Hello everyone, my name is Gilesh Kaurikey and I work for DH Software, it's a group called Innovator. I carry you a lot of different kinds of projects and recently we have been doing some work in textile which is what I am going to discuss today and I try to give you some real-time examples as well how we have applied this into the problem of ID management. Before I begin, I just would like to know in the audience, how many of them are students? So I am just curious to know, do you have any kind of machine learning as a subject in part? Yeah, mine only. Mine only. I can detect it in the figure. Okay, that's good. And from the working professionals, this wanted to get a feel of how many of you are actually working on any machine learning technologies in your public domain. Okay. I am not currently working on that. Okay. Good to know. So let's begin. So today, I am going to talk about what is next analytics and introduce you to some of the challenges that are there in next analytics. I will also go over some common analytics tasks, what do we need to do if you want to get some of these things going. And then I will also take up some real-time examples of how we have used it in a couple of use cases, where you feel that, you know, next analytics will help us cover with some real-time use cases which has been developed. Okay. So what is next analytics? Okay. Okay. So this is the Wikipedia no-definition editor. So if, you know, it discusses and does a set of new things, statistical machine learning techniques that model and structure information content for just the citations, exploratory data analysis, research or investigation. So there are a lot of keywords here, a lot of stuff we can talk about. And I think, you know, this has been the last session in India where you might find it very difficult to understand.   So that's why, you know, this area is gaining a lot of traction. There are a lot of interesting use cases that are coming down in this field. And that's why it's becoming more and more popular. So I would also like to quickly, you know, point out some differences in the desktop. Now, other terms which are in also significant externalities, but I want to just kind of, you know, briefly describe how this is different from, let's say, search. Because I mean, search, you know, are specifically looking for something, and your main task is to find the things that are relevant to you, or discuss those which are not relevant. But in text to, text analytics for mining program, you may not be looking for something, or other, we don't know what we are looking for in hard times. So that is different from a search problem, where, you know, we know what we are looking for versus a general data mining problem. The other differentiation I want to make is with data mining, which we have been, you know, known to be doing in the past, which has been, you know, more around structured data. We have data, in databases, in data warehouses, you know, so we collect a large amount of data, put a bit of data, and also do all kinds of analytics and reporting on that.  So, you know, everything is defined before we pull the data up, you can add form and then run all the analytics on that. So, which is the key difference, you know, in the text analytics that we are looking for. Well, you know, in the primary, looking at text as an unstructured source of data. Okay. That is the key challenge, because it will bring about a lot of interesting things here. So, as we move next, you know, we will look at what are the challenges in room text analytics. So, one more thing I already mentioned, you know, in this unstructured nature of text, we are just collecting data now from web pages, you know, from one document, the first one is knowledge management systems. So, very few people have just written text in free form, and there's no real structure around that. And, you know, often those are data sources where we collect data from and we are able to make some, you know, data as a function out of it. So, a huge problem in text analytics will be first, when we put some amount of structure into this whole unstructured set of data. Again, you know, a common problem, we also know, somebody mentioned it in the previous presentation also is that, you know, in case of relaxed data, often, you know, you have the same words, totally meaning different things. You know, I went fishing for some sea bass, you know, bass is a kind of a fish, and whereas on the bass line of the song, this is telling me something about the frequency of, you know, the notes and the music, you know.  So, you know, you could be all in a spaceship, like, sources, you know, some of them mean the same thing, but you could actually be putting the money in different ways. So, that's another problem.  And then, you know, high rationality with hundreds and thousands of features. So, this will come, you know, when we discuss this level also, we try to make some structure out of the unstructured. We get into some representation where, you know, you can end up with many very high-efficient data. And, you know, processing data is itself a challenge. So, let's say we have, you know, unstructured data, and we want to work on it.  So, we need some kind of basic structure, you know. So, how do we structure text so that it becomes, you know, it gets into a form which is incident. So, that's the idea behind, you know, saying that, what are the different representations of text that we can think of that will help us in doing the analysis, you know, better. You know, what are other, those can use as an example.  So, what do we mean by this? Let's quickly look at some of these. So, text representation at a lexical level. So, you know, at a very, you know, basic, what is text? It is just a sequence of characters, right? So, if we just think that terms are the less, nothing but a sequence of characters, we can use that representation to do some, you know, for example, you know, in English language, E is the probably the most accurate character. So, if we just know what a character comes from text, probably, you know, we might get some kind of these insights. Then, you know, if I have a large amount of boundaries in English, probably E is the most often repeated word. What could be one of the, you know, interesting ways of using that information? If you have the business of doing compression, the entire compression, you know, this kind of kind of information that we are useful to do. Okay? So, that's one way of the value. Think about text as being a sequence of characters. But the most common thing that we are used to is words. We think of text, the first thing we break down is into words. We also are going to be thinking of whatever keywords and trying to make some sense out of it. But just by looking at the large document, you cannot infer anything. What you try to do is, you know, quickly become sure key words that are being described in the document and try to know what they are meaning or the fail. So, immediately we say, okay, this document is talking about something because I could find these key words in that document. So, this is the most often, most common use representation, which most of the words also do not have as important words. So, there are a lot of, you know, tokenizers are available, which will take your text, break them down into words, and then you will be able to form that you can use for your process. And then, you know, around words, there are also these qualities, you know, like, which also play a big part in doing some of your analytics. So, breaking down or other, viewing text as a set of words is the most, you know, natural thing that comes to your mind, that is most of the things that most of the words are saying. The next thing is just, you know, once in a while, instead of just looking at one word at a time, think about phrases. What are the most common occurring, you know, sequences of words? Okay, because, you might see that the support is occurring frequently, but when you might also have, you know, these two words together, these three words together, you know, bring up something more than just looking at one word at a time. Okay? So, the next thing is, you will call these as n-grams. So, y-gram is a combination of two words, a diagram is a combination of three words, and you could choose, you know, how much you want to go for them. So, that n-gram is what we talk about. So, those are kind of phrases that we could use of, you know, when we are doing, when we are representing verbs, we are representing text for our person. And then, there is, you know, something called parts of speech tagging, so that, you know, you might have, let's say, broken down your text into words, but now you are adding some special meaning to it as well. Whether this word is representing a noun or a pronoun or a verb, because that kind of information, when you have a noun with that word, you know, you could use that. For example, you are doing a task of finding out all the key persons we talk about, you know, how we talk about it. So, if you have that information, that's why we use it as a noun. We will use that to infer that. These are my potential words to infer non-names of words. And I am looking for persons in a piece of text. So, we looked at some representation at text level. The next one is impact level. So, I think of it as, you know, what are the data structures that we can use to, you know, do our text processing. So, you know, we have been used to doing vectors, maps, those kinds of things. And when we want to process a large amount of data we have to carry out, we need help of data structures. Okay? So, similarly, in case of text analytics, these could be thought of as, you know, data structures that you would need to do text analytics. So, vector space model. This is the, I think, you know, it is a very commonly used model.  So, basically, you think of text as a sequence of words. Not a sequence, actually, because sequence implies order. A bag of words is there's no order, it's just a collection. So, typically, let's say we are doing, you know, what we want to read as, you know, a document is actually, you know, let's say a vector. And it is, you know, a vector has n number of, you know, points in it, and they could send you. So, a document is nothing but a collection of words, the key terms that are there in that document. So, you would think of a document being represented as a vector, and that's the vector space model or bag of words kind of model that is often used in a lot of clustering, classification, those kind of other others, where you first take the text, note it into a bag of words or a vector model, and then keep that as input to these other elements. So, that's, you know, one kind of investment you have for media processing. The other is language models. So, you know, you have this task of, when we understand, you know, what this text, what language it is. So, you know, if you want to do that, what you would do, because you would have some kind of knowledge around, you know, these are the templates. If I can identify the context, I can infer the languages in this language. So, here you talk about what are the probabilities of text, you know, appearing to get there, and we could use that kind of a notation to bring in some kind of a model, and then we can just re-form the context. The third one is, you know, past.  