CheckRedis in the Cloud Infrastructure Industry

CheckRed

This is part of the “Machine Learning” series on introducing machine learning from the very beginning. More articles are coming! < Previous article Already heard talking about Machine Learning in conferences, meetups or in my first article and want to learn more? You’re in the right place, the second step for you will be to discover the different kind of machine learning. I’ll introduce them to you through many practical examples. What you’ll know after reading ML comes in two flavors: and learning. Don’t judge them by their names, there aren’t opposite learning methods. supervised unsupervised The one you’ll choose only depends on your purpose. When you want the model to produce a formula which predicts a value from your data, you’re using supervised learning. Otherwise, if your goal isn’t to predict a value but rather finding patterns in your data, you’ll use unsupervised machine learning. Here is a diagram which sums up the concepts and vocabulary I want to teach you in this article. Don’t worry if classification and regression are unknown words to you. I’ll introduce them later with examples. Anyway, try to guess the answer to this question: Which kind of ML can help to find out if some items are often sold together? Supervised learning or Unsupervised learning? #1 Is my fruit tastes sweet or acid? I know, using machine learning to predict if the apple you’re going to eat is sweet or acid sounds like : You wanted a banana but what you got were a gorilla holding the banana and the entire jungle. — Joe Armstrong (Coders at work) Still, the purpose of this example is to predict if the fruit you’ll eat is either or . This goal is quite precise, you give a fruit to your ML model and it answers sweet or acid which is the taste of your fruit. sweet acid is what you’re looking for. Supervised learning with classification Let me explain, it’s supervised because you want to predict a specific value. With classification because this value isn’t a number, it can only get a defined set of values. Training dataset for our supervised ML Here is the training set we’ll give to our model to begin its training. It looks like a historical of the same question already asked with different fruits but the answer comes with. That’s why it’s called a training set, you give both the question and the answer for training. Your ML model will look for any kind of logic between each question and its answer. This link is actually function, it’s the function. Hypothesis The hypothesis with this training set will be as follows: Is this fruit sweet = Sum of this fruit sweet / Sum of this fruitLemons: 0/2 = 0% are sweet and 100% are acidApples: 3/4 = 75% are sweet and 25% are acid Let’s assume you’re a lemon eater. Even though you’re an expert about lemons tastes, you’ll ask you ML model to know if this lemon in your hand is sweet. Thank’s to its hypothesis it made up with the training data, it know 0% are sweet and 100% are acid so its answer will be . Indeed, as I explained before the answer is a value, not a probability. My question wasn’t right, a better would be: “How this lemon tastes?” acid With an apple in your hand, the answer would be sweet because it knows 75% of apples have a sweet taste. I only consider riped lemons, not limes! The training dataset we used to train our classification algorithm was simple. It could also include other pieces of information such as the current season and the fruit color. With more informations and training sets, the model will be able to improve its accuracy. #2 How much is this house worth? For this example, you can forget about fruits. You now evolved to a whole new level and will try to estimate houses prices. The purpose is clear, you want to sell or buy a house but you don’t know how much it worth. You’ve got property ads with plenty of details about properties. Those will be your training data for your ML model. Let’s sum up, you have a house with its living area and you want your model to give you its price. The goal is clear you’re asking for a value and this value is a number, it can be any price in a range. You must use . Supervised learning with regression Thus, the difference between and is only about the range of values the value you’re looking for can get. You’ll need classification for a discrete value and regression for a continuous value. classification regression Discrete values are counted whereas continious value are measured — Image from on Quora Luis Fok Now, let’s feed our model with a I’ve got from Kaggle. training dataset By the way, if you don’t know the Kaggle website I highly recommend you to check it out. You’ll find plenty of ML competitions with real-world datasets. Competitors also write articles to share their approach and techniques to deal with the ML competition problems. Plots sell price with living area SalePrice: sale price in dollars GrLivArea: living area in square foot The first approach is to draw a straight line roughly in the middle of all the dots on the chart. As mathematics taught us a straight line on a graph is nothing more than a formula which gives y with x: . y = f(x) = a * x + b It represents the as in the previous example. It’s a formula to get the price of the house with the house living area . hypothesis y x Thank’s to the hypothesis you can get either x from y or y from x Let’s say this morning you saw a property ad for a big property with a living area of 4,000 square feet. You want to check its price to see the owner didn’t overestimate its house. No need to calculate the and from the hypothesis, check the graph. It estimates a price of around $400,000 for a 4,000 square foot property. a b Using a different method to get the hypothesis, it can be more precise and answer a very different price. Now for a 4,000 square foot property, the estimated price is around $480,000. A more accurate hypothesis for this example The method used in the ML model to learn from the training data and create a hypothesis is very important, it’s called the . More this algorithm is well chosen and more the answers will be accurate. learning algorithm #3 Throw away junk emails Did you already ask yourself how some email are magically moved into the spam folder of your inbox? The magic part is actually using machine learning to figure out what makes an email a spam or not. In this case, the goal is clear. We want to know if an email is a spam or not. This is another example of classification, like the fruits example, there are only two possible results or . It’s a particular scenario of classification using only two classes (or categories). Besides, it also exists other variations with more than two classes. spam not spam Still, this is . Supervised learning with classification Let’s have an overview of how a supervised machine learning model works: The first step is the . As I explained before, you have to find a training set which contains both the question and its answer. In our example its a training set with the emails and their classification ( or ). training step spam not spam During its training, the ML algorithm will then build a function called to be able to classify any email. hypothesis The purpose of this training is to finally answer the same question with an email the model doesn’t know. The hypothesis function will provide the probability the given email is a spam. If this probability is less than 50%, the answer will be , otherwise, it’s a spam and it’ll move to the spam folder. not a spam #4 What people buy diapers with? This is one most famous history in machine learning domain. It’s about how Walmart used its customer data to get some business insights. Here is a recap about this project (including the diapers thing): Some time ago, Wal-Mart decided to combine the data from its loyalty card system with that from its point of sale systems. The former provided Wal-Mart with demographic data about its customers, the latter told it where, when and what those customers bought. Once combined, the data was mined extensively and many correlations appeared. Some of these were obvious; people who buy gin are also likely to buy tonic. They often also buy lemons. However, one correlation stood out like a sore thumb because it was so unexpected. On Friday afternoons, young American males who buy diapers (nappies) also have a predisposition to buy beer. No one had predicted that result, so no one would ever have even asked the question in the first place. Hence, this is an excellent example of the difference between data mining and querying. The answer is beer Interesting story, isn’t? Here the goal is unclear, you’re not trying to figure out some value. Actually, you don’t even have a question, your concern is just to find correlations in the data. This is what is best at. Unsupervised learning Sorry to disappoint you, but I have to tell you . Even if this story is used a lot at the university and for speeches. this story isn’t real #5 Explore your DNA Machine learning isn’t only used to predict sales. Scientists also use it heavily in various domains. In this example, we’ve got DNA analysis from many and our goal to gather people in different groups. For each individual, you’ll measure how much certain genes are expressed. Don’t let this graph overwhelm you, we’ve almost finished with it. Each little block is a gene. A green colour means the individual got this gene and a red colour means there is a very low possibility he has it. These colours show the degree to which different individuals do or do not have a specific gene. The purpose of our ML model it to gather individuals in categories from data which includes gene degree each people have. Now, its time for the question: Which kind of ML to use in this example? Supervised learning or Unsupervised learning? Be careful, the answer may not be what you’re expecting. Also, I’ll keep writing another sentence to hide the answer in the middle of a paragraph. If you answer is Supervised learning I’m sorry, it was a bit of trap that you fall into. Don’t worry, I’ll explain to you why it’s not the right answer. In reality, the model will group individual in categories but those are not defined beforehand in training data. Our question isn’t to know in which category the person we’ve picked is. The purpose is to find similar individuals and gather them in groups. Before you go Do you remember this schema I show you a the beginning? Maybe not, but you now may be able to understand all the words on it. Give it a try! Let’s sum up for the last time. The first step in machine learning is to train your model with a . Thank’s to those examples, the model will build a to be able to answer our question. training set hypothesis function The accuracy of the answer will be closely related to the training set quality and size. Besides, the learning algorithm used to build the hypothesis is also very important. If your training set is full of questions with answers, you do . When the answer value is continuous, then it’s , otherwise it’s . Supervised learning Regression Classification If you don’t know the question to ask and so your training set doesn’t contain question/answer sets, then you do . Unsupervised learning That’s it, you had an overview of the different kinds of machine learning. Remember it can help you to solve problems human can’t solve (or not fast enough). See you soon for a next article about the different kinds of ML. Please let me know if you’re interested in it, don’t forget to give this article some 👏 claps to support me. This is part of the “Machine Learning” series on introducing machine learning from the very beginning. < Previous article Have a good day!

