Kumois in the Big Data Analytics Industry

Kumo

kumo

The concepts of g, and have now become the new normal in our present COVID induced downturn. social distancin contactless approach safety first We have already started adapting by finding new ways to interact with new technologies or by enhancing the existing ones. One such technology which is already popular and has made a comeback is the . During this COVID time, QRCode helps in ensuring social distancing and business operations go hand in hand. QRCode QR Codes can easily be scanned from a short distance. QR Codes have a minimum radius of 25 cm (10 in), which then scales up depending on the size of the QR Code. QR Codes are the perfect tool for use cases such as: Contactless mobile payments Bitcoin and cryptocurrency payments. Convenient WiFi access. Contactless ticketing. Contactless entrance to buildings, offices, or events. General information sharing for social media links, PDFs, videos, images, and website links. And the list goes… In this article we will be creating a Kumologica API that will generate and host a QRCode which when read using a QRCode scanner will direct you to a simple floor access form. Kumologica is a free low-code development tool to build serverless integrations. You can learn more about Kumologica in this medium article or subscribe to our YouTube channel for the latest videos. Use case In this use case an employee approaches the service desk for an access to a floor. The service desk operator checks the employee id and generates a QRCode which is displayed on a screen in front of the service desk operator. The employee needs to scan the QRCode which will direct to the URL for a floor access form which needs to be completed by the employee and submitted to get the clearance. Prerequisite Kumologica designer installed in your machine. https://kumologica.com/download.html Implementation Open Kumologica Designer, click the button and choose . Home Create New Kumologica Project Enter name (for example QRCodeDemo), select directory for project and switch into Source From Existing Flow … 2. Copy and Paste the following flow 3. press Button. Create You should be seeing two flows as given below on the designer canvas. First flow is for generating the QRCode Second flow is for responding with the access form. When the code is imported the first flow will be having an empty node which is a missing QRCode node. QRCode node is not part of the core nodes and hence will not be showing up in the palette. In order to add the QRCode to the flow, go to option on the palette section which will open a page for you to select the nodes of your interest . In this case we will select the node and click . Post installation designer needs to be restarted. Once restarted the first flow will be completed with QRCode node. Add more nodes QRCode Install Now if you open the settings of the QRCode node , you should see the following : https://<<gatewayid>>.xyz.amazon.com/test/access QR String The URL provided as the QR string is the endpoint URL of the second flow. The second flow endpoint URL can be known only once the flow is deployed to your AWS account. After the first deployment update the QR string in QRCode node with the second endpoint URL and redeploy. This is done because both the QRCode generation API and Access From API are in the same project. Note: Testing Invoke the First flow (QRCode generation API) endpoint URL on your browser to see the QRCode displayed.QRCode displayed on the screen 2. Use your QRCode scanner in your mobile phone to scan the code. 3. Click on the second flow (Access From API) endpoint URL popped up to see the employee JSON data. Conclusion This article presented how easy Kumologica Designer to build an API to generate and host a QRCode. Remember is totally free to and use. Go ahead and give it a try, we would love to hear your feedback. Kumologica download In the meantime, if there is any specific use case that you would like to see in a future article please let us know in the comments section.

Building QR Code Generator in Serverless World using Kumologica

mapping is a key element in integration. Most of the prominent integration tools provide different capabilities for data mapping. In this article I thought of sharing on how data mapping can be achieved in Kumologica. Kumologica uses JSONata as the base for data mapping. JSONata is a Lightweight query and transformation language for data. It supports complex queries expression which can be achieved with minimal syntax and has a location path semantics of Xpath 3.1. Data JSON For more info on JSONata you may refer the following link. https://jsonata.org/ To understand the Kumologica data mapping lets create an which will accept a JSON data as an API request which will be mapped to a new JSON data structure and provide as API response. The request JSON data will also be sorted in descending order when mapping is done. Kumologica data mapper node will achieve both mapping and sorting function together. API Now let’s get started. Pre-requisite Downloaded and Installed Kumologica Designer.(Required) https://kumologica.com/download.html Walkthrough on tutorial.(Optional). https://medium.com/@kumologica Developing an API Open the Kumologica Designer and project. create a new Drag and drop node from the palette on to the designer canvas. EventListener Configure the node with the Source as . as and as EventListener API Gateway Verb POST path /employee. Drag and drop an node from the palette on to the designer. datamapper On the section of the datamapper node paste the following Sample Input [
       { : , : },
          { : , : },
       { : , : },
          { : , : }

    ] "Candidate_Name" "Arun" "Candidate_Age" "25" "Candidate_Name" "John" "Candidate_Age" "32" "Candidate_Name" "Sarah" "Candidate_Age" "22" "Candidate_Name" "Harry" "Candidate_Age" "28" On the section of the datamapper node paste the following mapping expression. Mapping $sort(
       $map(
           msg,
           (
               function($l){
                   { : $l.Candidate_Name, : $l.Candidate_Age
       
                   }

    }
           )

    ),
       function($l,$r){$l.Emp_Age < $r.Emp_Age}
    ) "EmpName" "Emp_Age" On the Result section (Bottom) of the datamapper node you could see the resultant output that would be generated by the node after running the mapping expression against the sample input JSON data provided. Following is the resultant output . [
        { : , : },
        { : , : },
        { : , : },
        { : , : }
    ] "EmpName" "Jhon" "Emp_Age" "32" "EmpName" "Harry" "Emp_Age" "28" "EmpName" "Arun" "Emp_Age" "25" "EmpName" "Sarah" "Emp_Age" "22" As you can see the output shown has mapped the JSON request to a new structure and also sorted the records in descending order. Result 6. Now Drag and drop the node on to the canvas. EventListener End 7. Open the settings for and configure the payload as and leave rest of the values as default. EventListener End msg.payload 8. Wire the EventListener node to Datamapper node and Datamapper node to EventListenerEnd node. The final flow would like as given below. Deployment Now let’s deploy the flow to AWS lambda. Before deployment select the AWS profile under the cloud tab. Also ensure that your AWS profile selected is having associated. If proper role is not associated the flow will fail to deploy to AWS. Kumologica Designer Role Now Click . Deploy Once the deployment is completed you will get the endpoint url to invoke the service from any of your favourite client. The url will look like as shown below. https://t7cq0rkdk.execute-api.ap-southeast-1.amazonaws.com/test/employee Conclusion The mapping mechanism in Kumologica was easy and follows function chaining which allows to build sophisticated query expressions with minimal syntax. The designer gives quick preview without deploying and running the whole flow which saves a lot of time. JSONata expression language looks more or less similar to Mulesoft Dataweave. So If you are familiar with dataweave then JSONata would require zero or minimal learning curve. If you want to try out this application you can clone the repo using the below given Github url or import the flow json into your project workspace in designer. https://github.com/ab73863/kumologica-datamapper References https://kumologica.com/ https://jsonata.org/

How To Do Data Mapping in Kumologica

254 Stories To Learn About Amazon

335 Stories To Learn About Serverless

277 Stories To Learn About Microservices

130 Stories To Learn About Low Code

Let's learn about via these 57 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Json /Learn Repo 1. Creating Developer-Friendly Data Services with Apache Cassandra Using data APIs and advanced data modeling will make it far easier for JSON-oriented developers to connect to Apache Cassandra. 2. Introducing Yet Another JWT Debugger Introducing a new JWT Debugger App - our way to contribute back to the developer community. The app is available on the web and as Desktop apps for Windows, Linux, and Mac. With JWT Debugger App we immediately highlight what really matters to you as a developer without any shenanigans. It is a very simple tool but we hope it will improve the developer experience. 3. How to Use .populate() With mongoDB How to use populate() method with mongoDB to itch data across referenced collections 4. How to Query Deeply Nested JSON Data in PSQL Recently I had to write a script, which should’ve changed some JSON data structure in a PSQL database. Here are some tricks I learned along the way. 5. How to Export Your HackerNoon Stories as a PDF The HackerNoon team is back at it with a new feature! You can now export your stories to a PDF or a JSON file. 6. The History of JSON and the People That Created It Douglas Crockford and Chip Morngingstar created the data exchange format that is now known as JSON. 7. Automate Simply With Webhook Automate anything with Webhook, a simple tool for your toolbox 8. With JSONB Data in PostgreSQL, You Can Get the Best of Both Worlds JSONB in PostgreSQL offers the best of relational and NoSQL. Here are two techniques that we use at AppLand to make JSONB data even more effective. 9. Custom Log Processor Integration – Fluentd vs Vector.dev By writing your tool, you gain more customization options, versatility, and other enhanced capabilities that can enrich your processing. 10. Defining Types: Using allOf in Swagger JSON Image by Devanath from Pixabay 11. 7 Kotlin Libraries Every Developer Should Know The most common Kotlin libraries that are used for cross-platform and Android development, testing, and documentation are considered in the article. 12. So, How are Observability and Monitoring Different, Actually? The similarities and differences between monitoring and observability and how to pair the two strategies 13. Ruby: How to read/write JSON File In Ruby read and write JSON file to hash can be achieved using File Handling. 14. How To Extract Key or Value From Hash in Ruby on Rails When I was recently working in one of the client projects, I had to
communicate with external MariaDB server to store records from
React/Rails app, that means I would get ActiveRecord hash from our app
which I had to convert to pure SQL query and send it to an external
server for storing. 15. A Quick Primer on Data Scraping Suppose you want to get large amounts of information from a website as quickly as possible. How can this be done? 16. Streamlining Form Validation with JSON Schema for Front-End and Back-End Efficiently validate forms with a single set of rules for both front-end and back-end using JSON Schema. 17. The Complete Microservice Tutorial: Introduction [Part 1] Project Link: https://github.com/Joker666/microservice-demo/ 18. An Interview with the Creator of JSON Douglas Crockford We had a nice chat with Douglas Crockford, a well-known American computer programmer who was involved in the development of the JavaScript language. 19. JSON Web Token: How To Secure Your Data With JWT A JSON Web Token (JWT) is an open standard (RFC 7519) that defines a way for securely transferring information between two parties. It can be used for an authentication system. As this information is digitally signed, it can be verified and trusted. 20. 8 Tips for Solving The JSON RPC Error In Metamask The most problem users come across is the internal JSON RPC error in Metamask. After a detailed search, I found there are numerous different solutions to it. 21. Hybrid Data Models: How To Have Your JSON Cake and Eat MariaDB Too By adding support for JSON, MariaDB provides the ultimate schema flexibility without sacrificing transactions or data integrity. 22. How To Do Data Mapping in Kumologica Data mapping is a key element in integration. Most of the prominent integration tools provide different capabilities for data mapping. In this article I thought of sharing on how data mapping can be achieved in Kumologica. Kumologica uses JSONata as the base for data mapping. JSONata is a Lightweight query and transformation language for JSON data. It supports complex queries expression which can be achieved with minimal syntax and has a location path semantics of Xpath 3.1. 23. How to Encode and Decode JSON with Scala-Circe Getting started with encoding and decoding JSON using Scala-Circe. 24. When Json Serializations Go Horribly Wrong From DateTime serialization issues, to the fact that serializing DateTime.MinValue causes a crash, because UTC time is less than minimal allowed DateTime value. 25. Fetch Data From a JSON File With a React & Redux App This guide explains how to use React and Redux to fetch data from JSON files in an easy and lightweight method through a real example and well explained steps. 26. Parsing JSON in C with Vely framework Parse JSON in C language with Vely framework 27. How to Use Data Science to Find the Best Seat in the Cinema (Part I) From the most popular seats to the most popular viewing times, we wanted to find out more about the movie trends in Singapore . So we created PopcornData — a website to get a glimpse of Singapore’s Movie trends — by scraping data, finding interesting insights, and visualizing them. 28. How To Query JSON in Couchbase via Collections and Scopes This week I’m attending the 3-day Couchbase Connect event and will be reporting on some of the topics that I find most interesting. 29. Extracting Information From Hash in Ruby on Rails When I was recently working in one of the client project, I had to communicate with external mariadb server to store records from react/rails app, that means I would get activerecord hash from our app which I had to convert to pure sql query and send it to external server for storing. If you have worked with sql queries previously then you must know that keys and values must be separated for insert operations like 30. Logging Vs. Monitoring: Best Practices for Logging [Part 2] Logging and Monitoring… this I tell you, brother. You can't have one without the other. 31. The Best Java Libraries that Every Developer Should Know This article describes Java libraries like Apache Commons, Guava, Log4j2, Logback, SLF4J, JUnit, TestNG, Mockito, Hamcrest, AssertJ, Jackson, Gson, JAXB, StAX 32. A Quick Guide to JSON Web Token [JWT] JSON Web Token (JWT) is an open standard (RFC 7519) for securely transmitting information between parties as JSON objects. 33. Improve Network API Performance For Your Apps Network APIs are everywhere in the modern world. If the data is the lifeblood of modern tech, then APIs are essentially vessels that carry it around distributed 34. Back-end Data and API Prototyping with Faker.js and JSON-Server Introduction 35. How to use Recursion to Traverse JSON Objects and the Filesystem I work primarily on application-level programs, so I tend to not use recursion very often. However, every now and then I need a function that is best solved recursively. It is important to be able to recognize when a problem is best solved recursively, and to be able to write it when the time comes. 36. Breaking Down MSON Template Queries Template Queries are dynamic templates constructed with MongoDB-style operators, which allow you to customize MSON components with less code. 37. Tree - AST Crushes JSON, XML, YAML, TOML, etc XML, JSON, TOML, YAML are bad relative to Tree data format 38. Debugging Gson, Moshi and Jackson JSON Frameworks in Production Parsing is a major source of production failures. Some are easy to track but some are insidious. Here's how you can debug them on the fly! 39. µjson — A minimal JSON parser and transformer in Go µjson is a minimal JSON parser and transformer that works on unstructured (and trusted) JSON. It works by parsing input and calling the given callback function when encountering each item. 40. Build Data-Driven Web App Without Backend During the last couple of decades websites' functionally has increased dramatically - from simple landing pages serving simple static ads to complex progressive web apps whose functionality close to native applications including user authorization, location tracking, bluetooth handling, and offline mode. 41. Using JSON Mapping to Work with APIs of Various Image Services Using JSON Mapping to Work with APIs of Various Image Services. This approach is applicable to any other services that are similar in type of returned results. 42. Coding 101: Who is Json? A.) Jason Voorhees (Friday the 13th) 43. Five Important Tips For Working With JWTs Nowadays, JSON Web Tokens are the most common way of proving identity information to APIs. The concepts behind JWTs are also quite easy to understand, and it takes only a couple of minutes to have the most basic authentication running. 44. Using Rainforest API With WordPress [A How-To Guide] Rainforest API is an alternative to the Amazon Product API provided to Amazon Associates. If you are thiking of building shopping comparisson sites with Amazon links and you don’t have an API key, or if you want to build a lot of pages very quickly without limitation, using Rainforest API is the answer. If you are serious about earning money as an Amazon Affiliate and want to make thousands of pages, do yourself a favor and start the project off right. 45. Writing Your First REST API: A How-to Guide As part of my Borum Jot project, I made a Web API for all my front-end platforms to create, retrieve, update, and delete (CRUD) data on the database. In this article, I'll discuss what exactly an API is and how I made my own Web API. 46. Today I Learned: Dealing with JSON DateTime when Unmarshal in Golang. Beware the date-time standard!! 47. What is SQL? And Where is it Used? What is SQL? Databases use it, query APIs use it, even energetic big data dashboards use it! Where did it come from and how are developers, DBAs, and app using. 48. Introducing Jdict Module in Python Providing attribute access to Python dictionary entries 49. 8 Most Popular Weather APIs for 2020 The climate is a hot issue and weather forecasting technology is suddenly cool. The urgent need for innovative weather and environmental conditions forecasting solutions is obvious whether you believe in climate change or not. 50. How to Win the Velo Writing Contest: A Detailed Cheat Sheet In order to display a non-static website - you'll need to have a data source and connect it with Velo. For example, you can create some sort of "fake API". 51. 3 Best Ways To Import JSON To Google Sheets [Ultimate Guide] 3 ways to pull JSON data into a Google Spreadsheet 52. How To Create a Simple Autocomplete Field And Connect it With Elasticsearch Autocomplete is a feature to predict the rest of a word a user is typing. It is an important feature to implement that can improve the user’s experience of your product. 53. How I'm Engineering a Versioned Database Storage Engine for Byte-Addressable NVM Feel free to contribute on GitHub 💚 54. JSON Web Auth Using Angular 8 and NodeJS The article is about interfacing an Angular 8 Project with a secure backend API. The Backend will be running on Node.JS. The security that will underlay the interfacing will be JSON Web Tokens. 55. Go: Handling JSON in MySQL 56. Querying Complex JSON Objects With SQL How many times have you run into a situation where you wish you could do a SQL join without getting duplicate rows back? What if we could get a list "column" returned instead? HarperDB’s ARRAY() function enables just that. In this post we’re going to take a look at a basic example of people with addresses and phone numbers. 57. Document Databases vs Relational Databases (Podcast Transcript) Amy Tom talks to Eric Bishard and Arun Vijayaraghavan about the differences between a Document Database and a Relational Database. Thank you for checking out the 57 most read stories about Json on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

