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Rookie actor Lee Seo-han has issued an apology after posting an obscene video filmed at former TREASURE member Bang Yedam&#x27;s studio. Read on!, Korean News - Times Now

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The trial of a former Kazakhstan minister charged with beating his wife to death has become the talking point across the nation, with some seeing it as a litmus test of President Kassym-Jomart Tokayev&#x27;s promise to build a fairer society.

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IT Services and IT Consulting\nSAP, Salesforce and Commerce Cloud, Docker, Azure, AWS, Anaplan, Cegid

VISEO

In the fast-moving world of , VeChain, a -based platform, distinguishes itself with an anti-fraud technology that has the potential to ultimately end product piracy. cryptocurrency blockchain Vechain’s proprietary technology helps to authenticate purchases and boost consumer confidence through reliable recordkeeping. Moreover, it can help companies to establish a higher level of transparency during their manufacturing and delivery processes. But what is VeChain, and how does it function? And most importantly – is its VET coin a good investment? Read on below for a deeper look into the world of VeChain, and find out what investment prospects this emerging technology holds right now. What Is Vechain? Let’s start by firstly understanding the role of in the realm of cryptocurrency. VeChain Currently, supply chain data for business procedures is compartmentalized in data silos among various stakeholders. It adversely affects information flow and acts as a dividing aspect among stakeholders. VeChain offers to provide a complete view of all organizational processes by taking away the concept of information data silos. Essentially, VeChain is a blockchain platform that helps to simplify information chain management. Its blockchain was originally conceived as a means of determining whether a real-life product is genuine or not. It helps prevent frauds and knock-offs; and now, multiple corporations use its blockchain to keep track of production, manufacturing, delivery, and much more. The blockchain purports the formation of self-executing with a definitive outcome without third-party trust or intrusion required. It facilitates the establishment of decentralized applications ( ), which can solve many enterprise problems, including: smart contracts DApps – VeChain uses smart chips technology to track every individual item, hence preventing duplication. Anti-counterfeit for Luxury Products – Using smart Internet of Things sensors to report critical data through the blockchain, companies can automatically monitor and ensure that food products aren’t spoiled during transportation and storage. Cold-chain for Logistics – companies can retain a tamper-free pool of data for vehicle data such as repair history, registration, insurance, and driver habits. Automobile Industry – companies can quantitatively track their carbon contributions to help decrease carbon emissions. The concept here is that consumers engaged in reduced carbon-emitting behaviors, for instance, buying products with reduced carbon, could be given the credits that can be redeemed for environment-friendly products or can be given as a donation. Going Green – collaborating with Bayer China, VeChain intends to use blockchain technology for solving problems related to digitalized clinical trial tracing. Clinical Trial Traceability VeChain aims to grow into a leading platform for initial coin offerings (ICOs) and lead transactions between Internet of Things (IoT) connected devices. How Does Vechain Work? The main goal of VeChain is to streamline the data flow of supply chains. The VeChain portal comprises of two specific tokens: to give value to VeChain’s network (funds the blockchain) VeChain Token (VET) to strengthen smart contract plans (empowers the technology) VeChainThor Energy (VTHO) The is this: As mentioned briefly in the previous section, VeChain allocates every product a distinct identity. It uses smart chips or Radio Frequency Identification (RFID) tags and sensors to track what happens to the product at every phase of the supply chain. way it works Moreover, all this information can be accessed in real-time by authorized stakeholders. It helps companies and customers ensure that each product is handled properly and verified for legitimacy during a purchase. The VeChain Portal claims to deliver a 360-degree outlook of all the essential information related to a product and its subsequent business processes. The platform provides access to authorized stakeholders, such as supply, storage, and transportation, to create greater market transparency. VeChain also publishes frequent financial reports to encourage transparency. VeChain aims to develop an intrusion-free and evenly deployed corporate system to allow clear information flow, promote effective cooperation, and allow velocity-based information sharing. As per VeChain’s philosophy, blockchain technology has the power to break the asymmetric information flow and allow for data ownership to flow back to and strengthen its user. To elaborate a real-life use of VeChain based management of operations, the portal can monitor and keep track of a long list of operational details. Information such as quality, validity, storage temperature, mode of transportation, and mile route delivery of a pack of medicines can be managed right from the manufacturing facility to the end customer, using the above-mentioned smart-chip sensors. What Are The Key Features of Vechain? The of VeChain as a blockchain and a cryptocurrency are: key features Like many blockchains, the VeChainThor blockchain is open source, which makes it suitable for hosting smart contracts and DApps. Open Source Blockchain: The VeChainportal will allow corporations to use the awesome power of blockchain for industries such as agriculture, liquor, and luxury goods. In-house IOT and Supply Chain Management Technology: Native fee delegation is a part of blockchain code, and the app users are not expected to hold VET or VTHO. They can write transactions related to gas costs as stated by sponsored developers are not applicable. Native Fee Delegation: The History of Vechain Having understood what VeChain is and what it is meant for, let’s delve a bit into its origins. Sunny Lu, Louis Vuitton China’s ex-Chief Information Officer (CIO), . The platform grew as a Bitse subsidiary, a leading and reputable blockchain company in China. VeChain is one of the few blockchains that has a considerable customer base amid well-established companies. founded VeChain in 2015 Since its inception, the company has been developing enterprise-focused solutions deployed on its proprietary blockchain platform. With a rising trend towards building better decentralization platforms within the blockchain industry, VeChain moved to a consortium blockchain before establishing the VeChain Foundation and ultimately starting their final migration to a Proof of Authority (PoA)-based public blockchain platform. In the early days, the VEN token operated through the Ethereum platform and its blockchain technology. More recently, VeChain created an independent blockchain platform and rebranded it to VeChainThor (VET) in 2018. In terms of strategic partnerships, VeChain has, over the years, formed alliances with many major corporations. These partnerships focus on helping to achieve their goal of blockchain-powered transparency in the management and supply industries. Among these agreements is a partnership with PricewaterhouseCoopers (PwC) in line with VeChain’s aim to reach the respected accounting firm’s client base. The collaboration will help improve product verification and tracking systems. Another major arm of VeChain is their partnership with Renault. Along with Microsoft and Viseo, the brand has created a car maintenance book that is digitally managed and tamper-proof. VeChain is the official partner of technology for the Gui’an- technology sector. Gui’an is the economic development zone in Central China. Other notable partnerships and live-use cases include: DNV GL Deloitte Walmart China BMW BYD Auto Bayer China H&M LVMH ENN AWS PICC ASI Group Vechain Price History VeChain price history is based on the VET Token used on the blockchain. Since about late 2018, the VET Token price mostly moved sideways in a range of $0.002-$0.009. However, even within this range, it has presented itself as being tremendously changeable at times, with prices jumping in the double-digit percentage range. In 2020, VeChain finally managed to create a stir in the market when the price rose to $0.02 in the course of a price rally in the middle of the year. It amounted to an increase of approximately 400 percent in just more than a month. Moreover, the success of VeChain is attracting further investors. The bull market that took place instantaneously propelled the VET Token price to over $0.03 shortly after the beginning of the year 2021. On January 21, VET reached its new all-time high price at $0.035. Since then, it has catapulted in price even more, reaching an all-time high on April 27 of $0.27. With a The market capitalization (or market cap) of a cryptocurrency is a measurement of its market value. In other words, it... at the time of writing of around $11 billion, VeChain ranks 14th in market cap among cryptocurrencies. The total supply is 86.712 million VET, with just over 64 million VET currently in circulation. market cap What Is The Future of Vechain? It must be obvious by now that the technology undoubtedly works; many investing partners believe in the project, and presently, the price is on a sturdy upswing. All in all, the prospects of VeChain looks promising. But before we make any , let’s note some expert opinions. price forecasts VET Price Predictions Remember that these predictions are only to form informed opinions and is not investment advice. Coinswitch Experts at predict a positive price trend for VeChain, saying the VET token may increase in price to $0.38, by 2023 – mostly propelled by partnerships with big names such as PwC, Renault, and others. Coinswitch Wallet Investor Analysts at also see VeChain as a worthwhile investment. They predict it could reach as high as $0.86 by 2025. Wallet Investor Digitalcoinprice.com ‘s forecast also assumes an overall profitable long-term investment, predicting it could reach $0.50 by 2025. Digitalcoinprice.com VET Price Factors You might be asking yourself: what is behind these ? positive upward trends For starters, this performance is mainly because of the large number of companies adopting the VeChainThor platform to develop apps that connect blockchain technology to the real world. Examples of these companies include LVMH, Walmart China, and BYD. Another factor is the social media hype. In recent times, traction surrounding Vechain on social media, especially on Reddit, has grown. It has led to many users buying the currency because of the ‘fear of missing out (FOMO) phenomenon, something that is well-known in the world of crypto. he Bottom Line T To sum up, VeChain is already among an elite list of companies that can pride themselves on developing a fully-functioning and coherent product that is being used by numerous industries. It’s fair to say that this is down to the team behind this project having taken an extremely innovative approach to their work. In a globalized world, where products cannot be manufactured centrally in one place anymore, tracking them has become an issue of immense importance. Hence, VeChain helps create the infrastructural basis to resolve this problem in a blockchain efficiently. If you’re thinking of investing in VeChain, and analyze the forecasts and expert predictions regarding its growth and usability, the price increases of 300% or more over the coming years do not seem unrealistic. Of course, as always it’s important to do your own research. Investors who want to buy the VET token must carefully educate themselves about VeChain and its technology and use crypto expert opinions only as a supporting tool before deciding to take the plunge and investing. So, with the newfound knowledge in crypto, why not sign up to Bybit? Sign up for a free trading account Disclaimer This article is intended for and only to be used for reference purposes only. No such information provided through Bybit constitutes advice or a recommendation that any investment or trading strategy is suitable for any specific person. These forecasts are based on industry trends, circumstances involving clients, and other factors, and they involve risks, variables, and uncertainties. There is no guarantee presented or implied as to the accuracy of specific forecasts, projections, or predictive statements contained herein. Users of this article agree that Bybit does not take responsibility for any of your investment decisions. Please seek professional advice before trading.

