These cookies and cakes are more than just good, they&#x27;re contest winners! Try something brand new fresh from the oven.

Presenting the Winners from Our Cakes & Cookies Contest

Shoppers were in uproar after a taste test ruled that the Co-Op&#x27;s budget Charlie cake was better than Marks and Spencer &#x27;s Colin the Caterpillar. But what do MailOnline readers think?

Battle of the caterpillar cakes! We asked MailOnline readers what their favourite supermarket version was - and YOU voted in your droves... find out the winner here! | Daily Mail Online

Morris the Caterpillar Cake was described as a little disappointing

Shockwaves as taste test reveals the best caterpillar cake is not Colin | The Independent

Colin the Caterpillar is a popular birthday cake choice amongst Brits but does the Marks &amp; Spencer icon now risk losing it&#x27;s crown? Vote in our MailOnline exclusive poll.

'Copycaterpillar' fury as taste test rules Co-Op's budget Charlie cake is better than M&S's original Colin the Caterpillar but furious customers say it doesn't even have a chocolate face - so what do YOU think? Take MailOnline's exclusive poll | Daily Mail Online

Tracking any and all ships - including those turning off their location trackers - using satellite data and ML

VAKE

For this article, I decided to do something different, reflecting on the past year and making two lists: the winners and the losers of 2023. Let’s start with the ones who hoped for a better year. 2023 Losers 1. SBF The inclusion of SBF here probably represents a bigger category, the smartasses who think they can just cheat their way up and cover up their sins with bribes and stacks of money. Well, SBF now is a first-person example that this is not always the case. The previous bear market cycle has shown those who were bathing without a suit, with the most classic revolving door becoming between crypto firms and jails. The US is trying to prove that anyone making their way up by cheating will be eventually brought to justice. Nonetheless, SBF is also a testament to the issues plaguing our system, where media and governmental protection can be bought, and everyone can turn a blind eye if they pay enough to cover them. As mentioned above, the SBF category honestly includes any other scammer out there, who was acting all cool and cocky (I see you Do Kwon) and has finally been put in jail for good. 2. Gary Gensler I feel sorry for this man; I can only think about a couple more examples of embarrassing politicians to the extent he does. The fact that he’s acting in bad faith is clear and proven by his past declarations. His crusade against crypto is his blood sacrifice to make it to the top, showing his soulless masters that he’s a good boy and will do everything in his power to safeguard the status quo. I really pity this guy; I am quite sure he cries out loud when he goes back home. For me, the biggest loser. 3. Elizabeth Warren Imagine being anti-banks and anti-crypto. Imagine making your whole career as an anti-bank Democrat, going against the biggest tool that can help decentralize the banking business, and probably offering them the only real competition. You can, by selling your soul. Is there anything else to say? Someone, please explain to her how crypto works. She doesn’t know it, but she advocated for crypto solutions for years, until those solutions were developed, and now.. back to the status quo. What a loser…. 4. CZ Makes only sense to put CZ as number 4 on this list. The person who has been the face of our industry for so long has pleaded guilty and reached an agreement for a multibillion-dollar fine to Binance. And this is not over yet, as CZ will have a trial in Q1 2024 and is currently prohibited from leaving the US. In retrospect, this was probably either granted or unimaginable, depending on where you land on the Binance-sympathy spectrum. For the crypto as a whole is the removal of a systemic risk, of an exchange possibly considered too big to fail. Nonetheless, I cannot but empathize with CZ. It’s hard to operate an exchange like Binance across multiple regulations. The message here is clear: you may be able to resist in the short term, but if they want, the US authorities will get you, even if you are an off-shore entity operating in the US. 5. Maximalism Probably the biggest loser of 2023 is maximalism. The continuation of the Ethereum Scaling Roadmap, with the development and deployment of Layer 2, Optimistic and ZK Rollups, new Data Availability Solutions, Retaking, and new Layer 1s focused on Gaming and Parallelization (Monad, Blast, Neon) have set out a clear path for the evolution of blockchains. One which is made by modularity and interoperability, and not siloed ecosystems. Those who have bet on their own closed systems fatigue to leverage network effects across protocols and networks alike. Those who made a living out of advocating for the technical superiority of their network are now with their backs against their wall, concerned about the survival of their arguments across a cross-chain ecosystem. On the other hand, those who have bet on cooperation and coordination are benefiting the most, from a win-win approach that is not always about self-interest. Being a maximalist in 2024 will be embarrassing. And now, onto the biggest winners! 2023 Winners 1. Coinbase Brian is by far our favorite tech baldie. We don’t have enough room in this piece to highlight every positive development at Coinbase this year. Nonetheless, in my eyes, Coinbase is the biggest company that is still aligned with the cryptocurrency . We have repeatedly seen how they questioned the SEC judgments, bringing them to court. ethos Aside from the fact that they are a CEX and, of course, care about their own business, Coinbase is a force for good within the industry and deserves the spot among the 2023 winners. Admitting a limited relevance of numbers themselves, Coinbase stock has been performing godly since the beginning of the year. In a year of trading troubles, Coinbase managed to shift the majority of its revenues to non-trading income. Within those, their Custody is especially set to have a great 2024 given that most ETFs propose Coinbase as their custodian. 2. Justin Sun Justin Sun is one of those god-like OG personalities within the crypto world. The lines between him and Tron are more blurred than ever. However, we cannot neglect Justin Sun his spot among the 2023 winners. Tron USDT has established itself as the stablecoin with the most volumes and is significantly used in third countries plagued by inflation. Arguably, this is the best use case of crypto as a whole nowadays and provides practical benefits to countless people worldwide. We don’t know whether this was Justin’s plan all along, or if he even cares at all, but he deserves his spot - what a humble king! 3. Paolo Ardoino Together with Pizza and Pasta, Paolo is another of the great Italian heritages starting with P. Crowned with the role of CEO, the king of Tether continues to be a trailblazer in the industry. In 2023, Tether reported over and is one of the , holding over $70b. $32b in excess reserves biggest holders of US treasuries The Tether dominance has found an unsuspected ally: the failure of US Banks (Silicon Valley Bank etc.) that hosted the accounts of USDC, USDT's main competitor. Tether’s hybrid structure merging offshore compliance and billions of US treasury holdings with close regulatory relationships (even with secret services), makes it an interesting example. Paolo has a golden aura within the sector: he’s a strong libertarian Bitcoiner, however, his company single-handedly has the shitcoin market on its shoulders, as well as having close ties with US regulators and regularly confiscating and freezing funds. We have seen other Bitcoiners fail from their thrones for much less; why does Paolo always get a pass within the community? 4. Layer 2s (L2) Chances are, you spent most of 2023 transacting on L2s. First Optimism and Arbitrum, then ZkSync or others like Scroll, Linea, or Layer Zero. The Ethereum scaling roadmap is moving forward, and there’s a lot of new kids on the block! While many argue that this contributes to taking volume and attention from Ethereum, this is exactly the whole point of scaling the system. A successful constellation of L2 is, in fact, a testament to the success of Ethereum. There’s not much else to say here: this is one of the most active areas in the whole sector, with some of the most obsessive innovations shipped recently. Stay tuned for 2024, when many of these networks will launch their mainnet. 5. Solana We cannot avoid putting Solana on the throne. What comes down must go up, and this was especially true for the price and activity of Solana, which exploded this year. What was regarded as a ghost-town network at the beginning of the year has managed to go through a re-invention including: Removing ties with bad actors. Reorganizing along community lines. Creating new tools and infra benefiting clients etc. Building a suite of DeFi products adding new use cases as well as avenues to stake and use SOL within DeFi. This combination resulted in renewed activity and volumes on-chain. In case you didn’t know: the dog has a hat. Solana is back. 6. Friend Tech The dApp of the year! during the first week of Friend Tech's inception, mentioning how I thought it had no future. RIP to my article Friend Tech went to capture most of the attention during a period that was narrative-dry. The situation slowed down eventually, raising concerns about the future of “Social Media in Web3.” However, I am quite confident that the Friend Tech hype will return in 2024, as we get closer to the airdrop. This is my top 10 winners and losers. Of course, I have forgotten many … Who do you think I should have mentioned? The author of this post is not associated with the projects mentioned in this article in any way. All of the content above is not financial advice, and you are always encouraged to do your own research. Also published here

