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Data source: https://www.footprint.network/@Beosin/Footprint-Beosin-Q3-Report-Beosin Download the full report: https://beosin.com/resources/Q3_2022_BLOCKCHAIN_SECURITY_REPORT.pdf 1. Q3 2022 Blockchain Security Overview A total of 37 major exploits were monitored, with a total loss of approximately $405 million In the third quarter of 2022, Beosin EagleEye monitored over 37 major attacks in the Web3 space, with total losses of approximately $405 million, down approximately 43.6% from $718.34 million in Q2 2022, and a decrease of 59.6% from the loss of $1,002.58 million in Q3 2021. From January to September 2022, assets lost in the Web3 space due to attacks totaled $2,317.91 million. July saw a significant decrease in attacks, making it the least loss amount from attacks since 2022. Hacker activity increased significantly in August and September. In terms of each month, , 92% of the amount lost came from cross-chain bridges and DeFi protocols. 22 of the 37 attacks occurred in the DeFi space. In terms of the project types , after a sharp drop in TVL from May to June, the trend of TVL of each chain tended to be stable this quarter. Late July to early August showed a slight upward trend in TVL, which was also the period with the highest number of attacks and loss amount in this quarter. In terms of TVL the number of losses on Ethereum reached $374.28 million this quarter, accounting for 92% of the total losses. The most frequently attacked chain was BNB Chain, which reached 16 times. In terms of chains, 92% of the loss amount was caused by contract vulnerability exploits and private key compromises. In terms of attack types, about $204.2 million of the stolen funds flowed into Tornado Cash, accounting for about 50.4% of the funds stolen in the quarter. Only about 4% of the stolen funds were recovered during the quarter. In terms of fund flows, only 40% of the rekt projects were audited. In terms of audits, 2. Overview of exploits Overall attacks fell in Q3 compared to Q2 In Q3 2022, 37 major attacks were monitored in the Web3 space, with a total loss of approximately $405 million. There were two attacks with losses of $100 million or more, three attacks with losses of $10 million or more, and 14 attacks with losses of $1 million or more. The security incidents with losses over $100 million were ($190 million) and ($160 million). Nomad Bridge Wintermute August 2022 was the most active month for hackers in the quarter, with losses of around $210.62 million. Total losses from attacks in July were $30.05 million, making it the lowest amount of losses in a month since 2022. 3. Types of rekt projects Cross-chain bridges and DeFi projects account for 92% of the loss amount In the third quarter of 2022, three cross-chain bridge attacks resulted in a total loss of approximately $190.25 million; 22 attacks in the DeFi space resulted in a total loss of $186.79 million. Approximately 92% of the attack loss amount came from the cross-chain bridge and DeFi protocols. As of September 2022, there were 10 major cross-chain bridge security incidents 2022, with over $1.4 billion in losses. Cross-chain bridges were the most affected area by attacks in 2022. In addition to cross-chain bridges and DeFi protocols, other types of projects attacked this quarter included NFTs, exchanges, DAOs, wallets, and MEV bots, making their overall types more diverse than in the previous quarter. 4. Loss amount by chain Losses on Ethereum amount to $374.3 million 12 major attacks occurred on Ethereum this quarter, with a total loss of $374.28 million, ranking first among all chains. Solana lost $18.37 million from 3 exploits. Chains with major attacks in two consecutive quarters include Ethereum, BNB Chain, Fantom, and Avalanche. BNB Chain saw the most attacks, with 16 exploits, and their corresponding projects are all unaudited. The amount of money involved in these 16 exploits is relatively small, with 14 incidents involving a single loss of less than $500,000. After experiencing a sharp drop in TVL from May to June, the trend of TVL across chains stabilized this quarter. TVL showed a slight upward trend in the period from late July to early August, which was also the period with the most attacks and loss amount this quarter. The crypto market generally moved slightly downward in September. After the Ethereum merge on September 15, the Ethereum TVL saw a continuous slight decline. 5. Analysis of Attack Types 92% of the lost amount was caused by contract vulnerability exploits and private key compromise In the third quarter, contract exploits continued to be the most common attack type. About 15 attacks are contract vulnerability exploits, accounting for 40.5 percent of the total number. Total losses from contract vulnerabilities amounted to $201.6 million, or 50.9 percent of total losses. The four private key compromises this quarter resulted in approximately $167.24 million in losses, the second largest amount of losses after contract vulnerability exploits. Compared with the previous quarter, the types of attacks in this quarter were more diverse. New attack types that emerged this quarter include BGP hijacking, misconfiguration, and supply chain attacks. Q3 market share of loss amount by attack type Q3 market share of count by attack type By contract vulnerabilities, the main vulnerabilities exploited this quarter include: validation issues, reentrancy, permission issues, improperly designed business logic or functions, and overflow vulnerabilities. These vulnerabilities are all discoverable and fixable during the audit phase. Q3 loss amount & count by contract vulnerabilities 6. Typical Security Incident Recap 6.1 Nomad Bridge $190 Million Incident On August 2, Nomad Bridge, a cross-chain platform that supports asset transfers across Ethereum, Moonbeam, Avalanche, Evmos, and Milkomeda, suffered a massive hack that cost the project $190 million. 6.2 Slope Wallet Incident on Solana On August 3, a large-scale Slope wallet theft incident occurred on Solana, with losses estimated at around $6 million. 6.3 Wintermute Private Key Compromise Incident On September 20, crypto market maker Wintermute was attacked with a loss of $160 million due to a private key compromise. 7. Fund Flow Analysis Approximately $204.2 million in stolen funds flowed into Tornado Cash On August 8, the US Department of the Treasury’s Office of Foreign Assets Control (OFAC) sanctioned Tornado Cash, prohibiting U.S. individuals or organizations from interacting with it. In the third quarter of 2022, approximately $204.2 million in stolen funds still flowed into Tornado Cash, representing 50.4 percent of the funds stolen in that quarter, which is lower than in the second quarter. Approximately $182.3 million of the stolen funds remained in the hacker’s address as the balance. Some stolen funds were bridged to addresses on other chains, and this portion is still counted as the hacker’s address balance. About $16.6 million of assets were recovered through on-chain negotiations and unsolicited returns from white hat hackers. In the third quarter of 2022, only about 4% of the stolen funds were recovered, a much lower percentage than in the second quarter. Around $1.92 million of stolen assets flowed into exchanges such as Binance and FixedFloat. Such incidents generally involved a small number of assets (usually around $10K to $100K) and the hackers transferred the stolen funds to the exchanges immediately after the attack, resulting in the projects failing to contact the exchanges in time to freeze the funds. Q3 fund flows 8. Project Audit Analysis Only 40% of the projects were audited In 2022, the percentage of rekt projects that were audited were: 70% in the first quarter, 52% in the second quarter, and 40% in the third quarter. The percentage of unaudited rekt projects shows an increasing trend quarter by quarter. Whether audited — count Whether audited — the amount Of all the rekt projects, the audited projects lost a total of $ 375.48 million, and the unaudited projects lost about $ 29.56 million in attacks. At first glance, it might seem that audits did not serve to protect the safe operation of the projects. However, a deeper analysis shows that most of these audited projects were attacked by non-contractual level issues such as private key compromise, supply chain attacks, DNS attacks, BGP hijacking, and misconfiguration. Among the unaudited projects, 85% were caused by contract vulnerabilities or flash loan attacks. It can be seen that professional audits are still effective in securing the project at the contract level to some extent, however, the safe operation of a protocol also requires a good job of offline risk control, safekeeping the private key, being alert to traditional network security attacks, and use third-party components carefully. Of course, in this quarter, some vulnerabilities should have been discovered in the audit phase but were not presented in the audit report, so it is recommended that the project seek a professional security company to conduct the audit. About Blockchain Security Alliance The Blockchain Security Alliance was launched by several units with diverse industry backgrounds, including university institutions, blockchain security companies, industry associations, fintech service providers, etc. The first batch of the alliance council includes Beosin, SUSS NiFT, NUS AIDF, BAS, FOMO Pay, Onchain Custodian, Semisand, Coinhako, ParityBit, and Huawei Cloud. The current members include Huobi University, Moledao, Least Authority, PlanckX, Coding Girls, Coinlive, Footprint Analytics, Web3Drive, and Digital Treasures Center. The members of the Security Alliance will work and cooperate to continuously secure the global blockchain ecosystem with their technical strengths. The Alliance Council also welcomes more people in blockchain-related fields to join and jointly defend the security of the blockchain ecosystem. Alliance Registration https://forms.gle/pb3NaUgS3a2Sswnc8 Contact Telegram：@kristenbeosin, @Web3Donny Email: market@beosin.com Alliance Member — Beosin Beosin is a Singapore-based leading global blockchain security company with 100+ security experts in formal verification and blockchain security. With the mission of “Securing Web3.0 Ecosystem”, Beosin provides integrated blockchain security products and services including code security audit, risk monitoring, alerting & blocking for projects, security compliance KYT & KYC, and stolen asset recovery. Beosin has currently provided security services to more than 2,000 blockchain enterprises worldwide, audited over 2,500 smart contracts, and protected over $500 billion of assets for clients. Alliance Member — Footprint Analytics Footprint Analytics is a tool to uncover and visualize data across the blockchain, including NFT and GameFi data. It currently collects, parses, and cleans data from 18 chains and lets users build charts and dashboards without code using a drag-and-drop interface as well as with SQL or Python. Also Published here