57 Stories To Learn About Json

193 Stories To Learn About AWS Lambda

Hey Hackers! I’m Amy Pravin Shah and I’m a psychiatrist and writer. First of all, a huge thank you to the HackerNoon community and staff for nominating me for a 2021 Noonies award! I’ve been nominated in the following categories please do check out these award pages and vote: HackerNoon Contributor of the Year - MENTAL-HEALTH: https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-mental-health As someone in the healthcare industry, I believe that the most exciting technology of the present is social media because of its ability to be both positive and negative for us all emotionally. Learn more about my thoughts and opinions on mental health and my journey in the tech industry via the interview below. 1. What do you do and why do you do it? (tell us your story) I currently work as a psychiatrist for adults. I am specializing in working with older adults and addiction medicine. I have been doing this work for over ten years in some capacity. I find helping people to be one of the most rewarding parts of my occupation. I notice that there is such stigma to talking about mental health problems for many people. It can be amazing to be a person that can help with mental health disorders because you can provide relief to people who might not have thought there was any help available to them. The work is emotional at times and you can put a lot of yourself into it, so it’s important to have other outlets and hobbies. Taking time out for self-care is important and everyone deserves that. One of the things I work on outside my job is community service. I am a member of a friendly neighborhood hackerspace. At the hackerspace, I have given free or low-cost workshops and classes about various topics. Some of the things I have taught are how to make bath bombs and how to make lip balm. I have also taught these classes for free at the local library. In addition to that, I am a volunteer curator of a startup newsletter for . I help as a volunteer organizer for in my local area. Techstars Startup Weekend 2. Tell us more about the things you create / write / manage / build! During my volunteer work as a Blogging Fellow at Hacker Noon, I have written some articles either focusing on mental health or including a mental health spin. These have widely circulated. It has been amazing to be a Blogging Fellow at Hacker Noon! I have learned a lot from the experience and I would recommend it to others. Some of the articles I have written include: Accessibility in the Workplace: "Companies Don't Give Me a Fair Shot," says a Blind Developer Why Senior Citizens Should Learn to Code The Effects of Energy Drinks on The Body: Cons of Caffeine Consumption In 2017, I have also started a charitable initiative called the #100Hats Project in which I and others make hats and other winter clothing items for people in need. We donate these items to local organizations for distribution for winter. An example of one of the hats is shown below. I make the items out of crochet and donated yarn. Every hat is unique. I don’t make them with a set pattern, but they turn out well in most cases.  It has been very touching to know that making a simple hat can really help someone. This project is still going on through the years and we were able to donate hats throughout the Pandemic. 3. How did you end up on your current career path? Do you like it? For various reasons, I think this path picked me. During the last 20 years, American attitudes about mental health treatment and self-care have greatly changed. And that is just my personal observation, but what I have noticed is that is becoming more permissible to talk about mental health topics. For example, college campuses are putting more money towards mental health services for students. That includes medical treatment and psychotherapy, but also self-care practices like meditation and yoga. I really like this direction, but it can be radically improved still in so many ways. 4. What tech are you most excited or passionate about right now and why? Social media has the opportunity, even the calling, to bring people together all around the world. I am most excited by its potential to be helpful even as I see the increasing negatives of social media. Social media companies have been able to connect parts of the world and make it easier to keep in touch with others for less expense and time. Family members who would only be able to call each other once a month before because of the expense now can freely video chat every evening. Relationships that previously might be strained by distance and finances are maintained more easily. All that being said, the clear negative impact of some aspects of social media cannot be ignored. The ambivalence about its nature makes social media fascinating to me. 5. What tech are you most worried about right now and why? I am most concerned about security and privacy and how it will develop in the future. To that end, I have taken classes on information security and talked to cybersecurity professionals. I have educated myself on the basics of cybersecurity knowing that my knowledge really only skims the surface. I have taken steps to learn more about computer technology and cybersecurity in the last 5 years. I have found that to be one of the better investments of my time and energy because though I do not understand everything, my learning has helped me understand what I do not know. 6. If we gave you 10 million dollars to invest in something today, what would you invest in and why? If I had 10 million dollars to invest in something today, I think the best use of my investment would be to donate the money to . Khan Academy During the Pandemic, I took some of the math classes on the Khan Academy website and I found them to be extremely high-quality ways to learn. Khan Academy levels the playing field for young people who might not have money for additional classes outside of school, like Kumon or other tutoring. I was surprised to see that Khan Academy also has an Association of Medical Colleges-backed curriculum for studying for the medical school admission test, MCAT! 7. What are you currently learning? Right now, I am studying psychiatry daily and reviewing my field’s current knowledge. But in addition to that, I am also planning to review math. I have used various resources for my study of math, but Khan Academy was the most entertaining and thorough. 8. What’s the best advice you’ve ever given someone? I think the best advice I have ever given anyone was to “Be Yourself and Believe in Yourself”  I am not sure if people have always found this to be the most helpful advice for . them As a psychiatrist, you never know what people really catch on to when you talk to them. What do people remember about the advice I gave? I am never really sure. 9. What’s the best advice you’ve ever received? The best teacher in any subject is your personal experience. That includes the mistakes you make. I think in my past people might have given me plenty of advice on a variety of topics, but the best teacher was my personal experience. About HackerNoon’s 2021 Noonie Awards The annual Noonie Awards celebrate the best and brightest of the tech industry, bringing together all who are making the Internet and the world of tech what it is today. Please be sure to , nominate, and vote for the people and companies who you think are making the biggest impact on the tech industry today. check out our award categories The 2021 Noonies are sponsored by: , , and . Thank you so much to these sponsors who are helping us celebrate the accomplishments of all our nominees. bybit Dottech Domains Avast