The Top Winners and Losers of Crypto in 2023

2023 সালে ক্রিপ্টোর শীর্ষ বিজয়ী এবং পরাজিত ব্যক্তি

Die Top-Gewinner und -Verlierer von Krypto im Jahr 2023

Los principales ganadores y perdedores de las criptomonedas en 2023

Les principaux gagnants et perdants de la crypto en 2023

2023 में क्रिप्टो के शीर्ष विजेता और हारने वाले

2023 年の仮想通貨の勝者と敗者

2023년 암호화폐 최고의 승자와 패자

Os principais vencedores e perdedores da criptografia em 2023

Лучшие победители и проигравшие в криптовалюте в 2023 году

2023'te Kripto'nun En Çok Kazananları ve Kaybedenleri

Những người thắng và kẻ thua hàng đầu về tiền điện tử năm 2023

2023 年加密货币的最大赢家和输家

This is a redacted version of the by CEO and COO sent to HackerNoon’s 1.3k shareholders. Picture: HackerNoon team eats ice cream at a company offsite. HackerNoon shareholders' newsletter David Smooke Linh Dao Smooke TL;DR Our team is humming along, building and integrating the HackerNoon CMS with the internet’s best publishing technologies. product development Valuation’s up: purchased $250,000 worth of HackerNoon common stock shares at . Forward Research $42.55/share, a $50M pre-money valuation ( ) has its first 200k+ votes and . Our business database now includes information on 60k+ companies. Startups of the Year video has trending startups all over the internet talking about their HackerNoon nomination Revenue’s risen 6 years in a row, but we fell short of our 2022 revenue goals, totaling , a YoY increase. redacted redacted Expenses are down YoY YtD. We cut costs at the top and the bottom, reducing management (Linh’s + David’s) compensation, reducing part-time staff, and eliminating all nonessential business costs. redacted We published 8,311 stories YTD, a 52% YoY increase. Read for editorial picks and for trending stories chosen by the people. HackerNoon Top Stories The TechBeat Product The HackerNoon product team’s been busy building and strengthening the platform, facilitating our mission to publishing quality tech content vetted by a 2nd human editor in this unpredictable macroeconomic landscape ( replacing as the next big thing, , and dry startups funding notwithstanding). AI web3 tech layoffs Product themes: Audience Development Content alone does not drive readership. We build publishing functionality to curate and distribute quality stories. The following features widen and deepen our relationship with new and existing writers and readers: ( ) - all published HackerNoon writers can now collect email subscribers via both profile and story page and have their new stories sent directly to subscribers’ inboxes. Subscribe for Writers video - all HackerNoon stories are now categorized into 1 of 22 niches that spin off into: 22 Parent Categories - with 250k+ subscribers and counting, these newsletters target readers interested in specific niches, rather than general top content from our daily or . 22 Category Specific Newsletters Noonification weekly company techbrief - audio content consumers can now find our top performing stories, read by AI for now, in their podcast feeds. 22 Category Specific Podcasts ( ) - our 2nd ever open source Github repo of top HackerNoon stories ranked by reading time The Learn Repo post explainer First open repo was our , which was downloaded ~2000 times on and this year. HackerNoon font Github Dafont - this feature combines historical tech events with a call back to interesting HackerNoon stories of years past, published on the same day. For example, did you know that on this day ( ), Hotmail Launched as a Free Email Service? On this Day in Tech History July 4, 1996 AI & Machine Learning Integrations Wouldn’t be a 2023 tech newsletter without mentioning once would it? 😛Jokes aside, these features are our practical across our CMS: AI use of AI - This reads the content of the story and suggests the catchiest headlines possible based off of existing content. AI Headline Generator - fully accessible to our community of 45k+ writers, the HackerNoon AI Image Generator comes with 5 different models from Stable Diffusion, MidJourney Diffusion to Kandinsky; with both a prompt and a negative prompt. AI Image Generator - To date, 4148 images have been prompted and 903 used as feature images by our writers. Community driven AI image library AI Assisting Human editors - In beta, it helps editors with 1st pass at checking for grammatical and formatting mistakes while . ChatGPT-4 is also used to rewrite highlighted text within the HackerNoon text editor. While not as astute as our editors, this tool can do basic preliminary checks on common glaring mistakes. Replacing detecting plagiarism and AI-content itself - These ensure accessibility and international accessibility for our top stories. With the community will also be able to improve these machine generated translations. Machine learning translations for all top stories annotations revamp Business Database 2,500 businesses already trusted HackerNoon with their content distribution, so we improve their reach further by creating various company rankings and indexes incorporating them, and other industry leaders. Redesign of to include better search, a more comprehensive ranking system, and 3 ways to filter. Check out Microsoft's company page . Learn more about Evergreen, our updated Tech Company Ranking System . Discover Companies page here here ( & ) - launched early May, this annual campaign features sleek new design, a more streamlined database (of this year’s 31k startups as well as winners of years past). This year’s nominees can also claim their accounts and customize their descriptions. 217k votes cast YTD, this campaign will announce the winners in January 2024. Startups of the Year 2023 announcement post SuperbCrew Interview with David Web3 Integrations Dethroned by AI though it’s been, is still one of our bets for the future, including our , and . web3 most recent round of funding our upcoming documentary - HackerNoon has published over 1 billion words, and now all those words and stories are backed up on the Arweave blockchain. Arweave Web3 Backups - Coil unfortunately decided to sunset their Web Monetization product. The business entity Coil continues as a HackerNoon shareholder. Sunset our Coil Integration - building on and writing contest payments, we’ve set up the option for a person’s visual identity to be an NFT. NFT as Profile Picture Web3 login Content Portability Writers of HackerNoon can now easily move their content in and out of the CMS with a couple simple clicks. Our Content Management System can now power contributor sites on any domain (beta). Some example sites: , , , , and . CoinWikis.com WritingContests.xyz HackerNoon.LinhDaoSmooke.com Blog.Slogging.com HackerNoon.tech Improved content Import and Export. Contributors can now export their content in , and , and in addition to importing by individual URL, contributors can now mass import content from other blogging platforms (beta). PDF, HTML, audio file access it on Arweave Managed Accounts - for our customers, we are building features and processes to manage larger volumes of content. Some of our best MA accounts include , , , , , & . Crypto Hayes BugSnag AmazonIVS Protocol Labs Courier Indrive For the most updated and frequent product updates, visit . We publish there every few weeks. Notably, check out , , and . HackerNoon.Tech this this this Valuation . This is great news for shareholders, who’ve previously purchased shares at StartEngine price of $8.20 and strategic investment price of $11.35/share . Forward Research purchased $250,000 worth of HackerNoon common stock shares at $42.55/ share, a $50M pre-money valuation in 2019 in 2020 As part of this strategic partnership, . Some news coverage links for reference: , , , , , , and . HackerNoon now backs up its entire text library on the Arweave blockchain TechStartups MarketWired Invezz Newswire PermaNews Arweave CEO Sam Williams AllyWatch We are not in a rush to raise more money, but will continue to opportunistically speak with other tech companies about owning HackerNoon shares. We want to align our interests with the most cutting edge publishing and technology companies. Revenue Our 2022 revenue plateaued, totaling just above our 2021 revenue ( YoY). While QoQ revenue increased in 2022 Q1, 2022 Q2 and 2022 Q3, 2022 Q4 was our worst revenue quarter since Q1 2021. While some of this is seasonal and some of this under our control, the massive tech layoffs and budget cuts greatly impacted our existing and prospective customers. For example, one of our previously trusted sales email lists had a 35% bounce rate due to layoffs. We have seen a slight QoQ rebound in Q1 2023 and a more promising early rebound in Q2 2023. We expect to hover around profitably in 2023, moving our expenses with the growth of revenue. redacted redacted redacted Some notable business realignments we did this year to combat macroeconomic challenges: ( ) and added 31k+ startups to the top of the HackerNoon business account freemium funnel. This campaign has its first 217k+ votes and . Startups of the Year launched video has trending startups all over the internet talking about their HackerNoon nomination Niche targeting simplification from to . This better aligned our ad inventory to our content relevancy ad placement philosophy into sellable units. All 100,000+ stories are now categorized into 22 buckets that pertain to distinct advertisement requirements, enabling advertisers to buy only those impressions and clicks that are relevant to their line of business. As a result, targeted ads turned out to be the biggest rising star of ad inventory, overtaking even writing contests. 30k+ tags 22 niche technology categories ( ): Given the diverse needs and expectations of advertisers with this inventory item, we divided it up into 3 different categories that attract customers from different price points. Writing Contests 2.0 what winners say /contest that runs for 1 month, no premium tags allowed. Mini-Contests: redacted minimum /contest that runs for 3 months, top 50 highest traffic tags are purchasable. Standard Writing Contests: redacted min /contest for customers who want to ‘throw in the kitchen sink,’ aggressively marketing it on HackerNoon and around the web. Mega-Contests: redacted Editorial Ranks HackerNoon.com as the 2,848th site on the entire internet, in the same realm as other sites pictured above like Bain.com, BHG.com, ICQ.com, WTO.org, WesternUnion.com, and McDonalds.com. Ahrefs YTD, we’ve published 8,311 stories, generating years of reading time. Our stories come with , , , , , and more. . With a leaner editorial staff, our focus has been on building stronger and longer-lasting relationships with our top users. See writers' testimonials , but people continue to trust HackerNoon as a place to learn and publish, citing distribution benefit, reputation, “bragging rights”, monetary compensations, and growing job opportunities. As always, visit for editorial picks, or for trending stories chosen by the people. translations in 7 languages emoji credibility indicators a lite version quotable images commenting All published writers now can also gain newsletters subscribers a-la substack directly on HackerNoon here HackerNoon top stories the TechBeat The entire Internet (if not the entire world) is talking about, and for good reason, and more broadly how we write, how we edit, and how we code. We’ve known this was coming, we’ve been before it was cool. We have integrated AI tools to , , , and even improve grammar, punctuation, and wording. For a deeper dive on our AI publishing strategy and progress, . ChatGPT LLMs changing publishing about GPT generate images suggest better headlines translate stories into 7 languages tune in to our VP Editorial Limarc on the WhatsAI podcast We are not using AI to take away jobs, but rather using it to help our editors edit faster, help our writers write better, and maybe even . create art that takes your breath away Here are some of the highlights so far this year: We published compared to just 2,822 stories in Q1 of 2022. We published 8,311 stories YTD, a 52% YoY increase. 4,196 stories in Q1 received and is close to cracking 1 Million total reads. This story 68,868 reads this year in Spanish alone This and cracked 100,000 reads. brand story went viral AI and ChatGPT have been the subject of many viral headlines this year: AI Sex Is Almost Here - And the World Isn't Ready for It Top 10 AI Tools to Check Out If You're Bored With ChatGPT How to Talk to ChatGPT: An Intro to Prompt Engineering The Best ChatGPT Prompts For Content Strategy and Creation Take ChatGPT With You: Introducing Ariana, the ChatGPT Assistant That Lives in WhatsApp Our alone have accumulated over 650,000 reads so far this year, and in April we just welcomed our biggest cohort yet of 13 new fellows. blogging fellowship stories Operations We cut costs at the top and the bottom, reducing management (Linh’s + David’s) compensation, reducing part-time staff, and eliminated all nonessential business costs. We did retain all our entire full-time staff of 17 people going forward. These are the people who make HackerNoon happen. As we walk the profitability line, we are very confident in this team’s ability. As we are management and we missed our revenue 2022 revenue goal, Linh and I reduced our compensation. We’ll re-evaluate this in January 2024 based on our 2023 revenue numbers. Across all teams, we also audited and greatly reduced all nonessential software and business expenses to get leaner. We also moved up infrastructure reducing costs projects, like increasing the use of smarter Caching, Svetle and Cloudflare. How to Support HackerNoon: We are launching a HackerNoon documentary called with screenings at tech events, a couple small movie theaters, and on Amazon Prime. Pls support by and let us know if you want to host a watch party once the film is released. Web 2.5 watching the trailer here , it’s the best physical product we’ve ever sold. Buy a HackerNoon hat Kind Regards, & COO Linh Dao Smooke CEO David Smooke If seeking more regular updates from HackerNoon, I recommend , , and/or . P.S. subscribing to the #hackernoon-product tag hackernoon.tech the #hackernoon tag

