Vizion is building a suite of tools \u0026 APIs to make logistics/supply chain data and events universally available

Vizion

What happens when an unstoppable force meets an immovable object? Well, in the case of and WordPress - they synergise. artificial intelligence Today the influence of WordPress on the World Wide Web can’t be underestimated. After all, is WordPress. Given its prevalence, it was only a matter of time before the ever-developing spectre of AI would wrap itself around the world’s favourite website builder. around one-third of the entire internet Artificial intelligence has been growing exponentially smarter year-on-year and is forecast to develop into an industry . worth $90 billion by 2025 Big business: AI revenues are forecast to skyrocket over the coming years. Image: Priceonomics It’s important to note that AI’s relationship with WordPress is nothing new, and users will have been using intuitive programs focussing on spelling and grammar as they construct their posts for years. In recent times, however, the progress that AI’s making can be plain to see. In August 2019, US-based voice AI organisation that enables users to search and shop by voice on website, mobile applications and smart speakers. speak2web debuted a WordPress plugin Let’s take a look at some of the revolutionary ways in which AI is influencing how we build websites on WordPress: Quality control A rough diagram covering how Akismet handles any spam entering your domains. Image: Akismet When it comes to running , there are plenty of opportunities for site owners to engage with their readers - not least through the dedicated comments sections that typically come alongside published content. websites via WordPress However, many less-genuine users try to take advantage of the comments sections of pages in a bid to spam their way into increasing their own blog or website’s search value. Fortunately, AI is on hand to automatically alleviate this burden. , spammy posts are automatically vetted to ensure that only organic responses see the light of day below any uploaded content. Through plugins like Akismet The plugin also utilises machine learning as a means of detecting spam commented uploaded by human users, and continually learns from its mistakes by recognising emerging patterns across the millions of websites that it already serves. Organic search Organic search is big business and can make all the difference between having content shared by thousands of visitors, or leaving the author preaching to what feels like an empty room. WordLift is an AI-driven service that not only offers SEO-based tips to help improve your content but actively analyses your posts to provide data-driven facts, figures and media in order to match and build contextual relationships among your articles to help bolster your on-page optimisation as well as the internal linking within your site. Another smart tool covered by . utilises the very best in AI voices to automatically convert your textual content into audio - helping to keep visitors on your site for longer and repurposing your articles for new audiences. Hacker Noon is the Play plugin for WordPress Play Intuitive layouts Vizion is a particularly powerful tool for AI-driven WordPress themes. Image: Theme Forest WordPress themes that are built with AI are capable of providing significant amounts of data analysis for traffic and visitor habits as well as supporting the use of chatbots within your pages. of smart layouts within WordPress, and can automatically draw on big data for marketing insights into user websites. Vizion is a strong developer AI can also encourage visitors to stay on your pages for longer thanks to tools like Bibblio and Recomendo that automatically suggest related content from within your pages. Through scouting out recurring keywords within your content, it’s possible for plugins to create a range of carefully selected further reading pieces that are designed to link directly to your pre-existing posts - all built into your layout. Chatbots They may still be a little rough around the edges, but chatbots are a revolutionary force when it comes to modern customer experience models online. While, at the time of writing, high-quality chatbot plugins for WordPress are a little few and far between, integrable tools like Landbot.io work well with Facebook Messenger functions and can be used for as well as pop-up chatbots. excellent conversational landing pages The benefits that artificially can bring to a WordPress site are huge - especially if you’re looking to make some waves in the world of e-commerce. With these smart little robots operating around the clock to answer your prospective customers’ questions and provide accurate product descriptions on-demand, they could be a driving force in maximising your sales. intelligent chatbots If the AI behind your chatbots is strong enough, they can even actively pre-empt your visitors’ motives and offer support unprompted. A brave new world indeed.

