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Los Angeles, United States, July 9th, 2024/Chainwire/--Phoenix Memecoin ($PHNX) is gaining attention for its unique combination of innovative technology and a strong, community-oriented approach. About Phoenix Memecoin ($PHNX) Phoenix Memecoin ($PHNX) is a new cryptocurrency designed to symbolize rebirth and innovation within the crypto world. Supported by a dedicated and rapidly growing community, Phoenix Memecoin aims to offer stability and long-term growth through its thoughtful tokenomics and strategic planning. Key Features of Phoenix Memecoin ($PHNX) Thoughtful Tokenomics: Phoenix Memecoin ($PHNX) employs a carefully designed tokenomics strategy to ensure stability and growth. The total supply of tokens is limited, providing a sustainable foundation. The presale offers tokens at a fixed rate of 2000 PHNX per 1 Solana (SOL), with a purchase limit of 30 SOL per wallet to promote broad participation and prevent market manipulation. Strategic Fund Allocation: Funds raised from the presale are allocated to liquidity, team development, exchange listings, partnerships, marketing, and token buyback and burn. This diversified approach aims to support the project’s long-term success. Community and Transparency: The Phoenix Memecoin team prioritizes transparency and community involvement. Active community members and content creators are rewarded for their contributions. Token holders can access exclusive content and events, enhancing engagement and value for the community. Roadmap and Governance: Phoenix Memecoin has a detailed roadmap outlining its plans, including presale, DEX listings, community events, and rewards. Governance mechanisms allow token holders to participate in project decisions, fostering a collaborative environment. Experienced Team: The project is led by a team of four developers with extensive experience in cryptocurrencies and a commitment to transparent communication. The team holds a modest 6% of the total tokens, ensuring that the focus remains on community growth and project sustainability. Phoenix Academy: The Phoenix Academy on the project's website offers free educational resources on cryptocurrencies, providing valuable knowledge without financial commitment. For More Information: Website Twitter (X) Telegram Reddit Tiktok Instagram Facebook The Phoenix Memecoin team is available on social media to respond to inquiries and engage with the community. Contact Jonathan Griffin contact@phoenixonsol.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. Los Angeles, United States, July 9th, 2024/Chainwire/--Phoenix Memecoin ($PHNX) is gaining attention for its unique combination of innovative technology and a strong, community-oriented approach. About Phoenix Memecoin ($PHNX) Phoenix Memecoin ($PHNX) is a new cryptocurrency designed to symbolize rebirth and innovation within the crypto world. Supported by a dedicated and rapidly growing community, Phoenix Memecoin aims to offer stability and long-term growth through its thoughtful tokenomics and strategic planning. Key Features of Phoenix Memecoin ($PHNX) Thoughtful Tokenomics: Phoenix Memecoin ($PHNX) employs a carefully designed tokenomics strategy to ensure stability and growth. The total supply of tokens is limited, providing a sustainable foundation. The presale offers tokens at a fixed rate of 2000 PHNX per 1 Solana (SOL), with a purchase limit of 30 SOL per wallet to promote broad participation and prevent market manipulation. Strategic Fund Allocation: Funds raised from the presale are allocated to liquidity, team development, exchange listings, partnerships, marketing, and token buyback and burn. This diversified approach aims to support the project’s long-term success. Community and Transparency: The Phoenix Memecoin team prioritizes transparency and community involvement. Active community members and content creators are rewarded for their contributions. Token holders can access exclusive content and events, enhancing engagement and value for the community. Roadmap and Governance: Phoenix Memecoin has a detailed roadmap outlining its plans, including presale, DEX listings, community events, and rewards. Governance mechanisms allow token holders to participate in project decisions, fostering a collaborative environment. Experienced Team: The project is led by a team of four developers with extensive experience in cryptocurrencies and a commitment to transparent communication. The team holds a modest 6% of the total tokens, ensuring that the focus remains on community growth and project sustainability. Phoenix Academy: The Phoenix Academy on the project's website offers free educational resources on cryptocurrencies, providing valuable knowledge without financial commitment. For More Information: Website Website Twitter (X) Twitter (X) Telegram Telegram Reddit Reddit Tiktok Tiktok Instagram Instagram Facebook Facebook The Phoenix Memecoin team is available on social media to respond to inquiries and engage with the community. Contact Jonathan Griffin contact@phoenixonsol.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Phoenix Memecoin ($PHNX) Launches With Innovative Tokenomics And Community Focus

ফিনিক্স মেমেকয়েন ($PHNX) উদ্ভাবনী টোকেনমিক্স এবং কমিউনিটি ফোকাসের সাথে চালু হয়েছে

Phoenix Memecoin ($PHNX) startet mit innovativer Tokenomics und Community-Fokus

Phoenix Memecoin ($PHNX) se lanza con tokenómica innovadora y enfoque comunitario

Phoenix Memecoin ($PHNX) est lancé avec des Tokenomics innovants et une orientation communautaire

फीनिक्स मेमेकॉइन ($PHNX) अभिनव टोकनोमिक्स और सामुदायिक फोकस के साथ लॉन्च हुआ

フェニックス・ミームコイン（$PHNX）が革新的なトークノミクスとコミュニティ重視でローンチ

Phoenix Memecoin($PHNX), 혁신적인 토큰경제학 및 커뮤니티 중심 출시

Phoenix Memecoin ($PHNX) é lançado com Tokenomics inovadores e foco na comunidade

Phoenix Memecoin ($PHNX) запускается с инновационной токеномикой и вниманием к сообществу

Phoenix Memecoin ($PHNX) Yenilikçi Tokenomikler ve Topluluk Odaklılığıyla Piyasaya Sürüldü

Phoenix Memecoin ($PHNX) ra mắt với công nghệ Tokenomics đổi mới và tập trung vào cộng đồng