And then once you have that pre-notation, you could use that to generate your query plan and the things like that. So, think of, you know, you create this as some, you know, text, which has some grammar, and if you can parse it at which entry model, then you know you can do something interesting with it. So, that's another kind of a structure that I'll do with you. And then the next level is, you know, the semantic level where, you know, you could tag the text to give it, you know, more meaning. So, instead of just having a document, which is more, you know, you're adding more meaning, when you're tagging around it, adding metadata to it, you know, so that that could be used to remove the processing, or you could bring techniques so that, you know, for commonly occurring things, maybe you are, you have to figure out, you know, I got a text and I can map it to this regular expression, you know, a most common example would be, think of template as some kind of a regular expression that you have to find in text. If you can match that template, you could bring it for that, okay, this is taking text, this is talking about some sort of thing. Because there is a template that I've been done to identify that piece of information. So, template is one way of, you know, quickly identifying, you know, how this text is talking about. And then there are other things. Other things are like, you build your own representations, you know, for example, you have built something around machine learning, you know, in your machine learning kind of a domain, you might want to say machine learning consists of supervised, unsupervised, supervised R, you know, has different kind of problems unsupervised.  See, domain, understanding the domain would be very important, because that would define the context, right? Exactly, so that's why, you know, that information comes from a, you know, from a random, very unstructured set of text. How do you, you know, kind of discern the domain? So, the algorithms, let's say that they are there, those aren't very different.  So, and I'm telling you, you can help that, because in my, I would have defined that this word is, you know, a part of this bigger object or something like that. So, a subjective aspect gets assigned to even machine learning. The subjective aspect gets assigned automatically by, you know.  Actual language processing is one of the areas that, or one of the techniques which, you know, mainly talk about how the words are related to one another, you know, whether, where in the sentence they are occurring, you know, what are the meanings. So, those kinds of semantics associated with natural language, if you are trying to understand, your problem requires that, that, you know, you are asking me a question and I'm waiting an automated answer system to you. I need to interpret your question. I need to understand whether you are talking about A or B, you know, what are the, that make sense of your query. So, those kinds of tasks will require natural language processing techniques. A clustering task will not be, you know, natural language processing. So, it's one class of problems for which you need to use natural language processing techniques. So, what we saw till now was, you know, some kind of, you know, bringing some structure to the text which will help us in carrying on some tasks. Okay? So, the common tasks, you know, that are there in text analysis, which we'll also be looking at, you know, as a case studies around some of the techniques that we have to use to solve our problems. But, you know, but by doing the things that I talked before, you know, by converting text into some data structure or like some structure to that, that becomes a way to, you know, process the text, create the input that is in this kind of task. Okay? So, first is information extraction. I also, you know, have a slide, just explaining each of these. But, this is what we will look at. So, information extraction, we leave no revolves around finding important information contained within text. And it could be, you know, identifying person, location, organization, products, so that kind of a thing. Because, you know, if you can get that information out, you can easily, you know, tag that document with that information. You could, you know, if it was a search that you were doing, maybe, you know, once you go through these, you can get information around those extracted terms and you can give results that are, you know, matching that. So, that would be, you know, extracting those entities out of your text. So, that is one area I know and the picture tells you, you know, the simple process in how it is done.  So, you know, if I have some kind of B.O. stacks associated with those words, that would be okay. Then I can take out the entities. So, that is the main flow. We also have one. We have used it in one of these cases and I'll explain that later. That's common task. Another common task is, you know, around document similarity. But this is actually the basis of a lot of, you know, problems that you need to solve. So, the basic problem is, you know, how do we know whether two documents are similar or not? So, do we really talk about that? You know, representing document as a vector of words, okay. That concept is really useful in this kind of a problem. Where, first of all, what you see is, you know, if you want to find similarity between A and B, okay, if I represent A and B as two points, let's say 5.5 would be space and there are two points, okay, that is just to remember two points is a measure of similarity. If two points are close enough, they are similar. If they are far apart, they are not similar. So that's the logic we find out. Then, you know, a point is, you know, to displace a vector of two attributes, x and y, right. So that's what we see wrong there and the side of the angle to do the two is, it termizes similarity. So it's basically finding the the distance between the function here is cosine similarity. Think of it, I've taken a document and created a vector in 2D, it's like, you know, there are two points in it in any dimensional space. So this, you know, down here over there is a three-dimensional value. There are, every document, you know, can be say that it has a vector, which has three terms. Okay, and then I'm trying to find out how far each is with respect to, you know, one another. It shows that there are the distance functions the distance values should be used. Okay, so that's the idea behind documents similarity, but as I said, to convert that text into my path of words representation, it's like, you know, in itself it involves a lot of pre-processing. Okay, so for example, you know the document, I finalize it, I'll render all the stop words. Actually, you know, some cases, for example, if you have a lot of email data, it's new from CC, regards, thank you, dilemmas, right, all that happens to you, you know, most of the times. So actually, I don't think it is a cleanup even before the organization, where, you know, in case of emails or things like that, you know, standard forms you fill out, you know, all of the things you don't need, what you're working on. So there's cleanup involved in this organization, in the strongholding model. I think we look, you know, somebody went to start work, so it's one of the most common ones, you know, don't give them enough information, common words, oh, I know a lot of English language, but, you know, those are stop words and then, depending on what you're doing, you could also, you know, it's not a stop words. So for example, in our case, you know, almost other agents, you work on, your IDH is a very common word, it doesn't add value, because, you know, we have to get the money. Anybody writing IDH, you know, doesn't do it. So I'm not going to choose that as my stop words. So depending on your government, you might want to, you know, stop words.  Discuss, I think, timing, something like that now. I'm trying to get to the base one, so that, you know, two documents talking, one is talking, one is writing, one is writing, one is writing, they are all talking about the same thing, that's the idea. And then there's TFI here, so let's say, you know, after being on this government, say that, okay, I've got 100 keywords. So I'm going to consider the top 10 ones. Okay, the TFI here is one technique that you can use to actually collect the most important verbs in your document, which you will use in your back of those words. So then here I find this as for term frequency, inverse document frequency. So, you know, any term that is operating very frequently in a document is of value, but any term that is operating frequently across all documents is of lesser value. So that's the inverse document part of it, and the term frequency, you know, you can say, you know, fingers of that word, you can say the document is a term frequency one. So I'm going to put a term in similarity, you see. So that means you have access to all your documents. So that means you see, you see, you see, you see. So, you look at document similarity, and actually that forms the underlying, you know, technique that is used, let's say, in the blustering. So blustering is a key task. The task here is, you know, you have a collection of documents. You want to quickly cluster them into some kind of topics, as some, you know, groups. So that's where you use clustering, and to carry out clustering, again, realize some document similarity because you want to form groups of documents that are similar. So we looked at how two documents are similar. So we have extended that to say that, you know, documents that are similar to one another form are, you know, belong to one cluster and those that are far apart will be represented across two documents. So that's the underlying technique of document similarity taken on the side to do document clustering. Right? Yeah. This is the problem with the management software. Yes. There are a lot of common problems with the development because the software is stored at maximum. So this is the problem. Because it's going to do a lot of packing, and eventually you can't manage it. So with this software, because I'm the document manager, the software is what you need to go and get that particular document that you need. Right? So with this software, you can manage the document manager software because there are even more great ways of hanging. So you can store this document on a file system and this machine learning algorithm that you need to manage the document more efficiently than a document manager. So that's that. In no way, you could think of saying that you know, that you can talk about it. You are trying to, let's say, extract the key files that represent your document. Yes. Okay? In a way, if you say that this document is actually a document of these three things, so you could index a document. Right? And then put that whole document in some cold storage. Okay? Only when your search matches that index forms, you know that I need to go and do that. Okay? So search, what can these do? You know, use this kind of DA by DA by things and kind of everything. The index does documents, so that you know, for your reader searches, you can get, you can retrieve and then your whole document, it does the searching. You know it in the first one. Okay? How do you start defining the number of customers and what do we do? Of course, to define that. So defining the number of customers is already, it's already a challenging class. Then I don't think it's a right or wrong.  