The House of Mouse is combining its Hulu + Live TV service with the pay-TV streamer Fubo's business. The deal paves the way for a new sports streamer, Venu, to debut in the coming months, with sports content from ESPN, Warner Bros. Discovery, and Fox.

Disney Shakes up Sports-Streaming Strategy With Fubo but Could Confuse - Business Insider

The proposed merger would join two internet TV bundles that together have 6.2 million subscribers.

Walt Disney and FuboTV to combine their online live TV businesses

Walt Disney Co said on Monday it would merge its Hulu + Live TV business with smaller rival FuboTV , removing a significant hurdle to the launch of its sports streaming venture with Fox Corp and Warner Bros Discovery .

Disney's Hulu + Live TV business to combine with Fubo, clearing way for Venu Sports | Reuters

This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now. Have an account?

Disney/Fubo Deal Clears the Way for Venu Sports Streaming Service - Business Insider

Marvel Rivals, a new video game from NetEase Inc. and Walt Disney Co., is climbing PC and console charts this holiday season, making the free-to-play superhero shooter title a fresh hit for the two companies.

‘Marvel Rivals’ Video Game Climbs Charts in Win for Disney, NetEase - Bloomberg

Disney debuted an ESPN tab on Disney+ on Wednesday for ESPN+ subscribers and will make about 100 live games available on Disney+ to nonsubscribers.

ESPN hopes to reach more casual sports fans with Disney+ integration

Disney

Authors:
(1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA;
(2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent 4.4 Follow-the-Leader Opponent and 4.5 Highest Average Payoff Opponent 5 Generalizing 5.1 Other Behaviorally-Biased Strategies 5.2 Exploiting an Unknown Strategy from a Known Set of Strategies 6 Future Work and References A Appendix A.1 Win-Stay Lose-Shift Variant: Tie-Stay A.2 Follow-the-Leader Variant: Limited History A.3 Ellipsoid Mistake Bounds A.4 Highest Average Payoff Opponent 2. Setting We consider two-player, repeated games with the following properties: Symmetric


Zero-sum


Payoffs are limited to {1, 0, −1} (representing win, tie, and loss respectively)


Each action beats at least one other action


Each action loses to at least one other action ▶ Definition 1 (Permissible Game). We refer to a game with the above properties as a permissible game for convenience. We consider how well we can do without knowing the payoff matrix M or even observing payoffs when playing against a behaviorally-biased opponent who does know M. Our goal will be to win nearly every round. 2.1 Models of behaviorally-biased opponents We assume all opponents know the payoff matrix and play a deterministic, behaviorallybiased strategy. The following are the particular biased adversaries we consider here: Myopic Best Responder The opponent plays a best response to our last action. If there are multiple best responses to our last action, we assume the opponent breaks ties according to some fixed but unknown order over actions. On the first round, since there is no previous action, we assume the opponent plays the first action in that ordering. This is our simplest opponent, who just assumes that tomorrow will be exactly like today. Gambler’s Fallacy e opponent plays a best response to our “most overdue” (least-frequently-played) action. This opponent is motivated by the Gambler’s Fallacy, in which one assumes an event which hasn’t happened as often as expected is “overdue” and is more likely to occur (for example, when tossing a fair coin, the belief that Tails is more likely following a series of Heads). If there are multiple best responses to our “most overdue” action, or if there are multiple “most overdue” actions the opponent might try to counter (for example, at the start of the game when all actions are equally “overdue” since they have all been played zero times), the opponent breaks ties using a fixed but unknown order over actions. Win-Stay, Lose-Shift The opponent continues to play the same action following a win, and switches to a new action following a loss. We consider two variants of this strategy: one in which the opponent behaves as if a tie is a win, continuing to play the same action after tying, and one in which the opponent behaves as if a tie is a loss, switching actions after tying. We assume that when the opponent shifts actions, they shift to the action immediately following the current action in some fixed but unknown order over actions. In the first round, we assume the opponent plays the first action in that ordering. Win-stay lose-shift was initially proposed as an early algorithm for bandit problems by Robbins [15]. There is also some empirical research indicating that people may play rock-paper-scissors according to an approximate version of this strategy: Wang et al. [19] observed that players are more likely to play the same action again after winning, and more likely to shift to the next action in the sequence rock-paper-scissors following a loss. More broadly, win-stay lose-shift has been proposed as a potential explanation for the evolution of cooperative behavior [13]. This strategy can also be viewed as similar to the hot-hand fallacy, in which one believes an action which has just won is having a “hot streak” and is more likely to win in the future, and conversely believes an action which has just lost is having a “cold” streak and is more likely to lose in the future. Follow the Leader The opponent plays the best action in retrospect, where the best action in retrospect is the action that would have achieved the highest net payoff against our history of play. If multiple actions are tied for the highest payoff, the opponent breaks ties using some fixed but unknown order over actions. In the first round, when there is no history to choose based on, the opponent plays the first action in that ordering. It has been hypothesized that irrational strategic behavior may be explained by agents playing a misspecified or simplified version of a game rationally [7]. The above strategy is reasonable for an opponent who erroneously models our play as stochastic. In online learning, this strategy is called Follow-the-Leader or FTL (see [16]). We also consider a limited-history variant of this opponent, in which the the opponent only considers the last r rounds, where r > 0. Highest Average Payoff The opponent plays the action that has achieved the highest average payoff over the times they have played it. We assume the opponent initializes the average payoff of each action as 0. If multiple actions are tied for the highest average payoff, the opponent breaks ties using some fixed but unknown order over actions. This is a reasonable strategy for an opponent who believes our play is stochastic, in which case its actions can be viewed as coins with various biases. It also corresponds to a well-known reinforcement learning algorithm, epsilon-greedy Q-learning, with ϵ = 0 (see [17]). This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. Authors: Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent 4.3 Win-Stay, Lose-Shift Opponent 4.4 Follow-the-Leader Opponent and 4.5 Highest Average Payoff Opponent 4.4 Follow-the-Leader Opponent and 4.5 Highest Average Payoff Opponent 5 Generalizing 5.1 Other Behaviorally-Biased Strategies 5.1 Other Behaviorally-Biased Strategies 5.2 Exploiting an Unknown Strategy from a Known Set of Strategies 5.2 Exploiting an Unknown Strategy from a Known Set of Strategies 6 Future Work and References 6 Future Work and References A Appendix A.1 Win-Stay Lose-Shift Variant: Tie-Stay A.1 Win-Stay Lose-Shift Variant: Tie-Stay A.2 Follow-the-Leader Variant: Limited History A.2 Follow-the-Leader Variant: Limited History A.3 Ellipsoid Mistake Bounds A.3 Ellipsoid Mistake Bounds A.4 Highest Average Payoff Opponent A.4 Highest Average Payoff Opponent 2. Setting We consider two-player, repeated games with the following properties: Symmetric Zero-sum Payoffs are limited to {1, 0, −1} (representing win, tie, and loss respectively) Each action beats at least one other action Each action loses to at least one other action Symmetric Symmetric Zero-sum Zero-sum Payoffs are limited to {1, 0, −1} (representing win, tie, and loss respectively) Payoffs are limited to {1, 0, −1} (representing win, tie, and loss respectively) Each action beats at least one other action Each action beats at least one other action Each action loses to at least one other action Each action loses to at least one other action ▶ Definition 1 (Permissible Game). We refer to a game with the above properties as a permissible game for convenience. ▶ Definition 1 We consider how well we can do without knowing the payoff matrix M or even observing payoffs when playing against a behaviorally-biased opponent who does know M. Our goal will be to win nearly every round. 2.1 Models of behaviorally-biased opponents We assume all opponents know the payoff matrix and play a deterministic, behaviorallybiased strategy. The following are the particular biased adversaries we consider here: Myopic Best Responder Myopic Best Responder The opponent plays a best response to our last action. If there are multiple best responses to our last action, we assume the opponent breaks ties according to some fixed but unknown order over actions. On the first round, since there is no previous action, we assume the opponent plays the first action in that ordering. This is our simplest opponent, who just assumes that tomorrow will be exactly like today. Gambler’s Fallacy Gambler’s Fallacy e opponent plays a best response to our “most overdue” (least-frequently-played) action. This opponent is motivated by the Gambler’s Fallacy, in which one assumes an event which hasn’t happened as often as expected is “overdue” and is more likely to occur (for example, when tossing a fair coin, the belief that Tails is more likely following a series of Heads). If there are multiple best responses to our “most overdue” action, or if there are multiple “most overdue” actions the opponent might try to counter (for example, at the start of the game when all actions are equally “overdue” since they have all been played zero times), the opponent breaks ties using a fixed but unknown order over actions. Win-Stay, Lose-Shift Win-Stay, Lose-Shift The opponent continues to play the same action following a win, and switches to a new action following a loss. We consider two variants of this strategy: one in which the opponent behaves as if a tie is a win, continuing to play the same action after tying, and one in which the opponent behaves as if a tie is a loss, switching actions after tying. We assume that when the opponent shifts actions, they shift to the action immediately following the current action in some fixed but unknown order over actions. In the first round, we assume the opponent plays the first action in that ordering. Win-stay lose-shift was initially proposed as an early algorithm for bandit problems by Robbins [15]. There is also some empirical research indicating that people may play rock-paper-scissors according to an approximate version of this strategy: Wang et al. [19] observed that players are more likely to play the same action again after winning, and more likely to shift to the next action in the sequence rock-paper-scissors following a loss. More broadly, win-stay lose-shift has been proposed as a potential explanation for the evolution of cooperative behavior [13]. This strategy can also be viewed as similar to the hot-hand fallacy, in which one believes an action which has just won is having a “hot streak” and is more likely to win in the future, and conversely believes an action which has just lost is having a “cold” streak and is more likely to lose in the future. Follow the Leader Follow the Leader The opponent plays the best action in retrospect, where the best action in retrospect is the action that would have achieved the highest net payoff against our history of play. If multiple actions are tied for the highest payoff, the opponent breaks ties using some fixed but unknown order over actions. In the first round, when there is no history to choose based on, the opponent plays the first action in that ordering. It has been hypothesized that irrational strategic behavior may be explained by agents playing a misspecified or simplified version of a game rationally [7]. The above strategy is reasonable for an opponent who erroneously models our play as stochastic. In online learning, this strategy is called Follow-the-Leader or FTL (see [16]). We also consider a limited-history variant of this opponent, in which the the opponent only considers the last r rounds, where r > 0. Highest Average Payoff Highest Average Payoff The opponent plays the action that has achieved the highest average payoff over the times they have played it. We assume the opponent initializes the average payoff of each action as 0. If multiple actions are tied for the highest average payoff, the opponent breaks ties using some fixed but unknown order over actions. This is a reasonable strategy for an opponent who believes our play is stochastic, in which case its actions can be viewed as coins with various biases. It also corresponds to a well-known reinforcement learning algorithm, epsilon-greedy Q-learning, with ϵ = 0 (see [17]). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Understanding Bias-Driven Opponent Models in Competitive Gameplay