The Q3 2022 Blockchain Security Report

A few months ago when I first started learning about Cryptocurrencies, It felt like I had been thrown into a whole new world with people using complex terms I didn't understand anything about. One concept in particular I struggled to understand was that of APIs. It took hours of tutorials and personal research to finally understand what APIs are and what they do. So, if you're a trainee web developer, crypto enthusiast, or a newbie to crypto trading, and you currently have a similar problem that I had, then this is the one tutorial you are going to need. Using simple language and easy-to-understand analogies, at the end of this tutorial, you'll save yourself hours of research time, and you will be able to answer the basic questions: What are Cryptocurrency APIs and how do they work?
Why are Crypto Currency APIs important?
What are the top APIs out there? Now let's dive in! What Are Cryptocurrency APIs, and How Do They Work? To understand this, we must first understand what APIs are. API is an acronym that stands for Application Programming Interface. Sounds complex? let's break it down. An application is software that helps people obtain information about specific things or carry out particular tasks like how the Expedia app on your phone helps you book flights on the go or the Amazon weather app helps you check weather forecasts. An interface in this context, is a program that helps 2 applications or websites communicate with each other, kinda like a middleman. So an API is basically a program or a “link” that helps two apps (or websites) “talk” to each other about a particular thing. So, when an app (let's call it app A) needs information about a particular thing or needs access to a service rendered by another app B, an API of app B is used. This API for app B serves as a link, a connection via which the specific information app A requires from app B is obtained. Wait … What?! Okay, okay. Let's break it down further using this analogy: Imagine you're opening a bakery, and you decide to sell cupcakes, muffins, and sourdough bread. For some reason, however,  you don't have the capacity to make sourdough bread of your own. What do you do? You reach out to another bakery that sells sourdough bread. Make an agreement for them to sell you sourdough bread when you request it. So, every time you need bread, you reach out to an employee of that bakery, and he brings you the bread. Now, bread isn't the only thing this second bakery sells, but it's the only thing you need from them. That's basically how an API works. When an app needs information that it cannot provide on its own, it uses the API of another app to gain access to that information from the other app. For a more technical analogy. Let's use a flight booking app to illustrate how APIs work: Imagine you're using a travel app like Expedia to book a flight. When you search for flights, the app doesn't have all the flight information stored locally. Instead, it uses specific APIs to fetch data from airlines' systems or other flight databases. Here's basically what happens behind the scenes: The travel app sends a request to the airline's API (e.g., "Get flights from Nigeria to New York on August 26th").


The airline's API processes the request and returns the relevant data (e.g., flight numbers, departure times, prices).


The travel app receives the data and displays it in a user-friendly format. So you see: The travel app is the "client" or "consumer" of the API. The airline's system is the "provider" of the API. The API acts as an intermediary, allowing the travel app to access the airline's data without having to replicate it locally. So, if I’m an app developer and the app I'm working on serves to provide specific information about the flight schedules from Qatar Airways for instance, I will use an API of Qatar Airways to get me and my future users access to that information. Now that we understand what an API is, applying that knowledge directly, we can say that cryptocurrency APIs are basically programs that enable different apps or websites to get access to specific data about Cryptocurrencies (i.e., chart updates, transactions, etc.) from other websites that directly monitor and interact with the Crypto Market. So, if I want information about the current status of Bitcoin on the Crypto Market, I open a website like Dervi. This website cannot monitor the Crypto market directly, so it uses an API to get information about current Bitcoin status from the Trading View website. Why Are Crypto Currency APIs Important? From what we've discussed earlier, we can say some of the importance of these APIs to Cryptocurrency are: Real-time data access: APIs provide up-to-date information on market prices, trading volumes, and blockchain data, enabling informed decisions.


Research and analysis: APIs provide data for researchers, analysts, and investors to study market trends and make predictions.