Phoenix Memecoin ($PHNX) 推出创新代币经济学和社区焦点

Generative AI (GenAI) is quickly becoming our everyday teammate, helping out behind the scenes in ways we might not even realize. In 2023, a McKinsey survey revealed that one-third of organizations actively use it in at least one business area, such as sales or accounting. But the most significant potential for business lies in software development. Costs of product development Imagine you have a great idea. To create a product out of it, you need to analyze the market, find a product-market fit, and hire strong specialists, including developers, designers, etc. The more complex the project, the more time you need. On average, planning and architecture, along with development and design, can take up to 6-7 months, while testing requires another 2-4 weeks. This process is not only time-consuming but also pricey. The exact cost depends heavily on the type of product and its niche. On average, a simple product, such as a basic web or mobile application with minimal features, can cost a business between $20,000 and $60,000. More advanced applications, integrated with third-party APIs, backend services, and databases, require from $60,000 to $90,000. A complex product, which includes feature-rich applications with advanced functionality, high-security demands, and potentially AI/ML components, calls for more than $100,000. Not every team can afford this level of spending. Given the current venture climate, it may be wise to explore as many cost-reducing strategies as possible. In this context, AI, especially Generative AI (GenAI), can be one of the most helpful tools. It is designed to generate new original content, from text to images, and even code, based on the patterns it learns from the data it is fed. Below, I'll delve into how GenAI can assist you at different stages of product development. Data analysis Generative AI can gather and analyze first-party customer data collected through their ecosystem, as well as reviews and insights on market trends from focus groups, social listening campaigns, and more. With this information, AI can identify customer pain points and product requests, which is crucial for testing your idea and finding a market fit for a future product. Among the AI models capable of this, Viable stands out due to its ability to aggregate and analyze feedback from various platforms, such as emails, chat logs, product reviews, and social media. Another example is Crayon, which leverages machine learning algorithms to analyze market trends and customer feedback, helping to identify customer needs. Soft development When it comes to product development, GenAI facilitates a range of tasks, from code generation to testing and even assisting in software design. Tools like GitHub Copilot, powered by OpenAI's Codex, assist developers by suggesting complete lines of code or entire functions based on the comments or partial code they write. For instance, in 2023, Shopify significantly boosted its productivity, efficiency, and code quality by integrating GitHub Copilot into their engineering processes. The tool helped generate complex Liquid code snippets (Shopify’s templating language), automate routine tasks, and offer best practices for code structure. It proved especially useful in accelerating the development of new themes and features, as well as in debugging existing code. There are also tools like DeepCode and Amazon CodeGuru that review existing code and suggest improvements or identify vulnerabilities. They can spot complex patterns and offer fixes to improve both performance and security. In addition to debugging code, generative AI can pretest upcoming software updates, reducing potential errors. Companies like Diffblue are integrating AI into automatic test generation, which can analyze existing code bases to create unit tests automatically, ensuring that new updates do not break existing functionalities. Design Product development is not only about code; creating an appealing interface is crucial as well. Tools like Stable Diffusion and Midjourney can generate visuals based on instructions from human designers. Many businesses leverage these tools to boost their productivity. For example, Airbnb utilizes AI to transform sketches into product code, drastically reducing the time from design to prototype. In the fashion and retail industry, Under Armour employs Adobe Sensei to enhance product images and quickly create engaging marketing content. Another major retailer, Home Depot, uses the Sensei AI model on their e-commerce platform to streamline search functions and adjust recommendations. Sales and marketing Once you have your product, generative AI can refine marketing strategies by performing A/B testing on elements like page layouts, SEO strategies, etc. It utilizes predictive analytics and data-driven insights to maximize investment returns. GenAI also excels at personalizing the customer experience and optimizing sales. According to McKinsey, generative AI significantly improves upon traditional AI methods in the early stages of sales, such as lead identification and targeting. Instead of basic web scraping and prioritization, it uses algorithms to detect patterns in customer and market data. This allows for better segmentation and targeting of audiences, efficiently identifying high-quality leads and enhancing the effectiveness of lead activation campaigns. In some cases, this can lead to a significant increase in sales, according to Visual Capitalist. Basic rules Thanks to its broad capabilities, GenAI is sometimes seen as a universal solution to solve all issues, either to ride the wave of hype or to impress investors. However, just as microscopes are not designed to hammer nails, GenAI is not a one-size-fits-all tool. You have to test different products until you find those that fit your goals. To maximize the benefits of this technology, it is also crucial to inspire people to use AI and dive deeper into its applications, or even hire someone with experience to lead a team and set an example. Given that this tool is still new, it might be beneficial to regularly meet to track progress, discuss blind spots, address issues, and find ways to improve collective work. Only by following these basic rules product development with AI may lead to success. Generative AI ( GenAI ) is quickly becoming our everyday teammate, helping out behind the scenes in ways we might not even realize. In 2023, a McKinsey survey revealed that one-third of organizations actively use it in at least one business area, such as sales or accounting. But the most significant potential for business lies in software development. GenAI Costs of product development Costs of product development Imagine you have a great idea. To create a product out of it, you need to analyze the market, find a product-market fit, and hire strong specialists, including developers, designers, etc. The more complex the project, the more time you need. On average, planning and architecture, along with development and design, can take up to 6-7 months, while testing requires another 2-4 weeks. This process is not only time-consuming but also pricey. The exact cost depends heavily on the type of product and its niche. On average , a simple product, such as a basic web or mobile application with minimal features, can cost a business between $20,000 and $60,000. More advanced applications, integrated with third-party APIs, backend services, and databases, require from $60,000 to $90,000. A complex product, which includes feature-rich applications with advanced functionality, high-security demands, and potentially AI/ML components, calls for more than $100,000. On average On average Not every team can afford this level of spending. Given the current venture climate, it may be wise to explore as many cost-reducing strategies as possible. In this context, AI, especially Generative AI (GenAI), can be one of the most helpful tools. It is designed to generate new original content, from text to images, and even code, based on the patterns it learns from the data it is fed. Below, I'll delve into how GenAI can assist you at different stages of product development. Data analysis Data analysis Generative AI can gather and analyze first-party customer data collected through their ecosystem, as well as reviews and insights on market trends from focus groups, social listening campaigns, and more. With this information, AI can identify customer pain points and product requests, which is crucial for testing your idea and finding a market fit for a future product. Among the AI models capable of this, Viable stands out due to its ability to aggregate and analyze feedback from various platforms, such as emails, chat logs, product reviews, and social media. Another example is Crayon, which leverages machine learning algorithms to analyze market trends and customer feedback, helping to identify customer needs. Soft development Soft development When it comes to product development, GenAI facilitates a range of tasks, from code generation to testing and even assisting in software design. Tools like GitHub Copilot, powered by OpenAI's Codex, assist developers by suggesting complete lines of code or entire functions based on the comments or partial code they write. For instance, in 2023, Shopify significantly boosted its productivity, efficiency, and code quality by integrating GitHub Copilot into their engineering processes. The tool helped generate complex Liquid code snippets (Shopify’s templating language), automate routine tasks, and offer best practices for code structure. It proved especially useful in accelerating the development of new themes and features, as well as in debugging existing code. boosted boosted There are also tools like DeepCode and Amazon CodeGuru that review existing code and suggest improvements or identify vulnerabilities. They can spot complex patterns and offer fixes to improve both performance and security. In addition to debugging code, generative AI can pretest upcoming software updates, reducing potential errors. Companies like Diffblue are integrating AI into automatic test generation, which can analyze existing code bases to create unit tests automatically, ensuring that new updates do not break existing functionalities. Design Product development is not only about code; creating an appealing interface is crucial as well. Tools like Stable Diffusion and Midjourney can generate visuals based on instructions from human designers. Many businesses leverage these tools to boost their productivity. For example, Airbnb utilizes AI to transform sketches into product code, drastically reducing the time from design to prototype. In the fashion and retail industry, Under Armour employs Adobe Sensei to enhance product images and quickly create engaging marketing content. Another major retailer, Home Depot, uses the Sensei AI model on their e-commerce platform to streamline search functions and adjust recommendations. Sales and marketing Sales and marketing Once you have your product, generative AI can refine marketing strategies by performing A/B testing on elements like page layouts, SEO strategies, etc. It utilizes predictive analytics and data-driven insights to maximize investment returns. GenAI also excels at personalizing the customer experience and optimizing sales. According to McKinsey , generative AI significantly improves upon traditional AI methods in the early stages of sales, such as lead identification and targeting. Instead of basic web scraping and prioritization, it uses algorithms to detect patterns in customer and market data. This allows for better segmentation and targeting of audiences, efficiently identifying high-quality leads and enhancing the effectiveness of lead activation campaigns. In some cases, this can lead to a significant increase in sales, according to Visual Capitalist. According to McKinsey According to McKinsey according according Basic rules Basic rules Thanks to its broad capabilities, GenAI is sometimes seen as a universal solution to solve all issues, either to ride the wave of hype or to impress investors. However, just as microscopes are not designed to hammer nails, GenAI is not a one-size-fits-all tool. You have to test different products until you find those that fit your goals. To maximize the benefits of this technology, it is also crucial to inspire people to use AI and dive deeper into its applications, or even hire someone with experience to lead a team and set an example. Given that this tool is still new, it might be beneficial to regularly meet to track progress, discuss blind spots, address issues, and find ways to improve collective work. Only by following these basic rules product development with AI may lead to success.