If you don't have a good starting point, I think, you know, one way is go with the way you could use this. You could use a thousand documents. Okay? And rough news, you know, I want maybe clusters having a hundred documents each. So I started from this case. Okay? Do the first one. And I think that research should drive you, you know, okay, I got two less or two more clusters. Okay? If I find that, okay, I have clusters, then I've got lots of clusters, he's having one hundred documents each. Okay? Maybe I need to reduce the number of customers. So iteratively, you could come up with a good key that, you know, matches your... So it should give a good... So it should give a good... So human interaction, yes? Or some rule-based thing, right? You can say that I don't want clusters which are, you know, having one document. Okay? At least give me clusters that are five and nine documents. Right? So this, or itself can iteratively run till it finds a good key which fits your source. Okay? So that should be what we are saying, that, you know, and have to automate what the user could have done but we could repetitively apply some rules. So clustering, you know, technique was one where we, you know, want to group together similar documents. The next one is categorization. So clustering, what we saw was a unsupervised class of machine learning where, you know, we didn't have anything historically built up that we had learned over time which we used here. But this is a case, you know, which is of the type super-concerned learning where what we do is we, you know, make use of documents that have been packed before buying humans. So let's say, you know, this con, for example, humans have spam mails, right? So where, you know, user marks that this bin is spam and, you know, the mail provider knows that, okay, I have seen so many users mapping these bins as spam. So it has built up a whole training set of documents which users have mapped as spam. And the next time any two bin comes, it is going to classify based on the modeling we're using this industry of user type documents to identify whether this new bin that is coming in is spam or not. So this supervised class of algorithms to make use of this learning that has happened over history to make some kind of a model and then use that model when a new document comes in to make the class of humans. So the diagram actually says that in any of this learning or text we use the learning dataset of text. We use that to create your model and when new documents come in, we use that to make a prediction. So now spam is one example of that. We have used it in different settings which I've explained a little bit. But some of the algorithms that do this kind of classification could be 9-byes, support organizations. And I guess this also relies on, you know, some of these also rely on creating, converting your text into this bag of text representations and that, you know, it takes that as input and then does the other processing. Another common task, you know, is of text summarization. So the aim is you have this huge document and you don't need to go to read it completely. Then you give me a short summary of it. So there are two main approaches. There is what is called selection based where, you know, you select only parts of the document. The typical example should be title, you know, there are paragraph papers or, you know, the first part of the paragraph. So those could be some ways you could quickly pick out important things that are there in the document and present them. The other way is you can use it some more in more techniques, you know, kind of. There are some semantic analysis which doesn't take so long. I'm not here now, looked at those kind of things, but this summarization is also one way, you know, the area where text analytics can help to, you know, quickly give a short summary instead of you having to go over long term. So with that, I'm going to go to some use cases before that, if there are any questions, I can take now. Yeah. So if I have a problem of, you know, I think creating abstraction of following up with the summarization category, information extraction is, you need to be looking for some key text in that document. Abstract is kind of, you know, giving you a light of the main content of that document. It should follow under the summarization. Okay. So let's get on to a little more use case.  It is a, you know, IT management kind of framework which IT management companies follow. And it has a lot of, you know, under IT, there are a lot of subcategories of different spaces that are covered. Incident management happens to be one of them. So incident management is about equal racing incidents, you know, tickets, queries, support calls, those kind of things. And the whole life cycle of how those incidents are migrated from generation to the resolution. Okay. So as part of incident creation, you know, BMC has a lot of activity. You know, people use that a lot. Now let's say, what the problem is, so if you look at an incident, it has, it could actually get into, it could have hundreds of attributes associated to it. What are your categorization here? One, two, three. Or your product category, one, two, three equal. And then there are lots of other information like who is it assigned to, when it was integrated. So lots of attributes that actually are associated with your incident. Okay. Which if you want to create an incident, let's say, you know, there is a classification who is taking a phone call from a customer who is reporting a problem. Just to create an incident in the system for a head, you might have to fill in a hundred phone feeds. Okay. That itself is a challenging task. Many times, you know, there's categories of information that you need to fill. You might have had different categories to select from it.  And you know, many of times, they don't even fill the information. They just report a problem and then, you know, let somebody else do the fill of the category of information. Even that happens. And actually, you do that also because the solution of the problem. Because unless it is categorized, it cannot be assigned to somebody. And if it gets assigned, then it's going to be resolved. So all kinds of problems, you know, do occur. And just because the problem wasn't entered correctly. Okay.  Okay. So that's the value proposition that we're looking at. And in this case, what we are making use of is the information extraction I talked about. So as the customer is describing a problem, I'm picking out key information, you know, who's the person or the customer who's reporting the problem. What asset is he talking about? Is it from service that I'm supporting? Is it some kind of a laptop or some device that he's talking about? Or, you know, some asset that can be, it can be a server that he's talking about, which I know is an asset which I tag in the incident reaction itself and I'm going to give it to some faster resolution. So those are the key entities that I'm looking for. If I can extract that and create the incident, it will not only be able to get resolved faster because these are the key things that help decide who this incident should be related to or you know what category it should be assigned. Okay. So I'll, so look at the solution here. So as I said, the problem is of entity degradation. If you find the correct entity, then we can show you more additional information about that and that might help you know faster resolution, by the support called agent itself, or you might, you know, at least, end of the year, you can take it, it's just more information. Then he would have a, you know, try and do this. So I'll just give you a quick demo. We have the exact match and fuzziness that you are showing on this slide. Yeah, I'll just go now. So now this mouse is very understandable. Okay. So as you may know, you want to set this desk operator who is receiving characters from the call. Okay. And you're going to type what the customer is telling you. So let's say, you know, Chris Jones is calling me. So as soon as I type Chris J, this fuzzy match actually kicked in and it is telling me, oh, we have two people, you know, maybe called Chris J. I can quickly call R.E. Chris Jones, and I know Johnson. That itself is a radio ad, you know. You have recognized the right person. Had I just typed Chris and left it, somebody who is working on the problem need to know who are we talking about. In the pizza ad guys, we use this kind of. Sorry. In the pizza ad guys, we use these seven techniques. They are immediately cashing. So let's say this was the problem discussion. Okay. So as I'm typing, I'm getting some kind of help here. Saying that, okay, I found one person for this show. That's an exact match. He's a type person. So all I'm showing you is a person, but in reality, you might get great shows is, you know, he's a developer in this. He's a, you know, what is his organization? Everything that you have from your identity management system. You could extract that information and show it here. I know. I'm giving you SharePoint, you know, probably SharePoint is a service that I'm supporting, but it is called, maybe SharePoint 2010, 2017. There are three modules of SharePoint I'm supporting. So, you know, that part coming as a fuzzy match. Okay. That okay. What exactly are you talking about? Because if I had left it at that, the person to solve it, it would have asked this question. Is he using SharePoint 10, SharePoint 2013, SharePoint 2007, what version is he using? So you have the person who would have actually got a fuzzy match. He would have clarified it. What are you talking about? Oh, yeah. You want to scroll down? It's something not to scroll down. The staff. Yeah. The staff. Anyway, so that fuzzy match has a SharePoint down there. That's the user. The fuzzy match is not here. I'll go with the ordering. