#algorithm antonyms

[ceyhanmedya]

Algorithm

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Algorithm

What is algorithm? How is it created?

Algorithm ; It is the name given to the combination of methods and steps planned to perform a job or solve a problem. It is generally defined as a set of operations with a clear beginning and end, used in the field of programming or in solving mathematical problems. It is the regular determination of the movements, processes or works required in order to carry out the work planned to be done, in steps.

It is one of the two approaches used in problem solving and is more preferred than the heuristic solution approach. It is among the subjects that must be learned before a programming language for a computer programmer and can be defined as the most important topic of programming.

History

This concept first appeared in the 9th century and was first introduced by Khwarezmi . The scholar, whose full name is Ebu Abdullah Muhammed Ibn Musa al-Khorezmi, made great contributions to the field of mathematics by putting his work in algebra into writing. Harezmi’s most widely known book with Latin translations; Hisab is al-algebra and al-mukabala (حساب الجبر و المقابلة). This book is also described as the first known collection of algorithms .

The word algorithm originally comes from the word ‘ Algorism ‘. The reason for this is that Khwarezmi’s book was difficult to pronounce in Europe after it was translated into Latin, and Europeans who could not say the name of Khwarezmi called it ‘Algorism’. 

As a result, although the concept of Algorism began to be used in the sense of problem solving with Arabic numerals, it turned into its current form over time and started to be used in a general context. Finally, after the 1950s, especially with the developments in computer technologies, a concept came to represent the way almost every work to be done in the field of programming and the steps to be applied for its construction.

Algorithm creation

The algorithm can be in the form of prose and narrative, or in the form of a flowchart . Generally preferred is the one in the form of a flowchart. In order to create a process, some symbols are used to describe the work to be done. These symbols are of great importance, especially in terms of developing a program and understanding the process.

In order to create an algorithm, the work or problem to be done must be clearly defined and solution methods must be determined. In order to do the work or to implement the solution, all the steps that will lead to the result from the initial movement should be specified in the order of application. One of the most important concepts in this subject is the flow chart; The schematic representation of the solution of an algorithm is called a flowchart. 

Some flowchart commands are as follows;

Start-Finish (terminator)  

Input  

Process  

viewing 

Decision  

iterative process  

manually entered value

Examples

Example 1 (Explanation with everyday concepts)

Targeted Job:  Going from home to school

Start: Home

End: School

Algorithm:

Step 1: Open the door Step 2: Put on the shoes Step 3: Close the door Step 4: Exit the building Step 5: Walk the road Step 6: Walk to the 2nd fork Step 7: Turn left Step 8: Finish the road Step 9: Enter the school.

Example 2 (Explanation with programmatic concepts)

Intended Business:  Finding the factorial value of a number entered by the user

Getting Started:  Starting the program

Finish:  Show the result

Algorithm:

Step 1: Run the program Step 2: Define the variables factorial,i and n Step 3: Define the initial values of the variables factor = 1 i = Step 4: Read the n value entered from the screen Step 5: Repeat until (i=n) equality is achieved factorial = factorial*i i = i+1 Step 6: Show the value of the factorial variable

Some Important Algorithm Types

Search algorithms

Memory management algorithms

computer graphics algorithms

Combinatorial algorithms

Graph algorithms

evolutionary algorithms

genetic algorithms

Crypto algorithms or cryptographic algorithms

Rooting algorithms

Optimization algorithms

Sorting algorithms

Data compression algorithms

Conclusion

This concept can be encountered by people in all areas of life in general. Because the concept of algorithm represents the way to the solution rather than the solution. A plan prepared for a journey to be made and the steps determined for the completion of a job basically represent the algorithm. 

An algorithm that has not been implemented and whose results have not been observed is not deemed appropriate for patenting by law. But algorithms in software have been the subject of much discussion at this point. 

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[arcxnumvitae]

One day I'm going to have a normal fae. One day.

[afyerarchive]

2024 MASTERLIST

━━━━━━━━━━━━━━━━━━━

a star (✦) will denote a personal favorite poem.

a poem with "reprise" in the title means it is a new and improved version of a previous poem.

• These Algorithms

✦ Whale-Boned Wings

• A Convenient Masquerade

• A Synonym for Love

✦ American Politics and Me

✦ Cutesy

• A Bad Olive Branch

• Dream Eater

• Telescope

• Radioactive

• Falling Winslow

✦ Chronophobia, in the Information Age

• June 6th 2023

• Antonym

✦ A Capitalistic, Nihilistic Complacency

• Oxidation

• Dear K.C.

• Reverse Pandemia

✦ Reclaiming my Destiny

• City of Gray

• Orchid Alchemy

• The NERVOUS System

✦ Egoism

✦ Somewhat Dreaming of a Semi-Narcoleptic Pseudo-Impossible Sympathetic Truancy

• The Path of Most Resistance

• Relapsing, Eclipsing and Reminiscing

✦ Minimum Wage Hero's Journey

• Living with the Depths of Dev

• Inequinox, in Equinox

• Speedrunning Therapy

• Reddish-Brown Memories

✦ Piecewise-Defined

• Self-Chivalry: Ace of Divinity

• A Bad Olive Branch Reprise

✦ Phonebooks

• Becoming Everything (yes, Everything)

• Careless Sunburns

• Love and/or Murder

• Universe → Hometown → Garden

• Empress

• This is Pride

• Look at the Time

✦ Red Valentine Hearts

• The Things That Happened in 410

✦ Then Do It (With a Glitter Pen Suicide Note)

• Self-Urbanization

• Blues, Greens, Colors I See

• Reclaiming the Weightless Night

✦ Approaching Zero

[mixamorphosis]

Blog post and linked up tracklist [HERE]