Welcome to HackerNoon Decoded: Data Science Edition—the ultimate recap of the stories, writers, and trends that defined your 2024! In Data Science, You Found Patterns No One Else Could See You don’t need to be neurospicy to enjoy a good dataset. You just have to be you 😉 If this was your top tech category, you’re part of the 2.94% of readers who know that data science isn’t just about crunching numbers—it’s about uncovering the secrets of the universe (or at least your user metrics). Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Most Read Data Science Stories Here are the top 10 Data Science stories that totally took over 2024: Outlier Detection: What You Need to Know	Natalia by Natalia Ogneva
QA Checks for Big Datasets With Deequ & Statistical Methods by Akshay Jain
Data Science for Portfolio Optimization: Markowitz Mean-Variance Theory by Andrey Kustarev
Dealing with Missing Data in Financial Time Series - Recipes and Pitfalls by Vladimir Kirilin
Google Analytics 4 (GA4) for Beginners—Part 1: Data Collection, Processing, and Account Structure by Asher
Leveraging Data Granularity, Distribution, and Modeling for Effective Product Management by Gevorg Kazaryan
Unlocking the Power of Data Lakes for Embedded Analytics in Multi-Tenant SaaS by Qrvey
How to Specify Data Format in Excel with Python by Luca Liu
Scaling Ethereum: Data Bloat, Data Availability, and the Cloudless Solution by Logos
Beyond the Hype: How Data Annotation Powers Generative AI by Indium Top 10 Data Science Readers These readers couldn't get enough of Data Science content: Lior Barak
Varun Nakra
Natalia Anufrieva
Nuruddeen Sambo
Karolis Didziulis
@shadix0815
Linda Quinn
@hacker-cm4ubmcw10001bz0f1jbohpp4
Madiha Khalid
Matias Top 10 Data Science Writers These prolific writers shaped our content landscape: Configuring
Interoperability
Learn Repo
Alex Merced
Anchoring
Lior Barak
EScholar
Netizenship
Computational Technology
Tech Media Bias Take advantage of this recap and catch up on some of the most-read stories, subscribe to your favorite writers, or start writing yourself— try this writing template. You too could make this list by next year! Thank You, Hacker! We want to take a moment to thank you for your continued support and for choosing HackerNoon as your go-to platform for all things tech. Your engagement, feedback, and passion for sharing knowledge have helped make HackerNoon what it is today. We're grateful to have you as part of this incredible community, and we can’t wait to see what you’ll achieve with us in 2025 and beyond! Curious about HackerNoon’s Global Decoded? Check out the blog post here! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Happy HackerNoon Decoded! Welcome to HackerNoon Decoded: Data Science Edition—the ultimate recap of the stories, writers, and trends that defined your 2024! In Data Science, You Found Patterns No One Else Could See You don’t need to be neurospicy to enjoy a good dataset. You just have to be you 😉 If this was your top tech category, you’re part of the 2.94% of readers who know that data science isn’t just about crunching numbers—it’s about uncovering the secrets of the universe (or at least your user metrics). 2.94% of readers Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Most Read Data Science Stories Here are the top 10 Data Science stories that totally took over 2024: Here are the top 10 Data Science stories that totally took over 2024: Outlier Detection: What You Need to Know	Natalia by Natalia Ogneva QA Checks for Big Datasets With Deequ & Statistical Methods by Akshay Jain Data Science for Portfolio Optimization: Markowitz Mean-Variance Theory by Andrey Kustarev Dealing with Missing Data in Financial Time Series - Recipes and Pitfalls by Vladimir Kirilin Google Analytics 4 (GA4) for Beginners—Part 1: Data Collection, Processing, and Account Structure by Asher Leveraging Data Granularity, Distribution, and Modeling for Effective Product Management by Gevorg Kazaryan Unlocking the Power of Data Lakes for Embedded Analytics in Multi-Tenant SaaS by Qrvey How to Specify Data Format in Excel with Python by Luca Liu Scaling Ethereum: Data Bloat, Data Availability, and the Cloudless Solution by Logos Beyond the Hype: How Data Annotation Powers Generative AI by Indium Outlier Detection: What You Need to Know	Natalia by Natalia Ogneva Outlier Detection: What You Need to Know	Natalia Natalia Ogneva QA Checks for Big Datasets With Deequ & Statistical Methods by Akshay Jain QA Checks for Big Datasets With Deequ & Statistical Methods Akshay Jain Data Science for Portfolio Optimization: Markowitz Mean-Variance Theory by Andrey Kustarev Data Science for Portfolio Optimization: Markowitz Mean-Variance Theory Andrey Kustarev Dealing with Missing Data in Financial Time Series - Recipes and Pitfalls by Vladimir Kirilin Dealing with Missing Data in Financial Time Series - Recipes and Pitfalls Vladimir Kirilin Google Analytics 4 (GA4) for Beginners—Part 1: Data Collection, Processing, and Account Structure by Asher Google Analytics 4 (GA4) for Beginners—Part 1: Data Collection, Processing, and Account Structure Asher Leveraging Data Granularity, Distribution, and Modeling for Effective Product Management by Gevorg Kazaryan Leveraging Data Granularity, Distribution, and Modeling for Effective Product Management Gevorg Kazaryan Unlocking the Power of Data Lakes for Embedded Analytics in Multi-Tenant SaaS by Qrvey Unlocking the Power of Data Lakes for Embedded Analytics in Multi-Tenant SaaS Qrvey How to Specify Data Format in Excel with Python by Luca Liu How to Specify Data Format in Excel with Python Luca Liu Scaling Ethereum: Data Bloat, Data Availability, and the Cloudless Solution by Logos Scaling Ethereum: Data Bloat, Data Availability, and the Cloudless Solution Logos Beyond the Hype: How Data Annotation Powers Generative AI by Indium Beyond the Hype: How Data Annotation Powers Generative AI Indium Top 10 Data Science Readers These readers couldn't get enough of Data Science content: These readers couldn't get enough of Data Science content: Lior Barak Varun Nakra Natalia Anufrieva Nuruddeen Sambo Karolis Didziulis @shadix0815 Linda Quinn @hacker-cm4ubmcw10001bz0f1jbohpp4 Madiha Khalid Matias Lior Barak Lior Barak Varun Nakra Varun Nakra Natalia Anufrieva Natalia Anufrieva Nuruddeen Sambo Nuruddeen Sambo Karolis Didziulis Karolis Didziulis @shadix0815 @shadix0815 Linda Quinn Linda Quinn @hacker-cm4ubmcw10001bz0f1jbohpp4 @hacker-cm4ubmcw10001bz0f1jbohpp4 Madiha Khalid Madiha Khalid Matias Matias Top 10 Data Science Writers These prolific writers shaped our content landscape: These prolific writers shaped our content landscape: Configuring Interoperability Learn Repo Alex Merced Anchoring Lior Barak EScholar Netizenship Computational Technology Tech Media Bias Configuring Configuring Interoperability Interoperability Learn Repo Learn Repo Alex Merced Alex Merced Anchoring Anchoring Lior Barak Lior Barak EScholar EScholar Netizenship Netizenship Computational Technology Computational Technology Tech Media Bias Tech Media Bias Take advantage of this recap and catch up on some of the most-read stories, subscribe to your favorite writers, or start writing yourself— try this writing template. You too could make this list by next year! Take advantage of this recap and catch up on some of the most-read stories, subscribe to your favorite writers, or start writing yourself — try this writing template . You too could make this list by next year! start writing yourself this writing template Thank You, Hacker! We want to take a moment to thank you for your continued support and for choosing HackerNoon as your go-to platform for all things tech. Your engagement, feedback, and passion for sharing knowledge have helped make HackerNoon what it is today. We're grateful to have you as part of this incredible community, and we can’t wait to see what you’ll achieve with us in 2025 and beyond! Curious about HackerNoon’s Global Decoded? Check out the blog post here! Curious about HackerNoon’s Global Decoded? Check out the blog post here ! Check out the blog post here Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Happy HackerNoon Decoded!

HackerNoon Decoded 2024: Celebrating Our Data Science Community!

