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The United States v Meta Platforms Court Filing October 24, 2023 is part of . You can jump to any part in this filing . This is part 11 of 100. HackerNoon’s Legal PDF Series here A. To maximize profit, Meta’s business model focuses on increasing young users’ engagement. 1. Meta monetizes young users’ attention through data harvesting and targeted advertising. 54. Meta’s core business model across its Social Media Platforms is monetizing user information and attention by increasing engagement, otherwise known as time spent, on its Platforms. Meta is constantly striving to sustain and increase user engagement on its Platforms so that it can sell more and better advertising opportunities to paying advertisers. 55. Meta generates most of its revenue from advertisers, who are able to use targeted advertising based on the personal data Meta collects for each user. As Meta’s CFO David Wehner indicated in a January 2019 earnings call: In terms of our ability to continue to grow the advertising business, it’s about working to develop the best—the best products we can to enable advertisers to achieve their end business results. Targeting obviously very is [sic] important in that. 56. When Meta succeeds in maintaining a user’s interest through its recommendation algorithms—thus keeping the user on a Platform for a longer time—Meta can collect more data on the user and serve the user more advertisements. 57. [Redacted] 58. Increasing the time spent on Meta’s Platforms increases the effective delivery of targeted ads—a pivotal factor in Meta’s ability to generate revenue. In an April 2019 earnings call, Meta’s CFO noted, “we’re relying on continuing to improve targeting. And so you’ve got— the risk there is of course the headwinds that we talked about on the ad targeting front and how that will play into U.S. growth as well.” 59-60. [Redacted] 61. Advertisers do not have long-term commitments to Meta’s Platforms. Accordingly, Meta must continue to deliver ads in an effective manner to retain paying advertisers and maintain and increase its revenue. 62. Meta has emphasized ad effectiveness as a top priority for future growth. As thenChief Operating Officer Sheryl Sandberg told investors on a 2019 earnings call, “[o]ver time our systems will do a better job deciding where your ads should be placed and even helping you target. And so you’re seeing us build tools in that direction as well.” 63. As Meta noted in its 2021 Annual Report to the SEC, “[t]he size of our user base and our users’ level of engagement across our products are critical to our success.” It noted that factors affecting Meta’s revenue generation include (1) “user engagement, including time spent on [Meta’s] products”; (2) increasing “user access to and engagement with [Meta’s] products”; (3) Meta’s ability “to maintain or increase the quantity or quality of ads shown to users”; (4) maintaining traffic to monetized features like the “Feed” and “Stories”; (5) the “effectiveness of [Meta’s] ad targeting”; and (6) the degree to which users engage with Meta’s ads. 64. Meta’s Recommendation Algorithms were designed with its business purpose in mind, namely, to capture users’ attention and keep them engaged on the Platforms. 65. These algorithms do not promote any specific message by Meta. Rather, the algorithms function on a user-by-user basis, detecting the material each individual is likely to engage with and then increasingly displaying similar material to maximize the time spent (and user data collected) on the Platforms. 66. [Redacted] 67. Meta’s algorithms apply not only to material generated by users but also to advertisements. As Sandberg expressed in a 2019 earnings call, “[a]cross all of our platforms and formats, we’re investing in AI [artificial intelligence] to make ads more relevant and effective. In Q4, we developed new AI ranking models to help people see ads they’re more likely to be interested in.” Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:23-cv-05448 retrieved on October 25, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Washingtonpost.com

Meta's Core Business Model Revolves Around Monetizing User Information and Attention

The Underground City; Or, The Black Indies by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . ON THE REVOLVING LADDER here CHAPTER XIII. ON THE REVOLVING LADDER The mining operations at New Aberfoyle continued to be carried on very successfully. As a matter of course, the engineer, James Starr, as well as Simon Ford, the discoverers of this rich carboniferous region, shared largely in the profits. In time Harry became a partner. But he never thought of quitting the cottage. He took his father’s place as overman, and diligently superintended the works of this colony of miners. Jack Ryan was proud and delighted at the good fortune which had befallen his comrade. He himself was getting on very well also. They frequently met, either at the cottage or at the works in the pit. Jack did not fail to remark the sentiments entertained by Harry towards Nell. Harry would not confess to them; but Jack only laughed at him when he shook his head and tried to deny any special interest in her. It must be noted that Jack Ryan had the greatest possible wish to be of the party when Nell should pay her first visit to the upper surface of the county of Stirling. He wished to see her wonder and admiration on first beholding the yet unknown face of Nature. He very much hoped that Harry would take him with them when the excursion was made. As yet, however, the latter had made no proposal of the kind to him, which caused him to feel a little uneasy as to his intentions. One morning Jack Ryan was descending through a shaft which led from the surface to the lower regions of the pit. He did so by means of one of those ladders which, continually revolving by machinery, enabled persons to ascend and descend without fatigue. This apparatus had lowered him about a hundred and fifty feet, when at a narrow landing-place he perceived Harry, who was coming up to his labors for the day. “Well met, my friend!” cried Jack, recognizing his comrade by the light of the electric lamps. “Ah, Jack!” replied Harry, “I am glad to see you. I’ve got something to propose.” “I can listen to nothing till you tell me how Nell is,” interrupted Jack Ryan. “Nell is all right, Jack—so much so, in fact, that I hope in a month or six weeks—” “To marry her, Harry?” “Jack, you don’t know what you are talking about!” “Ah, that’s very likely; but I know quite well what I shall do.” “What will you do?” “Marry her myself, if you don’t; so look sharp,” laughed Jack. “By Saint Mungo! I think an immense deal of bonny Nell! A fine young creature like that, who has been brought up in the mine, is just the very wife for a miner. She is an orphan—so am I; and if you don’t care much for her, and if she will have me—” Harry looked gravely at Jack, and let him talk on without trying to stop him. “Don’t you begin to feel jealous, Harry?” asked Jack in a more serious tone. “Not at all,” answered Harry quietly. “But if you don’t marry Nell yourself, you surely can’t expect her to remain a spinster?” “I expect nothing,” said Harry. A movement of the ladder machinery now gave the two friends the opportunity—one to go up, the other down the shaft. However, they remained where they were. “Harry,” quoth Jack, “do you think I spoke in earnest just now about Nell?” “No, that I don’t, Jack.” “Well, but now I will!” “You? speak in earnest?” “My good fellow, I can tell you I am quite capable of giving a friend a bit of advice.” “Let’s hear, then, Jack!” “Well, look here! You love Nell as heartily as she deserves. Old Simon, your father, and old Madge, your mother, both love her as if she were their daughter. Why don’t you make her so in reality? Why don’t you marry her?” “Come, Jack,” said Harry, “you are running on as if you knew how Nell felt on the subject.” “Everybody knows that,” replied Jack, “and therefore it is impossible to make you jealous of any of us. But here goes the ladder again—I’m off!” “Stop a minute, Jack!” cried Harry, detaining his companion, who was stepping onto the moving staircase. “I say! you seem to mean me to take up my quarters here altogether!” “Do be serious and listen, Jack! I want to speak in earnest myself now.” “Well, I’ll listen till the ladder moves again, not a minute longer.” “Jack,” resumed Harry, “I need not pretend that I do not love Nell; I wish above all things to make her my wife.” “That’s all right!” “But for the present I have scruples of conscience as to asking her to make me a promise which would be irrevocable.” “What can you mean, Harry?” “I mean just this—that, it being certain Nell has never been outside this coal mine in the very depths of which she was born, it stands to reason that she knows nothing, and can comprehend nothing of what exists beyond it. Her eyes—yes, and perhaps also her heart—have everything yet to learn. Who can tell what her thoughts will be, when perfectly new impressions shall be made upon her mind? As yet she knows nothing of the world, and to me it would seem like deceiving her, if I led her to decide in ignorance, upon choosing to remain all her life in the coal mine. Do you understand me, Jack?” “Hem!—yes—pretty well. What I understand best is that you are going to make me miss another turn of the ladder.” “Jack,” replied Harry gravely, “if this machinery were to stop altogether, if this landing-place were to fall beneath our feet, you must and shall hear what I have to say.” “Well done, Harry! that’s how I like to be spoken to! Let’s settle, then, that, before you marry Nell, she shall go to school in Auld Reekie.” “No indeed, Jack; I am perfectly able myself to educate the person who is to be my wife.” “Sure that will be a great deal better, Harry!” “But, first of all,” resumed Harry, “I wish that Nell should gain a real knowledge of the upper world. To illustrate my meaning, Jack, suppose you were in love with a blind girl, and someone said to you, ‘In a month’s time her sight will be restored,’ would you not wait till after she was cured, to marry her?” “Faith, to be sure I would!” exclaimed Jack. “Well, Jack, Nell is at present blind; and before she marries me, I wish her to see what I am, and what the life really is to which she would bind herself. In short, she must have daylight let in upon the subject!” “Well said, Harry! Very well said indeed!” cried Jack. “Now I see what you are driving at. And when may we expect the operation to come off?” “In a month, Jack,” replied Harry. “Nell is getting used to the light of our reflectors. That is some preparation. In a month she will, I hope, have seen the earth and its wonders—the sky and its splendors. She will perceive that the limits of the universe are boundless.” But while Harry was thus giving the rein to his imagination, Jack Ryan, quitting the platform, had leaped on the step of the moving machinery. “Hullo, Jack! Where are you?” “Far beneath you,” laughed the merry fellow. “While you soar to the heights, I plunge into the depths.” “Fare ye well. Jack!” returned Harry, himself laying hold of the rising ladder; “mind you say nothing about what I have been telling you.” “Not a word,” shouted Jack, “but I make one condition.” “What is that?” “That I may be one of the party when Nell’s first excursion to the face of the earth comes off!” “So you shall, Jack, I promise you!” A fresh throb of the machinery placed a yet more considerable distance between the friends. Their voices sounded faintly to each other. Harry, however, could still hear Jack shouting: “I say! do you know what Nell will like better than either sun, moon, or stars, after she’s seen the whole of them?” “No, Jack!” “Why, you yourself, old fellow! still you! always you!” And Jack’s voice died away in a prolonged “Hurrah!” Harry, after this, applied himself diligently, during all his spare time, to the work of Nell’s education. He taught her to read and to write, and such rapid progress did she make, it might have been said that she learnt by instinct. Never did keen intelligence more quickly triumph over utter ignorance. It was the wonder of all beholders. Simon and Madge became every day more and more attached to their adopted child, whose former history continued to puzzle them a good deal. They plainly saw the nature of Harry’s feelings towards her, and were far from displeased thereat. They recollected that Simon had said to the engineer on his first visit to the old cottage, “How can our son ever think of marrying? Where could a wife possibly be found suitable for a lad whose whole life must be passed in the depths of a coal mine?” Well! now it seemed as if the most desirable companion in the world had been led to him by Providence. Was not this like a blessing direct from Heaven? So the old man made up his mind that, if the wedding did take place, the miners of New Aberfoyle should have a merry-making at Coal Town, which they would never during their lives forget. Simon Ford little knew what he was saying! It must be remarked that another person wished for this union of Harry and Nell as much as Simon did—and that was James Starr, the engineer. Of course he was really interested in the happiness of the two young people. But another motive, connected with wider interests, influenced him to desire it. It has been said that James Starr continued to entertain a certain amount of apprehension, although for the present nothing appeared to justify it. Yet that which had been might again be. This mystery about the new cutting—Nell was evidently the only person acquainted with it. Now, if fresh dangers were in store for the miners of Aberfoyle, how were they possibly to be guarded against, without so much as knowing the cause of them? “Nell has persisted in keeping silence,” said James Starr very often, “but what she has concealed from others, she will not long hide from her husband. Any danger would be danger to Harry as well as to the rest of us. Therefore, a marriage which brings happiness to the lovers, and safety to their friends, will be a good marriage, if ever there is such a thing here below.” Thus, not illogically, reasoned James Starr. He communicated his ideas to old Simon, who decidedly appreciated them. Nothing, then, appeared to stand in the way of the match. What, in fact, was there to prevent it? They loved each other; the parents desired nothing better for their son. Harry’s comrades envied his good fortune, but freely acknowledged that he deserved it. The maiden depended on no one else, and had but to give the consent of her own heart. Why, then, if there were none to place obstacles in the way of this union—why, as night came on, and, the labors of the day being over, the electric lights in the mine were extinguished, and all the inhabitants of Coal Town at rest within their dwellings—why did a mysterious form always emerge from the gloomier recesses of New Aberfoyle, and silently glide through the darkness? What instinct guided this phantom with ease through passages so narrow as to appear to be impracticable? Why should the strange being, with eyes flashing through the deepest darkness, come cautiously creeping along the shores of Lake Malcolm? Why so directly make his way towards Simon’s cottage, yet so carefully as hitherto to avoid notice? Why, bending towards the windows, did he strive to catch, by listening, some fragment of the conversation within the closed shutters? And, on catching a few words, why did he shake his fist with a menacing gesture towards the calm abode, while from between his set teeth issued these words in muttered fury, “She and he? Never! never!” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (1998). The Underground City; Or, The Black Indies. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/1353/pg1353-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

