase two for the pilot one of the most important things we learned during our evaluation and selection of MGM tools was that accuracy can vary so drastically for a tool depending on the media content, and how those levels of accuracy translate to quality will be different for every use case. So, for example, a 30% word error rate for speech to text transcription may be unacceptable for using a transcript for captioning, but it might be good enough for keyword search. So we recognize that future amp users would benefit from being able to test tools on their own collections to make more informed assessments of both the values and the risks involved in applying the tools, rather than using any of our minimal assessments as recommendations. So what the evaluation module and amp is going to do it'll allow users to upload ground truth data for one or more files that they run through an MGM and then use built in tests to computationally compare the MGM output against this ground truth, or ideal output. And then they'll generate quantitative scores like precision and recall and then they'll be able to visualize those results. And if you're unfamiliar with how this kind of ground truth testing works for AI, we've got a link on our wiki to another presentation that goes deep into this. So these are all just early wireframes but as an MVP we're envisioning a simple visualization interface like this that is generated dynamically from the ground truth test outputs. And we'll let the user view bar charts or box plots to view and compare quantitative results for one or more outputs for one or more tools. We're also working on a view that will let users qualitatively review the comparison of MGM output to ground truth, so they can better understand how MGMs are failing on their collections. When they're trying to determine whether or not to apply a tool, or figure out what kind of workarounds or quality control mechanisms that they need to apply. So having worked with these AI tools in phase two, we on the core team understand the value of building evaluation support and to amp and helping users to integrate AI into their workflows confidently and responsibly. But it's been very challenging to conceptualize what these evaluation tools should look like just by interviewing users about their hypothetical use. So I think that with this evaluation component, we're going to see something similar to phase two where we experimented with tools within the core team, and then try to generate ideas and feedback collection with collection staff. But we just showed them demos and results and it wasn't until they were able to start working in the system themselves that we really started getting actionable feedback, and we even started seeing users get excited about using these tools. So our hope is that once collection staff are able to easily start using the tools on their own and have mechanisms for exploring the quality of the results. They'll learn how to use these tools to their advantage and then begin to materialize a practice of responsible use of AI. So probably this first version of this evaluation module will need to be improved after users start working with it, but we feel that's one of the benefits of this iterative development and we are very grateful to the Mellon Foundation for enabling us to build amp and this is like this. So finally, we're working on packaging amp for easier deployment, especially for organizations with minimal IT support. So we're taking a multi-tier approach to developing packaging with the end goal of creating a containerized environment. So the first tier will be being able to install amp directly on an operating system with help from detailed documentation and installation scripts. Then the next level will be having component level containers, and then finally we'll have some scripts and configuration to bring all of the containers up together. So we're still finalizing our packaging strategy, but in the meantime, anybody can try deploying amp and it's current in progress states at your own risk with our source code GitHub. Thanks, Sean. So I will just wrap up real quick talking about some of some of our ideas and my ideas about where this will go in the future. One is that that from the packaging work that Sean described, you know, we hope to see some other institutions start to experiment with the platform start to provide feedback, potentially, hopefully, you know, start to buy into it in some way in terms of integrating additional tools or improving deployability and so on, you know, potential, I think for there's not this is all Apache licensed open source. There's potential for software as a service models for offering something like this. We hope to work on integrating additional MGMs and target systems for output of metadata, you know, looking at additional opportunities where we could best achieve results by training our own models, building on what we did with applause detection into some other areas, which could include more work with music I see that as a whole other project that would kind of bring the music information retrieval community folks together with libraries and archives who are using tools like amp to take some of what exists in the research space and turn that into more productionizable tooling to support processing of archival music collections. We want to build integration with triple I F to be able to take the output of amp, turn it into web annotations and triple I F presentation manifest to be able to make it more portable to other tools. And participate as much as possible in this kind of emerging community around, you know, AI machine learning tools and workflows in libraries and cultural heritage, including for the AI for lamb community that if you are not familiar with, you can Google that at AI for and find out more. So that is it for the presentation. If you want to see more about read any of the reports that we mentioned see the documentation on evaluation of the different MGM tools and and and so on. You can go to go.iu.edu slash amp ppd to get to our wiki and feel free to contact us directly as well and with that. Thank you very much appreciate your time. And we'll open up for questions. So we do have a few minutes for questions before the next presentation starts at 1115. Thank you. Could you talk a little bit about where the metadata lives and how it links to the essence, whether it's linking to segmentation, or it's linked to time code or a set of time code things like that. Yeah, so internally amp uses custom JSON schema to represent the intermediate and final results of workflows, and within that JSON references time code so yeah the segmentation, or the terms found through any or transcription are tied back to the time code in that JSON representation the idea then is that would be transformed into whatever is needed to to bring it into a target system and access system or discovery system. Sean if you do want to add anything to that. It's a schema that we came up with ourselves just kind of as we will building the system assessing MGM since so we've been refining it as we go and you can reference that on the amp wiki. But turning it into as I mentioned into to web annotations that reference time code could make that more portable we are also working on kind of how does the, if someone has an, say a preservation system or repository and then we're bringing files into amp from that. How do we reference back to the file in that kind of system of record and so that then the output is tied back to that as well through, you know, essentially use of identifiers. And that's that's some of what we're exploring with with IU and with my PL is kind of what there's a lot of local implementation decision around that kind of thing so we want it we need to be as flexible as possible and how we handle. All right, well, let's we'll give us give the next presenter some time to set up because they start right 1115 so again thank you for your time and and feel free to talk with us afterwards as well. If you want us to elaborate or discuss any of this more we're happy to do so. Thanks a lot.