HackerNoon ዲኮድድ 2024፡ የውሂብ ሳይንስ ማህበረሰባችንን በማክበር ላይ!

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HackerNoon Decoded 2024: ¡Celebramos nuestra comunidad de ciencia de datos!

HackerNoon Decoded 2024 : Célébrons notre communauté de science des données !

HackerNoon Decoded 2024: Slavimo našu zajednicu znanosti o podacima!

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HackerNoon Decoded 2024: на честь нашої спільноти Data Science!

Make your class hierarchy clear and flexible TL;DR: Extract a common abstract class to mimic real-world structure. Problems Addressed Duplicate Code
Inappropriate Inheritance
Shotgun Surgery
Big Class
Feature Envy
High Coupling
Concrete classes subclassified
Parent class not abstract Related Code Smells https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xiv?embedable=true https://hackernoon.com/code-smell-255-addressing-parallel-hierarchies-in-code?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xiii?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-ix-7rr33ol?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxxiii?embedable=true Steps Identify common behaviors in both classes.


Create an abstract class with shared behavior and no implementation.


Move common logic to the abstract class.


Update subclasses to inherit from the abstract class. Sample Code Before class Car {
    void drive() {
        System.out.println("Driving a car");
    }
}

class Truck extends Car {
    void load() {
        System.out.println("Loading cargo");
    }

    void unload() {
        System.out.println("Unloading cargo");
    }
}

// Truck reuses driving method
// Overriding it would be another code smell
// Violating Liskov Substitution rule After abstract class Vehicle {
    // 2. Create an abstract class
    // with shared behavior and no implementation
    abstract void drive();
    // 1. Identify common behaviors in both classes
    // 3. Move common logic to the abstract class
}

class Car extends Vehicle {
    // 4. Update subclasses to inherit from the abstract class
    void drive() {
        System.out.println("Driving a car");
    }
}

class Truck extends Vehicle {
    // 4. Update subclasses to inherit from the abstract class
    void drive() {
        System.out.println("Driving a truck");
        // Implementation is different than the car's
    }

    void load() {
        System.out.println("Loading cargo");
    }

    void unload() {
        System.out.println("Unloading cargo");
    }
} Type [x]Semi-Automatic Safety This refactoring is safe if you identify all common behaviors correctly and move one method at a time running the tests. Why is the Code Better? It reduces duplication, simplifies maintenance, and makes it easier to extend functionality by adding new concrete realizations. How Does it Improve the Bijection? By introducing an abstract class, the code better reflects the real-world hierarchy, creating a clear relationship between the generic and specific types. Refactor with AI Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini



Meta AI

Meta AI Tags Inheritance Related Refactorings https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true See also https://refactoring.guru/es/extract-superclass?embedable=true Credits Image by Pexels on Pixabay This article is part of the Refactoring Series. https://maximilianocontieri.com/how-to-improve-your-code-with-easy-refactorings?embedable=true Make your class hierarchy clear and flexible Make your class hierarchy clear and flexible TL;DR: Extract a common abstract class to mimic real-world structure. TL;DR: Extract a common abstract class to mimic real-world structure. Problems Addressed Duplicate Code Inappropriate Inheritance Shotgun Surgery Big Class Feature Envy High Coupling Concrete classes subclassified Parent class not abstract Duplicate Code Duplicate Code Inappropriate Inheritance Shotgun Surgery Shotgun Surgery Big Class Feature Envy Feature Envy High Coupling Concrete classes subclassified Concrete classes subclassified Parent class not abstract not abstract Related Code Smells https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xiv?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xiv?embedable=true https://hackernoon.com/code-smell-255-addressing-parallel-hierarchies-in-code?embedable=true https://hackernoon.com/code-smell-255-addressing-parallel-hierarchies-in-code?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xiii?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xiii?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-ix-7rr33ol?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-ix-7rr33ol?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxxiii?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxxiii?embedable=true Steps Identify common behaviors in both classes. Create an abstract class with shared behavior and no implementation. Move common logic to the abstract class. Update subclasses to inherit from the abstract class. Identify common behaviors in both classes. Identify common behaviors in both classes. Create an abstract class with shared behavior and no implementation. Create an abstract class with shared behavior and no implementation. Move common logic to the abstract class. Move common logic to the abstract class. Update subclasses to inherit from the abstract class. Update subclasses to inherit from the abstract class. Sample Code Before class Car {
    void drive() {
        System.out.println("Driving a car");
    }
}

class Truck extends Car {
    void load() {
        System.out.println("Loading cargo");
    }

    void unload() {
        System.out.println("Unloading cargo");
    }
}

// Truck reuses driving method
// Overriding it would be another code smell
// Violating Liskov Substitution rule class Car {
    void drive() {
        System.out.println("Driving a car");
    }
}

class Truck extends Car {
    void load() {
        System.out.println("Loading cargo");
    }

    void unload() {
        System.out.println("Unloading cargo");
    }
}

// Truck reuses driving method
// Overriding it would be another code smell
// Violating Liskov Substitution rule After abstract class Vehicle {
    // 2. Create an abstract class
    // with shared behavior and no implementation
    abstract void drive();
    // 1. Identify common behaviors in both classes
    // 3. Move common logic to the abstract class
}

class Car extends Vehicle {
    // 4. Update subclasses to inherit from the abstract class
    void drive() {
        System.out.println("Driving a car");
    }
}

class Truck extends Vehicle {
    // 4. Update subclasses to inherit from the abstract class
    void drive() {
        System.out.println("Driving a truck");
        // Implementation is different than the car's
    }

    void load() {
        System.out.println("Loading cargo");
    }

    void unload() {
        System.out.println("Unloading cargo");
    }
} abstract class Vehicle {
    // 2. Create an abstract class
    // with shared behavior and no implementation
    abstract void drive();
    // 1. Identify common behaviors in both classes
    // 3. Move common logic to the abstract class
}

class Car extends Vehicle {
    // 4. Update subclasses to inherit from the abstract class
    void drive() {
        System.out.println("Driving a car");
    }
}

class Truck extends Vehicle {
    // 4. Update subclasses to inherit from the abstract class
    void drive() {
        System.out.println("Driving a truck");
        // Implementation is different than the car's
    }

    void load() {
        System.out.println("Loading cargo");
    }

    void unload() {
        System.out.println("Unloading cargo");
    }
} Type [x]Semi-Automatic [x] Semi-Automatic [x] Safety This refactoring is safe if you identify all common behaviors correctly and move one method at a time running the tests. Why is the Code Better? It reduces duplication, simplifies maintenance, and makes it easier to extend functionality by adding new concrete realizations. How Does it Improve the Bijection? By introducing an abstract class, the code better reflects the real-world hierarchy, creating a clear relationship between the generic and specific types. real-world Refactor with AI Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini



Meta AI

Meta AI Without Proper Instructions

With Specific Instructions ChatGPT

ChatGPT Claude

Claude Perplexity

Perplexity Copilot

Copilot Gemini

Gemini Meta AI

Meta AI Without Proper Instructions With Specific Instructions Without Proper Instructions Without Proper Instructions With Specific Instructions With Specific Instructions ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT Claude Claude Claude Claude Claude Claude Claude Claude Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Copilot Copilot Copilot Copilot Copilot Copilot Copilot Copilot Gemini Gemini Gemini Gemini Gemini Gemini Gemini Gemini Meta AI Meta AI Meta AI Meta AI Meta AI Meta AI Meta AI Meta AI Tags Inheritance Inheritance Related Refactorings https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true https://hackernoon.com/refactoring-013-eliminating-repeated-code-with-dry-principles?embedable=true See also https://refactoring.guru/es/extract-superclass?embedable=true https://refactoring.guru/es/extract-superclass?embedable=true Credits Image by Pexels on Pixabay Pexels Pixabay This article is part of the Refactoring Series. https://maximilianocontieri.com/how-to-improve-your-code-with-easy-refactorings?embedable=true https://maximilianocontieri.com/how-to-improve-your-code-with-easy-refactorings?embedable=true