State of the Noonion: Building, Selling and Storytelling @ HackerNoon

নুনিয়নের রাজ্য: নির্মাণ, বিক্রি এবং গল্প বলা @ হ্যাকারনুন

State of the Noonion: Bauen, Verkaufen und Geschichtenerzählen @ HackerNoon

Estado del mediodía: construcción, venta y narración @ HackerNoon

State of the Noonion : construire, vendre et raconter des histoires @ HackerNoon

नूनियन की स्थिति: निर्माण, बिक्री और कहानी सुनाना @ हैकरनून

真昼の様子: 構築、販売、ストーリーテリング @ HackerNoon

정오의 현황: 구축, 판매 및 스토리텔링 @ HackerNoon

Estado do meio-dia: construindo, vendendo e contando histórias @ HackerNoon

Öğlen Durumu: İnşa Etme, Satış ve Hikaye Anlatma @ HackerNoon

State of the Noonion: Xây dựng, Bán hàng và Kể chuyện @ HackerNoon

中午状态：建造、销售和讲故事 @ HackerNoon

If you've spent any time on the Internet in the last few weeks, you've seen tons of people experimenting with AI, especially ChatGPT. ChatGPT is a chatbot by OpenAI. Basically, you feed it a question, and it can give you some good answers. People were using it to do their homework, launch projects, and even publish books written entirely by AI. As a professional nerd, I wanted to see what the hype is all about. But aimed to put a different spin on it. Since I'm building , I wanted to create something in that space. a media company focused on web3 gaming So, I settled on using AI to create the story and artwork for an NFT game. It took me a day of work, but the results were astonishing. The Story Behind the Story As most nerds obsessed about gaming and crypto, I love traditional games inspired by a wide variety of popular intellectual property: I played all Batman video games from the Arkham series. I spent countless hours playing all Star Wars games, including the Lego Star Wars games. I finished the Spider-man PS4 game three times in one month. But I've also played a ton of web3 games such as Axie Infinity, Pegaxy, and Crabada. These games use non-fungible tokens (NFTs) to allow players to own game assets. So, I wanted to use AI to merge these concepts together - create an NFT Spider-Man game. And I wanted that game to be inspired by one of the best Spider-man games I've ever played - Spider-Man: Web of Shadows. Why that one in particular? Cuz I love games with a darker tone. And with that vague description, my gaming experiment with artificial intelligence started. Part 1: Creating the Story I started with a simple instruction: Here's ChatGPT's unedited response: "In the city of New York, a new threat has emerged. A shadowy organization known as the Syndicate has taken control of the city, using their advanced technology and powerful allies to rule with an iron fist. As the streets of New York descend into chaos, a young man named Peter Parker decides to take action. Using his amazing abilities as the superhero Spider-Man, he sets out to take down the Syndicate and restore order to the city. As Spider-Man, you swing through the streets and alleys of New York, taking on the Syndicate's minions and foiling their plans at every turn. But as you dig deeper into the Syndicate's operations, you realize that they are more powerful and dangerous than you ever imagined. To take down the Syndicate, you'll need all the help you can get. That's where the NFTs come in. As you progress through the game, you'll collect a variety of NFTs that represent different allies and allies from the Marvel Universe. Each NFT has its own unique abilities and powers, and by collecting and using them wisely, you'll be able to take on even the toughest foes. As you gather your forces and take the fight to the Syndicate, you'll discover that the web of shadows stretches far beyond the streets of New York. The fate of the entire world hangs in the balance, and only you, as Spider-Man, can save it." Okay, we are off to a good start. Let's figure out the next step: So, I guess I'll have to make the call here. Let's make this a multiplayer game where you play as Spider-Man and aim to defeat the Syndicate. But we need more info about that group. Okay. The AI is clearly pushing us towards a multiplayer game where we can play with different Marvel characters. So, let's see who it has in mind. Here's the unedited answer: "In the story I wrote, the characters from the Marvel Universe that could potentially help the player are represented by NFTs that the player can collect and use throughout the game. These NFTs could represent a variety of different characters, each with their own unique abilities and powers. Some examples of characters from the Marvel Universe that could potentially appear in the game as NFTs include: 1. Captain America: A patriotic superhero with superhuman strength, endurance, and agility, as well as a shield that can deflect bullets and energy blasts. 2. Iron Man: A billionaire genius inventor who uses a high-tech suit of armor to fight crime and protect the world. 3. Thor: The powerful Asgardian prince with superhuman strength and the ability to control the elements, as well as a enchanted hammer that can harness the power of the storms. 4. Hulk: A scientist transformed by gamma radiation into a giant, rampaging beast with incredible strength and durability. 5. Black Widow: A skilled spy and assassin with expertise in hand-to-hand combat, weapons, and stealth. 6. Hawkeye: A master marksman with superhuman accuracy and a wide array of trick arrows at his disposal. These are just a few examples of the many characters from the Marvel Universe that could potentially appear in the game as NFTs and help the player in battle. The specific characters included in the game would depend on the choices made by the developers." While I like these characters, they seem out of place for a Spider-Man game inspired by Web of Shadows. So, who are we going to team up with to defeat the evil Syndicate? Wait, I have an idea. I know who fits the story - Daredevil. Let's see how the chatbot will add him to the story: Mmm, that's good, but I'd like to play with Daredevil as well as Spider-Man. Let's use this prompt to figure out the full story. Here's the unedited full version by ChatGPT3: "In the city of New York, a new threat has emerged. A shadowy organization known as the Syndicate has taken control of the city, using their advanced technology and powerful allies to rule with an iron fist. As the streets of New York descend into chaos, two heroes rise to take on the Syndicate: Spider-Man and Daredevil. Together, they must use their incredible powers and skills to take down the Syndicate and restore order to the city. As a player, you can choose to play as either Spider-Man or Daredevil, each with their own unique abilities and playstyle. As Spider-Man, you can swing through the streets and alleys of New York, using your web-slinging skills and spider-sense to take on the Syndicate's minions. As Daredevil, you can use your superhuman senses and expert combat skills to take down your foes and defend the city. Together with your fellow players, you'll take on a variety of missions and challenges, ranging from small-scale objectives such as taking down Syndicate operatives or rescuing civilians, to larger, more epic battles against powerful bosses or factions. As you progress through the game, you'll collect a variety of NFTs that represent different allies and allies from the Marvel Universe, each with their own unique abilities and powers. The fate of the city hangs in the balance, and only you and your fellow heroes can save it. Will you rise to the challenge and bring down the Syndicate, or will you fall to their might? The choice is yours." God damn. Sounds epic! Now, we need an awesome title. Here are the unedited 30 titles: 1. "Spider-Man and Daredevil: Web of Shadows" 2. "Heroes United: Spider-Man and Daredevil" 3. "The Syndicate Showdown" 4. "The Fight for New York" 5. "Daredevil and Spider-Man: The Ultimate Team-Up" 6. "The Battle for the Big Apple" 7. "Defenders of the City" 8. "The Syndicate Wars" 9. "Heroes in Action: Spider-Man and Daredevil" 10. "The Ultimate Spider-Man and Daredevil Experience" 11. "The Spider-Man and Daredevil Chronicles" 12. "New York Under Siege" 13. "Heroes Assemble: Spider-Man and Daredevil" 14. "The Spider-Man and Daredevil Adventure" 15. "The Battle for Justice" 16. "The Syndicate's Last Stand" 17. "The Heroes of New York" 18. "The Spider-Man and Daredevil Epic" 19. "The Great Spider-Man and Daredevil Team-Up" 20. "The War for New York" 21. "The Spider-Man and Daredevil Mission" 22. "The Syndicate's Reign of Terror" 23. "Heroes Rising: Spider-Man and Daredevil" 24. "The Spider-Man and Daredevil Quest" 25. "New York in Peril" 26."The Spider-Man and Daredevil Showdown" 27. "The Ultimate Spider-Man and Daredevil Battle" 28."The Syndicate's Final Showdown" 29."Heroes Unite: Spider-Man and Daredevil" 30. "The Spider-Man and Daredevil Odyssey" Why 30? Cuz I've seen people experiment with these AI tools, and when it comes to headlines and titles, volume is the name of the game. But here's the problem - I didn't like any of these titles - most of them don't make much sense. So combined two of them, making the title of the game - "Spider-man and Daredevil: The Syndicate Wars." Again. DAT IS EPIC! And yes, English snobs, I know the right word is "That." I'm just excited. Now that we have the main story and title of the game let's dig deeper into the Syndicate. I asked ChatGPT how Syndicate members look - are they humans or superhuman? Here's what the chatbot had to say: That's not bad, but it's a little vague. So I'm going to use my own creativity here and mold these guys in the image of the Joker. Yes, I'm creating a Marvel game using DC characters as an inspiration for the villains. Let's see how ChatGPT would design the Syndicate members. Okay, this gives us plenty of room to play with. Now, it's time for the billion-dollar question - how would ChatGPT design this game to maximize fun using digital assets like non-fungible tokens? Here's the unedited version: "There are a number of ways that you could design a game using NFTs to maximize fun and enjoyment for players. Some potential strategies include: 1. Offering a wide variety of NFTs to collect and use: By offering a wide variety of NFTs that represent different characters, abilities, and items, players will have more options and choices when it comes to customizing their gameplay experience. This can add an extra layer of depth and replay value to the game. 2. Adding NFT-based gameplay mechanics: You could incorporate NFTs into the gameplay itself, using them as a way to unlock new abilities, powers, or items. For example, you could have players collect NFTs to unlock new characters or abilities, or use them to access special abilities or abilities in battle. 