ON THE REVOLVING LADDER

THE REVOLVER

I've always liked GUI, both desktop-based and browser-based, before you needed five years of training on the latter. That's the reason I loved and still love Vaadin: you can develop web UIs without writing a single line of HTML, JavaScript, and CSS. I'm still interested in the subject; a couple of years ago, I analyzed the state of JVM desktop frameworks. I also like the Rust programming language a lot. Tauri is a Rust-based framework for building desktop applications. Here's my view. Overview Build an optimized, secure, and frontend-independent application for multi-platform deployment.
-- Tauri website A Tauri app is composed of two modules: the client-side module in standard Web technologies (HTML, Javascript, and CSS) and the backend module in Rust. Tauri runs the UI in a dedicated Chrome browser instance. Users interact with the UI as usual. Tauri offers a binding between the client-side JavaScript and the backend Rust via a specific JS module, i.e, window.__TAURI__.tauri. It also offers other modules for interacting with the local system, such as the filesystem, OS, clipboard, window management, etc. Binding is based on strings. Here's the client-side code: const { invoke } = window.__TAURI__.tauri;

let greetInputEl;
let greetMsgEl;

greetMsgEl.textContent = await invoke("greet", { name: greetInputEl.value });  //1 Invoke the Tauri command named greet Here's the corresponding Rust code: #[tauri::command]                                                              //1
fn greet(name: &str) -> String {                                               //1
    format!("Hello, {}! You've been greeted from Rust!", name)
} Define a Tauri command named greet In the following sections, I'll list Tauri's good, meh, and bad points. Remember that it's my subjective opinion based on my previous experiences. The good Getting started:
Fortunately, it is becoming increasingly rare, but some technologies need to remember that before you're an expert, you're a newbie. The first section of any site should be a quick explanation of the technology, and the second a getting started. Tauri succeeds in this; I got my first Tauri app in a matter of minutes by following the Quick start guide.


Documentation:
Tauri's documentation is comprehensive, extensive (as far as my musings browsed them), and well-structured.


Great feedback loop:
I've experienced exciting technologies where the feedback loop, the time it takes to see the results of a change, makes the technology unusable. GWT, I'm looking at you. Short feedback loops contribute to a great developer experience.
In this regard, Tauri scores points. One can launch the app with a simple cargo tauri dev command. If the front end changes, Tauri reloads it. If any metadata changes, e.g., anything stored in tauri.conf.json, Tauri restarts the app. The only downside is that both behaviors lose the UI state.


Complete lifecycle management:
Tauri not only helps you develop your app but also provides the tools to debug, test, build, and distribute it. The meh At first, I wanted to create my usual showcase for desktop applications, a file renamer app. However, I soon hit an issue when I wanted to select a directory using the file browser button. First, Tauri doesn't allow the use of the regular JavaScript file-related API; Instead, it provides a more limited API. Worse, you need to explicitly configure which file system paths are available at build time, and they are part of an enumeration. I understand that security is a growing concern in modern software. Yet, I fail to understand this limitation on a desktop app, where every other app can access any directory. The bad However, Tauri's biggest problem is its design, more precisely, its separation between the front and the back end. What I love in Vaadin is its management of all things frontend, leaving you to learn the framework only. It allows your backend developers to build web apps without dealing with HTML, CSS, and JavaScript. Tauri, though a desktop framework, made precisely the opposite choice. Your developers will need to know frontend technologies. Worse, the separation reproduces the request-response model created using browser technologies to create UIs. Reminder: early desktop apps use the Observer model, which better fits user interactions. We designed apps around the request-response model only after we ported them on the web. Using this model in a desktop app is a regression, in my opinion. Conclusion Tauri has many things to like, mainly everything that revolves around the developer experience. If you or your organization uses and likes web technologies, try Tauri. However, it's a no-go for me: to create a simple desktop app, I don't want to learn how to center a div or about the flexbox layout, etc. To go further: Tauri I've always liked GUI, both desktop-based and browser-based, before you needed five years of training on the latter. That's the reason I loved and still love Vaadin : you can develop web UIs without writing a single line of HTML, JavaScript, and CSS. I'm still interested in the subject; a couple of years ago, I analyzed the state of JVM desktop frameworks . Vaadin state of JVM desktop frameworks I also like the Rust programming language a lot. Tauri is a Rust-based framework for building desktop applications. Here's my view. Overview Build an optimized, secure, and frontend-independent application for multi-platform deployment. -- Tauri website Build an optimized, secure, and frontend-independent application for multi-platform deployment. -- Tauri website Tauri website A Tauri app is composed of two modules: the client-side module in standard Web technologies (HTML, Javascript, and CSS) and the backend module in Rust. Tauri runs the UI in a dedicated Chrome browser instance. Users interact with the UI as usual. Tauri offers a binding between the client-side JavaScript and the backend Rust via a specific JS module, i.e , window.__TAURI__.tauri . It also offers other modules for interacting with the local system, such as the filesystem, OS, clipboard, window management, etc. i.e window.__TAURI__.tauri Binding is based on strings. Here's the client-side code: const { invoke } = window.__TAURI__.tauri;

let greetInputEl;
let greetMsgEl;

greetMsgEl.textContent = await invoke("greet", { name: greetInputEl.value });  //1 const { invoke } = window.__TAURI__.tauri;

let greetInputEl;
let greetMsgEl;