Talking intelligence: How AI is changing the way we use WordPress

Do you face difficulties in providing more accurate reporting and analysis?Are you unable to identify the latest trends, market conditions and respond quickly to change? Having problems in optimizing internal business process and driving decisions? What about obtaining important business reports? Is it challenging to convert raw data into actionable insights? If yes, then this blog is definitely for you! Well, if you are using Excel, it may be difficult to work with the large data which ultimately creates roadblocks for data-driven enterprises. And if you are using any other tools which have fewer competencies, it may be difficult to harness data for driving the business decision. So, how to make an informed decision with the use of data? Well, in such a scenario, the powerful Business Intelligence system can come to rescue. Gartner has recognized Microsoft as a leader in Analytics and BI platforms for 11 consecutive years. Why do organizations prefer to use Power BI? Before going in-depth let’s start with basics. What Is Power BI? is a cloud-based business analytics service by Microsoft enabling anyone to visualize and analyze data with greater speed, efficiency, and high understanding. Power BI Moreover Power BI:- Is an interactive data visualization tool which makes it easy to share data across the organization. Delivers valuable insights to make quick and informed decisions Transforms raw and unprocessed data into insightful and stunning visuals for officials to communicate and understand the business better. Provides access to the customized reports and interactive dashboards which help to have a complete 360-degree view of the business. Companies like Dell, Wipro, Metro Bank PLC, Edsby, Oraylis, Vizion solutions and many more are already using Power BI to grow their businesses. Have a look at this video <a href="https://medium.com/media/be842c09282afcb9593226e2f9e1dab9/href">https://medium.com/media/be842c09282afcb9593226e2f9e1dab9/href</a> Who Can Use Power BI? It can be used by to- create interactive data visualization from multiple data sources- deliver real-time insights across the organization Busines Analysts and IT personnel It can be used by to- simplify management- achieve compliance- get accurate and reliable reports- detect anomalies- streamline the workflow Enterprises can use to automate application lifecycle. Moreover, they can- Embed stunning visuals in apps, portals, websites, etc.- Develop and test visuals with open-source JavaScript libraries like D3, jQuery, and more. Developers Power BI REST API But, How Professionals Can Have Access To This Tool? Well, there are three ways to use Power BI : You can connect to data through multiple data sources, combine them into a data model and create compelling visuals. Power BI Desktop : It is a cloud-based, online service for collaboration with the teams and corporations. The reports created in Power BI desktop can be shared in Power BI service, where you can continue modifying them. You can also create dashboards based on your reports in Power BI service. Power BI Service : Power BI offers a set of mobile apps for iOS, Android and Windows 10 mobile devices, where you can view, interact and securely access reports, dashboards, and other data on the go — all in one app. Power BI Mobile Apps To be precise, the desktop is for creating content; the Service is for sharing the content; the mobile apps are for getting insights anywhere on any device. Image Credit: https://docs.microsoft.com To understand in a better way, you need to know the . Power BI architecture But before that, you must be aware of . Power BI components where each component plays an important part in Power BI solution. Power BI is a combination of components Overview Of Power BI’s Components Power BI offers several components that enable users to create and share data reports which include: : Facilitates data search capabilities and transform & merge data from multiple data sources. Power Query : In-memory data modeling component which enables highly-compressed data storage and extremely fast aggregation and calculation. Power Pivot : An interactive visualization tool which enables drag-and-drop interface to the users. Power View : a 3D geospatial data visualization tool Power Map : A natural language question and answering engine Power Q&A : By using this component you get periodic data refreshers and data feeds. Data Management Gateway : User can easily discover and reuse queries using Data Catalog. Metadata can be facilitated for search functionality. Data Catalog Let’s have an overview of the Power BI architecture Power BI Architecture: Sharing And Deploying Files For Power BI solution Connecting Data Power BI can connect data from a variety of data sources. There are two primary options for sourcing data: (1) Importing the data to power BI (2) Using the Direct Query. If you are importing a file to Power BI, it limits the data sets to 1 GB, but if the size is more than 1 GB, you can use Direct Query (you can build reports based on extremely large datasets). Transforming The Data Once the data is connected, you can transform it as per your needs. Transforming data includes changing the name of columns or tables, modifying the text to numbers, removing rows, adjusting rows, headers and much more. There is a number of available to shape your data. transformation options Reporting And Publishing Here reports are created which represent different findings and insights from that dataset. Basically, it is in the form of a , , , etc. It is highly interactive and customizable; which updates as the underlying data changes. After creating a report, you can publish them to Power BI services. graph pie-charts slicers Creating A Dashboard A power BI dashboard consists