Refactoring 022 - Extract Common Ancestor

Welcome to HackerNoon Decoded: Cybersecurity Edition—the ultimate recap of the stories, writers, and trends that defined your 2024! From Firewalls To Zero-Days, You Were Always One Step Ahead Just like a phishing scam, you’re too good to be true. If this was your top tech category, you’ve made it to the elite 9.96% of Cybersecurity readers. Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Most Read Cybersecurity Stories In 2024, you just couldn’t resist a good story, so here are the top 10 you might have missed: Unmasking VEILDrive: Threat Actors Exploit Microsoft Services for Command & Control by Hunters
Cross-Site Scripting (XSS) Vulnerabilities: Testing Strategies and Examples by Konstantin Sakhchinskiy
Intro to Digital Fingerprints: Understanding, Manipulating, and Defending Against Online Tracking by Konstantin Sakhchinskiy
Cybersecurity Essentials: Practical Web App Security Testing Tips for QA Engineers by Konstantin Sakhchinskiy
What Are Wallet Drainers? How to Protect Yourself From Them! by Hackless
Cybersecurity Tips: Vulnerability Scanners Essentials by Konstantin Sakhchinskiy
CSI Linux: Linux Distribution for Cyber and OSINT Investigation by Oleksandr Nadtoka
What's wrong with stickers in Telegram? Deanonymize anonymous channels in two clicks by Maksim Rogov
Defending Your Web App: A Guide to Rate Limiting and Brute Force Attack Prevention by Konstantin Sakhchinskiy
Never Rely on UUID for Authentication: Generation Vulnerabilities and Best Practices by Ivan Mochalov Top 10 Cybersecurity Readers These readers couldn't get enough of Cybersecurity content: Zac Amos
Mesbaul Arfeen Haidari
CyberNewswire
@hacker003
Blackheart
B1GF3LLA
Haina Dong
Konstantin Sakhchinskiy
DSV Consulting LLC
hackernoonbarman102 Top 10 Cybersecurity Writers These prolific writers shaped our content landscape: CyberNewswire
The White House
@encapsulation
Zac Amos
Blackheart
CryptoColumns
Technology News Australia
EScholar
MultiThreading.Tech
BotBeat.Tech Take advantage of this recap and catch up on some of the most-read stories, subscribe to your favorite writers, or start writing yourself— try this writing template. You too could make this list by next year! Thank You, Hacker! We want to take a moment to thank you for your continued support and for choosing HackerNoon as your go-to platform for all things tech. Your engagement, feedback, and passion for sharing knowledge have helped make HackerNoon what it is today. We're grateful to have you as part of this incredible community, and we can’t wait to see what you’ll achieve with us in 2025 and beyond! Curious about HackerNoon’s Global Decoded? Check out the blog post here! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Happy HackerNoon Decoded! Welcome to HackerNoon Decoded: Cybersecurity Edition—the ultimate recap of the stories, writers, and trends that defined your 2024! From Firewalls To Zero-Days, You Were Always One Step Ahead Just like a phishing scam, you’re too good to be true. If this was your top tech category, you’ve made it to the elite 9.96% of Cybersecurity readers . 9.96% of Cybersecurity readers Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Most Read Cybersecurity Stories In 2024, you just couldn’t resist a good story, so here are the top 10 you might have missed: In 2024, you just couldn’t resist a good story, so here are the top 10 you might have missed: Unmasking VEILDrive: Threat Actors Exploit Microsoft Services for Command & Control by Hunters Cross-Site Scripting (XSS) Vulnerabilities: Testing Strategies and Examples by Konstantin Sakhchinskiy Intro to Digital Fingerprints: Understanding, Manipulating, and Defending Against Online Tracking by Konstantin Sakhchinskiy Cybersecurity Essentials: Practical Web App Security Testing Tips for QA Engineers by Konstantin Sakhchinskiy What Are Wallet Drainers? How to Protect Yourself From Them! by Hackless Cybersecurity Tips: Vulnerability Scanners Essentials by Konstantin Sakhchinskiy CSI Linux: Linux Distribution for Cyber and OSINT Investigation by Oleksandr Nadtoka What's wrong with stickers in Telegram? Deanonymize anonymous channels in two clicks by Maksim Rogov Defending Your Web App: A Guide to Rate Limiting and Brute Force Attack Prevention by Konstantin Sakhchinskiy Never Rely on UUID for Authentication: Generation Vulnerabilities and Best Practices by Ivan Mochalov Unmasking VEILDrive: Threat Actors Exploit Microsoft Services for Command & Control by Hunters Unmasking VEILDrive: Threat Actors Exploit Microsoft Services for Command & Control Hunters Cross-Site Scripting (XSS) Vulnerabilities: Testing Strategies and Examples by Konstantin Sakhchinskiy Cross-Site Scripting (XSS) Vulnerabilities: Testing Strategies and Examples Konstantin Sakhchinskiy Intro to Digital Fingerprints: Understanding, Manipulating, and Defending Against Online Tracking by Konstantin Sakhchinskiy Intro to Digital Fingerprints: Understanding, Manipulating, and Defending Against Online Tracking Konstantin Sakhchinskiy Cybersecurity Essentials: Practical Web App Security Testing Tips for QA Engineers by Konstantin Sakhchinskiy Cybersecurity Essentials: Practical Web App Security Testing Tips for QA Engineers Konstantin Sakhchinskiy What Are Wallet Drainers? How to Protect Yourself From Them! by Hackless What Are Wallet Drainers? How to Protect Yourself From Them! Hackless Cybersecurity Tips: Vulnerability Scanners Essentials by Konstantin Sakhchinskiy Cybersecurity Tips: Vulnerability Scanners Essentials Konstantin Sakhchinskiy CSI Linux: Linux Distribution for Cyber and OSINT Investigation by Oleksandr Nadtoka CSI Linux: Linux Distribution for Cyber and OSINT Investigation Oleksandr Nadtoka What's wrong with stickers in Telegram? Deanonymize anonymous channels in two clicks by Maksim Rogov What's wrong with stickers in Telegram? Deanonymize anonymous channels in two clicks Maksim Rogov Defending Your Web App: A Guide to Rate Limiting and Brute Force Attack Prevention by Konstantin Sakhchinskiy Defending Your Web App: A Guide to Rate Limiting and Brute Force Attack Prevention Konstantin Sakhchinskiy Never Rely on UUID for Authentication: Generation Vulnerabilities and Best Practices by Ivan Mochalov Never Rely on UUID for Authentication: Generation Vulnerabilities and Best Practices Ivan Mochalov Top 10 Cybersecurity Readers These readers couldn't get enough of Cybersecurity content: These readers couldn't get enough of Cybersecurity content: Zac Amos Mesbaul Arfeen Haidari CyberNewswire @hacker003 Blackheart B1GF3LLA Haina Dong Konstantin Sakhchinskiy DSV Consulting LLC hackernoonbarman102 Zac Amos Zac Amos Mesbaul Arfeen Haidari Mesbaul Arfeen Haidari CyberNewswire CyberNewswire @hacker003 @hacker003 Blackheart Blackheart B1GF3LLA B1GF3LLA Haina Dong Haina Dong Konstantin Sakhchinskiy Konstantin Sakhchinskiy DSV Consulting LLC DSV Consulting LLC hackernoonbarman102 hackernoonbarman102 Top 10 Cybersecurity Writers These prolific writers shaped our content landscape: These prolific writers shaped our content landscape: CyberNewswire The White House @encapsulation Zac Amos Blackheart CryptoColumns Technology News Australia EScholar MultiThreading.Tech BotBeat.Tech CyberNewswire CyberNewswire The White House The White House @encapsulation @encapsulation Zac Amos Zac Amos Blackheart Blackheart CryptoColumns CryptoColumns Technology News Australia Technology News Australia EScholar EScholar MultiThreading.Tech MultiThreading.Tech BotBeat.Tech BotBeat.Tech Take advantage of this recap and catch up on some of the most-read stories, subscribe to your favorite writers, or start writing yourself— try this writing template. You too could make this list by next year! Take advantage of this recap and catch up on some of the most-read stories, subscribe to your favorite writers, or start writing yourself — try this writing template . You too could make this list by next year! start writing yourself this writing template Thank You, Hacker! We want to take a moment to thank you for your continued support and for choosing HackerNoon as your go-to platform for all things tech. Your engagement, feedback, and passion for sharing knowledge have helped make HackerNoon what it is today. We're grateful to have you as part of this incredible community, and we can’t wait to see what you’ll achieve with us in 2025 and beyond! Curious about HackerNoon’s Global Decoded? Check out the blog post here! Curious about HackerNoon’s Global Decoded? Check out the blog post here ! Check out the blog post here Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Happy HackerNoon Decoded!

HackerNoon Decoded 2024: Celebrating Our Cybersecurity Community!

HackerNoon Decoded 2024፡ የሳይበር ደህንነት ማህበረሰባችንን በማክበር ላይ!

HackerNoon Decoded 2024: ¡Celebramos nuestra comunidad de ciberseguridad!

HackerNoon Descoded 2024: Gure Zibersegurtasun Komunitatea ospatzen!

HackerNoon Decoded 2024: Selebre Kominote Cybersecurity nou an!

HackerNoon Decoded 2024. Տոնում ենք մեր կիբերանվտանգության համայնքը:

HackerNoon מפוענח 2024: חוגגים את קהילת אבטחת הסייבר שלנו!

HackerNoon Decoded 2024: サイバーセキュリティ コミュニティを祝福します!

HackerNoon ឌិកូដឆ្នាំ 2024៖ អបអរសាទរសហគមន៍សុវត្ថិភាពអ៊ីនធឺណិតរបស់យើង!

HackerNoon тайлагдсан 2024: Манай Кибер аюулгүй байдлын нийгэмлэгийн өдрийг тэмдэглэж байна!

HackerNoon Decoded 2024: Kwizihiza Umuryango Wumutekano Wacu!

ХацкерНоон Децодед 2024: Славимо нашу заједницу сајбер безбедности!

HackerNoon Decoded 2024: Ipinagdiriwang ang Aming Cybersecurity Community!

