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Algorithm Explanation The Knuth-Morris-Pratt (KMP) algorithm is a string-matching algorithm that is used to find all occurrences of a pattern string in a text string. It was developed by Donald Knuth, Vaughan Pratt, and James Morris in 1977 and is named after them. The KMP algorithm is a linear-time algorithm, which means that it can find all occurrences of a pattern string in a text string in O(n+m) time, where n is the length of the text string and m is the length of the pattern string. This makes it much faster than naive algorithms, which have a worst-case time complexity of O(nm). The KMP algorithm works by preprocessing the pattern string to generate a partial match table (also called the failure function or the next array). This table contains information about the pattern string that allows the algorithm to skip over unnecessary comparisons during the search. During the search, the KMP algorithm compares each character in the text string with the corresponding character in the pattern string. If a mismatch is found, the algorithm uses the information from the partial match table to determine where to start the next comparison. The KMP algorithm has many practical applications, such as in text editors, search engines, and DNA sequencing. It is also used in other algorithms, such as the Boyer-Moore algorithm, which is a faster string-matching algorithm that is based on the KMP algorithm. The “Algorithm Explanation” section was written by an AI. Stream-Based KMP Algorithm The KMP substring search algorithm can be modified to search for a pattern string within a stream of strings. This modification can be useful in various scenarios, such as searching for a particular keyword in a log file or searching for a sequence of characters in a video stream. To understand this modified algorithm, it is recommended to first have a good grasp of the original KMP pattern search algorithm. A great resource for learning KMP is , which provides an amazing explanation of the algorithm. Tushar Roy’s video guide The modified algorithm starts with checking for some edge cases and immediately returns false if the pattern or input is empty. Then, the algorithm creates a pattern table that defines how many characters can be safely ignored (not checked again) while the characters at the current position are not matched. Next, the algorithm iterates over the list of strings, representing the stream, and searches for the pattern. If the pattern is found in any of the strings, the algorithm returns true; otherwise, it continues searching until the end of the stream is reached. The modified version of the Knuth-Morris-Pratt (KMP) algorithm requires an additional check on the index inside each string from the stream. However, all the other parts of the algorithm remain the same. To see how this modified version differs from the original KMP algorithm, you can find the complete code on the author’s GitHub page. By comparing the two versions of the algorithm, you can gain a better understanding of how KMP can be applied in different scenarios. The complete code for this modified algorithm can be found in the provided link. Complete Code in Dart /// Time Complexity O(m + n), Space complexity O(n)
/// m - input, n - pattern length
bool searchSubstring(List<String> input, String pattern) {
  if (input.isEmpty || pattern.isEmpty) {
    return false;
  }
  /// table to build a pattern
  final List<int> patternTable = List<int>.filled(pattern.length, 0);

  int j = 0;
  int i = 1;
  final int patMaxIndex = pattern.length - 1;

  /// length of prefix which is suffix also
  /// Generate safe check positions for optimization
  /// It defines how much chars can be safely ignored (not checked again)
  /// while chars at current position not matched
  /// And return to position after safe prefix
  /// and check again with same char in the input
  while (i <= patMaxIndex) {
    if (pattern[i] == pattern[j]) {
      patternTable[i] = j + 1;
      j++;
      i++;
    } else if (j > 0) {
      j = patternTable[j - 1];
    } else {
      i += 1;
    }
  }

  int patIdx = 0; // for iteration over pattern;
  for (final String line in input) {
    int lineIdx = 0;
    while (lineIdx < line.length) {
      /// If it is last char in pattern - return start position of this pattern
      if (patIdx == pattern.length) {
        return true;
      }

      /// If chars match - iterate to the next
      if (line[lineIdx] == pattern[patIdx]) {
        patIdx++;
        lineIdx++;
      } else if (patIdx > 0) {
        /// if Value is not zero - means that we could return to pattern position
        /// safely ignore some prefix equal to suffix and check chars again
        patIdx = patternTable[patIdx - 1];
      } else {
        /// If value is zero - move to next char in line
        lineIdx++;
      }
    }
  }
  return false;
} Please refer to the and files from my GitHub for a complete code comparison. kmp_mod_substring_search kmp_substring_search https://github.com/VadymPinchuk/interview_questions?embedable=true

Understanding and Applying the Knuth-Morris-Pratt Algorithm: From String Matching to Stream Search