Integration with other services: APIs enable integration with payment systems, e-commerce platforms, and other services, expanding cryptocurrency adoption. In essence, cryptocurrency APIs facilitate innovation, automation, and accessibility in the cryptocurrency and blockchain ecosystem. What Are the Top Cryptocurrency APIs? Now that we understand what APIs are and can see why they are so important, let's look at some of the top Crypto CryptoCurrency APIs: Coin Market Cap API Coin Market Cap is one of the best cryptocurrency data APIs. So as a developer, incorporating it into your app or website will be really beneficial. You see it offers real-time cryptocurrency data and market information, such as: Historical cryptocurrency data since 2013
Live price updates for cryptocurrency assets
Market data, volume, and exchange information
Trading data and analytics
Data on non-fungible tokens (NFTs) Enterprise, professional, and free API plans This is an API that is used all over the world by cryptocurrency traders, developers, and financial firms. CoinGeko API CoinGeko is a big name amongst web developers! It offers many exciting features and has an easy-to-use interface. It has its own charts which it uses to track several coins in the Crypto market. Some of the important features it offers are these: Real-time and historical crypto prices,
Market capitalization data,
Trading volume and exchange data,
Metadata,
NFT collection floor prices. Coin Gecko API offers a free plan! As well as several paid plans with additional features and support. Binance: Binance is considered one of the largest cryptocurrency exchange platforms globally. It allows users to buy and sell digital assets. Its free API allows web developers to interact with the Binance platform and create custom trading applications like: Executing trades and checking their account balances.
Viewing their trading history using the API.
Enables developers to create custom trading strategies and automate their trading activities. The world of cryptocurrency is constantly changing, evolving, and expanding. APIs make it easier for web developers to build cryptocurrency-related apps, wallets, exchanges, and analytics tools. Which makes monitoring of trends within the Crypto market much easier for traders. A few months ago when I first started learning about Cryptocurrencies, It felt like I had been thrown into a whole new world with people using complex terms I didn't understand anything about. One concept in particular I struggled to understand was that of APIs. It took hours of tutorials and personal research to finally understand what APIs are and what they do. So, if you're a trainee web developer, crypto enthusiast, or a newbie to crypto trading, and you currently have a similar problem that I had, then this is the one tutorial you are going to need. Using simple language and easy-to-understand analogies, at the end of this tutorial, you'll save yourself hours of research time, and you will be able to answer the basic questions: What are Cryptocurrency APIs and how do they work? Why are Crypto Currency APIs important? What are the top APIs out there? What are Cryptocurrency APIs and how do they work? Why are Crypto Currency APIs important? What are the top APIs out there? Now let's dive in! What Are Cryptocurrency APIs, and How Do They Work? What Are Cryptocurrency APIs, and How Do They Work? To understand this, we must first understand what APIs are. API is an acronym that stands for Application Programming Interface. Sounds complex? let's break it down. An application is software that helps people obtain information about specific things or carry out particular tasks like how the Expedia app on your phone helps you book flights on the go or the Amazon weather app helps you check weather forecasts. An interface in this context, is a program that helps 2 applications or websites communicate with each other, kinda like a middleman. So an API is basically a program or a “link” that helps two apps (or websites) “talk” to each other about a particular thing. So, when an app (let's call it app A) needs information about a particular thing or needs access to a service rendered by another app B, an API of app B is used. This API for app B serves as a link, a connection via which the specific information app A requires from app B is obtained. Wait … What?! Wait … What?! Okay, okay. Let's break it down further using this analogy: Imagine you're opening a bakery, and you decide to sell cupcakes, muffins, and sourdough bread. For some reason, however,  you don't have the capacity to make sourdough bread of your own. What do you do? What do you do? You reach out to another bakery that sells sourdough bread. Make an agreement for them to sell you sourdough bread when you request it. So, every time you need bread, you reach out to an employee of that bakery, and he brings you the bread. Now, bread isn't the only thing this second bakery sells, but it's the only thing you need from them. That's basically how an API works. When an app needs information that it cannot provide on its own, it uses the API of another app to gain access to that information from the other app. For a more technical analogy. Let's use a flight booking app to illustrate how APIs work: Imagine you're using a travel app like Expedia to book a flight. When you search for flights, the app doesn't have all the flight information stored locally. Instead, it uses specific APIs to fetch data from airlines' systems or other flight databases. Here's basically what happens behind the scenes: The travel app sends a request to the airline's API (e.g., "Get flights from Nigeria to New York on August 26th"). The airline's API processes the request and returns the relevant data (e.g., flight numbers, departure times, prices). The travel app receives the data and displays it in a user-friendly format. The travel app sends a request to the airline's API (e.g., "Get flights from Nigeria to New York on August 26th"). The travel app sends a request to the airline's API (e.g., "Get flights from Nigeria to New York on August 26th"). The airline's API processes the request and returns the relevant data (e.g., flight numbers, departure times, prices). The airline's API processes the request and returns the relevant data (e.g., flight numbers, departure times, prices). The travel app receives the data and displays it in a user-friendly format. The travel app receives the data and displays it in a user-friendly format. So you see: The travel app is the "client" or "consumer" of the API. The travel app is the "client" or "consumer" of the API. The airline's system is the "provider" of the API. The airline's system is the "provider" of the API. The API acts as an intermediary, allowing the travel app to access the airline's data without having to replicate it locally. The API acts as an intermediary, allowing the travel app to access the airline's data without having to replicate it locally. So, if I’m an app developer and the app I'm working on serves to provide specific information about the flight schedules from Qatar Airways for instance, I will use an API of Qatar Airways to get me and my future users access to that information. Now that we understand what an API is, applying that knowledge directly, we can say that cryptocurrency APIs are basically programs that enable different apps or websites to get access to specific data about Cryptocurrencies (i.e., chart updates, transactions, etc.) from other websites that directly monitor and interact with the Crypto Market. So, if I want information about the current status of Bitcoin on the Crypto Market, I open a website like Dervi. This website cannot monitor the Crypto market directly, so it uses an API to get information about current Bitcoin status from the Trading View website. Why Are Crypto Currency APIs Important? Why Are Crypto Currency APIs Important? From what we've discussed earlier, we can say some of the importance of these APIs to Cryptocurrency are: Real-time data access: APIs provide up-to-date information on market prices, trading volumes, and blockchain data, enabling informed decisions. Research and analysis: APIs provide data for researchers, analysts, and investors to study market trends and make predictions. Integration with other services: APIs enable integration with payment systems, e-commerce platforms, and other services, expanding cryptocurrency adoption. Real-time data access: APIs provide up-to-date information on market prices, trading volumes, and blockchain data, enabling informed decisions. Real-time data access: APIs provide up-to-date information on market prices, trading volumes, and blockchain data, enabling informed decisions. Research and analysis: APIs provide data for researchers, analysts, and investors to study market trends and make predictions. Research and analysis: APIs provide data for researchers, analysts, and investors to study market trends and make predictions. Integration with other services: APIs enable integration with payment systems, e-commerce platforms, and other services, expanding cryptocurrency adoption. Integration with other services: APIs enable integration with payment systems, e-commerce platforms, and other services, expanding cryptocurrency adoption. In essence, cryptocurrency APIs facilitate innovation, automation, and accessibility in the cryptocurrency and blockchain ecosystem. What Are the Top Cryptocurrency APIs? What Are the Top Cryptocurrency APIs? Now that we understand what APIs are and can see why they are so important, let's look at some of the top Crypto CryptoCurrency APIs: Coin Market Cap API Coin Market Cap API Coin Market Cap is one of the best cryptocurrency data APIs. So as a developer, incorporating it into your app or website will be really beneficial. You see it offers real-time cryptocurrency data and market information, such as: Historical cryptocurrency data since 2013 Live price updates for cryptocurrency assets Market data, volume, and exchange information Trading data and analytics Data on non-fungible tokens (NFTs) Enterprise, professional, and free API plans Historical cryptocurrency data since 2013 Live price updates for cryptocurrency assets Market data, volume, and exchange information Trading data and analytics Data on non-fungible tokens (NFTs) Enterprise, professional, and free API plans This is an API that is used all over the world by cryptocurrency traders, developers, and financial firms. CoinGeko API CoinGeko API CoinGeko is a big name amongst web developers! It offers many exciting features and has an easy-to-use interface. It has its own charts which it uses to track several coins in the Crypto market. Some of the important features it offers are these: Real-time and historical crypto prices, Market capitalization data, Trading volume and exchange data, Metadata, NFT collection floor prices. Real-time and historical crypto prices, Market capitalization data, Trading volume and exchange data, Metadata, NFT collection floor prices. Coin Gecko API offers a free plan! As well as several paid plans with additional features and support. Binance: Binance: Binance is considered one of the largest cryptocurrency exchange platforms globally. It allows users to buy and sell digital assets. Its free API allows web developers to interact with the Binance platform and create custom trading applications like: Executing trades and checking their account balances. Viewing their trading history using the API. Enables developers to create custom trading strategies and automate their trading activities. Executing trades and checking their account balances. Viewing their trading history using the API. Enables developers to create custom trading strategies and automate their trading activities. The world of cryptocurrency is constantly changing, evolving, and expanding. APIs make it easier for web developers to build cryptocurrency-related apps, wallets, exchanges, and analytics tools. Which makes monitoring of trends within the Crypto market much easier for traders.