of a single page, often called canvas that tells a story through visualizations. You can create a dashboard by pinning the individual element or live report page. As it is limited to one page, a well-designed dashboard contains only the overview. Power BI Experience Your Data. Any Data, Any Way, Any Where Power BI: How It Can Be A Game-Changer For Enterprise? Gathers Data From All The Data Sources Power BI makes it easy to fetch data from the cloud services including Microsoft Dynamics, Salesforce, Google Analytics, Zen desk, Facebook and more. No matter where your data is available, power BI enables to shape the data from different sources; enabling you to create visual reports that you can easily share with others. Image Credit: https://powerbi.microsoft.com/en-us/desktop Rich Power BI Dashboards To Make Informed Decisions With Power BI, you can create personalized dashboards enabling it to monitor the business needs in real-time. It makes easy to solve the problems as they occur and provides a holistic view of the business empowering the teams to make informed decisions. You can easily embed the dashboards and BI reports in the applications to provide the unified user experience. Image Credit: https://community.powerbi.com Ask Questions- Get Answers In Real Time How about asking queries in natural language and getting answers in stunning interactive visualization? Well, with Power BI, you can ask a question and get an answer in the form of right chart and right graph. It has auto-search functionality which lets you scan the hidden insights in seconds. It stores all the information in a centralized location and makes interaction with fast and quick data. Image Credit: https://powerbicdn.azureedge.net Tell Stories With Interactive Reports The timeline storyteller is one of the trending custom visuals in Power BI that convey information and details that vary over time. It presents data in the form of circle, grids, spirals or any custom shapes, or even a simple linear list. It also has layouts, different scale representation like logarithmic and relative, controls for filtering, highlighting and annotation. You can decide how you want your data to be represented, scaled and designed in a timeline. Image Credit: https://timelinestoryteller.com Make Business Decisions From Anywhere Power BI offers native BI mobile apps for Android, iOS and Windows where anyone can securely access and view live Power BI dashboards and reports on any device. It is easy to access on-premise data stored in SQL server or data in the cloud; no matter wherever you are. With Power BI Mobile App You Can: View live dashboards and reports on a mobile device Directly share reports and dashboards from the app, which helps to keep the team on the same page Get push notifications for personal data alerts to your device Image Credit: https://powerbicdn.azureedge.net Other Perks Of Power BI is needed to implement Power BI; No technical support is required Little assistance Service provided by Microsoft and has tight integration with its other business tools such as Office 365, Microsoft Excel, Azure, and SQL Server with your existing business environment Integrates easily Helps you to set up automatic data refresh Users can on social media posts and emails, making them accessible to whoever may be interested in them publish their visualizations as Power BI can connect to the software like Spark, Hadoop, etc. and combine the data into a centralized dashboard Data is more accessible No memory and speed constraints Provides with new features and functions frequent updates The has grown significantly over the years community Looking At The Other Side Although Power BI shapes the business world and offers numerous advantages but with every advantage, there is always a corresponding disadvantage. Dashboards and reports can only be shared with users who have the same email domains or email domains listed with your Office 365 tenant. The list of Power BI components is very long and sometimes it becomes difficult to understand which component is required. Microsoft Power BI doesn’t allow you to build scheduled reports, personalized user views, personalized notifications, personalized security views Not all reports can be shared with colleagues as it needs a pro-license to work collaboratively Although, Power BI has few disadvantages; its unique features and functionalities are aligned with the business decisions. Thus it is loved by most of the organizations. How Power BI Is Different From Other BI Tools? To simplify this, let’s compare Power BI with Tableau and QlikView which are the industry-leading BI tools. Tableau doesn’t have a free version. The Personal version of Tableau cost $420 and the Professional version will cost you $840 annually. Whereas, the PRO version of Power BI will only $120 but the free version itself covers a lot more than Tableau. QlikView doesn’t support Predictive Analytics which makes it difficult to make data-based decisions whereas Power BI can help you to create predictive models and with the content pack of Power BI, you can visualize the predictive results. The Future Of Power BI The future belongs to the solution that is Flexible Affordable Innovative Integrates with other solution hassle-free Easy to configure and can be viewed anywhere on any device. Most importantly, it should have the features and functionalities that enable to make quick business decisions. And Power BI fits all these criteria. So, of-course future holds the best for Power BI and it is here to stay for many more years ahead. What is your opinion? Do you think businesses should use Power BI? Please express your opinion below or connect with us to know more about Power BI. : This post was previously published on our blog: Note here <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