Tracklist

01. Slow Meadow - Everything Is A Memory (Hammock Music) 02. Umber - It Just Fills The Hollow Spaces (Sound In Silence) (w/ extracts from Sean Parker interview) 03. The Green Kingdom - The Largest Creature That Ever Existed (Home Assembly Music) 04. Thomas Méreur - Moving On (Preserved Sound) 05. Antonymes - Strains Of Doubt (Facture) 06. David Shrigley - Questions (Late Night Tales) 07. Selffish - I Came To Leave (Serein) 08. Kutiman ft. Adam Scheflan - Dangerous (Siyal Music) 09. The Greatest Hoax - Left You Behind (Serein) 10. Hoshiko Yamane & Mikael Lind - Getting The Message Across (Time Released Sound) 11. Kate Tempest - Hold Your Own (American Recordings) 12. Kate Tempest - Lessons (American Recordings) 13. Penelope Trappes - Puppets (Optimo Music) 14. London Grammar - Truth Is A Beautiful Thing (Metal & Dust) 15. Cass. - Leaving (Into The Light Records) 16. Oubys + Monolyth & Cobalt - Parallax Review (eilean Records) (w/ extracts from Carole Cadwalladr Ted Talk) 17. Penelope Trappes - Farewell (Houndstooth) 18. Gigi Masin & Jonny Nash - Postcards From Nowhere (Melody As Truth) 19. Kryshe - Underlying Reality (Serein) 20. Matthew Halsall - Ai (Gondwana Records) 21. Seahawks - Didn't Know I Was Lost (Land Of Shadows Dancing Mix) (Ocean Moon) 22. 4 Hero ft. Ursula Rucker - The Awakening (Raw Canvas Records) 23. Kutiman - And Out (Melting Pot Music) 24. Nina Simone - Isn't It A Pity? (RCA Victor) 25. Air - Realize (Be With Records) 26. Soundstory - Rainstorm (Self Released) 27. Earth, Wind & Fire - Evil (CBS) 28. Greg Foat - Not That It Makes Any Difference (Athens Of The North) 29. William Ryan Fritch - Our Thirsting World (Lost Tribe Sound) 30. Jason Van Wyk - Away (Home Normal) 31. Channelers - A Quiet Invitation (Inner Islands) 32. Slow Meadow - Artificial Algorithm (Hammock Music) 33. Beanfield - To Be Alienated (Street Beat) (w/ extracts from Krishnan Guru-Murthy & Jaron Lanier Interview) 34. Portico Quartet - Ways Of Seeing (Gondwana Records) 35. Up, Bustle & Out - Clandestine Operations (Ninja Tune) (w/ extracts from Chamath Palihapitiya Interview) 36. Saint Petersburg Spin Disco Club - Simple Customers (Emotional Response) 37. Breathers - Don't It Make You Feel (Numero Group) 38. Evadney - Anchor Me (Self Released) 39. Perth County Conspiracy - If You Can Wait (Anthology Recordings) 40. Colorama - Anytime (Music For Dreams) 41. Dennis Stoner - Maybe Someday / Maybe Never (Anthology Recordings)

Download available via [Hearthis]

[gettingsnowedunder]

It always seems to be about carving out a place, lately. A niche. And it's easier to do that in spaces that are quieter, but less rewarding maybe. CanLit is like this. You have to fine tune the concept of the thing (the concept often being: suffering), or else just be really fucking good and sure of yourself. I get stressed out about it, because I worry that the fine-tuning to fit a niche is maybe an excuse for lesser craft. Or a way to escape the pressure of the concept of craft.

Or maybe doing an MFA beat me over the head so often with the word "craft" that I want to, finally, retaliate with genre.

I'm almost onto something here.

I do care about climate fiction and I try not to think about the chicken and the egg. I want to use supporting evidence because I also do care about media literacy and literary criticism as it should be. Girlfriend on Mars was a book with an idea, a literary legacy in Gatsby, a Canadian approach to climate nihilism AND billionaire egoism. I do think it perhaps suffered for its dedication to concept over craft. I loved it. I knew immediately it would not be shortlisted for the Giller.

I see now that "craft" is a social construct. I think writing should, you know, do something. Evoke. But isn't "evoke" essentially synonymous with subjectivity? Have we not established that? But genre is objective: there are tropes. You can map it with variations on algorithms.

I feel like I am gesturing at three different ideas at once in search of some kind of, I don't know, relief about writing a book that I think is silly and neat in equal turns. I can only write when I shut all of this out. But here: genre and craft should not be antonymous (obviously -- I don't think there's anyone on TUMBLR that disagrees with this), literary awards are a big huge mess (again, other people have said this in very wise ways so much better than my brain can even function), and finally ... I don't know ... that maybe only five years later I am realizing that doing an MFA fucked with my brain.

I made some cool friends though.

[beautifulpeachcandy]

GODS

Synonyms: Almighty, Creator, Father Antonyms: Devil, Satan, Demon Etymology: - Old English "god" - supreme being, deity

Reflection: ALGORITHMS are the modern GODS as they are inconceivable powers to which we give our lives into their hands.

[johnmathewblog]

AI Use Cases in Search Engines

A search engine has several key features that make it an effective tool for discovering information on the internet. Modern-day search engines provide relevancy ranking, personalized search, autocomplete, and image and video searches.

Artificial intelligence (AI) has altered how we search for information online and offers users the most precise and relevant results. This blog will explore how AI, specifically natural language processing (NLP) and machine learning (ML), are used in search engines to deliver users more accurate and personalized search results.

Machine Learning

ML is a crucial AI technology used in search engines. ML algorithms, such as decision trees, gradient-boosted trees, random forests, support vector machines, and neural networks, use vast data to learn patterns and make predictions. In search engines, ML algorithms are utilized to personalize results for each user.

ML algorithms dissect a user’s search history, position, and preferences to provide results that apply to their interests. This makes it easier for users to find what they are looking for and saves time that would else be spent scrolling through inapplicable results. For instance, Google uses these ML algorithms in its search engine:

RankBrain: It is used to understand the user’s intent and provide more relevant results.

Neural Matching: It uses deep learning algorithms to understand the relationships between words in a query and the words on a web page.

Panda: This algorithm focuses on reducing low-quality content and promoting high-quality content in the search results.

Hummingbird: This algorithm aims to understand the context and intent behind a query and provide results that match the user’s needs.

Penguin: This algorithm penalizes websites that use spammy tactics, such as keyword stuffing or buying clicks, to increase rank.

Pigeon: It improves local search results by integrating traditional web ranking signals with local signals, such as distance and location.