A Beginner's Introduction to Cryptocurrency APIs: The Basics You Need as a Trainee Web Developer

GAINS Associates has announced the launch of GainsPad, heralding a new era in Web3 fundraising. With a legacy spanning six years, a fully transparent team, and an unrivaled track record exemplified by the success of Gains Private, the inaugural launchpad, the new platform is poised to redefine the landscape of Initial Decentralized Offerings (IDOs). As the team embarks on this journey, they invite individuals worldwide to join them in shaping the future of crypto fundraising. GainsPad presents a monumental opportunity, magnitudes larger than anything witnessed before in the IDO market. The team is not just offering a platform; they are fostering a community where participants can partake in life-changing deals, embodying the spirit of empowerment and inclusivity. What Are The Key Highlights? GainsPad introduces a novel approach to IDOs, focusing on accessibility and fairness while maintaining the highest standards of transparency and security. The platform boasts several key components designed to enhance the investor experience. Firstly, there is the ‘Refund Policy,’ which helps safeguard investors against unforeseen circumstances. The policy ensures peace of mind during the token launch phase. Next, there is the ‘Tiered System.’ With varying tiers tailored to different levels of engagement, participants can maximize their investment potential and increase their chances of securing allocations. Finally, there is the ‘Whitelist Process.’ Through engaging educational content and quizzes, the team streamlines the whitelisting process, ensuring that only knowledgeable and committed community members gain access to exclusive opportunities. Alongside maintaining strong foundational principles, the team’s approach entails a dual strategy: seizing opportunities within burgeoning trends ahead of their peak and investing in emerging narratives and ecosystems preemptively. They also possess the capability to transparently raise funds on-chain by leveraging a consortium of angel investors under one unified entity. Moreover, GAINS offers comprehensive support to various projects, including in-house marketing initiatives and facilitation of introductions to the platform’s expansive network encompassing over 250 Key Opinion Leaders (KOLs), different launchpads, venture capitalists (VCs), marketing agencies, news platforms, exchanges, and market makers (MMs). Strategic Partnerships GainsPad is proud to collaborate with industry-leading partners such as ETHDubai, Polygon, BNB Chain, and Trust Wallet. These partnerships underscore the team’s commitment to excellence and their dedication to providing unparalleled value to the community. GAINS also boasts distinguished advisors such as Walid Benothman, known for his role in driving growth at 1INCH, Evan Luthra, a prominent Indian influencer, and Jonathan Faye, whose extensive experience includes a decade at the $125B AUM ODDO BHF, where he transitioned from a legal background to lead Open Innovation initiatives. Additionally, $GAINS has recently secured a listing on a premier centralized exchange. Key Figures: Evan Luthra Evan Luthra, a renowned figure within the cryptocurrency and tech world, has been named twice by Forbes as part of their esteemed 30 under 30 list. His expertise spans across multiple domains, from his prolific work in tech entrepreneurship to his passion for nonfungible tokens. Evan has been recognised by prestigious platforms like Forbes and Entrepreneur Magazine, thereby highlighting his impact as both an angel investor as well as a tech influencer. He is also a tech visionary with an impressive track record of creating more than 30 applications by the age of 17, in addition to fueling the growth of over 500 Web3 companies. As a tech visionary, Evan’s influence extends to a market cap exceeding a whopping $100 billion. Evans has also been actively involved in the crypto space for years, successfully managing a cryptocurrency investment bank, as well as investing in projects such as Ripple and Hashgraph. Evan has also shared his knowledge globally through platforms like TEDx alongside various institutions, educating audiences on topics ranging from cryptocurrencies and blockchain to the future of decentralized autonomous organizations. Walid Benothman Walid's Leadership roles in companies like 1inch and Curv demonstrate his expertise in driving growth and strategy operations. Walid was also an early employee at Ripple. His strategic initiatives at both Curv and Ripple played a significant role in advancing the mainstream adoption of blockchain technology and cryptocurrencies, thereby culminating in the acquisition of Curv by PayPal. Furthermore, from management consulting to creating the inaugural digitally managed semi-professional football club in Paris, Walid’s entrepreneurial spirit and foresight have also led to groundbreaking ventures at a consistent level.\ Jonathan Faye Jonathan has over ten years of relevant experience within the world of finance, as proven by his work at the aforementioned ODDO BHF, a French-German investment bank with €150b AUM. Although he began as a lawyer, Jonathan nevertheless played an important part in M&A, regulatory affairs, and private equity as well as VC. Eventually progressing to Global Head of Open Innovation, Jonathan concentrated on shaping corporate VC strategy while simultaneously pioneering blockchain technology for a modern age, supporting ventures such as Coinhouse and Ledger. Presently, Jonathan is in charge of JonVenture, his own advisory firm, providing key guidance to early-stage startups by utilizing his passion and experience for both angel investment and Web3. Investment Opportunity As a decentralized community VC established six years ago, GAINS operates a launchpad for private sales. Notably, the platform has deployed over $35 million for more than 300 projects, featuring notable successes such as Bloktopia (x1000), Quant Network (x500), Avalanche (x300), and Hashgraph (x50). GAINS CEO, Alexandre Raffin, stated that “At GAINS, we are driven by a relentless pursuit of excellence and a steadfast commitment to our community. With the launch of GainsPad, we are democratizing access to groundbreaking projects and revolutionizing the way individuals participate in Web3 fundraising.” To participate in GainsPad's IDOs, individuals are required to stake a minimum of 1,000 $GAINS, with additional tickets awarded for every 250 $GAINS staked. The aforementioned tiered system offers varying levels of bonuses and incentives, ensuring that each participant is rewarded according to their level of engagement and commitment. About GAINS GAINS Associates is the world's first decentralized venture capital firm, providing individuals holding $GAINS with access to the most promising crypto projects. With over six years of experience and a history of ultra-high return deals, GAINS is shaping the future of DeFi. GAINS stands for ‘Group Action Is Never Small’, symbolizing the collaborative spirit that is the essence of blockchain and decentralization. For individuals seeking to invest in the next generation of crypto unicorns, GainsPad represents the ultimate destination. GAINS remains steadfast in its mission to remove entry barriers to investing and empower individuals to seize opportunities in the crypto space. With a proven track record and a commitment to excellence, there is no better time than now to get started with GAINS as they have a fully doxxed team, an impressive track record for their first platform, Gains Private, that was launched back in 2021, and the fact that the team is finally ready to tap into the IDO market, which is a 10-20x bigger opportunity with potentially massive returns up for grabs. Visit the official website for further information and join GAINS’ Telegram channel to engage with the team members who are dedicated to assisting customers every step of the way. For regular updates, check out the X, LinkedIn, Discord, Facebook, Instagram, Medium, TikTok, and YouTube channels as well. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. GAINS Associates has announced the launch of GainsPad, heralding a new era in Web3 fundraising. With a legacy spanning six years, a fully transparent team, and an unrivaled track record exemplified by the success of Gains Private, the inaugural launchpad, the new platform is poised to redefine the landscape of Initial Decentralized Offerings (IDOs). GAINS Associates As the team embarks on this journey, they invite individuals worldwide to join them in shaping the future of crypto fundraising. GainsPad presents a monumental opportunity, magnitudes larger than anything witnessed before in the IDO market. The team is not just offering a platform; they are fostering a community where participants can partake in life-changing deals, embodying the spirit of empowerment and inclusivity. What Are The Key Highlights? GainsPad introduces a novel approach to IDOs, focusing on accessibility and fairness while maintaining the highest standards of transparency and security. The platform boasts several key components designed to enhance the investor experience. Firstly, there is the ‘ Refund Policy ,’ which helps safeguard investors against unforeseen circumstances. The policy ensures peace of mind during the token launch phase. Next, there is the ‘ Tiered System .’ With varying tiers tailored to different levels of engagement, participants can maximize their investment potential and increase their chances of securing allocations. Refund Policy Refund Policy Tiered System Tiered System Finally, there is the ‘Whitelist Process.’ Through engaging educational content and quizzes, the team streamlines the whitelisting process, ensuring that only knowledgeable and committed community members gain access to exclusive opportunities. Alongside maintaining strong foundational principles, the team’s approach entails a dual strategy: seizing opportunities within burgeoning trends ahead of their peak and investing in emerging narratives and ecosystems preemptively. They also possess the capability to transparently raise funds on-chain by leveraging a consortium of angel investors under one unified entity. Moreover, GAINS offers comprehensive support to various projects, including in-house marketing initiatives and facilitation of introductions to the platform’s expansive network encompassing over 250 Key Opinion Leaders (KOLs), different launchpads, venture capitalists (VCs), marketing agencies, news platforms, exchanges, and market makers (MMs). Strategic Partnerships GainsPad is proud to collaborate with industry-leading partners such as ETHDubai, Polygon, BNB Chain, and Trust Wallet. These partnerships underscore the team’s commitment to excellence and their dedication to providing unparalleled value to the community. GAINS also boasts distinguished advisors such as Walid Benothman, known for his role in driving growth at 1INCH, Evan Luthra, a prominent Indian influencer, and Jonathan Faye, whose extensive experience includes a decade at the $125B AUM ODDO BHF, where he transitioned from a legal background to lead Open Innovation initiatives. Additionally, $GAINS has recently secured a listing on a premier centralized exchange. Key Figures: Evan Luthra Evan Luthra Evan Luthra Evan Luthra, a renowned figure within the cryptocurrency and tech world, has been named twice by Forbes as part of their esteemed 30 under 30 list. His expertise spans across multiple domains, from his prolific work in tech entrepreneurship to his passion for nonfungible tokens. Evan has been recognised by prestigious platforms like Forbes and Entrepreneur Magazine, thereby highlighting his impact as both an angel investor as well as a tech influencer. He is also a tech visionary with an impressive track record of creating more than 30 applications by the age of 17, in addition to fueling the growth of over 500 Web3 companies. As a tech visionary, Evan’s influence extends to a market cap exceeding a whopping $100 billion. Evans has also been actively involved in the crypto space for years, successfully managing a cryptocurrency investment bank, as well as investing in projects such as Ripple and Hashgraph. Evan has also shared his knowledge globally through platforms like TEDx alongside various institutions, educating audiences on topics ranging from cryptocurrencies and blockchain to the future of decentralized autonomous organizations. Walid Benothman Walid Benothman Walid Benothman Walid's Leadership roles in companies like 1inch and Curv demonstrate his expertise in driving growth and strategy operations. Walid was also an early employee at Ripple. His strategic initiatives at both Curv and Ripple played a significant role in advancing the mainstream adoption of blockchain technology and cryptocurrencies, thereby culminating in the acquisition of Curv by PayPal. Furthermore, from management consulting to creating the inaugural digitally managed semi-professional football club in Paris, Walid’s entrepreneurial spirit and foresight have also led to groundbreaking ventures at a consistent level.\ Jonathan Faye Jonathan Faye Jonathan Faye Jonathan has over ten years of relevant experience within the world of finance, as proven by his work at the aforementioned ODDO BHF, a French-German investment bank with €150b AUM. Although he began as a lawyer, Jonathan nevertheless played an important part in M&A, regulatory affairs, and private equity as well as VC. Eventually progressing to Global Head of Open Innovation, Jonathan concentrated on shaping corporate VC strategy while simultaneously pioneering blockchain technology for a modern age, supporting ventures such as Coinhouse and Ledger. Presently, Jonathan is in charge of JonVenture, his own advisory firm, providing key guidance to early-stage startups by utilizing his passion and experience for both angel investment and Web3. Investment Opportunity As a decentralized community VC established six years ago, GAINS operates a launchpad for private sales. Notably, the platform has deployed over $35 million for more than 300 projects, featuring notable successes such as Bloktopia (x1000), Quant Network (x500), Avalanche (x300), and Hashgraph (x50). GAINS CEO, Alexandre Raffin, stated that “At GAINS, we are driven by a relentless pursuit of excellence and a steadfast commitment to our community. With the launch of GainsPad, we are democratizing access to groundbreaking projects and revolutionizing the way individuals participate in Web3 fundraising.” GAINS CEO, Alexandre Raffin, stated that “At GAINS, we are driven by a relentless pursuit of excellence and a steadfast commitment to our community. With the launch of GainsPad, we are democratizing access to groundbreaking projects and revolutionizing the way individuals participate in Web3 fundraising.” To participate in GainsPad's IDOs, individuals are required to stake a minimum of 1,000 $GAINS, with additional tickets awarded for every 250 $GAINS staked. The aforementioned tiered system offers varying levels of bonuses and incentives, ensuring that each participant is rewarded according to their level of engagement and commitment. About GAINS GAINS Associates is the world's first decentralized venture capital firm, providing individuals holding $GAINS with access to the most promising crypto projects. With over six years of experience and a history of ultra-high return deals, GAINS is shaping the future of DeFi. GAINS stands for ‘Group Action Is Never Small’, symbolizing the collaborative spirit that is the essence of blockchain and decentralization. For individuals seeking to invest in the next generation of crypto unicorns, GainsPad represents the ultimate destination. GAINS remains steadfast in its mission to remove entry barriers to investing and empower individuals to seize opportunities in the crypto space. With a proven track record and a commitment to excellence, there is no better time than now to get started with GAINS as they have a fully doxxed team, an impressive track record for their first platform, Gains Private, that was launched back in 2021, and the fact that the team is finally ready to tap into the IDO market, which is a 10-20x bigger opportunity with potentially massive returns up for grabs. Visit the official website for further information and join GAINS’ Telegram channel to engage with the team members who are dedicated to assisting customers every step of the way. For regular updates, check out the X , LinkedIn , Discord , Facebook , Instagram , Medium , TikTok , and YouTube channels as well. official website official website Telegram channel Telegram channel X X LinkedIn LinkedIn Discord Discord Facebook Facebook Instagram Instagram Medium Medium TikTok TikTok YouTube YouTube This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