Breaking the Stigma of Mental Health with Amy Pravin Shah, Noonies Nominee 2021

Ichimoku Cloud is a versatile technical analysis indicator rapidly rising in popularity among traders. Also known as Ichimoku Kinko Hyo which translates to ‘one look equilibrium chart’ - this is exactly what Ichimoku strives to be. An ‘all-in-one’ style indicator that incorporates multiple elements from different technical analysis tools. At just a glance traders are able to ascertain future support and resistance, momentum and trend direction. While the abundance of lines and shaded areas can leave traders new to Ichimoku Cloud confused and even a little intimidated. Once you single out and understand each separate element, mastering Ichimoku Cloud is easier than it looks. We take you through each component of Ichimoku Cloud and the strategies you can use to improve your trading. What is Ichimoku Cloud? Ichimoku Cloud essentially combines three indicators into a single chart so traders have multiple tests on price action. It can be used to analyse any tradeable asset, from stocks, options, futures etc. A key component of the indicator is the ‘cloud’ that acts as support and resistance. There are also five lines that represent different time intervals. These elements allow traders to decipher the strength of trend direction and gauge momentum which in turn can signal trading opportunities and optimal entry/exit points. There’s also an interesting origin story. Unlike most technical indicators that are formulated by statisticians and mathematicians, Ichimoku Cloud was developed by a Tokyo newspaperman Goichi Hosoda. By analysing the centuries-old Japanese rice markets, Hosoda came to the conclusion that markets are ‘human’ - they are social creatures in that they are both the product of and have an influence on human behaviour. Like people, markets can swing wildly but it is unusual to stay at extremes, there is always a return to equilibrium. After decades of endless computations and revisions, Hosoda and his team concluded that what is now known as Ichimoku Cloud, was the best chart representation of market reality and publicly shared the technique in 1968. The indicator is now heavily used by many Japanese trading rooms and rapidly growing in popularity worldwide. How does Ichimoku Cloud work? If you’ve never looked at an Ichimoku Cloud chart before, you’d be forgiven for thinking ‘what a mess’. The abundance of squiggly lines can create a cluttered chart that makes many traders dislike. However, for those that subsist, understanding Ichimoku Cloud is simpler than it looks. But to fully comprehend Ichimoku Cloud, we need to break it down into its individual components and build it back up. The Components of Ichimoku Cloud Tenkan Sen The Conversion Line -  The short-term line that is the most responsive to price action. Represents the average of the high and low divided by two for the previous nine time periods. Kijun Sen The Base Line - The long-term line that is less responsive to price action. Represents the average of the high and low divided by two for the previous 26 time periods. While the Tenkan Sen and Kijun Sen lines may look eerily similar to your traditional moving averages, there is one big distinction. They use the highest high and the lowest low over the time period instead of the closing price which is used in most moving average calculations. By taking the average of the price extremes and dividing by two, Tenkan Sen and Kijun Sen result in a line that more closely resembles support and resistance. While the difference in calculation may be subtle, the result is widely different even if the same number of periods are used. Chikou Span Lagging Span - Represents the closing price for the previous 26 time periods. Traders can easily gauge market sentiment and the intensity of trend direction by comparing current price movements with that of the previous movements. Senkou A Leading Span A - The future indicator for 26 time periods. It is calculated by taking the middle of Tenkan Sen and Kinjun Sen for the previous 26 time periods and plotting the values 26 time periods ahead of current price action. Senkou B Leading Span B - The second future or leading indicator. It is calculated by taking the average of the highest high and lowest low of the previous 52 time periods and plotting the values 26 time periods ahead of current price action. Kumo The Ichimoku Cloud - Is the shaded area between the Senkou A and Senkou B lines. It represents current and historical price action and it’s colour will change depending on the movements of the Senkou A and Senkou B lines. When Senkou A crosses above Senkou B, this signals an upward trend and the Kumo will be colored green (there may be a slight variation in color depending on your trading platform). Where Senkou A falls below Senkou B, this signals a downward trend and Kumo will typically be colored red. What is the best time frame for Ichimoku? Ichimoku Cloud can be used in any market and in any timeframe. What is the best timeframe largely depends on your trading style. Scalpers and day traders may benefit from using shorter time periods from anywhere form one min charts to six hour charts. Although it doesn't hurt to zoom out to larger time frames to understand the bigger market picture. Longer term swing traders or investors are better suited to looking at daily or weekly charts. As with all trading strategies, your trades will still need to be managed, so pick a time frame that you can reasonably monitor and follow. How can you use Ichimoku cloud for day trading? Trend Direction A simple way to see trend is to look at the colour of the cloud. When the cloud is shaded green, there is a positive trend. When the cloud is shaded red, there is a negative trend. You can also factor in the size of the cloud to determine the strength of the trend. Where the cloud between Senkou A and Senkou B is small, this signifies that the trend is weak. A large cloud can be interpreted as a strong trend. Another way to use the cloud is to look at current price action. Where current price is above the cloud, this further supports the strength of the bullish trend. Below the cloud, a bearish trend. And when the current price is located within the cloud, the market is consolidating and range bound. Furthermore, where the five lines are running parallel to each other, it is safe to assume that the market will continue in that direction. Support & Resistance Upper band of the prevailing cloud can act as an area of resistance while the lower band acts as an area of support. Where the current price breaks above the cloud this is akin to a breakout and below the cloud for a breakdown. This signifies a deep shift in market sentiment and the regular rules of supply and demand are in play. Crossovers The Tenkan Sen and Kijun Sen lines can be used in a similar fashion to moving average crossovers. When Tenkan Sen crosses above Kijun Sen, this is considered a bullish signal. When Tenkan Sen crosses below Kijun Sen, this is considered a bearish signal. Many traders will enter and exit trades based on these crossover signals Putting It All Together Where price action is trading above the cloud and this is coupled with a bullish crossover of Tenkan Sen and Kijun Sen, this indicates a powerful buy signal. The probability of the trade is even further increased where price action is above the Chiku Span. Many traders will hold this position until Tenkan Sen crosses back below Kijun Sen. Is Ichimoku cloud strategy good? The greatest strength of Ichimoku Cloud is that in just one glance, a trader can interpret a wealth of information. But don't forget that all indicators have weaknesses. The best way to combat these weaknesses is to understand, expect, and plan for them. We go through Ichimoku Cloud’s limitations below. Limitations Historical Data While Ichimoku Cloud provides future leading lines these data points are calculated using historical data and are just projected into the future. There is nothing inherently predictive in the formula. It is still a lagging indicator. It is still playing catch-up to current price action. Where there are quick market reversals, the indicator may give off late signals. As always, backup Ichimoku Cloud signals with your own price action analysis. Trading Range Ichimoku Cloud is in its prime element in trending markets. However, when the markets are range bound and no clear trend exists, Ichimoku Cloud has a tendency to give off false signals. As explained above, it's still a lagging indicator so it may be slow to react to quick market reversals. As always, be cautious. Don’t just jump into a trade unplanned. Wait for the price correction. Test support and resistance levels and then enter a trade once there is a confirmation of trend. Even though you may miss out on the start of the trend, you will be less susceptible to false signals. Busy Charts Ichimoku Cloud works best for the visual traders. However it is easy to get lost and feel overwhelmed by all lines and chaos. One thing we preach is to maintain clarity in your analysis, so one way to combat the ‘busyness’ of the chart is to focus on a set of lines at a time. Until of course, you become comfortable enough to glance at them and clearly understand their meaning. Most if not all charting software will allow traders to hide certain lines. If you’re looking specifically at the cloud and you find that the Tenkan Sen and Kijun Sen lines are distracting, get rid of them. Only include elements that are going to influence your trading decisions. Where Ichimoku Cloud was once relegated to the list of ‘exotic indicators’, traders are now turning to Ichimoku Cloud as a sophisticated and effective way of evaluating the markets. Be mindful not to get too carried away. While Ichimoku Cloud can be a fantastic asset, no indicator should be used on its own. No matter how great a technical analysis indicator is, there is no replacement for watching price action and key areas of support and resistance.

Ichimoku Cloud Explained

Crypto enthusiasts often face a conundrum when turning to cryptocurrency as a viable investment. An eternal question remains: To invest and forget, or to trade the notorious price swings for even more gains? shows a simple buy-and-hold strategy for Bitcoin the past six years would have yielded 1690% for investors. However, utilizing a long-short trading approach would have seen gains of over 2,500% at a minimum, or even 5,000% using a well-tweaked strategy. Academic research Needless to say, it’s easy to be allured by the prospect of trading cryptocurrencies with those statistics in mind. But as easy as it may look, trading is not for the faint-hearted, with even seasoned investors succumbing to sudden market movements and price drops — look no further than the “ in March this year which caused two very popular crypto funds, Cryptolab Capital and Adamant Capital, to fold overnight with their millions of dollars falling victim to the market. Black Thursday” event This is where a strategy becomes important. One could either take a fundamental approach towards the crypto market — looking at macro factors, correlations with the US equity indices, or Bitcoin miner-led market movements — or choose a technical strategy, meaning indicators and chart-reading to calculate probabilities of a market movement and place trades accordingly. A popular indicator is the Ichimoku Kinko Hyo, a so-called all-in-one trading system that was developed in the 1940s by Goichi Hosoda, a Japanese journalist. Its name translates to “equilibrium at a glance,” and allows traders to trade breakouts, identify support and resistance levels, and generally assess the group think behavior of the markets. “The Ichimoku Cloud has always been a comprehensive framework: it allows trend analysis, tells the trader when to stay out of the market completely, and provides clear signals with a high probability of success.” states Chairman Zen of MachinaTrader. “This combination allows the trader to act dispassionately and mechanistically in the market. And this also means that Ichimoku Cloud is ideal for creating an automated strategy." Here’s what the Ichimoku Kinko Hyo, or colloquially, the Ichimoku Cloud looks like: Daunted? Don’t be. The tool is easy to read and determine once each individual component is singled out. First, is the “Kumo” - the cloud: the big green and red zones in the chart, below, where price always seems to go and either establish a new trend or get rejected. Markets are usually directionless and sideways when trading in the Cloud, meaning traders usually sit out and avoid placing trades while price is inside the Cloud. The cloud is simply a calculation of the support and resistance equilibriums using previous price action of the past “x” periods. Generally, the longer the period “x” is, the less probability of a “false” signal (although absolutely nothing is absolutely certain in markets). Playing the Cloud is easy. Traders can go “long,” or bet on rising prices when price breaks above the Cloud, and “short,” or bet on decline if price breaks below the cloud. A green Cloud is a “support” area, one where the price is expected to see buying and subsequently continue an upward trend. Vice-versa for when the Cloud is red. In addition, the thicker the Cloud, the stronger its support/resistance. A thin Cloud is an indication that the market is unlikely to see much support/resistance when price approaches the Cloud. Hence, the thickness of the Cloud is an important consideration when trading the “Kumo”. Next is the Tenkan sen, or the “conversion line” as singled out (blue) in the chart below: The Tenkan sen calculates the average of the highest high and lowest low during the last “x” number of candles. Some traders feel that the Tenkan sen provides better points of support/resistance than a simple moving average. Here is a 21-period Tenkan sen and a 21-period simple moving average for comparison: Notice that when the Tenkan sen is flat, it indicates a trendless condition in the market during the last 21 candles. While an angled Tenkan sen shows the direction and relative momentum of price action during the last 21 candles. A moving average doesn’t respond in this manner, making the Tenkan sen a line that acts as both support/resistance and a directional indicator. Third, is the Kijun sen or the “base” line, which, similar to the Tenkan sen, is calculated from the highest high and lowest low averaged over the previous “x” number of candles. Why a duplicate? Because this component is calculated over a longer period than the Tenkan sen, thereby providing a second “slower” Tenkan sen for comparison, as well as, a longer timeframe indicator of support/resistance. A possible strategy — used by some traders — is to consider the Tenkan sen and Kijun sen in a similar manner as the popular “moving average crossover”. Trade long when the faster Tenkan sen crosses above the slower Kijun sen (indicating an uptrend) and trade short when the crossover occurs to the downside. Lastly, the Chikou span, or the “lagging” line. This one calculates the current price and shifts that back by “x” number of candles. The main signal given is “trend confirmation”. Once the Chikou span moves from above to below (or below to above) the Ichimoku Cloud it signals trend direction. In the chart, above, the lagging line breaks above the Kumo, providing a strong buy signal for traders. The move is said to be a “bullish” Kumo breakout, while the opposite of this - the Chikou breaking below the Cloud - is usually a “bearish” signal where traders might sell and offload positions. With these four components explained, the Ichimoku Cloud should now be more easy to understand and read. And here is why it is considered to be a “single, integrated strategy”: One glance at the chart provides the trader with an indication of trend (or lack thereof), support and resistance, potential breakouts, and similar past “setups”, making this freely available indicator a Swiss Army knife in any trader’s toolbox. Automate the Cloud? Ichimoku Cloud trading signals don’t necessarily require manual execution. Services like MachinaTrader, an online trading platform that allows traders to automate their strategies. A trader can code the logic of their strategy via the dashboard, or pick from already-coded algorithms (including Ichimoku Cloud), backtest its efficiency before deploying it live in the market. Traders can activate a terminal on the MachinaTrader dashboard and code their own complex trading strategies, define backtesting parameters, and much more. Traders can monetize successful strategies by making them available to other platform users. In addition, it allows you to choose to have a machine learning bot oversee select strategies. Bob, the trading robot can recommend improvements, leading to a more profitable trading strategy with suggestions of optimal parameters, for various market conditions, for indicators like the Ichimoku Cloud. The future of finance is here indeed.