Your Ideal Coworker: How To Develop an Innovative Product With a Help of Generative AI

Distributed Ledger Technology (DLT) is often paired with the Internet of Things (IoT) to get some interesting and automatic features in the real world, also registered and handled digitally. IoT devices are physical objects designed to connect to the Internet and can also perform effectively on a distributed network. Blockchains weren’t really built to face challenges like high energy costs or lack of needed speed. A Directed Acyclic Graph (DAG) like Obyte could be a better alternative, allowing inexpensive, fast, secure, and highly decentralized transactions or immutable data recording. In this vein, let’s look into some potential use cases for the powerful combination of IoT and Obyte. Track and Verify Assets The journey of any commodity or asset from its origin (manufacturers) to its final destination (common users) is long and complex, and it could be full of issues in the middle. This is especially true when the safety or authenticity of the product is essential for final consumers, and it could be tampered with before it reaches them. Luckily, decentralized networks can be paired with IoT devices to track and verify any product across the supply chain, assuring its final quality. Sensors and IoT devices can report the status of shipments in real time to a DLT, ensuring that all parties have access to accurate and tamper-proof records. This could be particularly useful for monitoring the transport conditions of perishable goods or high-value items, improving accountability, and reducing losses due to theft, fraud, or damage. Consider, for instance, a pharmaceutical product originating from a manufacturing plant. Each product could have an embedded IoT sensor, generating real-time data about its location, temperature, and humidity levels. As the product leaves the factory, the IoT device logs its status onto the Obyte DAG, ensuring an immutable and transparent record every step of the way. If any anomalies occur, such as temperature fluctuations that could compromise the product's integrity, stakeholders are instantly alerted, allowing for swift corrective actions. Upon arrival at the final destination, perhaps a pharmacy, the product's entire journey is readily accessible on the Obyte DAG, and all its journey could be consulted by accessing a simple QR code printed on its tags. Smart Vehicle Leasing and Sharing It’s also possible to implement a system where vehicles, such as electric scooters, bikes, or cars, can be leased or rented using tokens on a decentralized network like Obyte. Users could unlock and operate these vehicles by scanning a QR code and paying with any compatible tokens, or even creating new ones for this purpose. The vehicle's usage data (e.g., duration, distance traveled) would be recorded on the DAG, ensuring transparency and trust. Human-readable smart contracts and Autonomous Agents (acting like digital vending machines) could manage leases, maintenance schedules, and payments. All of this from the same wallet, maybe even with the help of a chatbot, but with no need for extra middlemen being involved. For example, imagine John wants to rent an electric scooter for a quick ride across town. He opens his Obyte wallet, locates a nearby scooter, and scans its QR code. Instantly, the app prompts him to pay the rental fee using Obyte-based tokens. After confirming the payment, the scooter unlocks, and John starts his journey. As he rides, the scooter's IoT sensors continuously track and record data such as distance traveled and time used onto the Obyte DAG. Upon reaching his destination, John parks the scooter and ends the rental via the same app. The smart contract processes the final payment based on the usage data. Additionally, it could automatically update the scooter’s maintenance schedule based on the recorded usage, ensuring it remains in optimal condition. Decentralized Health Monitoring IoT is widely used in healthcare already, and it includes common devices like wearable fitness trackers and medical sensors. If we pair them with a DAG like Obyte, all of our important medical information could be safely registered digitally, ready to be shared with health providers and researchers only if we decide to do so, when we decide to do so. Let’s consider a scenario like that one. Imagine Sarah is using a wearable fitness tracker to monitor her heart rate and daily activity. This device continuously collects health data and securely records it onto the Obyte DAG. One day, Sarah feels unwell and decides to consult a doctor via a telehealth service. Through her digital wallet, Sarah not only can pay for the consultation, but grant her doctor temporary access to her health records stored on the DAG. Then, the doctor reviews the data in real-time, including heart rate trends and activity levels, aiding in an accurate diagnosis. After the consultation, Sarah revokes access, ensuring her information remains private. This system could provide secure, efficient, and controlled sharing of vital health data. Waste Management System Immense waste and inefficient techniques to handle it are another of our big challenges to improve our environment these days. By using IoT sensors and the Obyte ledger, we could create a new decentralized system to address this issue. Sensors placed in waste bins and recycling containers could monitor fill levels and send data to the DAG. Smart contracts could automate the scheduling of waste collection services based on real-time data, ensuring efficient and timely pickups. Additionally, a new customized token could be created to reward users for proper recycling practices, incentivizing environmentally friendly behavior and improving overall waste management efficiency. In a city aiming to boost recycling efforts, a customized token named ‘RecycleCoin’ (RCC) could be created on Obyte. Residents could be provided with IoT-enabled recycling bins that track the amount and type of waste deposited. When someone recycles his plastic bottles, the bin records the data and automatically logs it onto the Obyte DAG. For every kilogram of recyclables, users would earn RCC, credited to their digital wallet automatically. These tokens could be redeemed for rewards, such as discounts at local stores, or they could acquire real value through free market supply and demand. This system would not only incentivize recycling but also ensure transparent tracking of waste management, fostering a more sustainable community. P2P Clean Energy Another way to help the environment through IoT and Obyte, and reduce our electrical bills in the process, could be peer-to-peer (P2P) energy trading. Households or businesses with surplus energy generated from renewable sources like solar panels could sell excess energy directly to other consumers in their local grid. IoT devices would measure the amount of energy produced and consumed, recording this data on the DAG. Smart contracts can facilitate automatic and transparent transactions based on real-time energy usage, ensuring that producers are paid promptly and consumers are charged fairly. This way, we could optimize energy distribution, reduce reliance on traditional energy suppliers, and promote the use of renewable energy sources. **  **If we went to put it into practice, let’s say that Alice’s home, equipped with IoT devices, measures her solar energy production and usage, recording this data on the Obyte ledger. One sunny afternoon, Alice's panels produce more energy than her household consumes. The IoT platform would automatically log the surplus energy onto the DAG and make it available for sale. Down the street, if Mike needs extra energy for his home office, his own IoT system and Obyte wallet could detect the availability and purchase Alice's excess energy via smart contract. The provider would be promptly paid and Mike would be fairly charged. All these examples highlight just a fraction of the immense possibilities that emerge when combining Obyte with connected devices, paving the way for innovative solutions across numerous sectors where they could enhance efficiency, security, and user experience in unprecedented ways. Featured Vector Image by vectorjuice / Freepik Distributed Ledger Technology (DLT) is often paired with the Internet of Things (IoT) to get some interesting and automatic features in the real world, also registered and handled digitally. IoT devices are physical objects designed to connect to the Internet and can also perform effectively on a distributed network. Distributed Ledger Technology (DLT) Blockchains weren’t really built to face challenges like high energy costs or lack of needed speed. A Directed Acyclic Graph (DAG) like Obyte could be a better alternative , allowing inexpensive, fast, secure, and highly decentralized transactions or immutable data recording. Obyte could be a better alternative Obyte could be a better alternative In this vein, let’s look into some potential use cases for the powerful combination of IoT and Obyte. Track and Verify Assets The