Well, we grew up on a farm, number one. In the summertime, we'd walk to school about two miles, and in the wintertime was a different story. We were driven by my father with sleigh-box and a couple of horses leading, and my father would be all frosted over with his breathing. So when he'd get us to school and we were safe, then he'd go back home and be there again in the afternoon to pick us up when school was finished. My mother was a city girl, so it was very difficult for her too. She had mattresses made for the sleigh-box, and we were all dress winter-like with quilts all around us, so we didn't get cold. Not like that today.


SMIIIIIIINUS GR- Let's start the video by actually answering the question I've asked in the title. What is the Rushing Beat series for Super Famicom? Well, it's a series of 3 beat-em-up games developed by Jailaco, titled Rushing Beat, Rushing Beat shot, Fukusei Toshi and Rushing Beat Shura. The first Rushing Beat was released outside of Japan as Rival Terf. Oh sorry, Rival Terf. Gotta consider the exclamation point there. It's known more for its embarrassingly bad cover rather than anything to do with the gameplay. Russian Beat Run, Fukusei Toshi, or Rushing Beat Chaos, the City of Clones, was released as Brawl Brothers in North America with a slightly less embarrassing cover, and as Brawl Brothers Rival Turf 2 in Europe. Rushing Beat Shura was released outside of Japan as The Peacekeepers. Did you get all that? Anyway, let's start with Rushing Beat or Rival Turf.  Rather than say the same old stuff I say about every beat em up, I want to take this opportunity to talk about the genre as a whole because I feel like it's treated a little unfairly as time goes on. Yes, I understand the principle gameplay of a beat em up is as simple as a game can possibly get. And the primary criticism is that you could finish just about any beat em up just by mindlessly mashing buttons, and you'd be exactly right. Take Rival Turf for example, when people want to criticize the beat em up genre this is what they'll point to. This game is as boring as the day is long, it's a poor Final Fight rip off even by Final Fight rip off standards, and there's seriously hardly anything unique here at all. It's not even that bad, it's just painfully generic. But just because Rival Turf is generic doesn't mean that every beat em up has the same trappings. Here's the thing, beat em ups kind of belong in their own universe, they're like the mindless dumb Steven Seagal Chuck Norris Jason Statham action movie of the gaming world. Are most of those movies bad by objective standards? Yes, but it's useless to compare Commando to Casablanca. You don't hold them to the same standards of objective quality because they're made for a specific purpose for a specific audience.   The point is, yeah, beat em ups are mindless button mashers, but that doesn't make them all the same by default. Some are still better than others. If you want another analogy, look at Death Metal or Country Music, sure it all sounds the same on the surface, but if you dig into it, there are significant differences that can lend substance and create something worthwhile for the listener. No, Rival Turf may not be worthwhile, but the Rushing Beat series gets better with its second installment, Brawl Brothers. There's five playable characters to choose from, and even in single player mode you have to pick two. Why? Because the three you don't pick are bosses, so yeah, who you pick is just important as who you don't pick. So that's pretty cool. And it gives the game some replay value as well. Also make sure to go to the options menu before getting started with Brawl Brothers and turn on Angry Mode. I have no idea why they defaulted this mode to off, but if you take enough damage, you start to flash red and you go into kind of a berserker mode and start whipping around bad guys. This seriously makes the game.  This mode is in Rival Turf 2, but it's implemented in a much better way here. Brawl Brothers is just plain faster and more polished than Rival Turf. You can run for one thing, and flipping punches from side to side is smoother. The level design has a bit more going for it, as you can electrocute guys or throw them into land mines. I will say the sewer stage really gets tedious as you wander around looking for where to go. Just so you know the door structure here was not in the Japanese version. So if this kind of stuff annoys you, get Rushing Beat Ron instead.  