greetMsgEl.textContent = await invoke("greet", { name: greetInputEl.value });  //1 Invoke the Tauri command named greet Invoke the Tauri command named greet greet Here's the corresponding Rust code: #[tauri::command]                                                              //1
fn greet(name: &str) -> String {                                               //1
    format!("Hello, {}! You've been greeted from Rust!", name)
} #[tauri::command]                                                              //1
fn greet(name: &str) -> String {                                               //1
    format!("Hello, {}! You've been greeted from Rust!", name)
} Define a Tauri command named greet Define a Tauri command named greet greet In the following sections, I'll list Tauri's good, meh, and bad points. Remember that it's my subjective opinion based on my previous experiences. The good Getting started:
Fortunately, it is becoming increasingly rare, but some technologies need to remember that before you're an expert, you're a newbie. The first section of any site should be a quick explanation of the technology, and the second a getting started. Tauri succeeds in this; I got my first Tauri app in a matter of minutes by following the Quick start guide. Documentation:
Tauri's documentation is comprehensive, extensive (as far as my musings browsed them), and well-structured. Great feedback loop:
I've experienced exciting technologies where the feedback loop, the time it takes to see the results of a change, makes the technology unusable. GWT, I'm looking at you. Short feedback loops contribute to a great developer experience.
In this regard, Tauri scores points. One can launch the app with a simple cargo tauri dev command. If the front end changes, Tauri reloads it. If any metadata changes, e.g., anything stored in tauri.conf.json, Tauri restarts the app. The only downside is that both behaviors lose the UI state. Complete lifecycle management:
Tauri not only helps you develop your app but also provides the tools to debug, test, build, and distribute it. Getting started: Fortunately, it is becoming increasingly rare, but some technologies need to remember that before you're an expert, you're a newbie. The first section of any site should be a quick explanation of the technology, and the second a getting started. Tauri succeeds in this; I got my first Tauri app in a matter of minutes by following the Quick start guide. Getting started: Fortunately, it is becoming increasingly rare, but some technologies need to remember that before you're an expert, you're a newbie. The first section of any site should be a quick explanation of the technology, and the second a getting started. Tauri succeeds in this; I got my first Tauri app in a matter of minutes by following the Quick start guide. Quick start Documentation: Tauri's documentation is comprehensive, extensive (as far as my musings browsed them), and well-structured. Documentation: Tauri's documentation is comprehensive, extensive (as far as my musings browsed them), and well-structured. Great feedback loop: I've experienced exciting technologies where the feedback loop, the time it takes to see the results of a change, makes the technology unusable. GWT, I'm looking at you. Short feedback loops contribute to a great developer experience. In this regard, Tauri scores points. One can launch the app with a simple cargo tauri dev command. If the front end changes, Tauri reloads it. If any metadata changes, e.g., anything stored in tauri.conf.json, Tauri restarts the app. The only downside is that both behaviors lose the UI state. Great feedback loop: I've experienced exciting technologies where the feedback loop, the time it takes to see the results of a change, makes the technology unusable. GWT , I'm looking at you. Short feedback loops contribute to a great developer experience. GWT In this regard, Tauri scores points. One can launch the app with a simple cargo tauri dev command. If the front end changes, Tauri reloads it. If any metadata changes, e.g. , anything stored in tauri.conf.json , Tauri restarts the app. The only downside is that both behaviors lose the UI state. cargo tauri dev e.g. tauri.conf.json Complete lifecycle management: Tauri not only helps you develop your app but also provides the tools to debug, test, build, and distribute it. Complete lifecycle management: Tauri not only helps you develop your app but also provides the tools to debug , test , build , and distribute it. debug test build distribute The meh At first, I wanted to create my usual showcase for desktop applications, a file renamer app . However, I soon hit an issue when I wanted to select a directory using the file browser button. First, Tauri doesn't allow the use of the regular JavaScript file-related API; Instead, it provides a more limited API. Worse, you need to explicitly configure which file system paths are available at build time, and they are part of an enumeration. file renamer app I understand that security is a growing concern in modern software. Yet, I fail to understand this limitation on a desktop app, where every other app can access any directory. The bad However, Tauri's biggest problem is its design, more precisely, its separation between the front and the back end. What I love in Vaadin is its management of all things frontend, leaving you to learn the framework only. It allows your backend developers to build web apps without dealing with HTML, CSS, and JavaScript. Tauri, though a desktop framework, made precisely the opposite choice. Your developers will need to know frontend technologies. Worse, the separation reproduces the request-response model created using browser technologies to create UIs. Reminder: early desktop apps use the Observer model , which better fits user interactions. We designed apps around the request-response model only after we ported them on the web. Using this model in a desktop app is a regression, in my opinion. Observer model Conclusion Tauri has many things to like, mainly everything that revolves around the developer experience. If you or your organization uses and likes web technologies, try Tauri. However, it's a no-go for me: to create a simple desktop app, I don't want to learn how to center a div or about the flexbox layout, etc. div To go further: To go further: Tauri Tauri Tauri

Why I Love Vaadin but Tauri Isn't for Me

Manual medical billing is full of long-standing problems that cause delays and errors. While automation seems like the ideal solution, it could do more harm than good if healthcare institutions don’t carefully consider integration-related ethical concerns. Fortunately, there are ways to ethically leverage the benefits of automation in health care. Automation’s Role in the Medical Billing Process Despite several ethical concerns, the healthcare industry leverages automation because it’s cost-effective and it improves productivity. These benefits are more than welcome amid shortages of medical professionals and rising operating expenses. Administrative costs account for 15%-25% of health expenditures in the United States — and billing and coding are the leading drivers. Another significant benefit of this technology is the elimination of human error. For example, autonomous coding software can assign codes to patients’ charts with 96% code-over-code accuracy for most outpatient specialties. These accuracy and efficiency improvements could improve patient outcomes and satisfaction. This technology automates repetitive processes, meaning it can automatically assign billing codes, identify record inconsistencies, detect unusual billing patterns, and remind patients of upcoming payments. Hospitals could substantially accelerate workflows with it. They can also save money by integrating this technology into their existing administrative processes. Examples of Medical Billing Automation Technology While the healthcare sector uses various automation technologies, the most common are: Robot Process Automation (RPA) Unlike the name suggests, RPA has nothing to do with robots. Rather, it’s a software technology following a predefined workflow. It completes its task repetitively or in response to specific triggers. It can handle various administrative tasks independently. Revenue Cycle Management (RCM) RCM is the process hospitals use to track patient-related financials. Seventy-five percent of hospitals and health systems in the U.S. deployed RCM technology during the COVID-19 pandemic. This technology automates everything from coding to collections. Artificial Intelligence (AI) AI is more versatile than RPA or RCM, as hospitals can tailor it to their needs. They can use it for backend administrative processes, deploy chatbots to answer frequently asked payment questions, or leverage machine learning (ML) for predictive forecasting. 5 Ethical Concerns of Automating Health Care Finance While automation technology could benefit hospitals, it may negatively affect patients. Healthcare institutions should examine several ethical considerations: System Apathy Automated tools replace humans in sensitive medical billing roles. Concerningly, they’re incapable of emotion and don’t care about context but interact with vulnerable people — those who have just had a medical scare, lost a loved one, or can’t afford to pay for their medication. Considering empathy contributes toward rebuilding patients’ trust and protecting companies’ reputations, automation technology may negatively affect hospitals just as much as patients. People generally dislike dealing with apathetic machines over humans. Algorithmic Bias Bias is a massive issue with AI systems since unconscious prejudices are often incorporated into training. Even if it’s unintentional, bias has long-lasting adverse impacts. For example, an ML model might disproportionately deny one demographic’s claims if it primarily trains on another. System Transparency As automation technology advances, tracing the logical framework it follows to reach conclusions becomes increasingly challenging. For instance, the black box issue with AI revolves around its inability to display a decision-making process, meaning every step before its output is essentially a mystery. A lack of transparency can worsen patient satisfaction and trust. Patient Privacy Industry experts worry automation will inadvertently expose patients’ personally identifiable information (PII), medical records, or financial details to bad actors. Considering that 30% of healthcare institutions automating with RCM use at least two third-party vendors, their fears aren’t unfounded. Outsourcing to run and maintain software can open them to data breach risks. Job Losses Medical billing professionals update patient information, generate invoices, process insurance claims, collect payments, and resolve disputes. Unfortunately, automation technology can take over their jobs almost entirely — meaning most face unemployment in the face of widespread adoption. How to Ensure Medical Billing Automation Is Ethical While medical billing automation aims to streamline administrative processes and improve interactions, it could inadvertently harm relationships and outcomes. You’d need to navigate ethical concerns carefully to increase accuracy and efficiency without compromising the equitable treatment of patients and workers. You can overcome automation’s ethical concerns with strategic integration and oversight. For example, you could develop an AI governance framework to ensure deployment and utilization stays transparent and moral. After all, ongoing management is vital to long-term success. Automated technology should also have a human element, whether someone is on standby for escalation or a professional audits the system’s output. This way, you can connect with patients and recognize how context may affect their ability to pay. Working with people can increase revenue while preserving reputation. Notably, even those not in charge of the technologies should have some training to ensure their occasional usage is equitable, safe, and secure. After all, you can only preserve patient privacy and help guarantee optimal outcomes if you fully understand how to navigate and manage the tools you leverage. The Moral Implications of Automated Medical Billing While you could increase accuracy and productivity with automation, it could also lower patient satisfaction and result in more disputes. Navigating the complex ethics around integration is time-consuming, but it is worth the effort to improve patient outcomes and preserve facilities’ reputations. Manual medical billing is full of long-standing problems that cause delays and errors. While automation seems like the ideal solution, it could do more harm than good if healthcare institutions don’t carefully consider integration-related ethical concerns. Fortunately, there are ways to ethically leverage the benefits of automation in health care. Automation’s Role in the Medical Billing Process Automation’s Role in the Medical Billing Process Despite several ethical concerns, the healthcare industry leverages automation because it’s cost-effective and it improves productivity. These benefits are more than welcome amid shortages of medical professionals and rising operating expenses. Administrative costs account for 15%-25% of health expenditures in the United States — and billing and coding are the leading drivers. 15%-25% of health expenditures 15%-25% of health expenditures Another significant benefit of this technology is the elimination of human error. For example, autonomous coding software can assign codes to patients’ charts with 96% code-over-code accuracy for most outpatient specialties. These accuracy and efficiency improvements could improve patient outcomes and satisfaction. with 96% code-over-code accuracy with 96% code-over-code accuracy This technology automates repetitive processes, meaning it can automatically assign billing codes, identify record inconsistencies, detect unusual billing patterns, and remind patients of upcoming payments. Hospitals could substantially accelerate workflows with it. They can also save money by integrating this technology into their existing administrative processes. Examples of Medical Billing Automation Technology While the healthcare sector uses various automation technologies, the most common are: Robot Process Automation (RPA) Unlike the name suggests, RPA has nothing to do with robots. Rather, it’s a software technology following a predefined workflow. It completes its task repetitively or in response to specific triggers. It can handle various administrative tasks independently. Revenue Cycle Management (RCM) RCM is the process hospitals use to track patient-related financials. Seventy-five percent of hospitals and health systems in the U.S. deployed RCM technology during the COVID-19 pandemic. This technology automates everything from coding to collections. deployed RCM technology deployed RCM technology Artificial Intelligence (AI) AI is more versatile than RPA or RCM, as hospitals can tailor it to their needs. They can use it for backend administrative processes, deploy chatbots to answer frequently asked payment questions, or leverage machine learning (ML) for predictive forecasting. 5 Ethical Concerns of Automating Health Care Finance While automation technology could benefit hospitals, it may negatively affect patients. Healthcare institutions should examine several ethical considerations: System Apathy Automated tools replace humans in sensitive medical billing roles. Concerningly, they’re incapable of emotion and don’t care about context but interact with vulnerable people — those who have just had a medical scare, lost a loved one, or can’t afford to pay for their medication. Considering empathy contributes toward rebuilding patients’ trust and protecting companies’ reputations, automation technology may negatively affect hospitals just as much as patients. People generally dislike dealing with apathetic machines over humans. contributes toward rebuilding patients’ trust contributes toward rebuilding patients’ trust Algorithmic Bias Bias is a massive issue with AI systems since unconscious prejudices are often incorporated into training. Even if it’s unintentional, bias has long-lasting adverse impacts. For example, an ML model might disproportionately deny one demographic’s claims if it primarily trains on another. System Transparency As automation technology advances, tracing the logical framework it follows to reach conclusions becomes increasingly challenging. For instance, the black box issue with AI revolves around its inability to display a decision-making process , meaning every step before its output is essentially a mystery. A lack of transparency can worsen patient satisfaction and trust. inability to display a decision-making process inability to display a decision-making process Patient Privacy Industry experts worry automation will inadvertently expose patients’ personally identifiable information (PII), medical records, or financial details to bad actors. Considering that 30% of healthcare institutions automating with RCM use at least two third-party vendors, their fears aren’t unfounded. Outsourcing to run and maintain software can open them to data breach risks. 30% of healthcare institutions 30% of healthcare institutions Job Losses Medical billing professionals update patient information, generate invoices, process insurance claims, collect payments, and resolve disputes. Unfortunately, automation technology can take over their jobs almost entirely — meaning most face unemployment in the face of widespread adoption. How to Ensure Medical Billing Automation Is Ethical While medical billing automation aims to streamline administrative processes and improve interactions, it could inadvertently harm relationships and outcomes. You’d need to navigate ethical concerns carefully to increase accuracy and efficiency without compromising the equitable treatment of patients and workers. You can overcome automation’s ethical concerns with strategic integration and oversight. For example, you could develop an AI governance framework to ensure deployment and utilization stays transparent and moral. After all, ongoing management is vital to long-term success. Automated technology should also have a human element, whether someone is on standby for escalation or a professional audits the system’s output. This way, you can connect with patients and recognize how context may affect their ability to pay. Working with people can increase revenue while preserving reputation. Notably, even those not in charge of the technologies should have some training to ensure their occasional usage is equitable, safe, and secure. After all, you can only preserve patient privacy and help guarantee optimal outcomes if you fully understand how to navigate and manage the tools you leverage. The Moral Implications of Automated Medical Billing While you could increase accuracy and productivity with automation, it could also lower patient satisfaction and result in more disputes. Navigating the complex ethics around integration is time-consuming, but it is worth the effort to improve patient outcomes and preserve facilities’ reputations.