journey of any commodity or asset from its origin (manufacturers) to its final destination (common users) is long and complex, and it could be full of issues in the middle. This is especially true when the safety or authenticity of the product is essential for final consumers, and it could be tampered with before it reaches them. Luckily, decentralized networks can be paired with IoT devices to track and verify any product across the supply chain, assuring its final quality. Luckily, decentralized networks can be paired with IoT devices to track and verify any product across the supply chain, assuring its final quality. Sensors and IoT devices can report the status of shipments in real time to a DLT, ensuring that all parties have access to accurate and tamper-proof records. This could be particularly useful for monitoring the transport conditions of perishable goods or high-value items, improving accountability, and reducing losses due to theft, fraud, or damage. Consider, for instance, a pharmaceutical product originating from a manufacturing plant. Each product could have an embedded IoT sensor, generating real-time data about its location, temperature, and humidity levels . As the product leaves the factory, the IoT device logs its status onto the Obyte DAG , ensuring an immutable and transparent record every step of the way. Each product could have an embedded IoT sensor, generating real-time data about its location, temperature, and humidity levels Obyte DAG Obyte DAG If any anomalies occur, such as temperature fluctuations that could compromise the product's integrity, stakeholders are instantly alerted, allowing for swift corrective actions. Upon arrival at the final destination, perhaps a pharmacy, the product's entire journey is readily accessible on the Obyte DAG, and all its journey could be consulted by accessing a simple QR code printed on its tags. Smart Vehicle Leasing and Sharing It’s also possible to implement a system where vehicles, such as electric scooters, bikes, or cars, can be leased or rented using tokens on a decentralized network like Obyte. Users could unlock and operate these vehicles by scanning a QR code and paying with any compatible tokens, or even creating new ones for this purpose. The vehicle's usage data (e.g., duration, distance traveled) would be recorded on the DAG, ensuring transparency and trust . Human-readable smart contracts and Autonomous Agents (acting like digital vending machines ) could manage leases, maintenance schedules, and payments. All of this from the same wallet, maybe even with the help of a chatbot, but with no need for extra middlemen being involved. The vehicle's usage data (e.g., duration, distance traveled) would be recorded on the DAG, ensuring transparency and trust Human-readable smart contracts Human-readable smart contracts vending machines vending machines For example, imagine John wants to rent an electric scooter for a quick ride across town. He opens his Obyte wallet, locates a nearby scooter, and scans its QR code. Instantly, the app prompts him to pay the rental fee using Obyte-based tokens. After confirming the payment, the scooter unlocks, and John starts his journey. As he rides, the scooter's IoT sensors continuously track and record data such as distance traveled and time used onto the Obyte DAG. Upon reaching his destination, John parks the scooter and ends the rental via the same app. The smart contract processes the final payment based on the usage data. Additionally, it could automatically update the scooter’s maintenance schedule based on the recorded usage, ensuring it remains in optimal condition. As he rides, the scooter's IoT sensors continuously track and record data such as distance traveled and time used onto the Obyte DAG. Decentralized Health Monitoring Decentralized Health Monitoring IoT is widely used in healthcare already, and it includes common devices like wearable fitness trackers and medical sensors. If we pair them with a DAG like Obyte, all of our important medical information could be safely registered digitally, ready to be shared with health providers and researchers only if we decide to do so, when we decide to do so. all of our important medical information could be safely registered digitally, ready to be shared with health providers and researchers only if we decide to do so, when we decide to do so. Let’s consider a scenario like that one. Imagine Sarah is using a wearable fitness tracker to monitor her heart rate and daily activity. This device continuously collects health data and securely records it onto the Obyte DAG. One day, Sarah feels unwell and decides to consult a doctor via a telehealth service. Through her digital wallet , Sarah not only can pay for the consultation, but grant her doctor temporary access to her health records stored on the DAG. digital wallet digital wallet Then, the doctor reviews the data in real-time, including heart rate trends and activity levels, aiding in an accurate diagnosis. After the consultation, Sarah revokes access, ensuring her information remains private. This system could provide secure, efficient, and controlled sharing of vital health data. Waste Management System Waste Management System Immense waste and inefficient techniques to handle it are another of our big challenges to improve our environment these days. By using IoT sensors and the Obyte ledger, we could create a new decentralized system to address this issue. Sensors placed in waste bins and recycling containers could monitor fill levels and send data to the DAG. Sensors placed in waste bins and recycling containers could monitor fill levels and send data to the DAG. Smart contracts could automate the scheduling of waste collection services based on real-time data, ensuring efficient and timely pickups. Additionally, a new customized token could be created to reward users for proper recycling practices, incentivizing environmentally friendly behavior and improving overall waste management efficiency. In a city aiming to boost recycling efforts, a customized token named ‘RecycleCoin’ (RCC) could be created on Obyte. Residents could be provided with IoT-enabled recycling bins that track the amount and type of waste deposited. When someone recycles his plastic bottles, the bin records the data and automatically logs it onto the Obyte DAG. In a city aiming to boost recycling efforts, a customized token named ‘RecycleCoin’ (RCC) could be created on Obyte. a customized token a customized token For every kilogram of recyclables, users would earn RCC, credited to their digital wallet automatically. These tokens could be redeemed for rewards, such as discounts at local stores, or they could acquire real value through free market supply and demand. This system would not only incentivize recycling but also ensure transparent tracking of waste management, fostering a more sustainable community. P2P Clean Energy P2P Clean Energy Another way to help the environment through IoT and Obyte, and reduce our electrical bills in the process, could be peer-to-peer (P2P) energy trading. Households or businesses with surplus energy generated from renewable sources like solar panels could sell excess energy directly to other consumers in their local grid. IoT devices would measure the amount of energy produced and consumed, recording this data on the DAG. help the environment help the environment Smart contracts can facilitate automatic and transparent transactions based on real-time energy usage, ensuring that producers are paid promptly and consumers are charged fairly . This way, we could optimize energy distribution, reduce reliance on traditional energy suppliers, and promote the use of renewable energy sources. Smart contracts can facilitate automatic and transparent transactions based on real-time energy usage, ensuring that producers are paid promptly and consumers are charged fairly ** **If we went to put it into practice, let’s say that Alice’s home, equipped with IoT devices, measures her solar energy production and usage, recording this data on the Obyte ledger. One sunny afternoon, Alice's panels produce more energy than her household consumes. The IoT platform would automatically log the surplus energy onto the DAG and make it available for sale. Down the street, if Mike needs extra energy for his home office, his own IoT system and Obyte wallet could detect the availability and purchase Alice's excess energy via smart contract. The provider would be promptly paid and Mike would be fairly charged. The IoT platform The IoT platform All these examples highlight just a fraction of the immense possibilities that emerge when combining Obyte with connected devices , paving the way for innovative solutions across numerous sectors where they could enhance efficiency, security, and user experience in unprecedented ways. All these examples highlight just a fraction of the immense possibilities that emerge when combining Obyte with connected devices Featured Vector Image by vectorjuice / Freepik Featured Vector Image by vectorjuice / Freepik Freepik Freepik