Everything You Need to Know About Vechain

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If you're in sales, HR, or customer support, you know the struggle to keep users engaged is real. Businesses are always trying to keep their users interested. But here's the thing: most of the time, the content just isn't engaging enough. Busy folks often ignore traditional strategies of user engagement, such as email campaigns. Think about how many marketing emails go unread in your inbox. So, how do you keep users engaged in the face of these challenges? That's where smart how-to videos come in. They use AI and interactive features to make learning more engaging and redefine the user experience. Did you know that according to a YouTube study done by Ipsos in 2017, when it comes to learning about new products, YouTube users are 3x more likely to prefer watching a video than reading the product’s instructions? That's how powerful videos can be for user engagement. Now, let's talk about what user engagement really means. What is User Engagement? User engagement is key for any digital project. It shows how much customers interact with a product, service, or content. It's about getting people involved, getting their feedback, and building their loyalty. Think of user engagement as the heartbeat of your business's success. It directly affects important things like the number of users, customer retention, satisfaction, and conversion rates. Engaged users aren't just customers. They're your brand's biggest fans. They're loyal customers. They're the reason for positive word-of-mouth advertising. Increasing user engagement can boost revenue, enhance brand reputation, and give you a competitive advantage in the market. It's that important. Smart How-to Videos vs User Engagement Based on the statistics mentioned above, it is clear that video content is highly effective for engaging users. But it's more than just numbers. Videos have a unique ability to evoke emotions and engage multiple senses, creating a deeper connection with your audience. They allow you to create immersive experiences that resonate on a personal level. Smart how-to videos are leading the charge in changing how users interact with businesses. They provide clear and concise product descriptions, demos, documentation, and training videos that are easy to understand and engaging to watch. Businesses are taking advantage of smart video content to connect with their audience and foster meaningful interactions. But what makes smart video content different, and how does it affect user engagement? Let's find out! How Teams Are Using How-to Videos to Achieve Optimal User Engagement Alright, so we've talked about how vital user engagement is for businesses. But making sure you're hitting those engagement targets? It's no walk in the park. There are challenges all over the place. Let's break it down and see how different teams are using how-to videos to amp up user engagement. Sales Teams Sales teams are using how-to videos to get more people interested and increase the number of users. Here's how: Dynamic Product Demonstrations: Simple screen recordings of how a product works is a great user engagement strategy that sales teams use. Sales teams are making dynamic how-to videos to show their products or services in action. These videos help people understand what the product does and why it's useful.
Personalized Outreach: Using AI technology, sales teams can make personalized how-to videos for each potential customer. These videos address the customer's needs directly and make them feel valued. HR Departments HR departments are using how-to videos to make training easier and improve the user experience for employees. Here's how they're doing it: Interactive Onboarding: Instead of using boring manuals, HR departments are making interactive how-to videos to help new employees get started. These videos show them what to do step by step and make learning fun.
Skills Development: HR departments are making video tutorials on different topics to help employees learn new things. Employees can watch these videos at their own pace and get better at their jobs. Customer Support Teams Customer support teams are using how-to videos to help customers fix problems on their own and improve the user experience. Here's how they're doing it: DIY Videos: How-to videos show customers how to fix things themselves, so they don't have to call for help. This saves time for the support team and makes customers happy.
Personalized Support: Customer support teams are using AI to give customers personalized help. AI can recommend the right how-to videos or answer questions in real-time, making support faster and more helpful. How AI is Changing User Engagement AI-powered how-to videos are transforming the way users learn and interact. From creating personalized content to boosting user engagement, AI is revolutionizing how-to videos like never before. Personalized Content Creation AI algorithms analyze huge amounts of data to understand user behaviors, preferences, and interests. This helps platforms like Guidde create personalized how-to videos tailored to each viewer. AI ensures personalized learning for each individual by recommending useful tutorials and adapting content to their specific needs. Enhanced User Engagement Traditional how-to videos often struggle to keep users engaged from start to finish. AI solves this problem by adding interactive elements like quizzes, polls, and branching scenarios to the video experience. These features make the content more engaging and encourage active participation, helping users retain information better. Efficient Content Creation Making high-quality how-to videos can be time-consuming and resource-intensive. AI simplifies this process by automating tasks such as video editing, scriptwriting, and voiceover narration. With AI's assistance, content creators can produce top-notch videos more efficiently, allowing for faster editing and growth opportunities. How Guidde Keeps Your Users Engaged Using Smart Videos Guidde is a game-changer, making it easy for businesses to create captivating and effective smart how-to videos. Here's how Guidde does it and keeps your users hooked: Easy Creation Process Guidde simplifies video creation, so anyone from any department can do it without needing special skills. With its user-friendly features and simple interface, Guidde makes creating how-to videos a breeze. Adapts on the Fly Guidde's smart videos adjust based on user feedback, tailoring the learning experience to each person. Whether it's speeding up the video or providing extra explanations, Guidde ensures everyone gets what they need. Uses Data to Improve Guidde gives insights into user engagement, helping businesses tweak their video content for better results. You can see real-time results of viewer behavior and preferences. Simply put, Guidde helps create videos that resonate with audiences. Measuring user engagement data is easy with Guidde. You can track user engagement metrics with Guidde. Some of these include the amount of time spent on a video and the user engagement rate. As you can see, Guidde helps businesses improve their brand awareness, content marketing strategies and create engaging videos that connect with their audience. Always Evolving Guidde is committed to innovation, staying on top of AI advancements to keep users engaged and interested. With Guidde, businesses can future-proof their video strategies and stay ahead of the curve. Final Words The future of user engagement with smart how-to videos is full of potential. With Guidde paving the way, businesses can stay ahead of the curve in user engagement, thanks to the cutting-edge video technology we have today. If you're in sales, HR, or customer support, you know the struggle to keep users engaged is real. Businesses are always trying to keep their users interested. But here's the thing: most of the time, the content just isn't engaging enough. Busy folks often ignore traditional strategies of user engagement, such as email campaigns. Think about how many marketing emails go unread in your inbox. So, how do you keep users engaged in the face of these challenges? That's where smart how-to videos come in. They use AI and interactive features to make learning more engaging and redefine the user experience. Did you know that according to a YouTube study done by Ipsos in 2017, when it comes to learning about new products, YouTube users are 3x more likely to prefer watching a video than reading the product’s instructions? That's how powerful videos can be for user engagement. YouTube 3x more likely to prefer watching a video 3x more likely to prefer watching a video Now, let's talk about what user engagement really means. What is User Engagement? User engagement is key for any digital project. It shows how much customers interact with a product, service, or content. It's about getting people involved, getting their feedback, and building their loyalty. Think of user engagement as the heartbeat of your business's success. It directly affects important things like the number of users, customer retention, satisfaction, and conversion rates. Engaged users aren't just customers. They're your brand's biggest fans. They're loyal customers. They're the reason for positive word-of-mouth advertising. Increasing user engagement can boost revenue, enhance brand reputation, and give you a competitive advantage in the market. It's that important. Smart How-to Videos vs User Engagement Based on the statistics mentioned above, it is clear that video content is highly effective for engaging users. But it's more than just numbers. Videos have a unique ability to evoke emotions and engage multiple senses, creating a deeper connection with your audience. They allow you to create immersive experiences that resonate on a personal level. Smart how-to videos are leading the charge in changing how users interact with businesses. They provide clear and concise product descriptions, demos, documentation, and training videos that are easy to understand and engaging to watch. Businesses are taking advantage of smart video content to connect with their audience and foster meaningful interactions. But what makes smart video content different, and how does it affect user engagement? Let's find out! How Teams Are Using How-to Videos to Achieve Optimal User Engagement Alright, so we've talked about how vital user engagement is for businesses. But making sure you're hitting those engagement targets? It's no walk in the park. There are challenges all over the place. Let's break it down and see how different teams are using how-to videos to amp up user engagement. Sales Teams Sales teams are using how-to videos to get more people interested and increase the number of users. Here's how: Here's how: Dynamic Product Demonstrations: Simple screen recordings of how a product works is a great user engagement strategy that sales teams use. Sales teams are making dynamic how-to videos to show their products or services in action. These videos help people understand what the product does and why it's useful. Personalized Outreach: Using AI technology, sales teams can make personalized how-to videos for each potential customer. These videos address the customer's needs directly and make them feel valued. Dynamic Product Demonstrations: Simple screen recordings of how a product works is a great user engagement strategy that sales teams use. Sales teams are making dynamic how-to videos to show their products or services in action. These videos help people understand what the product does and why it's useful. Personalized Outreach: Using AI technology, sales teams can make personalized how-to videos for each potential customer. These videos address the customer's needs directly and make them feel valued. HR Departments HR departments are using how-to videos to make training easier and improve the user experience for employees. Here's how they're doing it: Here's how they're doing it: Interactive Onboarding: Instead of using boring manuals, HR departments are making interactive how-to videos to help new employees get started. These videos show them what to do step by step and make learning fun. Skills Development: HR departments are making video tutorials on different topics to help employees learn new things. Employees can watch these videos at their own pace and get better at their jobs. Interactive Onboarding: Instead of using boring manuals, HR departments are making interactive how-to videos to help new employees get started. These videos show them what to do step by step and make learning fun. Skills Development: HR departments are making video tutorials on different topics to help employees learn new things. Employees can watch these videos at their own pace and get better at their jobs. Customer Support Teams Customer support teams are using how-to videos to help customers fix problems on their own and improve the user experience. Here's how they're doing it: Here's how they're doing it: DIY Videos: How-to videos show customers how to fix things themselves, so they don't have to call for help. This saves time for the support team and makes customers happy. Personalized Support: Customer support teams are using AI to give customers personalized help. AI can recommend the right how-to videos or answer questions in real-time, making support faster and more helpful. DIY Videos: How-to videos show customers how to fix things themselves, so they don't have to call for help. This saves time for the support team and makes customers happy. Personalized Support: Customer support teams are using AI to give customers personalized help. AI can recommend the right how-to videos or answer questions in real-time, making support faster and more helpful. How AI is Changing User Engagement AI-powered how-to videos are transforming the way users learn and interact. From creating personalized content to boosting user engagement, AI is revolutionizing how-to videos like never before. Personalized Content Creation AI algorithms analyze huge amounts of data to understand user behaviors, preferences, and interests. This helps platforms like Guidde create personalized how-to videos tailored to each viewer. AI ensures personalized learning for each individual by recommending useful tutorials and adapting content to their specific needs. Enhanced User Engagement Traditional how-to videos often struggle to keep users engaged from start to finish. AI solves this problem by adding interactive elements like quizzes, polls, and branching scenarios to the video experience. These features make the content more engaging and encourage active participation, helping users retain information better. Efficient Content Creation Making high-quality how-to videos can be time-consuming and resource-intensive. AI simplifies this process by automating tasks such as video editing, scriptwriting, and voiceover narration. With AI's assistance, content creators can produce top-notch videos more efficiently, allowing for faster editing and growth opportunities. How Guidde Keeps Your Users Engaged Using Smart Videos Guidde is a game-changer , making it easy for businesses to create captivating and effective smart how-to videos. Here's how Guidde does it and keeps your users hooked: Guidde is a game-changer Guidde is a game-changer Easy Creation Process Guidde simplifies video creation, so anyone from any department can do it without needing special skills. With its user-friendly features and simple interface, Guidde makes creating how-to videos a breeze. Adapts on the Fly Guidde's smart videos adjust based on user feedback, tailoring the learning experience to each person. Whether it's speeding up the video or providing extra explanations, Guidde ensures everyone gets what they need. Uses Data to Improve Guidde gives insights into user engagement, helping businesses tweak their video content for better results. You can see real-time results of viewer behavior and preferences. Simply put, Guidde helps create videos that resonate with audiences. Measuring user engagement data is easy with Guidde. You can track user engagement metrics with Guidde. Some of these include the amount of time spent on a video and the user engagement rate. As you can see, Guidde helps businesses improve their brand awareness, content marketing strategies and create engaging videos that connect with their audience. Always Evolving Guidde is committed to innovation, staying on top of AI advancements to keep users engaged and interested. With Guidde, businesses can future-proof their video strategies and stay ahead of the curve. Final Words The future of user engagement with smart how-to videos is full of potential. With Guidde paving the way, businesses can stay ahead of the curve in user engagement, thanks to the cutting-edge video technology we have today.

