Simple KDS for Clover is a free kitchen display system designed to easily integrate with Clover POS systems and turn any Android tablet into a powerful, one-touch KDS station

Simple KDS

simplekds

Let's say you're planning to throw an awesome party. Sounds fun, right? But how will you make sure it is successful? The indicator of success can be the profit you generate by attracting a large number of attendees to the party. Additionally, you would want to ensure that attendees will return for future parties. Who do you need to work with to coordinate all of this? At a minimum, you may want to involve: Venue owner DJ Security Bartender Promo manager Designer ...and this may not be an exhaustive list. All of these individuals will have a vested interest in the successful completion of the party. However, they may not necessarily have identical needs and expectations. For instance, the venue owner may desire to generate income by renting out the space and anticipate the venue to be cleaned without any noticeable harm after the party. Meanwhile, the DJ, for instance, anticipates an audience that will value his artistic expression, and an increase in his followers on , , etc. Spotify Instagram , meaning they are parties with a vested interest in the project. → All these individuals will be your stakeholders How to categorize your stakeholders? By classifying stakeholders into different subgroups, you will be able to develop strategies around: Communications (when and how to inform) Decision-making (who and when to involve) Project work (how to cultivate ownership and engagement in the project). Typically, stakeholders are categorized based on their , , and occasionally their . interest influence behavior/attitude Let's consider, for instance, a bartender who isn't particularly concerned about the success of the party. For him, the priority is to attract a large number of attendees and receive a commission from the bar sales. Therefore, the bartender has a low interest in the overall goal and a small level of influence, as his presence or absence does not determine whether the party will take place or not. When it comes to behavior, the bartender can sometimes act as a blocker. For instance, they might refuse to work until they have an XL shaker, stating that it is impossible to work without such equipment. While they may be correct, a bartender with a more positive work attitude would make an effort to assist in finding a solution to the problem. Based on the bartender profile and using a stakeholder categorization matrix, we can create a plan outlining how we will work with them, for example: Due to low influence, we will not include the bartender in making decisions regarding the party, such as the location, DJ, etc. Due to low interest, we are unlikely to inform the bartender about all the decisions related to the party, possibly only those that concern the bar. Knowing that there may be issues with the bartender, we will try to avoid them in advance to prevent disagreements from arising a day before the event. When working with people involved in organizing the party, you will be engaged in . stakeholder management Stakeholder management is a process that helps understand the needs and expectations of interested parties and build relationships with them. This will ultimately assist in hosting a successful party. How stakeholder management is assessed during the interview process? At interviews, this skill is assessed through the following questions: Describe a situation in which you had to work with a difficult stakeholder. How did you handle it? Share a situation in which you had to convince a stakeholder to collaborate on your project (provide feedback/resources, etc.). How do you communicate with stakeholders? Have you ever faced a situation in which your management denied you resources or the opportunity to work on a new project or change the strategy? How did you persuade them? How do you manage stakeholders' expectations?

Stakeholder Management 101 – The Simplest Explanation Ever

The Challenges of Decentralized Trading Platforms In the world of decentralized finance (DeFi), traders are often caught in a balancing act between user-friendliness and the sophisticated features that offer the best returns. On one side, traditional decentralized exchanges (DEXs) offer an alternative to centralized platforms, giving more control to users. However, the complexity of their interfaces can be daunting for many, especially newcomers. Traders are frequently met with cluttered dashboards, making the swift and intuitive execution of trades a challenging endeavor. Moreover, as users diversify their portfolios across various tokens and markets, keeping track of assets becomes an intricate task. The decentralized nature of these platforms, while ensuring security and transparency, can sometimes leave users grappling with scattered assets. Such fragmentation often leads to traders spending excessive time navigating the platform and less time making critical trading decisions. There's a clear demand in the market for an interface that seamlessly merges the power of decentralization with the simplicity of traditional trading platforms. Decentralized Finance: Navigating the Trade-Offs Between Sophistication and Simplicity remarked Berk Ozdogan, Head of Strategy at Dexalot. "Simple Swap has the potential to become the perfect conduit for the most capital-efficient liquidity to get to traders," As the DeFi sector grapples with the push and pull of advanced features and user-friendly interfaces, this sentiment captures the essence of a larger movement within the decentralized finance ecosystem. DeFi traders find themselves torn between two worlds. One offers the allure of advanced trading capabilities often seen in traditional financial markets, while the other promises a seamless and intuitive user experience. But what if there was a platform that married these two seemingly conflicting ideals? Dexalot: Pioneering the Future of Decentralized Trading Marrying DEX Simplicity with CEX Features Dexalot, an innovative decentralized exchange (DEX) functioning on an Avalanche Subnet, has unveiled its answer to this quandary with two groundbreaking features: SimpleSwap and SimpleView. Designed to elevate the trading experience, these features seamlessly merge the uncomplicated nature of a standard DEX with the advanced trading mechanisms of a centralized exchange (CEX). SimpleSwap, launching on September 19, replicates the familiarity of AMM atomic swaps predominant in the DeFi space but brings to the fore the advantages of a central limit order book (CLOB) exchange—tight spreads and unmatched pricing. Through this fusion, traders can effortlessly switch between tokens on a streamlined and clutter-free interface, enriching the existing order book model. Empowering Traders Across Platforms SimpleSwap's appeal extends beyond Dexalot's boundaries. The feature's compatibility with third-party aggregators means users aren’t confined to the Dexalot platform for their trading activities. This integration with various aggregators ensures that wherever users opt to trade, they can access the unparalleled onchain pricing that Dexalot champions for assets, including but not limited to WETH.e and BTC.b. A Bird’s Eye View of Portfolios Navigating a CLOB exchange can sometimes translate into a labyrinthine challenge for traders juggling assets across different markets. SimpleView is Dexalot's answer to this complexity. It offers traders a quick-glance account abstraction tool, streamlining portfolio management by providing a holistic snapshot of their assets. By resolving the tracking intricacies that traders face, SimpleView ensures that they can effortlessly discern their portfolio's value and composition. Endorsement and Growth Matthew Schmenk from Ava Labs lauds the endeavor, stating, "Dexalot's central limit order book architecture makes for some of the best swap pricing in all of blockchain and with SimpleSwap, Dexalot should win a lot of swap routes and volumes.” This assertion aligns with the platform's impressive growth metrics. Since its inception on the Avalanche Subnet, Dexalot has recorded over 17 million transactions and witnesses daily trading volumes reaching $5M. Spearheaded by a team of Wall Street veterans, the platform is positioned as a formidable player in the decentralized exchange arena. Their recent recognition, a significant incentive award from Avalanche’s Multiverse Fund, further underscores their rising prominence in the DeFi space. About Dexalot For those seeking the fusion of traditional centralized exchange dynamics with decentralized functionalities, Dexalot stands as a beacon. The Avalanche-driven Central Limit Order Book DEX fosters a transparent ecosystem where users can indulge in secure and efficient crypto trading, sans the challenges of slippage or custody risks. For more insights into their revolutionary approach to decentralized trading, visit . Dexalot : Vested Interest Disclosure This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program

Dexalot's Dual Triumph: Elevating Decentralized Trading with SimpleSwap and SimpleView

El doble triunfo de Dexalot: elevar el comercio descentralizado con SimpleSwap y SimpleView

Le double triomphe de Dexalot : élever le trading décentralisé avec SimpleSwap et SimpleView

डेक्सलॉट की दोहरी विजय: सिंपल स्वैप और सिंपल व्यू के साथ विकेंद्रीकृत व्यापार को बढ़ाना

Dexalot の 2 つの勝利: SimpleSwap と SimpleView による分散型取引の向上

O duplo triunfo da Dexalot: elevando a negociação descentralizada com SimpleSwap e SimpleView

Chiến thắng kép của Dexalot: Nâng cao giao dịch phi tập trung với SimpleSwap và SimpleView

Dexalot 的双重胜利：通过 SimpleSwap 和 SimpleView 提升去中心化交易

Cryptography has been around long before modern computers and has been a great way to protect messages. When it comes to providing information while keeping it concealed from those who wish to use it for ill intentions, you need and codes to encrypt messages to keep them confidential. Back in the days of Rome and Julius Caesar, code was invented - a form of basic encryption and one of the earliest encryption algorithms in humanity’s recorded history - that would allow his messages of military significance to stay safe from prying eyes. This code was named after Julius himself and it was called the cipher. cryptography Caesar According to the Roman historian Suetonius, the cipher was used to shift three letters to create a new code to protect messages. For example, if you want to encrypt the word/letter A, you would shift three letters down the alphabet and translate that ‘A’ into the letter ‘D’ & you decrypt the letter by going three letters back into an A. Suetonius describes the encryption in one of his writings: "If he had anything confidential to say, he wrote it in cipher, that is, by so changing the order of the letters of the alphabet, that not a word could be made out. If anyone wishes to decipher these, and get at their meaning, he must substitute the fourth letter of the alphabet, namely D, for A, and so with the others." - Suetonius, Life of Julius Caesar 56 One of the great things about the Caesar cipher is how easy this form of encryption is to recreate with modern code. Even with basic , you can easily turn some text into a basic encrypted cipher using the following function: Python def encrypt(text,s):
    result = ""
 
    
    for i in range(len(text)):
        char = text[i]
 
        # Encrypt the uppercase characters or lowercase letters as determined by an if statement
        # before adding to the result variable
        if (char.isupper()):
            result += chr((ord(char) + s-65) % 26 + 65)
 
        
        else:
            result += chr((ord(char) + s - 97) % 26 + 97)
 
    return result Unfortunately, the Caesar cipher probably would not work very well in a modern context since it is pretty easily broken since there are only so many different combinations of letter shifting you can use and, eventually, the code can be broken with brute force attacks. This is something you can also easily program in Python: def crack(message):

for i in range(len(LETTERS)):
   translated = ''
   for s in message:
      if s in LETTERS:
         num = LETTERS.find(symbol)
         num = num - i
         if num < 0:
            num = num + len(LETTERS)
         translated = translated + LETTERS[num]
      else:
         translated = translated + s

print('Potential Hacked Messages #%s: %s' % (i, translated)) Even Caesar himself worked to combine the Caesar cipher with more complicated systems in order to make more complicated encryptions. That all being said, the Caesar cipher is a pretty important part of the history of cryptography and is one of the earliest recorded encryption algorithms used in a practical context.

The Caesar Cipher: One of the Earliest, and Simplest, Encryptions

As a frontend developer, I have been working on building collaborative apps, in which the realtime performance of  multiplayer features is especially important. It needs to synchronize the state of users in less than 100ms between two users. In this article, I would like to demonstrate how " " helped me to implement these multiplayer features within 2 hours. Furthermore, presencejs is totally open source, and also does well in . Throughout my experience using this library, I have been impressed by its power and capabilities. presencejs security, low-latency, and geo-distributed deployment Therefore, I have decided to write this blog to introduce this excellent forward-looking JavaScript library to you. is a JavaScript library that enables the creation of real-time multiplayer/ collaboration web applications that supporting WebTransport. Presencejs It provides many ready-made components like Cursor-Chat, Live-Cursor, Grouphug etc. Certainly, we can create more functional custom components using the basic functions and API of presencejs. Through the ready-made components, I created a nextjs real-time web application demo in minutes. Presencejs: https://github.com/yomorun/presencejs Demo: https://cursorchat-grouphug-demo.vercel.app/ How did I create this demo? For this demo, I used the Presencejs library to create a real-time multi-user collaborative web application.  I chose a simple but effective design for the demo. I opted for a clean and modern look with a subtle color scheme that wouldn't distract from the content. Presencejs provides several pre-built components such as Cursor Chat, Realtime Cursor, and Grouphug. I used these components along with some custom features to build this demo. Here's a brief overview of how I created it: First, I installed and imported the Presencejs library into my code. npm install @yomo/cursor-chat-react
npm install @yomo/group-hug-react import CursorChat from '@yomo/cursor-chat-react';
import GroupHug from '@yomo/group-hug-react'; Next, I used the components provided by Presencejs to build my application. For this demo, I used  Cursor-Chat, and Grouphug components. // other code

import CursorChat from '@yomo/cursor-chat-react';
import GroupHug from '@yomo/group-hug-react';