5 Ways to Leverage Obyte and IoT Technology for Innovative Solutions

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Comment l'IA et la blockchain convergent : un aperçu de l'approche innovante d'IVS Crypto 2024

एआई और ब्लॉकचेन कैसे एक दूसरे से जुड़ रहे हैं: आईवीएस क्रिप्टो 2024 के अभिनव दृष्टिकोण पर एक नज़र

AIとブロックチェーンの融合: IVS Crypto 2024の革新的なアプローチを検証

AI와 블록체인이 융합되는 방식: IVS Crypto 2024의 혁신적인 접근 방식 살펴보기

Como a IA e o Blockchain estão convergindo: uma análise da abordagem inovadora do IVS Crypto 2024

Как ИИ и блокчейн объединяются: взгляд на инновационный подход IVS Crypto 2024

Yapay Zeka ve Blockchain Nasıl Birleşiyor: IVS Crypto 2024'ün Yenilikçi Yaklaşımına Bir Bakış

AI và Blockchain đang hội tụ như thế nào: Nhìn vào phương pháp tiếp cận đổi mới của IVS Crypto 2024

人工智能与区块链如何融合：了解 IVS Crypto 2024 的创新方法

**TORRANCE, United States / California, June 10th, 2024, CyberNewsWire/--**AI SPERA, a leader in Cyber Threat Intelligence (CTI) solutions, announced that it has started selling its paid threat detection data from its CTI search engine ‘Criminal IP” on the Snowflake Marketplace. Criminal IP is committed to offering advanced cybersecurity solutions through Snowflake, the leading cloud-based data warehousing platform. Criminal IP’s Intelligence for Fraud Detection and Privacy Protection is meticulously crafted to address the growing concerns surrounding fraudulent activities and privacy breaches. By aggregating data on known malicious and masked IP addresses, including those with historical abuse records such as IDS, malware, phishing, ransomware, and blocked IPs, this dataset equips organizations with actionable insights to identify and mitigate fraudulent activities in real time. Additionally, the product boasts advanced capabilities to detect servers infected by botnet and C2 software, as well as IP addresses leveraging masking services like VPNs, proxies, and hosting. This product is tailored to support fraud detection (FDS) and malicious IP plans, enabling organizations to bolster their security posture and streamline incident response protocols. Criminal IP's Intelligence for Threat Detection & Incident Response is designed to empower organizations to combat cyber threats effectively. This comprehensive cyber threat intelligence dataset provides invaluable insights into malicious IP addresses, leveraging data sourced from Criminal IP's Cyber Threat Intelligence Database (CTIDB). These new datasets on the Snowflake Marketplace offer granular, real-time threat intelligence, enabling organizations to safeguard digital assets, mitigate risks, and respond swiftly to security incidents. Snowflake's global customers can access a complimentary trial of up to 1,000 data items, with subscription options for daily updates. About AI Spera AI SPERA, a leader in Cyber Threat Intelligence (CTI) solutions, significantly expanded its reach by launching its flagship solution, Criminal IP, in 2023. Since then, the company has formed technical and business collaborations with over 40 renowned global security firms, including VirusTotal, Cisco, Tenable, Sumo Logic, and Quad9. Besides the CTI search engine, the company offers Criminal IP ASM, a SaaS-based Attack Surface Management Solution on AWS and Azure Marketplace, and Criminal IP FDS, an AI-based Anomaly Detection Solution used for credential stuffing prevention and fraud detection. Available in five languages (English, French, Arabic, Korean, and Japanese), the search engine provides a powerful service for users worldwide. Contact Michael Sena AI SPERA support@aispera.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **TORRANCE, United States / California, June 10th, 2024, CyberNewsWire/--**AI SPERA, a leader in Cyber Threat Intelligence (CTI) solutions, announced that it has started selling its paid threat detection data from its CTI search engine ‘Criminal IP” on the Snowflake Marketplace. Criminal IP is committed to offering advanced cybersecurity solutions through Snowflake, the leading cloud-based data warehousing platform. Criminal IP’s Intelligence for Fraud Detection and Privacy Protection is meticulously crafted to address the growing concerns surrounding fraudulent activities and privacy breaches. By aggregating data on known malicious and masked IP addresses, including those with historical abuse records such as IDS, malware, phishing, ransomware, and blocked IPs, this dataset equips organizations with actionable insights to identify and mitigate fraudulent activities in real time. Criminal IP’s Intelligence for Fraud Detection and Privacy Protection Criminal IP’s Intelligence for Fraud Detection and Privacy Protection Additionally, the product boasts advanced capabilities to detect servers infected by botnet and C2 software, as well as IP addresses leveraging masking services like VPNs, proxies, and hosting. This product is tailored to support fraud detection (FDS) and malicious IP plans, enabling organizations to bolster their security posture and streamline incident response protocols. Criminal IP's Intelligence for Threat Detection & Incident Response is designed to empower organizations to combat cyber threats effectively. This comprehensive cyber threat intelligence dataset provides invaluable insights into malicious IP addresses, leveraging data sourced from Criminal IP's Cyber Threat Intelligence Database (CTIDB). Criminal IP's Intelligence for Threat Detection & Incident Response Criminal IP's Intelligence for Threat Detection & Incident Response These new datasets on the Snowflake Marketplace offer granular, real-time threat intelligence, enabling organizations to safeguard digital assets, mitigate risks, and respond swiftly to security incidents. Snowflake's global customers can access a complimentary trial of up to 1,000 data items, with subscription options for daily updates. About AI Spera AI SPERA, a leader in Cyber Threat Intelligence (CTI) solutions, significantly expanded its reach by launching its flagship solution, Criminal IP, in 2023. Since then, the company has formed technical and business collaborations with over 40 renowned global security firms, including VirusTotal, Cisco, Tenable, Sumo Logic, and Quad9. technical and business collaborations technical and business collaborations Besides the CTI search engine, the company offers Criminal IP ASM, a SaaS-based Attack Surface Management Solution on AWS and Azure Marketplace, and Criminal IP FDS, an AI-based Anomaly Detection Solution used for credential stuffing prevention and fraud detection. Available in five languages (English, French, Arabic, Korean, and Japanese), the search engine provides a powerful service for users worldwide. search engine search engine Contact Michael Sena AI SPERA support@aispera.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. 