Microsoft Power BI In 10 Minutes [Quick Guide]

An early-stage Ripple advisor, CBDC inventor, and Air Force Major walk into a bar. What happens next is a conversation about the impact of privacy, the future of blockchains, and leveraging game theory for the good of all. This thread by Akasha Rose, Anish, Justin Roberti, Oliver Gale, Arthur Tkachenko and Ramadan Ameen occurred in slogging's official #amas channel, and has been edited for readability. Slogging.com Akasha Rose May 14, 2021, 9:30 AM Welcome Anish Mohammed, Oliver Gale and Ramadan Ameen from to the Hackernoon Slogging arena. Anish has been an early-stage advisor to Ripple, one of the original reviewers of Ethereum orange paper, and involved in the design & design review of dozens of blockchains and dApps, including Ocean and Boson. Oliver is an entrepreneur and CBDC inventor with 10 years experience building, advising and investing into blockchain and fintech projects. Ramadan is a Former Air Force Major, and start-up fractional CFO with 5 years at BlackRock working front, middle, and back-office with institutional relationships across the institutional spectrum. http://pantherprotocol.io Anish May 14, 2021, 1:27 PM Hi Akasha Rose thanks very much for giving us the opportunity. We would be happy to answer any questions you have for us. Justin Roberti May 14, 2021, 4:08 PM Anish hello thank you for joining us. Anish If you look at the wave of new retail investors that have bought BTC over the past two years, how many of them are aware of the custodianship of their crypto assets? It seems like the vast majority don't realize that Gemini, etc. is actually holding the asset -- when you integrate a cold storage/self custodial feature, are you targeting specifically institutional investors? Or is there a benefit for the retail buyer who has perhaps accumulated some gains during the mostly uninterrupted BTC bull run? Justin Roberti May 14, 2021, 4:18 PM Also, Anish I recently covered the proposal to the SEC about regulating fractional/limited run NFTs -- and now it seems that nearly everyone with any public profile is tokenizing some part of their lives. What can all these new NFT entrepreneurs do to protect themselves so they don't run into compliance issues when SEC establishes regulation (possibly as a security)? How does Panther help future-proof emerging compliance issues, which seem almost a given with NFTs since they are so enormously popular right now? Anish May 14, 2021, 5:06 PM The issues on compliance and regulatory authorities is something which is a tough problem. We are trying to achieve a middle of the line solution which is to allow users to have the ability to do selective disclosure.  As for all the NFT entrepreneurs, it's again a hard question to answer, there is always going to be regulatory arbitrage which folks try to leverage. Understanding the  risk and making the right trade-offs might be a good  path, again in this instance I shall add a disclaimer my legal expertise is limited Oliver Gale May 14, 2021, 5:45 PM The purpose of offering self custody is to enable users, retail or institutional, to have direct control of their own zAssets. One of the major advantages of DeFi is the ability to eliminate and mitigate counterparty risk of those third-party custodians. The ultimate value to users is sovereign private assets. We see a future in which institutions prefer to allow users to manage their own keys as it reduces costs of storage to the institutions Oliver Gale May 14, 2021, 5:47 PM NFTs or otherwise, the values to all users of a protocol like Panther is the ability to restore sovereignty over their data and who has access to it. Justin Roberti May 14, 2021, 5:53 PM Doesn't keeping custodianship of the key to the user also shift the risk of a lost key to the user? It's one of the first objections to crypto you hear from people outside the blockchain industry. Panther Protocol primarily focused on providing the privacy needed to enable "traditional" financial services to move to blockchain? (and therefore, more focused on enabling apps for enterprise and institutions, less for the retail buyer) Oliver Gale May 14, 2021, 7:20 PM The beautiful thing is the ability to implement different key management systems at the wallet level. The user can use centralized wallet issuers who manage their keys entirely on their behalf, they can use a secure multiparty computation scheme for managing 1 of n keys enabling things like recovery services in the event they or the institution loses a key, or they can manage their keys entirely on the client-side. The reason we hear so much about this outside the blockchain