3. Offering player-to-player trading: By allowing players to trade NFTs with each other, you can create a sense of community and encourage player interaction. This can make the game more social and engaging, and give players more reason to continue playing and collecting NFTs. 4. Providing NFT-based rewards and achievements: By offering NFTs as rewards for completing certain challenges or achievements in the game, you can give players extra incentives to keep playing and striving for progress. This can create a sense of accomplishment and give players a sense of progress and advancement. 5. Adding NFT-based mini-games or activities: You could also incorporate NFTs into mini-games or activities within the main game, giving players additional ways to interact with and collect them. This can add an extra layer of variety and fun to the game. Overall, by designing the game to make full use of NFTs and incorporating them into the gameplay and progression systems, you can create a more engaging and rewarding experience for players." This is a good start. Now, let's add NFTs in the context of the Spider-Man and Daredevil game we discussed. Here's ChatGPT's answer: "There are a number of ways that you could use NFTs in a Spider-Man and Daredevil game to enhance the gameplay and provide additional depth and replay value. Some possible examples include: 1. Collectible NFTs: Players could collect NFTs that represent different characters, allies, and allies from the Marvel Universe. These NFTs could be used to unlock new abilities, powers, or items, or to access special abilities or abilities in battle. 2. NFT-based upgrades: Players could use NFTs to upgrade their character's abilities, powers, or items, allowing them to customize their playstyle and approach to combat. 3. NFT-based rewards: Players could earn NFTs as rewards for completing certain challenges or achievements in the game, giving them extra incentives to keep playing and striving for progress. 4. NFT-based mini-games: The game could include mini-games or activities that revolve around collecting and using NFTs, providing additional ways for players to interact with and collect them. 5. Player-to-player trading: Players could trade NFTs with each other, allowing them to collect and use a wider variety of characters and abilities. This could create a sense of community and encourage player interaction. Overall, by incorporating NFTs into the gameplay and progression systems of a Spider-Man and Daredevil game, you can add an extra layer of depth and customization, as well as providing a way for players to collect and trade unique items and assets." This answer gave me some ideas. So here's what we will do. We will use NFTs for character upgrades. The more enemies we defeat, we gather XP, and we can use that XP to upgrade our characters. Each upgrade we make gives a new NFT that represents the newest (and badest) version of our character. Okay. We've made some progress. But I feel like something is missing... Like a main villain. Someone who will lead the Syndicate and match the darker tone of the game. I know the perfect guy for the job - the Kingpin. Hey, ChatGPT, what's his backstory? Here's the full unedited version: "The Kingpin, also known as Wilson Fisk, is a powerful and ruthless criminal mastermind in the Marvel Universe. He is often depicted as a major antagonist for characters like Spider-Man and Daredevil, and has a long and complicated backstory. In the story I wrote, the Kingpin could potentially be the main villain of the game, leading the Syndicate and pulling the strings behind their operations. Here is a possible backstory for the Kingpin in this context: Wilson Fisk was born into poverty in the Hell's Kitchen neighborhood of New York City. As a young man, he was determined to rise above his circumstances and become someone powerful and influential. He turned to a life of crime, using his intelligence, strength, and ruthlessness to climb the ranks of the criminal underworld. Over the years, Fisk became known as the Kingpin, a powerful and feared figure in the criminal world. He controlled a vast network of criminal enterprises, from gambling and extortion to drugs and weapons trafficking. He used his wealth and influence to corrupt politicians, judges, and law enforcement officials, ensuring that he could operate with impunity. As the leader of the Syndicate, the Kingpin uses his vast resources and connections to spread fear and chaos throughout the city. He is a formidable and ruthless opponent, willing to do whatever it takes to achieve his goals. Only Spider-Man and Daredevil stand a chance of taking him down and stopping his reign of terror." That's awesome! So here's what we've got so far: The main story, heroes, and villains of the game. The backstory of the Syndicate. Utility for our NFTs. I think that's all we need from ChatGPT, storywise. Now, it's time to go into the artwork. Part II: The Artwork We need to use AI to design the following characters: Spider-Man. Daredevil. Syndicate members. The Kingpin. Let's start with Spider-Man. I liked #2 - makes the most sense for the tone we are going for. Now, let's talk Daredevil. Let's go with #1 cuz it sounds epic. What about the Syndicate members? Let's go with number #9. Now, it's time to describe our main villain - Wilson Fisk, aka the Kingpin. Here are the 10 descriptions by ChatGPT: "Here are ten possible illustrations that could be used to visually describe how the Kingpin looks in the game: 1. A full body shot of the Kingpin in a suit and tie, exuding power and authority as he sits at his desk. 2. A close-up of the Kingpin's face, showing his cold and calculating expression as he plots his next move. 3. A shot of the Kingpin in action, using his vast resources and connections to manipulate events behind the scenes. 4. A dynamic shot of the Kingpin standing tall and proud, with his wealth and power on full display. 5. A dramatic pose of the Kingpin standing in front of a giant map of New York, plotting his next move. 6. A shot of the Kingpin using his intelligence and cunning to outmaneuver his enemies. 7. A shot of the Kingpin using his wealth and influence to bribe or coerce others to do his bidding. 8. A shot of the Kingpin in his private lair, surrounded by his loyal minions and advanced technology. 9. A shot of the Kingpin in his office, with a view of the city skyline in the background. 10. A shot of the Kingpin in a more casual pose, perhaps enjoying a drink or reflecting on his plans. Overall, these illustrations could help to convey the character and style of the Kingpin in the game, and give artists a sense of how to visually depict him." Let's go with number #3. Okay, so we have the descriptions for Spider-Man, Daredevil, Syndicate members, and Kingpin. Now, let's use the Midjourney to create the art. Why that tool? Cuz it's simple, easy to use, and free. You feed it a short prompt and it generates high-quality images. Here's an example for Spider-Man: All look epic. I'll take version #2 but will use version #1 for the main poster. Can't waste images like that. I want to see more variations of the second version. Let's see what the tool gives us: Fantastic. I've settled on this design: It's time for the man without fear - Daredevil. I love version 1 - works best for our game. Here's the final look: Time to see how our villains look. First, let's work on the Syndicate members. The V2 looks great, so I turned it into this: I wouldn't want to mess with that guy. Now, it's time for the big boss - The Kingpin. Following the same process with Midjourney, I've settled on this look: So, we have everything for our game. Part III: Putting It All Together Let's recap. Using ChatGPT, we created a story for a multiplayer Spider-Man and Daredevil NFT game called "Spider-man and Daredevil: The Syndicate Wars." The game has a darker tone and is inspired by "Spider-Man: Web of Shadows." The story takes place in New York City, where a shadowy organization called the Syndicate is running New York. The man behind the Syndicate is Wilson Fisk, the Kingpin. A powerful man who's controlling the city behind the scenes. It's up to Spider-Man and Daredevil to stop The Kingpin and his Syndicate from ruining New York and, potentially, the world. And let's not forget - this is an NFT game. As you go around the city, defeating enemies, you gather XP, and using that XP, you upgrade your character. Every time you upgrade your character, you get a new NFT that represents the latest version of your main hero. So this is the main story of the game and using Midjourney, we created some epic artwork. With a little magic from me and Canva.com, I designed our main poster for the game: Don't know about you, but I'm astonished at how good these tools are. I don't have any gaming, story, or design experience but managed to create a solid story and artwork for an NFT game using only AI. And remember, it all started with the simple idea of creating an NFT game based on Spider-Man: Web of Shadows. Can't wait to see how real game devs use these tools to create some epic web3 and traditional games. If you want to learn more about the web3 gaming space, subscribe to my free daily newsletter on web3 gaming.