Crypto-Trading with Ichimoku Cloud...And The World of Virtual Trading Assistants

Quick Overview Ethereum is one of the largest and most popular blockchain platforms, which utilizes a Turing-complete scripting language, Solidity, to enable developers to write smart contracts and decentralized applications (Dapps). Source: coinmarketcap.com Ether (ETH) is the native asset used within the Ethereum network and is used as a source of payment to miners and developers for the computational costs (gas) needed to verify blocks, execute smart contracts, and develop Dapps. Technology Evaluation The EVM (Ethereum Virtual Machine) is a quirky but solid technological development. Additionally, Ethereum developers are solving real, business use cases with the technology, e.g. Golem and Augur. Furthermore, Vitalik and team appear to be solid leaders and highly focused. However, there are a few issues to note: — The Solidity programming language is not great for smart contracts, which is a big use case for Ethereum. This comment is a bit old, but still . Essentially, Solidity was a good first attempt but the community has realized problems with it. However, the same thing happened with and look how popular that is! That being said, there are attempts to make better languages that integrate with the EVM, such as , but it is still in beta. Solidity relevant JavaScript Vyper — Ethereum is vulnerable to spam attacks to push up the price of gas (EOS rumors) or even just getting clogged by popular apps or ICOs (remember CryptoKitties?). There are several projects trying to solve this problem via sharding and off-chain transactions, e.g. and Plasma. Unfortunately, these solutions are hard to get right and time consuming (these projects have been trying for awhile), thus no guarantee of success. Spam Attacks Raiden Network Code Evaluation Source: Quality Analysis of Cryptocurrency Codebases (QUACC) How to read the stats: — is one measure of how complex the individual methods/functions that make up a program are. A program with a or a program with will be difficult to test its quality. you can’t really say that a “low cyclomatic complexity” is good, but rather a really high number is bad. For example, it is similar to asking “what’s a good weight for a human?” Well, maybe 100 pounds is good for a child, but it would be dangerously skinny for a 6'2 tall man, but everybody would agree that if a person weighed 650 pounds, it would be suboptimal. . The uncompiled version is a measure of the complexity of the code itself, whereas the compiled version is a measure of the code after it been converted into an executable (machine code); Cyclomatic complexity high average cyclomatic complexity high maximum cyclomatic complexity Note: Generally, a value lower than 10 is considered to be low-risk both metrics are beneath 10, thus low-risk. — These are problems with the code that automatic static analysis has determined to be , such as a possible null pointer dereference or opening a resource and never closing it. the number of errors in code and the number of warnings are both sometimes prone to false positives, thus the metric could be overstated. Number of errors in code relatively severe Note: At time of testing, Ethereum had 69 errors in its code. — These are problems with the code that our that our static analysis has determined to be . Looking through the Ethereum warnings, these are mostly from forcibly casting an unsigned (i.e., non-negative only) integer into a signed integer, superfluous code, or not initializing a variable in a constructor. Number of warnings in code less severe At time of testing, Ethereum had 219 warnings in its code. Network Analysis Currently, Ethereum is one of the biggest and most reputable platforms for Dapp developers. Despite the aforementioned shortcomings, coupled with a multitude of well-funded competitors, Ethereum still enjoys strong network effects from its first mover status. Given the switching cost between platforms for developers is notable but not insurmountable, the first-mover advantage is increasingly important to continued growth; especially in terms of Ethereum’s “killer app,” i.e. capital raising. Ethereum’s continued domination of the capital raising or ICO market indicates that developers and companies currently have faith in the ecosystem despite some of these ICOs being used by competitors, e.g. EOS and NEO, to raise large sums of money to compete with Ethereum. Regulatory Analysis Recently, the SEC announced that Bitcoin and Ether are not securities which means instead of being subject to Securities and Exchange rules like stocks or futures, they are commodities. This is positive because it removes itself from the potential ire of the SEC (for the time being) and paves the way for Ethereum derivatives creation (additional source of demand). However, the announcement did not drive up the price of ETH. Possible interpretations of this lack of price action could be that the government attitudes toward the overall cryptocurrency space is still sufficiently murky. Community Review Github Repositories There are 161 repositories on with over 40 contributors, and supports multiple languages and APIs for developers. Furthermore, Ethereum is doing a great job in lowering the barrier for adoption to attract new users. For example, in addition to Solidity, they also provide Python and Javascript plug-ins, so that different language developers can integrate and build upon the technology with few obstacles. Github Contributors The community has grown a lot since inception with a multitude of decentralized actors, contributing to several different repositories. For example, the frequency of updating popular repositories can be less 10 minutes per update. Another example is Go-Ethereum, which has 104 pull requests and 5,986 forks, but Solidity is by far the most popular repository with more than 3000 commits, constant pull requests and active users. Miners & Computing Nodes The number of miners and Ethereum nodes indicates the continued popularity of the platform. There are approximately 150 active miners via ethereumpool.co. Plus, the computing nodes in the picture below show the global nature of Ethereum’s network. Furthermore, miners are profit driven actors, thus the continued and growing pool of miners suggests sustained optimism regarding ETH’s future price appreciation in relation to their mining expenses. Source: Peter Szilagyi, Google Earth, ethernodes.org Technical Analysis EMA with Long Term Trends On the 1D chart, the bullish EMA cross (not shown), or , has not occurred which confirms ETH’s negative, 2018 linear price trend (Pearson’s R Correlation between time and price of 0.75). At time of writing, price is near the mean with a downward trend lag. If the existing linear trend holds, then a continued decline to a key support and 0.236 fibonacci retracement level (black and dashed line) of ~$350 is likely in the near term. If price fails to hold ~$350, then continued decreases to $200 or below become probable. Golden Cross This level is critical to hold. Source: tradingview.com Additionally, the (VFI) has consistently remained beneath 0, which may indicate stalling buying volume, which supports the “meandering” downward price theory. The VFI interpretation is a value above 0 is bullish and below 0 is bearish, with divergences between price and oscillator being high probability signals. volume flow indicator Ichimoku Clouds The uses four metrics to determine if a trend exists; the current price in relation to the Cloud, the color of the Cloud (red for bearish, green for bullish), the Tenkan (T) and Kijun (K) cross, Lagging Span (Chikou), and Senkou Span (A & B). Ichimoku Cloud The status of the current Cloud metrics on the 1D charts for both singled settings (10/30/60/30) and doubled settings (20/60/120/30) are bearish; price is below the Cloud, Cloud is bearish, the TK cross is bearish, and the Lagging Span is below the Cloud and price. A traditional long entry would occur with a price break above the Cloud, known as a , with price holding above the Cloud. From there, the trader would use either the Tenkan, Kijun, or Senkou A as their trailing stop. Kumo breakout Using (10/30/60/30), price will need to break above flat Senkou B level of ~$564 for a Kumo breakout and new bull trend. Using (20/60/120/30), price will need to break above flat Senkou B level of ~$603 for a Kumo breakout. If successful, price targets would be $617 and $700. The support levels to watch on the downside are $430, $380, and $350 (critical to hold). Source: tradingview.com Competitive Analysis ADA (Cardano) Cardano is the one competitor that should make Vitalik and team concerned, especially from a technology perspective. Cardano is much more focused on correctness, can do formally verified smart contracts, and has some smart minds working on it. However, despite the relatively high market cap, there still does not appear to be much activity happening from developer perspective. Ethereum is still the king of the hill regarding developer usage, which means that this could become a case of a technically superior product not getting market support. One potential reason is that Cardano uses a ML-derived programming language called Plutus, which looks an awful lot like Haskell, and to get people to even look at a language like that is difficult! However, Cardano’s management seems to have noticed the developer shortfall and has taken proactive steps to address it. For example, the CEO of Cardano has recently went on a global marketing campaign to promote the network and technology, and attract developers and users. In short, Cardano has the long term potential to be quite disruptive to Ethereum, but will need to make demonstrable strides in language friendliness and attracting developers to build “real” applications on the network. Price has consistently declined since early May with a Pearson’s R Correlation between time and price of 0.85. Using the slow, Ichimoku Cloud settings (20/60/120/30), price will need to breach $0.25 to re-ignite a new positive uptrend. M_ore fundamental and technical analysis of ADA_ here . Source: tradingview.com TRX (Tron) Tron is a blockchain technology originally built on Ethereum, but currently transitioning to its own mainnet. Tron is a highly controversial digital asset for reasons ranging from its gregarious CEO to allegedly . In theory, TRX will support higher transaction speeds and better scalability. The goal of Tron includes free and uncontrolled data storage on the distributed network, ICOs, and digital assets supported by their decentralized infrastructure. If TRX can execute upon a fraction of their promises, they could become a legitimate threat to Ethereum, but that is not in the near term. copying large portions of their code from other projects without attribution Price has consistently declined since early May with a Pearson’s R Correlation between time and price of 0.95. Using the slow, Ichimoku Cloud settings (20/60/120/30), price will need to breach $0.06 to re-ignite a new positive uptrend. M_ore fundamental and technical analysis of TRX_ here . Source: tradingview.com EOS EOS is one of the most hyped blockchain projects in recent time and similar to Tronix insofar as EOS is currently transitioning to its own mainnet and looking to attack Ethereum’s shortcomings. EOS will theoretically offer better scalability, faster transaction speeds, and will be free. However, instead of being decentralized like Ethereum, EOS will be more centralized with only 21 validator nodes, which is typically a trade-off for better speed and performance. Additionally, unlike Ethereum which requires users to pay “gas” for the transactions, EOS is free with the fees being paid by an inflationary issuance schedule. This “free” service coupled with notable investors in the project make EOS a phenom that has skyrocketed to the top 5 in market cap and top 4 in trading volume with little more than a white paper. It would appear that the EOS community has faith in the lead developer, Dan Larimer, who has a proven and solid track record with his programming capability widely recognized. Price has consistently declined since early May with a Pearson’s R Correlation between time and price of 0.91. Using the slow, Ichimoku Cloud settings (20/60/120/30), price will need to breach $13.60 — $14.40 to re-ignite a positive uptrend. Source: tradingview.com NEO NEO is known as the Chinese Ethereum and proposes a “Smart Economy” which includes digital assets, digital identity, and smart contracts. NEO’s Byzantine Consensus or Delegated Byzantine Fault Tolerance is the protocol NEO uses to verify the block creation by selecting delegates (bookkeepers) to reach consensus and approve a new block. In theory, Byzantine Consensus provides faster transaction speeds by cutting down the number of involved nodes, which also reduces costs. Additionally, NEO’s smart contracts support popular programming languages which may attract developers. Price has consistently declined since early May with a Pearson’s R Correlation between time and price of 0.91. Using the slow, Ichimoku Cloud settings (20/60/120/30), price will need to breach $60 to re-ignite a positive uptrend. M_ore fundamental and technical analysis of NEO_ here . Source: tradingview.com ZIL (Zilliqa) Zilliqa is a young blockchain project that is gaining attention and users as of late. The token is based on Ethereum’s technology, but it is independent of the Ethereum network given it was founded precisely to address some of Ethereum’s shortcomings such as scalability and increasing gas prices. Furthermore, Zilliqa smart contracts are written using their proprietary programming language, , and uses sharding. Given the aforementioned improvements, Zilliqa has apparently been quietly attracting Ethereum developers over to its platform. ERC-20 Scilla ZIL’s price has slowly been moving upward since the end of June 2018, despite the current bear market (black arrow). However, the negative, linear price trend using Pearson’s R Correlation between time and price is 0.88. Furthermore, price has bounced off the upper 2 standard deviation level (black circle), and has begun decreasing again. Using the slow, Ichimoku Cloud settings (20/60/120/30), price will need to breach $0.135 to re-ignite a positive uptrend. Source: tradingview.com BTC (Bitcoin) Despite both digital assets and networks solving different problems, they still compete in subtle ways given Bitcoin has the first-mover advantage, highest trading volume, price, and market cap. This market dominance coupled with some work happening in attracting better developer support for Bitcoin smart contracts could pose serious future risks to Ethereum, i.e. bitcoin maximalism. Similar to how bimetallism eventually fell in favor of the gold standard, blockchains seem to have a natural monopoly, thus we may see all non-Bitcoin cryptocurrencies fade away or at least be reduced dramatically. offers an in-depth look at the nuances of network effects within blockchain systems, which offers support and rebukes to the bitcoin maximalism theory . Kyle Samani here Price has consistently declined since early May with a Pearson’s R Correlation between time and price of 0.82. Using the slow, Ichimoku Cloud settings (20/60/120/30), price will need to breach $7900 — $8700 to re-ignite a positive uptrend. Source: tradingview.com Conclusion Currently, the bearish trend of 2018 still exists for ETH, thus it would behove investors to wait for a strong buy signal before entering a long position. However, in the longer term, we still believe ETH has a strong probability of being very successful given the community is healthy and active, has “killer app” leadership, and its scalability problems are being addressed. Despite the ever-growing threat by competing networks, we do believe Ethereum has several moats of protections including first mover advantage, strong community, and growing network effects. However, in the world of open-source competition, Ethereum will need to continually out-innovate their competition while retaining quality developers in order to maintain market leadership. One interesting point of note is that Ether should technically be used to buy gas on the network, which means that it may eventually be treated as a commodity instead of a currency, thus fluctuate based on developer demand. If this logic unfolds, then ETH’s price may plateau or even decline in the future since ETH’s store of value premise will be diminished, which will increase velocity. Special thanks to and Szuyu Chu for their contributions! William Laboon Disclaimer: This analysis has been designed for informational and educational purposes only. Readers are advised to conduct their own independent research into individual assets before making a purchase decision.