GainsPad Finally Launches And Ushers In A New Era Of Web3 Fundraising

This paper is available on arxiv under CC 4.0 license. Authors: (1) Muzhaffar Hazman, University of Galway, Ireland; (2) Susan McKeever, Technological University Dublin, Ireland; (3) Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs. Text-STILT 6 Conclusion In this work, we addressed the challenge of training multimodal meme sentiment classifiers on a limited number of labelled memes by incorporating unimodal sentiment analysis data. We did so by proposing the first instance of STILT that applies unimodal intermediate tasks to a multimodal target task. Specifically, we tested image-only and text-only sentiment classification as intermediate tasks in training a meme sentiment classifier. We showed that this approach worked – unimodal text improved meme classification performance to a statistically significant degree. This novel approach allowed us to train a meme classifier that outperforms meme-only finetuning with only 60% as many labelled meme samples. As possible explanations for our observations, we discuss apparent similarities and differences in the roles of image and text modalities between unimodal and multimodal sentiment analysis tasks. Acknowledgments This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224; and the provision of computational facilities and support from the Irish Centre for High-End Computing (ICHEC). References Christian Bauckhage. 2011. Insights into internet memes. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):42–49. Efrat Blaier, Itzik Malkiel, and Lior Wolf. 2021. 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Image sentiment polarity - dataset. Available at https://data.world/ crowdflower/image-sentiment-polarity. Accessed: 2023-01-15. Baishan Duan and Yuesheng Zhu. 2022. Browallia at memotion 2.0 2022 : Multimodal memotion analysis with modified ogb strategies (short paper). In DeFactify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Noam Gal, Limor Shifman, and Zohar Kampf. 2016. “it gets better”: Internet memes and the construction of collective identity. New Media & Society, 18(8):1698–1714. Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, and Ivan Marsic. 2018. Hybrid Attention based Multimodal Network for Spoken Language Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2379–2390, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Muzhaffar Hazman, Susan McKeever, and Josephine Griffith. 2023. 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Innovative Strategies in Meme Sentiment Analysis: The Impact of Unimodal Training