AI Created the Story and Artwork for a Spider-Man and Daredevil NFT Game

AI creó la historia y las ilustraciones para un juego NFT de Spider-Man y Daredevil

L'IA a créé l'histoire et les illustrations d'un jeu NFT Spider-Man et Daredevil

एआई ने स्पाइडर-मैन और डेयरडेविल एनएफटी गेम के लिए कहानी और कलाकृति बनाई

スパイダーマンとデアデビルの NFT ゲームのストーリーとアートワークを AI が作成

AI criou a história e arte para um jogo NFT do Homem-Aranha e Demolidor

AI đã tạo ra câu chuyện và tác phẩm nghệ thuật cho trò chơi NFT Người nhện và Kẻ liều mạng

AI 为蜘蛛侠和夜魔侠 NFT 游戏创造了故事和艺术作品

A new year has come, and new hacks and scams are brewing in the cryptocurrency world. Besides the classic old ransomware, phishing emails, and fake investment platforms, some other tactics to steal are getting quite popular among cybercriminals. These attacks have evolved in tandem with the maturation of the crypto space, emphasizing the pressing need for enhanced security measures. That’s an important decrease compared with 2022 and previous years, but it’s, by no means, an insignificant figure. Frauds, scams, darknet markets, malware, sanctioned entities, and other stolen funds in stablecoins, Bitcoin, Ether, and more altcoins are included in their report. According to estimates by over $24 billion were received by illicit addresses in 2023. Chainalysis, The message seems clear: we have to take good care of our coins. To do this, besides applying some basic measures, we also need to know about potential threats. Let’s discover what the scammers are up to lately. Smishing Maybe you’ve already received a text message (SMS) of this style: “Coinbase informs you of an approved transaction for $570 in BTC on 01/30/24. If you don’t recognize this operation, cancel here [Link].” Or perhaps a or friend could ask you for some coins after losing their wallet overseas, or a dubious crypto exchange is showing you an enticing offer to join their website. stranded relative The “smishing” pattern is similar in all cases: it this way: you receive an SMS, often from an unknown number, asking you for private information or urging you to click a link after using diverse excuses and fake identities. IBM defines “Smishing is a social engineering attack that uses fake mobile text messages to trick people into downloading malware, sharing sensitive information, or sending money to cybercriminals. The term “smishing” is a combination of “SMS”—or “short message service,” the technology behind text messages—and “phishing.” Coinbase this in their quarters, indeed. Some “smishers” tricked their employees via SMS into sharing institutional credentials. Luckily, the incident was quickly spotted, and the scammer was blocked. To avoid being a victim yourself, always check the phone number sending the text (even this number can be faked though), and don’t click on links sent via SMS unless you fully know and trust the source and recognize the domain the link leads to. experienced Romance Scams (Pig-butchering) This scheme isn’t new, but it’s growing exponentially. Losses of at least $1 billion in the US were reported for 2022, while the cybersecurity firm Verafin that Scammers are hunting especially non-tech savvy worldwide, but it could happen to anyone, everywhere. Whole criminal networks are working on it, and experts a higher severity for 2024. estimated $3.8 billion were stolen with these scams in 2023, taking up to $1 million per victim. elder people are predicting Social media, chats, apps, and forums have let us connect with all kinds of people globally, and they’re not always to trust. Scammers could start pretending to like the same things as their victims, visiting the same websites or online events to start a fake friendship. In other cases, they directly write via Facebook, Discord, Twitter (X), or dating apps like Tinder. They’re patient, as they can talk daily with their victims for months. They can include sobbing stories about a financial crisis, shipping fees, kids back home, a medical emergency, or you name it. Payments are often asked in cryptocurrency. Just when they have a certain level of trust, or even after declaring their undying love for the victim, they start asking for money or expensive favors. On the other hand, instead of asking directly for money or things, they can recommend a crypto investment platform, talking about how they got juicy earnings with it. Of course, this is all fake, and they’re either owners or part of the staff of such a fraudulent scheme. As the US Federal Trade Commission : “If an online love interest asks you for money [or to invest money] — that’s a scam.” advised Fake QR Codes / Quishing These days, it’s quite common to find Quick Response (QR) codes everywhere. They’re just small squares with a monochrome pattern inside, easy to scan with any smartphone to discover a wide array of digital stuff: restaurant menus, payment systems, websites, emails, app installers, crypto addresses, and… malware. Or fraudulent sites. Or not the crypto address you intended to send funds to. As the Aura cybersecurity team , QR codes aren’t always safe to scan. , and quickly share it digitally (via social media, chats, mail, fraudulent websites, etc.) or physically (by printing it). In this last case, it’s even common to paste a fraudulent QR code over a legitimate one —for instance, in parking lots. explained It’s pretty easy for anyone, everywhere, to create their own QR image with a customized link or data This type of scam is often called “Quishing,” from the fusion between QR and phishing. It can affect all kinds of QR users, including the ones with a crypto wallet. They could find an enticing offer or airdrop via social media, scan a QR code, and be sent to a malicious website that either asks for their private keys or installs malware. Fraudulent QR Generators Another way to scam crypto users using QR codes is by building a fraudulent QR Generator platform. In this vein, found out that in 2019. In these platforms, when the user tried to generate a QR code for their BTC address, the system would automatically generate a code for the scammer’s BTC address instead. ZenGo “4 out of the first 5 results presented when querying Google [Bitcoin QR Generator] were leading to scammer sites” That doesn’t seem to be the case anymore, as we’ve checked by ourselves. However, this type of scam isn’t over but has apparently moved to private coins like Monero (XMR), as pointed out. That’s why it’s always important to visually check every crypto address and URL before sending funds or typing credentials. recent reports AI Threats We’re likely in the Artificial Intelligence (AI) boom in every industry. And that also includes the crime sector. AI tools are growing in sophistication, availability, and user-friendliness, something that had to be noticed by cyber-criminals. , thanks to a myriad of free tools online. Today, it’s possible to , copy exact faces on video (deepfakes), and generate quite convincing written content clone voices Therefore, a scenario in which a loved one calls you asking for some urgent financial help, and you recognize their voice (even if it’s not them), it could happen. Several experts are already advising to create a family password to prevent this kind of scam. The AI videos may be more difficult to spot since cyber-criminals can modify any original video to make its participants look like someone else (like a celebrity) and/or talk about something else entirely —like an “incredible” crypto investment platform. That’s what happened to Ottawa News in January 2024. a story of an elderly couple that suffered a common crypto scam, and, two weeks later, a fake video of them appeared on social media, based entirely on the original story. They published However, in the fraudulent version, they were recommending the use of a dubious crypto investment platform. https://www.youtube.com/watch?v=e5y3qSB3PLo&embedable=true How to know what’s real, then? In the case of video deepfakes, to check facial expressions, blinking, lip movements, and the angles of light. If any of these traits look weird in some sense, it’s likely a deepfake. Besides, common sense could help a lot: is this famous person or news portal really recommending a get-rich-quick scheme? Probably not. If it sounds too good to be true, it’s likely not true. it’s advisable Fake Trading Bots Some other times, scammers don’t even need to use real AI technology, but just pretend they’re using it. Numerous crypto investment websites claim that they use the help of bots, automated trading algorithms, and AI, in general, to invest or trade with the funds given by their customers, promising impossibly huge returns. This is all false, of course. Only when the user tries to withdraw their supposed earnings is that they realize there are no funds there, crypto or otherwise. The US Commodity Futures Trading Commission (CFTC) : Commonly, they create fake dashboards for the victims to check on the “growth” of their investment, while in reality, they took everything from the start. warned about this “Fraudsters are exploiting public interest in artificial intelligence (AI) to tout automated trading algorithms, trade signal strategies, and crypto-asset trading schemes that promise unreasonably high or guaranteed returns. Don’t believe the scammers. AI technology can’t predict the future or sudden market changes.” Discord Hacks Discord is a useful communication platform, used by millions worldwide. That also includes most of the cryptocurrency world: it’s weird for a crypto project, coin, or brand not to have its own Discord server to share with its community. A fact that’s widely known by cybercriminals, who happily mingle inside that community, waiting for a chance to scam someone. This could be not that different from common phishing, but the major problem here is that Trusting the leaders and moderators, the users would click on those links and potentially lose their crypto funds and Non-Fungible Tokens (NFTs). hackers are targeting crypto servers on Discord and somehow snatching the accounts of the admins to publish fake announcements and malicious links. have suffered this attack, including popular NFT brands like the Bored Ape Yacht Club (BAYC), Mars Cats Voyage, Known Origin, and Homeless Friends. Other crypto servers like , , , , , , , and even have briefly passed through this hack as well, with different results. Numerous crypto projects Orbiter Finance Metakey Arbitrum Sei Network Polemos Valheim Sui Network Obyte It’s important to remember that, unlike cryptocurrencies, Discord and other chat platforms weren’t designed for security since the beginning. Always remember to check the announcements first in other sources (especially official websites/blogs) before sending any funds or typing credentials on external websites. Apply security measures! Now that you know some potential threats, you surely can apply some measures to protect your crypto funds and personal data. You can also install additional security tools, like a browser extension for (of course, be skeptical about anything that promises “security”). They can help you to identify and block phishing or fraudulent websites. Keep your devices and antivirus software updated. web3 security If you don’t know the sender (phone number, email, or URL), don’t open them. Never click on links of questionable origins, whether they arrive by SMS, email, or social media. , especially in cryptocurrency. Be cautious of emotional manipulation and maintain a healthy level of skepticism. Don’t blindly trust when asked for money or given investment recommendations or replace them with textcoins, usernames, or emails . This feature is available through the wallet (Send and Receive Tabs). Always double-check your crypto addresses and QR codes, in Obyte In Obyte, it’s possible to delete your backup words (after saving them elsewhere), , and connect via the private browser Tor. Be sure to activate every security feature available on social media (like 2FA) and in your personal crypto wallet. require a password for sending funds and opening the wallet Featured Vector Image by Freepik