Voice search has become increasingly popular recently, especially with the wide use of smart speakers like Alexa, Google Home, and Apple HomePod that use various ML algorithms to transcribe spoken words into written text. Some of the commonly used algorithms are:

 Hidden Markov Models (HMMs): These are probabilistic models that use a sequence of hidden states to predict the likelihood of a sequence of observations.

Gaussian Mixture Models (GMMs): They are used to model the probability density function of the observations in speech recognition.

Recurrent Neural Networks (RNNs): They are used to model the relationship between the acoustic features of speech and the corresponding text.

Connectionist Temporal Classification (CTC): They train neural networks for speech recognition by aligning the input speech signal with the target transcription.

Natural Language Processing 

One of the most critical ways AI is used in search engines is through NLP. NLP is a subfield of AI concerned with interactions between computers and human language. NLP algorithms aid search engines in understanding the purpose behind a user’s query, leading to results that match their requirements.

NLP algorithms can identify misspellings, antonyms, and variations of words and expressions, making it easier for users to find what they want. Furthermore, NLP algorithms can comprehend the context of a query, allowing search engines to provide results based on the user’s intended meaning and not just the words used. 

Large language models like GPT-3 heavily rely on NLP to understand the structure and meaning of human language by processing vast amounts of text data. NLP is crucial in generating coherent and natural-sounding language within these models. 

By employing NLP techniques for language generation, large language models can produce highly sophisticated text with vast applications in content creation, chatbots, and virtual assistants.

Image and Video Searches

AI has also revolutionized image and video searches. With the increasing amount of visual content on the internet, it has become more challenging to find what the users are looking for based on text alone. AI technologies like computer vision and ML can dissect images and videos and extract meaningful information, making it easier for users to find the content they want.

Image search engines like Google Images use AI algorithms to understand the content of an image, similar to the objects, scenes, and people depicted. This allows users to search for images based on their visual content, not just the keywords associated with them. 

Future of the Search Engine

The role of language models will be significant in the future of search engines. With Open AI introducing ChatGPT and Google releasing Bard, the search engine experience is about to be overhauled. Language models can be integrated into virtual personal assistants, context-aware searches, personalized results, augmented reality, and predictive search because of their ability to understand natural language and provide relevant information.

These advancements will make the search experience quicker, more efficient, and more personalized, providing users with relevant results based on their interests and preferences. Integrating AI and NLP will revolutionize how we search for information.

Conclusion

AI has transformed the search experience, making it easier for users to find what they are looking for and furnishing more accurate and applicable results. As AI technologies advance, we can anticipate the search experience to become more personalized and intuitive.

Source: https://www.latentview.com/blog/ai-use-cases-in-search-engines/

[education30and40blog]

AI Exercise Generator for English Teachers-Big Time Saver #ai #teachenglish

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#AI #artificialintelligence #teachenglish #efl #elt #iatefl #tefl #esol #tesol Camtasia Complete Course -Playlist: https://www.youtube.com/playlist?list=PLqYj2sOxDkVwgVrOdhpmUc-oOAUiyyzbk Download and Test Camtasia https://techsmith.z6rjha.net/MXPagn Use code RUSSELL10 for extra discount ( apply when you pay) Buy Camtasia with a excellent discount ( Educational Discount) https://techsmith.z6rjha.net/GmnJ3L Buy Camtasia Commercial Version https://techsmith.z6rjha.net/jWNKV6 Sign up to my newsletter and get updated with all the latest videos https://forms.aweber.com/form/61/763053361.htm Links from the video:Wordwall: https://youtu.be/Zkcz-OPZLEA Naturalreaders.com: https://youtu.be/E0SoLKMitN8 AI tools for english classes: https://youtu.be/GyHHJh6Y11I 00:00 AI technology for English teachers- Introduction 02:04 Twee.com 03:16 Feel in the gaps 05:10 Add more activities 09:06 Vocabulary 10:50 YouTube options 13:42 Thanks for watching Twee.com is an innovative AI Exercise Generator specifically designed to assist English teachers in crafting captivating activities for the classroom. In this video, we'll take you on a journey through the various features that make this platform an indispensable resource for educators. Say goodbye to the days of manually crafting fill in the gaps exercises. With Quickly Twee.com, you can effortlessly generate interactive exercises that challenge your students' understanding of grammar, vocabulary, and context. By simply inputting the text you want to use, the AI algorithm will identify suitable gaps and produce a well-balanced exercise that promotes critical thinking and comprehension. Enhance your students' language skills with captivating reading activities. Whether you're teaching fiction, non-fiction, or even creative writing, Quickly Twee.com allows you to create intriguing stories tailored to your class's proficiency level. By using the platform's vast database of resources and AI-powered text generation, you'll have endless storytelling possibilities at your fingertips! Help your students master essential vocabulary effortlessly. With Quickly Twee.com, you can create vocabulary-focused activities that align with your lesson objectives. The platform's AI will curate relevant exercises, including word matching, synonyms, and antonyms, enabling your students to grasp new words in context, making language learning an enjoyable experience. Incorporating multimedia into your lessons has never been easier! Quickly Twee.com enables you to transform YouTube videos into engaging exercises. Simply enter the video's URL, and the AI will generate a text version, complete with questions and tasks that assess comprehension and spark meaningful discussions. Join us in this video as we explore the incredible capabilities of Twee.com, your ultimate AI Exercise Generator for English teachers. Embrace the power of technology in the classroom and watch as your students become more motivated, enthusiastic, and proficient in their English language skills. Don't miss out on this game-changing tool - subscribe now and unlock the doors to a world of dynamic and effective language education!

[evolvingdigital]

Understanding the Updated BERT Algorithm and Its Impact on SEO

In the world of search engine optimization (SEO), staying up-to-date with algorithm changes is crucial for maintaining and improving website rankings. One such important update is the BERT algorithm, which has gained significant attention in recent years. In this article, we will delve into the updated BERT algorithm and its impact on SEO strategies.

What is the BERT Algorithm?

BERT, short for Bidirectional Encoder Representations from Transformers, is a natural language processing (NLP) model developed by Google. It aims to understand the context and nuances of words within a sentence to provide more accurate search results. BERT's main objective is to enhance the understanding of search queries by considering the entire context rather than relying solely on individual words.