Ethereum's (ETH ) In-Depth Analysis

Nota bene: This is not financial advice and I am not a financial advisor. I’m sure there’re plenty of licenced professionals in your jurisdiction. Speak to them, not strangers on the internet with Hyman Minsky avatars. — A few general remarks before I go through the list: I) You don’t to use indicators to trade successfully. There’re plenty of traders who chart ‘naked’ i.e. without any indicators and do so with tremendous success. That being said, indicators can certainly be helpful and supplement your analysis. need II) Less is more. Namely, in my experience the more successful traders are not necessarily those who know how to use and interpret the greatest number of indicators, but rather those who have truly mastered a couple of indicators and use them to their full potential. III) I would strongly advise against using any indicator as a crutch. If you’re unable to interpret price action and unable to find trade setups solely by looking at an empty candlestick chart, then you have no business using indicators which automate and map support and resistance for you. I sincerely believe that one is better served by learning price action first and using that as the basis of one’s technical analysis, and then adding the ‘fluff’ indicators afterwards to see if there’s any confluence. IV) Just because I didn’t include an indicator in this list doesn’t mean I think it’s a bad indicator. Without further ado, let’s get into my favourite indicators, why they’re my top picks, and how I use them. — 3. Ichimoku Cloud What it is Ichimoku Kinko Hyo, also known as the Ichimoku Cloud or simply as Cloud, is a ‘complete’ indicator which uses moving average data to show the trend of an instrument, the strength of that trend, dynamic areas of present and future support and resistance, and more. Why I like it I see the Ichimoku Cloud as a more powerful and complete version of MAs (Moving Averages) or EMAs (Exponential Moving Averages). While (E)MAs also show MA crosses and indicate possible areas of support and resistance, Cloud does a lot more. It shows more areas of support and resistance (Senkou Span A & and Senkou Span B), it can show targets for long/short positions in the case of an edge-to-edge trade (flat Kumo or just opposite edge of the Kumo Cloud), it gives breakout areas (Kumo breakout points), it can help you determine how much conviction to have in a position (TK cross relative to Cloud), and more. The list goes on. In short, it is like using (E)MAs on steroids. How I use it The first thing I do is adjust the inputs on the Ichimoku Cloud to 20/60/120/30 to better reflect the fact that cryptocurrency markets operate 24/7 (see below). If I am looking at an altcoin which doesn’t have enough price history for the Ichimoku Cloud to work properly, I will reduce the settings to 10/30/60/30. Adjusted settings for using the Ichimoku Cloud. I use the Ichimoku Cloud in two main ways. I) To see the direction of the macro trend. This is done by looking at price in relation to the Kumo Cloud on a high time frame like the 1D. At a glance, I can tell where the current trend is, how strong it is, and when it started/the last one reversed. To find confluence with areas of support and resistance that I already have on my chart. This is the most common way in which I use the Ichimoku Cloud. Namely, I will draw my lines/levels of support and resistance first, and then overlay the Ichimoku Cloud to see if any of my lines have confluence with the indicator e.g. a support line which lines up with the Kijun line will make me more confident about that support level holding, and the same applies if I were to have a resistance line which lines up with the top boundary of the Kumo Cloud i.e. the bullish Kumo breakout point. If you have what any of that means but you’re interested in learning more about how to interpret and use this indicator, I kindly refer you to no idea my YouTube video/lesson on the Ichimoku Cloud. — 2. Relative Strength Index (RSI) What it is The Relative Strength Index (henceforth ‘RSI’) is a momentum oscillator which measures the speed and change of price movements. In much more simple terms, this indicator is used by traders to get an idea of whether an instrument is overbought or oversold (though, as you will see, it is capable of doing much more than that). Why I like it I like the RSI because it appears to be a very straightforward indicator, but when used to its full potential, can convey a lot of information and generate very powerful and diverse trading signals. In other words, I like it because many cryptocurrency traders misuse it and only rely on overbought/oversold signals, not realising what else it is capable of. Another reason I like the RSI is because it can be used as a sort of all-in-one oscillator, thus relieving me of the need to add 3 different oscillators and clutter my chart just to get an idea of the strength of a trend. How I use it I use the RSI in two main ways. I) To identify market tops/bottoms and trend reversals by looking out for divergences. A divergence is when the highs/lows made by the RSI are different from the highs/lows made by the price. This is the most powerful and central use case of the RSI. Period. The founder of the RSI called divergences the “central characteristic of the Relative Strength Index” for a reason. Standard bearish divergence on BTC/USD marking the market top. II) To fine tune my entries and exits. Once I’ve identified a setup, I sometimes elect to base my exact entry and exit on a smaller time frame like the 15M or 30M. , once/if price reaches that zone, I will seek to enter when the RSI is oversold across lower time frames and I will seek to exit when the RSI is overbought across lower time frames. Having identified my ideal entry and exit zones in advance . Please read (II) carefully. I do not take trades purely based on RSI being overbought/oversold. What I am saying is once I’ve already identified a zone where I want to enter/exit, and price is in that zone, I will use the RSI on low time frames to enter around my predetermined range when an instrument is oversold and to exit around my predetermined range when an instrument is overbought If you’re unsure how to identify an RSI divergence, or want an in-depth explanation of this indicator more generally, I kindly refer you to my YouTube video/lesson on the RSI. — 1. Volume What it is Volume is an indicator which depicts the amount that an instrument was traded over a period of time. Why I like it I like the default volume indicator (shown below) as well as other volume-based indicators such as the VPVR, the OBV, et cetera. This is because volume, in my opinion, is the strongest tool that traders have to confirm their interpretation of price action. In other words, volume supplements traders’ interpretation of price action. The next section will make this more clear. Default volume indicator on Tradingview. How I use it I use volume in two main ways. I) To confirm the validity of a chart pattern. Chart patterns typically have a volume profile which, if met, renders the completion and success of the pattern more likely (see example below). If I’m trading a chart pattern, I check the volume profile to gauge how confident I should be that it will play out as expected. Excerpt from John J Murphy’s book ‘Technical Analysis of the Financial Markets. II) To ascertain whether the violation of a major support/resistance area is likely to stick. Put simply: if price is trying to break through a major resistance level, I want to see a sharp increase in buy volume to support such a push. At the same time, if price is trying to violate a major support, I want to see a corresponding increase in the sell volume at the same time. Thus, I use volume to ascertain whether there’re enough buyers/sellers in the market to break a major resistance/support/ — Thanks for reading my article and I hope you’ve found it helpful. I post charts and other technical analysis insights on my and in my . Twitter Telegram channel My full-length technical analysis lessons, which are all free, can be found here. — Until next time! Cred

My Top 3 Favourite Indicators for Technical Analysis of Cryptocurrencies

TOKYO, Japan, July 5th, 2024/Chainwire/--$DOP, the token of the Data Ownership Protocol(DOP) will be available for trading in 7 major cryptocurrency exchanges such as Bybit, Kucoin, HTX, Bitfinex, Gate.io, MEXC and WhiteBIT starting July 5th, 2024. The Data Ownership Protocol clarified that on Bybit, the token will be listed as $DOP1 to avoid confusion with an existing fiat coin. On KuCoin, Gate.io, Bitfinex, MEXC, and WhiteBIT, the token will be listed as $DOP. DOP launched its mainnet six weeks ago, following a highly successful testnet phase that saw participation from 2.67 million users. In the last six months, the DOP ecosystem has grown significantly. Over 1 million DOP wallets have been opened, and assets worth more than 10 million USD have been encrypted using the protocol, showcasing the increasing demand for user-centric data ownership solutions. “$DOP is the token of Data Ownership Protocol, a project that seeks to balance transparency and privacy on the blockchain, putting users in control of their data. The listing of our utility token marks a significant milestone and enhances our vision for the future of data ownership. We are thankful to our supporters and community who have believed in our mission from day one”, said Kohji Hirokado, co-founder of DOP and ex-core member at Cardano. Currently, the $DOP token serves two primary functions within the ecosystem: facilitating fee payments and granting community rewards. The usage of the protocol's data ownership features incurs fees payable in DOP tokens, creating a native demand for the token. This system incentivizes DOP token retention while implementing a deflationary mechanism through fee burning. It also rewards network supporters via staking distributions. To date, 210,000,000 DOP tokens have been allocated for staking rewards, with over 1.1 billion DOP tokens already staked. The listing announcement comes on the heels of Bybit's positioning as the world's second-largest cryptocurrency exchange by trading volume, surpassing Coinbase and trailing only Binance. Bybit's ascent has been remarkable, with its market share doubling from 8% to 16% since October 2023. To communicate the plans for the second half of 2024, DOP released a new roadmap following Q1's success. The plan introduces a $5 million developer grant program, expands protocol capabilities, and plans deployment on EVM-compatible chains beyond Ethereum, reducing gas fees and reaching more users. Moreover, the new developer SDK will enable developers to easily integrate and build dApps within the DOP ecosystem. Other objectives aim to enhance user security for managing NFTs by expanding functionality beyond ERC-20 tokens to include encryption and decryption of NFTs. About Data Ownership Protocol (DOP) The Data Ownership Protocol enables users to own their data. In crypto, financial data such as holdings, balances, and transaction history is publicly available on the blockchain. The mission of DOP technology is to let users decide what to share and with whom. DOP aims to empower individuals and businesses with more control over their data through selective transparency, utilizing zero-knowledge cryptography and other advanced technologies. For more information, users can visit Data Ownership Protocol's: Official Website | Twitter | Linkedin Data Ownership Protocol is the source of this content. This Press Release is for informational purposes only. The information does not constitute investment advice or an offer to invest. Contact Marketing DOP marketing@dop.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. TOKYO, Japan, July 5th, 2024/Chainwire/-- $DOP, the token of the Data Ownership Protocol(DOP) will be available for trading in 7 major cryptocurrency exchanges such as Bybit, Kucoin, HTX, Bitfinex, Gate.io, MEXC and WhiteBIT starting July 5th, 2024. TOKYO, Japan, July 5th, 2024/Chainwire/-- The Data Ownership Protocol clarified that on Bybit, the token will be listed as $DOP1 to avoid confusion with an existing fiat coin. On KuCoin, Gate.io, Bitfinex, MEXC, and WhiteBIT, the token will be listed as $DOP. DOP launched its mainnet six weeks ago, following a highly successful testnet phase that saw participation from 2.67 million users. In the last six months, the DOP ecosystem has grown significantly. Over 1 million DOP wallets have been opened, and assets worth more than 10 million USD have been encrypted using the protocol, showcasing the increasing demand for user-centric data ownership solutions. “$DOP is the token of Data Ownership Protocol, a project that seeks to balance transparency and privacy on the blockchain, putting users in control of their data. The listing of our utility token marks a significant milestone and enhances our vision for the future of data ownership. We are thankful to our supporters and community who have believed in our mission from day one”, said Kohji Hirokado, co-founder of DOP and ex-core member at Cardano. “$DOP is the token of Data Ownership Protocol, a project that seeks to balance transparency and privacy on the blockchain, putting users in control of their data. The listing of our utility token marks a significant milestone and enhances our vision for the future of data ownership. We are thankful to our supporters and community who have believed in our mission from day one”, said Kohji Hirokado, co-founder of DOP and ex-core member at Cardano. Currently, the $DOP token serves two primary functions within the ecosystem: facilitating fee payments and granting community rewards. The usage of the protocol's data ownership features incurs fees payable in DOP tokens, creating a native demand for the token. This system incentivizes DOP token retention while implementing a deflationary mechanism through fee burning. It also rewards network supporters via staking distributions. To date, 210,000,000 DOP tokens have been allocated for staking rewards, with over 1.1 billion DOP tokens already staked. The listing announcement comes on the heels of Bybit's positioning as the world's second-largest cryptocurrency exchange by trading volume, surpassing Coinbase and trailing only Binance. Bybit's ascent has been remarkable, with its market share doubling from 8% to 16% since October 2023. To communicate the plans for the second half of 2024, DOP released a new roadmap following Q1's success. The plan introduces a $5 million developer grant program, expands protocol capabilities, and plans deployment on EVM-compatible chains beyond Ethereum, reducing gas fees and reaching more users. Moreover, the new developer SDK will enable developers to easily integrate and build dApps within the DOP ecosystem. Other objectives aim to enhance user security for managing NFTs by expanding functionality beyond ERC-20 tokens to include encryption and decryption of NFTs. About Data Ownership Protocol (DOP) The Data Ownership Protocol enables users to own their data. In crypto, financial data such as holdings, balances, and transaction history is publicly available on the blockchain. The mission of DOP technology is to let users decide what to share and with whom. DOP aims to empower individuals and businesses with more control over their data through selective transparency, utilizing zero-knowledge cryptography and other advanced technologies. For more information, users can visit Data Ownership Protocol's: Official Website | Twitter | Linkedin Official Website Official Website Twitter Twitter Linkedin Linkedin Data Ownership Protocol is the source of this content. This Press Release is for informational purposes only. The information does not constitute investment advice or an offer to invest. Contact Marketing DOP marketing@dop.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