The Ethics of Automation in Medical Billing

Enhancing Product Development Through Customer Success Feedback

Optimizing Data Center Efficiency: A Deep Dive into Freecooling Techniques

Bahamut Foundation has announced the Grants program to support the growing ecosystem of Bahamut and foster the expansion of decentralized projects and applications. This initiative provides multi-faceted support for a wide range of projects based on categories mainly revolving but not limited to DeFi, gaming, and NFTs. Bahamut Blockchain has already witnessed the successful results of the grants program with the latest launch of 3 DeFi projects - Lolik, Mutuari, and SilkSwap. These platforms have successfully passed all the qualification stages and, after an extensive development process, are now live on the Bahamut blockchain. The appearance of new DeFi projects will surely increase the scalability and adaptability of the native coin of Bahamut, FTN, and expand its usability and relevance. Mutuari is a lending protocol made for decentralized lending and borrowing of assets directly on Bahamut. Mutuari gives the users access to diverse crypto assets, including FTN, USTD, USDC, stFTN, and is accessible through all EVM-supporting non-custodial wallets, among them Fastex Wallet, Metamask, and WalletConnect. The project has been selected and successfully completed the task under the category of “Lending Protocol” with a prize of 60.000 FTN. Lolik is the next project that has received a grant from Bahamut Foundation. It is a liquid staking platform that supports staking on leading blockchains such as Bahamut, with other blockchains, including Ethereum and Polygon, coming soon. With Lolik, each user can earn rewards by participating in the validation processes of the blockchain directly with their Web3 wallet. \And the last one is the DeFi trading platform, SilkSwapthat allows users to enjoy peer-to-peer trading and easy and fast transactions. The application stays true to a pure Web3 concept, totally eliminating any third-party interactions.The application process continues, accepting more and more applications. The step-by-step description of the application process and information on preferable categories and their funding are available on Bahamut.io/grants. The process is transparent and fair, keeping it real with web3. Bahamut Foundation is currently announcing the list of approved applications that will start building their projects on Bahamut through the Bahamut Arena contest and wishes them the best of luck and successful completion of their projects. Gotbit Labs Category - DEX aggregator Category Prize fund - 40,000 FTN Delivery deadline - 2 months Lost Lore Category - NFT Marketplace Category Prize fund - 40,000 FTNDelivery deadline- 4 months Blockstars (Greedy Lottery) Category - Decentralized Game Category Prize fund - 13,000 FTN Delivery deadline - 1 month Erinaceus RNG Oracle Category - RNG Oracle Category Prize fund - 50,000 FTN Delivery deadline - 3 months Erinaceus Price Feed Oracle Category - Price Feed oracle Category Prize fund - 50,000 FTN Delivery deadline - 3 months Additionally, the Bahamut Foundation is hosting a series of meetups at the__ftNFT Phygital Spaces__ located in Dubai, Venice, and Yerevan. These events are designed to promote community engagement by bringing together Web3  developers, newcomers, and enthusiasts. The meetups aim to foster growth and development within the global and local Web3 sector through knowledge exchange and collaboration. Moreover,  a partnership with Yerevan State University has been announced to support blockchain education and community development in Armenia's growing Web3 industry. This collaboration--formalized through a memorandum of cooperation--represents a significant step towards integrating educational and research programs focused on blockchain and virtual assets, underscoring a shared commitment to promoting blockchain education and community development. About Bahamut Foundation The Switzerland-based Bahamut Foundation stays at the forefront of innovation, offering support and funding to the projects that foster advancements in the field, generate value for the blockchain ecosystem, and bring new users into the world of Web3, laying the foundations for future generations to learn and build upon. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. Bahamut Foundation has announced the Grants program to support the growing ecosystem of Bahamut and foster the expansion of decentralized projects and applications. This initiative provides multi-faceted support for a wide range of projects based on categories mainly revolving but not limited to DeFi, gaming, and NFTs. Bahamut Blockchain has already witnessed the successful results of the grants program with the latest launch of 3 DeFi projects - Lolik, Mutuari, and SilkSwap. These platforms have successfully passed all the qualification stages and, after an extensive development process, are now live on the Bahamut blockchain. The appearance of new DeFi projects will surely increase the scalability and adaptability of the native coin of Bahamut, FTN, and expand its usability and relevance. Mutuari is a lending protocol made for decentralized lending and borrowing of assets directly on Bahamut. Mutuari gives the users access to diverse crypto assets, including FTN, USTD, USDC, stFTN, and is accessible through all EVM-supporting non-custodial wallets, among them Fastex Wallet, Metamask, and WalletConnect. The project has been selected and successfully completed the task under the category of “Lending Protocol” with a prize of 60.000 FTN. Mutuari Mutuari Lolik is the next project that has received a grant from Bahamut Foundation. It is a liquid staking platform that supports staking on leading blockchains such as Bahamut, with other blockchains, including Ethereum and Polygon, coming soon. With Lolik, each user can earn rewards by participating in the validation processes of the blockchain directly with their Web3 wallet. Lolik Lolik \And the last one is the DeFi trading platform, SilkSwapthat allows users to enjoy peer-to-peer trading and easy and fast transactions. The application stays true to a pure Web3 concept, totally eliminating any third-party interactions.The application process continues, accepting more and more applications. The step-by-step description of the application process and information on preferable categories and their funding are available on Bahamut.io/grants . The process is transparent and fair, keeping it real with web3. SilkSwap that allows users to enjoy peer-to-peer trading and easy and fast transactions. The application stays true to a pure Web3 concept, totally eliminating any third-party interactions. The application process continues, accepting more and more applications. The step-by-step description of the application process and information on preferable categories and their funding are available on SilkSwap Bahamut.io/grants Bahamut Foundation is currently announcing the list of approved applications that will start building their projects on Bahamut through the Bahamut Arena contest and wishes them the best of luck and successful completion of their projects. Gotbit Labs Category - DEX aggregator Category Prize fund - 40,000 FTN Delivery deadline - 2 months Lost Lore Category - NFT Marketplace Category Prize fund - 40,000 FTN Delivery deadline- 4 months Blockstars (Greedy Lottery) Category - Decentralized Game Category Prize fund - 13,000 FTN Delivery deadline - 1 month Erinaceus RNG Oracle Category - RNG Oracle Category Prize fund - 50,000 FTN Delivery deadline - 3 months Erinaceus Price Feed Oracle Category - Price Feed oracle Category Prize fund - 50,000 FTN Delivery deadline - 3 months Additionally, the Bahamut Foundation is hosting a series of meetups at the__ ftNFT Phygital Spaces __ located in Dubai, Venice, and Yerevan. These events are designed to promote community engagement by bringing together Web3  developers, newcomers, and enthusiasts. The meetups aim to foster growth and development within the global and local Web3 sector through knowledge exchange and collaboration. ftNFT Phygital Spaces Moreover,  a partnership with Yerevan State University has been announced to support blockchain education and community development in Armenia's growing Web3 industry. This collaboration--formalized through a memorandum of cooperation--represents a significant step towards integrating educational and research programs focused on blockchain and virtual assets, underscoring a shared commitment to promoting blockchain education and community development. About Bahamut Foundation The Switzerland-based Bahamut Foundation stays at the forefront of innovation, offering support and funding to the projects that foster advancements in the field, generate value for the blockchain ecosystem, and bring new users into the world of Web3, laying the foundations for future generations to learn and build upon. Bahamut Foundation Bahamut Foundation This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Bahamut Foundation Announces The Launch Of  Three DeFi Projects And The Winners Of Bahamut Arena

বাহামুত ফাউন্ডেশন তিনটি ডিফাই প্রজেক্ট চালু করার ঘোষণা করেছে এবং বাহামুত এরিনার বিজয়ীদের ঘোষণা করেছে

Bahamut Foundation gibt den Start von drei DeFi-Projekten und die Gewinner der Bahamut Arena bekannt