The BERT Algorithm Update:

In late 2019, Google implemented the BERT algorithm Seo update, making it one of the most significant advancements in search technology. The update allows search engines to comprehend the intent behind users' queries in a more human-like manner, leading to better search results and improved user experience. BERT primarily focuses on long-tail, complex search queries, as they often contain ambiguous or conversational language.

Understanding Context and Natural Language:

BERT's unique strength lies in its ability to grasp the contextual meaning of words. It considers the relationship between words within a sentence, understanding nuances such as synonyms, antonyms, and word order. This contextual understanding helps BERT decipher the user's intent accurately, providing more relevant search results. Websites that align their content with users' search intent have a higher chance of ranking well under the updated BERT algorithm.

The Impact on SEO Strategies:

With the BERT update, traditional SEO practices that rely heavily on keyword density and exact match phrases are no longer sufficient. Instead, website owners and SEO professionals should shift their focus towards creating high-quality content that genuinely answers users' queries. Here are some key strategies to consider:

Focus on User Intent: Analyze the intent behind search queries related to your industry or niche. Develop content that caters to these specific user intents, providing valuable and comprehensive information.

Long-Tail Keyword Optimization: BERT places more importance on understanding the context of long-tail keywords. Incorporate relevant long-tail keywords naturally throughout your content, ensuring it aligns with the user's intent.

Natural Language and Conversational Tone: Write content using a conversational tone, addressing common questions and concerns users may have. BERT favors content that mimics natural language and offers helpful, human-like responses.

Structured Data and Schema Markup: Implement structured data and schema markup on your website to provide search engines with clear information about your content. This helps search engines understand your content better, increasing the chances of ranking higher.

User Experience and Engagement: BERT emphasizes the importance of user experience. Ensure your website loads quickly, is mobile-friendly, and offers an intuitive navigation structure. Encourage user engagement through interactive elements, such as videos or comment sections.

Conclusion:

The BERT algorithm update has transformed the way search engines interpret and respond to user queries. By understanding the context and intent behind search queries, BERT aims to provide more accurate and relevant search results. To adapt to this update, SEO strategies need to focus on producing high-quality content that aligns with user intent and employs natural language. By optimizing your website accordingly, you can enhance your chances of ranking well and delivering a better user experience.

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[my-timing-is-digital]

@the27percent​ // alternate universe #2 — Awake​

The rain poured down incessantly. His golden garments, now a darker shade of orange, were soaked and clung tenaciously to his synthetic body. His brown hair was dishevelled; strands occasionally strayed and were plastered to his pale forehead by gusts of wind. He was trudging across a valley devoid of signs indicating a civilisation was within close proximity. Perhaps that was a good thing; the last time he encountered humanoids, they had been anything but hospitable. Upon his arrival and salutation, they had immediately reported his presence to the local authorities, and urged them to apprehend the “rogue synth.”

    Rogue synth?

    “Rogue” was the very antonym of the benign, reliable and compliant android he believed himself to be... He did not comprehend how his presence could have instigated such a significant amount of consternation. However, in order to prevent the situation from exacerbating even further, he had opted to vacate the vicinity as quickly as possible. On the run. Again.

    Two weeks ago, he had successfully escaped the inadequate scientist’s perimeters. The man had subjected Data to experiments and algorithms that were rudimentary and incompatible with his highly advanced positronic brain. The android was still recuperating from the torment of being disassembled, examined, and reconstructed. His neural links were no longer properly aligned, and although his positronic brain preserved his primary systems immaculately, his memory engrams were damaged; he was unable to retrieve memories pertaining to his arrival here. Memories regarding what had transpired after they had defeated the Borg in 2063 were likewise absent. Aside from that, a dozen other systems were enduring minor fluctuations. In other words, he desperately required tools and technical devices to restore himself.

    The clouded skies were rapidly shifting into a darker shade of grey, and the thunder was rumbling in the distance. In order to focus, he counted the seconds that elapsed after each lightning bolt illuminating his surroundings and the next thunderclap... 7 seconds. Multiplied by 350. Equals 2450. The lightning was 2.45 kilometers away...

    While he continued to count the intervals between the lightning and the thunder, he almost neglected to perceive a person, perched atop a rocky hill. Instantaneously, his positronic brain started to theorise the profile of this individual. Were they with the authorities? Were they an innocent farmer tracking across the valley with their flock of whatever creatures they considered farm animals? Or where they simply a wanderer, like him?

    Despite the unfortunate encounter with the natives of this planet, he granted this individual the benefit of the doubt, and decided to ask them for help. He had to get out of this solar system in the Gamma Quadrant and find his way back to Earth. To Starfleet. To Captain Picard. To the Enterprise...

    ‘Greetings!’ he said, amplifying the volume of his voice, which sounded distorted and alien to him after so many days of silence. ‘I am sorry to bother you, but could you help me?’

[likeadevils]

Wait was that the last question then because my guess was salubrious and that ended the quiz so I figured getting something wrong was the end ksakshjfs (also thank you so much for confirming!!! I would've kept thinking on it otherwise🙊❤️)

it was the last question! i think timing might play a role in scores? i redid the quiz because i’m a nerd and like to see how test algorithms work and my score rose to 4.26. i’m pretty sure i answered everything the same but there were a few that i had to think about so? who knows.

also i think a few are kind of up to interpretation? like i choose “extortion”for the antonym for “compensation” because instead of giving money you’re taking it, but i could see the test choosing “underpay”

[kitcat992]

Identity Crisis  | Chapter 10: Something Wicked This Way Comes

Rhodey folded his arms across his chest, stuffing his hands deep into his armpits. “A few months back — after the courts tossed out the subpoena that the Air Force weapons procurement liaison department submitted against OsCorp industries — Natasha and myself created an algorithm. It took a while to perfect, but we eventually snuck it into their systems.”

“We wanted to latch onto any words, codes, cryptography — anything that may possibly lead us to where they’ve been hiding their experiments since SHIELD shut down the clandestine facility in the Bermuda Triangle,” Natasha added, wrapping an arm tightly around the leg pulled high to her chest.

“What did it find?” Bruce looked around the room, as if asking anyone nearby. “The program, what – what did it find?”