Which type of ML tell us diapers and beer often sale together?

Red Pulse ICO, which was most awaited and called as the first ICO on NEO platform was scheduled to start on Oct 8th 0900 EDT. It has reached hard cap of 15 million USD in just 6 minutes past an hour. The Hype Red Pulse was touted as the first ICO based on so called ‘Ethereum of China’ — NEO. However, with the ban on ICOs and bitcoin exchanges by China has brought too much of confusion to investors believing and ready to invest in Red pulse. As expected, Red Pulse team has sent out communication saying they need to postpone the ICO, until all the regulations are met with. That’s a promising decision as it helped assure customers and investors about their commitment towards compliance and avoid unnecessary impediments from government later. Verification & KYC One interesting thing with good ICOs is they take KYC seriously. Red Pulse, as mentioned earlier to comply with all the rules have restricted investors from USA, Singapore and Mainland China(because of new ICO ban in place). They have done strict KYC and approved only candidates who have provided proper proof of residence in the form of passport or ID. Confused about what cryptocurrency is and Blockchain technology is? Head out to our educational chatbot powered by AI https://m.me/confusedcoin _Answers your basic questions regarding cryptocurrency_m.me/confusedcoin AI Powered Messenger ChatBot T — 1 Month Once verification is all done and laws are being complied, they have announced the new date of ICO as 8th Oct 2017. All the instructions are scheduled to be given only an hour before. Sombat and Jonathan from Redpulse team has been active on Slack channel. Also made as their community manager on their Slack channel — who has kept up to the speed and answered most of the questions investors had. crypto cointrading T — 1 Week RPX team has come up with a new twist saying investors would need a new NEO_GUI_RPX to participate in token sale as all existing NEO wallets doesn’t support token sale. Which inturn was a painful process of downloading the software from github, downloading chain.acc file (which accelerates sync up) and also updating your .net framework. (Remember to disable all firewalls and run as administrator) — Wait a second, as a normal investor who doesn’t know what all above means — will get scared for sure. Even with slight knowledge of computers and how to use them, you will be boggled and feel paranoid with so many security relaxations. Forgot to mention, this only works on windows machine! Mac & Linux users need to install VM and do it. T-2Hours RPX team sent out a notification to the relief of all mac users saying, has come up with a surprise announcement of updating NEON wallet to be compatible with token sale on NEO platform. That’s a very happy moment! At least for me (even after spending a day and night on installing windows wallet, importing NEON wallet, syncing it and rebuilding index for infinite amount of time). We should appreciate efforts of for their simple but working solution which doesnt need a lot of configurations and “Just Works”. City of Zion City of Zion T-25 Minutes We need a script hash to participate in the token sale. Red Pulse team decided to send this script hash just 25 minutes prior to the sale and sent out a mail to all whitelisted investors. Another twist here is, they have not included hash directly — There’s a link included, which on clicking will ask for your acceptance of agreement, firstname, lastname, address, city, state, country and then Submit. First thing, approximately 5000–10000 people opened the website at a time. RPX team should have scaled the website to serve atleast 50K requests as they know how many people are going to open the website (from member coount on slack/telegram channels) — Website crashed. Cloudfare started throwing 504 errors. Even after few minutes of refresh, SUBMIT button didn’t work as it was trhowing 504 error again in the background. T-10 Minutes Slack channel announcement — new mail is going to come with direct script hash included in it. Panic goes up as it’s only 9 minutes to go by the time we read the message. Hitting refresh on website now moved to email client — waiting eagerly for the Script hash as the clock is ticking. Sombat on slack channel was very good at live streaming the things happening on their end — communication was clear and he was updating investors properly on what they are going to receive and when. He also mentioned the script hash is gonna look like this Though not many know how token sale works on NEO platform and what role script hash has to play in it. 0xecc**************1df9 — which is helpful to avoid phishing by some random users. T-1 Minute There comes our mail containing script hash at exactly 8:59AM EDT — That’s it. One has to copy it and go to your hard installed windows RPX GUI or NEON wallet on Mac, paste it in mentioned space, enter amount of NEO below 27(hard cap per account in 1st hour) and “Submit for Sale”. By the end of first hour, it’s almost 70% goal reached. Later in the 2nd hour, where it is open for people who can invest up to $50,000 — there’s a confusion with the block number vesus countdown timer shown on Red Pulse website. Twitterati has mentioned that it’s about 18 seconds faster compared to the actual block number which triggers the acceptance for 2nd round — because of which few people who sent NEO a couple of seconds early couldn’t get their tokens (and got locked in for few weeks. Will be refunded in coming days/weeks). However, it didn’t last long. Hard cap was reached in less than 6 minutes of 2nd hour (higher contribution cap) and sale was concluded. Opinion Red Pulse team needs to be appreciated, along with NEO team helping them launch first ICO on NEO platform. was well appreciated by RPX team saying they have worked day and night to get the NEON wallet support the token sale. City of Zion Appreciate the efforts — but scalability issues of the website, complicated process of installing RPX GUI and dealing with sync issues etc and agreement being asked to sign just before the sale and script hash being sent just 1 minute prior to the sale are some things that could have been done right to avoid lot of confusion. If you are looking for detailed analysis on other upcoming ICOs and information regarding cryptocurrencies and blockchain technology in general — check out confusedcoin.com Confusedcoin explains about the technology and investment details about Bitcoin, Ethereum and other cryptocurrencies. Visit www.confusedcoin.com Confused Coin — A guide to cryptocurrency beginner NEO Platform for ICO Launch It’s not as bad as Ethereum. On Ethereum network, it was clogged when some initial ICOs were launched. On NEO, It just took a couple of seconds to respond saying “Submitted Successfully”. After a couple of minutes, it showed up on Token Balance If you’ve been part of this roller coast ride — let us know your experiences in comments. If you like the article 👏 for it and if you echo with this experience, show your 💕 by sharing.