Author:
(1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Table of Links Abstract and 1 Introduction 2 Related Work 3 Preliminaries 4 Approach 5.1 Experimental Setup 5.2 Performance of Dynamic Frontier PageRank 5.3 Strong Scaling of Dynamic Frontier PageRan 6 Conclusion, Acknowledgments, and References 5. EVALUATION 5.1 Experimental Setup 5.1.1 System used. We conduct experiments on a system equipped with an AMD EPYC-7742 processor, with 64 cores and operating at a frequency of 2.25 GHz. Each core has a 4 MB L1 cache, a 32 MB L2 cache, and shares a 256 MB L3 cache. The server is configured with 512 GB of DDR4 system memory and operates on Ubuntu 20.04. 5.1.3 Dataset. We use four graph classes sourced from the SuiteSparse Matrix Collection [14], as detailed in Table 1. The number of vertices in these graphs range from 3.07 million to 214 million, with edge counts spanning from 37.4 million to 1.98 billion. To address the impact of dead ends (vertices lacking out-links), a global teleport rank computation is needed in each iteration. We mitigate this overhead by adding self-loops to all vertices in the graph [1, 15]. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Author: (1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Author: Author: (1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Approach 4 Approach 5.1 Experimental Setup 5.1 Experimental Setup 5.2 Performance of Dynamic Frontier PageRank 5.2 Performance of Dynamic Frontier PageRank 5.3 Strong Scaling of Dynamic Frontier PageRan 5.3 Strong Scaling of Dynamic Frontier PageRan 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 5. EVALUATION 5.1 Experimental Setup 5.1.1 System used. We conduct experiments on a system equipped with an AMD EPYC-7742 processor, with 64 cores and operating at a frequency of 2.25 GHz. Each core has a 4 MB L1 cache, a 32 MB L2 cache, and shares a 256 MB L3 cache. The server is configured with 512 GB of DDR4 system memory and operates on Ubuntu 20.04. 5.1.1 System used. 5.1.3 Dataset. We use four graph classes sourced from the SuiteSparse Matrix Collection [14], as detailed in Table 1. The number of vertices in these graphs range from 3.07 million to 214 million, with edge counts spanning from 37.4 million to 1.98 billion. To address the impact of dead ends (vertices lacking out-links), a global teleport rank computation is needed in each iteration. We mitigate this overhead by adding self-loops to all vertices in the graph [1, 15]. 5.1.3 Dataset. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Dynamic Frontier PageRank Achieves Efficiency and Accuracy with Batch Updates and Parallel Computing

Author:
(1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Table of Links Abstract and 1 Introduction 2 Related Work 3 Preliminaries 4 Approach 5.1 Experimental Setup 5.2 Performance of Dynamic Frontier PageRank 5.3 Strong Scaling of Dynamic Frontier PageRan 6 Conclusion, Acknowledgments, and References 2. RELATED WORK A number of approaches have been proposed for performing incremental computation (updating PageRank values in a dynamic / evolving graph) of approximate PageRank. Chien et al. [6] identify a tiny region of the graph near the updated vertices and model the remainder of the graph as a single vertex in a new, much smaller graph. PageRanks are computed for the small graph and then transferred to the original graph. Chen et al. [5] propose a number of methods to estimate the PageRank score of a particular web page using only a small subgraph of the entire web, by expanding backwards from the target node following reverse hyperlinks. Bahmani et al. [2] analyze the efficiency of Monte Carlo methods for incremental computation of PageRank. Zhan et al. [24] propose a Monte Carlo based algorithm for PageRank tracking on dynamic networks, by maintaining 𝑅 random walks starting from each node. Pashikanti et al. [20] also follow a similar approach for updating PageRank scores on vertex and edge insertion/deletion. A few approaches have been proposed for updating exact PageRank scores on dynamic graphs. Zhang [25] presents a simple incremental Pagerank computation system for dynamic graphs on hybrid CPU and GPU platforms that incorporates the Update-GatherApply-Scatter (UGAS) computation model. A common approach used for Dynamic PageRank algorithm, given a small change to the input graph, is to find the affected region in the preprocessing step with Breadth-First Search (BFS) or Depth-First Search (DFS) traversal from the vertices connecting the edges that were inserted or deleted, and computing PageRanks only for that region [7, 12, 13, 22]. This approach was originally proposed by Desikan et al. [7]. Kim and Choi [13] use this approach with an asynchronous implementation of PageRank. Giri et al. [12] use this approach with collaborative executions on muti-core CPUs and massively parallel GPUs. Sahu et al. [22] use this approach on a Strongly Connected Component (SCC) based decomposition of the graph to limit the computation to SCCs that are reachable from updated vertices, on multi-core CPUs and GPUs (separately). Ohsaka et al. [18] propose an approach for locally updating PageRank using the Gauss-Southwell method, where the vertex with the greatest residual is updated first — however, their algorithm is inherently sequential. Further, Bahmani et al. [3] propose an algorithm to selectively crawl a small portion of the web to provide an estimate of true PageRank of the graph at that moment, while Berberich et al. [4] present a method to compute normalized PageRank scores that are robust to non-local changes in the graph. Their approaches are orthogonal to our Dynamic Frontier approach which focuses on the computation of the PageRank vector itself, not on the process of crawling the web or maintaining normalized scores. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Author: (1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Author: Author: (1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Approach 4 Approach 5.1 Experimental Setup 5.1 Experimental Setup 5.2 Performance of Dynamic Frontier PageRank 5.2 Performance of Dynamic Frontier PageRank 5.3 Strong Scaling of Dynamic Frontier PageRan 5.3 Strong Scaling of Dynamic Frontier PageRan 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 2. RELATED WORK A number of approaches have been proposed for performing incremental computation (updating PageRank values in a dynamic / evolving graph) of approximate PageRank. Chien et al. [6] identify a tiny region of the graph near the updated vertices and model the remainder of the graph as a single vertex in a new, much smaller graph. PageRanks are computed for the small graph and then transferred to the original graph. Chen et al. [5] propose a number of methods to estimate the PageRank score of a particular web page using only a small subgraph of the entire web, by expanding backwards from the target node following reverse hyperlinks. Bahmani et al. [2] analyze the efficiency of Monte Carlo methods for incremental computation of PageRank. Zhan et al. [24] propose a Monte Carlo based algorithm for PageRank tracking on dynamic networks, by maintaining 𝑅 random walks starting from each node. Pashikanti et al. [20] also follow a similar approach for updating PageRank scores on vertex and edge insertion/deletion. A few approaches have been proposed for updating exact PageRank scores on dynamic graphs. Zhang [25] presents a simple incremental Pagerank computation system for dynamic graphs on hybrid CPU and GPU platforms that incorporates the Update-GatherApply-Scatter (UGAS) computation model. A common approach used for Dynamic PageRank algorithm, given a small change to the input graph, is to find the affected region in the preprocessing step with Breadth-First Search (BFS) or Depth-First Search (DFS) traversal from the vertices connecting the edges that were inserted or deleted, and computing PageRanks only for that region [7, 12, 13, 22]. This approach was originally proposed by Desikan et al. [7]. Kim and Choi [13] use this approach with an asynchronous implementation of PageRank. Giri et al. [12] use this approach with collaborative executions on muti-core CPUs and massively parallel GPUs. Sahu et al. [22] use this approach on a Strongly Connected Component (SCC) based decomposition of the graph to limit the computation to SCCs that are reachable from updated vertices, on multi-core CPUs and GPUs (separately). Ohsaka et al. [18] propose an approach for locally updating PageRank using the Gauss-Southwell method, where the vertex with the greatest residual is updated first — however, their algorithm is inherently sequential. Further, Bahmani et al. [3] propose an algorithm to selectively crawl a small portion of the web to provide an estimate of true PageRank of the graph at that moment, while Berberich et al. [4] present a method to compute normalized PageRank scores that are robust to non-local changes in the graph. Their approaches are orthogonal to our Dynamic Frontier approach which focuses on the computation of the PageRank vector itself, not on the process of crawling the web or maintaining normalized scores. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Comparative Analysis of Incremental Methods for Updating PageRank on Dynamic Graphs