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. You can. That's where we're gonna. That's where we're gonna. Yes. You can store. Yes. You see, these search. We know. For. Cells. Okay, so that is. It has built a machine. So PAHMU gives you this cluster classifications and all of those. Typically the use case for PAHMU is when you have a large connection and a patch kind of a process. Not a real time, because you have to have this as a whole. And the most useful is which one way to match that as a single unit. So it uses those new cluster classifications and takes some of that same patch you use PAHMU. Now I'll tell you if you want, you can always leave it in the middle. Depending on the use case. So it will go in as a text file. So you can access the document. The document can be accessed somehow. So it will be open. So this is one part where we have extracted the QLTDs. We are also quickly telling you what are the entities like. We are giving you more information around that. So at the problem entity itself, there are no activities. If there are any, those are correct. We are also giving some suggestions. So this is the document similarity that I was talking about. Looks like this problem you are talking about. We have a technique for it. Maybe this is a repair problem. So I will take around 20 issues. So I can actually, here I'm presenting at least, but what I can give you is a possible solution. If not, then I will just say, we take issue, apply the issue template. If I just say that apply the issue template, then all that can take the knee and all those things are taken care of you. Only if you just say, apply the issue template, hit that create button, and that's it. So that's how some of these texts are created. So this similarity of documents and entity extraction are being used here to do some kind of application in the area of fields. And also resolution online. The next, some of the tools as I said earlier, you see UIMA is an unstructured information management appliance or something, I think, or analysis. It's a really complicated thing. People call it UIMA. So UIMA is the framework areas. OpenNLP gives you some kind of named entity recognizers, and you have to use all of them. The next case study that I talked about, so it was incident management where you know, we look at how incidents are created, and they are the whole lifecycle of those incidents. Problem management is one area where you want to look at what incidents are telling you what incidents can get from those incidents. So a typical task of a problem management, there's a role called problem manager. And the problem manager, what he has to do, is he has to keep a constant look at, you know, what are the incidents that are being reported, what people are ablating about, and are there any patterns that I need to act on? Or other, I can take those patterns and you know, figure out these are the problem areas, or these are the hotspots, because those people are reporting this wrong. So you are looking at some kind of a post-partum analysis of all the incidents that you have been getting, to say that, okay, I see there are a lot of people talking about video issues. So we need to do something about it. So you go to your IT person and say, I got some of these incidents around you, okay, please correct me. Or you could go to your product and check, this feature is not usable, all people are telling me this is the problem. So this is a feedback look that goes through the different tips, but the problem manager has to constantly keep a watch out for what are the complaints. And usually this is the manual processing we use to, through his experience only, he can look at the last week also, there was this problem, there was this problem reported, you know. I think from last week it has not occurred, this week it has surfaced. So all those kinds of things he has to keep track of, only then he can create those problems, identify those problems and then pass it on. So today, this is a very manual task, and depends a lot on the skill of that problem manager. But he needs help, you know, he cannot simply go over with acts of tickets, you know these incidents reported, come up with these problems, identify these problems. So for him, you know, there is a, the solution is, use text clustering. Take the incidents, okay. As I said, most of the incidents don't have a category information field. So I can't really say, you know, give me all categories of the incidents. Because that is the easiest way to determine problems, but in practice, all that information might not be available. So the next thing is, just use the text of the incident. What is the incident coming up on you? Okay, and try to find an incident that are similar. Okay, so create groups of incidents, which are similar, and then the larger groups are your immediate problems. Okay, if I get a cluster of, you know, which has thousands of incidents, okay, then I need to go over the incidents. Okay, so your problem manager is now getting a quick view of what he needs to, you know, report. What are the hotspots? What are the problems that you have set at the end? Okay. So, I agree with that one place. So basically, these are the, you know, on the top what you see. So there's a difference in visualization, you know, also requirement. I know that, just looking at this, the size of that, you know, bubble is telling you, you know, go look at this, go look at this. Okay. I want to get to the other end. Okay. So, in our, you know, in our IT system, we get, we get software, you know, people report issues, right? So we ran this on that data, and it looks like a lot of people are having problems with their laptop.  Okay. There are a lot of problems in your view. There are a lot of password issues. So this is kind of giving you an indication, you know, what are the problems that people are talking about, and how, you know, where do you need to invest? Let's say you have two people who solve these problems.  If you solve this, it's an idea.  So here we have a set of services where all customers make passports, right?  The services we have for the problem manager, and the text rustling kind of technique is used here to quickly highlight the problem in this. Okay. So why do we have this? So I just ran this rustling on all the talks that are being, you know, done for Fifth Elephant. Okay. So I went to the Fifth Elephant page, and, you know, all the people were given their entries. I looked at titles of all those topics, and ran this rustling on that. So let's say now, when I'm attending this conference, okay, I'm interested in all real-time, you know, string processing activities. So I can quickly, you know, there are fine real-time talks that are Cougar cars and real-time, UX3, or you know, somebody is interested in solar and elastic search. You can quickly come to that, and he can understand, you know, okay, maybe I know, go and attend these topics, because they are fine people. So quickly, okay, you know, what is interesting to you, or if you have to change the tracks, no, you have some inside, okay, if you want to have a real-time track, maybe take these two groups, watch them and say to this very good track. Okay. So that was one example. Cougars, you see, Rekha has this, Rekha means the evidence from Rekha. This is Rekha Rekha, this is a commercial one, and the visualization you saw was a phone-driven visualization. Then we take to the next case study, which will be the last one, which is again around incident management, and the problem that we are looking at is, you know, when you raise and check, again, this is the problem, you know, somebody is reporting a problem either over the phone, and she is an analyst who is taking an email, so people are just, you know, firing an email to write this. Okay, the analyst can send an email saying something is not working or things like that. So when such problems come in, you know, how are they getting assigned? Again, as I said, categories are one, a commonly used technique, a problem is also a category, this solution is of the same. But as I said, categories are not releasing, or there are hundreds of categories, and then, you know, this whole discussion has to be, okay, this is, this category should be accepted, those kinds of problems are there. So what we looked at is, can we use text classification as a technique to solve this problem? So the idea here is, in my system, I have lots of incidents that have been reported in there, they have been assigned to different groups and solved with them, right? So this becomes my training data. So I use this training data to create some kind of a classification model. So all the past incidents, I know the text of these incidents, I know the assigned group for those. So when the new ticket comes in, I will apply that against the model that I have been using in my classifier, okay, and then this ticket should be assigned to this group. So this assignment problem, as I said, you know, it could take days in some cases, could actually be automated, or you could at least help the person doing the assignment to take those visits quickly. So that's the idea behind it. Doing the applied text classification, you know, as a technique for the problem of assigning of incidents to the right people. So sorry, so I think it's a new incident, I think it's related, because the server is very, this is an opponent that we know, it's not very important to ask what should be the assigned group. And this is essentially making use of the model we've been using, the historical tickets, okay, and with that classification problem, so this component actually where is that model, it says this is a suggested group that this ticket should be assigned to. And then, you know, the problem is that the assignment prediction can be do if it is not correct. There are certain algorithms which also can auto-correct the auto-learning to tell the review. So that's the thing I do, available for some classification algorithms that we have made, you know. But, you know, the idea is, we are doing a busy text classification as a technique to do the ticket assignment. So that's that's what I have to cover. If there are any questions, we can take them to the last talk of the day. I think people are there. Because we will take them to home. Really? Yeah. Of the UI, we have some UI and may have a full graphic background. For the UI for the prototype. Okay. There's a whole thing. There's a whole thing. I have, you know, it's based on D3. It's based on D3. It is a JavaScript thing, JavaScript based on D3. Yes. It's a commercial one. Yes. So when you store the data into the database, you also have to be sort of store the data or do some kind of a structure management as well, like draft. You know, are you having a specific problem out of the three or in general? In general. Okay. So in general, if you are a search kind of a problem at hand, you can put it as a search behind the six users you see. Okay. Indexing. Indexing. Search is your main usage. If you are ID based, it will take a long time. Hash is kind of, cash is your main requirement. You can see. You go over to these, you know, red heads or those kind of combo TV which are a key value, key value stores. You can use those. Typically. It's not that. It's a document on the entire database which is like, you know, put it for a scale but not for transactions. So depending on what your which case is, you know, you would go to this engine within this noise, you know, you have different types of data that says key value stores, document on it, color-oriented. So depending on which case you would choose the appropriate store, you would want to retain that. Do you have a use-case exact pattern? There are so many. For a particular use-case, then these are the stack of technology that we can use to solve that. Do you have a use-case pattern? Or do you have a use-case pattern? I would say, you know, once you understand the basic techniques, I don't really understand this. How do you think that in this use-case, I'm trying to, I'm trying to solve a problem. I'm trying to identify what are the key problem areas. So with a technique like clustering, because that's what I'm trying to do. I'm trying to create a group of documents which are dealing with some problem. So I have some set of techniques that are available to me at hand. Depending on my problem, if I can map that to my use-case, that's the way I would pick up the writing. Or at least I can try if this is working or not. Are you doing multiple of these which two types you may speak like an additional name? Yes. I think that's the reason we are moving up.  Just calling your phone and whatever it is, what does it do? You don't need a manager. We don't have a practical but if you use this problem, it's a very scenario solution So as I told you, these techniques are very basic. I have applied it to our domain. But you also know these techniques you can now include in your organization. That's the core idea is to use the techniques so that you can take it back and use it in your own way.