Top 5: Crypto Crimes and Scammers to Avoid This Year

শীর্ষ 5: এই বছর এড়াতে ক্রিপ্টো ক্রাইমস এবং স্ক্যামাররা

Top 5: Krypto-Verbrechen und Betrüger, die Sie dieses Jahr vermeiden sollten

Top 5: Criptodelitos y estafadores que se deben evitar este año

Top 5 : Crimes cryptographiques et escrocs à éviter cette année

शीर्ष 5: इस वर्ष क्रिप्टो अपराध और घोटालेबाजों से बचना चाहिए

トップ 5: 今年避けるべき仮想通貨犯罪と詐欺師

상위 5개: 올해 피해야 할 암호화폐 범죄 및 사기꾼

Top 5: crimes criptográficos e golpistas a serem evitados este ano

Топ-5: криптопреступления и мошенники, которых следует избегать в этом году

İlk 5: Bu Yıl Kaçınılması Gereken Kripto Suçları ve Dolandırıcılar

Top 5: Tội phạm tiền điện tử và những kẻ lừa đảo cần tránh trong năm nay

前五名：今年要避免的加密货币犯罪和诈骗者

Is crypto age (generation) related? I don’t think so. I already . So, we have the best “old school” representative, Warren Buffett, who said that “if you ... owned all of the bitcoin in the world and you offered it to me for $25, I wouldn’t take it.” Nuff said. Then, we have the “icon” of the “younger generation” of entrepreneurs who has his fun with Dogecoin. Nuff done. Elon Musk : “don’t bet the farm on crypto!” wrote about it warned us all Talking about farms and farming, Bill Gates is convinced that the whole crypto-mania is “ .” Now, I know what is fool’s gold. My wife thinks that Matthew McConaughey is pure gold in . She isn’t referring to his acting, obviously. Honestly, I had to Google the theory Bill mentioned at his TechCrunch talk on climate change. 100% based on greater fool theory that movie In finance, suggests that one can sometimes make money through the purchase of overvalued assets — items with a purchase price drastically exceeding the intrinsic value — if those assets can later be resold at an even higher price. the greater fool theory So, in plain English, you’re a fool if you buy something “stupid,” but you are a “smart fool” if you can find even a “greater fool” to sell what you bought at a higher price, which will/would be worth nothing, eventually and inevitably. Bill doubled down by making a “joke” about NFTs. “Inside the Cult of Crypto” You know how it goes. One tweet leads to another. Was that “cult quote” pulled out from THE “Financial Times,” for real? I had to check. And, that speaks volumes, doesn’t it? illustration for the article Most groups identified as cults feature a single charismatic leader, something that the crypto world lacks. But many other classic hallmarks of culthood — apocalypticism, the promise of utopia for worthy believers, shunning of external critics and vitriolic denouncement of heretical insiders — are increasingly dominant. So, crypto is an “atypical cult,” but still a cult because “another major way in which crypto-cultism differs from traditional cults is the lack of physical gatherings. Beyond events such as Bitcoin Miami and “crypto cruises”, or scattered communities like El Salvador’s bitcoin beach, cryptocurrency advocates communicate online.” And, let’s keep in mind that “the biggest difference to traditional cults is the lack of a single leader figure.” So, it’s not all that bad. My crypto buddies and the crypto community still have a choice. They can either be “crypto fools” or “crypto cult members.” And You, Crypto-Kill-Bill, What Have You Been Up To? According to , a magazine of the proud American landowners, Bill is officially the Top Gun of the USA farmland owners. The Land Report In total, with assets totaling more than $690m. Gates owns approximately 242,000 acres of farmland But hey, it’s all about the perspective. “To put that into perspective, that’s nearly the size of Hong Kong.” The old and wise have spoken, crypto as the future of finance is broken! “And isn't it ironic... don't you think?” Yes, it is, my dear Alanis Morissette. We still haven’t figured out how to decentralize the Internet, not to mention the finances, decentralization of the farmland has already become the (next) problem. So, investing in crypto is foolish and cultish, but buying land the size of Honk Kong is … the least you can do for the “global green revolution.” OK Bill, if you say so.

Bill "The Farmer" vs. "Crypto Fools" 

Bill "The Farmer" contre "Crypto Fools"

बिल "किसान" बनाम "क्रिप्टो मूर्ख"

ビル「ザ・ファーマー」対「クリプト・フールズ」

Bill "The Farmer" so với "Crypto Fools"

比尔“农民”与“加密货币傻瓜”

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Muhammed Yusuf Kocyigit, Boston University;
(2) Anietie Andy, University of Pennsylvania;
(3) Derry Wijaya, Boston University. Table of Links Abstract and Intro
Related Works
Data
Method
Analysis and Results
Conclusion
Limitations
Ethics Statement and References
Appendix: Toxicity Measurement
Appendix: Correlation Over Time
Appendix: Wikidata
Appendix: Hyperparameter Sensitivity Ethics Statement Language and context are not simple things especially considering we are working on a summarization of n-grams instead of sentences. Hence the results and analysis must be evaluated with this in mind. Also the identification of bias can sometimes inadvertently be affected by the researchers’ own biases, despite our best efforts to be rigorous in our methodology. A risk lies in potentially over-simplifying or misrepresenting complex socio-cultural dynamics. We have worked to minimize this risk through methodological transparency, iterative analyses, and peer review. The study inherently involves discussing racially biased language, which could cause distress to some readers, particularly those from communities affected by such biases. Our presentation aims to be sensitive, focusing on systemic patterns rather than individuals or specific works. Lastly, there is a potential risk of misuse of our findings. The purpose of our study is to raise awareness and prompt action towards eliminating bias, but we are aware that the same information could be used to support biased arguments or perpetuate harmful stereotypes even though the content of books are not a representation of reality but the representation of the perception of the authors which could be source of bias in some cases. On the positive side, our research contributes to a better understanding of systemic racial biases in historical and cultural contexts. This deeper awareness can help us understand what we perceive as normal or what the next generation will perceive as normal putting a external reality check on the culture we inherit without knowing. As a part of our ethical commitment, we will make our data and code public to invite critique, replication, and improvement. Our research is not an end but a beginning, providing a stepping stone for further inquiries into racial bias. References Akyurek, A. F.; Kocyigit, M. Y.; Paik, S.; and Wijaya, D. T. ¨ 2022a. Challenges in Measuring Bias via Open-Ended Language Generation. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), 76–76. Akyurek, A. F.; Paik, S.; Kocyigit, M.; Akbiyik, S.; Run- ¨ yun, S. L.; and Wijaya, D. 2022b. On Measuring Social Biases in Prompt-Based Multi-Task Learning. In Findings of the Association for Computational Linguistics: NAACL 2022, 551–564. An, J.; Kwak, H.; and Ahn, Y.-Y. 2018. SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment. ArXiv, abs/1806.05521. Arseniev-Koehler, A. 2021. Theoretical foundations and limits of word embeddings: what types of meaning can they capture? Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021a. Investigating the Role of Negatives in Contrastive Representation Learning. Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021b. Investigating the Role of Negatives in Contrastive Representation Learning. Awasthi, P.; Dikkala, N.; and Kamath, P. 2022. Do More Negative Samples Necessarily Hurt in Contrastive Learning? Bassignana, E.; Basile, V.; and Patti, V. 2018. Hurtlex: A Multilingual Lexicon of Words to Hurt. In Italian Conference on Computational Linguistics. Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In NIPS Farrell, T.; Araque, O.; Fern ´ andez, M.; and Alani, H. 2020. ´ On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere. 12th ACM Conference on Web Science. Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16). Gerbner, G. 1998. Cultivation analysis: An overview. Mass communication and society, 1(3-4): 175–194. Goldberg, Y.; and Orwant, J. 2013. A Dataset of SyntacticNgrams over Time from a Very Large Corpus of English Books. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 241–247. Atlanta, Georgia, USA: Association for Computational Linguistics. Hagiwara, N.; Alderson, C. J.; and Mezuk, B. 2016. Differential effects of personal-level vs group-level racial discrimination on health among Black Americans. Ethnicity & disease, 26(3): 453. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1489–1501. Berlin, Germany: Association for Computational Linguistics. Harris, Z. S. 1954. Distributional Structure. ¡i¿WORD¡/i¿, 10(2-3): 146–162. Joseph, K.; and Morgan, J. H. 2020. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? In Annual Meeting of the Association for Computational Linguistics. Kozlowski, A. C.; Taddy, M.; and Evans, J. A. 2019. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review, 84(5): 905–949. Loon, A. v.; Giorgi, S.; Willer, R.; and Eichstaedt, J. 2022. Negative Associations in Word Embeddings Predict Antiblack Bias across Regions–but Only via Name Frequency. Proceedings of the International AAAI Conference on Web and Social Media, 16(1): 1419–1424. Lucy, L.; Tadimeti, D.; and Bamman, D. 2022. Discovering Differences in the Representation of People using Contextualized Semantic Axes. ArXiv, abs/2210.12170. Martinc, M.; Kralj Novak, P.; and Pollak, S. 2020. Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 4811–4819. Marseille, France: European Language Resources Association. ISBN 979-10-95546-34-4. Sorato, D.; Zavala-Rojas, D.; and del Carme Colominas Ventura, M. 2021. Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, 34–46. Online: Australasian Language Technology Association. Tripodi, R.; Warglien, M.; Sullam, S. L.; and Paci, D. 2019. Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914. In LChange@ACL. Valentini, F.; Slezak, D. F.; and Altszyler, E. 2022. The Dependence on Frequency of Word Embedding Similarity Measures. ArXiv, abs/2211.08203. Wijaya, D. T.; and Yeniterzi, R. 2011. Understanding semantic change of words over centuries. In Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, 35–40. Xu, H.; Zhang, Z.; Wu, L.; and Wang, C.-J. 2019. The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books. PLOS ONE, 14(11): e0225385. This paper is available on arxiv under CC 4.0 license. Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. Table of Links Abstract and Intro Related Works Data Method Analysis and Results Conclusion Limitations Ethics Statement and References Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Wikidata Appendix: Hyperparameter Sensitivity Abstract and Intro Abstract and Intro Related Works Related Works Data Data Method Method Analysis and Results Analysis and Results Conclusion Conclusion Limitations Limitations Ethics Statement and References Ethics Statement and References Appendix: Toxicity Measurement Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Correlation Over Time Appendix: Wikidata Appendix: Wikidata Appendix: Hyperparameter Sensitivity Appendix: Hyperparameter Sensitivity Ethics Statement Language and context are not simple things especially considering we are working on a summarization of n-grams instead of sentences. Hence the results and analysis must be evaluated with this in mind. Also the identification of bias can sometimes inadvertently be affected by the researchers’ own biases, despite our best efforts to be rigorous in our methodology. A risk lies in potentially over-simplifying or misrepresenting complex socio-cultural dynamics. We have worked to minimize this risk through methodological transparency, iterative analyses, and peer review. The study inherently involves discussing racially biased language, which could cause distress to some readers, particularly those from communities affected by such biases. Our presentation aims to be sensitive, focusing on systemic patterns rather than individuals or specific works. Lastly, there is a potential risk of misuse of our findings. The purpose of our study is to raise awareness and prompt action towards eliminating bias, but we are aware that the same information could be used to support biased arguments or perpetuate harmful stereotypes even though the content of books are not a representation of reality but the representation of the perception of the authors which could be source of bias in some cases. On the positive side, our research contributes to a better understanding of systemic racial biases in historical and cultural contexts. This deeper awareness can help us understand what we perceive as normal or what the next generation will perceive as normal putting a external reality check on the culture we inherit without knowing. As a part of our ethical commitment, we will make our data and code public to invite critique, replication, and improvement. Our research is not an end but a beginning, providing a stepping stone for further inquiries into racial bias. References Akyurek, A. F.; Kocyigit, M. Y.; Paik, S.; and Wijaya, D. T. ¨ 2022a. Challenges in Measuring Bias via Open-Ended Language Generation. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), 76–76. Akyurek, A. F.; Paik, S.; Kocyigit, M.; Akbiyik, S.; Run- ¨ yun, S. L.; and Wijaya, D. 2022b. On Measuring Social Biases in Prompt-Based Multi-Task Learning. In Findings of the Association for Computational Linguistics: NAACL 2022, 551–564. An, J.; Kwak, H.; and Ahn, Y.-Y. 2018. SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment. ArXiv, abs/1806.05521. Arseniev-Koehler, A. 2021. Theoretical foundations and limits of word embeddings: what types of meaning can they capture? Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021a. Investigating the Role of Negatives in Contrastive Representation Learning. Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021b. Investigating the Role of Negatives in Contrastive Representation Learning. Awasthi, P.; Dikkala, N.; and Kamath, P. 2022. Do More Negative Samples Necessarily Hurt in Contrastive Learning? Bassignana, E.; Basile, V.; and Patti, V. 2018. Hurtlex: A Multilingual Lexicon of Words to Hurt. In Italian Conference on Computational Linguistics. Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In NIPS Farrell, T.; Araque, O.; Fern ´ andez, M.; and Alani, H. 2020. ´ On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere. 12th ACM Conference on Web Science. Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16). Gerbner, G. 1998. Cultivation analysis: An overview. Mass communication and society, 1(3-4): 175–194. Goldberg, Y.; and Orwant, J. 2013. A Dataset of SyntacticNgrams over Time from a Very Large Corpus of English Books. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 241–247. Atlanta, Georgia, USA: Association for Computational Linguistics. Hagiwara, N.; Alderson, C. J.; and Mezuk, B. 2016. Differential effects of personal-level vs group-level racial discrimination on health among Black Americans. Ethnicity & disease, 26(3): 453. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1489–1501. Berlin, Germany: Association for Computational Linguistics. Harris, Z. S. 1954. Distributional Structure. ¡i¿WORD¡/i¿, 10(2-3): 146–162. Joseph, K.; and Morgan, J. H. 2020. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? In Annual Meeting of the Association for Computational Linguistics. Kozlowski, A. C.; Taddy, M.; and Evans, J. A. 2019. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review, 84(5): 905–949. Loon, A. v.; Giorgi, S.; Willer, R.; and Eichstaedt, J. 2022. Negative Associations in Word Embeddings Predict Antiblack Bias across Regions–but Only via Name Frequency. Proceedings of the International AAAI Conference on Web and Social Media, 16(1): 1419–1424. Lucy, L.; Tadimeti, D.; and Bamman, D. 2022. Discovering Differences in the Representation of People using Contextualized Semantic Axes. ArXiv, abs/2210.12170. Martinc, M.; Kralj Novak, P.; and Pollak, S. 2020. Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 4811–4819. Marseille, France: European Language Resources Association. ISBN 979-10-95546-34-4. Sorato, D.; Zavala-Rojas, D.; and del Carme Colominas Ventura, M. 2021. Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, 34–46. Online: Australasian Language Technology Association. Tripodi, R.; Warglien, M.; Sullam, S. L.; and Paci, D. 2019. Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914. In LChange@ACL. Valentini, F.; Slezak, D. F.; and Altszyler, E. 2022. The Dependence on Frequency of Word Embedding Similarity Measures. ArXiv, abs/2211.08203. Wijaya, D. T.; and Yeniterzi, R. 2011. Understanding semantic change of words over centuries. In Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, 35–40. Xu, H.; Zhang, Z.; Wu, L.; and Wang, C.-J. 2019. The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books. PLOS ONE, 14(11): e0225385.