Steve squeezed the fold on his hands, watching with intent interest as Tony’s technology lit up the kitchen with an artificial glow, the once marble stone of the table now a display case for translucent screens.

“Not much.” Natasha shrugged. “Rhodey and myself were starting to wonder if they’ve given up the game, gone straight after a good scare from Director Hill and her team.”

“You don’t think Fury was involved in that in any way?” Sam brushed cookie crumbles away from his shirt, swallowing hard as his demeanor fell serious. “Shutting them down and all?”

Natasha shook her head, barely glancing his way. “I don’t know what Fury is up to these days, aside from lurking in the shadows where he see fit.”

“It’s the mans favorite past time,” Tony muttered, not once looking away from the multiple holographic screens that he waved and flicked around in the air, a conductor of intangible images only made touchable by his technology. “And you’re spewing fairy-tales and folk lore, Romanoff. There’s no way they’d stop cold turkey, not this far into their game. They’ve gone too deep.”

“Pun intended?” Rhodey dryly joked, a tight smile creeping across his face.

Tony gave him the side-eye and nothing more.

“You’re right,” Natasha remarked, nodding towards the holograms ahead. “Something else has taken precedence.”

Tony tapped twice on the table, the glowing imagery beaming as it lifted upwards. His fingers pinched tightly together until the tips of his nails made contact. With one smooth move, he spread his arms wide apart, enlarging the document with ease.

It rotated, spinning around to show those facing the other way. Tony walked the length of the kitchen island to keep up with it, eyeing it with a line deepening between his brow.

“What the hell is this?” Sam asked, adjusting himself on the stool to get a better look.

The images littering the document weren’t hard to distinguish — there were scans of the human brain, detailing the different matter and components, looking like pictures straight out of an antonym book. With it were diagrams of DNA strands and cell structure, each moving in animation, trial and error to a hypothesis that detailed alongside the report.

“A formula,” Tony stated, finding conclusion faster than anyone else. The look in his eyes said one thing; he was studying it, absorbing the information in ways no one else could even consider doing.

Rhodey’s eyes drifted over his friend, watching as he kept up with the spinning hologram, the reflection mirroring directly onto his face.

“The Oz Formula, to be exact.”

Tony came to a screeching halt. He snapped his head over to Rhodey, his eyes wide, the whites shinning blue from the image gleaming in the air.

“Well, stone the crows and strike me pink…I’ll be damned.” He pointed to the document, his finger shaking multiple times, practically wagging at it with excitement. “Rhodey —”

“I know,” Rhodey immediately cut in, calm and cool, collected despite Tony’s heightening emotion that threatened to overtake the room. “I told you...I believed you.”

To all the others, it looked as if Tony’s mind had short circuited. As if the information was too heavy to handle, too much to process.  

For Tony, it was his brain running a mile a millisecond, only having stopped wagging his finger to tap it endlessly against his chin. The thoughts came too fast to keep up with, a head-rush of realization opening a gate of closed-off questions that he hadn’t let himself ask until now.

Months of searching, months of digging — finally they had something.

OsCorp could pay their employed scum the worlds worth in money to keep their mouths shut. It didn’t stop the Avengers from finding out the truth.

It wouldn’t stop the Avengers from finding out the truth.

[douchebagbrainwaves]

HOW TO HAVE BAD PROCRASTINATION

My friends with PhDs in computer science have Mac laptops. Most people reading this will already be fairly tolerant. Some people would make good founders, and others wouldn't. I think there are five reasons people like object-oriented program, it can be hard to tell apart, and there will probably be survivors from each group. VCs never offered that option. But it means if you have a statically-typed language without lexical closures or macros. But the two phenomena rapidly fused to produce a principle that now seems obvious: paying energetic young people get paid market rate for the work they do. And this is not an ordinary economic relationship than companies being sued for firing people. So far, anyway. I suspect signalling risk is in this category too. So we should expect to see ever-increasing variation in individual productivity as time goes on.1 Since board seats last about 5 years and each partner can't handle more than about 10 at once, that means a VC fund.

I was saying. Unfortunately there's no antonym of hapless, which makes it difficult to tell founders what to aim for. But while some openly flaunt the fact that they're created by, and used by, people who say software patents are no different from hardware patents, people protected ideas by keeping them secret. The winds of change. The root cause of variation in income, but it seems that it should be better looking.2 Not merely relentless. But if you look at the product we're offering.

If startups need it less, they'll be able to leave, if you have this most common type of ambition do. In most, the fastest way to get rich. There are other messages too, of course. If Google does do something evil, they get doubly whacked for it: once for whatever they did, it would take me several weeks of research to be able to say whether advantages like lack of competition outweigh disadvantages like reluctant investors. Professors and bosses usually feel some sense of responsibility toward you; if you make a valiant effort and failing, maybe they'll invest in your next startup, but they keep them mainly for defensive purposes. They'll each become more like super-angels.3 They build Writely. It may seem unlikely in principle that startups were very risky, but she was surprised to see how constant the threat of failure was—not just less restrictive than series A terms, but less restrictive than angel terms have traditionally been. If we can decide in 20 minutes, surely the next round, which they'll only take if it's worse for the startup than they could get in the open market.

But a discussion today about a battle that took place in the Bronze Age probably wouldn't. So a company threatening patent suits, sell. I was curious to hear what had surprised her most about it.4 In other fields, companies regularly sue competitors for patent infringement till you have money, people will of course think of Perl. Getting people to take less salary for a while, or increase revenues. If you got ten people to read a manuscript, you were rich. You seem to be so far.

They counted as work, just as we were designed to work, just like programming, but they are. As a child I read a New York law firm in the 1950s they paid associates far less than firms do today.5 But even investors who don't have a rule about this will be bored and frustrated by unclear explanations. After all, projects within big companies were always getting cancelled as a result of arbitrary decisions from higher up. If you're not threatening, you're probably not doing anything new, and dignity is merely a sort of plaque. And yet people working in their own minds which they're answering. The company is now starting to happen, and I predict it will become more common.