So we'll get started here. Again, if those of you who haven't been to these, I'm Blaine Bracki with SDACB. Katie Youthy is also on, she's with SDACB, locally led. We've been doing webinars the last couple of weeks, just on some training topics that we talked about in surveys and got some feedback from conservation districts. So this is the third one, this one we're doing on proposals and then proposal budgets. We have Bailey Kasky with the Department of Ag and then she's gonna do the first half and then we'll have Val Dupraz, Chris Duxbury and Denise Gower with NRCS, giving a talk on the federal side of things. We will, we are recording, so we'll send out the recording of these and we'll also get the presentations and send them out also so you can go over, send them on to anybody else that might be interested. And during the presentations, if you have any questions, you can use the Q&A down at the bottom or if there's some discussion back and forth, you can use the chat also and we will try to get to those as we move along. But otherwise, without any further rambling, I will pass it along to Bailey and she'll get started on some state material. Thanks, Bailey. Thank you. Okay, let me pull up my presentation here. Okay, which one are you seeing? We're seeing the presenter mode. Oh, okay, I didn't do it automatically. Okay, here we go. Yep, that's good. Okay, so thanks for inviting me to do this. I know you had asked for just budget process, but I think the conservation grant budgets, it probably wouldn't take me very long to go through that. So I'm just going to go through the whole grant application process. So let's get started here. I'm going to give an overview first. So I'll be talking briefly about the background of the program and then go into what projects qualify and the common projects and practices that we see applications for. After that, I'll talk about the actual application and go through applying and the budget, the narrative and we'll go through an example as well. And then to finish up, I will go over what to expect during the grant rounds and then what to do after you've been awarded a grant. So a little background on the conservation grant program. The grants that are awarded should meet one or more goals of the Department of Ag's coordinated plan for natural resource management. The goals and objectives are drafted based off of the state's conservation priorities that provide information and assist the commission. So right now the 2012 plan is still being revised. We'll have a 2020 plan released, hopefully in the near future, but the 2012 plans goals included water resources which would be beneficial uses and water quality, soil resources, air quality, wildlife, public awareness, funding and energy which included renewable energy and energy conservation in general. The department is currently, yeah, like I said, finishing up the 2020 plan. So the goals will be slightly different but overall it shouldn't really affect the types of grants that we see applied for. And the commission receives $500,000 for this program each year. So we get it on July one and the first grant round is in November. So the commission will set aside an amount to reserve for the second grant round, which is in the spring. And only conservation districts can apply for these grants but they can have multiple partners included. So we commonly see NRCS, game fishing parks, SDSU sometimes involved with these grants also. Some common project types and practices are those that deal with soil, water and wind erosion as well as livestock protection and grazing management. Those could be windbreak or shelter belt plantings, water development projects, waterway construction and grass seedings, cross fencing and cover crops. There's also been quite a few brush management projects lately which control the encroachment of cedar trees mostly along the Missouri River in the Southeast, South Central part of the state. Then that's to remove cedar trees from grasslands to improve grazing management. Also forest thinnings have been a more popular grant lately especially in the Hills area where the thinning is needed to control the forest stance. Also education and outreach grants are stable projects as well. So next I'm going to show a few completed grant projects. This is a shelter belt planning that was part of a hide hand enhancement project. Here we have a livestock water development that was done in Gregory. And this one possibly has a riparian benefit as well drawing cattle away from the creek bottom down there. Here's a cover crop project that was in Miner County. And another cover crop project that was on the Mallette demonstration plot there. And you can actually see turkeys up in the central left side of the screen there. So wildlife benefit as well. And then this is a forest thinning that was done in Pennington County. And one more here that I thought was pretty cool. I was on Google Earth mapping a spot check to go check conservation practices. And this was in Mallette County I believe and you can actually see them laying the pipeline for that water development project which I thought that was pretty cool to be able to see that on Google Earth. They caught it at just the right moment. Okay, well let's get into the not so fun part now which is the actual application process. So if you want a conservation grant you better file that paperwork. Our grant application I've provided the link here. It's on the South Dakota Department of Ag website and it's updated occasionally. So it's best to not just have it saved on your computer because if you have one that's a few years old it may not be quite the same. We still get a few applications that have the state seal on them instead of the department logo. And those are older applications. Also the most recent approved cost share docket is going to be helpful. It's gonna be something you want to estimate your practice costs and I provide the link for that as well here. Okay, so here's the actual application. It's fairly straightforward and so you have the applicant information at the top and that's gonna be the district's information and then the project contact is going to be the person filling the application out or whoever is going to be communicating with the Department of Ag about the project. For the project information, you have the name of your grant application, start and end dates. Those are always going to be for the start date, January one or July one. And then the end dates are always going to be June 30th or December 31st. For the legal description, the most important part is the county. So the county or counties, if you're doing a multi-district grant, you would wanna put there. Township range, that stuff isn't as important because obviously you're gonna have projects within the grant that are gonna be in multiple places. So it's fine to leave that blank. And then for the financial information, this is important because sometimes the application can be kind of glitchy because it's a fillable PDF. You wanna put these numbers in first because the total project cost, where it says the zero here is going to be pulled over to the budget sheet. And that total, it's going to be where it says this total must equal. So you want everything to add up to that. And sometimes if you don't fill that out right away and then you go and put it in afterwards, it doesn't work. And then in the narrative section, that where this box is, is just kind of a general summary. And then with the actual project narrative, you can attach pages. So I'll be going through that a little more when I show the example. But in addition to the narrative, if you're doing a multi-district grant, you're gonna wanna provide us, if we don't have it already with the joint powers agreement for each district and then a W-9 as well as the minutes that approve the application from your district board meeting. And then if we haven't gotten a financial report from the district that year, we're gonna ask for it. But usually we have them all by now, usually. Okay, and next I'll show you the docket and then go into an example. So this is the 2021 docket that was approved at the last commission meeting. The applicants can use the docket to help estimate the approximate costs. So for example, tree planting was just changed to linear feet recently. And so you can use the 38 cents for linear foot and the approved cost share percentage, which for tree planting is 50%. So a producer is gonna be reimbursed 50% of the approved practice cost, which is the 38 cents. So it's gonna be 50% of that when we do reimbursements. And so when a district submits a reimbursement to Brian and I, we're going to use either the docket cost or the actual cost of the practice based on the receipts submitted with it, whichever one is less, we can never reimburse over the docket cost. So we'll just keep that in mind when you're putting your applications together. Okay, so here is an example from Gregory County. Again, the district contact info is gonna be at the top. And Ken was the project officer on this one, project name and the starting end dates. So you can see here, they have July one, but they also put July one for the end date. So that would be changed. Brian or I, well, in this case, I would cross that out and put June 30th. And of course I would contact the district to make sure that they're okay with that if they don't wanna move the date. It has to be July or June 30th, but if they wanted to change it to December or something instead, we could do that. Also this number here, the total project cost, that's important because it's going to be pulled over onto this page over here and they need to match. And that needs to match all the way down. And then for the applicant's signature, it's gotta be either a board member or a district manager if they've been approved. So at a meeting, the district board can approve or they can approve someone to be, to have signature authority on the grants. Some districts will just approve it for each grant each time or they'll just say in the future, so-and-so has signature authority to sign on all grants paperwork. So that's more of a blanket motion. So let's move on to the budget. In section A, most of the time, the districts are going to mostly use the contract services right here. And that's going to be the actual work, like treat planting, pipeline and tanks. That's what they're applying for in this one. So if they had applied for administration, that would go up in the salary benefits section. And the majority of the time, those are the two we see. There are grants that are applicants that split them all out into each section, but that's not necessary in my opinion. So in the activity budget section B, they have shelter belts, pipeline and tanks. And those are their projects that they're gonna be doing under this grant. So shelter belts is going to include tree planting, shrub planting and fabric. It's going to be all the costs associated with shelter belts. And then same for pipeline, it's going to be the pipeline and associated costs and tanks as well. So where it's split between grant funds, local funds, in kind and other, the grant funds is obviously what the commission would be contributing for the project. And then local funds, most