Finally we come to Rushing Beat Shura, or the Peace Keepers. I always loved that title. Yeah, that's right, we're here to keep the peace by shoving our fists down your throat. Anyway, what jumps out here about this game is the sheer amount of story and text you have to skip through. I admire that they tried to do something different here and the story at least appears to be somewhat interesting, but to drive my earlier point even further into the ground, this is not why people play beat em ups. I just want to brutally enforce peace with knives and land mines and stuff. Peace Keepers is a great natural sequel to Brawl Brothers because it keeps everything well executed from the first game while making a lot of improvements. For example, the berserker mode here is even more insane.  That alone makes these games worth it. The game goes back to picking just one character and you don't fight the other playable characters in the main game anyway, but there is a one-on-one fighting mode that supports up to four players, so if you have a multi-tap device, Peace Keepers is worth getting just for that alone.  And don't sweat dying because you get a ton of continues here. You'll notice the X button does kind of a taunt that seems kind of pointless, but press X and A at the same time and you'll unleash a crazy-ass full-screen attack. That's pretty awesome. There's also branching paths here and there, which the game helpfully points out, and there's at least two different endings here as well. The only flaw about Peace Keepers is that, uh, where's the music? Seriously, did they forget to send the music over from Japan? There is an option to turn it on, but even then, the music is out of order compared to the North American release. So weird. So, yeah, the Rushing Beat series ranges from generic and forgettable to some real quality stuff. I definitely skip Rival Turf. You're not missing anything there, but Brawl Brothers is perfectly okay, and Peace Keepers is definitely a worthwhile pickup. But if you're going to go that route, I'd go with getting the Super Famicom version, Rushing Beat Shura. Just so you know, overall, there's not that many differences between the three Super Famicom games and their North American releases, other than name changes and face shots, and the other stuff I previously mentioned, like the structure change in Brawl Brothers. But the Rushing Beat Shura cartridge is like half of what Peace Keepers costs, and plus, you know, the music is actually turned on. Either way, Peace Keepers or Rushing Beat Shura is easily one of the better 16-bit beat-em-ups out there.


One of the major trends within the ASEAN region is the expansion of oil palm and rubber plantations into forest areas. For example, in Malaysia in 2014, 69% of arable land was already dedicated to oil palm. And another issue is that a lot of this commercial agriculture expansion is illegal. In Cambodia, Laos, PDR, Myanmar and Vietnam, 43 to 90% of this expansion is illegal. The most vulnerable forest types in the ASEAN region are P-forests and mangrove forests. Their loss is going to have significant impacts on livelihoods as well as climate change because of their major carbon sequestration potential. Another critical issue on land within the ASEAN region is the loss of soil fertility and stability from deforestation and commercial agricultural expansion. Deforestation can lead to soil erosion and the intensive use of chemicals for commercial agriculture can lead to lower soil fertility. Updated research on this is important because already in 1997, 53% of Thailand soil was categorized as having lost soil fertility. So because a lot of the commercial agricultural expansion within the ASEAN region is illegal, the ASEAN member states can better monitor vulnerable forest types like peak forests and mangrove forests.


AV-Test Institute identifies more than 350,000 new viruses and malicious programs each day. This includes malware, viruses, and unwanted applications. Millions of existing viruses continue to be identified every single day as well. Creating and releasing a new virus requires little capital and little effort, but an antivirus software can take months to catch up with current viruses. But did you know?


When I'm on the line and I'm riding a straight line, everything is perfectly straight in my body. So my legs point straight, my knees point straight, my toes point straight, my arms, my shoulders, everything is very square and straight.