Roller Coaster of Red Pulse ICO

Personal Recollections of Joan of Arc — Volume 1 by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Checked by the Folly of the Wise here Checked by the Folly of the Wise WE MARCHED out in great strength and splendor, and took the road toward Orleans. The initial part of Joan’s great dream was realizing itself at last. It was the first time that any of us youngsters had ever seen an army, and it was a most stately and imposing spectacle to us. It was indeed an inspiring sight, that interminable column, stretching away into the fading distances, and curving itself in and out of the crookedness of the road like a mighty serpent. Joan rode at the head of it with her personal staff; then came a body of priests singing the Veni Creator, the banner of the Cross rising out of their midst; after these the glinting forest of spears. The several divisions were commanded by the great Armagnac generals, La Hire, and Marshal de Boussac, the Sire de Retz, Florent d’Illiers, and Poton de Saintrailles. Each in his degree was tough, and there were three degrees—tough, tougher, toughest—and La Hire was the last by a shade, but only a shade. They were just illustrious official brigands, the whole party; and by long habits of lawlessness they had lost all acquaintanceship with obedience, if they had ever had any. But what was the good of saying that? These independent birds knew no law. They seldom obeyed the King; they never obeyed him when it didn’t suit them to do it. Would they obey the Maid? In the first place they wouldn’t know how to obey her or anybody else, and in the second place it was of course not possible for them to take her military character seriously—that country-girl of seventeen who had been trained for the complex and terrible business of war—how? By tending sheep. They had no idea of obeying her except in cases where their veteran military knowledge and experience showed them that the thing she required was sound and right when gauged by the regular military standards. Were they to blame for this attitude? I should think not. Old war-worn captains are hard-headed, practical men. They do not easily believe in the ability of ignorant children to plan campaigns and command armies. No general that ever lived could have taken Joan seriously (militarily) before she raised the siege of Orleans and followed it with the great campaign of the Loire. Did they consider Joan valueless? Far from it. They valued her as the fruitful earth values the sun—they fully believed she could produce the crop, but that it was in their line of business, not hers, to take it off. They had a deep and superstitious reverence for her as being endowed with a mysterious supernatural something that was able to do a mighty thing which they were powerless to do—blow the breath of life and valor into the dead corpses of cowed armies and turn them into heroes. To their minds they were everything with her, but nothing without her. She could inspire the soldiers and fit them for battle—but fight the battle herself? Oh, nonsense—that was their function. They, the generals, would fight the battles, Joan would give the victory. That was their idea—an unconscious paraphrase of Joan’s reply to the Dominican. So they began by playing a deception upon her. She had a clear idea of how she meant to proceed. It was her purpose to march boldly upon Orleans by the north bank of the Loire. She gave that order to her generals. They said to themselves, “The idea is insane—it is blunder No. 1; it is what might have been expected of this child who is ignorant of war.” They privately sent the word to the Bastard of Orleans. He also recognized the insanity of it—at least he thought he did—and privately advised the generals to get around the order in some way. They did it by deceiving Joan. She trusted those people, she was not expecting this sort of treatment, and was not on the lookout for it. It was a lesson to her; she saw to it that the game was not played a second time. Why was Joan’s idea insane, from the generals’ point of view, but not from hers? Because her plan was to raise the siege immediately, by fighting, while theirs was to besiege the besiegers and starve them out by closing their communications—a plan which would require months in the consummation. The English had built a fence of strong fortresses called bastilles around Orleans—fortresses which closed all the gates of the city but one. To the French generals the idea of trying to fight their way past those fortresses and lead the army into Orleans was preposterous; they believed that the result would be the army’s destruction. One may not doubt that their opinion was militarily sound—no, would have been, but for one circumstance which they overlooked. That was this: the English soldiers were in a demoralized condition of superstitious terror; they had become satisfied that the Maid was in league with Satan. By reason of this a good deal of their courage had oozed out and vanished. On the other hand, the Maid’s soldiers were full of courage, enthusiasm, and zeal. Joan could have marched by the English forts. However, it was not to be. She had been cheated out of her first chance to strike a heavy blow for her country. In camp that night she slept in her armor on the ground. It was a cold night, and she was nearly as stiff as her armor itself when we resumed the march in the morning, for iron is not good material for a blanket. However, her joy in being now so far on her way to the theater of her mission was fire enough to warm her, and it soon did it. Her enthusiasm and impatience rose higher and higher with every mile of progress; but at last we reached Olivet, and down it went, and indignation took its place. For she saw the trick that had been played upon her—the river lay between us and Orleans. She was for attacking one of the three bastilles that were on our side of the river and forcing access to the bridge which it guarded (a project which, if successful, would raise the siege instantly), but the long-ingrained fear of the English came upon her generals and they implored her not to make the attempt. The soldiers wanted to attack, but had to suffer disappointment. So we moved on and came to a halt at a point opposite Checy, six miles above Orleans. Dunois, Bastard of Orleans, with a body of knights and citizens, came up from the city to welcome Joan. Joan was still burning with resentment over the trick that had been put upon her, and was not in the mood for soft speeches, even to revered military idols of her childhood. She said: “Are you the bastard?” “Yes, I am he, and am right glad of your coming.” “And did you advise that I be brought by this side of the river instead of straight to Talbot and the English?” Her high manner abashed him, and he was not able to answer with anything like a confident promptness, but with many hesitations and partial excuses he managed to get out the confession that for what he and the council had regarded as imperative military reasons they so advised. “In God’s name,” said Joan, “my Lord’s counsel is safer and wiser than yours. You thought to deceive me, but you have deceived yourselves, for I bring you the best help that ever knight or city had; for it is God’s help, not sent for love of me, but by God’s pleasure. At the prayer of St. Louis and St. Charlemagne He has had pity on Orleans, and will not suffer the enemy to have both the Duke of Orleans and his city. The provisions to save the starving people are here, the boats are below the city, the wind is contrary, they cannot come up hither. Now then, tell me, in God’s name, you who are so wise, what that council of yours was thinking about, to invent this foolish difficulty.” Dunois and the rest fumbled around the matter a moment, then gave in and conceded that a blunder had been made. “Yes, a blunder has been made,” said Joan, “and except God take your proper work upon Himself and change the wind and correct your blunder for you, there is none else that can devise a remedy.” Some of these people began to perceive that with all her technical ignorance she had practical good sense, and that with all her native sweetness and charm she was not the right kind of a person to play with. Presently God did take the blunder in hand, and by His grace the wind did change. So the fleet of boats came up and went away loaded with provisions and cattle, and conveyed that welcome succor to the hungry city, managing the matter successfully under protection of a sortie from the walls against the bastille of St. Loup. Then Joan began on the Bastard again: “You see here the army?” “Yes.” “It is here on this side by advice of your council?” “Yes.” “Now, in God’s name, can that wise council explain why it is better to have it here than it would be to have it in the bottom of the sea?” Dunois made some wandering attempts to explain the inexplicable and excuse the inexcusable, but Joan cut him short and said: “Answer me this, good sir—has the army any value on this side of the river?” The Bastard confessed that it hadn’t—that is, in view of the plan of campaign which she had devised and decreed. “And yet, knowing this, you had the hardihood to disobey my orders. Since the army’s place is on the other side, will you explain to me how it is to get there?” The whole size of the needless muddle was apparent. Evasions were of no use; therefore Dunois admitted that there was no way to correct the blunder but to send the army all the way back to Blois, and let it begin over again and come up on the other side this time, according to Joan’s original plan. Any other girl, after winning such a triumph as this over a veteran soldier of old renown, might have exulted a little and been excusable for it, but Joan showed no disposition of this sort. She dropped a word or two of grief over the precious time that must be lost, then began at once to issue commands for the march back. She sorrowed to see her army go; for she said its heart was great and its enthusiasm high, and that with it at her back she did not fear to face all the might of England. All arrangements having been completed for the return of the main body of the army, she took the Bastard and La Hire and a thousand men and went down to Orleans, where all the town was in a fever of impatience to have sight of her face. It was eight in the evening when she and the troops rode in at the Burgundy gate, with the Paladin preceding her with her standard. She was riding a white horse, and she carried in her hand the sacred sword of Fierbois. You should have seen Orleans then. What a picture it was! Such black seas of people, such a starry firmament of torches, such roaring whirlwinds of welcome, such booming of bells and thundering of cannon! It was as if the world was come to an end. Everywhere in the glare of the torches one saw rank upon rank of upturned white faces, the mouths wide open, shouting, and the unchecked tears running down; Joan forged her slow way through the solid masses, her mailed form projecting above the pavement of heads like a silver statue. The people about her struggled along, gazing up at her through their tears with the rapt look of men and women who believe they are seeing one who is divine; and always her feet were being kissed by grateful folk, and such as failed of that privilege touched her horse and then kissed their fingers. Nothing that Joan did escaped notice; everything she did was commented upon and applauded. You could hear the remarks going all the time. “There—she’s smiling—see!” “Now she’s taking her little plumed cap off to somebody—ah, it’s fine and graceful!” “She’s patting that woman on the head with her gauntlet.” “Oh, she was born on a horse—see her turn in her saddle, and kiss the hilt of her sword to the ladies in the window that threw the flowers down.” “Now there’s a poor woman lifting up a child—she’s kissed it—oh, she’s divine!” “What a dainty little figure it is, and what a lovely face—and such color and animation!” Joan’s slender long banner streaming backward had an accident—the fringe caught fire from a torch. She leaned forward and crushed the flame in her hand. “She’s not afraid of fire nor anything!” they shouted, and delivered a storm of admiring applause that made everything quake. She rode to the cathedral and gave thanks to God, and the people crammed the place and added their devotions to hers; then she took up her march again and picked her slow way through the crowds and the wilderness of torches to the house of Jacques Boucher, treasurer of the Duke of Orleans, where she was to be the guest of his wife as long as she stayed in the city, and have his young daughter for comrade and room-mate. The delirium of the people went on the rest of the night, and with it the clamor of the joy-bells and the welcoming cannon. Joan of Arc had stepped upon her stage at last, and was ready to begin. 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. Mark Twain (2004). Personal Recollections of Joan of Arc — Volume 1. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/2874/pg2874-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Checked by the Folly of the Wise