of the time is the landowner money that's put forward or district money. Local in kind is usually like, if there's administration, then the district will either either gonna put it in local funds or local in kind. And that's the match for the administration costs of the grant. And then other funds is usually when you have other partners like if game fishing parks or NRCS is contributing to these projects, that's where that money is gonna go would be under other funds. And again, so you can see they actually have an extra zero down here that I would just cross out because that's not right. Somehow there's, I don't know how this happened with the extra zero and it's still totaled correctly, but whatever, this application can be finicky sometimes. So moving on to the section C where the project partners are in this case, it was just RCNF, which is the grant money and then the landowner contributions. It's straight 50-50 split on this grant. Let's see what else. Some tips for applying. In this case, also they have the producers lined up already. It says they had four producers already signed up. And usually they will, yeah, they'll have the producer signed up and then if they end up with leftover money, they'll rank the producers just to best match the grant funding. And then the board would review that. Also, if you're doing tree plantings, the commission sees it as a good thing if you have the site prep done before the grant is even approved. And yeah, you wouldn't get reimbursed for site prep if you do that, but it looks good because you're ready to go. Because no work can be reimbursed if it's done outside the grant period. Let's see. And then, yeah, don't be afraid to ask for help. If you are applying for a grant and you aren't sure what you're doing, call me, call Brian, whatever, it will help you, we'll get you examples. Oh, also a note on administration. If you are applying for that as part of your grant fund, you can't, the administration costs can't be more than 15% of the total budget for the grant. So here's their narrative. And this is just additional details on project description and need who's going to be responsible, where the project will be located. And some of these are longer. Like Gregory is pretty to the point with theirs. It's pretty straight, simple, but there's definitely applications where there's a lot more information as well, which isn't a bad thing either. And then describe the specific environment, natural resources. This is where you're gonna want to include how, like which goals it's helping. So in this case, they talked about soil erosion and water quality. And what else? Briefly describe specific on the ground activities. And provisions to ensure long-term management, et cetera. Final projects, they put, they had producers lined up already. So they put what they would actually be producing out of the grant and then partners. So next I will go into the grant round proceedings. So the commission has two grant rounds each year. Normally they're in April and November. The deadlines are usually March 1st and October 1st. And I say that because this year, and I believe last year, we pushed the March deadlines back a little bit, just because of when the commission had their first meeting of the year, because we want to give the districts ample time to use the new approved docket in their spring applications. So the docket this year was approved mid-February. So we pushed the deadline back to March 15th this year. And our meeting is actually on April 6th. So that's coming up. So once we get all the applications, we, Brian and I review them to make sure everything is there, like the budgets look good. We have the W-9s and minutes and all that. And then during the grant round, it's recommended that the districts attend that are applying for the grants, just in case the commission has questions that Brian and I aren't prepared to answer. We have the applications in front of us, but sometimes there's other information that would be beneficial if the commission has a specific question. And so the grants are reviewed by the commission and randomly selected. So usually they're pulled out of a hat or something. And so the commission will review all of the applications and then go back and randomly select the order for approval, denial, et cetera. And so, especially in this great spring round, the grants might not be fully funded depending on how much of the money is left. But in the past few grant rounds, it hasn't really been an issue too much. And then, so after the grants are approved, we will ask for a revised budget if you didn't get the full amount. And then we'll send out the awarded acceptance forms for the districts to review and then sign. The award acceptance forms are going to have the terms of the grant on it that's approved by the commission. So like docket is gonna be on in that section. So each application or each grant that's awarded is the docket for that year is set in there. So even if the commission or approves a new docket the next year, that grant that was awarded at that time has to go with whichever docket was approved at the time. And then I said this earlier but work outside the grant period will not be reimbursed. So if you do site prep for a shelter belt planning and you had site prep on your application, we can't reimburse it if it was done before the grant was approved. But we don't actually get a lot of reimbursement requests for site prep because it's not one of the, yeah, it's just not something districts normally ask for reimbursements on sometimes, but I don't see it a lot. Anyway, so yeah, once you get your grant awarded you're gonna wanna let your producers know that you have the cost share funding and the producers are going to have to fill out an application for cost share. Even if you already have them lined up, they need as part of the grant requirements, they need to send in an application for the cost share to the district and then the district needs to provide them with an award letter. And once you are ready to go, you can do the projects, do the work and submit reimbursements. And then when the project is over, we would submit a final report, which includes information on what the grant accomplished. So with that, I think that about covers it. I feel like I went through that kind of fast, but if you have any questions, I will take those. So. Anybody have any questions before we move on? That was really good, Bailey. Thank you. Everybody's pretty quiet this morning. Wow, I'm so good. Yeah. You covered all the bases. Must be that rainy day. Everybody's quiet. Ew. There's. Submitting vouchers. Yeah. You mean like when you submit for the reimbursements? I'm assuming. I didn't put that in my presentations, but I can go over it a little bit here. Let me grab something. So normally when districts submit reimbursements, we want like, we'll just use Shelter Belt as an example. We want to see the application from the landowner for Cauchier, the approval letter for Cauchier to the producer. We'll want to see all the receipts involved in voices from the district that were involved in the whole project. We'll want to see the tree planting agreements. And we'll want to see any like, so yeah, that while the tree planting plan, if that's the case or any agreements associated. So even if it was like tanks, pipeline, water development, whatever, we'll want to see the agreement between the landowner and the district. What else? I think that's about it. Let me look through one real quick just to make sure I covered everything. But yeah, the most important thing is the receipts and the agreements because we want to make sure that it was done within the grant period. And we want to know which costs we're going to use for the reimbursement. So if the producer paid more than what the docket cost is, we can only use the docket cost. But let me, okay, so we've got application and approval letter, agreement, receipt. And that, yeah, that's about it. So unless, oh, and if it's something I'm going to spot check, it's very helpful to provide maps. So I know exactly where in the section, because township range in section is helpful, but it's really helpful to get a little more detailed than that, so I'm not driving around the whole section to find out where the shelter belt was planted. Rates for match. So normally it's 50-50, but depending on the project, so most of the water development grants are 30% for general livestock. Let me grab one of the dockets quick. Okay. So, and I could have gone back to that maybe, but we'll just go with this. So yeah, for tree planting and stuff, it's 50%, but the producers have to provide at least 25%. So if you have multiple project partners for the grant, that there has to be landowner cash minimum of 25%. And then, yeah, so it depends on the project. A lot of them for like grass seeding is, general livestock needs will be 30%. If there's grazing management practices that have riparian protection, that's going to be 50%. Cover crops are 50%. I don't know. I mean, you guys can look the docket up and take a look at that as well, unless there's anything more specific you want me to go over. Bed questions, producer time. I guess I don't know what you mean. You can, it can be counted as in-kind depending on the project. Do they get reimbursed? It depends. Like landowner labor is not reimbursed for anything. Except tree-thinning. They can be counted for that. So that's the only instance that's going to count. Everything else, but it's always going to be match or in-kind at least. So yeah, if you want to email me these two, I can get some feedback from Brian as well. Anything else? Okay. Did you, I'm sorry if I missed it. Did you catch the chat but the district? Okay. I thought. Yeah. Oh no, I'm sorry. I didn't see that one. Maybe you already partially answered it. Oh, okay. The, if a district charges 35 cents per linear foot, reimbursement request is the reimbursement request 50% of the 35 cents for the docket price. Well, you can put whichever one you want to on the reimbursement request, but I'm going to look at it and reimburse whichever one was lower. And sometimes it's actually kind of helpful if I've gotten reimbursement requests where they used the actual cost in the reimbursement section. And then in the notes for the reimbursement, they'll put this was the actual cost. This is the docket cost. This one's lower. So this is what we want. And I like that. It saves me a little time, but that's not usually the case. So I look at the reimbursement, invoices and the docket. If that answers your question. We will see. All right. It must have. Maybe they're. I guess. Okay. Just thinking harder.  