La Fundación Bahamut anuncia el lanzamiento de tres proyectos DeFi y los ganadores del Bahamut Arena

La Fondation Bahamut annonce le lancement de trois projets DeFi et les gagnants de la Bahamut Arena

बहामुट फाउंडेशन ने तीन DeFi परियोजनाओं के शुभारंभ और बहामुट एरिना के विजेताओं की घोषणा की

バハムート財団、3つのDeFiプロジェクトの立ち上げとバハムートアリーナの優勝者を発表

Bahamut Foundation, 3개의 DeFi 프로젝트 출시 및 Bahamut Arena 우승자 발표

Fundação Bahamut anuncia o lançamento de três projetos DeFi e os vencedores da Bahamut Arena

Фонд Бахамут объявляет о запуске трех проектов DeFi и победителях Bahamut Arena

Bahamut Vakfı Üç DeFi Projesinin Lansmanını ve Bahamut Arena'nın Kazananlarını Açıkladı

Bahamut Foundation thông báo ra mắt ba dự án DeFi và những người chiến thắng Bahamut Arena

Bahamut 基金会宣布启动三个 DeFi 项目以及 Bahamut Arena 获胜者

When we talk about broad-spectrum finance, we are not only talking about purely super-complicated products, but also about insurance, and also in decentralized finance (DeFi), we also find this type of instrument. Traditional insurance In this case we find ourselves faced with 2 parties, the insurer and the insured, the first undertakes to cover any future damage and the second undertakes to pay an agreed amount to cover the risk to the insured, in the event that potential damage may occur in the future and call the insurer to compensate for any damage. There are insurances of various kinds that cover different objects or situations, think of the normal insurance of our cars or large household appliances, but also of the more complex ones such as in the case of theft of a jewelry store or potential disasters that can cancel a concert and in that case the insurer will undertake to compensate the users for any ticket or other eventualities established in the contract, let's think about what happened during the pandemic, especially with important sporting events, which thanks to these insurances, were able to cover part of the costs. Crypto insurance Even in the crypto and blockchain world, the first projects of this kind have begun to appear, although still at a very small and not widespread enough level given that there are particular difficulties and risks that many do not want to take on, think of possible errors in the code of the smart contract or an external attack that causes the loss of funds, all episodes that have occurred and will occur in the future. Nexus Mutual However, a project that has certainly stood out in this area is that of Nexus Mutual, founded by Hugh Karp, which is a decentralized insurance company that uses the blockchain (in this case on Ethereum) to create a mutual or a shared risk pool with other users and in this way open up to the insurance of smart contracts, in fact in this case the focus is on the most important part of the project in this sector, because it is the heart around which the whole system revolves. The system also plans to govern everything with the relevant NXM tokens and therefore take part in decisions regarding whether to cover the problem that has arisen or not, given that it is the members who decide who and how much to cover after the damage has occurred. Recall that the Nexus Mutual team operates as a traditional company, and is regulated by the Financial Conduct Authority (Bank of England) and this allows it to have the name “Mutual” in the project name. To participate in this project and therefore also use this form of coverage you need to become a member of Nexus Mutual, which is interesting but requires going through a KYC (Know Your Customer) procedure since we will respond and interface personally with the our funds and in the event of a loss, also bear the same. With this system we can cover practically every smart contract that we find on the Ethereum blockchain, even if it must have at least some interesting and minimum parameters because it would not make sense to cover a smart contract that has little value within it or that is not used. Before proceeding to see how this protocol works, there is a detail that we must point out is that if it is true that Nexus Mutual offers coverage for smart contracts, it is also true that it does not use its system, a risk that could put user funds at risk, even though this has never happened and the related smart contract has been audited by Solidified. Obviously there is other interesting information regarding this protocol, such as the governance model, tokens and so on, which in this case we will not delve into but which is a good idea to read on the relevant website and therefore read up carefully by also reading the project's whitepaper to have an overview. How to cover the funds Once we have connected to the site, we will have to log in with our wallet, so as to be able to access the platform and here we will be able to see all the various options available and also search for the relevant protocol we are interested in: For example, let's search for the Aave protocol and see what protocol is available: In our case we choose v3 and open the page with all the details relating to this coverage, what it covers and how much it costs: So we choose both the time and the amount we want to cover and continue to become members of the protocol: On this page we then have to enter all the information for the KYC part such as our personal data, among other things there is the possibility to choose whether we are acting as an individual or as a company, also allowing companies to cover their own funds in case of future problems: In this way and thanks to this but also other protocols, it is possible to protect ourselves from any attacks or problems regarding those protocols we use, and therefore having an additional level of security for our funds, especially if they are important. Conclusion In conclusion, decentralized finance (DeFi) is evolving rapidly and decentralized insurance is becoming an increasingly important aspect of protecting user funds and Nexus Mutual is an example of a decentralized insurance platform that uses the Ethereum blockchain to create a shared risk mutual among users. Thanks to this system, it is possible to cover the smart contracts used in DeFi protocols, protecting yourself from any future attacks or problems, however, it is important to underline that the use of these platforms requires a KYC procedure and the active participation of users in the governance of the protocol, and in more, it is essential to carefully read up on the characteristics of the project and the coverage offered before using these services because decentralized insurance represents an important step towards greater security and protection of funds in DeFi. When we talk about broad-spectrum finance, we are not only talking about purely super-complicated products, but also about insurance, and also in decentralized finance (DeFi), we also find this type of instrument. Traditional insurance In this case we find ourselves faced with 2 parties, the insurer and the insured, the first undertakes to cover any future damage and the second undertakes to pay an agreed amount to cover the risk to the insured, in the event that potential damage may occur in the future and call the insurer to compensate for any damage. There are insurances of various kinds that cover different objects or situations, think of the normal insurance of our cars or large household appliances, but also of the more complex ones such as in the case of theft of a jewelry store or potential disasters that can cancel a concert and in that case the insurer will undertake to compensate the users for any ticket or other eventualities established in the contract, let's think about what happened during the pandemic, especially with important sporting events, which thanks to these insurances, were able to cover part of the costs. Crypto insurance Even in the crypto and blockchain world, the first projects of this kind have begun to appear, although still at a very small and not widespread enough level given that there are particular difficulties and risks that many do not want to take on, think of possible errors in the code of the smart contract or an external attack that causes the loss of funds, all episodes that have occurred and will occur in the future. Nexus Mutual However, a project that has certainly stood out in this area is that of Nexus Mutual, founded by Hugh Karp, which is a decentralized insurance company that uses the blockchain (in this case on Ethereum) to create a mutual or a shared risk pool with other users and in this way open up to the insurance of smart contracts, in fact in this case the focus is on the most important part of the project in this sector, because it is the heart around which the whole system revolves. The system also plans to govern everything with the relevant NXM tokens and therefore take part in decisions regarding whether to cover the problem that has arisen or not, given that it is the members who decide who and how much to cover after the damage has occurred. Recall that the Nexus Mutual team operates as a traditional company, and is regulated by the Financial Conduct Authority (Bank of England) and this allows it to have the name “Mutual” in the project name. To participate in this project and therefore also use this form of coverage you need to become a member of Nexus Mutual, which is interesting but requires going through a KYC (Know Your Customer) procedure since we will respond and interface personally with the our funds and in the event of a loss, also bear the same. With this system we can cover practically every smart contract that we find on the Ethereum blockchain, even if it must have at least some interesting and minimum parameters because it would not make sense to cover a smart contract that has little value within it or that is not used. Before proceeding to see how this protocol works, there is a detail that we must point out is that if it is true that Nexus Mutual offers coverage for smart contracts, it is also true that it does not use its system, a risk that could put user funds at risk, even though this has never happened and the related smart contract has been audited by Solidified. Obviously there is other interesting information regarding this protocol, such as the governance model, tokens and so on, which in this case we will not delve into but which is a good idea to read on the relevant website and therefore read up carefully by also reading the project's whitepaper to have an overview. How to cover the funds Once we have connected to the site, we will have to log in with our wallet, so as to be able to access the platform and here we will be able to see all the various options available and also search for the relevant protocol we are interested in: For example, let's search for the Aave protocol and see what protocol is available: In our case we choose v3 and open the page with all the details relating to this coverage, what it covers and how much it costs: So we choose both the time and the amount we want to cover and continue to become members of the protocol: On this page we then have to enter all the information for the KYC part such as our personal data, among other things there is the possibility to choose whether we are acting as an individual or as a company, also allowing companies to cover their own funds in case of future problems: In this way and thanks to this but also other protocols, it is possible to protect ourselves from any attacks or problems regarding those protocols we use, and therefore having an additional level of security for our funds, especially if they are important. Conclusion In conclusion, decentralized finance (DeFi) is evolving rapidly and decentralized insurance is becoming an increasingly important aspect of protecting user funds and Nexus Mutual is an example of a decentralized insurance platform that uses the Ethereum blockchain to create a shared risk mutual among users. Thanks to this system, it is possible to cover the smart contracts used in DeFi protocols, protecting yourself from any future attacks or problems, however, it is important to underline that the use of these platforms requires a KYC procedure and the active participation of users in the governance of the protocol, and in more, it is essential to carefully read up on the characteristics of the project and the coverage offered before using these services because decentralized insurance represents an important step towards greater security and protection of funds in DeFi.