The Future of User Engagement Via Smart How-To-Videos

স্মার্ট হাউ-টু-ভিডিওর মাধ্যমে ব্যবহারকারীর ব্যস্ততার ভবিষ্যত

Die Zukunft der Benutzereinbindung durch intelligente How-To-Videos

El futuro de la participación del usuario a través de vídeos instructivos inteligentes

L'avenir de l'engagement des utilisateurs via des vidéos pratiques intelligentes

स्मार्ट हाउ-टू-वीडियो के माध्यम से उपयोगकर्ता सहभागिता का भविष्य

スマートなハウツー動画によるユーザーエンゲージメントの未来

스마트 How-To 비디오를 통한 사용자 참여의 미래

O futuro do envolvimento do usuário por meio de vídeos inteligentes de instruções

Будущее взаимодействия с пользователями посредством умных видеороликов с практическими рекомендациями

Akıllı Nasıl Yapılır Videoları Yoluyla Kullanıcı Etkileşiminin Geleceği

Tương lai của sự tương tác của người dùng thông qua video hướng dẫn thông minh

通过智能视频提高用户参与度的未来

**TOKYO, Japan, May 8th, 2024/Chainwire/--**Slash Vision Labs is thrilled to introduce SVL Alpha-Base (’Alpha-Base’), an innovative addition to the SVL Portal tailored to offer SVL token stakers exclusive alpha opportunities from our esteemed partner projects. Alpha-Base represents a significant leap forward in our mission to empower and engage our community. By partnering with leading projects in the crypto space, Slash Vision Labs will create exclusive alpha opportunities for SVL token stakers, offering early access to token launches and potential airdrops. With three notable projects already onboarded and one more set to be announced in the coming days, Alpha-Base is poised to become the go-to destination for those seeking to stay ahead of the curve. Through SVL Alpha-Base, SVL token stakers will gain access to exclusive opportunities while our partner projects gain an active community already onboarded onto SVL. By bringing a vibrant and active community of SVL to their projects, our partners can benefit from increased engagement and participation in their platform and upcoming token launches. "Our mission with SVL Alpha-Base is to provide our community with the tools and opportunities needed to thrive in the ever-evolving crypto landscape. We aim to create a unified ecosystem that unlocks massive opportunities for every participant of the SVL ecosystem.”— Shinsuke Sato, Founder of Slash Vision Labs. Stay tuned for further updates and announcements as we prepare to launch SVL Alpha-Base and onboard many promising new projects. About Slash Vision Labs SVL (Slash Vision Labs) is pioneering the transformation of the cryptocurrency payments landscape with its innovative ecosystem, built on the Mantle Network. Embracing decentralization and community empowerment, SVL redistributes 100% of protocol fees back into the ecosystem, rewarding token stakers and fostering active participation. SVL's flagship products, Slash Card and Slash Payment, redefine the digital payments landscape. Slash Card, the first-ever compliant credit card in Japan, bridges fiat and crypto economies, offering unmatched convenience. Meanwhile, Slash Payment, a crypto-to-crypto swap payment solution, empowers over 4,000 merchants, processing over 200 million USD worth of crypto transactions. For media inquiries or further information, please contact: Masato Kato Business Development Slash Vision Labs hello@slash.vision Website | X | Discord | Medium | Whitepaper Contact Slash Team hello@slash.vision This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **TOKYO, Japan, May 8th, 2024/Chainwire/--**Slash Vision Labs is thrilled to introduce SVL Alpha-Base (’Alpha-Base’), an innovative addition to the SVL Portal tailored to offer SVL token stakers exclusive alpha opportunities from our esteemed partner projects. Alpha-Base represents a significant leap forward in our mission to empower and engage our community. By partnering with leading projects in the crypto space, Slash Vision Labs will create exclusive alpha opportunities for SVL token stakers, offering early access to token launches and potential airdrops. With three notable projects already onboarded and one more set to be announced in the coming days, Alpha-Base is poised to become the go-to destination for those seeking to stay ahead of the curve. Through SVL Alpha-Base, SVL token stakers will gain access to exclusive opportunities while our partner projects gain an active community already onboarded onto SVL. By bringing a vibrant and active community of SVL to their projects, our partners can benefit from increased engagement and participation in their platform and upcoming token launches. "Our mission with SVL Alpha-Base is to provide our community with the tools and opportunities needed to thrive in the ever-evolving crypto landscape. We aim to create a unified ecosystem that unlocks massive opportunities for every participant of the SVL ecosystem.”— Shinsuke Sato, Founder of Slash Vision Labs. "Our mission with SVL Alpha-Base is to provide our community with the tools and opportunities needed to thrive in the ever-evolving crypto landscape. We aim to create a unified ecosystem that unlocks massive opportunities for every participant of the SVL ecosystem.”— Shinsuke Sato, Founder of Slash Vision Labs. Stay tuned for further updates and announcements as we prepare to launch SVL Alpha-Base and onboard many promising new projects. About Slash Vision Labs SVL (Slash Vision Labs) is pioneering the transformation of the cryptocurrency payments landscape with its innovative ecosystem, built on the Mantle Network. Embracing decentralization and community empowerment, SVL redistributes 100% of protocol fees back into the ecosystem, rewarding token stakers and fostering active participation. SVL's flagship products, Slash Card and Slash Payment, redefine the digital payments landscape. Slash Card, the first-ever compliant credit card in Japan, bridges fiat and crypto economies, offering unmatched convenience. Meanwhile, Slash Payment, a crypto-to-crypto swap payment solution, empowers over 4,000 merchants, processing over 200 million USD worth of crypto transactions. For media inquiries or further information, please contact: Masato Kato Business Development Slash Vision Labs hello@slash.vision hello@slash.vision hello@slash.vision Website | X | Discord | Medium | Whitepaper Website Website X X Discord Discord Medium Medium Whitepaper Whitepaper Contact Slash Team hello@slash.vision 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

Slash Vision Labs Unveils SVL Alpha-Base: A New Gateway To Exclusive Alpha Opportunities

In this exclusive interview, Andrey Nayman, co-founder of Magic Square, shares his journey in the blockchain industry and the inspirations behind creating a community-driven App Store. Ishan Pandey: Hi Andrey, great to have you here. To start off, could you share a bit about your background and what inspired you to co-found Magic Square? Andrey Nayman: I've been in the blockchain industry since 2013. My experiences leading various tech companies inspired me to start Magic Square. We're building a community-driven App Store that makes using decentralized apps simpler and safer. I’m passionate about using my blockchain knowledge to create something valuable and innovative for users worldwide. Ishan Pandey: Magic Square offers a unique ecosystem for discovering Web3 projects. Can you explain what sets Magic Store apart from other platforms in the space? Andrey Nayman: Magic Square is all about putting users first. Imagine it like a big online store, but instead of selling physical items, we present Web3 Apps. Magic Store is a way for users to discover all trusted Dapps and engage with them in one place. What makes us different is that we check every app really carefully before putting it in our store. This makes our platform safe and easy to use, whether you're new to Web3 or already an expert. Ishan Pandey: Given the diverse offerings within the Magic Square ecosystem like Magic Karma and Hot Offers, how do these features enhance the user experience and engagement? Andrey Nayman: Magic Karma and Hot Offers are innovative features designed to enhance the user experience on Magic Square. Magic Karma serves as our reputation system – users receive daily rewards for contributing to the Magic Ecosystem, such as trying out new apps or participating in community activities, earning them points. These points can then be exchanged for exciting rewards. On the other hand, Hot Offers form our engagement layer, where users can complete tasks provided by the listed projects and receive real payments for their efforts. Together, these features keep our users happy and engaged while using our platform. Ishan Pandey: The $SQR token plays a central role in the Magic Square community. Could you discuss how it integrates into the ecosystem and what its benefits are for users? Andrey Nayman: The $SQR token is like the heart of Magic Square. Users earn $SQR as a reward for doing things on our platform every day. You can stake $SQR to unlock special benefits if you're a member. You'll get paid in $SQR when you earn rewards through Hot Offers. We also use $SQR to reach the right people in our marketing efforts. To join Launchpad sales, you must stake even more $SQR. So, $SQR is important for everyone using Magic Square. It's what keeps everything running smoothly and helps our community grow. Ishan Pandey: Community-driven platforms depend heavily on user