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 6. Conclusion and Future Work In this paper, we summarize the approaches used by the participants for the Memotion 3 task and analyze the results. Due to the multi-modal nature of the dataset, all teams use a pre-trained image and text embedding models. However, each team presents a novel model pipeline. The highest scores achieved in Task A, Task B and Task C of Memotion 3.0 are 34.41%, 79.77% and 59.82% respectively, which shows there is significant room for improvement. On analysis of the results and the mis-classified examples on the test set, we find that "Sarcasm" and "Humour" are difficult to identify, especially in code-mixed memes. While we address Hind-English code-mixed memes in this paper, future work could include exploring other languages/language pairs. A unified baseline model to analyze memes in multiple languages could also be an interesting possibility. References [1] R. Dawkins, The selfish gene, Granada Publishing Lim, 1979. [2] A. Marwick, Memes, Contexts 12 (2013) 12–13. [3] N. Akhther, Internet memes as form of cultural discourse: A rhetorical analysis on facebook, 2018. doi:10.31234/osf.io/sx6t7. [4] S. 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Aguilar, S. Kar, S. Pandey, S. PYKL, B. Gambäck, T. Chakraborty, T. Solorio, A. Das, SemEval-2020 task 9: Overview of sentiment analysis of code-mixed tweets, in: Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020. [37] B. R. Chakravarthi, et al., Findings of the shared task on offensive language identification in Tamil, Malayalam, and Kannada, in: Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, 2021. [38] B. R. Chakravarthi, V. Muralidaran, R. Priyadharshini, J. P. McCrae, Corpus creation for sentiment analysis in code-mixed tamil-english text, arXiv:2006.00206 (2020). [39] B. R. Chakravarthi, N. Jose, S. Suryawanshi, E. Sherly, J. P. McCrae, A sentiment analysis dataset for code-mixed malayalam-english, arXiv preprint arXiv:2006.00210 (2020). [40] A. Hande, R. Priyadharshini, B. R. Chakravarthi, KanCMD: Kannada CodeMixed dataset for sentiment analysis and offensive language detection, in: Workshop on Computational Modeling of People’s Opinions, Personality, and Emotion’s in Social Media, 2020. [41] S. Dowlagar, R. Mamidi, Graph convolutional networks with multi-headed attention for code-mixed sentiment analysis, in: Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, 2021, pp. 65–72. [42] J. Risch, A. Stoll, M. Ziegele, R. Krestel, hpidedis at germeval 2019: Offensive language identification using a german bert model., in: KONVENS, 2019. [43] D. Tula, P. Potluri, S. Ms, S. Doddapaneni, P. Sahu, R. Sukumaran, P. Patwa, Bitions@DravidianLangTech-EACL2021: Ensemble of multilingual language models with pseudo labeling for offence detection in Dravidian languages, in: Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, 2021. [44] D. Tula, M. S. Shreyas, V. Reddy, P. Sahu, S. Doddapaneni, P. Potluri, R. Sukumaran, P. Patwa, Offence detection in dravidian languages using code-mixing index-based focal loss, SN Computer Science 3 (2022). doi:10.1007/s42979-022-01190-1. [45] Y. Ma, L. Zhao, J. Hao, XLP at SemEval-2020 task 9: Cross-lingual models with focal loss for sentiment analysis of code-mixing language, in: Semeval, 2020. [46] M. Ali, S. T. Kandukuri, S. Manduru, P. Patwa, A. Das, Pesto: Switching point based dynamic and relative positional encoding for code-mixed languages (student abstract), AAAI (2022). doi:10.1609/aaai.v36i11.21587. [47] A. Hu, S. Flaxman, Multimodal sentiment analysis to explore the structure of emotions, in: KDD, 2018. [48] R. Jha, V. Kaki, V. Kolla, S. Bhagat, P. Patwa, A. Das, S. Pal, Image2tweet: Datasets in Hindi and English for generating tweets from images, in: Proceedings of the 18th International Conference on Natural Language Processing (ICON), 2021. [49] R. Gomez, J. Gibert, L. Gomez, D. Karatzas, Exploring hate speech detection in multimodal publications, 2019. arXiv:1910.03814. [50] B. Zadeh, et al., Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph, in: ACL, 2018. [51] D. Kiela, H. Firooz, A. Mohan, V. Goswami, A. Singh, P. Ringshia, D. Testuggine, The hateful memes challenge: Detecting hate speech in multimodal memes, Neurips (2020). [52] S. Suryawanshi, B. R. Chakravarthi, Findings of the shared task on troll meme classification in Tamil, in: Speech and Language Technologies for Dravidian Languages, 2021. [53] E. Hossain, O. Sharif, M. M. Hoque, MUTE: A multimodal dataset for detecting hateful memes, in: Proceedings of the 2nd AACL Student Research Workshop, 2022. [54] Z. Xie, L. Liu, Y. Wu, L. Zhong, L. Li, Learning text-image joint embedding for efficient cross-modal retrieval with deep feature engineering, ACM Transactions on Information Systems 40 (2021) 1–27. URL: https://doi.org/10.1145%2F3490519. doi:10.1145/3490519. [55] V. Krishna, S. Suryavardan, S. Mishra, S. Ramamoorthy, P. Patwa, M. Chakraborty, A. Chadha, A. Das, A. Sheth, Imaginator: Pre-trained image+text joint embeddings using word-level grounding of images, 2023. arXiv:2305.10438. [56] N. Gunti, S. Ramamoorthy, P. Patwa, A. Das, Memotion analysis through the lens of joint embedding (student abstract), Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022). doi:10.1609/aaai.v36i11.21616. [57] J. Lu, D. Batra, D. Parikh, S. Lee, Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks, 2019. arXiv:1908.02265. [58] L. H. Li, M. Yatskar, D. Yin, C.-J. Hsieh, K.-W. Chang, Visualbert: A simple and performant baseline for vision and language, 2019. arXiv:1908.03557. [59] S. Ramamoorthy, N. Gunti, S. Mishra, S. Suryavardan, A. Reganti, P. Patwa, A. Das, T. Chakraborty, A. Sheth, A. Ekbal, et al., Memotion 2: Dataset on sentiment and emotion analysis of memes, in: Proceedings of De-Factify: Workshop on Multimodal Fact Checking and Hate Speech Detection, 2022. [60] K. N. Phan, G.-S. Lee, H.-J. Yang, S.-H. Kim, Little flower at memotion 2.0 2022: Ensemble of multi-modal model using attention mechanism in memotion analysis, in: Proceedings of De-Factify: Workshop on Multimodal Fact Checking and Hate Speech Detection, 2022. [61] T. Morishita, G. Morio, S. Horiguchi, H. Ozaki, T. Miyoshi, Hitachi at SemEval-2020 task 8: Simple but effective modality ensemble for meme emotion recognition, in: SemEval, 2020. [62] Y. Guo, J. Huang, Y. Dong, M. Xu, Guoym at SemEval-2020 task 8: Ensemble-based classification of visuo-lingual metaphor in memes, in: SemEval, 2020. [63] G.-A. Vlad, G.-E. Zaharia, D.-C. Cercel, et al., Upb at semeval-2020 task 8: Joint textual and visual modeling in a multi-task learning architecture for memotion analysis, 2020. arXiv:2009.02779. [64] T. T. Nguyen, N. T. Pham, N. D. Nguyen, et al., Hcilab at memotion 2.0 2022: Analysis of sentiment, emotion and intensity of emotion classes from meme images using single and multi modalities, in: Proceedings of De-Factify: Workshop on Multimodal Fact Checking and Hate Speech Detection, 2022. [65] G. G. Lee, M. Shen, Amazon pars at memotion 2.0 2022: Multi-modal multi-task learning for memotion 2.0 challenge, Proceedings of De-Factify: Workshop on Multimodal Fact Checking and Hate Speech Detection (2020). [66] M. Bhange, N. Kasliwal, Hinglishnlp: Fine-tuned language models for hinglish sentiment detection, arXiv preprint arXiv:2008.09820 (2020). [67] A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al., An image is worth 16x16 words: Transformers for image recognition at scale, arXiv:2010.11929 (2020). [68] W. Yu, D. Kolossa, wentaorub at Memotion 3: Ensemble learning for multi-modal meme classification, in: Proceedings of De-Factify 2: Workshop on Multimodal Fact Checking and Hate Speech Detection, CEUR, 2023. [69] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, et al., Learning transferable visual models from natural language supervision, in: ICML, 2021. [70] X. Li, X. Yin, C. Li, P. Zhang, et al., Oscar: Object-semantics aligned pre-training for vision-language tasks, 2020. arXiv:2004.06165. [71] Y.-C. Tang, K.-D. Wang, T.-Y. Ou, W.-C. Peng, NYCU_TWO at Memotion 3: Good foundation, good teacher, then you have good meme analysis, in: Proceedings of De-Factify 2: Workshop on Multimodal Fact Checking and Hate Speech Detection, CEUR, 2023. [72] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, 2021. arXiv:2103.14030. [73] X. Guo, J. Ma, A. Zubiaga, NUAA-QMUL-AIIT at Memotion 3: Multi-modal fusion with squeeze-and-excitation for internet meme emotion analysis, in: Proceedings of De-Factify 2: Workshop on Multimodal Fact Checking and Hate Speech Detection, CEUR, 2023. [74] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, V. Stoyanov, Roberta: A robustly optimized bert pretraining approach (2019). [75] X. Zhai, J. Puigcerver, A. Kolesnikov, et al., A large-scale study of representation learning with the visual task adaptation benchmark, 2020. arXiv:1910.04867. [76] G. Ke, Q. Meng, T. Finley, et al., Lightgbm: A highly efficient gradient boosting decision tree, in: Neurips, 2017. [77] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: CVPR, 2016. [78] F. Schroff, D. Kalenichenko, J. Philbin, FaceNet: A unified embedding for face recognition and clustering, in: CVPR, 2015.