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Criminal IP Unveils Innovative Fraud Detection Data Products On Snowflake Marketplace

**LONDON, UK, April 17th, 2024/Chainwire/--**In a pioneering move within the industry, MetaWin in collaboration with law firm BCLP, has successfully tokenized a Tesla Cybertruck, transforming it into a digital asset wrapped in a legally binding contract and minted as a Non-Fungible Token (NFT). This NFT, which represents full ownership rights to The Cybertruck, has been launched on the Ethereum Base Layer 2 blockchain operated by cryptocurrency giant Coinbase. This contest marks a significant milestone as the world’s first tokenized vehicle asset, offering participants an opportunity to win a Cybertruck through a transparent, on-chain process. Entry into the competition is free and easily accessible via a Web3 wallet, with significantly reduced gas prices on the Base Layer 2 blockchain compared to Ethereum's Main Net. Rebecca Hanwell, Operations Manager at MetaWin, highlighted, "By leveraging the Ethereum Base Layer 2 blockchain, we not only minimize transaction costs but also ensure complete transparency and immediate transfer of ownership for every aspect of this contest - from entry registration to prize allocation." Participants can enhance their chances of winning by acquiring MetaWin NFTs directly from the platform. Each purchase includes complimentary entries, seamlessly registered on-chain at the time of purchase. At the conclusion of the competition, the winner can choose to have the Cybertruck delivered or opt for an alternative prize of $125,000 in USDC/T, if delivery is not feasible. The Tesla Cybertruck, renowned for its unique design and celebrity endorsements, serves as an extraordinary prize. It remains under the stewardship of MetaWin, with its VIN number detailed in the NFT contract. Rebecca Hanwell further noted, "The Cybertruck not only represents an exceptional vehicle but also embodies the technological innovation driving our vision at MetaWin. Hosting this contest on the blockchain ensures that every step of the process is as revolutionary as the prize itself." The competition is now open for entries, with the drawing scheduled for June 1, 2024. The winner will receive their prize promptly upon the contest's conclusion, showcasing the efficiency and potential of blockchain technology in modernising traditional asset ownership. For more information and to participate in this extraordinary contest, visit MetaWin.com. About Metawin MetaWin is the premier platform for on-chain prize competitions and instant win games, offering a diverse range of entertaining challenges for users to enjoy. By harnessing cutting-edge blockchain technology, MetaWin provides a transparent, fair, and secure gaming environment, making it the go-to destination for blockchain enthusiasts and gamers alike. Follow us on social media: X| Instagram Join our community: Telegram | Discord Contact PR Team Metawin press@metawin.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **LONDON, UK, April 17th, 2024/Chainwire/--**In a pioneering move within the industry, MetaWin in collaboration with law firm BCLP, has successfully tokenized a Tesla Cybertruck, transforming it into a digital asset wrapped in a legally binding contract and minted as a Non-Fungible Token (NFT). This NFT, which represents full ownership rights to The Cybertruck, has been launched on the Ethereum Base Layer 2 blockchain operated by cryptocurrency giant Coinbase. MetaWin MetaWin This contest marks a significant milestone as the world’s first tokenized vehicle asset, offering participants an opportunity to win a Cybertruck through a transparent, on-chain process. Entry into the competition is free and easily accessible via a Web3 wallet, with significantly reduced gas prices on the Base Layer 2 blockchain compared to Ethereum's Main Net. Rebecca Hanwell, Operations Manager at MetaWin , highlighted, MetaWin MetaWin "By leveraging the Ethereum Base Layer 2 blockchain, we not only minimize transaction costs but also ensure complete transparency and immediate transfer of ownership for every aspect of this contest - from entry registration to prize allocation." "By leveraging the Ethereum Base Layer 2 blockchain, we not only minimize transaction costs but also ensure complete transparency and immediate transfer of ownership for every aspect of this contest - from entry registration to prize allocation." Participants can enhance their chances of winning by acquiring MetaWin NFTs directly from the platform. Each purchase includes complimentary entries, seamlessly registered on-chain at the time of purchase. At the conclusion of the competition, the winner can choose to have the Cybertruck delivered or opt for an alternative prize of $125,000 in USDC/T, if delivery is not feasible. The Tesla Cybertruck, renowned for its unique design and celebrity endorsements, serves as an extraordinary prize. It remains under the stewardship of MetaWin, with its VIN number detailed in the NFT contract. Rebecca Hanwell further noted, "The Cybertruck not only represents an exceptional vehicle but also embodies the technological innovation driving our vision at MetaWin. Hosting this contest on the blockchain ensures that every step of the process is as revolutionary as the prize itself." Rebecca Hanwell further noted, "The Cybertruck not only represents an exceptional vehicle but also embodies the technological innovation driving our vision at MetaWin. Hosting this contest on the blockchain ensures that every step of the process is as revolutionary as the prize itself." The competition is now open for entries, with the drawing scheduled for June 1, 2024. The winner will receive their prize promptly upon the contest's conclusion, showcasing the efficiency and potential of blockchain technology in modernising traditional asset ownership. For more information and to participate in this extraordinary contest, visit MetaWin.com. The competition is now open for entries, with the drawing scheduled for June 1, 2024. The competition is now open for entries, with the drawing scheduled for June 1, 2024. MetaWin.com. MetaWin.com. About Metawin MetaWin is the premier platform for on-chain prize competitions and instant win games, offering a diverse range of entertaining challenges for users to enjoy. By harnessing cutting-edge blockchain technology, MetaWin provides a transparent, fair, and secure gaming environment, making it the go-to destination for blockchain enthusiasts and gamers alike. Follow us on social media: X | Instagram X X Instagram Instagram Join our community: Telegram | Discord Telegram Telegram Discord Discord Contact PR Team Metawin press@metawin.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. here. here.