participation. How does Magic Square encourage community involvement in project voting and validation? Andrey Nayman: Community is at the core of every crypto project. At Magic Square, we believe in bringing the crypto community together in one place. Here, users can decide which projects make it onto our platform, shaping their future. Our community moderators actively gather feedback, which our product team uses to create a roadmap aligned with their needs. To make our community feel valued and engaged, we offer daily rewards through Karma Points and exciting opportunities through Hot Offers. We also host regular surveys where users can share their ideas and feedback, ensuring our platform evolves based on their input. Ishan Pandey: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Can you elaborate on how this program works and what its advantages are for both users and developers? Andrey Nayman: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Here's how it works: Users can easily monetize their communities by sharing affiliate offers from listed projects. With one click, they share these offers on social media. When their friends engage and convert, users receive instant payouts. This benefits the user by allowing them to earn effortlessly while supporting their favorite projects. For developers, Magic Boost expands their reach and boosts user acquisition, enhancing app visibility and credibility. Ishan Pandey: Looking forward, what are some key developments or milestones that the Magic Square community can anticipate in the near future? Andrey Nayman: In the near future, our Launchpad will be a big focus. It's designed to help projects grow steadily and connect with our community. We'll customize our approach, using both public and private sales, to give projects the best chance of success. With our active community, we'll make sure these projects get plenty of attention. Stay tuned for more updates as we work together to shape the future of Web3! Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. In this exclusive interview, Andrey Nayman, co-founder of Magic Square, shares his journey in the blockchain industry and the inspirations behind creating a community-driven App Store. Ishan Pandey: Hi Andrey, great to have you here. To start off, could you share a bit about your background and what inspired you to co-found Magic Square? Ishan Pandey: Hi Andrey, great to have you here. To start off, could you share a bit about your background and what inspired you to co-found Magic Square? Ishan Pandey: Hi Andrey, great to have you here. To start off, could you share a bit about your background and what inspired you to co-found Magic Square? Andrey Nayman: I've been in the blockchain industry since 2013. My experiences leading various tech companies inspired me to start Magic Square. We're building a community-driven App Store that makes using decentralized apps simpler and safer. I’m passionate about using my blockchain knowledge to create something valuable and innovative for users worldwide. Andrey Nayman: Ishan Pandey: Magic Square offers a unique ecosystem for discovering Web3 projects. Can you explain what sets Magic Store apart from other platforms in the space? Ishan Pandey: Magic Square offers a unique ecosystem for discovering Web3 projects. Can you explain what sets Magic Store apart from other platforms in the space? Ishan Pandey: Magic Square offers a unique ecosystem for discovering Web3 projects. Can you explain what sets Magic Store apart from other platforms in the space? Andrey Nayman: Magic Square is all about putting users first. Imagine it like a big online store, but instead of selling physical items, we present Web3 Apps. Magic Store is a way for users to discover all trusted Dapps and engage with them in one place. What makes us different is that we check every app really carefully before putting it in our store. This makes our platform safe and easy to use, whether you're new to Web3 or already an expert. Andrey Nayman: Ishan Pandey: Given the diverse offerings within the Magic Square ecosystem like Magic Karma and Hot Offers, how do these features enhance the user experience and engagement? Ishan Pandey: Given the diverse offerings within the Magic Square ecosystem like Magic Karma and Hot Offers, how do these features enhance the user experience and engagement? Ishan Pandey: Given the diverse offerings within the Magic Square ecosystem like Magic Karma and Hot Offers, how do these features enhance the user experience and engagement? Andrey Nayman: Magic Karma and Hot Offers are innovative features designed to enhance the user experience on Magic Square. Magic Karma serves as our reputation system – users receive daily rewards for contributing to the Magic Ecosystem, such as trying out new apps or participating in community activities, earning them points. These points can then be exchanged for exciting rewards. On the other hand, Hot Offers form our engagement layer, where users can complete tasks provided by the listed projects and receive real payments for their efforts. Together, these features keep our users happy and engaged while using our platform. Andrey Nayman: Ishan Pandey: The $SQR token plays a central role in the Magic Square community. Could you discuss how it integrates into the ecosystem and what its benefits are for users? Ishan Pandey: The $SQR token plays a central role in the Magic Square community. Could you discuss how it integrates into the ecosystem and what its benefits are for users? Ishan Pandey: The $SQR token plays a central role in the Magic Square community. Could you discuss how it integrates into the ecosystem and what its benefits are for users? Andrey Nayman: The $SQR token is like the heart of Magic Square. Users earn $SQR as a reward for doing things on our platform every day. You can stake $SQR to unlock special benefits if you're a member. You'll get paid in $SQR when you earn rewards through Hot Offers. We also use $SQR to reach the right people in our marketing efforts. Andrey Nayman: To join Launchpad sales, you must stake even more $SQR. So, $SQR is important for everyone using Magic Square. It's what keeps everything running smoothly and helps our community grow. Ishan Pandey: Community-driven platforms depend heavily on user participation. How does Magic Square encourage community involvement in project voting and validation? Ishan Pandey: Community-driven platforms depend heavily on user participation. How does Magic Square encourage community involvement in project voting and validation? Ishan Pandey: Community-driven platforms depend heavily on user participation. How does Magic Square encourage community involvement in project voting and validation? Andrey Nayman: Community is at the core of every crypto project. At Magic Square, we believe in bringing the crypto community together in one place. Here, users can decide which projects make it onto our platform, shaping their future. Our community moderators actively gather feedback, which our product team uses to create a roadmap aligned with their needs. To make our community feel valued and engaged, we offer daily rewards through Karma Points and exciting opportunities through Hot Offers. We also host regular surveys where users can share their ideas and feedback, ensuring our platform evolves based on their input. Andrey Nayman: Ishan Pandey: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Can you elaborate on how this program works and what its advantages are for both users and developers? Ishan Pandey: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Can you elaborate on how this program works and what its advantages are for both users and developers? Ishan Pandey: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Can you elaborate on how this program works and what its advantages are for both users and developers? Andrey Nayman: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Here's how it works: Users can easily monetize their communities by sharing affiliate offers from listed projects. With one click, they share these offers on social media. When their friends engage and convert, users receive instant payouts. This benefits the user by allowing them to earn effortlessly while supporting their favorite projects. For developers, Magic Boost expands their reach and boosts user acquisition, enhancing app visibility and credibility. Andrey Nayman: Ishan Pandey: Looking forward, what are some key developments or milestones that the Magic Square community can anticipate in the near future? Ishan Pandey: Looking forward, what are some key developments or milestones that the Magic Square community can anticipate in the near future? Ishan Pandey: Looking forward, what are some key developments or milestones that the Magic Square community can anticipate in the near future? Andrey Nayman: In the near future, our Launchpad will be a big focus. It's designed to help projects grow steadily and connect with our community. We'll customize our approach, using both public and private sales, to give projects the best chance of success. With our active community, we'll make sure these projects get plenty of attention. Stay tuned for more updates as we work together to shape the future of Web3! Andrey Nayman: Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