Overview of Memotion 3: Sentiment & Emotion
Analysis of Codemixed Hinglish - Conclusion

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 5. Results The performance in Task A i.e. sentiment classification is presented in Table 1. Our proposed baseline achieved a score of 33.28%. Out of the five final submissions, four teams managed to surpass the baseline. The top two teams, namely, NUAA-QMUL-AIIT [73] and NYCU_TWO [71] improved on the baseline by 1.1% and 0.9% respectively. The difference in F1 scores for this task is minimal across all participants. Table 2 shows the leaderboard for Task B. Three teams out-performed the baseline for this task and top performing team wentaorub [68] improves on the final score by 5.4%. Team CUFE performs lower than the baseline score by 3.4%, however, they present the highest score on the Table 2 Leaderboard of teams on Task B: Emotion analysis {H:Humor, S:Sarcasm, O:Offense, M:Motivation}. All the scores are in percentage. The teams are ranked by their average F1 scores (overall) across all the four emotions. Motivation emotion is the easiest to detect while offense is the most difficult to detect. Offensive sub-task and on the Sarcasm sub-task, jointly with NYCU_TWO [71]. wentaorub [68], NYCU_TWO, and NUAA-QMUL-AIIT achieved the same score on Motivation, which is 4% higher than the baseline. NYCU_TWO also scored the highest in the Humour sub-task, improving on the baseline by 4.5%. Based on these F1 scores, we can deduce that the Motivation class is the easiest to detect, similar to Memotion 2.0 [59], despite some teams performing poorly on this class. However, in this iteration, the Offensive class is the hardest to detect, instead of the Sarcasm class. No single team performs the best on all the classes. Table 3 Leaderboard of teams on Task C: Emotion intensity detection {H:Humor, S:Sarcasm, O:Offense, M:Motivation}. All scores are in percentage. The teams are ranked by average F1 scores (overall) across all the four emotions. Intensity of sarcasm is by far the most difficult to detect. Motivation has only two intensities as opposed to four intensity levels for other emotions. As shown in the leaderboard for Task C in Table 3, all the participating teams outperform the baseline in this task. The minimum improvement in F1 score is 0.8% by team CUFE and the maximum improvement in the final score is 7.5% by wentaorub [68]. The submission by wentaorub exceeds all other teams in the sub-tasks Humour, Offensive and Motivation. NUAA-QMUL-AIIT [73] matched the highest score of wentaorub in the Motivation category, while CUFE achieved the highest score in the Sarcasm sub-task. The Motivation class has significantly higher F1 scores than other classes, due to it having only 2 intensities whereas other classes have 4 intensities each. The best overall score is only 59.82%, which shows the difficulty of the task. A major observation when comparing performances with the previous iteration of the task [7] is that the performance on Task A is much worse in Memotion 3. Overall Scores in Tasks B and C have remained about the same compared to Memotion 2. For each individual task, we analyze the memes from the test set that all participants made wrong predictions on. For Task A, there are 121 memes where all participants mis-classified the label, out of which 66 memes were true negative sentiment followed closely by true positive memes. For task B, there are 231 such memes, majority of which belong to "humor" and "sarcasm" class. Finally, for Task C, 421 memes are mis-classified by all the systems - most memes mis-classified by all teams are "Very Funny", "Very Sarcastic", "Slightly Offensive". Some such examples for Task A, B and C are shown in figures 1, 2, and 3 respectively. Further, we note that most of such difficult examples have code-mixed text. As for the overall performances, only two teams - NUAA-QMUL-AIIT and NYCU_TWO [73, 71] - perform better than the baseline in all tasks.

Overview of Memotion 3: Sentiment & Emotion
Analysis of Codemixed Hinglish -Results

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 4. Participating systems There were 47 team registrations for the task in the Memotion 3.0 task page, of which 5 teams made submissions for the final test set of the dataset. The results for all three tasks are given in the following section and an overview of the 4 teams that presented their description papers are provided below. Table 1 Leaderboard of teams on Task A: Sentiment Analysis. [68] use CLIP [69] for individual text and image embeddings, before concatenating them and passing them through multi-headed attention layers for classification. They also use the OSCAR [70] model in this approach and an ensemble of their models are used for the final submission. Datasets such as Facebook Hateful memes [51], MMHS150k [49] etc. are used for pre-training. This architecture helped them achieve the best performance in Task B and C. wentaorub [71] propose a two model pipelines, namely Coopoerative Teaching Model (CTM) for task A and Cascaded Emotion Classifier (CEC) for task B and C. A fusion of multimodal embeddings from pre-trained Swin-Transformer [72] and CLIP are passed to the CTM and CEC pipelines. CEC helps leverage task C predictions for task B by jointly training the model. This team attained the best results in 3 out of the 4 labels in Task B. NYCU_TWO [73] refer to their approach as Squeeze-and-Excitation Fusion or SEFusion. The textual features from pre-trained RoBERTa [74] and visual features from CLIP-ViT [75] are fused to obtain multi-modal embeddings. The fusion is the SEFusion module, which uses a learned activation of the squeezed features, allowing for weighted fusion of multi-modal embeddings. This approach led to NUAA-QMUL-AIIT being the 1st ranked team in Task A. NUAA-QMUL-AIIT used a LightGBM [76] classifier for classification on every individual emotion or label in all Tasks. The inputs to the classifier were pre-trained Hinglish-DistilBERT for text embeddings and ResNet18 [77] for image embeddings. Other features, such as occurrence of characters, word count etc. from text and number of faces in the memes (extracted using Facenet’s [78] multitask cascaded CNN), were also used. CUFE obtained the highest score in 2 labels in Task B and 1 label in Task C. CUFE

Overview of Memotion 3: Sentiment & Emotion
Analysis of Codemixed Hinglish -Participating Systems