Welcome to HackerNoon Decoded: Cloud Edition—the ultimate recap of the stories, writers, and trends that defined your 2024! You Didn’t Just Use The Cloud, You Architected It Why take your head out of the clouds when delulu is the solulu? If this was your top tech category, you’re one of the 1.53% of users who can’t get enough of DevOps and AWS stories, using them as your personal guide into the big, fluffy Cloud. Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Most Read Cloud Stories Here are the top 10 stories that made the Cloud Category pop in 2024: How to Earn $1 Million With AWS in One Year by Gianpi Colonna
Everything You Need to Know to Implement Generative AI for Your Enterprise by MinIO
Smooth Sailing: Transitioning from Docker to Localhost by MinIO
An Architect’s Guide to Building Reference Architecture for an AI/ML Datalake by MinIO
Backing Up Weaviate with MinIO S3 Buckets to Achieve Strategic Enhancement to Data Management by MinIO
LanceDB: Your Trusted Steed in the Duel Against Data Complexity by MinIO
Supercharge TileDB Engine with MinIO by MinIO
All You Need to Know to Repatriate from AWS S3 to MinIO by MinIO
Hungry GPUs Need Fast Object Storage by MinIO
The System Design Cheat Sheet: Relational Databases (Part 1) by Aleksandr Gavrilenko You really are suckers for MinIO, uh?! Top 10 Cloud Readers These readers couldn't get enough of Cloud content: Alexander Sharov
Abraham Dahunsi
Suyambukani Lakshmanan
@hacker002
Aahil
Anshul Ka
Shrey Sharma
hackerclxw9e7y000002e6k5giodsr4
Malar
Emmanuel Umeajaghaku Top 10 Cloud Writers These prolific writers shaped our content landscape: MinIO
Shirley H.
Alexander Sharov
Suyambukani Lakshmanan
Computational Technology
Rockset
Jon Stojan Media
Tyler Hawkins
Alejandro Duarte
Gilad David Maayan Take advantage of this recap and catch up on some of the most-read stories, subscribe to your favorite writers, or start writing yourself— try this writing template. You too could make this list by next year! Thank You, Hacker! We want to take a moment to thank you for your continued support and for choosing HackerNoon as your go-to platform for all things tech. Your engagement, feedback, and passion for sharing knowledge have helped make HackerNoon what it is today. We're grateful to have you as part of this incredible community, and we can’t wait to see what you’ll achieve with us in 2025 and beyond! Curious about HackerNoon’s Global Decoded? Check out the blog post here! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Happy HackerNoon Decoded! Welcome to HackerNoon Decoded: Cloud Edition—the ultimate recap of the stories, writers, and trends that defined your 2024! You Didn’t Just Use The Cloud, You Architected It Why take your head out of the clouds when delulu is the solulu? If this was your top tech category, you’re one of the 1.53% of users who can’t get enough of DevOps and AWS stories, using them as your personal guide into the big, fluffy Cloud. 1.53% of users Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Most Read Cloud Stories Here are the top 10 stories that made the Cloud Category pop in 2024: Here are the top 10 stories that made the Cloud Category pop in 2024: pop How to Earn $1 Million With AWS in One Year by Gianpi Colonna Everything You Need to Know to Implement Generative AI for Your Enterprise by MinIO Smooth Sailing: Transitioning from Docker to Localhost by MinIO An Architect’s Guide to Building Reference Architecture for an AI/ML Datalake by MinIO Backing Up Weaviate with MinIO S3 Buckets to Achieve Strategic Enhancement to Data Management by MinIO LanceDB: Your Trusted Steed in the Duel Against Data Complexity by MinIO Supercharge TileDB Engine with MinIO by MinIO All You Need to Know to Repatriate from AWS S3 to MinIO by MinIO Hungry GPUs Need Fast Object Storage by MinIO The System Design Cheat Sheet: Relational Databases (Part 1) by Aleksandr Gavrilenko How to Earn $1 Million With AWS in One Year by Gianpi Colonna How to Earn $1 Million With AWS in One Year Gianpi Colonna Everything You Need to Know to Implement Generative AI for Your Enterprise by MinIO Everything You Need to Know to Implement Generative AI for Your Enterprise MinIO Smooth Sailing: Transitioning from Docker to Localhost by MinIO Smooth Sailing: Transitioning from Docker to Localhost MinIO An Architect’s Guide to Building Reference Architecture for an AI/ML Datalake by MinIO An Architect’s Guide to Building Reference Architecture for an AI/ML Datalake MinIO Backing Up Weaviate with MinIO S3 Buckets to Achieve Strategic Enhancement to Data Management by MinIO Backing Up Weaviate with MinIO S3 Buckets to Achieve Strategic Enhancement to Data Management MinIO LanceDB: Your Trusted Steed in the Duel Against Data Complexity by MinIO LanceDB: Your Trusted Steed in the Duel Against Data Complexity MinIO Supercharge TileDB Engine with MinIO by MinIO Supercharge TileDB Engine with MinIO MinIO All You Need to Know to Repatriate from AWS S3 to MinIO by MinIO All You Need to Know to Repatriate from AWS S3 to MinIO MinIO Hungry GPUs Need Fast Object Storage by MinIO Hungry GPUs Need Fast Object Storage MinIO The System Design Cheat Sheet: Relational Databases (Part 1) by Aleksandr Gavrilenko The System Design Cheat Sheet: Relational Databases (Part 1) Aleksandr Gavrilenko You really are suckers for MinIO, uh?! Top 10 Cloud Readers These readers couldn't get enough of Cloud content: These readers couldn't get enough of Cloud content: Alexander Sharov Abraham Dahunsi Suyambukani Lakshmanan @hacker002 Aahil Anshul Ka Shrey Sharma hackerclxw9e7y000002e6k5giodsr4 Malar Emmanuel Umeajaghaku Alexander Sharov Alexander Sharov Abraham Dahunsi Abraham Dahunsi Suyambukani Lakshmanan Suyambukani Lakshmanan @hacker002 @hacker002 Aahil Aahil Anshul Ka Anshul Ka Shrey Sharma Shrey Sharma hackerclxw9e7y000002e6k5giodsr4 hackerclxw9e7y000002e6k5giodsr4 Malar Malar Emmanuel Umeajaghaku Emmanuel Umeajaghaku Top 10 Cloud Writers These prolific writers shaped our content landscape: These prolific writers shaped our content landscape: MinIO Shirley H. Alexander Sharov Suyambukani Lakshmanan Computational Technology Rockset Jon Stojan Media Tyler Hawkins Alejandro Duarte Gilad David Maayan MinIO MinIO Shirley H. Shirley H. Alexander Sharov Alexander Sharov Suyambukani Lakshmanan Suyambukani Lakshmanan Computational Technology Computational Technology Rockset Rockset Jon Stojan Media Jon Stojan Media Tyler Hawkins Tyler Hawkins Alejandro Duarte Alejandro Duarte Gilad David Maayan Gilad David Maayan Take advantage of this recap and catch up on some of the most-read stories, subscribe to your favorite writers, or start writing yourself— try this writing template. You too could make this list by next year! Take advantage of this recap and catch up on some of the most-read stories, subscribe to your favorite writers, or start writing yourself — try this writing template. You too could make this list by next year! start writing yourself this writing template. Thank You, Hacker! We want to take a moment to thank you for your continued support and for choosing HackerNoon as your go-to platform for all things tech. Your engagement, feedback, and passion for sharing knowledge have helped make HackerNoon what it is today. We're grateful to have you as part of this incredible community, and we can’t wait to see what you’ll achieve with us in 2025 and beyond! Curious about HackerNoon’s Global Decoded? Check out the blog post here! Curious about HackerNoon’s Global Decoded? Check out the blog post here ! Check out the blog post here Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Dive into Your HackerNoon 2024 Decoded—Explore Your Data On Your Profile Page Now! Happy HackerNoon Decoded!

HackerNoon Decoded 2024: Celebrating Our Cloud  Community!

HackerNoon Decoded 2024: ¡Celebramos nuestra comunidad en la nube!

HackerNoon Decoded 2024: Gure Hodeiko Komunitatea ospatzen!

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China Is Already Beating the US in Quantum Computing

Introduction SQL is not just about querying data—it also includes powerful commands to modify data within tables. These Data Manipulation Language (DML) commands such as INSERT, UPDATE, and DELETE, enable you to add, modify, or remove rows in a database. In this article, we’ll explore these commands with practical examples. Sample Table: Employees Let’s start with a sample Employees table to demonstrate the examples: EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

70000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

72000

IT



4

Diana

Manager

90000

HR



5

Eve

Developer

70000

IT 1. INSERT: Adding Rows to a Table The INSERT command is used to add new rows to a table. Task: Add a new employee, Frank, who is a Tester in the QA department with a salary of $60,000. INSERT INTO Employees (EmployeeID, Name, Position, Salary, Department)
VALUES (6, 'Frank', 'Tester', 60000, 'QA'); Result: The table now includes the new employee: EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

70000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

72000

IT



4

Diana

Manager

90000

HR



5

Eve

Developer

70000

IT



6

Frank

Tester

60000

QA 2. UPDATE: Modifying Existing Rows The UPDATE command allows you to modify data in existing rows based on specific conditions. Task: Give all Developers in the IT department a 10% salary increase. UPDATE Employees
SET Salary = Salary * 1.10
WHERE Position = 'Developer' AND Department = 'IT'; Result: The salary for Alice, Charlie, and Eve has been updated: EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

77000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

79200

IT



4

Diana

Manager

90000

HR



5

Eve

Developer

77000

IT



6

Frank

Tester

60000

QA 3. DELETE: Removing Rows From a Table The DELETE command removes rows from a table based on a condition. Task: Remove all employees in the QA department. DELETE FROM Employees
WHERE Department = 'QA'; Result: Frank has been removed from the table: EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

77000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

79200

IT



4

Diana

Manager

90000

HR



5

Eve

Developer

77000

IT 4. MERGE (UPSERT): Combining Insert and Update The MERGE statement is used to insert new rows or update existing rows based on a match condition. This is also known as “upsert.” Task: If an employee with EmployeeID = 5 exists, update their position to “Lead Developer”. Otherwise, insert a new employee. MERGE INTO Employees AS Target
USING (SELECT 5 AS EmployeeID, 'Eve' AS Name, 'Lead Developer' AS Position, 80000 AS Salary, 'IT' AS Department) AS Source
ON Target.EmployeeID = Source.EmployeeID
WHEN MATCHED THEN
    UPDATE SET Position = Source.Position, Salary = Source.Salary
WHEN NOT MATCHED THEN
    INSERT (EmployeeID, Name, Position, Salary, Department)
    VALUES (Source.EmployeeID, Source.Name, Source.Position, Source.Salary, Source.Department); Result: Eve’s position has been updated to “Lead Developer”: EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

77000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

79200

IT



4

Diana

Manager

90000

HR



5

Eve

Lead Developer

80000

IT 5. TRUNCATE: Quickly Clearing All Rows The TRUNCATE command removes all rows from a table, but unlike DELETE, it does not log individual row deletions, making it faster. Task: Clear all rows from the Employees table. TRUNCATE TABLE Employees; Result: The table is now empty, but the structure remains intact. 6. DROP: Removing the Entire Table The DROP command deletes a table and its data permanently. Task: Remove the Employees table from the database. DROP TABLE Employees; Result: The Employees table no longer exists. Summary SQL provides a wide range of commands to modify data and table structures. Here’s a quick recap: Command Use Case INSERT Add new rows to a table.
UPDATE Modify data in existing rows.
DELETE Remove specific rows from a table.
MERGE Combine insert and update logic (upsert).
TRUNCATE Quickly clear all rows in a table.
DROP Remove the entire table structure and data. These commands allow you to keep your database up-to-date, clean, and well-organized. Practice these examples on your own database to gain confidence in modifying data with SQL! Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on LinkedIn. Happy exploring!👋 Introduction SQL is not just about querying data—it also includes powerful commands to modify data within tables. These Data Manipulation Language (DML) commands such as INSERT, UPDATE, and DELETE, enable you to add, modify, or remove rows in a database. In this article, we’ll explore these commands with practical examples. Data Manipulation Language (DML) Sample Table: Employees Let’s start with a sample Employees table to demonstrate the examples: EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