Hello my friends and welcome to beautiful Indianapolis, Indiana. I've actually never been here before in my life and I'm really excited to be here because I am here for the Places for Bikes conference put on by People for Bikes and it's a conference with hundreds of elected leaders from all over the country who want to make biking better in their communities and you know I love bikes and that's why I'm here and we're going to make a lot of new friends this week. People come here because they want to improve their cities overall and they want to do it through bicycling and they want to do it quickly and in a smart way so they're going to get inspiration and best practices and just going to get fired up and feel like they can achieve and we're going to help them do that and we're going to do it in 48 hours starting right now. This has been a remarkable journey here in Indianapolis over the last eight years or so bicycling for this city. We set out and got very very aggressive and had a goal of putting 200 miles of bike lanes and trails into place by the end of my term in 2015. It's time for draft and draft is an event they do all over the country actually and it's a mix of beer and speakers and inspiration and just mingling and bike love. The mission of the UCI is to promote cycling worldwide working with 190 national federations but it's not only to develop bike racing within the 190 federations it's also promoting cycling as a form of transport. All right that was awesome these people are amazing I am super inspired and it was only one afternoon I still have two full days left. Better get some rest. We're getting to see examples of the the the best outreach work the best encouragement work the best infrastructure work that's going on in the country. Building better places to bike in Tucson does great things for our community especially obviously for public health and environmental health but also for the equity of our community. You know Tucson's a relatively poor city and we have a lot of people who depend on biking and walking and transit to get around so it's really important that we're serving the needs of all Tucsonans but also the more we make bicycling accessible for everyone for my mom for my grandparents for our kids the better our community is going to be the healthier we're going to be the better connected our community is going to be the more visible and beautiful and thriving will be will be a place people want to come. Look I love bikes because of the flexibility that it provides that it provides you to be able to commute just to be able to enjoy recreationally the community that I live in. Bikes are just a core part of a core part of life so in Tennessee we're making cycling easier for people by one number one big thing is policy we're putting in place policy to make sure that the way we design our roads throughout the state are more conducive to cycling. I think a lot of careful thought and attention has gone into selecting the speakers of this conference. It's one thing to get a national figure who's inspiring it has an inspiring story but to have someone who cut their teeth working at the local level in the mayor's office making decisions that ultimately would shape the future of their city that's what so many of us are trying to do and Valerie Jarrett was a truly inspiring keynote speaker. What gives me optimism by now is being here with you and you see people who are you could all be at work and you're here advocating for something you believe in so to the degree that we can all not spend time kind of being critical of things we can't control but look at what we can do and figure out how to be intentional and positive in your own lives. Since this is a bike conference we probably should ride some bikes. Tell me about the Caltill Trail. Yep so it's an eight mile urban bike and pedestrian path in downtown Indianapolis it also doubles as a linear park it connects all of the city's greenways it intersects with our bike lanes that are on street our bus system and we have the great pacer's bike share system that was built on top of the trail. Unlike in these twinkly lights our tour guide just said that there's been over four million dollars invested in art along the cultural trail that's pretty impressive. It's always lit so that safety is never really someone's concern if they're on the cultural trail. I gotta be honest I really didn't know anything about Indianapolis before coming here and now that I'm here I'm really impressed. Alright let's play with some e-bikes. Electric bikes get more people out on bikes and life on two wheels we all believe life on two wheels is a little bit better. This is amazing I had no idea how much fun this would be like you can choose which level of boost that you want for your ride and it just makes a little bit smoother a little bit more supercharged and you can kind of choose how much energy you want to expend.  One of our goals is basically putting as many smiles on the road as we can because it's fun to ride and it's a good exercise so we'd like to see more and more of these parodies all over the states. I mean you can't like have a bad time on a bicycle ride they're just pure joy lots of fun and you know they make people happy and bring people together. So I'm really excited tonight we are unveiling our much anticipated city ratings which is a new system to evaluate how cities are doing for bikes. We have cities in the audience who are getting awards and they don't know what they're getting yet so we're really we're really pumped to present them with a little something to thank them for their progress on helping build better places for bikes. Fantastic because we are all working towards the same thing right and we want to make cycling better we want to make we want to grow it nationally and so we each as different communities have a role to play to make cycling great and so we learn from one another and that's what it's all about. The city rating system is a way for cities to understand what kind of impact they're having for bicycling in their community on an annual basis. It's data-driven it uses numbers and facts and figures from more than 47,000 different communities here in the US and lets us set achievable goals in the future that says hey we've done a lot of really great things for bicycling but we still have a lot of work to do and let's get that work done fast but ultimately let's set targets and goals that help us achieve international success. I don't want to keep going to Copenhagen and Amsterdam to show cities how bicycling can be done. I want to go to Tucson Arizona I want to go to Oklahoma City I want to go to places here in the US that are world-class examples for bicycling and this tool lets cities set those benchmarks and move to make them a reality much faster. All right I am leaving here very very inspired I've only been here 48 hours but it seems like an entire week I've gotten to meet people from all over the country and the world and everybody out there is doing such great things to make cycling better in their communities and that leaves me with a lot of hope so I will see you out there hopefully on your bikes. If you like bike content if you like getting outside if you like getting off your couch and enjoying the best of this world please like and subscribe I'm always producing fun videos see you next time