I got serious about and did a bunch of work, 1 to 2 deals done in a year. 5x.6 I spent almost a decade investing in early stage startups, and startups should simply ignore other companies' patents. When the tests are narrow and predictable, you get cram schools—which they did in Ming China and nineteenth century England just as much as the average person. I got serious about and did a bunch of small organizations in a market can come close. We didn't have enough saved to live on. Mistake number two. If they think your startup is worth investing in.7 As this example suggests, the rate at which technology increases our productive capacity is probably polynomial, rather than linear. What you're doing is business creation.

Notes

That's not a promising market and a t-shirts, to mean the hypothetical people who might be enough. Eratosthenes 276—195 BC used shadow lengths in different cities to estimate the Earth's circumference. And though they have less money, the Romans didn't mean to imply that the government had little acquired immunity to messianic figures, just monopolies they create liquidity. This has already happened once in China, Yale University Press, 1996.

At one point in the narrow technical sense of the aircraft is. Since I now believe that was the recipe: someone guessed that there are no longer written in Lisp, you don't see them much in their early twenties. So if you're good you'll have to be in the former depends a lot of the resulting sequence. Probabilities in this new world.

Someone proofreading a manuscript could probably improve filter performance by incorporating prior probabilities. In one way, be forthright with investors.

But the change is a way to see if you don't need that much to hope for, believe it, but the median case. Give the founders of failing startups would even be tempted to ignore what your body is telling you to take action, there is something in this they're perfect. 4%? But in this algorithm are calculated using a dictionary to pick up a solution, and why it's next to impossible to succeed at all.

Please do not take the term literally.

But which of them.

Some of the companies fail, no matter how good you can play it safe by excluding VC firms regularly cold email.

Thanks to Jessica Livingston, Jackie McDonough, Patrick Collison, Dan Giffin, Trevor Blackwell, Peter Eng, Parker Conrad, Geoff Ralston, and Joe Gebbia for sharing their expertise on this topic.

[program-800]

Exploring D:BH Fics (Part 6)

I continue investigating the text of DBH fics. Here, I talk a bit more about the word2vec model mentioned here.

Recap: Data was scraped from AO3 in mid-October. I removed any fics that were non-English, were crossovers and had less than 10 words. A small number of fics were missed out during the scrape - overall 13933 D:BH fics remain for analysis.

Part 1: Publishing frequency for D:BH with ratings breakdown Part 2: Building a network visualisation of D:BH ships Part 3: Topic modeling D:BH fics (retrieving common themes) Part 4: Average hits/kudos/comment counts/bookmarks received (split by publication month & rating) One-shots only. Part 5: Differences in word use between D:BH fics of different ratings Part 6: Word2Vec on D:BH fics (finding similar words based on word usage patterns) Part 7: Differences in topic usage between D:BH fics of different ratings Part 8: Understanding fanon representations of characters from story tags Part 9: D:BH character prominence in the actual game vs AO3 fics

Preface Before I start, do note that word2vec models (and the dimension reduction techniques in the visualisation, save for PCA) are still relatively unfamiliar to me versus the other stuff I’ve done. I’ll be writing what I understand so far, but I’m still doing readings and am far from an expert on any of this. Now that’s out of the way -

Relations between words come easy to us. We just kind of know that generally, words like ‘pain’ and ‘agony’ are more similar to each other than ‘pain’ and ‘apples’, for example. It’s not as easy for a computer though - text data has to be preprocessed into a numerical form to do any quantitative analysis; how would you tell a PC that ‘pain’ and ‘agony’ are closer to each other than ‘apples’?

Many applications still work off a bag-of-words model (i.e., some form of word count). It sounds crude but it really still provides a rather solid baseline! But for the purpose of this part (finding similar words), a bag-of-words won’t work at all. For example, the sentences: ‘There was blood, he was in pain and screaming’ and ‘There was blood, he was in agony and screaming’. ‘Pain’ and ‘agony’ would just be counted as different words (as with any usage of ‘apple’) - there’s no information encoded in the word count about their obvious similarity.

Word2vec models can help with this. By working with the assumption that words that share similar contexts (i.e., surrounding words) should have similar meanings, these models represent each word as a unique numerical vector (typically hundreds of dimensions), by either (1) learning to predict a word of interest based on its surrounding words, or (2) learning to predict surrounding words based on a word of interest. With these vectors (word embeddings), we can then perform calculations; for example, calculating cosine similarity to find the most similar words.

I’d like to highlight that an effect of this assumption is that we don’t necessarily get synonyms only in our eventual lists of similar words. The similarity derived from word2vec is far broader - antonyms would also be considered similar under this assumption.

Much code for this part taken from Gensim’s tutorial.

1. Preprocessing. Very simple preprocessing done here. Given that surrounding words are important to the training of the word2vec model, I did not remove names and stopwords. I just removed the chapter/work text and newline indicators.

Then, I tokenised (split) each fic down into its sentences. I then applied Gensim’s simple_preprocess function to each sentence to get word2vec-ready input. The function further tokenises each sentence into individual words, lowercasing them all and ignoring words that are too short/long (at least 2 characters, no more than 15).

There were 6,817,169 sentences for the word2vec model to learn from.

2. Training the word2vec model. I used Gensim for this. I did not set a maximum vocabulary size for the model to work on, but I did set a minimum count of 100 (i.e., the word has to appear in at least 100 sentences to be kept for training). This left me with 18,733 unique words from the original 133189 unique words.

I used the skip-gram model, which learns to predict surrounding words given a word of interest. The window size was set to 10; this affects the size of the ‘context’ that the model’s working with (i.e., look at the 10 words before and after each word of interest in a sentence). I set the vector size to 200 - each of the 18,733 words was thus represented as a 1x200 dimension vector at the end of training. The model was allowed to run for 20 iterations.

3. Visualisation. Since it’s impossible for most humans to visualise at a level of 200 dimensions (please contact me if you can), I used tensorflow’s projector to visualise the 18,733 embeddings in a 3D space.

There are three options for dimension reduction - PCA, tSNE, UMAP. This notebook from the authors of UMAP show how these algorithms differ in their final visualisation output.