How to Insure Your DeFi Plays with Nexus Mutual

The headline might be a slight exaggeration — I'm not Elon Musk. However, I am bootstrapping a venture studio called Evernomic with a focus on the media industry. Our portfolio includes several newsletters, media brands, SaaS projects, service-based startups, high-authority websites, external contracts, and more. Evernomic is also quite a unique organization when it comes to our team and how we allocate our time and resources. Our team is now comprised of over 20 people, with the oldest being 26 years old and the average age is just over 21 years old. Such a young team has its advantages, but it also comes with the drawbacks associated with the stereotypes often held against younger people in business. At the end of the day, we don't have that chronological experience behind us, which also applies to time management. In addition, one value we all share is despising a future of becoming one-dimensional. We don't just aspire to be successful at business or any other aspect of life. We exercise and take care of our health, we spend much of our time learning new things, we like to socialize and make memories, we pay attention to how we present ourselves, and we pursue hobbies such as writing and public speaking. Now, this begs the question of how we manage our time so as not to fall behind on our responsibilities. Maintenance and Expansion Framework I have developed what I call the Maintenance and Expansion Framework. I divide my workload and things that require headspace into two categories: Maintenance: ongoing commitments requiring my continued attention on a regular basis that are necessary for me to keep going in the same trajectory. In short, these are things I must do to maintain my current progress.
Expansion: one-time or open-ended tasks that improve the overall condition of my life, whether it's in business, fitness, or happiness. This may sound controversial to openly admit, but I avoid commitments, otherwise defined as Maintenance Tasks, at all costs. That's the danger zone. It takes away from my ability to expand and take on more things when I'm faced with better opportunities in the future. I always prefer to leave some free margin to try new things, waste some time, let things go wrong without detrimental consequences, and so on. Therefore, I'm very picky with the things I commit to, and I try to minimize them. Conversely, I'm unafraid of the Expansion side of things. I can take on several things simultaneously as long as I know it will be over soon. The only decisions that truly concern me are those that lock me into a certain future. A typical day for me includes my daily routine, immediately tackling my Maintenance Tasks and later tackling three or four Expansion Tasks at a time. This division gives a game-changing order to my life. I know what the bare minimum of what I should be doing is, and it lets me focus on multiple domains within my life and business simultaneously. Collaborative Portfolio In addition to how I personally approach time management, our strategy revolves around creating a system that goes "full circle." This means that every project we take on must contribute in one way or another to other projects in our portfolio as well. I wrote a post some time ago about how, with this approach, we technically have more than 24 hours in a day because every hour we spend on one project also leads to some sort of progress for our other projects. To give you an example, we are planning on launching an ad network in the following months. What's the biggest challenge with such a business? Finding sufficient creators and advertisers to make the marketplace functional — the chicken and egg problem. Looking at companies who have been most successful at growing an ad network, such as Alphabet or Meta, they have always had at least one side of the equation satisfied prior. In Meta's case, they already had access to a large audience who were using their free products before they began rolling out advertisements. Over a year prior to our scheduled launch date, we acquired FindNewsletters.com, an authority newsletter directory. We have yet to make a penny off of this acquisition. However, we now have direct contacts with most newsletter creators, which gives us an extensive creator network to start with. Our job is now much simpler; we only need to find advertisers. Within Evernomic, we try to build similar systems and frameworks through which we push our projects that give us a streamlined and efficient approach. Moreover, tasks get delegated effectively, which minimizes commitments to low-yield tasks among our more experienced members. Naturally, focusing on so many things simultaneously has its drawbacks, too. I often fail to match deadlines, I often don't end up acting on plans I've had, and I often don't reach my goals. In fact, at any given moment, I'm probably not doing too well. However, I've learned that time has a magic of its own. Whenever I've looked back at my progress over a few months, I was pleasantly surprised. Now, I trust the process and keep pushing forward regardless. The headline might be a slight exaggeration — I'm not Elon Musk. However, I am bootstrapping a venture studio called Evernomic with a focus on the media industry. Our portfolio includes several newsletters, media brands, SaaS projects, service-based startups, high-authority websites, external contracts, and more. Evernomic is also quite a unique organization when it comes to our team and how we allocate our time and resources. Our team is now comprised of over 20 people, with the oldest being 26 years old and the average age is just over 21 years old. Such a young team has its advantages, but it also comes with the drawbacks associated with the stereotypes often held against younger people in business. At the end of the day, we don't have that chronological experience behind us, which also applies to time management. In addition, one value we all share is despising a future of becoming one-dimensional. We don't just aspire to be successful at business or any other aspect of life. We exercise and take care of our health, we spend much of our time learning new things, we like to socialize and make memories, we pay attention to how we present ourselves, and we pursue hobbies such as writing and public speaking. Now, this begs the question of how we manage our time so as not to fall behind on our responsibilities. Maintenance and Expansion Framework I have developed what I call the Maintenance and Expansion Framework. I divide my workload and things that require headspace into two categories: Maintenance: ongoing commitments requiring my continued attention on a regular basis that are necessary for me to keep going in the same trajectory. In short, these are things I must do to maintain my current progress. Expansion: one-time or open-ended tasks that improve the overall condition of my life, whether it's in business, fitness, or happiness. Maintenance: ongoing commitments requiring my continued attention on a regular basis that are necessary for me to keep going in the same trajectory. In short, these are things I must do to maintain my current progress. Maintenance: Expansion: one-time or open-ended tasks that improve the overall condition of my life, whether it's in business, fitness, or happiness. Expansion: This may sound controversial to openly admit, but I avoid commitments, otherwise defined as Maintenance Tasks, at all costs. That's the danger zone. It takes away from my ability to expand and take on more things when I'm faced with better opportunities in the future. I always prefer to leave some free margin to try new things, waste some time, let things go wrong without detrimental consequences, and so on. Therefore, I'm very picky with the things I commit to, and I try to minimize them. Conversely, I'm unafraid of the Expansion side of things. I can take on several things simultaneously as long as I know it will be over soon. The only decisions that truly concern me are those that lock me into a certain future. A typical day for me includes my daily routine, immediately tackling my Maintenance Tasks and later tackling three or four Expansion Tasks at a time. This division gives a game-changing order to my life. I know what the bare minimum of what I should be doing is, and it lets me focus on multiple domains within my life and business simultaneously. Collaborative Portfolio In addition to how I personally approach time management, our strategy revolves around creating a system that goes "full circle." This means that every project we take on must contribute in one way or another to other projects in our portfolio as well. I wrote a post some time ago about how, with this approach, we technically have more than 24 hours in a day because every hour we spend on one project also leads to some sort of progress for our other projects. To give you an example, we are planning on launching an ad network in the following months. What's the biggest challenge with such a business? Finding sufficient creators and advertisers to make the marketplace functional — the chicken and egg problem. Looking at companies who have been most successful at growing an ad network, such as Alphabet or Meta, they have always had at least one side of the equation satisfied prior. In Meta's case, they already had access to a large audience who were using their free products before they began rolling out advertisements. Over a year prior to our scheduled launch date, we acquired FindNewsletters.com, an authority newsletter directory. We have yet to make a penny off of this acquisition. However, we now have direct contacts with most newsletter creators, which gives us an extensive creator network to start with. Our job is now much simpler; we only need to find advertisers. Within Evernomic, we try to build similar systems and frameworks through which we push our projects that give us a streamlined and efficient approach. Moreover, tasks get delegated effectively, which minimizes commitments to low-yield tasks among our more experienced members. Naturally, focusing on so many things simultaneously has its drawbacks, too. I often fail to match deadlines, I often don't end up acting on plans I've had, and I often don't reach my goals. In fact, at any given moment, I'm probably not doing too well. However, I've learned that time has a magic of its own. Whenever I've looked back at my progress over a few months, I was pleasantly surprised. Now, I trust the process and keep pushing forward regardless.

How I Manage Over a Dozen Startups

Interview with Dr. Yonatan Sompolinsky of Kaspa

This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk;
(2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com;
(3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk;
(4) Jener Rasmussen, jener@jener.dk;
(5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com;
(6) Asger G. Weirsøe, asger@weircon.dk;
(7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk;
(8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Definitions Model Checking Tool Overview Evaluation Conclusion and References 5 Evaluation To evaluate our tool, we run several experiments. In our experiments we compare our global algorithm, our local algorithm using our various search strategies, and the established tool PRISM-games. We use several different concurrent game case studies. Some of these are PRISM-games case studies adapted to LCGS for CGAAL and determinised if needed. Others are well-known algorithms and games constructed during the development of CGAAL. We make the PRISM-lang and LCGS implementations of models as identical as possible, such that the state spaces are comparable. Some of the concurrent games and related ATL formulae used in the experiments are presented below. Queries marked with ⊺ (resp. ) are satisfied (resp. not satisfied), and queries marked with † may terminate early as we are not required to compute the entire fixed-point: • Mexican-Standoff: In this model N cowboys stand in a circle, each with a revolver. At each moment they can choose to shoot another cowboy or do nothing. If a cowboy is hit by B bullets, they are incapacitated. We run the following queries: • Gossiping Girls: In this model, N nodes each know a piece of information. The nodes can exchange information by calling each other. Our experiments include both a fully connected network of nodes and a circular network of nodes. We run the following queries: • Robot Coordination: In this model, four robots move orthogonally on a N by N grid. Each robot needs to reach the opposite corner of where they start, but crashing into each other is fatal. Werun the following queries: Each experiment is run with a time limit of two hours, allocated 128 GB of memory, and has 32 cores available regardless of how many worker threads CGAAL may spawn. All experiments are run on several identical AMD EPYC 7642 based servers, allowing only one experiment per node at a time to reduce noise in the results. Results Here we provide a select set of experimental results that roughly exemplify our findings in general. For a more thorough set of results along with all of our experimental data and the Python script processing it, see the git repository https://github.com/d702e20/evaluation-results/. We find that the local on-the-fly algorithm is often one order of magnitude faster than the global algorithm regardless of the search strategy. However, the global algorithm can compete with and sometimes outperforms the local algorithm in cases where the local algorithm cannot terminate early. These matches observations made by A. Dalsgaard et al. [4] and M. C. Jensen et al. [10]. A general trend in our experiments is that we see an increase in the execution speed of our distributed implementation as we increase the number of compute threads available. The only times we do not see a speed-up with an increased number of compute threads are when the models are small enough such that the overhead of managing the additional threads is significant. Similarly, a single thread performs better than a few threads in many cases, since there is no communication overhead with only one compute thread. In general, depending on the search strategy employed, we see an improvement on a scale of one to two orders of magnitude when increasing the number of compute threads as exemplified in Figure 9d. Figure 9: Select experimental results. CGAAL-results in Figures 9a to 9c show the best result for each search strategy when varying between 1 and 32 threads. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. This paper is available on Arxiv under CC 4.0 license. Authors: Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Introduction Definitions Definitions Model Checking Model Checking Tool Overview Tool Overview Evaluation Evaluation Conclusion and References Conclusion and References 5 Evaluation To evaluate our tool, we run several experiments. In our experiments we compare our global algorithm, our local algorithm using our various search strategies, and the established tool PRISM-games. We use several different concurrent game case studies. Some of these are PRISM-games case studies adapted to LCGS for CGAAL and determinised if needed. Others are well-known algorithms and games constructed during the development of CGAAL. We make the PRISM-lang and LCGS implementations of models as identical as possible, such that the state spaces are comparable. Some of the concurrent games and related ATL formulae used in the experiments are presented below. Queries marked with ⊺ (resp. ) are satisfied (resp. not satisfied), and queries marked with † may terminate early as we are not required to compute the entire fixed-point: • Mexican-Standoff: In this model N cowboys stand in a circle, each with a revolver. At each moment they can choose to shoot another cowboy or do nothing. If a cowboy is hit by B bullets, they are incapacitated. We run the following queries: • Mexican-Standoff: • Gossiping Girls: In this model, N nodes each know a piece of information. The nodes can exchange information by calling each other. Our experiments include both a fully connected network of nodes and a circular network of nodes. We run the following queries: • Gossiping Girls: N • Robot Coordination: In this model, four robots move orthogonally on a N by N grid. Each robot needs to reach the opposite corner of where they start, but crashing into each other is fatal. Werun the following queries: • Robot Coordination: N N Each experiment is run with a time limit of two hours, allocated 128 GB of memory, and has 32 cores available regardless of how many worker threads CGAAL may spawn. All experiments are run on several identical AMD EPYC 7642 based servers, allowing only one experiment per node at a time to reduce noise in the results. Results Here we provide a select set of experimental results that roughly exemplify our findings in general. For a more thorough set of results along with all of our experimental data and the Python script processing it, see the git repository https://github.com/d702e20/evaluation-results/. Results We find that the local on-the-fly algorithm is often one order of magnitude faster than the global algorithm regardless of the search strategy. However, the global algorithm can compete with and sometimes outperforms the local algorithm in cases where the local algorithm cannot terminate early. These matches observations made by A. Dalsgaard et al. [4] and M. C. Jensen et al. [10]. A general trend in our experiments is that we see an increase in the execution speed of our distributed implementation as we increase the number of compute threads available. The only times we do not see a speed-up with an increased number of compute threads are when the models are small enough such that the overhead of managing the additional threads is significant. Similarly, a single thread performs better than a few threads in many cases, since there is no communication overhead with only one compute thread. In general, depending on the search strategy employed, we see an improvement on a scale of one to two orders of magnitude when increasing the number of compute threads as exemplified in Figure 9d. Figure 9: Select experimental results. CGAAL-results in Figures 9a to 9c show the best result for each search strategy when varying between 1 and 32 threads. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