Everything Bad is Good for You is a that points out that some things we assume are bad (like TV) have tremendous benefits to our well-being. I love the premise of disrupting the conventional narrative and was reminded of that constantly when debating some of the more controversial features and problems in Java. It’s a feature, not a bug… pop culture book One of my favorite things about Java is its tendency to move slowly and deliberately. It doesn’t give us what we want right away. The Java team understands the requirements and looks at the other implementations, then learns from them. I’d say Java’s driving philosophy is that the early bird is swallowed by a snake. https://www.youtube.com/watch?v=cWdkMfmpsps&embedable=true Checked Exceptions One of the most universally hated features in Java is checked exceptions. They are the only innovative feature Java introduced as far as I recall. Most of the other concepts in Java existed in other languages, checked exceptions are a brand new idea that other languages rejected. They aren’t a “fun” feature, I get why people don’t like them. But they are an amazing tool. The biggest problem with checked exceptions is the fact that they don’t fit nicely into functional syntax. This is true for nullability as well (which I will discuss shortly). That’s a fair complaint. Functional programming support was tacked onto Java and in terms of exception handling it was poorly done. The Java compiler could have detected checked exceptions and required an error callback. This was a mistake made when these capabilities were introduced in Java 8. E.g. if these APIs were better introduced into Java we could have written code like this: api.call1()
    .call2(() -> codeThatThrowsACheckedException())
    .errorHandler(ex -> handleError(ex))
    .finalCall(); The compiler could force us to write the callback if it was missing which would satisfy the spirit of the checked exceptions perfectly. This is possible because checked exceptions are a feature of the compiler, not the JVM. A compiler could detect a checked exception in the lambda and require a specially annotated exception handling callback. errorHandler Why wasn’t something like this added? This is probably because of the general dislike of checked exceptions. No one attempted to come up with an alternative. No one likes them because no one likes the annoying feature that forces you to tidy up after yourself. We just want to code, checked exceptions force us to be responsible even when we just want to write a simple hello world… This is, to a great extent, a mistake… We can declare that main throws an exception and create a simple hello world without handling checked exceptions. In large application frameworks like Spring, checked is wrapped with a version of the same class. You might think I’m against that but I’m not. It’s a perfect example of how we can use checked exceptions to clean up after the fact. Cleanup is performed internally by Spring, at this point the exception-handling logic is no longer crucial and can be converted to a runtime exception. SQLException RuntimeException I think a lot of the hate towards the API comes from bad versions of this exception such as or encoding exceptions. These exceptions are often thrown for constant input that should never fail. That’s just redundant and a bad use of language capabilities. Checked exceptions should only be thrown when there’s cleanup we can do. That’s an API problem, not a problem with the language feature. MalformedURLException Null Pouring hate on null has been trending for the past 15+ years. Yes, I know . I think people misuse it. that quote Null is a fact of life today, whether you like it or not. It’s inherent in everything: databases, protocols, formats, etc. Null is a deep part of programming and will not go away in the foreseeable future. The debate over null is pointless. The debate that matters is whether the cure is better than the disease and I’m yet unconvinced. What matters isn’t if null was a mistake, what matters is what we do now. To be fair, this directly correlates to your love of functional programming paradigms. Null doesn’t play nicely in FP which is why it became a punching bag for the FP guys. But are we stepping back or stepping forward? Let’s break this down into three separate debates: Performance Failures Ease of programming Performance Null is fast. Super fast. Literally free. The CPU performs a null check for us and handles exceptions as interrupts. We don’t need to write code to handle null. The alternatives can be very low overhead and can sometimes translate to null for CPU performance benefits. But this is harder to tune. Abstractions leak and null is the way our hardware works. For most intents and purposes, it is better. There is a caveat. We need the ability to mark some objects as non-null for better memory layout (as Valhalla plans to do). This will allow for better memory layout and can help speed up code. Notice that we can accomplish this while maintaining object semantics, a marker would be enough. I would argue that null takes this round. Failures People hate NullPointerException. This baffles me. NullPointerException is one of the best errors to get. It’s the . The error is usually simple to understand and even when it isn’t; it isn’t far off. It’s an easy bug to fix. The alternative might include initializing an empty object which we need to verify or setting a dummy object to represent null. fail-fast principle Open a database that has been around long enough and search for “undefined”. I bet it has quite a few entries… That’s the problem with non-null values. You might not get a failure immediately. You will get something far worse. A stealth bug that crawls through the system and pollutes your data. Since null is so simple and easy to detect there’s a vast number of tools that can deal with it both in runtime and during development. When people mention getting a null pointer exception in production I usually ask: what would have been the alternative? If you could have initialized the value to begin with then why didn’t you do it? Java has the final keyword, you can use that to keep non-null stateful values. Mutable values are the main reason for uninitialized or null values. It’s very possible that a non-null language wouldn’t fail. But would its result be worse? In my experience, corrupt data in storage is far worse. The problem is insidious and hides under the surface. There’s no clue as to the origin of the problem and we need to set “traps” to track it down. Give me a fail-fast any day. In my opinion, null has this one hands down… Ease of Programming An important point to understand is that null is a requirement of modern computing. Our entire ecosystem is built on top of null. Languages like Kotlin demonstrate this perfectly, they have null and non-null objects. This means we have duplication. Every concept related to objects is expressed twice, and we need to maintain semantics between null and non-null. This raises the bar of complexity for developers new to such languages and makes for some odd syntax. This in itself would be fine if the complexity paid off. Unfortunately, such features only resolve the most trivial non-issue cases of null. The complex objects aren’t supported since they contain null retrieved from external sources. We’re increasing language complexity for limited benefit. Boilerplate This used to be a bigger issue in the past but looking at a typical Java file vs. TypeScript or JavaScript the difference isn’t as big. Still, people nitpick. A smart engineer I know online called the use of semicolons in languages: "Laziness". I don’t get that. I love the semicolon requirement and am always baffled by people who have a problem with that. As an author it lets me format my code while ignoring line length. I can line break wherever I want, the semicolon is the part that matters. If anything, I would have loved to cancel the ability to write conditional statements without the curly braces e.g.: if(..) x();
else y(); That’s terrible. I block these in my style requirements; they are a recipe for disaster with an unclear beginning or end. Java forces organization, this is a remarkable thing. Classes must be in a specific file and packages map to directories. This might not matter when your project is tiny, but as you handle a huge code base, this becomes a godsend. You would instantly know where to look for clues. That is a powerful tool. Yet, it leads to some verbosity and some deep directory structures. But Java was designed by people who build 1M LoC projects, it scales nicely thanks to the boilerplate. We can’t say the same for some other languages. Moving Fast Many things aren’t great in Java, especially when building more adventurous startup projects. That’s why I’m so . I think it’s a way to patch Java with all the “cool stuff” we want while keeping the performance, compatibility and stability we love. excited about Manifold This can let the community move forward faster and experiment, while Java as a platform can take the slow and steady route. Final Word Conventional wisdom is problematic. Especially when it is so one-sided and presents a single-dimension argument in which a particular language feature is inferior. There are tradeoffs to be made and my bias probably shines through my words. However, the cookie cutter counterpoints don’t cut it. The facts don’t present a clear picture to their benefit. There’s always a tradeoff and Java has walked a unique tightrope. Even a slight move in the wrong direction can produce a fast tumbling-down effect. Yet it maintains its traction despite the efforts of multiple different groups to display it as antiquated. This led to a ridiculous perception among developers of Python and JavaScript as “newer” languages. I think the solution for that is two-fold. We need to educate about the benefits of Java's approach to these solutions. We also need solutions like Manifold to explore potential directions freely. Without the encumberment of the JCP. Having a working proof of concept will make integrating new ideas into Java much easier. Also published . here

Everything Bad in Java is Good for You — Why Nulls and Checked Exceptions Are Your Friends