$DOP Announces Listing On 7 Exchanges Including BYBIT, Kucoin, Gate.io, And Bitfinex

Authors:
(1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work;
(2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work;
(3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro
Motivation
Related Work
Dataset
Experiments and Evaluation
Results and Analysis
Conclusion and Future Work
Limitations and References
A. Appendix 7. Conclusion and Future Work In this paper, we have reported a first-of-its-kind work on automatic plot and script generation from prompts. Automatic evaluation, human rating using the Likert scale, and qualitative observations by professional scriptwriters from our industry partner (a large and well-reputed media platform)- all vindicate the power of our rich dataset and GPT3 in script generation. We hope our work will help television show writers, game show writers, and so on. There are several future directions: (i) the imbalance in the Bollywood plot dataset needs to be rectified; (ii) there is a lot of variation in Indian script because of multilingualism, which needs addressing; (iii) the most obvious weakness of GPT-3 is not being able to handle factual data and numbers, causing hallucination and preventing the automatic generation of documentaries and biographies. Detection and resolution of hallucination is anyway a growing need for language models. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Authors: Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro Motivation Related Work Dataset Experiments and Evaluation Results and Analysis Conclusion and Future Work Limitations and References A. Appendix Abstract and Intro Abstract and Intro Motivation Motivation Related Work Related Work Dataset Dataset Experiments and Evaluation Experiments and Evaluation Results and Analysis Results and Analysis Conclusion and Future Work Conclusion and Future Work Limitations and References Limitations and References A. Appendix A. Appendix 7. Conclusion and Future Work In this paper, we have reported a first-of-its-kind work on automatic plot and script generation from prompts. Automatic evaluation, human rating using the Likert scale, and qualitative observations by professional scriptwriters from our industry partner (a large and well-reputed media platform)- all vindicate the power of our rich dataset and GPT3 in script generation. We hope our work will help television show writers, game show writers, and so on. There are several future directions: (i) the imbalance in the Bollywood plot dataset needs to be rectified; (ii) there is a lot of variation in Indian script because of multilingualism, which needs addressing; (iii) the most obvious weakness of GPT-3 is not being able to handle factual data and numbers, causing hallucination and preventing the automatic generation of documentaries and biographies. Detection and resolution of hallucination is anyway a growing need for language models. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

"Kurosawa": A Script Writer's Assistant: Conclusion and Future Work

"কুরোসাওয়া": একজন স্ক্রিপ্ট লেখকের সহকারী: উপসংহার এবং ভবিষ্যতের কাজ

„Kurosawa“: Assistent eines Drehbuchautors: Fazit und zukünftige Arbeit

"Kurosawa": asistente de guionista: conclusión y trabajo futuro

"Kurosawa" : un assistant de scénariste : conclusion et travaux futurs

"कुरोसावा": एक पटकथा लेखक का सहायक: निष्कर्ष और भविष्य का कार्य

「黒澤」脚本家アシスタントのまとめと今後の仕事

『쿠로사와』 각본가 어시스턴트: 결론과 앞으로의 작업

"Kurosawa": Assistente de Roteiro: Conclusão e Trabalho Futuro

«Куросава»: помощник сценариста: заключение и дальнейшая работа

"Kurosawa": Bir Senaryo Yazarının Asistanı: Sonuç ve Gelecekteki Çalışmalar

"Kurosawa": Trợ lý biên kịch: Kết luận và công việc trong tương lai

“黑泽明”：编剧助理：结论和未来工作

Authors:
(1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work;
(2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work;
(3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro
Motivation
Related Work
Dataset
Experiments and Evaluation
Results and Analysis
Conclusion and Future Work
Limitations and References
A. Appendix A. Appendix A.1. Ethics Consideration We have taken all the scripts from IMDB and IMSDb databases. The website has a disclaimer regarding using its scripts for research, which can be found at this link https://imsdb.com/ disclaimer.html. We have used the scripts fairly and without copyright violation. A.2. Annotator Profiles We required the help of external annotators in two cases: (i) Manually Annotating the Scripts and (ii) Creating scenes and their descriptions from the scripts. For the first task, we took the help of 10 annotators. Their ages ranged from 21-28, and all were Asian. They were given detailed guidelines with examples for annotating. There were also periodic sessions to confirm their understanding and solve their doubts and mistakes. For the second task, we took the help of two annotators. Both of them are Asian females aged between 21-23. Both of them were given detailed guidelines for the scene-writing task. A few data points were picked randomly and checked to find out and correct conceptual mistakes. The annotators had bachelors and masters degree in STEM and Arts. A.3. Evaluation Metrics The evaluation metrics are described below: • Perplexity (PPL): Perplexity is one of the most common metrics for evaluating language models. They are computed as exponential of entropy. The smaller the value of the PPL, the greater the fluency of the generated text. • BLEU: BiLingual Evaluation Understudy is a common metric in many NLP tasks, especially in the field of Machine Translation. It measures the overlap between the generated output and gold standard data. Although this metric does not consider the model’s creativity, we can deduce the difference between the candidate text and the reference text using BLEU. The higher the BLEU measure, the better it is. • ROUGE: Recall-Oriented Understudy for Gisting Evaluation is typically used for evaluating automatic summarization. In our case, it measures the longest overlapping sequence between the generated and original plots. The higher the ROUGE measure, the better it is. • N-grams: We measure the redundancy and diversity of the movie plots by computing the repetition and distinction n-gram scores. A.4. Screenplay Structure A movie script or a screenplay has a different format than a story. A script is a group of scenes. Each of these scenes consists of a few major components, which are discussed below: Scene Headings/Sluglines- This component describes the when and where of the scene. It can be thought of as the first shot that a camera takes of a new scene. For example, INT. - RESTAURANT - NIGHT indicates that the scene starts inside a restaurant at night. Sluglines are normally written in capital letters and are left-aligned. Character Names- they are mentioned every time a character is going to utter a dialogue. The name of each character is mentioned in uppercase and is centre aligned. Dialogues- dialogues are the lines that the characters say. They appear right after the character name in a script and are centrally aligned. Action Lines- action lines describe almost everything about a scene. They can be described as the narration of each script. Action lines can be present after either dialogues or sluglines and are left-aligned. Transitions- a transition marks the change from one scene to the next. They also depict how a scene is ended. For example, DISSOLVE, FADE, and CUT are different keywords used to indicate a transition. They are usually in upper case and are right-aligned. Figure 8 shows an example of the screenplay elements. A.5. Story Templates Over time various templates have been developed that help to create stories. One of the most famous templates is the 3-act structure (Field, 1979). This structure divides a story into a setup, confrontation, and resolution. In this work, we have used the 4-act structure which we now describe in detail. Act 1- This is the opening/introduction act. It describes the protagonist’s character and briefly introduces the movie’s theme. The act ends with the start of a new journey for the protagonist. Act 2A- Due to the vast span of Act 2, it can be divided into two acts. This act usually contains the start of a love story. It also entertains the audience as the protagonist tries to adapt to their new journey. The act ends as the movie’s midpoint, one of the film’s critical moments, with either a very positive or negative scene. Act 2B- This act usually contains the protagonist’s downfall. The villain or antagonist starts to gain an advantage, and the protagonist loses something or someone significant. The act ends with the protagonist realizing their new mission after reaching rock bottom. Act 3— The protagonist has realized the change required in them and sets out to defeat the antagonist in a thrilling finale. The movie then ends by displaying a welcome change in the protagonist that was lacking in the beginning. A.6. Fine-Tuning GPT-3 GPT-3 was deemed publicly available last year by OpenAI (Brown et al., 2020). Its best model has 175B parameters, which is much more than GPT2’s 2.9B parameters. We have fine-tuned multiple plot generation models with GPT-3 along with a scene generation model. The multiple combinations of plot generation models are short or long prompts and with or without genres. The GPT-3 model and hyperparameters remain the same for all the above combinations. We have fine-tuned the GPT-3 Curie model for four epochs. For generating text, GPT-3 offers various hyperparameters to tune and get closer to our desired results. For testing, we set other hyperparameters as follows: the temperature as 0.7, top-p as 1, frequency penalty as 0.1, presence penalty as 0.1, and max tokens as 900. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Authors: Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro Motivation Related Work Dataset Experiments and Evaluation Results and Analysis Conclusion and Future Work Limitations and References A. Appendix Abstract and Intro Abstract and Intro Motivation Motivation Related Work Related Work Dataset Dataset Experiments and Evaluation Experiments and Evaluation Results and Analysis Results and Analysis Conclusion and Future Work Conclusion and Future Work Limitations and References Limitations and References A. Appendix A. Appendix A. Appendix A.1. Ethics Consideration We have taken all the scripts from IMDB and IMSDb databases. The website has a disclaimer regarding using its scripts for research, which can be found at this link https://imsdb.com/ disclaimer.html. We have used the scripts fairly and without copyright violation. A.2. Annotator Profiles We required the help of external annotators in two cases: (i) Manually Annotating the Scripts and (ii) Creating scenes and their descriptions from the scripts. For the first task, we took the help of 10 annotators. Their ages ranged from 21-28, and all were Asian. They were given detailed guidelines with examples for annotating. There were also periodic sessions to confirm their understanding and solve their doubts and mistakes. For the second task, we took the help of two annotators. Both of them are Asian females aged between 21-23. Both of them were given detailed guidelines for the scene-writing task. A few data points were picked randomly and checked to find out and correct conceptual mistakes. The annotators had bachelors and masters degree in STEM and Arts. A.3. Evaluation Metrics The evaluation metrics are described below: • Perplexity (PPL): Perplexity is one of the most common metrics for evaluating language models. They are computed as exponential of entropy. The smaller the value of the PPL, the greater the fluency of the generated text. • Perplexity (PPL): • BLEU: B i L ingual E valuation U nderstudy is a common metric in many NLP tasks, especially in the field of Machine Translation. It measures the overlap between the generated output and gold standard data. Although this metric does not consider the model’s creativity, we can deduce the difference between the candidate text and the reference text using BLEU. The higher the BLEU measure, the better it is. • BLEU: B L E U • ROUGE: R ecall- O riented U nderstudy for G isting E valuation is typically used for evaluating automatic summarization. In our case, it measures the longest overlapping sequence between the generated and original plots. The higher the ROUGE measure, the better it is. • ROUGE: R O U G E • N-grams: We measure the redundancy and diversity of the movie plots by computing the repetition and distinction n-gram scores. • N-grams: A.4. Screenplay Structure A movie script or a screenplay has a different format than a story. A script is a group of scenes. Each of these scenes consists of a few major components, which are discussed below: Scene Headings/Sluglines- This component describes the when and where of the scene. It can be thought of as the first shot that a camera takes of a new scene. For example, INT. - RESTAURANT - NIGHT indicates that the scene starts inside a restaurant at night. Sluglines are normally written in capital letters and are left-aligned. Scene Headings/Sluglines- Character Names - they are mentioned every time a character is going to utter a dialogue. The name of each character is mentioned in uppercase and is centre aligned. Character Names Dialogues - dialogues are the lines that the characters say. They appear right after the character name in a script and are centrally aligned. Dialogues Action Lines - action lines describe almost everything about a scene. They can be described as the narration of each script. Action lines can be present after either dialogues or sluglines and are left-aligned. Action Lines Transitions - a transition marks the change from one scene to the next. They also depict how a scene is ended. For example, DISSOLVE, FADE, and CUT are different keywords used to indicate a transition. They are usually in upper case and are right-aligned. Transitions Figure 8 shows an example of the screenplay elements. A.5. Story Templates Over time various templates have been developed that help to create stories. One of the most famous templates is the 3-act structure (Field, 1979). This structure divides a story into a setup, confrontation, and resolution. In this work, we have used the 4-act structure which we now describe in detail. Act 1 - This is the opening/introduction act. It describes the protagonist’s character and briefly introduces the movie’s theme. The act ends with the start of a new journey for the protagonist. Act 1 Act 2A - Due to the vast span of Act 2, it can be divided into two acts. This act usually contains the start of a love story. It also entertains the audience as the protagonist tries to adapt to their new journey. The act ends as the movie’s midpoint, one of the film’s critical moments, with either a very positive or negative scene. Act 2A Act 2B - This act usually contains the protagonist’s downfall. The villain or antagonist starts to gain an advantage, and the protagonist loses something or someone significant. The act ends with the protagonist realizing their new mission after reaching rock bottom. Act 2B Act 3 — The protagonist has realized the change required in them and sets out to defeat the antagonist in a thrilling finale. The movie then ends by displaying a welcome change in the protagonist that was lacking in the beginning. Act 3 A.6. Fine-Tuning GPT-3 GPT-3 was deemed publicly available last year by OpenAI (Brown et al., 2020). Its best model has 175B parameters, which is much more than GPT2’s 2.9B parameters. We have fine-tuned multiple plot generation models with GPT-3 along with a scene generation model. The multiple combinations of plot generation models are short or long prompts and with or without genres. The GPT-3 model and hyperparameters remain the same for all the above combinations. We have fine-tuned the GPT-3 Curie model for four epochs. For generating text, GPT-3 offers various hyperparameters to tune and get closer to our desired results. For testing, we set other hyperparameters as follows: the temperature as 0.7, top-p as 1, frequency penalty as 0.1, presence penalty as 0.1, and max tokens as 900. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