MetaWin Announces Innovative TOKENIZED Tesla Cybertruck Contest On Ethereum's Base Layer 2 Protocol

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. Caption enriched samples for improving hateful memes detection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9350–9358, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Ana-Maria Bucur, Adrian Cosma, and Ioana Iordache. 2022. BLUE at memotion 2.0 2022: You have my image, my text and my transformer. In De-Factify @ AAAI 2022. First Workshop on Multimodal FactChecking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. BoolQ: Exploring the surprising difficulty of natural yes/no questions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2924–2936, Minneapolis, Minnesota. Association for Computational Linguistics. CrowdFlower. 2016. 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. Meme sentiment analysis enhanced with multimodal spatial encoding and face embedding. In Artificial Intelligence and Cognitive Science, pages 318–331, Cham. Springer Nature Switzerland. Junguang Jiang, Yang Shu, Jianmin Wang, and Mingsheng Long. 2022. Transferability in deep learning: A survey. arXiv preprint, arXiv:2201.05867. Vishal Keswani, Sakshi Singh, Suryansh Agarwal, and Ashutosh Modi. 2020. IITK at SemEval-2020 task 8: Unimodal and bimodal sentiment analysis of Internet memes. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1135–1140, Barcelona (online). International Committee for Computational Linguistics. Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, et al. 2020. The hateful memes challenge: Detecting hate speech in multimodal memes. Advances in Neural Information Processing Systems, 33:2611–2624. Liisi Laineste and Piret Voolaid. 2017. Laughing across borders: Intertextuality of internet memes. The European Journal of Humour Research, 4(4):26–49. Gwang Gook Lee and Mingwei Shen. 2022. Amazon pars at memotion 2.0 2022: Multi-modal multi-task learning for memotion 2.0 challenge. In De-Factify @ AAAI 2022. First Workshop on Multimodal FactChecking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Quinn McNemar. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2):153–157. Shreyash Mishra, S Suryavardan, Parth Patwa, Megha Chakraborty, Anku Rani, Aishwarya Reganti, Aman Chadha, Amitava Das, Amit Sheth, Manoj Chinnakotla, Asif Ekbal, and Srijan Kumar. 2023. Memotion 3: Dataset on sentiment and emotion analysis of codemixed hindi-english memes. arXiv preprint, arXiv:2303.09892. Niklas Muennighoff. 2020. Vilio: state-of-the-art visiolinguistic models applied to hateful memes. arXiv preprint arXiv:2012.07788. Thanh Van Nguyen, Nhat Truong Pham, Ngoc Duy Nguyen, Hai Nguyen, Long H. Nguyen, and YongGuk Kim. 2022. Hcilab at memotion 2.0 2022: Analysis of sentiment, emotion and intensity of emotion classes from meme images using single and multi modalities (short paper). In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Asaf Nissenbaum and Limor Shifman. 2017. Internet memes as contested cultural capital: The case of 4chan’s /b/ board. New Media & Society, 19(4):483– 501. Parth Patwa, Sathyanarayanan Ramamoorthy, Nethra Gunti, Shreyash Mishra, S Suryavardan, Aishwarya Reganti, et al. 2022. Findings of memotion 2: Sentiment and emotion analysis of memes. In De-Factify @ AAAI 2022. First Workshop on Multimodal FactChecking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Kim Ngan Phan, Gueesang Lee, Hyung-Jeong Yang, and Soo-Hyung Kim. 2022. Little flower at memotion 2.0 2022 : Ensemble of multi-modal model using attention mechanism in memotion analysis (short paper). In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Jason Phang, Thibault Fevry, and Samuel R. Bowman. ´ 2019. Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks. arXiv preprint, arXiv:1811.01088. Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019. WiC: the word-in-context dataset for evaluating context-sensitive meaning representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1267–1273, Minneapolis, Minnesota. Association for Computational Linguistics. Clifton Poth, Jonas Pfeiffer, Andreas Ruckl ¨ e, and Iryna ´ Gurevych. 2021. What to pre-train on? Efficient intermediate task selection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10585–10605, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela. 2021. DynaSent: A dynamic benchmark for sentiment analysis. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2388–2404, Online. Association for Computational Linguistics. Shraman Pramanick, Md Shad Akhtar, and Tanmoy Chakraborty. 2021a. Exercise? i thought you said ’extra fries’: Leveraging sentence demarcations and multi-hop attention for meme affect analysis. Proceedings of the International AAAI Conference on Web and Social Media, 15(1):513–524. Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty. 2021b. MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4439–4455, Punta Cana, Dominican Republic. Association for Computational Linguistics. Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R. Bowman. 2020. Intermediate-task transfer learning with pretrained language models: When and why does it work? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5231–5247, Online. Association for Computational Linguistics. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, et al. 2021. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748–8763. PMLR. Sathyanarayanan Ramamoorthy, Nethra Gunti, Shreyash Mishra1, S Suryavardan, Aishwarya Reganti, Parth Patwa, Amitava Das, Tanmoy Chakraborty, Amit Sheth, Asif Ekbal, and Chaitanya Ahuja. 