Authors:
(1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk);
(2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk);
(3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction
Course information
Motivation and intervention
Findings
Discussion
Conclusion and References 3 Motivation and intervention The pre-lecture activities were made available to students via the University of Glasgow’s virtual learning environment Moodle [2]. As such, it is straightforward for course instructors to check if students are engaging with these activities and crucially, given the flipped learning approach, whether they are engaging with them in advance of the in-person sessions. As discussed in Section 2.1, the University of Glasgow Learning Analytics Policy allows staff to undertake learning analytics activities which enable interventions to assist students learning. From the year in which the flipped learning approach was introduced (2020-21) staff on the course were therefore monitoring engagement in pre-lecture activities through activity logs on Moodle. For the purpose of this study, only engagement with the formative reading quizzes (pre-lecture quizzes) is measured, where activity completion can be indicated by certain conditions being met (e.g. whether a student has received a grade for a quiz), rather than students being able to manually mark completion (e.g. for link to reading from course textbook). This means that we cannot measure and compare engagement across all pre-lecture activities and we may be misrepresenting true engagement as some students may choose to read the textbook but not complete the reading quiz (or vice versa). However, this should give a more reliable estimate of meaningful engagement in activities since students have to engage somewhat with the activity (e.g. start and submit the quiz) rather than being able to manually mark that the activity is complete by checking a button. This does not fully guard against non-meaningful engagement (e.g. a student could begin the quiz, complete no questions, submit, and still be counted as having completed this activity) but performance in these activities (e.g. questions attempted and marks achieved) was also checked to understand the extent of meaningful engagement. Despite students indicating that they enjoyed the flipped learning approach through course evaluation forms, when looking at the data it was clear that engagement in the pre-lecture quizzes was extremely low. For example, the average completion of pre-lecture quizzes in advance of the active learning session taking place was 6.88% in semester 1 of 2022-23. This was of concern to course instructors given the in-class sessions involved spending less time presenting material and more time engaging in active learning activities as per the goal of flipped learning. This pattern was persistent over a number of years. Given that the introduction of the new courses coincided with the Covid-19 pandemic, it was also difficult to disentangle the lack of engagement with the flipped learning material, and the general lack of engagement HE institutions saw over the course of the pandemic ([Wester et al., 2021]). The academic year of 2022-23 saw a return to campus for the majority of teaching but the issue of low engagement in pre-lecture activities persisted. This is demonstrated in Figure 1 (a) which shows the cumulative mean percentage of students engaging in the pre-lecture quizzes for each week across an 11 week period in semester 1 of 2022-23. Each coloured line represents the cumulative average percentage of students who have engaged in the collection of quizzes associated with one week of teaching over the course of the semester (an average is shown here since each week may have a number of quizzes for students to complete). Of most interest is the level of engagement in the pre-lecture quizzes for the week in which the material is covered in class, which is represented by the first point for each weekly line in Figure 1 (a). These figures are also presented in column 1 of Table 1. This figure starts off low (around 27%) for week 1 and continues to drop for each week of material, before reaching a point around week 7 where less than 5% of students have engaged with the pre-lecture quizzes on the week in which the material is covered in class. Another interesting feature of Figure 1 (a) is the pattern of engagement for each collection of weekly quizzes over the semester. Although the engagement in the weekly material does increase slightly over the semester, at most only about half of the students in the class have ever engaged with the material at all on average for week 1, and after week 8 this drops to less than 25%. Interestingly, for all weeks an increase in average engagement can be seen between weeks 10 and 11 and we believe this to be related to exam preparation as the exam takes place immediately after the course finishes. These features are clearly of huge concern to the course instructors as they indicate that not only are students not engaging with the pre-lecture quizzes ‘on time’ to benefit from a flipped approach, the majority of students aren’t engaging with the materials at all across the entire duration of the course. In an attempt to tackle this low engagement the decision was made to add an element of gamification to the course which would encourage students to engage in the pre-lecture activities. This was implemented through the the Level Up! Moodle plug-in[3] which was introduced to the course in semester 2 of 2022-23. Level Up! allows students to gain points by participating in the course via Moodle. Points were awarded for the completion for a variety of course activities including the pre-lecture activities, tutorial questions, lab materials, and assessed quizzes and points were awarded based on completion only, i.e. the points system was endeavour-based and not merit-based. In an attempt to explicitly tackle engagement in the pre-lecture activities, full points were only awarded if the activity was completed before the associated lecture/tutorial/lab with half-points being awarded thereafter. Figure 2 shows how this plug-in is integrated within the course Moodle page with the left-hand image showing the Level Up! block that students can see on the main course page and the right-hand image showing the leaderboard view which shows students where they rank individually on an anonymous leaderboard throughout the semester. There is also an option to view where the students’ tutorial group rank in a team leaderboard. Level Up! was initially introduced in an attempt to increase engagement in pre-lecture activities in semester 2 of the 2022-23 academic year and, after initial success, introduced from semester 1 of the 2023-24 academic year. This allows us to make a number of comparisons which form our questions of interest: How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! between the same students across two courses.


How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! within the same course across two academic years. This paper is available on arxiv under CC BY 4.0 DEED license. [2] https://moodle.org/ [3] https://moodle.org/plugins/block_xp Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Authors: Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction Course information Motivation and intervention Findings Discussion Conclusion and References Abstract and Introduction Abstract and Introduction Course information Course information Motivation and intervention Motivation and intervention Findings Findings Discussion Discussion Conclusion and References Conclusion and References 3 Motivation and intervention The pre-lecture activities were made available to students via the University of Glasgow’s virtual learning environment Moodle [2]. As such, it is straightforward for course instructors to check if students are engaging with these activities and crucially, given the flipped learning approach, whether they are engaging with them in advance of the in-person sessions. As discussed in Section 2.1, the University of Glasgow Learning Analytics Policy allows staff to undertake learning analytics activities which enable interventions to assist students learning. From the year in which the flipped learning approach was introduced (2020-21) staff on the course were therefore monitoring engagement in pre-lecture activities through activity logs on Moodle. For the purpose of this study, only engagement with the formative reading quizzes (pre-lecture quizzes) is measured, where activity completion can be indicated by certain conditions being met (e.g. whether a student has received a grade for a quiz), rather than students being able to manually mark completion (e.g. for link to reading from course textbook). This means that we cannot measure and compare engagement across all pre-lecture activities and we may be misrepresenting true engagement as some students may choose to read the textbook but not complete the reading quiz (or vice versa). However, this should give a more reliable estimate of meaningful engagement in activities since students have to engage somewhat with the activity (e.g. start and submit the quiz) rather than being able to manually mark that the activity is complete by checking a button. This does not fully guard against non-meaningful engagement (e.g. a student could begin the quiz, complete no questions, submit, and still be counted as having completed this activity) but performance in these activities (e.g. questions attempted and marks achieved) was also checked to understand the extent of meaningful engagement. Despite students indicating that they enjoyed the flipped learning approach through course evaluation forms, when looking at the data it was clear that engagement in the pre-lecture quizzes was extremely low. For example, the average completion of pre-lecture quizzes in advance of the active learning session taking place was 6.88% in semester 1 of 2022-23. This was of concern to course instructors given the in-class sessions involved spending less time presenting material and more time engaging in active learning activities as per the goal of flipped learning. This pattern was persistent over a number of years. Given that the introduction of the new courses coincided with the Covid-19 pandemic, it was also difficult to disentangle the lack of engagement with the flipped learning material, and the general lack of engagement HE institutions saw over the course of the pandemic ([Wester et al., 2021]). The academic year of 2022-23 saw a return to campus for the majority of teaching but the issue of low engagement in pre-lecture activities persisted. This is demonstrated in Figure 1 (a) which shows the cumulative mean percentage of students engaging in the pre-lecture quizzes for each week across an 11 week period in semester 1 of 2022-23. Each coloured line represents the cumulative average percentage of students who have engaged in the collection of quizzes associated with one week of teaching over the course of the semester (an average is shown here since each week may have a number of quizzes for students to complete). Of most interest is the level of engagement in the pre-lecture quizzes for the week in which the material is covered in class, which is represented by the first point for each weekly line in Figure 1 (a). These figures are also presented in column 1 of Table 1. This figure starts off low (around 27%) for week 1 and continues to drop for each week of material, before reaching a point around week 7 where less than 5% of students have engaged with the pre-lecture quizzes on the week in which the material is covered in class. Another interesting feature of Figure 1 (a) is the pattern of engagement for each collection of weekly quizzes over the semester. Although the engagement in the weekly material does increase slightly over the semester, at most only about half of the students in the class have ever engaged with the material at all on average for week 1, and after week 8 this drops to less than 25%. Interestingly, for all weeks an increase in average engagement can be seen between weeks 10 and 11 and we believe this to be related to exam preparation as the exam takes place immediately after the course finishes. These features are clearly of huge concern to the course instructors as they indicate that not only are students not engaging with the pre-lecture quizzes ‘on time’ to benefit from a flipped approach, the majority of students aren’t engaging with the materials at all across the entire duration of the course. In an attempt to tackle this low engagement the decision was made to add an element of gamification to the course which would encourage students to engage in the pre-lecture activities. This was implemented through the the Level Up! Moodle plug-in[3] which was introduced to the course in semester 2 of 2022-23. Level Up! allows students to gain points by participating in the course via Moodle. Points were awarded for the completion for a variety of course activities including the pre-lecture activities, tutorial questions, lab materials, and assessed quizzes and points were awarded based on completion only, i.e. the points system was endeavour-based and not merit-based. In an attempt to explicitly tackle engagement in the pre-lecture activities, full points were only awarded if the activity was completed before the associated lecture/tutorial/lab with half-points being awarded thereafter. Figure 2 shows how this plug-in is integrated within the course Moodle page with the left-hand image showing the Level Up! block that students can see on the main course page and the right-hand image showing the leaderboard view which shows students where they rank individually on an anonymous leaderboard throughout the semester. There is also an option to view where the students’ tutorial group rank in a team leaderboard. Level Up! was initially introduced in an attempt to increase engagement in pre-lecture activities in semester 2 of the 2022-23 academic year and, after initial success, introduced from semester 1 of the 2023-24 academic year. This allows us to make a number of comparisons which form our questions of interest: How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! between the same students across two courses. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! within the same course across two academic years. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! between the same students across two courses. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! between the same students across two courses. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! within the same course across two academic years. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! within the same course across two academic years. 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 [2] https://moodle.org/ [3] https://moodle.org/plugins/block_xp