world is there is a misunderstanding of the choices available and the flexibility afforded by this technology. Akasha Rose May 15, 2021, 6:03 AM Hey Oliver and Anish, thanks for joining us. Can you tell us more about Panther Protocol, what it does, who it's for, and why we should use it? Arthur Tkachenko May 15, 2021, 4:46 PM What crypto-community should tell Elon about his latest "move"? While I agree, mining eating a lot of electricity. But data centers eat like 1% of energy worldwide. Should Tesla ban servers too? Anish May 15, 2021, 10:32 PM Akasha Rose thanks for the question, we generally answer this in two parts,  generally goes with real-world impact and use case and benefits. I try to explain how we are different, and we add to the table from a technology perspective. We try to marry both cryptography and game theory to provide privacy to users. Zero-knowledge proofs alone would only provide limited value add in providing privacy. What we aim to do is to leverage some game theory to make sure there are enough participants in the protocol that privacy is a very highly likely outcome. The example that I generally use is that of using zero-knowledge proofs to prove one membership to a group of one to that a group of hundred thousand. We are leveraging game theory to both price privacy and to find equilibrium condition. Akasha Rose May 15, 2021, 11:39 PM Awesome, I look forward to hearing from you about the real-world impact. For the complete crypto noob, can you explain what zero-knowledge proofs are and why they are important? Would also like to hear more about the game theory involved and the different factors you have weighed up in the mechanism design if that's a possibility. i.e. what objectives did you put first in designing the incentives? Anish May 16, 2021, 12:23 AM Akasha Rose happy to try to explain zero-knowledge in simple terms. In general, this is more like you have a room with just one door, if it's just you and me - I would be the prover and you would be the verifier. I make the claim that I can get out of the room without using the door. You would then close the door behind me once I enter the room. A few minutes later you see me outside. So my claim that I can escape from the room without using the door is proven to be true. But you as verifier don't know how I did it Anish May 16, 2021, 12:24 AM The reason zero-knowledge proofs are important is until recently, there was a trade-off between privacy and authenticity. Zero-knowledge proofs can provide authenticity still providing privacy. Anish May 16, 2021, 12:28 AM Akasha Rose, for more detail I would point to our white paper when it gets released. However, I shall try answering this. In general privacy in mixers implies we need to incentivise folks to participate in the privacy mixing pools - loosely speaking. The larger the size of the pools the better privacy, but it becomes more expensive. So it becomes the optimisation of where the equilibrium condition exists where we can provide privacy at the optimal price point. Oliver Gale May 16, 2021, 1:35 AM The most important factor for any open source community is the community itself. The people that align to the vision, mission and values of the project take ownership of it and in that way it decentralizes, becomes anti-fragile and ultimately, when successful, makes its way into the cultural fabric of digital and real-world society. Panther aims to enhance, protect and preserve human sovereignty. Restoring privacy using zero-knowledge proofs allows for all users to control their data and thereafter share it according to their preferences. Oliver Gale May 16, 2021, 1:37 AM What are zero-knowledge proofs? Proofs that someone knows a value without revealing the underlying information. In a simple sense, in most disclosure settings the important thing is to satisfy some set of conditions. With zkps we have the ability to prove things like AML transaction compliance without revealing the underlying data. Akasha Rose May 16, 2021, 8:26 PM What do you think the future holds for Ripple and Ethereum? Anish ? will they continue to dominate by Market Cap?  