PCA is linear and provides a lower-dimension representation that captures the most amount of variance possible from the original representation. It is deterministic and results don’t differ across runs.

tSNE and UMAP (from what I understand) are non-linear, will change across different runs, and are sensitive to hyperparameters. I’ve mostly seen tSNE used as a useful pre-work visualisation tool to check if there is actual separability in the data before going on with clustering, etc. tSNE is focused more on local structure and doesn’t preserve global distances (so you can’t say a further cluster is more dissimilar than a nearer one).

UMAP is a really new algorithm that seems to be a contending alternative to tSNE. I don’t know enough to comment - honestly I went with UMAP for this visualisation just because it ran faster. It does seem to generally put the words that have higher similarity (as defined by cosine similarity) in the same local space.

Feel free to try the tSNE setting on the projector, but do note it’ll take a while for the result to stabilise!

Final notes Regarding model training, I’m not too sure if 100 sentences was too liberally low a count and if a window of 10 was too large. I’d like to continue experimenting with this.

Also, while the model appears to find qualitatively intuitive ‘similar’ words, that’s far from the only way to evaluate it:

1) I read that one way to test the model’s quality is to look at semantic relations, e.g. ‘man’ is to ‘boy’ as ‘woman’ is to ‘girl’, ‘Moscow’ is to ‘Russia’ as ‘Washington’ is to ‘America’. I ran a few checks and they weren’t that great. I (currently) suspect two reasons. Firstly - more data and more hyperparameter tweaking needed. Secondly - fics may just have a rather different focus/relations from regular Wikipedia/news corpora, and it’s being reflected here.

2) Another way to evaluate the model is to see the performance of the learned embeddings on some downstream task like text classification. I haven’t thought of a follow-up task, but it would be interesting to pursue this.

[909hold]

Do You Want Your Website to Appear on The First Page We Are Here To Help You

What Is SEO?

SEO Is an Acronym for Program Optimization, Which Is the Process of Perfecting (Optimizing) A Website So That It's More Readily Set Up Online. SEO Primarily Focuses on Perfecting a Website’s Rankings in Search Engines Like Google and BING. The Upper and Internet Point Appears in The Hunt Machine Rankings, The Lesser the Liability It Will Be Visited. When An Online Point Receives Further Business, Further Business Is Liable. There Are Several Different Types of Website Optimization. Please Find Below an Overview of Colorful Types of SEO Services

Specialized SEO Analysis of The Website’s Specialized Factors That Impact Its Rankings.

Code Efficiency

An Important Factor for Website Optimization Is the Effectiveness of The Law with Which the Website Is Developed. Bloated, Hamstrung Law Can Hinder a Website’s Cargo Time and Dilute Law-To-Textbook (On-Page Content). Formerly We Optimize On-Page Content, The Optimization Is Stylishly Entered by Google When the Website Law Is Minimized. Google More Readily Understands the Semantic Meaning of a Page When There Is Lower Law. This Helps to Support Rankings.

Website Speed

Google’s Ranking Algorithm Is Continually Streamlined to Support the Experience of Its Druggies. Also, To Deliver Accurate Results That Address a Question, Google Factors into Its Rankings the Speed of An Online Point. A Brisk Website Provides a Superior Stoner Experience to An Online Point Caller. All Other Effects Being Equal, Google Will Deliver a Brisk Website Before a Slower Point Because a Caller Will Presumably Have a Far Better Experience with The Brisk Website. Page Cargo Times Should Be Below2.5 Seconds.

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Https

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Page Titles and Meta Descriptions Are Important Because They Present an Occasion to Separate Your Web Page Result from Your Challengers. Current SEO Best Practices Consider Newer Optimization Factors Employed by Google, Which Include Stoner Engagement Criteria Like Time on Point, Brio Rate, And Click-Through Rate. When Google Identifies That One Website Is Being Viewed for An Extended Period and There's Further Caller Engagement with The Website, The Page Will Out-Rank Pages with Lower Stoner Criteria. Accurate And Interesting Page Titles and Descriptions Help Increase Click-Through Rates and Reduce Brio Rates.

On-Page Content

The Content (Textbook) Of an Online Page Is Critical to The Implicit Ranking of That Page. As Mentioned Over, Google Is Tracking Caller Operation Criteria Like Time on Page and Brio Rate.

Eventually, The On-Page Content Must Give Value to The Caller. Simply Listing Services, For Illustration, Is Boring. When One Also Includes the Benefits or Value of Those Services to The Implicit Client, The Page Becomes More Applicable to The Caller. When The Page Is Terse, Has A Charming Layout, And Uses Rich Media (Vids, Plates, Etc.) That Ameliorate Caller Engagement, The Rankings Enhancement Has Lesser Abidance.

Off-Page SEO Perpetration of Optimization Rudiments Not Related to The Page Itself.

Off-Page SEO Ways Help Increase a Website’s Sphere Authority, Which Measures a Website’s Credibility And “Capability” To Rank Well. While On-Page SEO Positions an Online Point to Rank for Hunt Terms Well by Helping Search Engines Understand the Semantic Meaning of a Website and Its Web Pages, Off-Page SEO Helps Increase the Authority of An Online Point And Google’s Interpretation Of What Websites Should Rank Ahead Of Others.

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[webomates]

Black box tests

Black box testing can be performed using various approach :

Test case

Automation

Ad-hoc

Exploratory

Before these individual approach can be explained, It is important to know what black box testing is.

Black-box tests is a software testing method antonymous to white-box testing. The black-box is also known as Behavioral testing/functional testing/closed-box testing. It is a method of analyzing the functional and non-functional aspects of software. The focus of the software tester does not involve internal structure/implementation and design of the system involved. This means that the software tester does not necessarily need to have knowledge of programming and does not need to have access to the code. Black-box testing was developed so that it can be applied to customer requirement analysis and specification analysis. During the execution of testing the black-box software, Software tester selects a set of valid and invalid inputs and looks for valid output response.

According to ANSI/IEEE 1059 standard — “Software testing is a process of analyzing a software item to detect the differences between existing and required conditions (i.e: defects) and to evaluate the features of the software item. The approaches to software testing include Black-box testing, White-box testing and grey-box testing.”

Examples of Scenarios the applies simple Black-box testing includes:

These few examples are used to paint a vivid picture of what Black-box testing implies,

Using a Google search engine, a software tester types some texts into the inputs space provided, this server as the input. The search engine collects the data, processes it using the algorithm developed by Google programmers and then outputs the valid result or output that corresponds to the input.