export default function Home(){
// other code

return(

// other code

<CursorChat />
<Grouphug />

// other code

)
} Then, I added some custom features . I got a public_key from and created a file .env in the main path. I entered the code below: AllegroCloud.io PUBLIC_KEY=**** And I saved this file, then ran the demo locally. Finally, I deployed my application online using Next.js so that others can access it. Overall, creating a real-time web application using Presencejs is straightforward. Presencejs provides many pre-built components that allow developers to quickly build their own applications. Additionally, Presencejs supports custom development, which allows advanced developers to create more complex features. I hope this demo inspires more developers to try using Presencejs to create their own real-time web applications. The special benefits of presencejs From my experience with it, presencejs has been particularly helpful to me in the following ways: The support to WebTransport makes presencejs show better performance than other collaboration js lib. Compatible with websocket Ready-made components and customized development. Geo-distributed architecture lets me build amazing and fast response web applications. Both serverless and self-hosting. Benefits in detail: Support for WebTransport:
Presencejs supports WebTransport, which is a newer real-time application protocol based on the QUIC protocol. It establishes reliable, secure, low-latency connections without requiring handshakes and negotiations like websocket. It also supports advanced features such as multiplexing and connection migration. This provides faster connection establishment speed, data transfer speed, and latency. Compatible with websocket:
Presencejs also supports websocket. When the user's browser doesn't support WebTransport, presencejs will replace the communication protocol with websocket. Ready-made components and customized development:
Presencejs provides ready-made components like grouphug, livecursor, and cursorchat, which allow real-time web applications. For advanced developers, the complete toolkit can be used to create custom components. Geo-distributed architecture backend:
Presencejs is a serverless architecture framework using the YoMo framework for real-time data processing. This provides faster response speed, stable performance, and secure service. Both serverless and self-hosting:
Presencejs supports self-hosting, which provides greater control over data and applications. Final Notes and Thoughts In conclusion, presencejs offers a variety of benefits that make it an excellent choice for developers looking to build real-time web applications. Its support for WebTransport and websocket protocols, ready-made components, customizable development, geo-distributed architecture, and serverless or self-hosting options provide developers with a versatile toolkit to create amazing applications. By adopting presencejs, developers can ensure their applications have faster response times, greater stability, and improved security. Furthermore, as an open-source product, presencejs will continue to improve and evolve in the coming years. Overall, presencejs is a forward-thinking product that can meet the real-time needs of the web today and in the future. As a developer experience personnel of AllegroCloud and a frontend developer, I am fully aware of the shortcomings of Presence. However, as an open-source product, Presence possesses a more robust vitality and a more transparent technological reserve. In the coming years, Presence will continue to be updated and iterated, providing even higher-quality services.

PresenceJS: The Simplest Way to Power Realtime Experience in My Collaborative Apps

This is by (source: ). Twitter Thread Compound248 12-01-2022 Dear Media, Do you want a simple description of HOW did what he did? @SBF_FTX Why does nobody seem capable of pinning Sam down? His SECRET is, when asked pointed questions, he switches to market-plumbing speak, hiding behind lingo. Let me help you understand SBF's moves. 🧵 👇 Anytime you hear or read "Alameda," replace with, "Sam's personal account." When Alameda borrows FTX customer money, that means "Sam borrows customer money": Alameda is 100% owned by Sam & a few friends. Alameda had no clients. "Alameda's capital" is Sam's, Caroline's, etc. 👇 Sam says he poorly "risk-managed" the FTX and Alameda relationship. Recall: Alameda=Sam. What transpired? • Alameda (Sam) Had Liquidity Issues • Alameda (Sam) Organized a Loan from FTX • To Paper the Loan, Alameda Posted Collateral to FTX • The Collateral Was Pretend Money 3) Alameda (Sam) Had Issues: Nobody has nailed down what happened: was Alameda *always* a fraud or did it suffer losses at some point and then get desperate (perhaps the Terra Luna implosion in the spring)? This is a question begging for an answer. I don't know, but... https://tenor.com/bhNAA.gif?embedable=true 4)...Sam claims he was no longer closely involved in Alameda (again, Alameda = Sam's personal capital). Sam claims Caroline & Team ran it independently (other than its venture investments). This claim gives Sam cover to state he doesn't know the details about Alameda's issues. 5) Alameda (Sam) Organized a Loan from FTX: At some point - time uncertain - Alameda (Sam) needed access to liquidity. It almost certainly had margin loans from 3rd parties being called (3rd parties like Gemini or Binance...maybe FTX?). To repay these, Alameda needed liquidity. 6) To Paper the Loan, Alameda Posted Collateral to FTX: FTX client assets were the liquidity that Alameda (Sam) needed to repay its other loans. To pretend this was OK, FTX needed to hold collateral from Alameda as security against the loan (in case Alameda (Sam) didn't repay). 7) When you borrow cash on margin, you generally have to post MORE collateral than you borrow. Ex: you might borrow against your Schwab account. Schwab holds your account's assets as protection against you defaulting on the loan. This lets you turn investments into liquidity. 8) Elon's margin loan is a prominent example: $TSLA Elon can borrow $1 for every $5 of TSLA stock he posts (giving him cash to do anything, like buy , without selling TSLA). $TWTR After borrowing from the facility, if TSLA stock falls below a certain threshold (say $1 to $3)... 9)...Elon needs to either add more collateral to the margin facility (post more ) or pay down the loan. $TSLA If he does neither, the lender can liquidate the it holds as collateral and repay the loan from the proceeds. $TSLA Now back to Alameda (Sam)... 10) Together, FTX & Alameda (Sam & Sam) developed a few shitcoins...err "tokens": the most relevant are and . $FTT $SRM Both FTX & Alameda received huge grants of and (I'm just going to use going forward). $FTT $SRM $FTT F&A (Sam) controlled the VAST majority of the issuance... 11)...and only a tiny portion of / traded. Alameda was the largest trader. If you own 95% of something and then manipulate the price of the other 5%, you can make your 95% look REALLY valuable - just keep the price of that 5% up and look how valuable your 95% becomes! $FTT $SRM https://tenor.com/va2h.gif?embedable=true Guess what Alameda (Sam) used as collateral for those margin loans we were talking about? and . $FTT $SRM And it doesn't appear that FTX required a big haircut on those. Remember that Elon can borrow $1 for every $5 of stock he posts? That's an 80% haircut. But... $TSLA 13)...it doesn't appear FTX required a substantial haircut on the that Alameda (Sam) posted. Ultimately, using these shitcoins...I mean tokens, as collateral, Alameda (Sam) "borrowed" $9-$10 billion from FTX. That money was, almost by definition, FTX customer money. $FTT 14) You might recall that Sam famously described tokens like these to and as "magic money in a box." It was recognized immediately as Sam describing a Ponzi. But most of us didn't realize *Sam* was issuing these magic tokens. @TheStalwart @tracyalloway http://youtube.com/watch?v=KZYqL79GDXU&t=1292s Sam may argue this is just how banks (and brokerages) work: they take deposits, they lend them out based on credit judgments to earn a spread, and - as long as depositors don't ask for deposits back en masse - all is perfectly fine. But let me tell you what banks don't do: 16) Banks do not: • Lend 70% of their deposits to one borrower • Have that borrower be the founder of the bank • Accept made-up "tokens" as collateral - the actual worst collateral ever conceived • Have almost all that collateral be just two (made up) assets 17) • Apply de minimis haircuts to collateral • Accept derivatives of the bank itself as collateral! is not random - it relates to FTX $FTT As said, Goldman Sachs should not take equity as collateral - it is circular risk. And definitely not pretend-GS equity. @matt_levine $GS 18) This Collateral Was Pretend Money Alameda (Sam) & FTX (also Sam) invented intrinsically worthless tokens, keeping almost all of it for themselves. They traded a tiny percentage, controlling the trading price. This manipulated the implied value of all of Sam's tokens... $FTT 19)...then used the resultant implied token value as collateral to extract ("borrow") customer funds from FTX. The bogus collateral was one giant identical (non-diversified) risk, funding related-party transactions. And FTX lied about doing it via misleading Terms of Service. 20) When Sam says, "he poorly risk-managed" it means, "I, FTX's CEO Sam, should not have allowed Alameda Sam to use pretend Sam-money as collateral to take real-$$ from Sam's customers." It's like in Dumb & Dumber when Lloyd spends all the found money, but replaced it with IOUs. https://tenor.com/buMGg.gif?embedable=true This insanity could have lasted longer, except Sam made one horrible mistake. At FTX's founding, Binance/ was a seed investor. As FTX evolved into a full-fledged Binance competitor, FTX reacquired Binance's seed stake using a mix of payments. One of those was... @cz_binance https://tenor.com/bONrD.gif?embedable=true 22)... ! $FTT Amazingly, the only big part of 's float that Sam did not control was controlled by his arch-f'ing-rival: CZ. $FTT When it became public that Alameda was relying heavily on as collateral, CZ understood his wasn't worth the magic paper it wasn't written on. $FTT $FTT 23) If CZ didn't know before, he knew then: all of Sam's prior "bailouts" were attempts to prevent Alameda *itself* from being liquidated - it was built on magic token money. And CZ was the biggest independent owner of the magic money. CZ knew there were zero natural buyers. $FTT 24) CZ held the keys to killing his loudest, most annoying competitor & he used them. Good on CZ - hopefully Binance itself is sound. We don't know what's next or where the $10B of customer $$ went. Perhaps Alameda lost it fair & square, perhaps it's stolen. Time MAY tell. -END The key question is, “who at FTX approved $10B of loans to Alameda (Sam)?” Sam says he didn’t understand the exposure. He was CEO. Who approved it? My “worlds colliding” dream is for to reply to my viral true crime thread. 👆 @elonmusk @SBF_FTX Like this to make my dream come true.😅 ...or Retweet. Feature image generated via prompt of ‘The Simplest Guide of What Happened With SBF That You Will Ever Come Across’ HackerNoon Stable Diffusion