Or will other blockchains take over? Anish May 17, 2021, 12:29 AM Full disclaimer - I am guilty of being one of the original reviewers of Ethereum Orange Paper, and have given talks at Swarm summit. However in my humble opinion, given the massive network effect and the very large developer community - if Ethereum manages to solve some of the scaling pains and delivers eth 2.0 in reasonable time scale it is very likely that Ethereum would still the major “flavour”. Some of the recent developments in Eth ecosystem such as EIP1559 indicates folks involved in ecosystem design decisions are keeping their ears close to the ground and looking at possibilities. In many ways, Ethereum is a victim of its own success and it should be able to leverage that success to be a dominant player in future. Oliver Gale May 17, 2021, 12:32 AM Ripple has a relatively large network effect of users, financial resources, strong lindy effects, and is likely to successfully defend itself in legal actions brought against them by the SEC, in my view, so it will continue to perform in the near term. Longer-term, I see Ripple as a niche payments protocol. Akasha Rose May 17, 2021, 12:41 AM Both those perspectives are very interesting! Do you think there will ever be room for a privacy token like Panther or one of the others to be in the top 3 by market cap? Anish May 17, 2021, 12:51 AM There are two perspectives to this, one is that of something which I describe as “institutional”, entities that depend on their market strategies to earn their revenue, and second is that of the average users in the world of central bank digital currencies. For the above-mentioned institutionals - loss of privacy might result in loss of “alpha” - or their ability to have their own strategy. To compare this to real-world, take Renaissance Technologies the worlds largest hedge funds - we hardly know their strategies. However in Defi world, as it stands that doesn't exist. Following our chain of thought, assuming that CBDC’s don't really support privacy, then arbitrage - which is driven by data - especially in the case of FANG- I believe would give rise to increasing need for privacy for average retail users, once they are cognizant of what is happening and the price they pay for lack of privacy. Assuming if either one of the above turns out to be true, the chances that privacy protocol such as Panther being in the top three could be pretty high. Akasha Rose May 17, 2021, 12:59 AM Oh, that's really interesting, I hadn't seen it like that! Anish May 17, 2021, 1:08 AM You are most welcome 🙂, our team is very diverse, so it gives us a different perspective. And we are open to being challenged 🙂, so ideas get their tires kicked as well .... Oliver Gale May 17, 2021, 1:44 AM Of course, there are different perspectives on what drives value to those protocols which are dominant today and emerging protocols seeking to offer different properties, like interoperability or transaction throughout, to the market. In my view, there are a few core properties of public blockchains that make them contenders for dominance in the market. First of all, a blockchain solves the Byzantine General’s problem of coordinating actions amongst adversaries who have an incentive to collaborate but also to betray each other under certain circumstances. As such, it’s fair to say that each of these protocols imposes friction into the network in order to reach consensus. The real question then is what compromises are worth making on the consensus mechanism to achieve the use case, eg. store of value vs. payments. Outside of the consensus cornerstone of value are a wide range of use cases for protocols built for layer 1 or layer 2. When we began Panther we assessed the industry from first principles and it’s clear to us that privacy is a ubiquitous use case and a digital service demanded by every party in some way. By creating a protocol that effectively prices and creates network effects for the privacy of any digital good in a multi-chain manner there is an opportunity to enhance all networks in this way. In this light as a meta protocol, Panther is positioning itself to become a ubiquitous service and a leading blockchain. Akasha Rose May 17, 2021, 4:36 AM Well, that is very good to hear! Akasha Rose May 17, 2021, 4:37 AM @channel any further questions for the Pather Protocol team before we wrap up? It's been a very enlightening thread! Thanks, Panther team for joining! We'll see you next time! Ramadan Ameen May 21, 2021, 10:55 AM :slogging-slack-blogging: 1 🔥 1 💚 1 🎯 1 Akasha Rose, it was our pleasure! Thank you so much for hosting us and we look forward to doing this again very soon. About Panther Protocol Panther Protocol is an end-to-end privacy protocol for DeFi. Panther provides DeFi users with fully collateralized privacy-enhancing digital assets, leveraging crypto-economic incentives and zkSNARKs technology. Users are able to mint zero-knowledge zAssets by depositing digital assets from any blockchain into Panther vaults. zAssets will become an ever-expanding asset class for users who want their transactions and strategies the way they should always have been: private.