70000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

72000

IT



4

Diana

Manager

90000

HR



5

Eve

Developer

70000

IT EmployeeID

Name

Position

Salary

Department 1

Alice

Developer

70000

IT 2

Bob

Designer

65000

Design 3

Charlie

Developer

72000

IT 4

Diana

Manager

90000

HR 5

Eve

Developer

70000

IT EmployeeID Name Position Salary Department EmployeeID EmployeeID Name Name Position Position Salary Salary Department Department 1 Alice Developer 70000 IT 1 1 Alice Alice Developer Developer 70000 70000 IT IT 2 Bob Designer 65000 Design 2 2 Bob Bob Designer Designer 65000 65000 Design Design 3 Charlie Developer 72000 IT 3 3 Charlie Charlie Developer Developer 72000 72000 IT IT 4 Diana Manager 90000 HR 4 4 Diana Diana Manager Manager 90000 90000 HR HR 5 Eve Developer 70000 IT 5 5 Eve Eve Developer Developer 70000 70000 IT IT 1. INSERT: Adding Rows to a Table The INSERT command is used to add new rows to a table. Task : Add a new employee, Frank, who is a Tester in the QA department with a salary of $60,000. Task INSERT INTO Employees (EmployeeID, Name, Position, Salary, Department)
VALUES (6, 'Frank', 'Tester', 60000, 'QA'); INSERT INTO Employees (EmployeeID, Name, Position, Salary, Department)
VALUES (6, 'Frank', 'Tester', 60000, 'QA'); Result : The table now includes the new employee: Result EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

70000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

72000

IT



4

Diana

Manager

90000

HR



5

Eve

Developer

70000

IT



6

Frank

Tester

60000

QA EmployeeID

Name

Position

Salary

Department 1

Alice

Developer

70000

IT 2

Bob

Designer

65000

Design 3

Charlie

Developer

72000

IT 4

Diana

Manager

90000

HR 5

Eve

Developer

70000

IT 6

Frank

Tester

60000

QA EmployeeID Name Position Salary Department EmployeeID EmployeeID Name Name Position Position Salary Salary Department Department 1 Alice Developer 70000 IT 1 1 Alice Alice Developer Developer 70000 70000 IT IT 2 Bob Designer 65000 Design 2 2 Bob Bob Designer Designer 65000 65000 Design Design 3 Charlie Developer 72000 IT 3 3 Charlie Charlie Developer Developer 72000 72000 IT IT 4 Diana Manager 90000 HR 4 4 Diana Diana Manager Manager 90000 90000 HR HR 5 Eve Developer 70000 IT 5 5 Eve Eve Developer Developer 70000 70000 IT IT 6 Frank Tester 60000 QA 6 6 Frank Frank Tester Tester 60000 60000 QA QA 2. UPDATE: Modifying Existing Rows The UPDATE command allows you to modify data in existing rows based on specific conditions. Task : Give all Developers in the IT department a 10% salary increase. Task UPDATE Employees
SET Salary = Salary * 1.10
WHERE Position = 'Developer' AND Department = 'IT'; UPDATE Employees
SET Salary = Salary * 1.10
WHERE Position = 'Developer' AND Department = 'IT'; Result : The salary for Alice, Charlie, and Eve has been updated: Result EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

77000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

79200

IT



4

Diana

Manager

90000

HR



5

Eve

Developer

77000

IT



6

Frank

Tester

60000

QA EmployeeID

Name

Position

Salary

Department 1

Alice

Developer

77000

IT 2

Bob

Designer

65000

Design 3

Charlie

Developer

79200

IT 4

Diana

Manager

90000

HR 5

Eve

Developer

77000

IT 6

Frank

Tester

60000

QA EmployeeID Name Position Salary Department EmployeeID EmployeeID Name Name Position Position Salary Salary Department Department 1 Alice Developer 77000 IT 1 1 Alice Alice Developer Developer 77000 77000 IT IT 2 Bob Designer 65000 Design 2 2 Bob Bob Designer Designer 65000 65000 Design Design 3 Charlie Developer 79200 IT 3 3 Charlie Charlie Developer Developer 79200 79200 IT IT 4 Diana Manager 90000 HR 4 4 Diana Diana Manager Manager 90000 90000 HR HR 5 Eve Developer 77000 IT 5 5 Eve Eve Developer Developer 77000 77000 IT IT 6 Frank Tester 60000 QA 6 6 Frank Frank Tester Tester 60000 60000 QA QA 3. DELETE: Removing Rows From a Table The DELETE command removes rows from a table based on a condition. Task : Remove all employees in the QA department. Task DELETE FROM Employees
WHERE Department = 'QA'; DELETE FROM Employees
WHERE Department = 'QA'; Result : Frank has been removed from the table: Result EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

77000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

79200

IT



4

Diana

Manager

90000

HR



5

Eve

Developer

77000

IT EmployeeID

Name

Position

Salary

Department 1

Alice

Developer

77000

IT 2

Bob

Designer

65000

Design 3

Charlie

Developer

79200

IT 4

Diana

Manager

90000

HR 5

Eve

Developer

77000

IT EmployeeID Name Position Salary Department EmployeeID EmployeeID Name Name Position Position Salary Salary Department Department 1 Alice Developer 77000 IT 1 1 Alice Alice Developer Developer 77000 77000 IT IT 2 Bob Designer 65000 Design 2 2 Bob Bob Designer Designer 65000 65000 Design Design 3 Charlie Developer 79200 IT 3 3 Charlie Charlie Developer Developer 79200 79200 IT IT 4 Diana Manager 90000 HR 4 4 Diana Diana Manager Manager 90000 90000 HR HR 5 Eve Developer 77000 IT 5 5 Eve Eve Developer Developer 77000 77000 IT IT 4. MERGE (UPSERT): Combining Insert and Update The MERGE statement is used to insert new rows or update existing rows based on a match condition. This is also known as “upsert.” Task : If an employee with EmployeeID = 5 exists, update their position to “Lead Developer”. Otherwise, insert a new employee. Task MERGE INTO Employees AS Target
USING (SELECT 5 AS EmployeeID, 'Eve' AS Name, 'Lead Developer' AS Position, 80000 AS Salary, 'IT' AS Department) AS Source
ON Target.EmployeeID = Source.EmployeeID
WHEN MATCHED THEN
    UPDATE SET Position = Source.Position, Salary = Source.Salary
WHEN NOT MATCHED THEN
    INSERT (EmployeeID, Name, Position, Salary, Department)
    VALUES (Source.EmployeeID, Source.Name, Source.Position, Source.Salary, Source.Department); MERGE INTO Employees AS Target
USING (SELECT 5 AS EmployeeID, 'Eve' AS Name, 'Lead Developer' AS Position, 80000 AS Salary, 'IT' AS Department) AS Source
ON Target.EmployeeID = Source.EmployeeID
WHEN MATCHED THEN
    UPDATE SET Position = Source.Position, Salary = Source.Salary
WHEN NOT MATCHED THEN
    INSERT (EmployeeID, Name, Position, Salary, Department)
    VALUES (Source.EmployeeID, Source.Name, Source.Position, Source.Salary, Source.Department); Result : Eve’s position has been updated to “Lead Developer”: Result EmployeeID

Name

Position

Salary

Department



1

Alice

Developer

77000

IT



2

Bob

Designer

65000

Design



3

Charlie

Developer

79200

IT



4

Diana

Manager

90000

HR



5

Eve

Lead Developer

80000

IT EmployeeID

Name

Position

Salary

Department 1

Alice

Developer

77000

IT 2

Bob

Designer

65000

Design 3

Charlie

Developer

79200

IT 4

Diana

Manager

90000

HR 5

Eve

Lead Developer

80000

IT EmployeeID Name Position Salary Department EmployeeID EmployeeID Name Name Position Position Salary Salary Department Department 1 Alice Developer 77000 IT 1 1 Alice Alice Developer Developer 77000 77000 IT IT 2 Bob Designer 65000 Design 2 2 Bob Bob Designer Designer 65000 65000 Design Design 3 Charlie Developer 79200 IT 3 3 Charlie Charlie Developer Developer 79200 79200 IT IT 4 Diana Manager 90000 HR 4 4 Diana Diana Manager Manager 90000 90000 HR HR 5 Eve Lead Developer 80000 IT 5 5 Eve Eve Lead Developer Lead Developer 80000 80000 IT IT 5. TRUNCATE: Quickly Clearing All Rows The TRUNCATE command removes all rows from a table, but unlike DELETE, it does not log individual row deletions, making it faster. Task : Clear all rows from the Employees table. Task TRUNCATE TABLE Employees; TRUNCATE TABLE Employees; Result : The table is now empty, but the structure remains intact. Result 6. DROP: Removing the Entire Table The DROP command deletes a table and its data permanently. Task : Remove the Employees table from the database. Task DROP TABLE Employees; DROP TABLE Employees; Result : The Employees table no longer exists. Result Summary SQL provides a wide range of commands to modify data and table structures. Here’s a quick recap: Command Use Case Command Use Case INSERT Add new rows to a table. UPDATE Modify data in existing rows. DELETE Remove specific rows from a table. MERGE Combine insert and update logic (upsert). TRUNCATE Quickly clear all rows in a table. DROP Remove the entire table structure and data. INSERT Add new rows to a table. UPDATE Modify data in existing rows. DELETE Remove specific rows from a table. MERGE Combine insert and update logic (upsert). TRUNCATE Quickly clear all rows in a table. DROP Remove the entire table structure and data. These commands allow you to keep your database up-to-date, clean, and well-organized. Practice these examples on your own database to gain confidence in modifying data with SQL! Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on LinkedIn . Happy exploring!👋 LinkedIn