The Simplest Guide of What Happened With SBF That You Will Ever Come Across

Load Balancing Strategies for Applications: From Infrastructure to Code

One of the most effective ways to cultivate an engaged and productive team is to actively demonstrate appreciation for your employees’ hard work and dedication. When individuals feel genuinely valued by their employer, it translates into improved morale, heightened job satisfaction, increased productivity, and greater overall retention. Showing genuine gratitude doesn't have to be an elaborate or costly endeavor. Even seemingly simple yet thoughtful gestures can go a long way in making your staff feel recognized and energized about their contributions to the company. Whether through personalized acknowledgments, unique incentives, or fostering a strong sense of community, there are myriad creative approaches to expressing appreciation. Personalized recognition A simple but powerful way to make employees feel appreciated is through personalized recognition of their efforts. According to Asim Hafeez, Owner and Operator of Empower Energy Solutions, “Showing your employees that you genuinely care through personal gestures that recognize their value and contributions resonates deeply.” This recognition can be as easy as direct, one-on-one praise or a handwritten thank-you note acknowledging specific contributions. Whatever approach you take, make sure to tell your employees face-to-face that they are doing well. Public recognition is also highly impactful. “Recognizing employees' achievements in team meetings or through company-wide communications makes them feel valued and seen by their peers and the broader organization,” says Amanda Webster, vice president of Fund & Grow. Simple acts like announcing an “Employee of the Month” or giving shout-outs during meetings can motivate individuals and foster a culture of collaboration. Team building and community Creating a strong sense of community and team cohesion is another way to make employees feel truly part of something bigger. “Organize events that allow employees to relax and bond outside work, such as team dinners, outings, or recreational activities,” Webster suggests. “These events can enhance team cohesion and make everyone feel like part of a community.” Jay Avigdor, CEO and president of Velocity Capital Group, takes this principle even further by striving to build a truly family-like atmosphere. “Everyone here is far more than an employee — we're all family,” he says. “Team-building activities, outings, and fostering an authentic sense of belonging will pay dividends in productivity and retention efforts.” Professional development opportunities Investing in both your employees’ professional and personal growth demonstrates a vested interest in their long-term success. “I can’t stress enough how everything stems from genuine care,” Hafeez notes. “Investing in your employees’ personal growth and professional development shows that you care, and they will be more willing to offer the same care right back into your company.” For example, Webster states, “Investing in your employees' growth by providing training, workshops, or courses shows that you value their development and future.” However, providing learning opportunities is only one facet. “Consistent feedback and open communication are essential,” says Asha Tarry, a licensed psychotherapist, caregiver coach, and owner of Behavioral Health Consulting Services LMSW, PLLC. “Employees like periodic updates on their work performance because it allows them to take inventory of themselves and set short-term goals they can review with their managers.” As Tarry explains, this dialogue allows employees to gain clarity on expectations and voice any needs for further training or support. It also facilitates an environment where growth is prioritized and shows you are actively invested in their professional journeys. Unique incentives and perks Creative incentives and fun workplace perks can infuse excitement and tangible rewards into your appreciation efforts. Avigdor, for example, utilizes an innovative “spinning wheel” system. “The more we fund, the more spins they get,” Avigdor explains. “For every $1 million we fund, the team gets a spin at the end of the month for items like new Airpods, cash prizes of $150 or $200, mystery prizes, and gift cards. If we fund more, it means they’ve worked harder, and the more spins they get.” Avigdor goes on to describe additional creative perks at his company. “One day, we had breakfast that came to the office with an omelet chef and a guy who makes professional espressos,” he says. “We even shut down the office once early and had a ventriloquist come for some entertainment!” Other ideas include office treats, creative spaces to recharge, or unique team bonuses. Showing genuine care At the core of any effective appreciation strategy is genuine care and interest in your employees as whole individuals. Hafeez suggests asking questions like, “When was the last time we asked them what their dreams were? When was the last time we had them submit a list of their dreams and followed up with them?” Finding ways to add value to employees' lives outside of work, whether funding a class they're interested in or connecting them to resources, shows that you see them as multidimensional people. “I think that's where the good stuff comes in — when we take genuine interest in adding value to their lives outside,” Hafeez says. Equally important is leading with empathy and consideration. “Being a considerate and respectful speaker had an impact on my self-esteem and performance,” Tarry reflects. “It created more anxiety not only in me, but I saw it firsthand in my colleagues. Conversely, when I worked with managers who were ‘people leaders,’ my mood was better.” When fostering a culture of appreciation within your organization, it’s clear that the simplest gestures often yield the most significant impact. Whether through personalized acknowledgments, fostering a strong sense of community, or investing in professional growth, the key lies in demonstrating genuine care for your employees. By recognizing and celebrating their contributions publicly and privately, you can boost morale and cultivate a sense of belonging and camaraderie. Furthermore, providing opportunities for growth and development, along with creative incentives and perks, reinforces your commitment to their success and well-being. Ultimately, leading with empathy and consideration establishes a foundation of trust and support, paving the way for a motivated and engaged workforce. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author One of the most effective ways to cultivate an engaged and productive team is to actively demonstrate appreciation for your employees’ hard work and dedication. When individuals feel genuinely valued by their employer, it translates into improved morale, heightened job satisfaction, increased productivity, and greater overall retention. Showing genuine gratitude doesn't have to be an elaborate or costly endeavor. Even seemingly simple yet thoughtful gestures can go a long way in making your staff feel recognized and energized about their contributions to the company. Whether through personalized acknowledgments, unique incentives, or fostering a strong sense of community, there are myriad creative approaches to expressing appreciation. Personalized recognition Personalized recognition A simple but powerful way to make employees feel appreciated is through personalized recognition of their efforts. According to Asim Hafeez, Owner and Operator of Empower Energy Solutions , “Showing your employees that you genuinely care through personal gestures that recognize their value and contributions resonates deeply.” Empower Energy Solutions Empower Energy Solutions This recognition can be as easy as direct, one-on-one praise or a handwritten thank-you note acknowledging specific contributions. Whatever approach you take, make sure to tell your employees face-to-face that they are doing well. Public recognition is also highly impactful. “Recognizing employees' achievements in team meetings or through company-wide communications makes them feel valued and seen by their peers and the broader organization,” says Amanda Webster, vice president of Fund & Grow . Simple acts like announcing an “Employee of the Month” or giving shout-outs during meetings can motivate individuals and foster a culture of collaboration. Fund & Grow Fund & Grow Team building and community Team building and community Creating a strong sense of community and team cohesion is another way to make employees feel truly part of something bigger. “Organize events that allow employees to relax and bond outside work, such as team dinners, outings, or recreational activities,” Webster suggests. “These events can enhance team cohesion and make everyone feel like part of a community.” Jay Avigdor, CEO and president of Velocity Capital Group , takes this principle even further by striving to build a truly family-like atmosphere. “Everyone here is far more than an employee — we're all family,” he says. “Team-building activities, outings, and fostering an authentic sense of belonging will pay dividends in productivity and retention efforts.” Velocity Capital Group Velocity Capital Group Professional development opportunities Professional development opportunities Investing in both your employees’ professional and personal growth demonstrates a vested interest in their long-term success. “I can’t stress enough how everything stems from genuine care,” Hafeez notes. “Investing in your employees’ personal growth and professional development shows that you care, and they will be more willing to offer the same care right back into your company.” For example, Webster states, “Investing in your employees' growth by providing training, workshops, or courses shows that you value their development and future.” However, providing learning opportunities is only one facet. “Consistent feedback and open communication are essential,” says Asha Tarry, a licensed psychotherapist, caregiver coach, and owner of Behavioral Health Consulting Services LMSW, PLLC. “Employees like periodic updates on their work performance because it allows them to take inventory of themselves and set short-term goals they can review with their managers.” As Tarry explains, this dialogue allows employees to gain clarity on expectations and voice any needs for further training or support. It also facilitates an environment where growth is prioritized and shows you are actively invested in their professional journeys. Unique incentives and perks Unique incentives and perks Creative incentives and fun workplace perks can infuse excitement and tangible rewards into your appreciation efforts. Avigdor, for example, utilizes an innovative “spinning wheel” system. “The more we fund, the more spins they get,” Avigdor explains. “For every $1 million we fund, the team gets a spin at the end of the month for items like new Airpods, cash prizes of $150 or $200, mystery prizes, and gift cards. If we fund more, it means they’ve worked harder, and the more spins they get.” Avigdor goes on to describe additional creative perks at his company. “One day, we had breakfast that came to the office with an omelet chef and a guy who makes professional espressos,” he says. “We even shut down the office once early and had a ventriloquist come for some entertainment!” Other ideas include office treats, creative spaces to recharge, or unique team bonuses. Showing genuine care Showing genuine care At the core of any effective appreciation strategy is genuine care and interest in your employees as whole individuals. Hafeez suggests asking questions like, “When was the last time we asked them what their dreams were? When was the last time we had them submit a list of their dreams and followed up with them?” Finding ways to add value to employees' lives outside of work, whether funding a class they're interested in or connecting them to resources, shows that you see them as multidimensional people. “I think that's where the good stuff comes in — when we take genuine interest in adding value to their lives outside,” Hafeez says. Equally important is leading with empathy and consideration. “Being a considerate and respectful speaker had an impact on my self-esteem and performance,” Tarry reflects. “It created more anxiety not only in me, but I saw it firsthand in my colleagues. Conversely, when I worked with managers who were ‘people leaders,’ my mood was better.” When fostering a culture of appreciation within your organization, it’s clear that the simplest gestures often yield the most significant impact. Whether through personalized acknowledgments, fostering a strong sense of community, or investing in professional growth, the key lies in demonstrating genuine care for your employees. By recognizing and celebrating their contributions publicly and privately, you can boost morale and cultivate a sense of belonging and camaraderie. Furthermore, providing opportunities for growth and development, along with creative incentives and perks, reinforces your commitment to their success and well-being. Ultimately, leading with empathy and consideration establishes a foundation of trust and support, paving the way for a motivated and engaged workforce. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Empowering Your Team: 5 Easy Ways To Show Employee Appreciation

আপনার দলকে ক্ষমতায়ন করা: কর্মচারীর প্রশংসা দেখানোর 5টি সহজ উপায়

Bestärken Sie Ihr Team: 5 einfache Möglichkeiten, Ihren Mitarbeitern Wertschätzung zu zeigen

Empoderar a su equipo: 5 formas sencillas de mostrar aprecio a los empleados

Donner du pouvoir à votre équipe : 5 façons simples de montrer l'appréciation de vos employés

अपनी टीम को सशक्त बनाना: कर्मचारियों की सराहना दिखाने के 5 आसान तरीके

チームに力を与える: 従業員に感謝の気持ちを伝える 5 つの簡単な方法

팀 역량 강화: 직원에게 감사를 표시하는 5가지 쉬운 방법

Capacitando sua equipe: 5 maneiras fáceis de mostrar a valorização dos funcionários

Расширение возможностей вашей команды: 5 простых способов показать признательность сотрудникам

Ekibinizi Güçlendirmek: Çalışan Takdirini Göstermenin 5 Kolay Yolu

Trao quyền cho nhóm của bạn: 5 cách dễ dàng để thể hiện sự đánh giá cao của nhân viên

赋予团队权力：表达对员工赞赏的 5 种简单方法

SEO 101: Content Readability Matters!