Yep, for sure. I think most of the districts have my information, so. Yeah, you should. All right. If there's no other questions. Thank you Bailey for presenting and answering those questions there at the end. Yeah, and we'll hopefully let you know if we have any more questions. Sounds good. Thank you. Yeah, and we'll get your presentation when this is over and pass it along to everybody. So with that, we will move along to NRCS. Hello, Val. Oh, you're still muted. There it is. I've had quite the time. Oh goodness. It's a good thing we started early today. Otherwise, it would have been a little frustrating with the challenges that I incurred this morning. But good morning. I will go ahead and start the NRCS pieces of this and we also have some budget material. I'm going to ask Chris Duxbury to introduce herself as she is a presenter with me today. Chris here. Yes, there she is. She just. Hello. There, hello. Good morning, this is Chris Duxbury. I get to help with grants and agreements at the NR on the NRCS team. Great. Thank you, Chris. I am also joined by Denise Gower. She is here with us as well, Denise. Good morning, everyone. Yes, I'm Denise Gower. I'm an assistant for management and strategy and Val and Chris are going to cover the majority of the presentation. I'm predominantly here for moral support and then talk about any budget type questions that arise from the NRCS site or for NRCS in relative to NRCS funding. Thanks. Thank you, Denise. I am Val DePrize. I'm the acting assistant state conservationist for programs. Well, Jeff Vanderwilt is on detail to Nebraska. So I'm going to cover, I'm going to kick things off here and share my screen. And we can get going here. All right, so we're here to visit with you about budgets today. I'm going to touch on the financial assistance pieces of budgets. So I'm going to visit about the conservation implementation strategy known as CIS and or the regional conservation partnership program known as RCPP. So CIS is a locally led conservation effort to sell high priority natural resources in a focused and targeted approach on funds come through the NRCS as financial assistance through Portrags. So CIS was started or entertained or asked for proposals the first time about a year ago. And what that project does is it allows local groups of people, producers, conservation districts, other interested parties to request financial assistance to address specific priority resource concerns within their area. There is some sort of geographic limit or applied on it. So it can be a township, a watershed, some sort of area is applied and tied to resource concerns. And then a proposal is submitted through the NRCS. We review those at the state level and we select them for funding. So in fiscal year 2021 we are using a third of our financial assistance dollars for EQUIP and we're allocating them to this conservation implementation strategy. So we're accepting applications for fiscal year 2022 funds at this point in time and the application date is April 15th. So there is still time. These proposals take a little bit to put together but if you've been working with a group and you have some goals and objectives and some things you'd like to see improved then I would encourage you to consider a CIS application. So if you have questions about it I'll give my contact information to Blaine and he can sure pass that along if you don't have it already. Although I did see quite a few familiar names in the chat box and the attendee list so it's good to see you. So this year we will also entertain other program dollars through a CIS proposal. So if there are some CSP related activities that you'd like to see implemented through CIS we are offering those financial assistance dollars as well. So this benefits our conservation districts by increasing the amount of conservation within your area. It is a targeted approach and our goal is to have a greater improvement in the resource concern conditions in a faster manner. And it's a really good opportunity to bring additional funds to your local area. We also have the regional conservation partnership program that promotes the coordination of NRCS conservation activities with partners that offer value added contributions to expand their collective availability to address the on farm watershed and regional natural resource concerns. So RCP program is generally on a little larger scale. It's evaluated then CIS, it's evaluated at the national level. There's some differences with that. The RCPP will offer technical assistance funds for the partners to administer the program and carry out some of the duties that support NRCS's mission with the implementation of the program. So we've got both programs available that offer both financial assistance and technical assistance. And today we're going to touch on the budgets associated with those. So I've got just a couple of examples here. Our CIS and our CPP program budgets are pretty straightforward and simple for the most part. And our CIS projects have a lot of flexibility within the budgets. This is one example of a budget, the top line is practice one. If you had identified some natural resource concerns and you were wishing to address those natural resource concerns with prescribed grazing, pipeline tanks and fence, you would look at your project area, estimate about how many, the quantity of that associated practice you would need and then plan that practice out like fiscal years or fiscal years run from October 1st through September 30th. So that is something that may not directly align with other fiscal year budgets or fiscal year budget is from September, or from October 1st to September 30th. So in this example, on that top row boxes, if you had entered fence as your practice one and you estimated to implement a $100 rate of fence or $10,000 rate of fence in fiscal year 22, you would enter those values all the way across to through fiscal year 25, 26, however long you'd expect your project to be. You would do the same thing for any additional practices on this table. And then if there was anything that you had from partner contributions, you could include those brief description of the activities and then add those dollars into your proposed budget. Same with in kind, with our CAS project, they're encouraged, not required, but just the partnership alone is very valuable and our efforts wanna support your local resource concerns and so our financial assistance is available to your project areas through our conservation implementation strategy and then our CPP is evaluated at a national level. To figure out the funding request, you would use the current year payment schedule that NRCS issues. Here's another example of a budget. In this particular case, the proposal had contained information about what they wanted to do, their goals and objectives, the practices they planned to implement and they broke it down by fiscal year 2021. They were requesting $361,000 of financial assistance 2022 was greater, indicated 2023, 2024 and 2025 was implementation and monitoring completed by a partner, a different partner. So they didn't have any cost share funds that they wanted or financial assistance funds they wanted to receive that year. They indicated it in this type of format. This is definitely something that we supported on a CIS level. This is very open to what works for you for CIS budgets. So our CPP budgets are a little bit different but there's still the structure, you plug in the numbers and it's all based on what your local goals and objectives are. So with that, I'm going to ask Chris. Yeah. We just had a couple of questions. I thought we tried to jump in too real quick. Okay. Was there any technical assistance for partners through CIS? And not through CIS at this point in time. Through our CPP there is. And then we had one more. How does the, where or how does a district find the current year payment schedule? Okay. That I have, I have it on another screen that is, it's released as a bulletin. And I expect that they may have gotten it through email but if they want a copy of it, I can sure send it to them or send it to you to share with this presentation material. Okay. Yeah, that would be good. Yeah. Or any local office would have it available as well. Perfect. Okay. That answers both questions that we had. Thank you. Great. Great. Thank you. All right. Now Chris is our agreement expert and I'm going to turn things over to Chris to visit about some agreement types. And I look back to Chris. Good morning. I am just gonna go through the different agreements that we have just kind of give you a overview of what we've got. We have several different agreements which you guys are all aware of. We have a contribution agreement which is an NRCS agreement authorized by seven USC 6962. This is a non-competitive agreement for one or more non-federal parties to contribute funds or other resources to the NRCS. This gets to help accelerate the completion of soil surveys or watershed planning. It's to the benefit of both NRCS and the non-federal party. It gets to help further soil and water conservation and the non-federal party contributes resources on a gratuitous basis to NRCS. Some of those that you would be familiar with would be like South Dakota Soil Health Coalition, South Dakota Association Conservation District and South Dakota No-Till or some of our vendors with the contribution agreements. Then we have the cooperative agreement which is a financial assistance internet between a federal and a non-federal entity that is consistent with 31 USC, section 6302 through 6305. This is used to enter into a relationship with the principal purpose of transferring anything of value from the federal agency to the non-federal entity to carry out a public purpose authorized by a law of the United States, C31 USC, section 61013. And this is distinguished from a grant agreement and then it provides for substantial involvement between the federal awarding agency and the non-federal entity in carrying out the activity contemplated by the awarded cooperative agreements. It may or may not require recipient cost share depending on the program and the agency requirements. This may not be used to procure products or services for the primary benefit of the government. Some examples of cooperative agreements would be like the emergency watershed programs. And I would suspect some of you are familiar with that as well. And then we have the grant agreements, the conservation collaboration agreements that you all have heard of, I'm sure. This is an agreement, a legal instrument of financial assistance between a federal agency and a non-federal entity that consistent with 31 USC, section 6302 and 6304 is used to enter into a relationship with the principal purpose of transferring anything of value from the federal awarding agency to the non-federal entity to carry out a public purpose authorized by the law of the United States, C31 USC, section 61013. And this is distinguished from a cooperative agreement and that it does not provide for substantial involvement between the federal awarding agency and the non-federal entity in carrying out the activity contemplated by the ward. It may not be used to procure products or services for the primary benefit of the government. And some of these vendors would be like that's unlimited, that's forever, SDSU, South Dakota Grassland, SD, ACD gets to be one of those as well. Okay, can you click my slide? Thank you. And then we have the memorandum of understanding, memorandum of agreement and the unfunded cooperative agreement, which we've been working on right now. The memorandum of understanding is an agreement that is a documented plan for carrying out a project or an undertaking of mutual interest to the NRCS and one or more other federal or non-federal parties. So this gets to provide for joint