Andrey Nayman Reveals the Vision Behind Magic Square's Unique Web3 Ecosystem

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Rui Cao, Singapore Management University;
(2) Ming Shan Hee, Singapore University of Design and Technology;
(3) Adriel Kuek, DSO National Laboratories;
(4) Wen-Haw Chong, Singapore Management University;
(5) Roy Ka-Wei Lee, Singapore University of Design and Technology
(6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Related Work Preliminary Proposed Method Experiment Conclusion and References Appendix APPENDIX A DETAILS FOR IMPLEMENTATION We implement all models under the PyTorch Library with the CUDA11.2 version. We use the Tesla V 100 GPU, each with a dedicated memory of 32GB. For models specifically implemented for hateful meme detection, we take the codes published from the author for reimplementation [4]. For pre trained models which can be found under the Huggingface Library, we use the packages from Huggingface [5], specifically the BERT [4], VisualBERT [18] and the BLIP model. Gor ViLBERT [23], we take the released code from the authors [6]. For ALBEF [17] and BLIP-2 [15], we use the packages under the LAVIS Library [7] For each meme image, we constrain the total length of the meme text and the generic image caption (either from the captioning model or by asking about the content of the image) to be 65. For each additional questions, we restrict its length to be shorter than 20. If the concatenation of the sentence exceeds the limited length, the sentence will be truncated, otherwise, if the sentence is shorted than the limited length, it will be padded. We set the number of training epochs to be 10 for all models. The number of model parameters are summarized in Table 11. B FULL ABLATION STUDY RESULTS Due to the limitation of space, we only show results of accuracy in ablation studies in Table 6. The full results including both the AUC and the accuracy are provided in Table 12. C VISUALIZATION CASES In Section 5.5, we provide visualization of cases for comparing ProCapPromptHate with the basic PromptHate. Due to space constraints, we omit examples from the other two datasets. We provide more visualization cases in this part. The cases from the HarM dataset are illustrated in Table 9 and the cases from the MAMI dataset are shown in Table 10. D RESULTS WITH PRO-CAP ABOUT ONE TARGET In Section 5, we only report results when models use Pro-Cap from all probing questions. In this part, we report results (with entities) when using the answers from a single probing question in Table 13. According to the results, we observe models using answers to a single probing question are all powerful and some even surpass heuristically asking all probing questions (e.g., using the question asking about nationality on FHM is better than using all probing questions). It points out using all probing captions may not be the optimal solution and may generate irrelevant image descriptions. For instance, confronted with a hateful meme targeting at black people, it is meaningless to ask the religion of people in the image. Interestingly, on MAMI, when only using answers to the probing question about gender reaches teh best performance. It is because MAMI contains only hateful memes about woman. A promising direction would train the model to dynamically select probing questions essential for meme detection for different memes. [4] CLIP-BERT/MOMENTA: https://github.com/LCS2-IIITD/MOMENTA;DisMultiHate: https://gitlab.com/bottle_shop/safe/dismultihate; PromptHate: https://gitlab.com/bottle_shop/safe/prompthate [5] https://huggingface.co/ [6] https://github.com/facebookresearch/vilbert-multi-task [7] https://github.com/salesforce/LAVIS heuristically asking all probing questions (e.g., usin This paper is available on arxiv under CC 4.0 license. Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Preliminary Preliminary Proposed Method Proposed Method Experiment Experiment Conclusion and References Conclusion and References Appendix Appendix APPENDIX A DETAILS FOR IMPLEMENTATION We implement all models under the PyTorch Library with the CUDA11.2 version. We use the Tesla V 100 GPU, each with a dedicated memory of 32GB. For models specifically implemented for hateful meme detection, we take the codes published from the author for reimplementation [4]. For pre trained models which can be found under the Huggingface Library, we use the packages from Huggingface [5], specifically the BERT [4], VisualBERT [18] and the BLIP model. Gor ViLBERT [23], we take the released code from the authors [6]. For ALBEF [17] and BLIP-2 [15], we use the packages under the LAVIS Library [7] For each meme image, we constrain the total length of the meme text and the generic image caption (either from the captioning model or by asking about the content of the image) to be 65. For each additional questions, we restrict its length to be shorter than 20. If the concatenation of the sentence exceeds the limited length, the sentence will be truncated, otherwise, if the sentence is shorted than the limited length, it will be padded. We set the number of training epochs to be 10 for all models. The number of model parameters are summarized in Table 11. B FULL ABLATION STUDY RESULTS Due to the limitation of space, we only show results of accuracy in ablation studies in Table 6. The full results including both the AUC and the accuracy are provided in Table 12. C VISUALIZATION CASES In Section 5.5, we provide visualization of cases for comparing ProCapPromptHate with the basic PromptHate. Due to space constraints, we omit examples from the other two datasets. We provide more visualization cases in this part. The cases from the HarM dataset are illustrated in Table 9 and the cases from the MAMI dataset are shown in Table 10. D RESULTS WITH PRO-CAP ABOUT ONE TARGET In Section 5, we only report results when models use Pro-Cap from all probing questions. In this part, we report results (with entities) when using the answers from a single probing question in Table 13. According to the results, we observe models using answers to a single probing question are all powerful and some even surpass heuristically asking all probing questions (e.g., using the question asking about nationality on FHM is better than using all probing questions). It points out using all probing captions may not be the optimal solution and may generate irrelevant image descriptions. For instance, confronted with a hateful meme targeting at black people, it is meaningless to ask the religion of people in the image. Interestingly, on MAMI, when only using answers to the probing question about gender reaches teh best performance. It is because MAMI contains only hateful memes about woman. A promising direction would train the model to dynamically select probing questions essential for meme detection for different memes. [4] CLIP-BERT/MOMENTA: https://github.com/LCS2-IIITD/MOMENTA;DisMultiHate: https://gitlab.com/bottle_shop/safe/dismultihate; PromptHate: https://gitlab.com/bottle_shop/safe/prompthate [5] https://huggingface.co/ [6] https://github.com/facebookresearch/vilbert-multi-task [7] https://github.com/salesforce/LAVIS heuristically asking all probing questions (e.g., usin

Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection: Appendix

প্রো-ক্যাপ: বিদ্বেষপূর্ণ মেম সনাক্তকরণের জন্য একটি হিমায়িত দৃষ্টি-ভাষা মডেল ব্যবহার করা: পরিশিষ্ট

Pro-Cap: Nutzung eines Frozen Vision-Language-Modells zur Erkennung hasserfüllter Memes: Anhang

Pro-Cap: Aprovechamiento de un modelo de visión y lenguaje congelado para la detección de memes de odio: Apéndice

Pro-Cap : Exploiter un modèle de langage de vision gelé pour la détection des mèmes haineux : Annexe

प्रो-कैप: घृणास्पद मीम का पता लगाने के लिए फ्रोज़न विज़न-लैंग्वेज मॉडल का लाभ उठाना: परिशिष्ट

Pro-Cap: ヘイトミーム検出のための凍結視覚言語モデルの活用: 付録

Pro-Cap: 혐오스러운 밈 탐지를 위해 고정된 시각-언어 모델 활용: 부록

Pro-Cap: Aproveitando um modelo de linguagem de visão congelada para detecção de memes odiosos: Apêndice

Pro-Cap: Использование модели Frozen Vision-Language для обнаружения ненавистных мемов: Приложение

Pro-Cap: Nefret Dolu Memlerin Tespiti için Dondurulmuş Görüş-Dil Modelinden Yararlanma: Ek

Pro-Cap: Tận dụng Mô hình ngôn ngữ tầm nhìn đông lạnh để phát hiện Meme đáng ghét: Phụ lục