Exploring the Impact of Gamification in Flipped Classrooms: Motivation and intervention

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References METHODS As illustrated in Figure 1, this study was conducted in five steps. In the first step, the query sets were generated from previously collected Google Trends data. The daily trends for both Germany and the US were collected daily at 3 am CET from November 10th, 2021, until March 31st, 2022. Since the daily number of trending queries can vary, the query sets for Germany and the US are not the same size. Two thousand nine hundred sixteen trending queries for Germany and 2,819 for the US were initially collected. However, since some topics of interest are longer lived than a single day, the same topics can be trending on multiple days. Therefore, after removing duplicates, our query sets consist of 1,821 queries for Germany and 2,126 queries for the US. The queries have a wide range, including topics like celebrities (e.g., "Sandra Bullock"), sports (e.g., "NFL," "Liverpool vs. Southampton"), seasonal events (e.g., "Macy's Thanksgiving Day Parade," "Restaurants open on Thanksgiving"), headline news (e.g., "TikTok classaction lawsuit settlement"), and more. In step 2, for each query, the top 10 results from all four search engines were collected. We made sure to request results for the appropriate language and location by using URL parameters. Furthermore, we decided to limit the comparison to the top 10 results because users usually only consider these. Again, for our study, only the implications for the user perception are relevant. The Web scraping component of the Relevance Assessment Tool (RAT; Sünkler et al., 2022) was used to collect the data. The scraping took place between April 6th and 9th, 2022, with 230,315 results (Germany: 109,604; US: 120,711) gathered. It is important to note that only organic search results were considered. A fair comparison can be ensured when ranked result lists instead of vertical inserts of universal search results (e.g., Google News and Bing News) are considered. We also ignored ads since they are not part of the search engine's web index. During step 3, the python library urllib was used to get the domain of each search result. To ensure a fair comparison between all search engines, in step 4, we removed results for any query that had less than ten results in any of the search engines. Additionally, the data was checked and stripped of any errors that might have occurred during the collection, like duplicate results. This resulted in a further reduced dataset consisting of 1,672 queries and 66,880 results for Germany and 1,865 queries and 74,600 results for the US. Lastly, the extraction of top-level domains was further refined in this step. We used basic string matching to unify URLs that pointed to the same domains. For example, the following three URLs would have been counted as separate domains without the string matching: https://www.facebook.com, "http://www.facebook.com", and "http://www.facebook.com/". Finally, in step five, we used different methods of comparing the data collected. An initial comparison is made by using simple descriptive statistics. To measure source concentration, we adapt the Gini coefficient, a measure of statistical dispersion used to measure income or wealth inequality (Gini, 1936). The Gini coefficient is a single number ranging from 0 to 1, where 0 represents perfect equality, and 1 is the maximum inequality. As Ortega et al. (2008) demonstrated, adapting this measure to the distribution of values in different domains is appropriate. In the case of search result sets, this allows us to compare the various search engines easily. The lower the Gini coefficient, the more equally the results are distributed over all domains contributing to a particular result set. Following that, we calculate the Jaccard similarity index (González et al., 2008). Puschmann (2019) demonstrates the usage of the Jaccard index to measure the similarity between two result sets. The Jaccard index is a single number ranging from 0 to 1, where 0 means that the two sets are entirely dissimilar, while 1 means that they are identical. We calculate the Jaccard index for every two-way combination of our four search engines (e.g., Google and Bing, Bing and Metager, etc.) for all stages between the top 1 and top 10 results (e.g., Google and Bing position 1 to 5). This paper is available on arxiv under CC 4.0 license. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References METHODS As illustrated in Figure 1, this study was conducted in five steps. In the first step, the query sets were generated from previously collected Google Trends data. The daily trends for both Germany and the US were collected daily at 3 am CET from November 10th, 2021, until March 31st, 2022. Since the daily number of trending queries can vary, the query sets for Germany and the US are not the same size. Two thousand nine hundred sixteen trending queries for Germany and 2,819 for the US were initially collected. However, since some topics of interest are longer lived than a single day, the same topics can be trending on multiple days. Therefore, after removing duplicates, our query sets consist of 1,821 queries for Germany and 2,126 queries for the US. The queries have a wide range, including topics like celebrities (e.g., "Sandra Bullock"), sports (e.g., "NFL," "Liverpool vs. Southampton"), seasonal events (e.g., "Macy's Thanksgiving Day Parade," "Restaurants open on Thanksgiving"), headline news (e.g., "TikTok classaction lawsuit settlement"), and more. In step 2, for each query, the top 10 results from all four search engines were collected. We made sure to request results for the appropriate language and location by using URL parameters. Furthermore, we decided to limit the comparison to the top 10 results because users usually only consider these. Again, for our study, only the implications for the user perception are relevant. The Web scraping component of the Relevance Assessment Tool (RAT; Sünkler et al., 2022) was used to collect the data. The scraping took place between April 6th and 9th, 2022, with 230,315 results (Germany: 109,604; US: 120,711) gathered. It is important to note that only organic search results were considered. A fair comparison can be ensured when ranked result lists instead of vertical inserts of universal search results (e.g., Google News and Bing News) are considered. We also ignored ads since they are not part of the search engine's web index. During step 3, the python library urllib was used to get the domain of each search result. To ensure a fair comparison between all search engines, in step 4, we removed results for any query that had less than ten results in any of the search engines. Additionally, the data was checked and stripped of any errors that might have occurred during the collection, like duplicate results. This resulted in a further reduced dataset consisting of 1,672 queries and 66,880 results for Germany and 1,865 queries and 74,600 results for the US. Lastly, the extraction of top-level domains was further refined in this step. We used basic string matching to unify URLs that pointed to the same domains. For example, the following three URLs would have been counted as separate domains without the string matching: https://www.facebook.com, "http://www.facebook.com", and "http://www.facebook.com/". Finally, in step five, we used different methods of comparing the data collected. An initial comparison is made by using simple descriptive statistics. To measure source concentration, we adapt the Gini coefficient, a measure of statistical dispersion used to measure income or wealth inequality (Gini, 1936). The Gini coefficient is a single number ranging from 0 to 1, where 0 represents perfect equality, and 1 is the maximum inequality. As Ortega et al. (2008) demonstrated, adapting this measure to the distribution of values in different domains is appropriate. In the case of search result sets, this allows us to compare the various search engines easily. The lower the Gini coefficient, the more equally the results are distributed over all domains contributing to a particular result set. Following that, we calculate the Jaccard similarity index (González et al., 2008). Puschmann (2019) demonstrates the usage of the Jaccard index to measure the similarity between two result sets. The Jaccard index is a single number ranging from 0 to 1, where 0 means that the two sets are entirely dissimilar, while 1 means that they are identical. We calculate the Jaccard index for every two-way combination of our four search engines (e.g., Google and Bing, Bing and Metager, etc.) for all stages between the top 1 and top 10 results (e.g., Google and Bing position 1 to 5). This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Comparative Analysis of Search Engine Results Using Google Trends Data