2022. Memotion 2: Dataset on sentiment and emotion analysis of memes. In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Elad Segev, Asaf Nissenbaum, Nathan Stolero, and Limor Shifman. 2015. Families and Networks of Internet Memes: The Relationship Between Cohesiveness, Uniqueness, and Quiddity Concreteness. Journal of Computer-Mediated Communication, 20(4):417–433. Lanyu Shang, Yang Zhang, Yuheng Zha, Yingxi Chen, Christina Youn, and Dong Wang. 2021. AOMD: An Analogy-Aware Approach to Offensive Meme Detection on Social Media. Inf. Process. Manage., 58(5). Chhavi Sharma, Deepesh Bhageria, William Scott, Srinivas PYKL, Amitava Das, Tanmoy Chakraborty, et al. 2020. SemEval-2020 task 8: Memotion analysisthe visuo-lingual metaphor! In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 759–773, Barcelona (online). International Committee for Computational Linguistics. Shivam Sharma, Mohd Khizir Siddiqui, Md. Shad Akhtar, and Tanmoy Chakraborty. 2022. Domainaware self-supervised pre-training for label-efficient meme analysis. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 792–805, Online only. Association for Computational Linguistics. Limor Shifman. 2014. The cultural logic of photo-based meme genres. Journal of Visual Culture, 13(3):340–358. Pranaydeep Singh, Aaron Maladry, and Els Lefever. 2022. Combining language models and linguistic information to label entities in memes. In Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations, pages 35–42, Dublin, Ireland. Association for Computational Linguistics. Shardul Suryawanshi, Bharathi Raja Chakravarthi, Pranav Verma, Mihael Arcan, John Philip McCrae, and Paul Buitelaar. 2020. A dataset for troll classification of TamilMemes. In Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation, pages 7–13, Marseille, France. European Language Resources Association (ELRA). Cagri Toraman, Furkan S¸ahinuc¸, and Eyup Yilmaz. 2022. Large-scale hate speech detection with crossdomain transfer. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2215–2225, Marseille, France. European Language Resources Association. Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, and Samuel R. Bowman. 2019. Can you tell me how to get past sesame street? sentence-level pretraining beyond language modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4465–4476, Florence, Italy. Association for Computational Linguistics. Ron Zhu. 2020. Enhance multimodal transformer with external label and in-domain pretrain: Hateful meme challenge winning solution. arXiv preprint, arXiv:2012.08290. Yan Zhuang and Yanru Zhang. 2022. Yet at memotion 2.0 2022 : Hate speech detection combining bilstm and fully connected layers (short paper). In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI.

Innovative Strategies in Meme Sentiment Analysis: The Impact of Unimodal Training

**SINGAPORE, Singapore, April 5th, 2024/Chainwire/--**Solv Protocol, the leading native yield platform that tokenizes and aggregates yields from diverse sources, is launching its new point system. The system will go live on April 5, and is designed to reward users investing in Solv vaults like SolvBTC, the platform’s native yield solution for Bitcoin. The Solv point system introduces a dynamic way for users to earn rewards based on their investment activities. Those who stake in Solv vaults will accrue points proportionate to their staked amounts. Additionally, the system features a referral program, enabling users to invite friends and family to participate. All earned points can be used to receive future Solv airdrops, fostering enhanced engagement and allowing users to actively contribute to the protocol's growth and success. Backed by Blockchain Capital, Binance, Nomura, and other prominent investors, Solv Protocol currently boasts a Total Value Locked (TVL) of over $202 million since its launch in the second quarter of last year. The recent introduction of SolvBTC, the world’s first-ever yield-bearing Bitcoin, underscores Solv’s commitment to building a vital liquidity layer that empowers the vast BTCFi ecosystem. SolvBTC is already available on Ethereum mainnet, Arbitrum, BNB Chain, Merlin Chain, and other networks. More than 2,000 BTC is now entrusted with Solv. The $SOLV token will serve as the governance token for Solv Protocol, giving $SOLV holders the opportunity to participate in asset and resource selection, as well as gain additional governance-related benefits and rewards. With the launch of the new point system and the continued success of SolvBTC, Solv Protocol continues to solidify its position as a vital liquidity layer within the DeFi landscape. This exciting development marks yet another important milestone in Solv’s journey towards empowering users and revolutionizing the world of DeFi asset management. About Solv Protocol: is a native yield platform and unified liquidity gateway that tokenizes and aggregates high-quality yields from various sources. With core assets like SolvBTC, SolvUSD, and SolvETH, the protocol serves as a vital liquidity layer across ecosystems. Solv Protocol Solv Protocol is backed by prominent investors including Binance Labs and Blockchain Capital, and audited by Quantstamp, Certik, and others. The protocol’s maiden product, SolvBTC, addresses a significant gap in the Bitcoin ecosystem by providing a native yield solution that gives BTC holders unprecedented opportunities for growth and participation. | | | | | | Website dApp X Telegram Discord LinkedIn GitHub Contact Ethean Yu Ethean@Solv.finance This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Solv Protocol Introduces Innovative Point System To Reward Users, Reveals Airdrop Plans

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