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com];
(2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za];
(3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za];
(4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction
Methodology
Neural Network Methodology
Numerical results, implementation and discussion
Conclusion, Acknowledgments, and Funding
Availability of data, code and materials, Contributions and Declarations
References 3. Neural Network Methodology The neural network (NN) algorithm is used for solving problems relating to dimensionality reduction [42, 49], visualization of data [31], clustering [11, 52] and classification issues [37, 40]. It is also used in regression [2, 21, 45] in solving regression problems since they do not require prior knowledge about the distribution of the data. The NN algorithm often has the tendency to predict with higher accuracy than other techniques due to the NN’s capability to fit any continuous functions [18]. Mathematically, the NN can be referred to as a directed graph having neurons as vertices and links as edges. Different forms of NN depend on the connectivity of their neurons, and the simplest one is the Multi-layer perceptron, also known as the feedforward NN. Basically, a NN can have only one input and one output layer in its simplest form, and this can be written as: where m is the number of input variables, Φ is the activation function, wi,k is the weight of the input layer i with respect to the output k, b is the bias, and the input vector x is connected to the output k (denoting the kth neuron in the output layer) through a biased weighted sum. In the presence of hidden layers positioned between the input layers and the output layers, the output can be written as: where m1 is the number of input variables, m2 is the number of nodes in the hidden layer, Φ : R → R refers to non-linear activation functions for each of the layers in the brackets, and Φout is the possibly new output function. Recently, the NN has become an indispensable tool for learning the solutions of differential equations, and this section will utilize the potential of the NN in solving PDE-related problems [1, 10, 20, 50]. Also, the NN has been implemented in solving equations without analytical solutions. For instance, Nwankwo et al. [38] considered the solution of the American options PDE by incorporating the Landau transformation and solving the corresponding transformed function via a neural network approach. In many scientific and industrial contexts, there is a need to solve parametric PDEs using NN techniques and Khoo et al. [24] developed such a technique which solves PDE with inhomogeneous coefficient fields. For high-dimensional parametric PDE, [14] analyzed the deep parametric PDE method to solve high-dimensional parametric PDEs with much emphasis on financial applications. In the following, we describe the Bitcoin options with the corresponding Black-Scholes pricing PDE: The above equation which is a non-homogeneous PDE can be re-written as Standard theorems guarantee a classical smooth solution exists for the pricing PDE (3.13) given the assumed dynamics. The continuity and linear growth conditions on the coefficient functions ensure they satisfy Lipschitz continuity. Contingent claims theory shows that Lipschitz continuity is sufficient for existence and uniqueness under the stochastic process specifications. In particular, the smooth past price dependence in the diffusion coefficient allows the application results for delayed Black-Scholes equations. Therefore, the Cauchy problem is well-posed under the model assumptions, and a smooth pricing solution is guaranteed to exist based on the theorems. This provides the theoretical foundation for using a neural network to approximate the price function numerically. Thus, employing the ANN to solve the PDE, we introduce an approximating function ζ(t, S : θ) with parameter set θ. The loss or cost function associated with this training is measured by how well the approximation function satisfies the differential operator, boundary conditions and the terminal condition of the option pricing PDE. These are given respectively as Suppose L(ζ) = 0, then ζ(t, S : θ) is a solution of the PDE in equation (3.13). The major aim is to obtain a set of parameters θ such that the function ζ(t, S : θ) minimizes the observed error of L(ζ). The procedure for seeking a good parameter set by minimizing this loss function using the stochastic gradient descent (SGD) optimizer is called “training”. Thus, using a Machine Learning approach, and in this case, the artificial NN, it will be feasible to minimize the function L(ζ) by applying the SGD approach on the sequence of asset and time points which are drawn at random. A part of the input samples, fully dependent on the mini-batch size, is drawn within each iteration to estimate the gradient of the given objective function. The gradient is then estimated over these mini-batches due to the limitations of the computer memory [30]. The training of NN is classified into first identifying the network’s hyperparameters, that is, the architectural structure and the loss function of the network. Next, use the SGD to optimize or estimate the loss minimizer, and then the derivatives of the loss function are computed using the backpropagation techniques. Essentially, the process of searching and identifying the best θ parameter by minimizing the loss function using the gradient descent-based optimizers is referred to as “training”. The whole procedure is well illustrated in the algorithm below [46], and can be implemented in solving the PDE in equation (3.13): The learning rate α is employed to scale the intensity of the parameter updates during the gradient descent. Choosing the learning rate, as the descent parameter α, in the network training is crucial since this factor plays a significant role in aiding the algorithm’s convergence to the true solution in the gradient descent. It equally affects the pace at which the algorithm learns and whether or not the cost function is minimized. When α implies divergence, and thus, the optimal point can be missed. Also, a fixed value of α can most likely make the local optima overshoot after some iterations, leading to divergence. Defining the learning rate as a dynamic decreasing function is preferable since it allows the algorithm to identify the needed point rapidly. Another limitation in employing the gradient descent method is obtaining the value of the initial parameters of the loss function. If the value is significantly close to the local optimum, then the slope of the loss function reduces, thereby leading to the optimal convergence. Otherwise, there will be no convergence as the solution explodes abnormally. 3.1. Solution of bitcoin option pricing PDE While the pricing PDE has a proven smooth solution under the model dynamics, obtaining the analytical form is intractable. Numerical methods must be used to approximate the solution. Neural networks provide a flexible parametric approach based on their universal approximation theoretical results. Given sufficient network capacity, neural networks can represent a wide class of functions, including solutions to PDEs like the pricing equation. However, challenges remain in finding the optimal network parameters to recover the true solution robustly. While existence is guaranteed, the non-convex optimization of neural networks does not assure convergence to global optimality. Care must be taken in specifying the network architecture, loss function, regularization, and training methodology to promote the learning of the pricing function. Subject to these caveats, neural networks present a promising computational technique for approximating smooth pricing solutions, circumventing discretization of the domain. We note the limitations of using neural networks as function approximators. However, their generalization capabilities provide a methodology for data-driven extraction of the pricing mapping across the entire domain. This avoids the constraints of methods like grid-based techniques that rely on local consistency. Future work should explore neural network training enhancements and theoretical guarantees to ensure robust solutions. This paper is available on arxiv under CC 4.0 license. Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction Methodology Neural Network Methodology Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations References Abstract and Introduction Abstract and Introduction Methodology Methodology Neural Network Methodology Neural Network Methodology Numerical results, implementation and discussion Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations Availability of data, code and materials, Contributions and Declarations References References 3. Neural Network Methodology The neural network (NN) algorithm is used for solving problems relating to dimensionality reduction [42, 49], visualization of data [31], clustering [11, 52] and classification issues [37, 40]. It is also used in regression [2, 21, 45] in solving regression problems since they do not require prior knowledge about the distribution of the data. The NN algorithm often has the tendency to predict with higher accuracy than other techniques due to the NN’s capability to fit any continuous functions [18]. Mathematically, the NN can be referred to as a directed graph having neurons as vertices and links as edges. Different forms of NN depend on the connectivity of their neurons, and the simplest one is the Multi-layer perceptron, also known as the feedforward NN. Basically, a NN can have only one input and one output layer in its simplest form, and this can be written as: where m is the number of input variables, Φ is the activation function, wi,k is the weight of the input layer i with respect to the output k, b is the bias, and the input vector x is connected to the output k (denoting the kth neuron in the output layer) through a biased weighted sum. In the presence of hidden layers positioned between the input layers and the output layers, the output can be written as: where m1 is the number of input variables, m2 is the number of nodes in the hidden layer, Φ : R → R refers to non-linear activation functions for each of the layers in the brackets, and Φout is the possibly new output function. Recently, the NN has become an indispensable tool for learning the solutions of differential equations, and this section will utilize the potential of the NN in solving PDE-related problems [1, 10, 20, 50]. Also, the NN has been implemented in solving equations without analytical solutions. For instance, Nwankwo et al. [38] considered the solution of the American options PDE by incorporating the Landau transformation and solving the corresponding transformed function via a neural network approach. In many scientific and industrial contexts, there is a need to solve parametric PDEs using NN techniques and Khoo et al. [24] developed such a technique which solves PDE with inhomogeneous coefficient fields. For high-dimensional parametric PDE, [14] analyzed the deep parametric PDE method to solve high-dimensional parametric PDEs with much emphasis on financial applications. In the following, we describe the Bitcoin options with the corresponding Black-Scholes pricing PDE: The above equation which is a non-homogeneous PDE can be re-written as Standard theorems guarantee a classical smooth solution exists for the pricing PDE (3.13) given the assumed dynamics. The continuity and linear growth conditions on the coefficient functions ensure they satisfy Lipschitz continuity. Contingent claims theory shows that Lipschitz continuity is sufficient for existence and uniqueness under the stochastic process specifications. In particular, the smooth past price dependence in the diffusion coefficient allows the application results for delayed Black-Scholes equations. Therefore, the Cauchy problem is well-posed under the model assumptions, and a smooth pricing solution is guaranteed to exist based on the theorems. This provides the theoretical foundation for using a neural network to approximate the price function numerically. Thus, employing the ANN to solve the PDE, we introduce an approximating function ζ(t, S : θ) with parameter set θ. The loss or cost function associated with this training is measured by how well the approximation function satisfies the differential operator, boundary conditions and the terminal condition of the option pricing PDE. These are given respectively as Suppose L(ζ) = 0, then ζ(t, S : θ) is a solution of the PDE in equation (3.13). The major aim is to obtain a set of parameters θ such that the function ζ(t, S : θ) minimizes the observed error of L(ζ). The procedure for seeking a good parameter set by minimizing this loss function using the stochastic gradient descent (SGD) optimizer is called “training”. Thus, using a Machine Learning approach, and in this case, the artificial NN, it will be feasible to minimize the function L(ζ) by applying the SGD approach on the sequence of asset and time points which are drawn at random. A part of the input samples, fully dependent on the mini-batch size, is drawn within each iteration to estimate the gradient of the given objective function. The gradient is then estimated over these mini-batches due to the limitations of the computer memory [30]. The training of NN is classified into first identifying the network’s hyperparameters, that is, the architectural structure and the loss function of the network. Next, use the SGD to optimize or estimate the loss minimizer, and then the derivatives of the loss function are computed using the backpropagation techniques. Essentially, the process of searching and identifying the best θ parameter by minimizing the loss function using the gradient descent-based optimizers is referred to as “training”. The whole procedure is well illustrated in the algorithm below [46], and can be implemented in solving the PDE in equation (3.13): The learning rate α is employed to scale the intensity of the parameter updates during the gradient descent. Choosing the learning rate, as the descent parameter α, in the network training is crucial since this factor plays a significant role in aiding the algorithm’s convergence to the true solution in the gradient descent. It equally affects the pace at which the algorithm learns and whether or not the cost function is minimized. When α implies divergence, and thus, the optimal point can be missed. Also, a fixed value of α can most likely make the local optima overshoot after some iterations, leading to divergence. Defining the learning rate as a dynamic decreasing function is preferable since it allows the algorithm to identify the needed point rapidly. Another limitation in employing the gradient descent method is obtaining the value of the initial parameters of the loss function. If the value is significantly close to the local optimum, then the slope of the loss function reduces, thereby leading to the optimal convergence. Otherwise, there will be no convergence as the solution explodes abnormally. 3.1. Solution of bitcoin option pricing PDE 3.1. Solution of bitcoin option pricing PDE While the pricing PDE has a proven smooth solution under the model dynamics, obtaining the analytical form is intractable. Numerical methods must be used to approximate the solution. Neural networks provide a flexible parametric approach based on their universal approximation theoretical results. Given sufficient network capacity, neural networks can represent a wide class of functions, including solutions to PDEs like the pricing equation. However, challenges remain in finding the optimal network parameters to recover the true solution robustly. While existence is guaranteed, the non-convex optimization of neural networks does not assure convergence to global optimality. Care must be taken in specifying the network architecture, loss function, regularization, and training methodology to promote the learning of the pricing function. Subject to these caveats, neural networks present a promising computational technique for approximating smooth pricing solutions, circumventing discretization of the domain. We note the limitations of using neural networks as function approximators. However, their generalization capabilities provide a methodology for data-driven extraction of the pricing mapping across the entire domain. This avoids the constraints of methods like grid-based techniques that rely on local consistency. Future work should explore neural network training enhancements and theoretical guarantees to ensure robust solutions.

Neural Network for Valuing Bitcoin: Neural Network Methodology

Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less.
Reliability: No reliance on third-party services reduces failure chances.
Scalability: No extra costs as usage grows. Cons Resource Intensive: Each app instance consumes significant memory.
More Dependencies: Requires packages for decoding and looking up GeoIP data.
Challenging Updates: Updates require manual intervention or another self-hosted service. Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources.
Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint.
Fewer Dependencies: Often only requires an HTTP client.
Updates without hassle: Third-party will handle updates Cons Slower Responses: Calls to third-party services can introduce significant latency.
Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes.
Scaling Costs: Costs can escalate as usage increases. Introducing Atlas: Combining the Best of Both Worlds Faced with these challenges, I developed Atlas—a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Cost Efficiency: No additional costs with scale in terms of third-party fees. Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md. Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/. Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8: > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage.


Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement.


Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request.


Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable.


Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration! Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less. Reliability: No reliance on third-party services reduces failure chances. Scalability: No extra costs as usage grows. Speed : IP lookups in milliseconds or less. Speed Reliability : No reliance on third-party services reduces failure chances. Reliability Scalability : No extra costs as usage grows. Scalability Cons Resource Intensive: Each app instance consumes significant memory. More Dependencies: Requires packages for decoding and looking up GeoIP data. Challenging Updates: Updates require manual intervention or another self-hosted service. Resource Intensive : Each app instance consumes significant memory. Resource Intensive More Dependencies : Requires packages for decoding and looking up GeoIP data. More Dependencies Challenging Updates : Updates require manual intervention or another self-hosted service. Challenging Updates Using External GeoIP APIs (Often Paid) Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources. Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Fewer Dependencies: Often only requires an HTTP client. Updates without hassle: Third-party will handle updates Minimal Resource Use : Outsourcing GeoIP data and logic doesn't burden your app's resources. Minimal Resource Use Ease of Implementation : Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Ease of Implementation Fewer Dependencies : Often only requires an HTTP client. Fewer Dependencies Updates without hassle : Third-party will handle updates Updates without hassle Cons Slower Responses: Calls to third-party services can introduce significant latency. Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes. Scaling Costs: Costs can escalate as usage increases. Slower Responses : Calls to third-party services can introduce significant latency. Slower Responses Increased Unreliability : Greater likelihood of failures due to network issues or third-party downtimes. Increased Unreliability Scaling Costs : Costs can escalate as usage increases. Scaling Costs Introducing Atlas : Combining the Best of Both Worlds Introducing Atlas Faced with these challenges, I developed Atlas —a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Atlas Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency : Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates : It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates Cost Efficiency: No additional costs with scale in terms of third-party fees. Cost Efficiency : No additional costs with scale in terms of third-party fees. Cost Efficiency Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API : A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free : Atlas is Open Source and freely available under Apache-2.0 License. Open and Free By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Why Rust? Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md . Atlas README.md Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/ . https://atlas-rs.fly.dev/swagger-ui/ Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8 : Fly.io 8.8.8.8 > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas : The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues : If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features : Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation : As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word : If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. special thanks to oschwald oschwald maxminddb Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration!

IP Geo-Location: Local Database vs. Remote API—Finding the Middle Ground With Atlas