Protect People From Themselves: Reducing Default Risks in P2P Lending

Exciting news! Our monthly product update is here! Get ready for the latest mobile app update, Apple’s Memoji-inspired pixelated avatars, “April Shower Brings May Flowers” Color Theme, better story pages layout, fresh top writers ranking page, and more!🚀 This product update reflects changes to the platform fromMarch 1st until May 3rd, 2024. New for Writers New Mobile App Update: Version 2.0 Just Landed The 2.0 Version has brought HackerNoon’s original polling data, and active technology polls, into The HackerNoon App to help you stay on the pulse of the internet’s trends. Stay in the loop with the latest tech discussions, cast your vote, and contribute your insights to poll topics—all from your phone! Here's how: Navigate to the homepage, scroll down, and tap on "Poll of the Week"  - this will lead you to a collection of completed polls. Protip: When you see a topic you want to elaborate on, such as this one, simply click “✏️about the results” and it will direct you to a fully editable draft. You can also create a new draft on the web versions of HackerNoon polls as well! Simply search for any polls in our Poll database and click on “Write about the results” to get to a new draft with polls results in the feature image. This release also includes major improvements to the mobile writing experience. We added hundreds of story templates to make it easier to start writing and made the headline story function more like Apple Notes and Google Keep, editable in the story draft itself and within story settings. Breakthrough ideas can come from tiny keyboards! And that's not all. We improved and sped up authentication to save you a few precious moments upon opening the HackerNoon App, and changed the user tab so that once you tap it, you’ll get instantly directed to your user profile where you can easily update your information or navigate to settings. Check it out for yourself: Keep the feedback coming! We’ll keep improving! Emoji Credibility Indicators are Live on GitHub and Figma Emoji Credibility Indicators by HackerNoon are now live on  Figma and Github! Our open-source pixelated emoji pack was designed to communicate context about a story's content on HackerNoon and you can be amongst the first to try them! What will you get from this open-source package? Pixelated Emojis, designed on a 24px grid.
Well crafted SVGs
Fully customizable
Ready to use for development via GitHub Repo and Figma! Sounds good? Try them out today and tell us about your experience here.
Designing HackerNoon's Emoji Credibility Indicators: Get the developer’s perspective here.
Learn more about Emoji Credibility Indicators here.
Read about all our emoji credibility indicators, their meaning, and how to use them here. HackerNoon's Enhanced Top Writers Ranking: Explore the New Page for Tech Categories' Leaders The latest evolution of HackerNoon's Top Writers ranking now features a dedicated page for each of our Tech Categories. This feature aims to spotlight HackerNoon’s exceptional contributors and refine and enhance readers’ experience, making it easier to discover and engage with the tech’s leading voices. You can now navigate to all Tech Categories from the top nav. Check it out! What's New? Dedicated Pages: Each Tech Category now boasts its own dedicated Top Writers page, offering a comprehensive showcase of the most prolific writers. If you visit https://hackernoon.com/writers/programming you’ll see each top writer’s profile, including name, handle, bio and avatar, tags of expertise, and their placement on the ranking.
Real-Time Updates: Our Top Writers ranking is updated daily, ensuring users remain up-to-date with the latest contributors in their favorite categories. How Does This Improve Your Experience? If you’re a reader, this ranking is a great place to find new authors and subscribe to their work. And if you’re a writer… well, wouldn’t you like to land a top spot? Find out how here. Happy reading! New for Readers Create Your Own Pixelated Avatar With Hackernoon https://www.youtube.com/watch?v=WFc3ryAkCv4&embedable=true Pixelated avatars have just landed on HackerNoon, and now you have the power to create your own! Add some fun to your profile, and improve your experience at HackerNoon while ensuring your profile reflects your true essence. Get your customized avatar: Go to your profile page settings.
Click the “Create Avatar” button - you can’t miss it!
Have fun creating your avatar!
Hit “Save Avatar” and it’ll automatically replace your old placeholder image. New Theme Alert🌸 "In a world of petals and pixels, let your content flourish." - GPT, Chat Experience the all-new HackerNoon theme by clicking the paintbrush icon, choosing BlossomBytes, and hitting save. Watch as the design gets instantly transformed: Protip: you can also customize HackerNoon in many other ways via HackerNoon’s Choose Your Color setting. Simply click on the paintbrush to the right of the yellow banner ad, and see for yourself! One-Tap Google Signup and Login? Yup, HackerNoon Has It! HackerNoon continues to simplify the signup process, and if you have a Google account, then this one is for you - meet the One-Tap Google Signup and Login. This feature leverages the security and convenience of Google's authentication system, making it easy to access HackerNoon with a single tap, whether you're a seasoned writer or a new reader eager to explore our platform, ensuring a hassle-free experience. All you need is: Google's Chrome browser on your computer;
To be logged in to your Google account. With these two requirements checked off, you’ll be able to visit hackernoon.com and with just one tap, sign up or log in. Here’s what it looks like: Remember: You must be logged in to Google for the one-tap login to work!
If, after being logged in to your Google account, you still don’t see the one-tap option, check the permissions for HackerNoon on your browser. What if I don’t have a Google account? No problem! You can either follow our regular Signup flow (learn more here and here), just make sure you have an active email, a GitHub, or an X account, and you’re all set! Click here to create a HackerNoon account! Story Page Redesign Story pages have received a fresh makeover! Translations and TLDR summaries now share the spotlight with the story title, enhancing navigation. Easily identify stories with multiple authors in the "About the Author" section, along with various CTAs. Enjoy the playful pixelated icons for bookmarking, reacting, commenting, and sharing, adding a fun touch to interaction. Moreover, wider stories now occupy more screen space, ensuring better readability. New for Brands Tech Press Release Page For our beloved brands, a new package just landed: Tech Press Release! Publish your announcements onto HackerNoon and join our trusted +4K brands worldwide. If you’re undecided about whether or not to partner with HackerNoon, look at our Reader Demographics section right here. That's all folks! Exciting news! Our monthly product update is here! Get ready for the latest mobile app update, Apple’s Memoji-inspired pixelated avatars , “April Shower Brings May Flowers” Color Theme, better story pages layout, fresh top writers ranking page , and more!🚀 pixelated avatars Color fresh top writers ranking page This product update reflects changes to the platform from March 1st until May 3rd, 2024. March 1st New for Writers New Mobile App Update: Version 2.0 Just Landed New Mobile App Update: Version 2.0 Just Landed New Mobile App Update: Version 2.0 Just Landed The 2.0 Version has brought HackerNoon’s original polling data , and active technology polls , into The HackerNoon App to help you stay on the pulse of the internet’s trends. Stay in the loop with the latest tech discussions, cast your vote, and contribute your insights to poll topics—all from your phone! Here's how: Navigate to the homepage, scroll down, and tap on "Poll of the Week"  - this will lead you to a collection of completed polls. 