"Kurosawa": A Script Writer's Assistant: Appendix

"কুরোসাওয়া": একজন স্ক্রিপ্ট রাইটার সহকারী: পরিশিষ্ট

„Kurosawa“: Der Assistent eines Drehbuchautors: Anhang

"Kurosawa": asistente del guionista: Apéndice

"Kurosawa" : l'assistant d'un scénariste : annexe

"कुरोसावा": एक पटकथा लेखक का सहायक: परिशिष्ट

「黒澤」脚本家アシスタント 付録

"쿠로사와": 각본가의 어시스턴트: 부록

"Kurosawa": Assistente de Roteiro: Apêndice

«Куросава»: Помощник сценариста: Приложение

"Kurosawa": Bir Senaryo Yazarının Asistanı: Ek

"Kurosawa": Trợ lý biên kịch: Phụ lục

“黑泽明”：编剧助理：附录

Authors:
(1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work;
(2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work;
(3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro
Motivation
Related Work
Dataset
Experiments and Evaluation
Results and Analysis
Conclusion and Future Work
Limitations and References
A. Appendix Abstract Storytelling is the lifeline of the entertainment industry- movies, TV shows, and stand-up comedies, all need stories. A good and gripping script is the lifeline of storytelling and demands creativity and resource investment. Good scriptwriters are rare to find and often work under severe time pressure. Consequently, entertainment media are actively looking for automation. In this paper, we present an AIbased script-writing workbench called KUROSAWA which addresses the tasks of plot generation and script generation. Plot generation aims to generate a coherent and creative plot (600–800 words) given a prompt (15–40 words). Script generation, on the other hand, generates a scene (200–500 words) in a screenplay format from a brief description (15–40 words). Kurosawa needs data to train. We use a 4-act structure of storytelling to annotate the plot dataset manually. We create a dataset of 1000 manually annotated plots and their corresponding prompts/storylines and a gold-standard dataset of 1000 scenes with four main elements — scene headings, action lines, dialogues, and character names — tagged individually. We fine-tune GPT-3 with the above datasets to generate plots and scenes. These plots and scenes are first evaluated and then used by the scriptwriters of a large and famous media platform ErosNow[1]. We release the annotated datasets and the models trained on these datasets as a working benchmark for automatic movie plot and script generation. 1. Introduction Movies are one of the most popular sources of entertainment for people worldwide and can be a strong medium for education and social awareness. The impact and influence of film industries can be gauged from the fact that Hollywood movies invest *These authors contributed equally to this work 1 https://erosnow.com/ 100s of millions of dollars and often make boxoffice collections of billions of dollars. The first motion picture The Great Train Robbery, 1903— black & white with no sound— was created at the beginning of the 20th century. Since then, the art has gone through several transformations, and now people can instantly access 4K HD movies of their liking on any smart device. Throughout the history of film, two of the contributors to a film’s blockbuster success have been the quality of its plot and the manner of storytelling. The appeal of the movie decreases drastically if the viewers find the plot drably predictable. Writing a creative and exciting script is, therefore, a critical necessity and is extremely challenging. Add to this the constraints of time and budget, and the need for (at least partial) automation in script writing becomes obvious. AI-based story generation has been used before. Based on the engagement-reflection cognitive explanation of writing, the computer model MEXICA (Pérez and Sharples, 2001) generates frameworks for short tales. BRUTUS (Bringsjord and Ferrucci, 1999) creates short stories with predetermined themes like treachery. With the arrival of pre-trained transformer models, automatic story generation has got a shot in the arm. Transformer models like GPT-2 and GPT-3 are extensively used for text generation. These models have shown the capability of generating creative text, albeit sometimes with hallucinations (Zhao et al., 2020). Text generated by these models also sometimes lacks coherence and cohesiveness. On the other hand, template-based models can generate coherent text but lack creativity in generating new characters and events in the plot (Kale and Rastogi, 2020). The process of creating a movie generally starts with an idea which is then used to create a plot which is used as the base to build the movie script (Figure 1). Novel datasets are an important feature of this paper. We closely studied the plots and prompts of movies from Bollywood and Hollywood. Such plots and prompts were scraped from Wikipedia[2] and IMDb[3], respectively. The plots are then annotated using the 4-act story structure- an extension of the well-known 3-act structure (Field, 1979). The 4-act structure and the annotation methods are explained in detail in appendix A.5 and section 4, respectively. We introduce a dataset of 1000 Hollywood movie scenes and their short descriptions. The scripts are scraped from IMSDb[4]. The scenes are annotated with the four major components of a screenplay: sluglines, action lines, character names and dialogues, described in details in appendix A.4 We introduce a workbench which we call “Kurosawa”, consisting of datasets and a pair of GPT-3 (Brown et al., 2020) models fine-tuned with the said datasets. One GPT-3 model generates a movie plot given a short description of the storyline (15– 40 words), while the other creates a scene based on a short description of the required scene. Importantly, we have provided the “Kurosawa” platform to one of the biggest media platforms engaged in the business of making movies and TV shows, producing music and soundtrack etc.- to help script and content writers from different film industries create new movie plots. Our contributions in this work are as follows: • To the best of our knowledge, this is the first work on generating movie scenes from a scene description. • We create and publicly release two datasets: (a) a parallel dataset of 1000 movie storylines and their corresponding plots, (b) a parallel dataset of 1000 movie scenes and their corresponding descriptions. In (a), we link available movie storylines from IMDb with available corresponding movie plots from Wikipedia. In (b), we link available movie scenes from IMSDb with corresponding descriptions from IMDb. • We manually annotate movie plots according to a 4-act structure which is an extension of the well-known 3-act structure (Field, 1979). Professional scriptwriters from the media and entertainment industry guided us very closely. • We manually annotate movie scenes with four major components of a scene: sluglines, action lines, character names and dialogues, along with a short description of the scene. • We introduce “Kurosawa”: a workbench that consists of multiple datasets and models which can assist script and scene writers in the film industry. This paper is available on arxiv under CC 4.0 DEED license. [1] https://erosnow.com/ [2] https://www.wikipedia.org/ [3] https://www.imdb.com/ [4] https://www.imsdb.com/ Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Authors: Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro Motivation Related Work Dataset Experiments and Evaluation Results and Analysis Conclusion and Future Work Limitations and References A. Appendix Abstract and Intro Abstract and Intro Motivation Motivation Related Work Related Work Dataset Dataset Experiments and Evaluation Experiments and Evaluation Results and Analysis Results and Analysis Conclusion and Future Work Conclusion and Future Work Limitations and References Limitations and References A. Appendix A. Appendix Abstract Storytelling is the lifeline of the entertainment industry- movies, TV shows, and stand-up comedies, all need stories. A good and gripping script is the lifeline of storytelling and demands creativity and resource investment. Good scriptwriters are rare to find and often work under severe time pressure. Consequently, entertainment media are actively looking for automation. In this paper, we present an AIbased script-writing workbench called KUROSAWA which addresses the tasks of plot generation and script generation. Plot generation aims to generate a coherent and creative plot (600–800 words) given a prompt (15–40 words). Script generation, on the other hand, generates a scene (200–500 words) in a screenplay format from a brief description (15–40 words). Kurosawa needs data to train. We use a 4-act structure of storytelling to annotate the plot dataset manually. We create a dataset of 1000 manually annotated plots and their corresponding prompts/storylines and a gold-standard dataset of 1000 scenes with four main elements — scene headings, action lines, dialogues, and character names — tagged individually. We fine-tune GPT-3 with the above datasets to generate plots and scenes. These plots and scenes are first evaluated and then used by the scriptwriters of a large and famous media platform ErosNow[1]. We release the annotated datasets and the models trained on these datasets as a working benchmark for automatic movie plot and script generation. 1. Introduction Movies are one of the most popular sources of entertainment for people worldwide and can be a strong medium for education and social awareness. The impact and influence of film industries can be gauged from the fact that Hollywood movies invest *These authors contributed equally to this work 1 https://erosnow.com/ 100s of millions of dollars and often make boxoffice collections of billions of dollars. The first motion picture The Great Train Robbery, 1903— black & white with no sound— was created at the beginning of the 20th century. Since then, the art has gone through several transformations, and now people can instantly access 4K HD movies of their liking on any smart device. Throughout the history of film, two of the contributors to a film’s blockbuster success have been the quality of its plot and the manner of storytelling. The appeal of the movie decreases drastically if the viewers find the plot drably predictable. Writing a creative and exciting script is, therefore, a critical necessity and is extremely challenging. Add to this the constraints of time and budget, and the need for (at least partial) automation in script writing becomes obvious. AI-based story generation has been used before. Based on the engagement-reflection cognitive explanation of writing, the computer model MEXICA (Pérez and Sharples, 2001) generates frameworks for short tales. BRUTUS (Bringsjord and Ferrucci, 1999) creates short stories with predetermined themes like treachery. With the arrival of pre-trained transformer models, automatic story generation has got a shot in the arm. Transformer models like GPT-2 and GPT-3 are extensively used for text generation. These models have shown the capability of generating creative text, albeit sometimes with hallucinations (Zhao et al., 2020). Text generated by these models also sometimes lacks coherence and cohesiveness. On the other hand, template-based models can generate coherent text but lack creativity in generating new characters and events in the plot (Kale and Rastogi, 2020). The process of creating a movie generally starts with an idea which is then used to create a plot which is used as the base to build the movie script (Figure 1). Novel datasets are an important feature of this paper. We closely studied the plots and prompts of movies from Bollywood and Hollywood. Such plots and prompts were scraped from Wikipedia[2] and IMDb[3], respectively. The plots are then annotated using the 4-act story structure- an extension of the well-known 3-act structure (Field, 1979). The 4-act structure and the annotation methods are explained in detail in appendix A.5 and section 4, respectively. We introduce a dataset of 1000 Hollywood movie scenes and their short descriptions. The scripts are scraped from IMSDb[4]. The scenes are annotated with the four major components of a screenplay: sluglines, action lines, character names and dialogues, described in details in appendix A.4 We introduce a workbench which we call “Kurosawa”, consisting of datasets and a pair of GPT-3 (Brown et al., 2020) models fine-tuned with the said datasets. One GPT-3 model generates a movie plot given a short description of the storyline (15– 40 words), while the other creates a scene based on a short description of the required scene. Importantly, we have provided the “Kurosawa” platform to one of the biggest media platforms engaged in the business of making movies and TV shows, producing music and soundtrack etc.- to help script and content writers from different film industries create new movie plots. Our contributions in this work are as follows: Our contributions in this work are as follows: • To the best of our knowledge, this is the first work on generating movie scenes from a scene description. • We create and publicly release two datasets: (a) a parallel dataset of 1000 movie storylines and their corresponding plots, (b) a parallel dataset of 1000 movie scenes and their corresponding descriptions. In (a), we link available movie storylines from IMDb with available corresponding movie plots from Wikipedia. In (b), we link available movie scenes from IMSDb with corresponding descriptions from IMDb. • We manually annotate movie plots according to a 4-act structure which is an extension of the well-known 3-act structure (Field, 1979). Professional scriptwriters from the media and entertainment industry guided us very closely. • We manually annotate movie scenes with four major components of a scene: sluglines, action lines, character names and dialogues, along with a short description of the scene. • We introduce “Kurosawa”: a workbench that consists of multiple datasets and models which can assist script and scene writers in the film industry. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv [1] https://erosnow.com/ [2] https://www.wikipedia.org/ [3] https://www.imdb.com/ [4] https://www.imsdb.com/