Pro-Cap：利用冻结视觉语言模型进行仇恨模因检测：附录

Authors:
(1) Rui Cao, Singapore Management University;
(2) Ming Shan Hee, Singapore University of Design and Technology;
(3) Adriel Kuek, DSO National Laboratories;
(4) Wen-Haw Chong, Singapore Management University;
(5) Roy Ka-Wei Lee, Singapore University of Design and Technology
(6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Related Work Preliminary Proposed Method Experiment Conclusion and References Appendix ABSTRACT Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from noninformative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot visual question answering (VQA) manner. Specifically, we prompt a frozen PVLM by asking hateful contentrelated questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection. The good performance of models with Pro-Cap on three benchmarks validates the effectiveness and generalization of the proposed method.[1] CCS CONCEPTS • Computing methodologies → Natural language processing; Computer vision representations. KEYWORDS memes, multimodal, semantic extraction ACM Reference Format: Rui Cao, Ming Shan Hee, Adriel Kuek, Wen-Haw Chong, Roy Ka-Wei Lee, and Jing Jiang. 2023. Pro Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection. In Proceedings of the 31st ACM International Conference on Multimedia (MM ’23), October 29-November 3, 2023, Ottawa, ON, Canada. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3581783.3612498 Disclaimer: This paper contains violence and discriminatory content that may be disturbing to some readers. 1 INTRODUCTION Memes, which combine images with short texts, are a popular form of communication in online social media. Internet memes are often intended to express humor or satire. However, they are increasingly being exploited to spread hateful content across online platforms. Hateful memes attack individuals or communities based on their identities such as race, gender, or religion [5, 8, 12, 27]. The propagation of hateful memes can lead to discord online and may potentially result in hate crimes. Therefore, it is urgent to develop accurate hateful meme detection methods. The task of hateful meme detection is challenging due to the multimodal nature of memes. Detection involves not only comprehending both the images and the texts but also understanding how these two modalities interact. Previous work [14, 28, 35, 36] learns cross-modal interactions from scratch using hateful meme detection datasets. However, it may be difficult for models to learn complicated multimodal interactions with the limited amount of data available from these datasets. With the development of Pretrained Vision-Language Models (PVLMs) such as VisualBERT [18] and ViLBERT [23], recent work leverage these powerful PVLMs to facilitate the hateful meme detection task. A common approach is to fine-tune PVLMs with task-specific data [9, 20, 26, 34, 37]. However, it is less feasible to fine-tune the larger models such as BLIP-2 [15] and Flamingo [1] on meme detection because there are billions of trainable parameters. Therefore, computationally feasible solutions other than direct fine-tuning are needed to leverage large PVLMs in facilitating hateful meme detection. Different from the approach above using PVLMs, PromptHate[2] is a recently proposed model that converts the multimodal meme detection task into a unimodal masked language modeling task. It first generates meme image captions with an off-the-shelf image caption generator, ClipCap [25]. By converting all input information into text, it can prompt a pre-trained language model along with two demonstrative examples to predict whether or not the input is hateful by leveraging the rich background knowledge in the language model. Although PromptHate achieves state-of-the-art performance, it is significantly affected by the quality of image captions, as shown in Table 1. Image captions that are merely generic descriptions of images may omit crucial details [14, 37], such as the race and gender of people, which are essential for hateful content detection. But with additional image tags, such as entities found in the images and demographic information about the people in the images, the same model can be significantly improved, as shown in Table 1. However, generating these additional image tags is laborious and costly. For instance, entity extraction is usually conducted with the Google Vision Web Entity Detection API [2], which is a paid service. Ideally, we would like to find a more affordable way to obtain entity and demographic information from the images that is critical for hateful content detection. Both above-mentioned approaches (i.e., one using PVLMs and the other converting the task to a unimodal task) have their pros and cons. In this paper, we combine the ideas from these two approaches and design a hateful meme detection method that leverages the power of a frozen PVLM to complement the unimodal approach of PromptHate. Specifically, we use a set of “probing” questions to query a PVLM (BLIP-2 [15] in our experiments) for information related to common vulnerable targets in hateful content. The answers obtained from the probing questions will be treated as image captions (denoted as Pro-Cap) and used as input to a trainable hateful meme detection model. Figure 1 illustrates the overall workflow of the method. We refer to the step of using probing questions to generate the captions as probing-based captioning. Our proposed method fills existing research gaps by: 1) Leverage a PVLM without any adaptation or fine-tuning, thereby reducing computational cost; 2) Instead of explicitly obtaining additional image tags with costly APIs, we utilize the frozen PVLM to generate captions that contain information useful for hateful meme detection. To the best of our knowledge, this is the first work that to leverage PVLMs in a zero-shot manner through question answering to assist in the hateful meme detection task. To further validate our method, we test the effect of the generated Pro-Cap on both PromptHate[2] and a BERT-based[4] hateful meme detection model. Based on the experimental results, we observe that PromptHate with Pro-Cap (denoted as Pro-CapPromptHate) significantly surpasses the original PromptHate without additional image tags (i.e., about 4, 6, and 3 percentage points of absolute performance improvement on FHM [12], MAMI [5], and HarM [28] respectively). ProCapPromptHate also achieves comparable results with PromptHate with additional image tags, indicating that probing-based captioning can be a more affordable way of obtaining image entities or demographic information. Case studies further show that Pro-Cap offers essential image details for hateful content detection, enhancing the explainability of models to some extent. Meanwhile, ProCapBERT clearly surpasses multimodal BERT-based models of similar sizes (i.e., about 7 percentage points of absolute improvement with VisualBERT on FHM [12]), proving the generalization of the probing-based captioning method. [1] Code is available at: https://github.com/Social-AI-Studio/Pro-Cap [2] https://cloud.google.com/vision/docs/detecting-web This paper is available on arxiv under CC 4.0 license. Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Authors: Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Preliminary Preliminary Proposed Method Proposed Method Experiment Experiment Conclusion and References Conclusion and References Appendix Appendix ABSTRACT Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from noninformative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot visual question answering (VQA) manner. Specifically, we prompt a frozen PVLM by asking hateful contentrelated questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection. The good performance of models with Pro-Cap on three benchmarks validates the effectiveness and generalization of the proposed method.[1] CCS CONCEPTS • Computing methodologies → Natural language processing; Computer vision representations. • Computing methodologies → Natural language processing; Computer vision representations. KEYWORDS memes, multimodal, semantic extraction ACM Reference Format: Rui Cao, Ming Shan Hee, Adriel Kuek, Wen-Haw Chong, Roy Ka-Wei Lee, and Jing Jiang. 2023. Pro Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection. In Proceedings of the 31st ACM International Conference on Multimedia (MM ’23), October 29-November 3, 2023, Ottawa, ON, Canada. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3581783.3612498 Disclaimer: This paper contains violence and discriminatory content that may be disturbing to some readers. Disclaimer: This paper contains violence and discriminatory content that may be disturbing to some readers. Disclaimer: This paper contains violence and discriminatory content that may be disturbing to some readers. 1 INTRODUCTION Memes, which combine images with short texts, are a popular form of communication in online social media. Internet memes are often intended to express humor or satire. However, they are increasingly being exploited to spread hateful content across online platforms. Hateful memes attack individuals or communities based on their identities such as race, gender, or religion [5, 8, 12, 27]. The propagation of hateful memes can lead to discord online and may potentially result in hate crimes. Therefore, it is urgent to develop accurate hateful meme detection methods. The task of hateful meme detection is challenging due to the multimodal nature of memes. Detection involves not only comprehending both the images and the texts but also understanding how these two modalities interact. Previous work [14, 28, 35, 36] learns cross-modal interactions from scratch using hateful meme detection datasets. However, it may be difficult for models to learn complicated multimodal interactions with the limited amount of data available from these datasets. With the development of Pretrained Vision-Language Models (PVLMs) such as VisualBERT [18] and ViLBERT [23], recent work leverage these powerful PVLMs to facilitate the hateful meme detection task. A common approach is to fine-tune PVLMs with task-specific data [9, 20, 26, 34, 37]. However, it is less feasible to fine-tune the larger models such as BLIP-2 [15] and Flamingo [1] on meme detection because there are billions of trainable parameters. Therefore, computationally feasible solutions other than direct fine-tuning are needed to leverage large PVLMs in facilitating hateful meme detection. Different from the approach above using PVLMs, PromptHate[2] is a recently proposed model that converts the multimodal meme detection task into a unimodal masked language modeling task. It first generates meme image captions with an off-the-shelf image caption generator, ClipCap [25]. By converting all input information into text, it can prompt a pre-trained language model along with two demonstrative examples to predict whether or not the input is hateful by leveraging the rich background knowledge in the language model. Although PromptHate achieves state-of-the-art performance, it is significantly affected by the quality of image captions, as shown in Table 1. Image captions that are merely generic descriptions of images may omit crucial details [14, 37], such as the race and gender of people, which are essential for hateful content detection. But with additional image tags, such as entities found in the images and demographic information about the people in the images, the same model can be significantly improved, as shown in Table 1. However, generating these additional image tags is laborious and costly. For instance, entity extraction is usually conducted with the Google Vision Web Entity Detection API [2], which is a paid service. Ideally, we would like to find a more affordable way to obtain entity and demographic information from the images that is critical for hateful content detection. Both above-mentioned approaches (i.e., one using PVLMs and the other converting the task to a unimodal task) have their pros and cons. In this paper, we combine the ideas from these two approaches and design a hateful meme detection method that leverages the power of a frozen PVLM to complement the unimodal approach of PromptHate. Specifically, we use a set of “probing” questions to query a PVLM (BLIP-2 [15] in our experiments) for information related to common vulnerable targets in hateful content. The answers obtained from the probing questions will be treated as image captions (denoted as Pro-Cap) and used as input to a trainable hateful meme detection model. Figure 1 illustrates the overall workflow of the method. We refer to the step of using probing questions to generate the captions as probing-based captioning. Our proposed method fills existing research gaps by: 1) Leverage a PVLM without any adaptation or fine-tuning, thereby reducing computational cost; 2) Instead of explicitly obtaining additional image tags with costly APIs, we utilize the frozen PVLM to generate captions that contain information useful for hateful meme detection. To the best of our knowledge, this is the first work that to leverage PVLMs in a zero-shot manner through question answering to assist in the hateful meme detection task. To further validate our method, we test the effect of the generated Pro-Cap on both PromptHate[2] and a BERT-based[4] hateful meme detection model. Based on the experimental results, we observe that PromptHate with Pro-Cap (denoted as Pro-CapPromptHate) significantly surpasses the original PromptHate without additional image tags (i.e., about 4, 6, and 3 percentage points of absolute performance improvement on FHM [12], MAMI [5], and HarM [28] respectively). ProCapPromptHate also achieves comparable results with PromptHate with additional image tags, indicating that probing-based captioning can be a more affordable way of obtaining image entities or demographic information. Case studies further show that Pro-Cap offers essential image details for hateful content detection, enhancing the explainability of models to some extent. Meanwhile, ProCapBERT clearly surpasses multimodal BERT-based models of similar sizes (i.e., about 7 percentage points of absolute improvement with VisualBERT on FHM [12]), proving the generalization of the probing-based captioning method. [1] Code is available at: https://github.com/Social-AI-Studio/Pro-Cap [2] https://cloud.google.com/vision/docs/detecting-web This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection

প্রো-ক্যাপ: বিদ্বেষপূর্ণ মেম সনাক্তকরণের জন্য একটি হিমায়িত দৃষ্টি-ভাষা মডেল ব্যবহার করা

Pro-Cap: Nutzung eines Frozen Vision-Language-Modells zur Erkennung hasserfüllter Memes

Pro-Cap: Aprovechamiento de un modelo de visión-lenguaje congelado para la detección de memes de odio

Pro-Cap : exploiter un modèle de langage de vision figé pour la détection des mèmes haineux

प्रो-कैप: घृणास्पद मीम का पता लगाने के लिए फ्रोज़न विज़न-लैंग्वेज मॉडल का लाभ उठाना

Pro-Cap: 凍結された視覚言語モデルを活用して憎悪的なミームを検出する

Pro-Cap: 혐오스러운 밈 탐지를 위해 고정된 비전-언어 모델 활용

Pro-Cap: aproveitando um modelo de linguagem de visão congelada para detecção de memes odiosos

Pro-Cap: использование модели Frozen Vision-Language для обнаружения ненавистных мемов

Pro-Cap: Nefret Dolu Memlerin Tespiti için Donmuş Görüş-Dil Modelinden Yararlanmak

Pro-Cap: Tận dụng mô hình ngôn ngữ tầm nhìn đông lạnh để phát hiện meme đáng ghét