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 3. Task Details 3.1. Tasks Memotion 3 is the 3rd iteration of the Memotion task. The challenge consists of three sub-tasks: 1. Task A - Sentiment analysis of memes: Given a meme, the system is supposed to classify the meme’s sentiment as positive, negative or neutral. 2. Task B - Overall emotion analysis of memes: This task’s goal is to pinpoint certain emotions connected to a given meme. Whether a meme is humorous, sarcastic, offensive, or motivating should be indicated by the system. There are multiple categories in which a meme can fit. 3. Task C - Classifying the intensity of meme emotions: The task is to determine the degree to which a particular emotion is being expressed. The ranking of these emotions is as follows: a) Humour: Not funny, funny, very funny and hilarious b) Sarcasm: Not Sarcastic, little sarcastic, very sarcastic and extremely sarcastic c) Offensive: Not offensive, slightly offensive, very offensive and hateful offensive d) Motivation: Not motivational, motivational 3.2. Dataset The tasks were conducted on the Memotion 3 dataset [5]. It consists of Hindi-English codemixed memes, which were collected from selenium based web crawler and they were gathered from various public platforms like Reddit, Google Images, etc and annotated manually. The dataset consists of 10,000 meme images divided into a train-val-test split of 7000-1500-1500. Each meme is annotated for its sentiment, emotion and intensity of emotion. Images also have their corresponding OCR text extracted with the help of Google Vision APIs and their respective URLs. For more details of the dataset, please refer to [5]. 3.3. Evaluation As mentioned previously, there are three tasks. Scoring is done for each task separately, and separate leaderboards are generated. For each task, we use weighted average F1 score to measure the performance of a model. The participants had access to only train and validation set. They were asked to submit a maximum of 3 submissions on the test set for each task, the best of which was selected as part of the leaderboard. 3.4. Baselines For multi-modal data, it is crucial to take into account both the visual and textual properties, particularly in the case of memes where the context can only be recorded using a mix of both elements. For textual features, we employ a multilingual form of BERT, namely HinglishBERT (BERT base-multilingual-cased) [66], which is tuned on Hinglish data. The trained Vision Transformer (ViT) model provides the visual properties [67]. The Hinglish-BERT embedding is concatenated with the pooled output from the ViT model. After passing through an MLP, the combined features are then categorised in a final classification layer. For more details about the baseline, please refer [5].

Overview of Memotion 3: Sentiment & Emotion
Analysis of Codemixed Hinglish -Task Details

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 2. Related Work Sentiment and emotion analysis: There has been significant research on sentiment analysis for text over many years [8]. Work on sentiment analysis using ML methods like SVM, logistic regression, random forest, XGBoost, k-nearest neighbor has been done in [9, 10, 11]. Works which use DL methods include [12, 13, 14]. For detailed surveys on sentiment analysis in social media, please refer to [15, 16]. The HaHa shared task provides a dataset for humor detection on social media [17]. Öhman et al [18] release an English dataset to detect eight emotions like joy, sadness, disgust etc. A comprehensive survey of textual emotion detection is provided in [19] Hatespeech detection: It is important to detect hatespeech to keep social media safe for everyone including minorities. Towards this goal, researchers have curated and released annotated datasets [20]. The offenseval shared task [21] at SemEval 2019 releases an annotated dataset of 14 English tweets to detect the type and target of offensive language. The HatEval shared task releases English and Spanish datasets to detect hate towards women and immigrants. Patwa et al. [22] conduct a shared task on Hindi hostile tweets detection. The TRAC workshop series [23, 24, 25] conducts multiple shared tasks to detect aggression and misogyny in English, Hindi, and Bengali datasets. Methods to detect hatespeech in text include CNNs and RNNS [26, 27, 28, 29], Bert-like models [30, 31, 32], incorporating linguistic characteristics [33] etc. Codemixed Language Processing : Codemixed language processing is a challenging task because the informal mixing of 2 or more languages and the proliferation of unique number of ways to write the same word [34, 35]. The Sentimix task [36] at semeval 2020 focused on sentiment analysis of Hinglish and Spanish-English tweets. [37] organized a shared task on detecting offense in 3 codemixed dravidian languages - Tamil [38], Malayalam[39] and Kannada[40]. Methods explored to tackle codemixing include graph convolutional netowrks [41], BERT based models [42, 43], modifying loss function [44, 45] or positional embeddings [46] to incorporate codemixing etc. Multimodal analysis : Although most of the existing research focuses on unimodal (text) analysis, the use of multi-modal content likes memes and videos is fast increasing. Multimodal datatsets having text and image, or videos are useful for tasks like image captioning, hatespeech detection, emotion analysis in videos, sentiment analysis [47, 48, 49, 50] etc among other tasks. The Hateful Memes Challenge dataset [51] and the multioff dataset [4] address hatespeech and offense detection in memes. However, they are binary classification task and the memes are in English whereas memotion 3 has multi-class and multi-label tasks on code-mixed data. There have been very few works on code-mixed meme analysis. [52] conduct a shared task to detect trolling in Tamil codemixed memes whereas [53] release a dataset to detect hate in codemixed Bengali memes. Popular methods for multimodal learning include image-text joint embeddings [54, 55, 56] and transformer based models [57, 58]. Previous iteration of Memotion : Memotion 1 [6] and Memotion 2[7] shared task released datasets of 10k memes each [6, 59]. These datasets were annotated on the same tasks as Memotion 3. However, both these datasets only focus on English memes, where as in memotion 3, we focus on Hinglish codemixed memes. Methods like ensembling [60, 61, 62] and bert-like models [63, 64, 65] were common across memotion 1 and Memotion 2.

Overview of Memotion 3: Sentiment & Emotion
Analysis of Codemixed Hinglish -Related Work

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References Abstract Analyzing memes on the internet has emerged as a crucial endeavor due to the impact this multi modal form of content wields in shaping online discourse. Memes have become a powerful tool for expressing emotions and sentiments, possibly even spreading hate and misinformation, through humor and sarcasm. In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23. The task released an annotated dataset of Hindi-English code-mixed memes based on their Sentiment (Task A), Emotion (Task B), and Emotion intensity (Task C). Each of these is defined as an individual task and the participants are ranked separately for each task. Over 50 teams registered for the shared task and 5 made final submissions to the test set of the Memotion 3 dataset. CLIP, BERT modifications, ViT etc. were the most popular models among the participants along with approaches such as Student-Teacher model, Fusion, and Ensembling. The best final F1 score for Task A is 34.41, Task B is 79.77 and Task C is 59.82. Memes, codemixed, multimodal, Hindi-English Keywords 1. Introduction The term meme is derived from the Ancient Greek word mimema, meaning imitated thing, which comes from the verb mimeisthai, meaning to mimic. The term was coined by Richard Dawkins, a British evolutionary biologist, in his book The Selfish Gene [1]. Dawkins used the concept of a meme to explain the spread of cultural phenomena and ideas, drawing parallels between memes and genes. Dawkins provided examples of memes such as melodies, catchphrases, fashion, and arch-building technology in his book. Interestingly, the word meme is a self-describing term, also known as autological, meaning that it is a meme itself. The reach of online content is vast and has the capacity to reach a massive viewership. Memes propagate through multiple mediums including social media, messaging, apps, and emails. An article on memes quotes "When you plant a fertile meme in my mind, you literally, parasitize my brain" [2]. This quote indicates the impact of memes on a viewer’s mind. It spreads to others who find it motivating, offensive, amusing, or relatable in some ways [3]. Memes are used to convey an opinion. When a person comes across a meme that they find relatable, they often share it with others who they think would find relatable. Different people interpret memes differently which means what can be offensive for one person might not be for other. This difference in interpretation is a result of cultural, gender, and demographic differences. The memes can also be used to spread offensive and hateful content [4]. Identifying and halting the dissemination of hateful memes is an arduous undertaking for both human beings and AI models, as it requires comprehending the intricate subtleties and socio-political contexts that form their interpretation. The deficiencies of current hate speech moderation techniques highlight the pressing need to enhance the effectiveness of automated hate speech detection. Given their subtlety and multi-modal nature, memes pose a more challenging issue to address compared to only text. In this paper, we present the findings of the Memotion 3 shared task, where participants were provided with an annotated dataset of 10k memes [5], and were tasked with detecting the sentiment, emotion and emotion intensity of the meme. Unlike the previous iteration of memotion [6, 7] which provided English memes, current iteration studies codemixed HindiEnglish (Hinglish) memes. The paper is organized as follows: we describe the related work and the task in section 2 and 3 respectively; Section 4 contains the details of the dataset we collected for Memotion analysis: Memotion 3.0; followed by a brief description of baseline models and their results in 5. We conclude and mention some possible future works in section 6.

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