It's been a while since my last appearance, but I'm excited to be back and to share something truly special with you. In this article, we'll explore my top 10 Google Sheets features that are guaranteed to boost your productivity, speed up your workflow, and make your data handling more efficient. So, without further ado, let's dive into these game-changing tools! https://youtu.be/iHAvhGO0Df0?si=j6ePVf_Y5814jupV&embedable=true Autofill One of the simplest yet most effective features in Google Sheets is Autofill. Imagine you need to input numbers from 1 to 100—instead of typing each one, simply start the sequence, grab the blue dot at the corner of the cell, and drag down. Google Sheets will fill the rest for you. This works not just with numbers, but with days of the week and months of the year too, saving you a significant amount of time. Removing Whitespace and Duplicates When working with large datasets, cleaning up data is crucial. Google Sheets makes this easy with tools to eliminate unwanted whitespace and duplicates. Just select your range, use the "Trim whitespace" option under Data Cleanup, and voilà—clean, tidy cells. Similarly, the "Remove duplicates" feature ensures that only unique values remain, streamlining your data for better analysis. Alternating Colors Enhancing readability can significantly improve the way you interact with your data. The "Alternating colors" feature applies different colors to alternating rows, making your spreadsheet easier to read and reducing errors when reviewing large blocks of data. Embedding Sheets in Web Pages For those who wish to integrate Google Sheets with their websites, the ability to embed sheets is invaluable. Simply choose to embed your document, copy the provided HTML code, and paste it into your website. This allows you to display live, interactive data directly on your web pages. Data Validation for Dates Data integrity can be maintained by restricting input to certain types of data. For example, you can set a cell to only accept dates. This is done through data validation, where you select "Date" as the criteria, and Google Sheets will provide a handy date picker to ensure accuracy. Importing HTML Need to pull data from a webpage into your spreadsheet? The "ImportHTML" function is your friend. Just write the formula, specify the URL, and choose the table you wish to import. This feature not only saves time but also ensures you can quickly update your spreadsheet with the latest web data. Extracting Unique Values To identify unique entries from a range, the "Unique" function is perfect. It extracts distinct values from your selected range, allowing you to see just the unique information without any repeats. Quick Sheet Creation If you need to create a new spreadsheet quickly, just type "sheets.new" in your Chrome address bar. This shortcut instantly opens a new Google Sheet, helping you get to work right away without navigating through menus. Import Range Sometimes, you might need to share data from a spreadsheet without giving access to the entire file. "Import Range" allows you to import specific ranges from one Google Sheet to another, keeping the data synchronized without manual intervention. Google Translate in Sheets Google Sheets can even translate text directly within cells using the "Google Translate" function. Simply specify the cell, the source language, and the target language, and the translation updates in real-time. Bonus Tip Column Stats For data analysts, the "Column Stats" add-on provides quick statistical insights into data columns without needing to apply complex formulas. This tool is excellent for a high-level overview of data characteristics. Inserting Images in Cells A lesser-known but handy feature is the ability to insert images directly into cells. This functionality allows for a more organized display of visual data, which can be particularly useful for inventory lists, directories, and catalogs. I hope you find these tips helpful for making the most out of Google Sheets. If you enjoyed this article, please consider liking and sharing it. Your feedback is always welcome, so feel free to drop a comment below. Don't forget to subscribe for more useful content, and I look forward to seeing you in future articles! Thank you for your support and happy sheeting! It's been a while since my last appearance, but I'm excited to be back and to share something truly special with you. In this article, we'll explore my top 10 Google Sheets features that are guaranteed to boost your productivity, speed up your workflow, and make your data handling more efficient. So, without further ado, let's dive into these game-changing tools! https://youtu.be/iHAvhGO0Df0?si=j6ePVf_Y5814jupV&embedable=true https://youtu.be/iHAvhGO0Df0?si=j6ePVf_Y5814jupV&embedable=true Autofill One of the simplest yet most effective features in Google Sheets is Autofill. Imagine you need to input numbers from 1 to 100—instead of typing each one, simply start the sequence, grab the blue dot at the corner of the cell, and drag down. Google Sheets will fill the rest for you. This works not just with numbers, but with days of the week and months of the year too, saving you a significant amount of time. Removing Whitespace and Duplicates When working with large datasets, cleaning up data is crucial. Google Sheets makes this easy with tools to eliminate unwanted whitespace and duplicates. Just select your range, use the "Trim whitespace" option under Data Cleanup, and voilà—clean, tidy cells. Similarly, the "Remove duplicates" feature ensures that only unique values remain, streamlining your data for better analysis. Alternating Colors Enhancing readability can significantly improve the way you interact with your data. The "Alternating colors" feature applies different colors to alternating rows, making your spreadsheet easier to read and reducing errors when reviewing large blocks of data. Embedding Sheets in Web Pages For those who wish to integrate Google Sheets with their websites, the ability to embed sheets is invaluable. Simply choose to embed your document, copy the provided HTML code, and paste it into your website. This allows you to display live, interactive data directly on your web pages. Data Validation for Dates Data integrity can be maintained by restricting input to certain types of data. For example, you can set a cell to only accept dates. This is done through data validation, where you select "Date" as the criteria, and Google Sheets will provide a handy date picker to ensure accuracy. Importing HTML Need to pull data from a webpage into your spreadsheet? The "ImportHTML" function is your friend. Just write the formula, specify the URL, and choose the table you wish to import. This feature not only saves time but also ensures you can quickly update your spreadsheet with the latest web data. Extracting Unique Values To identify unique entries from a range, the "Unique" function is perfect. It extracts distinct values from your selected range, allowing you to see just the unique information without any repeats. Quick Sheet Creation If you need to create a new spreadsheet quickly, just type "sheets.new" in your Chrome address bar. This shortcut instantly opens a new Google Sheet, helping you get to work right away without navigating through menus. Import Range Sometimes, you might need to share data from a spreadsheet without giving access to the entire file. "Import Range" allows you to import specific ranges from one Google Sheet to another, keeping the data synchronized without manual intervention. Google Translate in Sheets Google Sheets can even translate text directly within cells using the "Google Translate" function. Simply specify the cell, the source language, and the target language, and the translation updates in real-time. Bonus Tip Column Stats For data analysts, the "Column Stats" add-on provides quick statistical insights into data columns without needing to apply complex formulas. This tool is excellent for a high-level overview of data characteristics. Inserting Images in Cells A lesser-known but handy feature is the ability to insert images directly into cells. This functionality allows for a more organized display of visual data, which can be particularly useful for inventory lists, directories, and catalogs. I hope you find these tips helpful for making the most out of Google Sheets. If you enjoyed this article, please consider liking and sharing it. Your feedback is always welcome, so feel free to drop a comment below. Don't forget to subscribe for more useful content, and I look forward to seeing you in future articles! Thank you for your support and happy sheeting!

Top 10 Favorite Google Sheets Features for Everyday Work

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de;
(2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl;
(3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de;
(4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction
Related Work
Methodology
Experimental Design
Experimental Results
Conclusion
Ethics Statement and References III. METHODOLOGY A. System Architecture The CUP framework is based on a two-tower architecture for representation learning (one ”tower” for users, the other for items, following the prevalent architecture in neural information retrieval with query and document/passage encodings). The two towers are jointly trained, coupled by the shared loss function and ground truth. Figure 1 shows a pictorial overview. User reviews and items descriptions are fed into BERT followed by a feed-forward network to learn latent representations. Downstream, the vectors are simply compared by a dot product for scores that indicate whether the user likes an item or not. Importantly, unlike prior works, we allow the top-most layer of BERT itself to be fine-tuned as part of the end-to-end training process. The per-item text usually comprises book titles, tags like categories or genre labels, which can be coarse (e.g., “thriller”) or fine-grained (e.g., “Scandinavian crime noir”), and a short description of the book contents. The per-user text can comprise the titles and tags of her training-set books and the entirety of her review texts, which vary widely in length and informativeness, hence the need for smart text selection. In the following, we present the CUP training procedure in Subsection III-B, the test/deployment-time inference in Subsection III-C, the judicious selection of input text snippets in Subsection III-D, and the selection of negative training samples in Subsection III-E. B. Training Another optional mechanism is to process multiple chunks of bounded-length text, which could be of interest for long user reviews. In this case, the per-user representations are learned for each chunk, and there is an additional max-pooling step between the FFN and the dot product, to combine the per-chunk vectors. We use this option for comparison in experiments. As it incurs m times higher computational and energy costs, with m being the number of chunks, we do not favor it. A user-item pair is classified with score calculated by with <, > denoting dot product and σ for the sigmoid function. We use the Adam optimizer to minimize the binary cross entropy loss between predicted labels and the ground truth with sampled negatives. During training we update the topmost layer of BERT, which allows end-to-end training of all components. This is an important difference to earlier works with similar architectures, which use frozen BERT to encode user and item representations. C. Inference Prediction for Ranking. At test time, a prediction is made for user-item pairs. We encode the item description by running it through the trained network, and we compare it to the already learned user vector, which is based on the user’s training-time reviews (none for the given test item). The scores for different test items, computed by the final dot product, yield the ranking of the candidate items. This is a very efficient computation, following established practice in neural information retrieval [37]. Search-based Recommendation. In a deployed system (as opposed to lab experiments with test samples), a typical usage mode would be search-based re-ranking: a user provides context with a tag-based query or an example of a specific liked item, which can be thought of as query-by-example. The user’s expectation is to see a ranked list of recommended items that are similar to her positive sample (as opposed to recommendations from all kinds of categories). The system achieves this by first computing approximate matches to the query item (i.e., similarity-search neighbors), and then re-ranking a shortlist of say top-100 candidates. The CUP framework supports this mode, by using a light-weight BM25 retrieval model, querying all unlabeled items with the category and textual description of the positive point at hand, and keeping the top-100 highest scoring matches. D. Coping with Long and Noisy Texts For constructing user profiles from text, the simplest idea would be to concatenate all available reviews into a long token sequence. Two problems arise, though. User reviews are a noisy mix of descriptive elements (e.g., “the unusual murder weapon”), sentiment expressions (e.g., “it was fun to read”) and personal but irrelevant statements (e.g., “I read only on weekends”). Only the first aspect is helpful for content-based profiling (as the sentiment is already captured by the fact that the user likes the book). Second, the entirety of user-provided text can be too long to be fully digested by the Transformer. Even when it would fit into the token budget, the computational and energy cost is quadratic in the number of input tokens. Therefore, we tightly limit the tokens for each user’s text profile to 128, and devise a suite of light-weight techniques for judiciously selecting the most informative pieces. Variants of our methodology are to create concise profiles by generative language models upfront (e.g., using T5 or ChatGPT), or to digest multiple chunks of text with downstream aggregation by max-pooling or averaging. We include these techniques in our experimental comparisons, but emphasize that both of these alternatives have much higher computational cost and climate footprint. Our techniques for selecting the most informative parts of user reviews to construct concise profiles, are as follows: • Weighted Phrases: selected unigrams, bigrams or trigrams, ordered by descending tf-idf weights, where tf is the frequency of the phrase in all of the given user’s reviews, and idf is pre-computed on Google books n-grams to capture informativeness. • Weighted Sentences: selected sentences, ordered by descending idf weights, where a sentence’s total weight is the sum of the per-word idf weights normalized by sentence length. • Similar Sentences: selected sentences, ordered by descending similarity scores computed via Sentence-BERT [38] for comparing the user-review sentences against the sentences of the item description. To ensure that the selected set is not dominated by a single item, the sentences are picked from different items in a round-robin manner. • ChatGPT-generated Profiles: feeding all reviews of a user, in large chunks, into ChatGPT and instructing it to characterize the user’s book interests with a few short keyphrases. The output size is limited per chunk with a total budget of 128 tokens, to enforce concise profiles. • T5-generated Keywords: using a T5 model fine-tuned for keyword generation, to cast each user’s review text into a set of keywords, concatenated to create the profile. We give examples of selected profile constructions for a Goodreads and an Amazon user in Table VII. An online demo of the CUP framework is accessible on our website[2]. E. Coping with Unlabeled Data A challenge for training in the data-poor regime is how to handle the extreme skew between positive samples and unlabeled data points for sparse users. The crux in many recommender applications is that there are extremely few, if any, explicitly negative samples, such as books rated with low marks. This holds also for the datasets in this work. Therefore, we introduce and experiment with two different techniques to construct negative training samples from unlabeled data: • Uniform random samples. Under the closed world assumption (CWA), aka Selected Completely At Random, negative training points are sample uniformly from all unlabeled data. This is the baseline technique, widely used in prior works. • Weighted pos-neg samples. Prior works on PU learning [39], with positive and unlabeled data and without explicitly negative samples, is largely based on treating unlabeled points as pairs of samples, one positive and one negative with fractional weights. The weights can be based on (learned estimates of) class priors, but the extreme skew in our data renders these techniques ineffective. Instead we leverage the fact that relatedness measures between item pairs can be derived from tagging features or interaction data. We compute item-item relatedness via basic matrix factorization of the user-item matrix for the entire dataset. The relatedness of two items is set to the scalar product between their latent vectors, re-scaled for normalization between 0 and 1. Then, each originally unlabeled sample is cloned, with one instance positive with weight proportional to its average relatedness to the user’s explicitly positive points. The negative clone’s weight is set to the complement. [2] https://sirup.mpi-inf.mpg.de/ This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction Related Work Methodology Experimental Design Experimental Results Conclusion Ethics Statement and References Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Design Experimental Design Experimental Results Experimental Results Conclusion Conclusion Ethics Statement and References Ethics Statement and References III. METHODOLOGY A. System Architecture A. System Architecture The CUP framework is based on a two-tower architecture for representation learning (one ”tower” for users, the other for items, following the prevalent architecture in neural information retrieval with query and document/passage encodings). The two towers are jointly trained, coupled by the shared loss function and ground truth. Figure 1 shows a pictorial overview. User reviews and items descriptions are fed into BERT followed by a feed-forward network to learn latent representations. Downstream, the vectors are simply compared by a dot product for scores that indicate whether the user likes an item or not. Importantly, unlike prior works, we allow the top-most layer of BERT itself to be fine-tuned as part of the end-to-end training process. The per-item text usually comprises book titles, tags like categories or genre labels, which can be coarse (e.g., “thriller”) or fine-grained (e.g., “Scandinavian crime noir”), and a short description of the book contents. The per-user text can comprise the titles and tags of her training-set books and the entirety of her review texts, which vary widely in length and informativeness, hence the need for smart text selection. In the following, we present the CUP training procedure in Subsection III-B, the test/deployment-time inference in Subsection III-C, the judicious selection of input text snippets in Subsection III-D, and the selection of negative training samples in Subsection III-E. B. Training B. Training Another optional mechanism is to process multiple chunks of bounded-length text, which could be of interest for long user reviews. In this case, the per-user representations are learned for each chunk, and there is an additional max-pooling step between the FFN and the dot product, to combine the per-chunk vectors. We use this option for comparison in experiments. As it incurs m times higher computational and energy costs, with m being the number of chunks, we do not favor it. A user-item pair is classified with score calculated by with <, > denoting dot product and σ for the sigmoid function. We use the Adam optimizer to minimize the binary cross entropy loss between predicted labels and the ground truth with sampled negatives. During training we update the topmost layer of BERT, which allows end-to-end training of all components. This is an important difference to earlier works with similar architectures, which use frozen BERT to encode user and item representations. C. Inference C. Inference Prediction for Ranking. At test time, a prediction is made for user-item pairs. We encode the item description by running it through the trained network, and we compare it to the already learned user vector, which is based on the user’s training-time reviews (none for the given test item). The scores for different test items, computed by the final dot product, yield the ranking of the candidate items. This is a very efficient computation, following established practice in neural information retrieval [37]. Prediction for Ranking. Search-based Recommendation. In a deployed system (as opposed to lab experiments with test samples), a typical usage mode would be search-based re-ranking: a user provides context with a tag-based query or an example of a specific liked item, which can be thought of as query-by-example. The user’s expectation is to see a ranked list of recommended items that are similar to her positive sample (as opposed to recommendations from all kinds of categories). The system achieves this by first computing approximate matches to the query item (i.e., similarity-search neighbors), and then re-ranking a shortlist of say top-100 candidates. The CUP framework supports this mode, by using a light-weight BM25 retrieval model, querying all unlabeled items with the category and textual description of the positive point at hand, and keeping the top-100 highest scoring matches. D. Coping with Long and Noisy Texts D. Coping with Long and Noisy Texts For constructing user profiles from text, the simplest idea would be to concatenate all available reviews into a long token sequence. Two problems arise, though. User reviews are a noisy mix of descriptive elements (e.g., “the unusual murder weapon”), sentiment expressions (e.g., “it was fun to read”) and personal but irrelevant statements (e.g., “I read only on weekends”). Only the first aspect is helpful for content-based profiling (as the sentiment is already captured by the fact that the user likes the book). Second, the entirety of user-provided text can be too long to be fully digested by the Transformer. Even when it would fit into the token budget, the computational and energy cost is quadratic in the number of input tokens. Therefore, we tightly limit the tokens for each user’s text profile to 128, and devise a suite of light-weight techniques for judiciously selecting the most informative pieces. Variants of our methodology are to create concise profiles by generative language models upfront (e.g., using T5 or ChatGPT), or to digest multiple chunks of text with downstream aggregation by max-pooling or averaging. We include these techniques in our experimental comparisons, but emphasize that both of these alternatives have much higher computational cost and climate footprint. Our techniques for selecting the most informative parts of user reviews to construct concise profiles, are as follows: • Weighted Phrases: selected unigrams, bigrams or trigrams, ordered by descending tf-idf weights, where tf is the frequency of the phrase in all of the given user’s reviews, and idf is pre-computed on Google books n-grams to capture informativeness. • Weighted Sentences: selected sentences, ordered by descending idf weights, where a sentence’s total weight is the sum of the per-word idf weights normalized by sentence length. • Similar Sentences: selected sentences, ordered by descending similarity scores computed via Sentence-BERT [38] for comparing the user-review sentences against the sentences of the item description. To ensure that the selected set is not dominated by a single item, the sentences are picked from different items in a round-robin manner. • ChatGPT-generated Profiles: feeding all reviews of a user, in large chunks, into ChatGPT and instructing it to characterize the user’s book interests with a few short keyphrases. The output size is limited per chunk with a total budget of 128 tokens, to enforce concise profiles. • T5-generated Keywords: using a T5 model fine-tuned for keyword generation, to cast each user’s review text into a set of keywords, concatenated to create the profile. We give examples of selected profile constructions for a Goodreads and an Amazon user in Table VII. An online demo of the CUP framework is accessible on our website[2]. E. Coping with Unlabeled Data E. Coping with Unlabeled Data A challenge for training in the data-poor regime is how to handle the extreme skew between positive samples and unlabeled data points for sparse users. The crux in many recommender applications is that there are extremely few, if any, explicitly negative samples, such as books rated with low marks. This holds also for the datasets in this work. Therefore, we introduce and experiment with two different techniques to construct negative training samples from unlabeled data: • Uniform random samples. Under the closed world assumption (CWA), aka Selected Completely At Random, negative training points are sample uniformly from all unlabeled data. This is the baseline technique, widely used in prior works. Uniform random samples. • Weighted pos-neg samples. Prior works on PU learning [39], with positive and unlabeled data and without explicitly negative samples, is largely based on treating unlabeled points as pairs of samples, one positive and one negative with fractional weights. The weights can be based on (learned estimates of) class priors, but the extreme skew in our data renders these techniques ineffective. Instead we leverage the fact that relatedness measures between item pairs can be derived from tagging features or interaction data. We compute item-item relatedness via basic matrix factorization of the user-item matrix for the entire dataset. The relatedness of two items is set to the scalar product between their latent vectors, re-scaled for normalization between 0 and 1. Then, each originally unlabeled sample is cloned, with one instance positive with weight proportional to its average relatedness to the user’s explicitly positive points. The negative clone’s weight is set to the complement. [2] https://sirup.mpi-inf.mpg.de/