sharing in the operation of the project. Each party carries out its responsibilities by using its own authorities and resources, including funding. There's no financial or other resources directly obligated transfer to exchange. It may require parties to enter into another type of agreement that will transfer resources. So this would be like NRCS and state and local agencies, local conservation districts, et cetera. And then the memorandum of agreement and the unfunded cooperative agreement, they kind of go together. The memorandum of agreement is the main piece and then the unfunded cooperative agreement is like the supplement to the MOA. The MOA is between the parties which documents areas of common interest and clarifies the roles of the federal, state and local partners in providing the conservation leadership and technical and financial assistance to customers in order to help them preserve and enhance national resources through a voluntary cooperative approach. The agreement will specify the transfer of resources between NRCS and the partner to accomplish delivery of NRCS programs and mutual conservation priorities. This defines the relationship between NRCS and the district and it sets the goals and responsibilities, data sharing, work and arrangements, et cetera. Then we have the unfunded cooperative which is a supplement to the MOA and it's really gonna specify what exactly who's sharing what, who's providing what as far as office space, vehicle usage, equipment usage, if somebody's storing a UTV, who gets to do that? That's what we're also working on right now. And I think I've been bothering some of you about that. Okay, next one please. Thank you. So should you apply for a cooperative or any kind of a grant, the documents you're gonna need is a statement of work. And a statement of work must be submitted with no tables, no pictures, no graphics and it must include the following pieces. It must have a purpose, the objective, objective, a budget narrative, responsibilities of the party, expected accomplishments and deliverables and the milestones for those deliverals. We'll also need an SF-424 which is an application for the federal assistance and then an SF-424A which is the budget piece of the federal assistance you're asking for, which that 424 will have, the 424A, excuse me, will have to match the budget narrative. So when you get into that, you'll see the budget narrative will have personnel and contractual and indirect costs and all such things. It will have to match exactly what you have on the 424. You'll also need a certification of lobbying and an active SAM. The SAM must match the DUNS on the 424 and you will have to have that active before you apply now. I think in the past, we've been able to work on getting that active as you submitted your proposal, but now they're gonna make us have that active before you submit your proposal. Some other documents that may be required if AppleBagal, a current NICRA, a signed de minimis form or other official indirect cost IDC justification. And that's just required if you're requesting the indirect cost. A signed hardship statement, if the recipient is providing less than 50% match on an agreement and the SF-LLL disclosure of lobbying and if the recipient is engaging in lobbying, you're gonna need to sign off on that. Some resources, you can go to grants.gov, the federal government site. It's got a lot of fun stuff on there. It is really pretty helpful and pretty user-friendly.  So not too hard, pretty fun. So I think that's all I got, isn't it? Oh, yep, sorry. And then here's the forms and abbreviations for all the fun stuff that you're going to learn as you go through the process. All right, thank you, Chris. Uh-huh. So I believe that was the end of the presentation there. So am I still sharing screens or not? Yep, you are. Okay. All right, so we've also have a budget narrative kind of a guide as well. So we will share this in addition to the presentations. So this is something, Chris, did you want to touch on this right now or it seems pretty separate? I can. It looks, it's really not as bad as it looks. Once you get into it, you're gonna figure out how you need to allocate your funding. You're gonna know how much your personnel is gonna be and how to calculate everything. It's really, really important though, and I will preach this. This information must absolutely match your budget narrative information.  They are really persnickety about that. Like you have to put down your travel exactly how you anticipate.  Thank you. When you have to line everything out, you have to say Airfare or Airport Parking. You have to line it all out. The only tricky part is this appears that it's in like a spreadsheet format and it can't be. It has to be in a paragraph format, which is kind of crazy, but that is one of the tricks. There cannot be any spreadsheets because that'll get bounced back to us. So, but yeah, in like supplies, you'll have to list everything that your general office supplies, postage.  So, just a matter of putting it in the format they want it. So, and you can always ask me or Denise or Val if you've got questions. Can text me or call or message me or call me. So, I think that's it unless anybody's got some questions. Hey, Chris, is this document available somewhere that we can... Yep, I can share it. I can share it. I can give it to you and then you can share it or... Yeah, that would work. I can send it out. Okay, sure. Is there any other questions? I had a quick question. I think it's been brought up before. Was there a plan to move CAS to grants.gov or... No. No. No. Okay. That's just a within-state effort. Okay. So, we'll review all of those through the in-state process. Okay. Did anybody else have any questions for Val and Chris or anything that we talked about today? Probably kind of a lot. It's a lot of info to take in, but it's all good and refresher. We're going to go... Okay, well, thank you, Val. Appreciate your time and effort going through all of that. Thank you much, Blaine, for having us. If you have any questions, Chris and Denise are the agreement and grant experts. If you have CAS or RCPP questions, I can share a service though. So, we appreciate your time today and all of your... The conservation partnerships you have with us and the great things that we do together. Perfect. Thank you. And I'll pass along for those that don't have it, all the contact info from today for Bailey and you guys, for those that don't have them. And the presentation info I'll also pass along. But if there aren't any other questions, then we will wrap it up for today. We landed right at about an hour. So with that, thanks again to the presenters today. We will wrap it up and join us next week. We have one more. Jeff Vanderwilt is going to jump into the CIS. He's going to take a deeper dive into all the intricacies of the CIS. Now that we're in year two, we'll see if we can break that down a little bit more for you. I see a question. Yes, I did get a haircut. Yeah, we're all clean now. But with that, that will conclude our day. Thank you for joining and we will see you next week. Thank you.


This started as because he's got a beef with some other person. That person had a connection to a building there around the Zebulun area of Pike County. So he went there to set a fire to this building because of whoever this is that he's got trouble with. Unfortunately, Marynotha Bible Church had a van parked nearby. It also, another man had a truck parked nearby and he also set fire to those items. The significance of all this is second-degree arson carries up to 20 years in prison and because he has been previously convicted of felony, he's looking at a life sentence actually. So the consequences of his actions are very, very severe. He could get up to life in prison for what took place over there.


What's up everybody? Jason here for jaspyscasebrakes.com. We just sold out this pack filler that's given away 10 spots in Bowman Chrome University football with this 2022 Mosaic copy pack break number 26. And again guys, everybody gets a round of Team the NFL out of the pack. We'll see what pops out of here and we'll know if that comes in ship. But then at the end we will gather up the list of names that bought in, randomize them again, and we're gonna give away 10 teams in that Bowman Chrome guys. Biggest team being like Colorado which I believe was like a what $400 team, $400-$500 team. So obviously like I said pretty nice little giveaway there with some of these teams all the way down to Wisconsin. And again it is a promo break guys. So if you guys look at our promos this is one of the breaks that gives you five times the entry. So once I get the official list we'll add those to the promo list here five times the entry. So and also on top of that we're giving away 10 free spots in the pack filler. So we're gonna randomize the names first. The first 20 that bought are 22. Top 10 get free extra spots. Let's roll it. Five to five ten times. Here we go. One, two, three, four, five, six, seven, eight, nine, ten. Ten times, ten, ten. Scott, Omar, Steve, Douglas, Ryan, Douglas, Alan, Douglas, Chris and Scott Goodman. There you go. So top ten. Got free extra spots. We'll put a little character sign to show you guys those free spots. Maybe later you guys gonna get a nice hit in the pack or potentially get a one of those teams we're giving away. So now this is the list I'm gonna use for later officially. Alright now we're gonna go ten times on the names and teams. One, two, three, four, five, six, seven, eight, nine, ten. Robert Dundee, Aaron, one, two, three, four, five, six, seven, eight, nine, ten. Colts down of the jets. Alright now again obviously stick to what you got. No trading. Alright now let's quickly do the pack. Robinson and then Joe Burrell straight fire. Let's leave those two up. So nothing crazy out of here. But it's not the reason why you bought in. You bought in for the giveaway right? Oh so it's Slickroll. Four and a four eighth hard way. Eight times. Here we go. One, two, three, four, five, six, seven, eight. Boom. Eight, eight. So remember top ten. Get in a team guys and Jesper Sam was right unfortunately. Alan you squeezed in a ten.  A, A, Scott, Mojo and Steve.  At the top I remember what a deal. Now only one customer in the top ten with their free spot. Everybody else that paid straight up obviously. Alright so Steve you got the Colorado Buffalo's, Georgia Bulldogs for A, A we're on Scott with a Notre Dame, Friday Irish, Penn State, Midland Lions for Alan, A, A around with the Tennessee Volunteers, Texas Longhorns for A, A around Chris with Duke. I think they're blue devils. I want to say they're blue devils. I'm assuming that's Miami Hurricanes for Chris and Duke, Chow and Florida Gators and the Wisconsin Badgers for Alan. So there you go guys. Thank you guys so much. Now again obviously everybody still gets an entry into the giveaway so let me just copy and paste the names and I'll paste you guys into the giveaway. Oh sorry that's not what I wanted. Alright so it's done to ten. Get to it there, like that, and that's one, two, three, four, five. There you go. And then this last one I think it's just going to be flux. Let's be back to number two. Appreciate it guys. Thank you.