How We Evaluated CGAAL: The Experiments That We Ran

The Evolution of Data Center Cooling: From Air-Based Methods to Free Cooling

In recent years, the gaming world has revolutionized with the rise of cryptocurrencies. This innovative blend of gaming and blockchain technology has opened up a new frontier for gamers, developers, and investors alike. As the industry continues to evolve, what can we expect from this exciting intersection of crypto and gaming? Decentralized gaming platforms, powered by blockchain technology, are at the forefront of the crypto-gaming revolution. These platforms offer players true ownership of in-game assets through the use of non-fungible tokens (NFTs). NFTs allow gamers to buy, sell, and trade digital items with real-world value, creating a thriving economy within the game ecosystem. Expect to see more decentralized gaming platforms emerge, offering players unprecedented control over their gaming experience. One of the most revolutionary aspects of crypto gaming is the concept of play-to-earn. Unlike traditional games where players spend countless hours grinding for virtual rewards, crypto games reward players with cryptocurrency for their time and effort. This creates new opportunities for gamers to monetize their skills and gameplay, turning gaming into a lucrative income stream. As play-to-earn mechanics become more prevalent, expect to see a shift in the way gamers approach gaming as both a hobby and a profession. Developers themselves aren’t backing down. They’re creating games that fans can find attractive, with the goal being that these games will become leaders in the field. This interview is with the CCO of iCandy, Lim Jenn Yu, heading the Cryptant Crab Prime Project - a new, groundbreaking blockchain game that is looking to transform the industry in its own way. 1. What is the concept behind your game, and what makes it different? While many have ported web2 games directly into web3, CryptantCrab Prime is made to cater to the unique nature of the web3 community. This expands beyond the somewhat unsustainable play-to-earn mechanics and gets players directly involved in the ecosystem. It revolves around the simple concept of having pairs of elemental crabs that are matched with thousands of other crabs as they rise to the top of the arena. One of the distinctive features of our game is that each Crab is generally hatched with a mish-mash of different parts and each one has a heart that hides a hidden strength that they have. Over time, players are able to either mutate or xenograft these parts or create a perfect Crab combination. By bringing a fusion of Pokemon x Hearthstone x RPG x Web 3, it will create a uniquely different gaming experience. 2. Will CryptantCrab incorporate native tokens? How will they be used? Yes. The Crab Token will serve as the native token on Arbitrum to facilitate our ecosystem. The Crab Token will serve as the key entry ticket for the ETH prize pool Tournament; it is also used to fuel various transactions in empowering CryptantCrabs in our new features Fossil Binding and Stamina Restoration. The new Elemental Enchanter features allow not just involved players but also spectators to have an active stake in the Arena and Tournament results. This opens up a new avenue for involving Token holders in the ecosystem outside the GameFi aspect. 3. Are there any additional prospects for blockchain gaming that you see? The way that I see it, for blockchain gaming, is that it will continue to grow toward a player’s control of their assets and how they can modify, track, and store information. This will be an integral part of the next level of the gaming ecosystem that includes the trade between players. The economic component in the gaming ecosystem is one of the crucial parts that blockchain gaming brings. Opening up greater involvement of player-to-player trade in a transparent manner, the use of the public ledger for assets will greatly benefit players who have to resort to black markets and peer-to-peer trading in conventional web2 games. 4. How do you see blockchain gaming evolving in the future, and the impact this might have on the gaming industry? From the coming trends developing in the gaming industry, we believe that there will be an increase in adoption that will make blockchain gaming indistinguishable from web2 gaming. A lot of blockchain integrations will be on the backend and players will benefit from these with almost no barrier. We are seeing a lot of developers working on Web 3 integration into OAuth2, and we look forward to this development that will truly make full use of blockchain technology for the general masses. Web3 gaming will also introduce new transformative ways for the overall gaming experience. We foresee that Web 3 games will return to having a solid foundation, making sure that they build strong game ecosystem designs while keeping them easily accessible to Web3 audiences. Conclusion Following a market downturn and subsequent recovery, crypto gaming is looking to make a comeback. However, for it to truly be actualized, this sub-industry itself will need to evolve significantly. And with the future itself looking bright, all eyes will be on audacious developers looking to take the landscape by storm. In recent years, the gaming world has revolutionized with the rise of cryptocurrencies. This innovative blend of gaming and blockchain technology has opened up a new frontier for gamers, developers, and investors alike. As the industry continues to evolve, what can we expect from this exciting intersection of crypto and gaming? Decentralized gaming platforms, powered by blockchain technology, are at the forefront of the crypto-gaming revolution. These platforms offer players true ownership of in-game assets through the use of non-fungible tokens (NFTs). NFTs allow gamers to buy, sell, and trade digital items with real-world value, creating a thriving economy within the game ecosystem. Expect to see more decentralized gaming platforms emerge, offering players unprecedented control over their gaming experience. One of the most revolutionary aspects of crypto gaming is the concept of play-to-earn. Unlike traditional games where players spend countless hours grinding for virtual rewards, crypto games reward players with cryptocurrency for their time and effort. This creates new opportunities for gamers to monetize their skills and gameplay, turning gaming into a lucrative income stream. As play-to-earn mechanics become more prevalent, expect to see a shift in the way gamers approach gaming as both a hobby and a profession. Developers themselves aren’t backing down. They’re creating games that fans can find attractive, with the goal being that these games will become leaders in the field. This interview is with the CCO of iCandy, Lim Jenn Yu, heading the Cryptant Crab Prime Project - a new, groundbreaking blockchain game that is looking to transform the industry in its own way. 1. What is the concept behind your game, and what makes it different? While many have ported web2 games directly into web3, CryptantCrab Prime is made to cater to the unique nature of the web3 community. This expands beyond the somewhat unsustainable play-to-earn mechanics and gets players directly involved in the ecosystem. It revolves around the simple concept of having pairs of elemental crabs that are matched with thousands of other crabs as they rise to the top of the arena. One of the distinctive features of our game is that each Crab is generally hatched with a mish-mash of different parts and each one has a heart that hides a hidden strength that they have. Over time, players are able to either mutate or xenograft these parts or create a perfect Crab combination. By bringing a fusion of Pokemon x Hearthstone x RPG x Web 3, it will create a uniquely different gaming experience. 2. Will CryptantCrab incorporate native tokens? How will they be used? Yes. The Crab Token will serve as the native token on Arbitrum to facilitate our ecosystem. The Crab Token will serve as the key entry ticket for the ETH prize pool Tournament; it is also used to fuel various transactions in empowering CryptantCrabs in our new features Fossil Binding and Stamina Restoration. The new Elemental Enchanter features allow not just involved players but also spectators to have an active stake in the Arena and Tournament results. This opens up a new avenue for involving Token holders in the ecosystem outside the GameFi aspect. 3. Are there any additional prospects for blockchain gaming that you see? The way that I see it, for blockchain gaming, is that it will continue to grow toward a player’s control of their assets and how they can modify, track, and store information. This will be an integral part of the next level of the gaming ecosystem that includes the trade between players. The economic component in the gaming ecosystem is one of the crucial parts that blockchain gaming brings. Opening up greater involvement of player-to-player trade in a transparent manner, the use of the public ledger for assets will greatly benefit players who have to resort to black markets and peer-to-peer trading in conventional web2 games. 4. How do you see blockchain gaming evolving in the future, and the impact this might have on the gaming industry? From the coming trends developing in the gaming industry, we believe that there will be an increase in adoption that will make blockchain gaming indistinguishable from web2 gaming. A lot of blockchain integrations will be on the backend and players will benefit from these with almost no barrier. We are seeing a lot of developers working on Web 3 integration into OAuth2, and we look forward to this development that will truly make full use of blockchain technology for the general masses. Web3 gaming will also introduce new transformative ways for the overall gaming experience. We foresee that Web 3 games will return to having a solid foundation, making sure that they build strong game ecosystem designs while keeping them easily accessible to Web3 audiences. Conclusion Conclusion Following a market downturn and subsequent recovery, crypto gaming is looking to make a comeback. However, for it to truly be actualized, this sub-industry itself will need to evolve significantly. And with the future itself looking bright, all eyes will be on audacious developers looking to take the landscape by storm.