Recommendations by Concise User Profiles from Review Text: Methodology

Starting Simple: The Strategic Advantage of Baseline Models in Machine Learning

Learn Multithreading in Python Like A Pro Using These 30 Questions

Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? The Overlooked Problem With E-Waste Most people don’t think twice about what happens to their electronics when they throw them away or trade them in. In almost every case, their old devices become e-waste. Common examples include phones, computers, printers, monitors, smart devices and wearables. Almost all electronics contain materials like nickel, flame retardants, and cadmium that either are or can become hazardous if disposed of improperly. For example, lithium-ion batteries can produce noxious smoke if damaged and toxic liquid if exposed to water. In landfills, these devices poison the air, waterways, and soil by leeching heavy metals and poisonous chemicals. Since e-waste is a relatively new problem, it remains largely unaddressed. Today, it accounts for roughly 70% of the toxic waste in U.S. landfills. Only a fraction is ever formally recycled, leaving the rest to pollute local environments for thousands of years until they naturally break down. While there’s no breakdown of where the country’s e-waste comes from, the consumer electronics sector is likely responsible for most of it. People have been buying more than ever in recent years — computer purchases surged by 34% during the COVID-19 pandemic — suggesting they’ve been throwing away more than ever. The main focus of e-waste is its environmental risks, for good reason. However, that’s not its only problem. Many people don’t realize they’re essentially handing all their device’s data directly to a cybercriminal when they dispose of it improperly. E-waste hacking is becoming increasingly profitable for cybercriminals as people toss more electronics every year. Unfortunately, buying something new is often cheaper than having something repaired. Once a device leaves the owner’s hands and lands in the trash, it’s forfeit — along with every shred of personally identifiable information (PII) and sensitive data it ever stored. What E-Waste Hacking Looks Like While e-waste hacking sounds futuristic, it’s concerningly simplistic. Once an electronic device is thrown away or given to a third party for disposal, it often ends up in a landfill. At some point along the journey, people pick through the waste for hard drives to recover data. They either misuse it themselves or sell it to a third party for a profit. There’s 100 times more gold in one ton of smartphones than in one ton of gold ore. Thus, urban mining — extracting usable material like copper, glass, aluminum or gold from discarded electronics — is becoming popular in places like Ghana, China and India. How does e-waste hacking work? Usually, cybercriminals can pry out a device’s physical drives and recover the data from them, as many people forget to wipe or destroy those. While they might have to use specialized tools to bypass security measures, they often only have to plug it in to retrieve sensitive information. Back in 2009, a group of journalism students from the University of British Columbia bought a $40 hard drive at an open-air market in Ghana for an international reporting course. Once they plugged it in, a multi-million dollar U.S. defense contract between the Pentagon, the Department of Homeland Security and a military contractor was sitting there, waiting to be read. What Are the Security Risks of E-Waste? Once cybercriminals get ahold of an improperly disposed-of device’s storage system, they can see the previous owner’s PII. This could include names, social security numbers, addresses and contact information. They also gain access to whatever files remain intact. Regarding e-waste generated by consumer electronics, individuals become susceptible to identity theft, phishing scams, cyberattacks and financial fraud. If cybercriminals choose to sell their information on the dark web, they remain at risk for the rest of their lives. While individuals are usually the focus of cybersecurity concerns related to e-waste, businesses are also at risk. If even a single employee improperly disposes of their old work laptop, the company’s data breach risk skyrockets. E-waste hacking can expose proprietary information, give cybercriminals a way to infiltrate the brand’s network legitimately or lead to an uptick in cyberattacks. Considering the average data breach cost over $4.45 million in 2023, the consequences could be catastrophic. How Do Electronics End up in Hackers' Hands? The logistics of electronics going from a garbage can into a hacker’s hands is more straightforward than most people would assume. One of the simplest means is through theft, where the person retrieves the thrown-away device from the trash and takes it home. While most cybercriminals don’t make dumpster diving a pastime, many know where to go — or who to call — to get their hands on recently thrown-out electronics. Considering roughly 800 laptops worth of e-waste are generated every second in the U.S., the simplicity of such a plan shouldn’t come as a surprise. Another illicit means cybercriminals use to get their hands on PII is through a third party like a recycling center, buy-back platform or trade-in program. In this scenario, the intermediary sells used consumer electronics to them directly. Whether due to negligence or an effort to make a quick profit, sensitive data doesn’t get wiped before the sale. One of the most concerning ways e-waste ends up in hackers’ hands is through cargo ships. According to a 2016 study — one of the few on the matter — the U.S. exports up to 40% of its e-waste for dismantling or recycling. Apparently, there have been multiple cases where countries have had to reject cargo loads of e-waste shipments. While some say this claim is untrue, the U.S. has set a precedent by exporting plastic waste for decades. Despite international law making it illegal, it has increased its exports to places like Malaysia and Indonesia. Moreover, while most shipments are categorized as recycling, they end up in landfills, meaning e-waste export isn’t an unreasonable concept. What to Do to Avoid E-Waste Hacking While most people’s first thought is to avoid e-waste hacking by holding on to their technology, unused electronics are attractive targets to thieves. Besides, lost devices pose a host of other cybersecurity risks. The only way to guarantee security is through proper disposal channels. 1. Shred Data Storage Systems People must get used to “shredding” their hard disk drives and solid state drives (SSDs) like they do their sensitive documents. Once they wipe their device, they should render their storage systems unusable by physically destroying them and their chips. 2. Use Darik's Boot and Nuke Darik's Boot and Nuke (DBAN) is a free, open-source software people use to wipe physical drives. While it’s effective, it should only be used by individuals and not businesses, as it doesn’t meet any formal destruction requirements like NIST or HIPAA. Notably, it does not work on all devices and will not work for SSDs due to compatibility issues. 3. Leverage Data Encryption People should proactively encrypt every shred of data on their devices even if they use DBAN since data residue — recoverable digital traces of data — remains on electronics and is recoverable by hackers even after the manual erasure process. As a bonus, this measure can protect them from experiencing the effects of various cyberattacks. 4. Use Reputable Third Parties Simply destroying and throwing away electronics to prevent e-waste hacking is irresponsible because it is incredibly damaging to the environment. Instead, people should find reputable, transparent third parties to ensure their devices don’t end up being sold off or landfilled. The World Cannot Ignore the Issue of E-Waste Even without the security risks e-waste hacking poses, improperly disposed-of electronics are a problem. They poison the air, waterways and soil for thousands of years as they sit in landfills. Individuals and enterprises must do their part to prevent this from happening for the good of data protection and the environment. Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? The Overlooked Problem With E-Waste The Overlooked Problem With E-Waste Most people don’t think twice about what happens to their electronics when they throw them away or trade them in. In almost every case, their old devices become e-waste. Common examples include phones, computers, printers, monitors, smart devices and wearables. Almost all electronics contain materials like nickel, flame retardants, and cadmium that either are or can become hazardous if disposed of improperly. For example, lithium-ion batteries can produce noxious smoke if damaged and toxic liquid if exposed to water. In landfills, these devices poison the air, waterways, and soil by leeching heavy metals and poisonous chemicals. Since e-waste is a relatively new problem, it remains largely unaddressed. Today, it accounts for roughly 70% of the toxic waste in U.S. landfills. Only a fraction is ever formally recycled, leaving the rest to pollute local environments for thousands of years until they naturally break down. 70% of the toxic waste 70% of the toxic waste While there’s no breakdown of where the country’s e-waste comes from, the consumer electronics sector is likely responsible for most of it. People have been buying more than ever in recent years — computer purchases surged by 34% during the COVID-19 pandemic — suggesting they’ve been throwing away more than ever. computer purchases surged by 34% computer purchases surged by 34% The main focus of e-waste is its environmental risks, for good reason. However, that’s not its only problem. Many people don’t realize they’re essentially handing all their device’s data directly to a cybercriminal when they dispose of it improperly. E-waste hacking is becoming increasingly profitable for cybercriminals as people toss more electronics every year. Unfortunately, buying something new is often cheaper than having something repaired. Once a device leaves the owner’s hands and lands in the trash, it’s forfeit — along with every shred of personally identifiable information (PII) and sensitive data it ever stored. What E-Waste Hacking Looks Like While e-waste hacking sounds futuristic, it’s concerningly simplistic. Once an electronic device is thrown away or given to a third party for disposal, it often ends up in a landfill. At some point along the journey, people pick through the waste for hard drives to recover data. They either misuse it themselves or sell it to a third party for a profit. There’s 100 times more gold in one ton of smartphones than in one ton of gold ore. Thus, urban mining — extracting usable material like copper, glass, aluminum or gold from discarded electronics — is becoming popular in places like Ghana, China and India. 100 times more gold 100 times more gold How does e-waste hacking work? Usually, cybercriminals can pry out a device’s physical drives and recover the data from them, as many people forget to wipe or destroy those. While they might have to use specialized tools to bypass security measures, they often only have to plug it in to retrieve sensitive information. Back in 2009, a group of journalism students from the University of British Columbia bought a $40 hard drive at an open-air market in Ghana for an international reporting course. Once they plugged it in, a multi-million dollar U.S. defense contract between the Pentagon, the Department of Homeland Security and a military contractor was sitting there, waiting to be read. bought a $40 hard drive bought a $40 hard drive What Are the Security Risks of E-Waste? Once cybercriminals get ahold of an improperly disposed-of device’s storage system, they can see the previous owner’s PII. This could include names, social security numbers, addresses and contact information. They also gain access to whatever files remain intact. Regarding e-waste generated by consumer electronics, individuals become susceptible to identity theft, phishing scams, cyberattacks and financial fraud. If cybercriminals choose to sell their information on the dark web, they remain at risk for the rest of their lives. While individuals are usually the focus of cybersecurity concerns related to e-waste, businesses are also at risk. If even a single employee improperly disposes of their old work laptop, the company’s data breach risk skyrockets. E-waste hacking can expose proprietary information, give cybercriminals a way to infiltrate the brand’s network legitimately or lead to an uptick in cyberattacks. Considering the average data breach cost over $4.45 million in 2023, the consequences could be catastrophic. cost over $4.45 million cost over $4.45 million How Do Electronics End up in Hackers' Hands? The logistics of electronics going from a garbage can into a hacker’s hands is more straightforward than most people would assume. One of the simplest means is through theft, where the person retrieves the thrown-away device from the trash and takes it home. While most cybercriminals don’t make dumpster diving a pastime, many know where to go — or who to call — to get their hands on recently thrown-out electronics. Considering roughly 800 laptops worth of e-waste are generated every second in the U.S., the simplicity of such a plan shouldn’t come as a surprise. roughly 800 laptops worth of e-waste roughly 800 laptops worth of e-waste Another illicit means cybercriminals use to get their hands on PII is through a third party like a recycling center, buy-back platform or trade-in program. In this scenario, the intermediary sells used consumer electronics to them directly. Whether due to negligence or an effort to make a quick profit, sensitive data doesn’t get wiped before the sale. One of the most concerning ways e-waste ends up in hackers’ hands is through cargo ships. According to a 2016 study — one of the few on the matter — the U.S. exports up to 40% of its e-waste for dismantling or recycling. Apparently, there have been multiple cases where countries have had to reject cargo loads of e-waste shipments. up to 40% of its e-waste up to 40% of its e-waste While some say this claim is untrue, the U.S. has set a precedent by exporting plastic waste for decades. Despite international law making it illegal , it has increased its exports to places like Malaysia and Indonesia. Moreover, while most shipments are categorized as recycling, they end up in landfills, meaning e-waste export isn’t an unreasonable concept. international law making it illegal international law making it illegal What to Do to Avoid E-Waste Hacking While most people’s first thought is to avoid e-waste hacking by holding on to their technology, unused electronics are attractive targets to thieves. Besides, lost devices pose a host of other cybersecurity risks. The only way to guarantee security is through proper disposal channels. 1. Shred Data Storage Systems People must get used to “shredding” their hard disk drives and solid state drives (SSDs) like they do their sensitive documents. Once they wipe their device, they should render their storage systems unusable by physically destroying them and their chips. 2. Use Darik's Boot and Nuke Darik's Boot and Nuke (DBAN) is a free, open-source software people use to wipe physical drives. While it’s effective, it should only be used by individuals and not businesses, as it doesn’t meet any formal destruction requirements like NIST or HIPAA. Notably, it does not work on all devices and will not work for SSDs due to compatibility issues. 3. Leverage Data Encryption People should proactively encrypt every shred of data on their devices even if they use DBAN since data residue — recoverable digital traces of data — remains on electronics and is recoverable by hackers even after the manual erasure process. As a bonus, this measure can protect them from experiencing the effects of various cyberattacks. 4. Use Reputable Third Parties Simply destroying and throwing away electronics to prevent e-waste hacking is irresponsible because it is incredibly damaging to the environment. Instead, people should find reputable, transparent third parties to ensure their devices don’t end up being sold off or landfilled. The World Cannot Ignore the Issue of E-Waste Even without the security risks e-waste hacking poses, improperly disposed-of electronics are a problem. They poison the air, waterways and soil for thousands of years as they sit in landfills. Individuals and enterprises must do their part to prevent this from happening for the good of data protection and the environment.