Astounding Stories of Super-Science April 1930, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Monsters of Moyen - Chapter III: Wings of To-morrow Astounding Stories of Super-Science April 1930: Monsters of Moyen - CHAPTER III. Wings of To-morrow AS Prester Kleig climbed into the enclosed passenger pit of the monoplane—a Mayther—his ears seemed literally to be ringing with the drumming, mighty voice of Moyen. But now that voice, instead of merely speaking, rang with sardonic laughter. He had never heard the laughter of Moyen, but he could guess how it would sound. That airplane of the slanted wings, the bulbous, almost bulletlike fuselage, what of it? It was simple, as Kleig looked back at his memoried glimpse of it. The submarine was a metal fish made with human hands; the airplane aped the birds. The strange ship which had caused the destruction of the Stellar, was a combination fish and bird—which merely aped nature a bit further, as anyone who had ever traversed tropical waters would have instantly recognized. But what did it portend? What ghastly terrors of Moyen roamed the deeps of the Atlantic, of the Pacific, the oceans of the world? How close were some of these to the United States? The pale eyes of Moyen, he was sure, were already turned toward the West. PRESTER KLEIG sighed as he seated himself beside Carlos Kane. Then Kane pressed one of the myriad of buttons on the dash, and Kleig lifted his eyes to peer through the skylight, to where that single press of a button had set in motion the intricate machinery of the helicopter. A four-bladed fan lifted on a slender pedestal, sufficiently high above the surface of the wing for the vanes to be free of the central propeller. Then, automatically, the vanes became invisible, and the Mayther lifted from the sandy beach as lightly, and far more straightly, than any bird. As the ship climbed away for the skies, and through the transparent floor the beach and the Atlantic fell away below the ship, a sigh of relief escaped Kleig. This was living! Up here one was free, if only for a moment, and the swift wind of flight brushed all cobwebs from the tired human brain. He watched the slender needle of the altimeter, as it moved around the face of the dial as steadily as the hands of a clock, around to thirty thousand, thirty-five, forty. Then Carlos Kane, every movement as effortless as the flight of the silvery winged Mayther, thrust forth his hand to the dash again, pressed another button. Instantly the propellers vanished into a blur as the vanes of the helicopter dropped down the slender staff and the vanes themselves fitted snugly into their appointed notches atop the wing. FOR a second Carlos Kane glanced at the tiny map to the right of the dash, and set his course. It was a matter of moments only, but while Kane worked, Prester Kleig studied the instruments on the dash, for it had been months since he had flown, save for his recent half-dreamlike experience. There was a button which released the mechanism of the deadly guns, fired by compressed air, all operated from the noiseless motor, whose muzzles exactly cleared the tips of Mayther's wings, two guns to each wing, one on the entering edge, one on the trailing edge, fitted snugly into the adamant rigging. Four guns which could fire to right or left, twin streams of lead, the number of rounds governed only by the carrying power of the Mayther. Prester Kleig knew them all: the guns in the wings, the guns which fired through the three propellers, and the guns set two and two in the fuselage, to right and left of the pits, which could be fixed either up or down—all by the mere pressing of buttons. It was marvelous, miraculous, yet even as Kleig told himself that this was so, he felt, deep in the heart of him, that Moyen knew all about ships like these, and regarded them as the toys of children. Kane touched Kleig on the shoulder, signaling, indicating that the atmosphere in the pits had been regulated to their new height, and that they could remove their helmets and oxygen tanks without danger. WITH a sigh Prester Kleig sat back, and the two friends turned to face each other. "You certainly look done in, Kleig," said Kane sympathetically. "You must have been through hell, and then some. Tell me about this Moyen; that is, if you