2.0 Version original polling data active technology polls Protip: When you see a topic you want to elaborate on, such as this one, simply click “✏️about the results” and it will direct you to a fully editable draft. You can also create a new draft on the web versions of HackerNoon polls as well! Simply search for any polls in our Poll database and click on “Write about the results” to get to a new draft with polls results in the feature image. Protip : When you see a topic you want to elaborate on, such as this one , simply click “✏️about the results” and it will direct you to a fully editable draft. You can also create a new draft on the web versions of HackerNoon polls as well! Simply search for any polls in our Poll database and click on “Write about the results” to get to a new draft with polls results in the feature image. Protip such as this one This release also includes major improvements to the mobile writing experience. We added hundreds of story templates to make it easier to start writing and made the headline story function more like Apple Notes and Google Keep, editable in the story draft itself and within story settings. Breakthrough ideas can come from tiny keyboards! story templates And that's not all. We improved and sped up authentication to save you a few precious moments upon opening the HackerNoon App, and changed the user tab so that once you tap it, you’ll get instantly directed to your user profile where you can easily update your information or navigate to settings. Check it out for yourself: Keep the feedback coming! We’ll keep improving! Keep the feedback coming! We’ll keep improving! the feedback Emoji Credibility Indicators are Live on GitHub and Figma Emoji Credibility Indicators are Live on GitHub and Figma Emoji Credibility Indicators are Live on GitHub and Figma Emoji Credibility Indicators are Live on GitHub and Figma Emoji Credibility Indicators by HackerNoon are now live on Figma and Github ! Figma Figma Github Our open-source pixelated emoji pack was designed to communicate context about a story's content on HackerNoon and you can be amongst the first to try them! What will you get from this open-source package? What will you get from this open-source package? Pixelated Emojis, designed on a 24px grid. Well crafted SVGs Fully customizable Ready to use for development via GitHub Repo and Figma! Pixelated Emojis, designed on a 24px grid. Well crafted SVGs Fully customizable Ready to use for development via GitHub Repo and Figma ! GitHub Repo GitHub Repo Figma Figma Sounds good? Try them out today and tell us about your experience here. Designing HackerNoon's Emoji Credibility Indicators: Get the developer’s perspective here. Learn more about Emoji Credibility Indicators here. Read about all our emoji credibility indicators, their meaning, and how to use them here. Sounds good? Try them out today and tell us about your experience here . here here Designing HackerNoon's Emoji Credibility Indicators : Get the developer’s perspective here . Designing HackerNoon's Emoji Credibility Indicators Designing HackerNoon's Emoji Credibility Indicators here here Learn more about Emoji Credibility Indicators here . here here Read about all our emoji credibility indicators, their meaning, and how to use them here . here here HackerNoon's Enhanced Top Writers Ranking: Explore the New Page for Tech Categories' Leaders HackerNoon's Enhanced Top Writers Ranking: Explore the New Page for Tech Categories' Leaders HackerNoon's Enhanced Top Writers Ranking: Explore the New Page for Tech Categories' Leaders The latest evolution of HackerNoon's Top Writers ranking now features a dedicated page for each of our Tech Categories . This feature aims to spotlight HackerNoon’s exceptional contributors and refine and enhance readers’ experience, making it easier to discover and engage with the tech’s leading voices. HackerNoon's Top Writers ranking HackerNoon's Top Writers ranking Tech Categories Tech Categories You can now navigate to all Tech Categories from the top nav. Check it out! You can now navigate to all Tech Categories from the top nav. Check it out! all Tech Categories What's New? What's New? Dedicated Pages: Each Tech Category now boasts its own dedicated Top Writers page, offering a comprehensive showcase of the most prolific writers. If you visit https://hackernoon.com/writers/programming you’ll see each top writer’s profile, including name, handle, bio and avatar, tags of expertise, and their placement on the ranking. Real-Time Updates: Our Top Writers ranking is updated daily, ensuring users remain up-to-date with the latest contributors in their favorite categories. Dedicated Pages: Each Tech Category now boasts its own dedicated Top Writers page, offering a comprehensive showcase of the most prolific writers. If you visit https://hackernoon.com/writers/programming you’ll see each top writer’s profile, including name, handle, bio and avatar, tags of expertise, and their placement on the ranking. https://hackernoon.com/writers/programming https://hackernoon.com/writers/programming Real-Time Updates: Our Top Writers ranking is updated daily, ensuring users remain up-to-date with the latest contributors in their favorite categories. How Does This Improve Your Experience? How Does This Improve Your Experience? If you’re a reader, this ranking is a great place to find new authors and subscribe to their work. And if you’re a writer… well, wouldn’t you like to land a top spot? Find out how here . Happy reading! here here New for Readers Create Your Own Pixelated Avatar With Hackernoon Create Your Own Pixelated Avatar With Hackernoon Create Your Own Pixelated Avatar With Hackernoon Create Your Own Pixelated Avatar With Hackernoon https://www.youtube.com/watch?v=WFc3ryAkCv4&embedable=true https://www.youtube.com/watch?v=WFc3ryAkCv4&embedable=true Pixelated avatars have just landed on HackerNoon , and now you have the power to create your own! just landed on HackerNoon Add some fun to your profile, and improve your experience at HackerNoon while ensuring your profile reflects your true essence. Get your customized avatar: Get your customized avatar: Go to your profile page settings. Click the “Create Avatar” button - you can’t miss it! Have fun creating your avatar! Hit “Save Avatar” and it’ll automatically replace your old placeholder image. Go to your profile page settings . profile page settings profile page settings Click the “Create Avatar” button - you can’t miss it! Have fun creating your avatar! Hit “Save Avatar” and it’ll automatically replace your old placeholder image. New Theme Alert 🌸 New Theme Alert "In a world of petals and pixels, let your content flourish." - GPT, Chat "In a world of petals and pixels, let your content flourish." - GPT, Chat Experience the all-new HackerNoon theme by clicking the paintbrush icon, choosing BlossomBytes , and hitting save. Watch as the design gets instantly transformed: BlossomBytes Protip: you can also customize HackerNoon in many other ways via HackerNoon’s Choose Your Color setting. Simply click on the paintbrush to the right of the yellow banner ad, and see for yourself! Protip: you can also customize HackerNoon in many other ways via HackerNoon’s Choose Your Color setting. Simply click on the paintbrush to the right of the yellow banner ad, and see for yourself! Choose Your Color One-Tap Google Signup and Login? Yup, HackerNoon Has It! One-Tap Google Signup and Login? Yup, HackerNoon Has It! One-Tap Google Signup and Login? Yup, HackerNoon Has It! HackerNoon continues to simplify the signup process, and if you have a Google account, then this one is for you - meet the One-Tap Google Signup and Login. This feature leverages the security and convenience of Google's authentication system, making it easy to access HackerNoon with a single tap, whether you're a seasoned writer or a new reader eager to explore our platform, ensuring a hassle-free experience. All you need is: All you need is: Google's Chrome browser on your computer; To be logged in to your Google account. Google's Chrome browser on your computer; To be logged in to your Google account. With these two requirements checked off, you’ll be able to visit hackernoon.com and with just one tap, sign up or log in. Here’s what it looks like: hackernoon.com hackernoon.com Remember: You must be logged in to Google for the one-tap login to work! If, after being logged in to your Google account, you still don’t see the one-tap option, check the permissions for HackerNoon on your browser. Remember: You must be logged in to Google for the one-tap login to work! Remember: If, after being logged in to your Google account, you still don’t see the one-tap option, check the permissions for HackerNoon on your browser. What if I don’t have a Google account? What if I don’t have a Google account? No problem! You can either follow our regular Signup flow ( learn more here and here ), just make sure you have an active email, a GitHub, or an X account, and you’re all set! Click here to create a HackerNoon account! learn more here learn more here here here here here Story Page Redesign Story Page Redesign Story Page Redesign Story pages have received a fresh makeover! Translations and TLDR summaries now share the spotlight with the story title, enhancing navigation. Easily identify stories with multiple authors in the "About the Author" section, along with various CTAs. Enjoy the playful pixelated icons for bookmarking, reacting, commenting, and sharing, adding a fun touch to interaction. Moreover, wider stories now occupy more screen space, ensuring better readability. New for Brands New for Brands Tech Press Release Page Tech Press Release Page Tech Press Release Page Tech Press Release Page For our beloved brands, a new package just landed: Tech Press Release ! Tech Press Release Tech Press Release Publish your announcements onto HackerNoon and join our trusted +4K brands worldwide. If you’re undecided about whether or not to partner with HackerNoon, look at our Reader Demographics section right here. If you’re undecided about whether or not to partner with HackerNoon, look at our Reader Demographics section right here . here here That's all folks!

CHECKRED

checkred.com

-- employees

Since n.d.

COMPANY RANKING

CHECKRED

EVERGREEN INDEX #2164

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CheckRed WIKI

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CHECKRED

checkred.com

-- employees

Since n.d.

COMPANY RANKING

CHECKRED

EVERGREEN INDEX #2164

RELATED COMPANIESRANK

STORIES ABOUT CHECKRED

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