What Is E-Waste Hacking?

ই-ওয়েস্ট হ্যাকিং কি?

Was ist Elektroschrott-Hacking?

¿Qué es el pirateo de residuos electrónicos?

Qu’est-ce que le piratage des déchets électroniques ?

ई-कचरा हैकिंग क्या है?

電子廃棄物ハッキングとは何ですか?

전자 폐기물 해킹이란 무엇입니까?

O que é hackeamento de lixo eletrônico?

Что такое взлом электронных отходов?

E-Atık Hacking Nedir?

Hack rác thải điện tử là gì?

什么是电子垃圾黑客？

Language models (LMs) represent a highly versatile category of technology, processing text inputs to generate textual outputs. Contemporary research has illuminated that this adaptable text-to-text framework can be harnessed to address a myriad of tasks without necessitating task-specific adjustments (i.e., absent fine-tuning or architectural changes) through the use of prompting methods to enable precise zero and few-shot inference. In essence, LMs are initially trained on extensive, unlabelled text corpora (guided by a language modeling objective), after which the LMs are directed via textual prompts to resolve specific problems, thereby allowing the pre-trained model to be efficiently repurposed for varied applications. Despite the immense promise of LMs as task-neutral foundation models, initial endeavours to apply pre-trained LMs to downstream tasks (e.g., GPT and GPT-2 [4, 5]) encountered significant challenges. This overview will explore how recent advancements have improved upon these preliminary efforts, leading to LMs that deliver significantly enhanced task-neutral performance. A critical discovery in this research trajectory is the enhanced capability of LMs as they are scaled up. (from [1] and [2]) More detailed examination reveals that large LMs (LLMs) are (i) more sample-efficient compared to their smaller counterparts and (ii) exhibit greater proficiency in task-neutral transfer to downstream applications. Notably, the performance of these LLMs demonstrates predictable patterns relative to various parameters (e.g., model scale and training data volume). This empirical evidence has culminated in the development of GPT-3, a colossal LLM with 175 billion parameters, which not only surpasses its predecessors in task-neutral performance but also exceeds contemporary, supervised deep learning approaches in certain domains. Key concepts necessary for understanding LMs have been discussed in my previous posts, including the language modeling objective, the architecture of decoder-only transformer models, and their integration to forge robust foundation models. For a more thorough understanding, refer to the provided link. Background I will offer a succinct overview of these concepts here, while also delineating a few additional notions pertinent to comprehending LLMs like GPT-3. Modern LMs utilize standard pre-training methods to tackle a broad spectrum of tasks without subsequent modifications (i.e., no architectural adjustments, fine-tuning, etc.). Employing a voluminous corpus of unlabelled text, we pre-train our LM with a language modeling objective that (i) extracts a text sample from our corpus and (ii) attempts to predict the succeeding word. This represents a form of self-supervised learning, where the correct next word can always be verified by examining the data in our corpus. Language modeling at a glance Modern LMs employ decoder-only transformer architectures, which apply a series of layers that include masked self-attention and forward transformations to the input. Masked self-attention, as opposed to bidirectional self-attention, precludes the model from 'peeking ahead' in a sequence to predict the subsequent word. Architecture Beyond these decoder-only layers, the LM architecture encompasses embedding layers that store vectors for all potential tokens within a predefined vocabulary size. Through these embedding layers, raw text is transformed into a matrix that the model can process, achieved by: Tokenizing raw text into discrete tokens (i.e., words or sub-words), Retrieving the corresponding embedding vector for each token, Concatenating these token embeddings to form a matrix or sequence of token vectors, Integrating position (and other) embeddings to each token vector. By pre-training LMs over expansive text corpora, we obtain models capable of accurately predicting the next token within a given token sequence. However, to utilize these models for language understanding tasks such as sentence classification and language translation, we do not modify the LM. Instead, we leverage the model’s inherent generic text-to-text input-output structure by supplying textual "prompts," such as: Adaptation “Translate this sentence to English: <sentence> =>”
“Summarize the following document: <document> =>”. With these problem-solving prompts, a proficient LM should yield a textual sequence that addresses the query effectively. For scenarios requiring a selection from a predetermined set of responses (e.g., multiple choice or classification), the LM can be used to assess the likelihood of generating each potential response and select the most probable outcome. The essence of modern LLMs lies in utilizing language model pre-training as a mechanism to develop generic foundation models capable of resolving diverse challenges without the need for adaptation or fine-tuning. While previous LMs like GPT and GPT-2 [4, 5] demonstrated inferior performance relative to tailored or supervised language understanding methods, this learning paradigm holds considerable promise and—as evidenced by GPT-3—can yield impressive results when the underlying LM is substantially enlarged. Main takeaway Power Laws In this overview, we will frequently refer to the concept of power laws. To illustrate, consider a statement from a research paper: "The LM’s test loss demonstrates a power law dependency on the number of model parameters." This assertion establishes a correlation between two variables—the loss and the number of model parameters—wherein adjustments to one variable induce proportionate, scale-independent alterations in the other. For clarity, a power law is mathematically expressed as follows. In this equation, x and y represent the variables under examination, while a and p influence the power law's shape and dynamics. When this power law (with a = 1, p = 0.5, and 0 < x, y < 1) is graphed, converting both axes to a logarithmic scale reveals a distinct linear trend, characteristic of power laws. Power laws describe how one variable scales as a power of another. Our discussions will include an inverse version of a power law, represented as: This equation employs a negative exponent for p, producing a graph where one variable decreases as the other increases. In our analysis of LMs, we observe that the LM loss decreases in accordance with a power law across various dimensions, such as model or dataset size. Further elaboration on this will follow in subsequent sections. Additional Useful Concepts In addition to the foundational principles of language modeling, there are several ancillary concepts that will be beneficial to understand moving forward. The primary focus of the papers discussed in this overview is on scaling models like GPT and GPT-2 [4, 5]. As models grow in complexity, training them becomes increasingly challenging due to heightened computational and memory demands. Distributed training methods, which utilize additional hardware resources (e.g., more servers/GPUs), facilitate the scalability of large training operations. Distributed Training There are various strategies for distributing the training workload of neural networks. One such strategy is data parallel training, which involves: Taking a large mini-batch, Dividing it into smaller sub-batches, Performing computations for each sub-batch in parallel across different GPUs, Consolidating the results from each GPU to update the central model. This method enhances training efficiency by parallelizing computations across multiple GPUs. Alternatively, model-parallel training divides the model itself across several GPUs. For instance, each layer—or even sub-components of each layer—might be allocated to different GPUs. This distribution allows the forward pass of the training process to occur across multiple devices, enabling the training of larger models and potentially improving efficiency through sophisticated pipelining and parallelization. For the purposes of this overview, it is important to recognize that leveraging multiple GPUs can make training of large language models (LLMs) more manageable and efficient. Data and model parallel training represent popular techniques in distributed training. There are numerous other strategies and considerations in this field that significantly enhance practical outcomes in deep learning. Utilizing large batches in data parallel training is generally advantageous for computational efficiency. However, excessively large batches may impair model performance and escalate computational costs, requiring more hardware. This phenomenon of diminishing returns becomes evident beyond a certain batch size; see below. Critical Batch Size (from [3]) Research has empirically determined the 'critical batch size', a metric that estimates the optimal batch size across various applications. Beyond this critical size, performance gains and computational efficiency start to diminish. As we will explore, some research adopts this critical batch size as a standard for efficient resource utilization during training. Language models generate text sequences in response to prompts through an autoregressive process, predicting each subsequent word based on the previous context. The simplest method continuously selects the most likely next word, but this approach may not yield the optimal overall sequence. Beam Search Beam search offers an alternative: instead of choosing the single most probable next word, it selects the top-k most probable options at each step, maintaining a list of potential sequences. At the conclusion of the generation process, the most likely overall sequence from this list is selected. This method provides a more effective approximation of the best sequence by considering multiple potential continuations at each step, thereby enhancing the quality of the generated text. Key Insights and Strategic Implications The foundational goal of GPT models was to develop universal language models capable of addressing a broad spectrum of tasks without necessitating task-specific tuning. This ambition is grounded in the premise that a profound comprehension of language modeling—specifically, the prediction of subsequent words in sequences—can be extrapolated to a variety of applications. Initially, models such as GPT and GPT-2 did not meet these expectations, delivering subpar performance in task-agnostic settings compared to supervised models. However, this overview has demonstrated that amplifying the scale of these language models (LMs) is an effective strategy for achieving high-performance, task-agnostic language understanding capabilities. This approach culminated in the conceptualization and deployment of GPT-3, an enormously scaled language model (approximately 100 times larger than GPT-2) that significantly outstrips the task-agnostic performance of its predecessors. The principle of scaling—enhancing model performance through the augmentation of model size, data volume, and computational resources—has been pivotal. Empirical data suggest that to optimize the efficacy of language model training, it is critical to substantially expand the model's dimensions while moderately increasing the dataset and batch size used during pre-training. This results in models that not only exhibit greater sample efficiency but also improve performance in accordance with power laws related to model size, data volume, and computational investment. Scaling Laws The advent of GPT-3, a model boasting 175 billion parameters, exemplifies the practical validation of these scaling theories. When such a colossal model is pre-trained across extensive text corpora, it demonstrates marked enhancements in task-agnostic, few-shot learning capabilities. Although GPT-3 may still lag behind specialized, supervised methods in certain benchmarks, subsequent studies have convincingly shown that as LLMs increase in size, their capacity for in-context learning also intensifies. Despite GPT-3’s architectural similarities to GPT-2, engineering a model of this magnitude underscores the transformative potential and scalability of foundational language models. Prospects of Scaling In conclusion, the journey from the initial GPT models to GPT-3 highlights a significant shift in the understanding and application of language models. The scalability of these systems not only advances their performance but also broadens their applicability across a diverse range of language understanding tasks, setting a new benchmark for future developments in the field of artificial intelligence. Bibliography [1] Kaplan, Jared, et al. “Scaling laws for neural language models.” arXiv preprint arXiv:2001.08361 (2020). [2] Brown, Tom, et al. “Language models are few-shot learners.” Advances in neural information processing systems 33 (2020): 1877–1901. [3] McCandlish, Sam, et al. “An empirical model of large-batch training.” arXiv preprint arXiv:1812.06162 (2018). [4] Radford, Alec, et al. “Improving language understanding by generative pre-training.” (2018). [5] Radford, Alec, et al. “Language Models are Unsupervised Multitask Learners.” [6] Child, Rewon, et al. “Generating long sequences with sparse transformers.” arXiv preprint arXiv:1904.10509 (2019). [7] Raffel, Colin, et al. “Exploring the limits of transfer learning with a unified text-to-text transformer.” J. Mach. Learn. Res. 21.140 (2020): 1–67. [8] Liu, Yinhan, et al. “Roberta: A robustly optimized bert pretraining approach.” arXiv preprint arXiv:1907.11692 (2019).