"Kurosawa", A Script Writer's Assistant: Abstract and Intro

"কুরোসাওয়া", একটি স্ক্রিপ্ট লেখকের সহকারী: বিমূর্ত এবং ভূমিকা

„Kurosawa“, Assistent eines Drehbuchautors: Zusammenfassung und Einleitung

"Kurosawa", asistente de guionista: resumen e introducción

"Kurosawa", assistant d'un scénariste : résumé et introduction

"कुरोसावा", एक पटकथा लेखक का सहायक: सार और परिचय

「黒澤」脚本家アシスタント：概要と紹介

각본가 어시스턴트 "구로사와" 개요 및 소개

"Kurosawa", Assistente de Roteiro: Resumo e Introdução

«Куросава», помощник сценариста: Аннотация и вступление

"Kurosawa", Bir Senaryo Yazarının Asistanı: Özet ve Giriş

"Kurosawa", Trợ lý biên kịch: Tóm tắt và giới thiệu

《黑泽明》，编剧助理：摘要和简介

Authors:
(1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work;
(2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work;
(3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro
Motivation
Related Work
Dataset
Experiments and Evaluation
Results and Analysis
Conclusion and Future Work
Limitations and References
A. Appendix 6. Results and Analysis We present our observations and evaluations. The nature of our task makes human evaluation take precedence over automatic evaluation (it is for automatic movie script generation, after all!). The qualitative analysis of our generated plots and scenes is based on feedback from 5 professional scriptwriters of our industry partner, the well-known media platform. 6.1. Plot Generation 6.1.1. Automatic Evaluation Table 1 shows auto-evaluation scores for the multiple GPT-3 plot generation models. 6.1.2. Human Rating We conducted human evaluation on Hollywood annotated short input model. The evaluation was done by five groups of 3 people, with each group having been assigned 10 unique plots. The ratings given for the 5 features are in Figure 5. The average scores for fluency, creativity, likability, coherence and relevance are 3.98, 3.29, 2.97, 2.65 and 2.55, respectively. Fluency of almost 4 is an indicator of the power of GPT-3 as a language model. Creativity and likeability are respectable at a value of around 3.0. The low BLEU scores support the average creativity score (Table 1). Figure 5 indicates that coherence and relevance still have major room for improvement. The MAUVE (Pillutla et al., 2021) value measures the gap between neural text and human text. We have separately calculated the MAUVE scores for 20 plots and 50 plots. The weighted average of the MAUVE scores for the two experiments is 0.48 which is reasonably good. 6.1.3. Qualitative Observations Professional scriptwriters from our industry partner have given the following observations: Non-annotated Hollywood Plots • The build-up is creative and interesting, but the ending becomes incoherent. • Some characters which are introduced in the beginning are never mentioned again. • The output is not portraying the key points or the theme mentioned in the input. Annotated Hollywood Plots • The plots are much more coherent, and the endings are logical. • There is still hallucination present (a common feature of all models). • The longer inputs made the plots more attentive to the key points. Annotated Hollywood Plots with Genres included • Along with the above points, now the plots generated are more tilted towards the genre or genres of the movie the writer wants to create. • Addition of genre gives some control over the kind of plot generated by the model. Annotated Bollywood plots • The outputs show incoherence in the last two paragraphs and repetition of the same characters throughout the plot. • The flow of the plot is not fast enough, i.e., the plot does not move ahead much. • Many of the outputs have a 1990s theme around them, where the characters are separated and then find each other later. This is due to a skewed dataset with lesser modern plots. 6.2. Scene Generation We fine-tuned GPT-3 for scene generation with our dataset. We generated ten scenes using the models mentioned in 5.1. Figure 7 in the appendix. shows an example of a completely generated scene. 6.2.1. Human Ratings We conducted a human evaluation on 10 scenes generated by the above model. 5 people evaluated the scenes using the Likert Scale. The ratings for the five features can be seen in Figure 5. The average scores for fluency, creativity, likability, coherence, and relevance are 4.48, 3.9, 3.48, 3.46 and 3.86, respectively. All of the values are above the neutral mark and imply that the generated scenes are close to human-written scenes. 6.2.2. Qualitative Observations In this section, we analyze the quality of the scenes generated by the GPT-3 model. This analysis has been done by professional scriptwriters from the previously mentioned media company. • The model produces a well-structured scene. • It can create new characters and fabricate dialogues even when they are unimportant. • The key points from the input can be found in the output. • There are some lines that are repetitive. • The output is not completely coherent. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Authors: Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro Motivation Related Work Dataset Experiments and Evaluation Results and Analysis Conclusion and Future Work Limitations and References A. Appendix Abstract and Intro Abstract and Intro Motivation Motivation Related Work Related Work Dataset Dataset Experiments and Evaluation Experiments and Evaluation Results and Analysis Results and Analysis Conclusion and Future Work Conclusion and Future Work Limitations and References Limitations and References A. Appendix A. Appendix 6. Results and Analysis We present our observations and evaluations. The nature of our task makes human evaluation take precedence over automatic evaluation (it is for automatic movie script generation, after all!). The qualitative analysis of our generated plots and scenes is based on feedback from 5 professional scriptwriters of our industry partner, the well-known media platform. 6.1. Plot Generation 6.1.1. Automatic Evaluation Table 1 shows auto-evaluation scores for the multiple GPT-3 plot generation models. 6.1.2. Human Rating We conducted human evaluation on Hollywood annotated short input model. The evaluation was done by five groups of 3 people, with each group having been assigned 10 unique plots. The ratings given for the 5 features are in Figure 5. The average scores for fluency, creativity, likability, coherence and relevance are 3.98, 3.29, 2.97, 2.65 and 2.55 , respectively. Fluency of almost 4 is an indicator of the power of GPT-3 as a language model. Creativity and likeability are respectable at a value of around 3.0. The low BLEU scores support the average creativity score (Table 1). Figure 5 indicates that coherence and relevance still have major room for improvement. 3.98, 3.29, 2.97, 2.65 2.55 The MAUVE (Pillutla et al., 2021) value measures the gap between neural text and human text. We have separately calculated the MAUVE scores for 20 plots and 50 plots. The weighted average of the MAUVE scores for the two experiments is 0.48 which is reasonably good. 0.48 6.1.3. Qualitative Observations Professional scriptwriters from our industry partner have given the following observations: Non-annotated Hollywood Plots Non-annotated Hollywood Plots • The build-up is creative and interesting, but the ending becomes incoherent. • Some characters which are introduced in the beginning are never mentioned again. • The output is not portraying the key points or the theme mentioned in the input. Annotated Hollywood Plots Annotated Hollywood Plots • The plots are much more coherent, and the endings are logical. • There is still hallucination present (a common feature of all models). • The longer inputs made the plots more attentive to the key points. Annotated Hollywood Plots with Genres included Annotated Hollywood Plots with Genres included • Along with the above points, now the plots generated are more tilted towards the genre or genres of the movie the writer wants to create. • Addition of genre gives some control over the kind of plot generated by the model. Annotated Bollywood plots Annotated Bollywood plots • The outputs show incoherence in the last two paragraphs and repetition of the same characters throughout the plot. • The flow of the plot is not fast enough, i.e., the plot does not move ahead much. • Many of the outputs have a 1990s theme around them, where the characters are separated and then find each other later. This is due to a skewed dataset with lesser modern plots. 6.2. Scene Generation We fine-tuned GPT-3 for scene generation with our dataset. We generated ten scenes using the models mentioned in 5.1. Figure 7 in the appendix. shows an example of a completely generated scene. 6.2.1. Human Ratings We conducted a human evaluation on 10 scenes generated by the above model. 5 people evaluated the scenes using the Likert Scale. The ratings for the five features can be seen in Figure 5. The average scores for fluency, creativity, likability, coherence, and relevance are 4.48, 3.9, 3.48, 3.46 and 3.86 , respectively. All of the values are above the neutral mark and imply that the generated scenes are close to human-written scenes. 4.48, 3.9, 3.48, 3.46 3.86 6.2.2. Qualitative Observations In this section, we analyze the quality of the scenes generated by the GPT-3 model. This analysis has been done by professional scriptwriters from the previously mentioned media company. • The model produces a well-structured scene. • It can create new characters and fabricate dialogues even when they are unimportant. • The key points from the input can be found in the output. • There are some lines that are repetitive. • The output is not completely coherent. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

"Kurosawa": A Script Writer's Assistant: Results and Analysis

"কুরোসাওয়া": একজন স্ক্রিপ্ট লেখকের সহকারী: ফলাফল এবং বিশ্লেষণ

„Kurosawa“: Assistent eines Drehbuchautors: Ergebnisse und Analyse

"Kurosawa": un asistente de guionista: resultados y análisis

"Kurosawa" : un assistant de scénariste : résultats et analyse

"कुरोसावा": एक पटकथा लेखक का सहायक: परिणाम और विश्लेषण

「黒澤」脚本家アシスタント：結果と分析

"쿠로사와": 각본가의 조수: 결과 및 분석

"Kurosawa": um assistente de roteirista: resultados e análises

«Куросава»: помощник сценариста: итоги и анализ

"Kurosawa": Bir Senaryo Yazarının Asistanı: Sonuçlar ve Analiz

"Kurosawa": Trợ lý biên kịch: Kết quả và phân tích

“黑泽明”：编剧助理：结果与分析

Stories, News & Mentions about KUMO

Kumo’s Stories on HackerNoon