A Novel Method for Analysing Racial Bias: Ethics Statement and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Muhammed Yusuf Kocyigit, Boston University;
(2) Anietie Andy, University of Pennsylvania;
(3) Derry Wijaya, Boston University. Table of Links Abstract and Intro
Related Works
Data
Method
Analysis and Results
Conclusion
Limitations
Ethics Statement and References
Appendix: Toxicity Measurement
Appendix: Correlation Over Time
Appendix: Wikidata
Appendix: Hyperparameter Sensitivity Limitations Reliability of Word Embeddings The representation power and how much word embeddings can model our beliefs is an active point of discussion. This line of research is critical because it allows differentiating under which conditions word representations generate good abstractions to analyze. The critique can be summarized in two main lines: structural and statistical problems. Structural problems (Arseniev-Koehler 2021) relate to the Distributional Hypothesis (Harris 1954) that current word embeddings are based on. This is criticized by structuralism in multiple aspects. Primarily issues arise when the phrase a word is known by the company it keeps is interpreted as a word is the company it keeps. The representations of words or groups that we present are not to say that the content represents them precisely as the representations, but they are more closely linked to certain concepts if they are closer in the semantic space. The second important issue we must be aware of is that concepts are not always dichotomous or binary. They can be graded or related in multiple ways. We, however, in line with An, Kwak, and Ahn (2018) assume a dichotomous nature to a set of adjectives. The statistical problems mainly stem from the frequency of different words/concepts. Valentini, Slezak, and Altszyler (2022) shows that word representations tend to yield higher similarity scores between high-frequency words than between high and low-frequency words. Joseph and Morgan (2020) show that word representations produce similarity measures that better represent human beliefs in with higher frequency words. Loon et al. (2022) argue that word embedding methods like word2vec cluster high-frequency words together and low-frequency words together. They argue that people have an intuitive positive bias where positive words are more frequent and if a racial/social group(black names in their case) is less frequently represented in the corpus compared to their control group(white names). They are just likely to be related to negative words and this relation would not necessarily be an indication of bias in the corpus. Structural problems are related to how we analyze the results and make inferences from them. However, in this paper, we target statistical problems on two levels. First, we look at the distribution of the context words in the primary data where the word vectors were trained and observe that the context words for these groups are not significantly different, and on the frequency of names, we sample equal amounts of (word, context) pairs for both African Americans and White Americans and train the embeddings for these groups from scratch rather than using a pre-trained embedding that would depend on the frequency of names in the initial corpus. One specific limitation that we had to work around in this project was the issue of false positive matches. Even though we search for exact name+surname combination in books the resulting n-grams are not necessarily always about the same person that we intent on. While a person with the same name+surname combination likely belongs to the same racial group in our analysis, this might not be the same for other use cases making this a relevant limitation to consider. Another relevant limitation is the data size limitation. We have replicated our results with a manually curated name subsets however a smaller subset of n-grams was not a viable option. We have also observed that decades before 1850s return too few samples and the results are not meaningful for those decades. The question of how many n-grams would be enough for a new application is still an open question that we were not able to answer. This question would also extend the scope of this paper beyond a reasonable limit. This paper is available on arxiv under CC 4.0 license. Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. Table of Links Abstract and Intro Related Works Data Method Analysis and Results Conclusion Limitations Ethics Statement and References Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Wikidata Appendix: Hyperparameter Sensitivity Abstract and Intro Abstract and Intro Related Works Related Works Data Data Method Method Analysis and Results Analysis and Results Conclusion Conclusion Limitations Limitations Ethics Statement and References Ethics Statement and References Appendix: Toxicity Measurement Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Correlation Over Time Appendix: Wikidata Appendix: Wikidata Appendix: Hyperparameter Sensitivity Appendix: Hyperparameter Sensitivity Limitations Reliability of Word Embeddings The representation power and how much word embeddings can model our beliefs is an active point of discussion. This line of research is critical because it allows differentiating under which conditions word representations generate good abstractions to analyze. The critique can be summarized in two main lines: structural and statistical problems. Structural problems (Arseniev-Koehler 2021) relate to the Distributional Hypothesis (Harris 1954) that current word embeddings are based on. This is criticized by structuralism in multiple aspects. Primarily issues arise when the phrase a word is known by the company it keeps is interpreted as a word is the company it keeps. The representations of words or groups that we present are not to say that the content represents them precisely as the representations, but they are more closely linked to certain concepts if they are closer in the semantic space. The second important issue we must be aware of is that concepts are not always dichotomous or binary. They can be graded or related in multiple ways. We, however, in line with An, Kwak, and Ahn (2018) assume a dichotomous nature to a set of adjectives. The statistical problems mainly stem from the frequency of different words/concepts. Valentini, Slezak, and Altszyler (2022) shows that word representations tend to yield higher similarity scores between high-frequency words than between high and low-frequency words. Joseph and Morgan (2020) show that word representations produce similarity measures that better represent human beliefs in with higher frequency words. Loon et al. (2022) argue that word embedding methods like word2vec cluster high-frequency words together and low-frequency words together. They argue that people have an intuitive positive bias where positive words are more frequent and if a racial/social group(black names in their case) is less frequently represented in the corpus compared to their control group(white names). They are just likely to be related to negative words and this relation would not necessarily be an indication of bias in the corpus. Structural problems are related to how we analyze the results and make inferences from them. However, in this paper, we target statistical problems on two levels. First, we look at the distribution of the context words in the primary data where the word vectors were trained and observe that the context words for these groups are not significantly different, and on the frequency of names, we sample equal amounts of (word, context) pairs for both African Americans and White Americans and train the embeddings for these groups from scratch rather than using a pre-trained embedding that would depend on the frequency of names in the initial corpus. One specific limitation that we had to work around in this project was the issue of false positive matches. Even though we search for exact name+surname combination in books the resulting n-grams are not necessarily always about the same person that we intent on. While a person with the same name+surname combination likely belongs to the same racial group in our analysis, this might not be the same for other use cases making this a relevant limitation to consider. Another relevant limitation is the data size limitation. We have replicated our results with a manually curated name subsets however a smaller subset of n-grams was not a viable option. We have also observed that decades before 1850s return too few samples and the results are not meaningful for those decades. The question of how many n-grams would be enough for a new application is still an open question that we were not able to answer. This question would also extend the scope of this paper beyond a reasonable limit.