Understanding Scaling Law through Data Science Lenses

Mastering Scheduling and Tax Execution in Spring Boot

Non-Fungible Tokens (NFTs) took the digital world by storm in recent years, providing a novel foundation for extreme innovation in digital art, collectibles, and other assets using blockchain technology. However, after an initial frenzy of excitement and exorbitant sales, the NFT market experienced a significant slowdown. Now, signs are pointing to a potential resurgence. But what factors are necessary for NFTs to make a successful comeback? In this article, we'll delve into the key elements the NFT market needs to reignite its momentum. For NFTs to regain traction, they need to offer more than just digital bragging rights. Enhancing the utility of NFTs by enabling interoperability across different platforms and applications could unlock new use cases and value propositions. For example, NFTs could be integrated into gaming ecosystems, virtual worlds, or even financial services, allowing users to leverage their digital assets in diverse ways. Nevertheless, several industry participants have looked clearly into the market and are now working to ensure that developers in the NFT sub-genre have the resources that they need to really optimize their profitability and efficiency. Olly Wilson, CEO and co-founder of the leading NFT data aggregator, SimpleHash, contributed to our research to highlight some of the key issues that need to be tackled to start seeing scale. 1. What role do you envision SimpleHash playing in the NFT space? We abstract away all of the complexity of dealing with NFT data for developers. Instead of worrying about standards, support for different chains, integrating with multiple marketplaces, and spending time and money on NFT storage and compute, we handle it for them. Instead of worrying about infrastructure, crypto teams working with NFTs can focus on building the best end-user experience possible. 2. How do you see the data gap evolving for NFTs? Our original thesis was that NFT activity was rapidly going multi-chain (i.e., not just on Ethereum), and that’s absolutely borne out over the past two years. As an example, Solana and Bitcoin Ordinals NFTs now see more volume than Ethereum. The number of EVM L2s continues to proliferate, as do other L1s - and each of these ecosystems have a constantly changing set of standards to support. NFT marketplaces continue to pop up.  Plus, as NFT media becomes more complex, the storage, compute, and real-time processing becomes even more of a challenge. Taking all of this together, this creates a significant “data gap” where most teams are better off offloading this set of problems to a trusted provider. 3. Do you believe that there are more use cases that can be seen with NFTs? Yes - some of the use cases I’m most excited by include loyalty and reward programs that are actually better than their off-chain equivalents. There’s still massive untapped potential for NFTs within games too. Because of where we fit in the stack and are generalized across all chains, we can serve any of these use cases - whether their biggest needs are metadata, history, or market prices. In terms of how SimpleHash fits in – we’ll continue to abstract away the complexity of working with this data, regardless of use case. Developers and creators building the next big on-chain game shouldn’t have to care about indexing arbitrary tokens on the chains they want to support, nor should they have to cache media, metadata, or other essential data on the NFTs themselves. By working with us, they can focus on what matters - building an awesome end-user experience. The same is true for any other conceivable use case - we’ll be there in the future as a reliable partner who can say “no matter what kind of app you’re building, if you’re using NFTs or tokens, let us do the heavy lifting on setting up your infra, while you do what you do best” – ideally they don’t even have to think about us! 4. What bespoke resources does your platform plan to provide to improve the standing of NFTs among NFT collectors? SimpleHash has a mission to help all stakeholders within the NFT and broader digital asset ecosystem. We make it super easy for developers to build sophisticated applications that help inform anything a collector might care about. For example, we have a bespoke set of endpoints to provide wallet valuation, along with those that show the top and trending collections. Getting even more granular, we offer a full set of market data streams - including all NFT sales, listings, bids and floor prices across the main marketplaces and chains. Using all these, app developers can help provide detailed insights to collectors on which of their collections are doing well, which might be up and coming, along with the details on specific holders and trading patterns. We firmly believe that more information, and easier access to tooling is crucial for future development of the space. This extends to chain ecosystems too – much of the excitement within the NFT community has come from lower cost chains, like Base (where we have a special subsidized plan program), Zora, and Solana – so supporting these chains from the get-go has been crucial for us. 5. What do you believe is the future of the NFT space in general? Digital assets (of which NFTs are just one form) will underpin a huge part of the future internet, and we intend on helping power it all. As mentioned, the most exciting future we see for NFTs include loyalty and reward programs, core primitives in games, proving provenance of content in an AI content generated world, and yes, collectibles / PFPs too. We also expect the space to democratize somewhat too, given the activity trends on lower-cost chains (Base and Solana most notably). The days of paying hundreds of dollars just to get access to a mint are over - and we’re excited to see what creators and developers do when it becomes much easier (and cheaper) to build on these networks. Conclusion As the NFT market looks to make a comeback, it’s important to focus on reliable and solid projects with proper use cases. And even though the skepticism remains, there are several projects that are looking rather interesting across the board. Non-Fungible Tokens (NFTs) took the digital world by storm in recent years, providing a novel foundation for extreme innovation in digital art, collectibles, and other assets using blockchain technology. However, after an initial frenzy of excitement and exorbitant sales, the NFT market experienced a significant slowdown. Now, signs are pointing to a potential resurgence. But what factors are necessary for NFTs to make a successful comeback? In this article, we'll delve into the key elements the NFT market needs to reignite its momentum. For NFTs to regain traction, they need to offer more than just digital bragging rights. Enhancing the utility of NFTs by enabling interoperability across different platforms and applications could unlock new use cases and value propositions. For example, NFTs could be integrated into gaming ecosystems, virtual worlds, or even financial services, allowing users to leverage their digital assets in diverse ways. Nevertheless, several industry participants have looked clearly into the market and are now working to ensure that developers in the NFT sub-genre have the resources that they need to really optimize their profitability and efficiency. Olly Wilson, CEO and co-founder of the leading NFT data aggregator, SimpleHash, contributed to our research to highlight some of the key issues that need to be tackled to start seeing scale. 1. What role do you envision SimpleHash playing in the NFT space? We abstract away all of the complexity of dealing with NFT data for developers. Instead of worrying about standards, support for different chains, integrating with multiple marketplaces, and spending time and money on NFT storage and compute, we handle it for them. Instead of worrying about infrastructure, crypto teams working with NFTs can focus on building the best end-user experience possible. 2. How do you see the data gap evolving for NFTs? Our original thesis was that NFT activity was rapidly going multi-chain (i.e., not just on Ethereum), and that’s absolutely borne out over the past two years. As an example, Solana and Bitcoin Ordinals NFTs now see more volume than Ethereum. The number of EVM L2s continues to proliferate, as do other L1s - and each of these ecosystems have a constantly changing set of standards to support. NFT marketplaces continue to pop up.  Plus, as NFT media becomes more complex, the storage, compute, and real-time processing becomes even more of a challenge. Taking all of this together, this creates a significant “data gap” where most teams are better off offloading this set of problems to a trusted provider. 3. Do you believe that there are more use cases that can be seen with NFTs? Yes - some of the use cases I’m most excited by include loyalty and reward programs that are actually better than their off-chain equivalents. There’s still massive untapped potential for NFTs within games too. Because of where we fit in the stack and are generalized across all chains, we can serve any of these use cases - whether their biggest needs are metadata, history, or market prices. In terms of how SimpleHash fits in – we’ll continue to abstract away the complexity of working with this data, regardless of use case. Developers and creators building the next big on-chain game shouldn’t have to care about indexing arbitrary tokens on the chains they want to support, nor should they have to cache media, metadata, or other essential data on the NFTs themselves. By working with us, they can focus on what matters - building an awesome end-user experience. The same is true for any other conceivable use case - we’ll be there in the future as a reliable partner who can say “no matter what kind of app you’re building, if you’re using NFTs or tokens, let us do the heavy lifting on setting up your infra, while you do what you do best” – ideally they don’t even have to think about us! 4. What bespoke resources does your platform plan to provide to improve the standing of NFTs among NFT collectors? SimpleHash has a mission to help all stakeholders within the NFT and broader digital asset ecosystem. We make it super easy for developers to build sophisticated applications that help inform anything a collector might care about. For example, we have a bespoke set of endpoints to provide wallet valuation, along with those that show the top and trending collections. Getting even more granular, we offer a full set of market data streams - including all NFT sales, listings, bids and floor prices across the main marketplaces and chains. Using all these, app developers can help provide detailed insights to collectors on which of their collections are doing well, which might be up and coming, along with the details on specific holders and trading patterns. We firmly believe that more information, and easier access to tooling is crucial for future development of the space. This extends to chain ecosystems too – much of the excitement within the NFT community has come from lower cost chains, like Base (where we have a special subsidized plan program), Zora, and Solana – so supporting these chains from the get-go has been crucial for us. 5. What do you believe is the future of the NFT space in general? Digital assets (of which NFTs are just one form) will underpin a huge part of the future internet, and we intend on helping power it all. As mentioned, the most exciting future we see for NFTs include loyalty and reward programs, core primitives in games, proving provenance of content in an AI content generated world, and yes, collectibles / PFPs too. We also expect the space to democratize somewhat too, given the activity trends on lower-cost chains (Base and Solana most notably). The days of paying hundreds of dollars just to get access to a mint are over - and we’re excited to see what creators and developers do when it becomes much easier (and cheaper) to build on these networks. Conclusion Conclusion As the NFT market looks to make a comeback, it’s important to focus on reliable and solid projects with proper use cases. And even though the skepticism remains, there are several projects that are looking rather interesting across the board.

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