A New Frontier For Crypto Gaming: What To Expect

Memes and Narrative Strategies in the Russo-Ukrainian War: A Comprehensive Study

রুশো-ইউক্রেনীয় যুদ্ধে মেমস এবং বর্ণনামূলক কৌশল: একটি ব্যাপক অধ্যয়ন

Meme und Erzählstrategien im Russisch-Ukrainischen Krieg: Eine umfassende Studie

Memes y estrategias narrativas en la guerra ruso-ucraniana: un estudio exhaustivo

Mèmes et stratégies narratives dans la guerre russo-ukrainienne : une étude approfondie

रूस-यूक्रेनी युद्ध में मीम्स और कथात्मक रणनीतियाँ: एक व्यापक अध्ययन

ロシア・ウクライナ戦争におけるミームと物語戦略：包括的研究

러시아-우크라이나 전쟁의 밈과 서사 전략: 종합 연구

Memes e estratégias narrativas na guerra russo-ucraniana: um estudo abrangente

Мемы и нарративные стратегии в российско-украинской войне: комплексное исследование

Rus-Ukrayna Savaşında Memler ve Anlatı Stratejileri: Kapsamlı Bir Çalışma

Memes và chiến lược kể chuyện trong Chiến tranh Nga-Ukraina: Một nghiên cứu toàn diện

俄乌战争中的模因与叙事策略：综合研究

Authors:
(1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA;
(2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA;
(3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA;
(4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA;
(5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Related Work Methodology Experiment Result Discussion and References Abstract Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have demonstrated their ability in a multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a leading example of LLMs, in processing the sentiment analysis of Internet memes. Memes, which include both verbal and visual aspects, act as a powerful yet complex tool for expressing ideas and sentiments, demanding an understanding of societal norms and cultural contexts. Notably, the detection and moderation of hateful memes pose a significant challenge due to their implicit offensive nature. This project investigates GPT’s proficiency in such subjective tasks, revealing its strengths and potential limitations. The tasks include the classification of meme sentiment, determination of humor type, and detection of implicit hate in memes. The performance evaluation, using datasets from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative understanding of GPT responses against human annotations. Despite GPT’s remarkable progress, our findings underscore the challenges faced by these models in handling subjective tasks, which are rooted in their inherent limitations including contextual understanding, interpretation of implicit meanings, and data biases. This research contributes to the broader discourse on the applicability of AI in handling complex, context-dependent tasks, and offers valuable insights for future advancements. Index Terms—memotion analysis, hateful memes detection, GPT model I. INTRODUCTION Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have recently gathered substantial interest from both academia and industry. Due to their exceptional capacity to comprehend, generate, and engage in complex linguistic tasks, these models are revolutionizing the development and application of AI algorithms. Equipped with sophisticated algorithms and vast training datasets, LLMs have demonstrated advanced conversational capabilities, processing textual input and output to participate in detailed and meaningful dialogues [6], [10], [11]. OpenAI’s ChatGPT [12] is a prominent representative among these LLMs. It is an AI chatbot backed by the generative pretrained transformer (GPT) model and has attracted a broad societal interest. The proficiency of ChatGPT is particularly noticeable in the realm of question answering, spanning multiple sectors including, but not limited to, healthcare [7] and finance [8]. This model uses its linguistic capabilities to understand questions and generate human-like answers, marking a crucial milestone in AI-driven communication. Nevertheless, despite these impressive feats, LLMs, including GPT, confront considerable challenges when addressing subjective tasks, one such task being the interpretation and annotation of internet memes. Internet memes, modern digital artifacts, encompass a variety of cultural and sociological attitude, and serve as a type of social shorthand among online communities [1], [2]. These memes blend verbal and graphic elements to express complex concepts or feelings in a concise manner. However, the multimodal nature of memes, which combine various images with informal, often humorous or ironic text, introduces an intricate layer of complexity. To comprehend and analyze memes, one must manage the subtle interplay of visual clues, linguistic content, cultural allusions, and common community knowledge. This complexity, along with how subjective memes can be, makes it really tough for LLMs to accurately interpret memes and analyze their sentiment. Sentiment Analysis (SA) primarily concentrate on interpreting user sentiments from textual content. However, due to the proliferation and pervasiveness of multimodal social media data, such as online memes, SA must diversify to handle multimodal data sources. This diversification becomes even more complex when considering ’hateful memes’, which cleverly combine text and images to spread offensive or harmful messages under the guise of humor [3]–[5]. Detecting and moderating such content not only requires understanding the text-image interaction but also a grasp of the cultural context and, in many cases, specific sub-cultural expertise. These challenges call for ongoing research and development in the field, pushing the boundaries and exploring the potential of LLMs like GPT. In this project, we focus on the capabilities and limitations of LLMs like GPT in dealing with subjective and nuanced tasks, particularly internet meme sentiment analysis. This exploration forms the crux of this research, contributing to the broader discussion on the evolving potential and limitations of AI. In this project, we delve into the intricacies of subjectivity, assessing GPT’s performance in tasks necessitating an understanding of societal norms and cultural contexts. Our investigation primarily revolves around two research questions: 1. Can GPT accurately detect implicitly hateful memes given a specific prompt? 2. Can GPT effectively conduct sentiment analysis of memes, including classifying the sentiment of each meme as positive or negative, and categorizing the type of humor into three sub-classes: sarcastic, humorous, and offensive? We draw upon datasets from Facebook’s hateful memes [3] and SemEval-2020 Task 8: Multimodal Memotion Analysis [9] and for this project. The experimental section of the study will present a comparative analysis highlighting the constraints and limitations of GPT’s responses. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Work Related Work Methodology Methodology Experiment Result Experiment Result Discussion and References Discussion and References Abstract Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have demonstrated their ability in a multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a leading example of LLMs, in processing the sentiment analysis of Internet memes. Memes, which include both verbal and visual aspects, act as a powerful yet complex tool for expressing ideas and sentiments, demanding an understanding of societal norms and cultural contexts. Notably, the detection and moderation of hateful memes pose a significant challenge due to their implicit offensive nature. This project investigates GPT’s proficiency in such subjective tasks, revealing its strengths and potential limitations. The tasks include the classification of meme sentiment, determination of humor type, and detection of implicit hate in memes. The performance evaluation, using datasets from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative understanding of GPT responses against human annotations. Despite GPT’s remarkable progress, our findings underscore the challenges faced by these models in handling subjective tasks, which are rooted in their inherent limitations including contextual understanding, interpretation of implicit meanings, and data biases. This research contributes to the broader discourse on the applicability of AI in handling complex, context-dependent tasks, and offers valuable insights for future advancements. Index Terms —memotion analysis, hateful memes detection, GPT model Index Terms I. INTRODUCTION Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have recently gathered substantial interest from both academia and industry. Due to their exceptional capacity to comprehend, generate, and engage in complex linguistic tasks, these models are revolutionizing the development and application of AI algorithms. Equipped with sophisticated algorithms and vast training datasets, LLMs have demonstrated advanced conversational capabilities, processing textual input and output to participate in detailed and meaningful dialogues [6], [10], [11]. OpenAI’s ChatGPT [12] is a prominent representative among these LLMs. It is an AI chatbot backed by the generative pretrained transformer (GPT) model and has attracted a broad societal interest. The proficiency of ChatGPT is particularly noticeable in the realm of question answering, spanning multiple sectors including, but not limited to, healthcare [7] and finance [8]. This model uses its linguistic capabilities to understand questions and generate human-like answers, marking a crucial milestone in AI-driven communication. Nevertheless, despite these impressive feats, LLMs, including GPT, confront considerable challenges when addressing subjective tasks, one such task being the interpretation and annotation of internet memes. Internet memes, modern digital artifacts, encompass a variety of cultural and sociological attitude, and serve as a type of social shorthand among online communities [1], [2]. These memes blend verbal and graphic elements to express complex concepts or feelings in a concise manner. However, the multimodal nature of memes, which combine various images with informal, often humorous or ironic text, introduces an intricate layer of complexity. To comprehend and analyze memes, one must manage the subtle interplay of visual clues, linguistic content, cultural allusions, and common community knowledge. This complexity, along with how subjective memes can be, makes it really tough for LLMs to accurately interpret memes and analyze their sentiment. Sentiment Analysis (SA) primarily concentrate on interpreting user sentiments from textual content. However, due to the proliferation and pervasiveness of multimodal social media data, such as online memes, SA must diversify to handle multimodal data sources. This diversification becomes even more complex when considering ’hateful memes’, which cleverly combine text and images to spread offensive or harmful messages under the guise of humor [3]–[5]. Detecting and moderating such content not only requires understanding the text-image interaction but also a grasp of the cultural context and, in many cases, specific sub-cultural expertise. These challenges call for ongoing research and development in the field, pushing the boundaries and exploring the potential of LLMs like GPT. In this project, we focus on the capabilities and limitations of LLMs like GPT in dealing with subjective and nuanced tasks, particularly internet meme sentiment analysis. This exploration forms the crux of this research, contributing to the broader discussion on the evolving potential and limitations of AI. In this project, we delve into the intricacies of subjectivity, assessing GPT’s performance in tasks necessitating an understanding of societal norms and cultural contexts. Our investigation primarily revolves around two research questions: 1. Can GPT accurately detect implicitly hateful memes given a specific prompt? 2. Can GPT effectively conduct sentiment analysis of memes, including classifying the sentiment of each meme as positive or negative, and categorizing the type of humor into three sub-classes: sarcastic, humorous, and offensive? We draw upon datasets from Facebook’s hateful memes [3] and SemEval-2020 Task 8: Multimodal Memotion Analysis [9] and for this project. The experimental section of the study will present a comparative analysis highlighting the constraints and limitations of GPT’s responses. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Is GPT Powerful Enough to Analyze the Emotions of Memes?

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