Privacy Blockchains With Teeth | Panther Protocol AMA

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

In the rapidly evolving landscape of artificial intelligence, one name has emerged as a trailblazer in the realm of AI-driven entertainment: Jan Zoltkowski. As the founder of JanitorAI, Zoltkowski has captured the attention of millions with his innovative approach to creating engaging and immersive AI experiences. Through his technical prowess and unwavering commitment to pushing the boundaries of what's possible, Zoltkowski has established himself as a visionary leader in the AI industry. JanitorAI, launched in June of last year, quickly gained traction, attracting over one million users within its first week. The platform's success can be attributed to Zoltkowski's keen understanding of the untapped potential of AI in the entertainment sector. While many AI startups focus on productivity and efficiency, Zoltkowski recognized the opportunity to harness the power of AI to create captivating and interactive experiences that resonate with users on a deeper level. One of the key factors contributing to JanitorAI's rapid growth is its predominantly female user base, with over 70% of its users being women. This demographic breakdown is a testament to Zoltkowski's ability to create an inclusive and welcoming environment that caters to a diverse range of interests and preferences. By fostering a strong sense of community and actively engaging with users, Zoltkowski has built a loyal following that continues to propel JanitorAI forward. Behind the scenes, Zoltkowski and his team are constantly pushing the envelope in terms of technological advancements. Faced with the challenge of accommodating millions of users, Zoltkowski led the charge in reworking JanitorAI's entire tech stack to ensure seamless performance at scale. This required a significant investment in infrastructure and a deep understanding of the intricacies of AI deployment. When JanitorAI faced a cease and desist order from OpenAI for violating their terms of service, Zoltkowski refused to let this setback derail his vision. Instead, he pivoted and deployed state-of-the-art language models (LLMs) on-premises, managing hundreds of GPUs to maintain the platform's performance. Zoltkowski's team initially relied on fine-tuning open-source LLMs but quickly discovered that incremental training of their own models based on RNN architectures yielded superior results compared to transformer-based models. Moderation has been another significant challenge for JanitorAI, given the platform's reliance on user-generated content. Zoltkowski has tackled this issue head-on by leveraging advanced tools like AWS Rekognition and employing a dedicated team of human moderators. While it remains an ongoing battle, Zoltkowski's proactive approach has ensured that JanitorAI maintains a safe and enjoyable environment for its users. Throughout his journey, Zoltkowski has remained committed to building a product that users crave and actively engaging with the JanitorAI community. He personally replies to support requests, recognizing that the community is the platform's greatest asset. This hands-on approach has fostered a deep connection between Zoltkowski and JanitorAI's users, further solidifying the platform's position as a leader in AI-driven entertainment. Looking to the future, Zoltkowski envisions JanitorAI as the go-to hub for all things entertainment and AI. Just as ChatGPT has become synonymous with productivity, Zoltkowski aims to make JanitorAI the ultimate destination for the most exciting and engaging AI experiences. By staying at the forefront of academic research and continuously incorporating the latest advancements in AI, Zoltkowski is poised to revolutionize the way we interact with and consume entertainment. In a world where the lines between reality and virtual experiences are increasingly blurred, Jan Zoltkowski stands as a beacon of innovation and possibility. Through his technical expertise, unwavering dedication, and ability to inspire a passionate community, Zoltkowski has positioned JanitorAI as a force to be reckoned with in the AI industry. As he continues to push the boundaries of what's possible, there's no doubt that Jan Zoltkowski and JanitorAI will shape the future of entertainment in profound and exciting ways.