think you care to talk about him." "Talk about him!" repeated Kleig. "Talk about him? It will be a relief! There has been nothing, and nobody, on my mind save Moyen for weary months on end. If I don't talk to someone about him, I'll go mad, if I'm not mad already. Moyen? A monster with the face of an angel! What else can one say about him? A devil and a saint, a brute whose followers would go with him into hell's fire, and sing him hosannas as they were consumed in agony! The greatest mob psychologist the world has ever seen. He's a genius, Kane, and unless something is done, the Western world, all the world, is doomed to sit at the feet, listen to the commands, of Moyen! "He isn't an Oriental; he isn't a European; he isn't negroid or Indian; but there is something about him that makes one thing of all of these, singly and collectively. His body is twisted and grotesque, and when one looks at his face, one feels a desire to touch him, to swear eternal fealty to him—until one looks into his pale eyes, eyes almost milky in their paleness—and gets the merest hint of the thoughts which actuate him. If he has a failing I did not find it. He does not drink, gamble...." "And women?" queried Kane, softly. KLEIG was madly in love with the sister of Kane, Charmion, and this thing touched him nearest the heart, because Charmion was one of her country's most famous beauties, about whom Moyen must already have heard. "Women?" repeated Kleig musingly, his black eyes troubled, haunted. "I scarcely know. He has no love for women, only because he has no capacity for any love save self-love. But when I think of him in this connection I seem to see Moyen, grown to monster proportions, sitting on a mighty throne, with nude women groveling at his feet, bathed in tears, their long hair in mantles of sorrow, hiding their shamed faces! That sounds wild, doesn't it? But it's the picture I get of Moyen when I think of Moyen and of women. Many women will love him, and have, perhaps. But while he has taken many, though I am only guessing here, he has given himself to none. Another thing: His followers—well, he sets no limits to the lusts of his men, requiring only that every soldier be fit for duty, with a body strong for hardship. You understand?" Kane understood; and his face was very pale. "Yes," he said, his voice almost a whisper, "I understand, and as you speak of this man I seem to see a city in ruins, and hordes of men marching, bloodstained men entering houses ... from which, immediately afterward, come the screams of women ... terror-stricken women...." He shuddered and could not go on for the very horror of the vision that had come to him. But Kleig stared at him as though he saw a ghost. "Great God, Carl!" he gasped. "The same identical picture has been in my mind, not once but a thousand times! I wonder...." Was it an omen of the future for the West? Deep in his soul Prester Kleig fancied he could hear the sardonic laughter of the half-god, Moyen. A TINY bell rang inside the dash, behind the instruments. Kane had set direction finders, had pressed the button which signaled the Washington-control Station of the National Radio, thus automatically indicating the exact spot above land, by grid-coordinates, where the Mayther should start down for the landing. An hour later they landed on the flat roof of the new Capitol Building, sinking lightly to rest as a feather, nursed to a gentle landing by the whirring vanes of the helicopter. Prester Kleig, surrounded by uniformed guards who tried to shield him from the gaze of news-gatherers crowded there on the roof-top, hurried him to the stairway leading into the executive chambers, and through these to the Secret Chamber which only a few men knew, and into which not even Carlos Kane could follow Prester Kleig—yet. But one man, one news-gatherer, had caught a glimpse of the face of Kleig, and already he raced for the radio tower of his organization, to blazon to the Western world the fact that Kleig had come back. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Astounding Stories. 2009. Astounding Stories of Super-Science, April 1930. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/29390/29390-h/29390-h.htm#Monsters_of_Moyen This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Wings of To-morrow