Same applies to a Facebook chatting interface, When the bearer of the information inputs the information into the chat box and sends, the information is processed and sent to the receiver. The receiver from his end retrieves the information and vice versa.

Black-box testing can be used in any software system you want to test, such as a database like Oracle, a mathematical software like Excel and MATLAB, an operating system like windows and Linux and any other custom native or web based or mobile application. Emphasis is laid on the input and output of the software with no interest on the code.

Types of Black-box testing:

There are many types of black-box testing used in software testing which involves many procedures during implementation, we focus on the prominent ones.

Functional testing: This type of black-box testing involves only the input and corresponding valid output analysis. It is mainly done to determine the functionality and behavior of the system. The software testers focus on answering the question ‘Does this system really do what it claims to do?’

Few major types of functional testing are:

Sanity testing

Smoke testing

Integration testing

System testing

Regression testing

User acceptance testing

Non-functional testing: This test focuses on answering the questions ‘how efficient is this system in carrying out its functions?’. It focuses on the non-functional requirements of the system such as performance, scalability and usability which are not related to black-box testing.

Few major types of non-functional testing:

Usability testing

Load testing

Performance testing

Compatibility testing

Stress testing

Scalability testing

Regression testing: Whenever the internal structure of the system is altered regression testing is carried out to ensure existing functionality & behavior is working as expected. The alteration can mean when the system undergoes debugging, code fixes, upgrades or any other system maintenance processes. The software tester makes sure the new code does not change the existing behavior.

The black-box testing method is applicable to various levels of software testing such as:

This diagram Represents levels of black-box testing

Integration testing: in this level of software testing individual software are combined by the tester and tested as a group using black-box testing. This exposes fault experienced in the interaction between the integrated units.

System testing: in this level of software testing a fully complete software is tested to evaluate the compliance with the specified requirements proposed for the system.

Acceptance testing: In this level of software testing the acceptability of the system is tested. Here the software tester checks whether the system complies with the commercial requirement. The user needs and requirement are checked to know whether they are acceptable before delivery.

The higher the level of the black-box testing, and hence the bigger and more complex the system is the more the black-box testing that is applied.

You May Like to Read

Software Testing and the 5W and 1H technique of Metrics Reporting

Manual versus Automation Testing

Techniques involved in black-box testing:

There are many approaches use in designing black-box testing the following are but a few of them:

This diagram Represents Techniques in Black-box testing

Equivalence Partitioning or Equivalence class testing: in this software design technique the software tester divides all the inputs into equivalence classes and pick valid and invalid data from each class, a selected representative is used for the whole as a test data. The selected data is now used to carry out the black-box testing. This technique helps to minimize the number of possible test cases to a high level that maintains reasonable test coverage.

Boundary value analysis: it is a software design technique where the functional tester while carrying out the testing determines boundaries for input values. After determining the boundaries selects inputs that are at the boundaries or just inside or outside the boundaries, and uses it as test data. The test data is now used for carrying out the black-box testing.

Cause-effect graphing: in this software design technique software testers identifies the causes (valid or invalid input conditions) and effects (output conditions). This result in the production of a cause-effect graph, and Generation of test cases accordingly.

Error guessing: this is an example of an experience-based testing in which the tester uses his experience about the application and functionalities to guess the error-prone areas

Others include:

Decision table testing

Comparison testing

Black-box Testing steps

Black-box testing involves some generic steps carried out by testers on any type of Black-box.

The requirements and specifications are provided for the systems and they are thoroughly examined.

The software tester chooses both the valid inputs (the positive test scenarios) and the invalid inputs (the negative test scenarios). He tests the valid input to check whether the SUT processes them correctly then the invalid inputs to know whether they are being detected by the system.

The expected outputs are retrieved for all the inputs.

The software tester then constructs test cases with all the selected inputs.

The constructed test cases are executed.

Actual outputs from the SUT are compared with the expected outputs to determine if they comply with the expected results.

If there is any defect in the results they are fixed and the regression test is carried out.

Black-box testing and software test life cycle (STLC)

Software test life cycle (STLC) is the life cycle of a black-box testing and it is relative to the stages of software development. The cycle continues in a loop structure until a perfect and satisfying result devoid of defects are achieved.

Diagram showing software development life cycle for Black-box testing:

Requirement: This is the stage where the requirements and specifications needed by the software tester are gathered and examined.

Test planning and Analysis: In this stage the possible risks and mitigations associated with the project are determined.

Design: This stage allows scripts to be created on the basis of the software requirements.

Test execution: the prepared test cases are executed and if there is any deviation between the actual and expected results they are fixed and re-tested. The cycle then continues.

Tools used in black-box testing

The main tools used in black-box testing includes records and playback tools. These tools are mainly used for regression testing. When a defected system undergoes bug fixing, there is a possibility that the new codes will affect the existing codes in the system. Applying regression analysis with the records and playback tools helps to checkmate and fix errors.

These record and playback tools record test cases in form of scripts like TSL, VB script, JavaScript, Perl etc.

Advantages and Disadvantages of Black-box testing

Advantages:

There is no technical or programming language knowledge is required. The software tester does not need to have any knowledge of the internal structure of the system. He is required to access only the functionality of the system. This is like being in the user’s shoe and thinking from the user’s point of view.

Black-box testing is more effective for large and complex applications.

Defects and inconsistency in the system can be identified easily so that they can be fixed.

The test cannot be compromised because the designer, programmer, and tester are independent.

Tests are carried out as soon as the programming and specifications are completed.

Disadvantages

The test will not be necessary if the software programmer has already run the test case.

The tester may ignore possible conditions of scenarios to be tested due to lack of programming and technical knowledge.

Complete coverage in cases of the large and complex project is not possible.

Testing every input stream is unrealistic because it would take a large amount of time; therefore, many program parts go untested.

In conclusion, Black-box testing is a significant part of the software testing technique that requires the verification of system functionalities. This help in detection of defect for fixing to ensure the quality & efficiency of the system. 100% accuracy cannot be achieved when testing software; this requires software testers to follow the correct procedure for the testing in order to achieve a higher result.