A Novel Method for Analysing Racial Bias: Collection of Person Level References: Limitations

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 5. Conclusion This paper has considered the valuation of the bitcoin call option when the bitcoin price dynamics follow a bivariate Merton jump-diffusion model. This research generally provided a novel pricing framework that described the behaviour of bitcoin prices and the model parameter estimation with the intent of pricing the corresponding derivatives. Since bitcoin is normally affected by investors’ attention and sentiment, we used the continuous-time stochastic jump-diffusion process to capture the dynamics of the bitcoin prices and the transaction volumes affecting the price. In the methodological aspect of the research, we extended the classical Black-Scholes model in order to obtain the extended Black-Scholes equation for the bitcoin options. By introducing the artificial NN, we proposed a trial solution that solves the associated BlackScholes PDE for the bitcoin call options with European features. As a result, the original constrained optimization problem was transformed into an unconstrained one. The numerical results considered both the normal Black-Scholes model and the Merton jump-diffusion model, and it was observed that the latter resulted in a more efficient valuation process. Hence, we can conclude that the NN can be employed efficiently in solving complex PDE-related problems and can be applied to pricing certain financial derivatives without analytical forms. For the Model I and II comparison, we noted that the Black-Scholes used in connection with Model II showed the out-of-the-money feature of the call option. The option absolutely pays off when the underlying price falls below the strike price. The JMD overprices the out-of-the-money options for Model II, whereas the BlackScholes overprices the call options in Model I. Hence, one of the limitations of this research lies in finding the optimal neural network configuration which ensures the fair pricing of bitcoin options using the proposed two models. This optimal feature will be incorporated to avoid over-pricing or under-pricing of these option values, and future research will focus on this. One of the limitations of the approach used in this paper is that it may be prone to artificial Google searches affecting sentiment-based data analysis and decisions. Even though search-based measures appear to be more transparent than other social media-driven measures, they seem limited as the volume of data is concerned. Social media like Twitter provides high-frequency data (second, minute, hour, daily, . . . ), which can provide meaningful and instantaneous insights into the price dynamics of cryptocurrencies and bitcoin in particular. Our future work will first examine the correlation between search-based data and tweets data, then compare their performance when included in the valuation process. The tweets sentiment scores will be computed using the stateof-the-art Lexicon-based sentiment analyzer Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, this paper focused exclusively on jump-diffusion models for cryptocurrency pricing. Levy processes allow the modelling of heavy-tailed return distributions and large deviations from the mean. Expanding the set of stochastic processes considered would provide a more thorough treatment of cryptocurrency dynamics. Processes like variance gamma, normal inverse Gaussian, and generalized hyperbolic Levy motions have shown promise in modelling assets with frequent extreme moves. Given Bitcoin’s volatility clustering and significant outliers, applying Levy processes could potentially improve model fitting. We acknowledge the limitations of only exploring a jumpdiffusion framework presently. Incorporating alternatives like Levy processes would strengthen the generalizability and robustness of the modelling approach. Building on the initial foundation proposed here, researchers could examine a wider set of stochastic processes for capturing empirically observed features. Comparative testing using historical data would elucidate the relative effectiveness of diffusions, Levy processes, and other probabilistic models. Extending this work to Levy processes represents a valuable progression for future research. The flexibility of Levy’s motions shows potential for modelling emerging cryptocurrency returns. We hope this paper provides a starting point that can be incrementally improved by incorporating innovations like heavy-tailed processes. Evaluating a range of stochastic models will ultimately enhance financial engineering techniques tailored specifically to cryptocurrencies. Acknowledgement The second author thanks the Research Centre of AIMS-Cameroon for hosting him during the preparation of this manuscript. Funding This research received no external funding. 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 5. Conclusion This paper has considered the valuation of the bitcoin call option when the bitcoin price dynamics follow a bivariate Merton jump-diffusion model. This research generally provided a novel pricing framework that described the behaviour of bitcoin prices and the model parameter estimation with the intent of pricing the corresponding derivatives. Since bitcoin is normally affected by investors’ attention and sentiment, we used the continuous-time stochastic jump-diffusion process to capture the dynamics of the bitcoin prices and the transaction volumes affecting the price. In the methodological aspect of the research, we extended the classical Black-Scholes model in order to obtain the extended Black-Scholes equation for the bitcoin options. By introducing the artificial NN, we proposed a trial solution that solves the associated BlackScholes PDE for the bitcoin call options with European features. As a result, the original constrained optimization problem was transformed into an unconstrained one. The numerical results considered both the normal Black-Scholes model and the Merton jump-diffusion model, and it was observed that the latter resulted in a more efficient valuation process. Hence, we can conclude that the NN can be employed efficiently in solving complex PDE-related problems and can be applied to pricing certain financial derivatives without analytical forms. For the Model I and II comparison, we noted that the Black-Scholes used in connection with Model II showed the out-of-the-money feature of the call option. The option absolutely pays off when the underlying price falls below the strike price. The JMD overprices the out-of-the-money options for Model II, whereas the BlackScholes overprices the call options in Model I. Hence, one of the limitations of this research lies in finding the optimal neural network configuration which ensures the fair pricing of bitcoin options using the proposed two models. This optimal feature will be incorporated to avoid over-pricing or under-pricing of these option values, and future research will focus on this. One of the limitations of the approach used in this paper is that it may be prone to artificial Google searches affecting sentiment-based data analysis and decisions. Even though search-based measures appear to be more transparent than other social media-driven measures, they seem limited as the volume of data is concerned. Social media like Twitter provides high-frequency data (second, minute, hour, daily, . . . ), which can provide meaningful and instantaneous insights into the price dynamics of cryptocurrencies and bitcoin in particular. Our future work will first examine the correlation between search-based data and tweets data, then compare their performance when included in the valuation process. The tweets sentiment scores will be computed using the stateof-the-art Lexicon-based sentiment analyzer Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, this paper focused exclusively on jump-diffusion models for cryptocurrency pricing. Levy processes allow the modelling of heavy-tailed return distributions and large deviations from the mean. Expanding the set of stochastic processes considered would provide a more thorough treatment of cryptocurrency dynamics. Processes like variance gamma, normal inverse Gaussian, and generalized hyperbolic Levy motions have shown promise in modelling assets with frequent extreme moves. Given Bitcoin’s volatility clustering and significant outliers, applying Levy processes could potentially improve model fitting. We acknowledge the limitations of only exploring a jumpdiffusion framework presently. Incorporating alternatives like Levy processes would strengthen the generalizability and robustness of the modelling approach. Building on the initial foundation proposed here, researchers could examine a wider set of stochastic processes for capturing empirically observed features. Comparative testing using historical data would elucidate the relative effectiveness of diffusions, Levy processes, and other probabilistic models. Extending this work to Levy processes represents a valuable progression for future research. The flexibility of Levy’s motions shows potential for modelling emerging cryptocurrency returns. We hope this paper provides a starting point that can be incrementally improved by incorporating innovations like heavy-tailed processes. Evaluating a range of stochastic models will ultimately enhance financial engineering techniques tailored specifically to cryptocurrencies. Acknowledgement The second author thanks the Research Centre of AIMS-Cameroon for hosting him during the preparation of this manuscript. Funding This research received no external funding.

Neural Network for Valuing Bitcoin: Conclusion, Acknowledgments, and Funding

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VAKE

vake.ai

-- employees

Since n.d.

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VAKE

EVERGREEN INDEX #11692

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VAKE WIKI

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