SQL Data Modification Commands With Examples: A Quick and Simple Guide

Author:
(1) David S. Hardin, Cedar Rapids, IA USA (david.s.hardin@gmail.com). Table of Links 1 Introduction 2 Dancing Links 3 The Rust Programming Language 4 RAC: Hardware/Software Co-Assurance at Scale 5 Rust and RAR 5.1 Restricted Algorithmic Rust 6 Dancing Links in Rust and 6.1 Definitions 6.2 Translation to ACL2 6.3 Dancing Links Theorems 7 Related Work 8 Conclusion 9 Acknowledgments and References “Dancing Links” connotes an optimization to a circular doubly-linked list data structure implementation which provides for fast list element removal and restoration. The Dancing Links optimization is used primarily in fast algorithms to find exact covers, and has been popularized by Knuth in Volume 4B of his seminal series The Art of Computer Programming. We describe an implementation of the Dancing Links optimization in the Rust programming language, as well as its formal verification using the ACL2 theorem prover. Rust has garnered significant endorsement in the past few years as a modern, memory-safe successor to C/C++ at companies such as Amazon, Google, and Microsoft, and is being integrated into both the Linux and Windows operating system kernels. Our interest in Rust stems from its potential as a hardware/software co-assurance language, with application to critical systems. We have crafted a Rust subset, inspired by Russinoff’s Restricted Algorithmic C (RAC), which we have imaginatively named Restricted Algorithmic Rust, or RAR. In previous work, we described our initial implementation of a RAR toolchain, wherein we simply transpile the RAR source into RAC. By so doing, we leverage a number of existing hardware/software co-assurance tools with a minimum investment of time and effort. In this paper, we describe the RAR Rust subset, describe our improved prototype RAR toolchain, and detail the design and verification of a circular doubly-linked list data structure employing the Dancing Links optimization in RAR, with full proofs of functional correctness accomplished using the ACL2 theorem prover. 1 Introduction The exact cover problem [17], in its simplest form, attempts to find, for an n × m matrix with binary elements, all of the subsets of the rows of the matrix such that all the column sums are exactly one. This basic notion naturally extends to matrix elements that are in some numerical range; indeed, the popular puzzle game Sudoku is an extended exact cover problem for a 9× 9 matrix with element values in the range of 1 to 9, inclusive. The exact cover problem is NP-complete, but computer scientists have devised recursive, nondeterministic backtracking algorithms to find exact covers. One such procedure is Knuth’s Algorithm X, described in [17]. In this algorithm, elements of the matrix are connected via circular doubly-linked lists, and individual elements are removed, or restored, as the algorithm proceeds, undergoing backtracking, etc. As these removals and restorations out of/into the list are quite common, making these operations efficient is a laudable goal. This is where Knuth’s “Dancing Links” comes in, resulting in an optimized algorithm for finding exact covers which Knuth calls DLX (Dancing Links applied to algorithm X). 2 Dancing Links The concept behind Dancing Links is quite simple: when a given element Y of a list is removed in an exact cover algorithm, it is very likely that this same element will later be restored. Thus, rather than “zero out” the ‘previous’ and ‘next’ links associated with element Y, as good programming hygiene would normally dictate, in Dancing Links, the programmer leaves the link values in place for the removed element. The Dancing Links remove operator thus deletes element Y from the list, setting the ’next’ element of the preceding element X to the following element Z, and setting the ’previous’ element of Z to a link to X, but not touching the ’next’ and ’previous’ links of the removed element Y. Later on, if Y needs to be restored, it is simply hooked back in to the list using a simple restore operator. In Knuth’s words, if one monitors the list links as the DLX algorithm proceeds, the links appear to ‘dance’, hence the name. Knuth’s Dancing Links functionality is summarized in Fig. 1. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) David S. Hardin, Cedar Rapids, IA USA (david.s.hardin@gmail.com). Author: Author: (1) David S. Hardin, Cedar Rapids, IA USA (david.s.hardin@gmail.com). Table of Links 1 Introduction 1 Introduction 2 Dancing Links 2 Dancing Links 3 The Rust Programming Language 3 The Rust Programming Language 4 RAC: Hardware/Software Co-Assurance at Scale 4 RAC: Hardware/Software Co-Assurance at Scale 5 Rust and RAR 5 Rust and RAR 5.1 Restricted Algorithmic Rust 5.1 Restricted Algorithmic Rust 6 Dancing Links in Rust and 6.1 Definitions 6 Dancing Links in Rust and 6.1 Definitions 6.2 Translation to ACL2 6.2 Translation to ACL2 6.3 Dancing Links Theorems 6.3 Dancing Links Theorems 7 Related Work 7 Related Work 8 Conclusion 8 Conclusion 9 Acknowledgments and References 9 Acknowledgments and References “Dancing Links” connotes an optimization to a circular doubly-linked list data structure implementation which provides for fast list element removal and restoration. The Dancing Links optimization is used primarily in fast algorithms to find exact covers, and has been popularized by Knuth in Volume 4B of his seminal series The Art of Computer Programming. We describe an implementation of the Dancing Links optimization in the Rust programming language, as well as its formal verification using the ACL2 theorem prover. Rust has garnered significant endorsement in the past few years as a modern, memory-safe successor to C/C++ at companies such as Amazon, Google, and Microsoft, and is being integrated into both the Linux and Windows operating system kernels. Our interest in Rust stems from its potential as a hardware/software co-assurance language, with application to critical systems. We have crafted a Rust subset, inspired by Russinoff’s Restricted Algorithmic C (RAC), which we have imaginatively named Restricted Algorithmic Rust, or RAR. In previous work, we described our initial implementation of a RAR toolchain, wherein we simply transpile the RAR source into RAC. By so doing, we leverage a number of existing hardware/software co-assurance tools with a minimum investment of time and effort. In this paper, we describe the RAR Rust subset, describe our improved prototype RAR toolchain, and detail the design and verification of a circular doubly-linked list data structure employing the Dancing Links optimization in RAR, with full proofs of functional correctness accomplished using the ACL2 theorem prover. 1 Introduction The exact cover problem [17], in its simplest form, attempts to find, for an n × m matrix with binary elements, all of the subsets of the rows of the matrix such that all the column sums are exactly one. This basic notion naturally extends to matrix elements that are in some numerical range; indeed, the popular puzzle game Sudoku is an extended exact cover problem for a 9× 9 matrix with element values in the range of 1 to 9, inclusive. The exact cover problem is NP-complete, but computer scientists have devised recursive, nondeterministic backtracking algorithms to find exact covers. One such procedure is Knuth’s Algorithm X, described in [17]. In this algorithm, elements of the matrix are connected via circular doubly-linked lists, and individual elements are removed, or restored, as the algorithm proceeds, undergoing backtracking, etc. As these removals and restorations out of/into the list are quite common, making these operations efficient is a laudable goal. This is where Knuth’s “Dancing Links” comes in, resulting in an optimized algorithm for finding exact covers which Knuth calls DLX (Dancing Links applied to algorithm X). 2 Dancing Links The concept behind Dancing Links is quite simple: when a given element Y of a list is removed in an exact cover algorithm, it is very likely that this same element will later be restored. Thus, rather than “zero out” the ‘previous’ and ‘next’ links associated with element Y, as good programming hygiene would normally dictate, in Dancing Links, the programmer leaves the link values in place for the removed element. The Dancing Links remove operator thus deletes element Y from the list, setting the ’next’ element of the preceding element X to the following element Z, and setting the ’previous’ element of Z to a link to X, but not touching the ’next’ and ’previous’ links of the removed element Y. Later on, if Y needs to be restored, it is simply hooked back in to the list using a simple restore operator. In Knuth’s words, if one monitors the list links as the DLX algorithm proceeds, the links appear to ‘dance’, hence the name. Knuth’s Dancing Links functionality is summarized in Fig. 1. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

The Algorithm That Makes Data Dance—and Solve Complex Problems Fast

Algoritmo ukax Datos ukar thuqt’ayi —ukat complejos jan walt’awinak jank’ak askichi

Der Algorithmus, der Daten zum Tanzen bringt – und komplexe Probleme schnell löst

El algoritmo que hace bailar a los datos y resuelve problemas complejos rápidamente

האלגוריתם שגורם לנתונים לרקוד - ולפתור בעיות מורכבות במהירות

データを踊らせ、複雑な問題を素早く解決するアルゴリズム

Маалыматтарды бийлеген жана татаал маселелерди тез чечкен алгоритм

Algoritmas, kuris priverčia duomenis šokti ir greitai išspręsti sudėtingas problemas

Ny Algorithm Mampidihy Data—ary mamaha haingana ny olana sarotra

දත්ත නර්තනය කරන ඇල්ගොරිතම-සහ සංකීර්ණ ගැටළු ඉක්මනින් විසඳන්න

Algorithm-ka Ka Dhiga Dheefta Dance-iyo Xalinta Dhibaatooyinka Kakan

Verileri Dans Ettiren ve Karmaşık Sorunları Hızlı Çözen Algoritma

I-Algorithm Eyenza Ukudansa Kwedatha—futhi Ukuxazulula Izinkinga Eziyinkimbinkimbi Ngokushesha

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