Jan Zoltkowski: The Visionary Behind JanitorAI's Limitless Entertainment Experience

Jan Zoltkowski: JanitorAI এর সীমাহীন বিনোদন অভিজ্ঞতার পেছনের স্বপ্নদর্শী

Jan Zoltkowski: Der Visionär hinter dem grenzenlosen Unterhaltungserlebnis von JanitorAI

Jan Zoltkowski: el visionario detrás de la experiencia de entretenimiento ilimitado de JanitorAI

Jan Zoltkowski : le visionnaire derrière l'expérience de divertissement illimitée de JanitorAI

जान ज़ोल्टकोव्स्की: जेनिटोरएआई के असीमित मनोरंजन अनुभव के पीछे की दूरदर्शी सोच

Jan Zoltkowski: JanitorAI の無限のエンターテイメント体験を支える先見の明のある人

Jan Zoltkowski: JanitorAI의 무한한 엔터테인먼트 경험 뒤에 숨은 선구자

Jan Zoltkowski: o visionário por trás da experiência de entretenimento ilimitada da JanitorAI

Ян Жольтковски: Провидец, стоящий за безграничными развлечениями JanitorAI

Jan Zoltkowski: JanitorAI'nin Sınırsız Eğlence Deneyiminin Arkasındaki Vizyoner

Jan Zoltkowski: Người có tầm nhìn đằng sau trải nghiệm giải trí vô hạn của JanitorAI

Jan Zoltkowski：JanitorAI 无限娱乐体验背后的远见者

Hey Hackers! We’ve got some exciting news  🥁🥁🥁 Web 2.5, HackerNoon’s documentary on the next-generation internet is now available on demand! ! Watch it here Web 2.5: A HackerNoon Film The Web 2.5 Documentary by HackerNoon centers on the internet’s evolution and complex history. In a light-hearted interview lasting just under half an hour, HackerNoon’s team interrogates the different phases of the Internet, from an era of dial-up modems to one slowly defining what Web 3 is. The internet is not a flawless, mysterious machine operating in a black box; it is a dynamic tool collaboratively built by users worldwide, and its evolution is a shared responsibility. As , HackerNoon COO, points out, the ball has already been dropped a few times. She identifies 2016 as a turning point for Web 2 and the tech ecosystem, highlighting issues such as weaponized ad-based tech algorithms, mass distrust in the government, struggling publishing industries, and pervasive misinformation as the straws that broke the camel’s back. Linh Dao Smooke “I think we desperately need a reimagined internet because somewhere along the way we lost the plot. We all wanted it to be a force for good but we ended up driving people further apart” The dialogue continues with the HackerNoon team further investigating the broken state of the Internet, questioning whether Web 3 is the solution, and discussing the company's commitment to keeping information on the Internet free, user-friendly, and easily accessible. “We live between social media and the traditional Forbes of the world, bringing you a high-quality tech story without all the noise,” said David Smooke, HackerNoon Founder & CEO. Is Web 3 really the future, or is it just a passing trend for people looking to make a quick buck? The HackerNoon team has its answer and now, they want to help you find yours. Watch the Web 2.5 documentary, available on demand now!

Web 2.5: HackerNoon's Vision for a New Wave Internet 

ওয়েব 2.5: একটি নতুন তরঙ্গ ইন্টারনেটের জন্য হ্যাকারনুনের দৃষ্টিভঙ্গি

Web 2.5: HackerNoons Vision für ein New Wave Internet

Web 2.5: La visión de HackerNoon para una nueva ola de Internet

Web 2.5 : la vision de HackerNoon pour une nouvelle vague Internet

वेब 2.5: हैकरनून का नए दौर के इंटरनेट का विजन

Web 2.5: 新しい波インターネットに対する HackerNoon のビジョン

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