The Count of Monte Cristo, Illustrated: Chapter 50 - The Morrel Family

The Count of Monte Cristo, Illustrated: Chapter 29 - The House of Morrel & Son

HackerNoon Reporter: Please tell us briefly about your background. I’m a high school dropout with a Harvard MBA… Not exactly the combination you’d expect but it gives me a unique perspective on our business. When I was 14, my mom was asked to work the 6am shift at the local hospital or lose her job. She liked that job so when her schedule changed, so did mine. Someone had to take my 4 year old brother to daycare across town, didn’t they? This made me late to school, missing 1st and eventually 2nd periods, until eventually I dropped out altogether. I started working a few jobs at a time to help my family make ends meet. I can still remember the weights of each ingredient used for the menu at Taco Bell. Crazy how something like that sticks with you. I managed to get my high school diploma in a single year and work my way to Cornell where I graduated college. That took me to GE, Google, and eventually HBS where I started Syrg. We’re all about giving back to the people who gave me a chance when my family and I needed it most, while helping the countless folks who grew up like me but haven't been so fortunate. What's your startup called? And in a sentence or two, what does it do? Syrg is helping hourly employers transition into a world where there are more jobs than people willing to work them. We’re empowering employers to recruit and retain the best team members with a suite of automated tools by putting the needs of workers front and center. What is the origin story? Before getting my MBA at Harvard or working at Google, I was a high school dropout working 2-3 hourly jobs at a time. As I bounced from job to job, I realized my employers were always short-staffed and scrambling for hires. I was willing to work for any of my previous employers if I needed some extra cash and found it strange they never reached out to me proactively. While getting my MBA, I met my cofounder Rob, a former McKinsey consultant who worked in business transformation and turnarounds. He had specific experience optimizing workforce operations for hourly employers and a deep appreciation that there was more to life than making PowerPoints and Excel models. Syrg is the first step in my vision for transforming hourly work and recruiting, and Rob was the obvious match to help make that a reality. What do you love about your team, and why are you the ones to solve this problem? I love our team because we’re all so passionate about making a difference. Syrg is born out of my personal story and it’s been incredible to see my own passion reflected by everyone at the company. This combination of personal experience, intense passion, deep expertise, and commitment to a growth mindset allows us to execute extremely well. I certainly wouldn’t bet against us. If you weren’t building your startup, what would you be doing? Seriously, looking for a startup to build. At the moment, how do you measure success? What are your core metrics? We care more about helping hourly workers and their employers more than anything, so that’s how we measure our success. There are a number of growth related metrics we focus on, but number of satisfied customers, number of hourly workers engaged, and number of jobs filled are towards the top of the list today. What’s most exciting about your traction to date? In Spring 2019, we launched our first product that enabled former employees, primarily W2 union-represented janitors who cleaned commercial buildings, to work shifts on an “on-demand” basis for supplemental income to make ends meet. We completed nearly 10K shifts. When the pandemic hit, we suffered 90% revenue loss and no shifts passed through our platform. We pivoted and re-launched Syrg in July 2020. Since then, we have signed over 200 paying franchisee customers in 13 months across brands including McDonald’s, Burger King, Domino’s, Taco Bell, Wendy’s, Pizza Hut, and Dunkin’. This represents more than 3,000 individual stores and over 3M job seekers on our network who we engage to fill both full-time and part-time permanent positions. What technologies are you currently most excited about, and most worried about? And why? I’m most excited about Blockchain, but not in a fanboy, dot-bubble, crypto-only superficial kind of way, but because Blockchain has the potential to do for assets what the internet has done for information. Blockchain has the ability to raise billions out of poverty and drive equality. I’m most worried about technology that leaves space debris and pollution. We gotta get out of here! What drew you to get published on HackerNoon? What do you like most about our platform? HackerNoon is a place where technologists strut their stuff. Having written most of the code of our initial product and spent years as an arcade junky, it’s an honor to get featured among like minded nerds. What advice would you give to the 21-year-old version of yourself? Start a practice in which you sit down and focus on an object, any object. Do it up to 60 minutes long and try not to break your attention from the object. Do this everyday for at least 7 years. You’re going to be unstoppable. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Since we can only perceive a billionth of all of what we experience in any given moment, it makes our reality completely subjective. So then, it’s only worth adopting the perspective to view life as a piece of art and endeavor to paint a masterpiece. Vote for Syrg for Startup of the Year, Boston, Massachussets.

"Blockchain has the Ability to Raise Billions out of Poverty" Rahkeem Morris, Syrg Co-Founder & CEO.

This paper is under CC 4.0 license. available on arxiv Authors: (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 5 THE UNDISCOVERED COUNTRY AI copilots will transform how we search. Tasks are central to people’s lives and more support is needed for complex tasks in search settings. Some limited support for these tasks already exists in search engines, but copilots will expand the task frontier to make more tasks actionable and address the “last mile” in search interaction: task completion [58]. Moving forward, search providers should invest in “better together” experiences that utilize copilots plus traditional search, make these joint experiences more seamless for searchers, and add more support for their use in practice, e.g., help people to quickly understand copilot capabilities and potential and/or recommend the best modality for the current task or task stage. This includes experiences where both modalities are offered separately and can be selected by searchers and those where there is unification and the selection happens automatically based on the query and the conversation context. The foundation models that power copilots have other search-related applications, e.g., for generating and applying intent taxonomies [43] or for evaluation [19]. We must retain a continued focus on human-AI cooperation, where searchers stay in control while the degree of system support increases as needed [44], and on AI safety. Searchers need to be able to trust copilots in general but also be able to verify their answers with minimal effort. Overall, the future is bright for IR, and AI research in general, with the advent of generative AI and the copilots that build upon it. Copilots will help augment and empower searchers in their information seeking journeys. Computer science researchers and practitioners should embrace this new era of assistive agents and engage across the full spectrum of exciting practical and scientific opportunities, both within information seeking as we focused on here, and onwards into other important domains such as personal productivity [5] and scientific discovery [22]. REFERENCES [1] Eugene Agichtein, Ryen W White, Susan T Dumais, and Paul N Bennet. 2012. Search, interrupted: understanding and predicting search task continuation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 315-324. [2] Marcia J Bates. 1990. 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Navigating Complex Search Tasks with AI Copilots: The Undiscovered Country and References