Pro-Cap：利用冻结视觉语言模型进行仇恨模因检测

Recently, Apple released the Vision Pro, and I was fortunate enough to become a proud owner. Since then, I've been deeply engaged in developing my application and exploring various apps and trends within the Vision Pro App Store. One thing that grabbed my attention is that new apps are coming out every day, but only a few fit well with the immersive virtual reality experience. I guess many developers struggle to find documentation and examples for Vision Pro features. However, utilizing the capabilities of Vision Pro can set your application apart and provide users with an enhanced experience. In this article, I'll be sharing some tips and tricks I've uncovered while crafting my application. These tips will not only help your application to stand out but also ensure its seamless integration and native feel within VisionOS. Dark Surroundings Effect I'm sure you've noticed that when viewing a video or photo, the background behind the window is darkened, in the settings it's called "auto-dimming". To get the same effect you need to use .preferredSurroundingsEffect(.systemDark) on your view and then the space around the user will darken and your window will remain bright, this will help to keep the focus on the content. Hidden Window Controls Line When viewing a video or content, you want the user to have no distractions and create the effect of a window floating in the air, for this Apple has given us the ability to hide the bottom bar of the window that is used to move or close the current window. With .persistentSystemOverlays(.hidden) you can hide this control bar on your window, it will disappear with the default animation and only appear when the user interacts with the window. Video Reflection Effect If you open the standard Apple TV application and turn on a movie, you will notice that the movie window has a reflection that changes with each frame, giving a more immersive effect. To get a glow effect around your video, you need to use VideoPlayerComponent and enable isPassthroughTintingEnabled, then the effect will be the same as in Apple TV or HBO Max. Here is what the documentation says: This is to enable passthrough tinting during video playback that shows up around the video taking the average color of the frame and tinting the passthrough with that color to emphasize the video. Window Resizing You may want to prevent the user from resizing the window, there is a way to do that: onAppear {
    guard let windowScene = UIApplication.shared.connectedScenes.first as?UIWindowScene else { return }
    windowScene.requestGeometryUpdate(.Vision(resizingRestrictions: UIWindowScene.ResizingRestrictions.none))
} You can also specify additional size settings in this method The full signature of the method looks like this: windowScene.requestGeometryUpdate(.Vision(size:, minimumSize:, maximumSize:, resizingRestrictions:)) Hands Visibility If your application uses immersive space and you don’t want the user to see their hands, or you want to replace them with virtual hands, as is done in AmazeVR, then you need to use .upperLimbVisibility(.hidden) Ornament I'm sure you've seen sidebars in VisionOS applications that look like TabBars. Apple has introduced a new View method for VisionOS that allows you to "expand" the window and add ornaments on either side of it. .ornament(
    visibility: .visible,
    attachmentAnchor: .scene(.bottom),
    contentAlignment: .center
) {
    HStack {
        Button("Play", systemImage: "play.fill") {
        }
        Button("Stop", systemImage: "stop.fill") {
        }
        
    }
    .labelStyle(.iconOnly)
    .padding(.all)
    .glassBackgroundEffect()
} Сore location An interesting fact that I was able to verify experimentally is that CMMotionManager's methods only work in immersive space and not in the normal window view. Conclusion VisionOS provides many tools to create immersive applications that bring users into the virtual world and make them feel part of it. While more and more applications are being released for Vision Pro, only a few utilize its full range of features. That's where your chance to distinguish comes in — use them and take advantage. Unfortunately, developers struggle to find easily accessible documentation and examples. That's why I'm writing this article. I hope my tips will help you make your application better. Recently, Apple released the Vision Pro, and I was fortunate enough to become a proud owner. Since then, I've been deeply engaged in developing my application and exploring various apps and trends within the Vision Pro App Store. Apple One thing that grabbed my attention is that new apps are coming out every day, but only a few fit well with the immersive virtual reality experience. I guess many developers struggle to find documentation and examples for Vision Pro features. However, utilizing the capabilities of Vision Pro can set your application apart and provide users with an enhanced experience. In this article, I'll be sharing some tips and tricks I've uncovered while crafting my application. These tips will not only help your application to stand out but also ensure its seamless integration and native feel within VisionOS. Dark Surroundings Effect I'm sure you've noticed that when viewing a video or photo, the background behind the window is darkened, in the settings it's called "auto-dimming". To get the same effect you need to use .preferredSurroundingsEffect(.systemDark) on your view and then the space around the user will darken and your window will remain bright, this will help to keep the focus on the content. .preferredSurroundingsEffect(.systemDark) Hidden Window Controls Line When viewing a video or content, you want the user to have no distractions and create the effect of a window floating in the air, for this Apple has given us the ability to hide the bottom bar of the window that is used to move or close the current window. With .persistentSystemOverlays(.hidden) you can hide this control bar on your window, it will disappear with the default animation and only appear when the user interacts with the window. .persistentSystemOverlays(.hidden) Video Reflection Effect If you open the standard Apple TV application and turn on a movie, you will notice that the movie window has a reflection that changes with each frame, giving a more immersive effect. To get a glow effect around your video, you need to use VideoPlayerComponent and enable isPassthroughTintingEnabled , then the effect will be the same as in Apple TV or HBO Max. Here is what the documentation says: VideoPlayerComponent isPassthroughTintingEnabled This is to enable passthrough tinting during video playback that shows up around the video taking the average color of the frame and tinting the passthrough with that color to emphasize the video. This is to enable passthrough tinting during video playback that shows up around the video taking the average color of the frame and tinting the passthrough with that color to emphasize the video. Window Resizing You may want to prevent the user from resizing the window, there is a way to do that: onAppear {
    guard let windowScene = UIApplication.shared.connectedScenes.first as?UIWindowScene else { return }
    windowScene.requestGeometryUpdate(.Vision(resizingRestrictions: UIWindowScene.ResizingRestrictions.none))
} onAppear {
    guard let windowScene = UIApplication.shared.connectedScenes.first as?UIWindowScene else { return }
    windowScene.requestGeometryUpdate(.Vision(resizingRestrictions: UIWindowScene.ResizingRestrictions.none))
} You can also specify additional size settings in this method The full signature of the method looks like this: windowScene.requestGeometryUpdate(.Vision(size:, minimumSize:, maximumSize:, resizingRestrictions:)) windowScene.requestGeometryUpdate(.Vision(size:, minimumSize:, maximumSize:, resizingRestrictions:)) Hands Visibility If your application uses immersive space and you don’t want the user to see their hands, or you want to replace them with virtual hands, as is done in AmazeVR, then you need to use .upperLimbVisibility(.hidden) .upperLimbVisibility(.hidden) Ornament I'm sure you've seen sidebars in VisionOS applications that look like TabBars . Apple has introduced a new View method for VisionOS that allows you to "expand" the window and add ornaments on either side of it. TabBars View .ornament(
    visibility: .visible,
    attachmentAnchor: .scene(.bottom),
    contentAlignment: .center
) {
    HStack {
        Button("Play", systemImage: "play.fill") {
        }
        Button("Stop", systemImage: "stop.fill") {
        }
        
    }
    .labelStyle(.iconOnly)
    .padding(.all)
    .glassBackgroundEffect()
} .ornament(
    visibility: .visible,
    attachmentAnchor: .scene(.bottom),
    contentAlignment: .center
) {
    HStack {
        Button("Play", systemImage: "play.fill") {
        }
        Button("Stop", systemImage: "stop.fill") {
        }
        
    }
    .labelStyle(.iconOnly)
    .padding(.all)
    .glassBackgroundEffect()
} Сore location An interesting fact that I was able to verify experimentally is that CMMotionManager's methods only work in immersive space and not in the normal window view. Conclusion VisionOS provides many tools to create immersive applications that bring users into the virtual world and make them feel part of it. While more and more applications are being released for Vision Pro, only a few utilize its full range of features. That's where your chance to distinguish comes in — use them and take advantage. Unfortunately, developers struggle to find easily accessible documentation and examples. That's why I'm writing this article. I hope my tips will help you make your application better.

VisionOS Development: Tips and Tricks for Building Apple Vision Pro Apps

VisionOS ডেভেলপমেন্ট: অ্যাপল ভিশন প্রো অ্যাপস তৈরির জন্য টিপস এবং ট্রিকস

VisionOS-Entwicklung: Tipps und Tricks zum Erstellen von Apple Vision Pro-Apps

Desarrollo de VisionOS: consejos y trucos para crear aplicaciones Apple Vision Pro

Développement VisionOS : trucs et astuces pour créer des applications Apple Vision Pro

विज़नओएस डेवलपमेंट: ऐप्पल विज़न प्रो ऐप्स बनाने के लिए टिप्स और ट्रिक्स

VisionOS 開発: Apple Vision Pro アプリを構築するためのヒントとコツ

VisionOS 개발: Apple Vision Pro 앱 구축을 위한 팁과 요령

Desenvolvimento VisionOS: dicas e truques para criar aplicativos Apple Vision Pro

Разработка VisionOS: советы и рекомендации по созданию приложений Apple Vision Pro

VisionOS Development: Apple Vision Pro Uygulamaları Geliştirmeye İlişkin İpuçları ve Püf Noktaları

Phát triển VisionOS: Mẹo và thủ thuật để xây dựng ứng dụng Apple Vision Pro

VisionOS 开发：构建 Apple Vision Pro 应用程序的技巧和窍门

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