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Alyvia Walters, Rutgers University, USA; (2) Tawfiq Ammaris, Rutgers University, USA; (3) Kiran Garimella, Rutgers University, USA; (4) Shagun Jhaver, Rutgers University, USA. Table of Links Abstract & Introduction Background and Related Work Methods The Tools of CRT Meme Production Knowledge Production in a Post-Truth World Conclusion & References Conclusion This study entices us to continue asking how we might confront mis-/disinformation in our current moment. It becomes especially urgent as we encounter the fact that much of the information circulating through highly transmissible media, such as memes, is not only incorrect but is also fungible: in the case of CRT-centered memes, CRT could “mean” almost anything race-related to forward each camp’s agenda, and seemingly very few care to engage with an institutional definition. When politicized definitions are a practice in power assertion, the discursive work these definitions do–“correct” or not–is more necessary than ever to understand. As Cassam (2021) warns us, it is important not to mistake a lack of engagement with “true” definitions as a disregard for the truth as a whole, and assuming that mis- /disinformation is merely bullshit is perhaps an unproductive lens through which to view knowledge production. We must take these definitions seriously, as they are, from a political epistemology stance, thoughtfully crafted messages that are “true” in some way to their consumers. There are several fruitful routes that we can identify for further work around the production and consumption of these memes as they relate to knowledge-building practices. In the space of production, contacting those who created these media objects would potentially lend useful insight about how they, as creators, gained their own understanding of CRT, and why they chose to disseminate this information in these particular ways attached to these particular visual formats. The Media Research Center would be an interesting first place to start, as they crafted each of their anti-CRT memes in the same aesthetic format with the same rhetorical appeals to Black spokespeople. Additionally, study of those who consume these memes is warranted to uncover how users’ encounters with these media shape their understandings of CRT and their opinions on it. Further, we argue that platforms, too, have some responsibility to contextualize memes such as these through content moderation practices. We acknowledge that this is more than a simple technical issue: filters for racist material, for example, would not flag memes as nuanced as these, and indeed, platforms would likely encounter bad publicity around censorship if any of the memes included in this study were removed. However, there are ways to approach this information landscape through socio-technical solutions, such as by providing the public, experts, and other cultural gatekeepers the ability to contextualize information on social networking sites (Morrow et al. 2022). By adding “notes,” or otherwise interacting with the information in such a way that its complex relationships to institutional fact are immediately evident to users who may encounter that information, platforms could greatly diminish the power of partisan information masquerading as fact. Finally, educational curricula and the students who learn from them would deeply benefit from incorporating critical media consumption practices into their core goals and outcomes. It is no longer possible to separate learning from media consumption in the everyday lives of the vast majority of students in the U.S., and we all suffer when there is a lack of commitment to creating critical media consumers who are trained to think before believing–and even more importantly, re-circulating–a politicized meme. Training young people on how mis-/disinformation and hate speech are disguised as fact and/or humor in memes is an important step forward in strengthening our information landscape and democratic future. Teachers are extraordinarily overburdened already, but a curriculum that integrates media literacy as a guiding principle would partially shift the burden from teachers directly and instead task those who guide the direction of school districts nationwide with creating pathways to teach this skill in all subject areas. There is no simple solution to curtailing the circulation of harmful visual media, as it is neither a purely tech issue nor purely a lack of education: this is a social issue which can only be resolved through the engagement of a wide variety of actors. It is incumbent upon all of us to take these seemingly insignificant memes seriously for their social impact and what they reveal about current ideological trends. It is crucial to better understand how bottom-up knowledge production on politicized topics, such as CRT, occurs on social media, particularly through compact, madeto-share media such as memes. 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Online Knowledge Production in Polarized Political Memes: Conclusion & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 7 Conclusion We present a method that efficiently scales thermodynamic stability prediction from single mutations to higher-order mutations. Our key insight is that the effects of mutations on the same protein are correlated. Thus, for a target protein, it suffices to run a deep backbone once and decode the effect of all mutations simultaneously using a shallow decoder. With the AlphaFold model as our backbone, our method outperforms existing methods on a variety of single and multiple mutation benchmarks. 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