Integritee provides a platform to enable collaboration between businesses on sensitive data while giving all parties control and transparency about what their data is used for.

Integritee

Redefining AI Data Provenance In the fast changing field of artificial intelligence and decentralised networks, a new actor has emerged: Rivalz. This revolutionary platform is more than simply a technology development; it is a leader in AI data provenance. Imagine a world in which each bit of data has a clear and verifiable past, similar to a well-documented pedigree. This is the world Rivalz is building with its AI-powered DePIN RollApp, which uses cutting-edge Dymension technology. Rivalz's native token, $RIZ, allows transactions, powers node infrastructure, and enables data monetization, lays the groundwork for a powerful AI-app ecosystem. The technology is intended to serve as a foundation for the creation of an ideal AI data market, with an emphasis on assuring data authenticity and integrity—key qualities that are sometimes difficult to achieve in digital settings. The Team and Vision Rivalz is guided by individuals that have strong roots in artificial intelligence, data management, and Web3 technologies. The team that guides Rivalz consists of approximately thirty professionals. They have been at the forefront of developing solutions that combine these sectors since the year 2019, with the intention of bringing about a new age of data-driven artificial intelligence applications. The initiative is bolstered by the assistance of well-known organisations in the industry, such as Magnus Capital and Delphi Ventures, amongst others. This provides an additional layer of legitimacy and financial support, which promises considerable growth and innovation. Key Components of Rivalz - RX - Rivalz Platform: Here, users can create AI Personas, essentially digital identities that can interact within the AI ecosystem, expanding into roles such as AI NPCs (Non-Player Characters), AI Companions, and AI Agents. - RECO - Rivalz Ecosystem Collaboration: This segment focuses on fostering collaborations across various fields such as DID (Digital Identity), GameFi (Gaming Finance), and SocialFi (Social Finance), creating a network of partners leveraging the Rivalz infrastructure. - RNET - Rivalz Network: Dedicated to enhancing neural network infrastructure, RNET supports the development and operation of modular AI applications and agents. At its core, the Rivalz network relies on rNodes and Validator zNodes, essential for maintaining network integrity and efficiency. These nodes are analogous to the foundations of a building, crucial for the stability and functionality of the entire structure. Why Follow Rivalz? A window into the future of artificial intelligence and blockchain integration is provided by Rivalz, which is accessible to enthusiasts, developers, and casual spectators alike. Because of the ever-increasing complexity of the digital world, platforms such as Rivalz are absolutely necessary in order to guarantee the dependability and safety of the data that is used to power artificial intelligence applications. This dedication to the provenance of data is what distinguishes Rivalz as a pioneer in the field of artificial intelligence technologies for the future. 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. Redefining AI Data Provenance In the fast changing field of artificial intelligence and decentralised networks, a new actor has emerged: Rivalz. This revolutionary platform is more than simply a technology development; it is a leader in AI data provenance. Imagine a world in which each bit of data has a clear and verifiable past, similar to a well-documented pedigree. This is the world Rivalz is building with its AI-powered DePIN RollApp, which uses cutting-edge Dymension technology. Rivalz's native token, $RIZ, allows transactions, powers node infrastructure, and enables data monetization, lays the groundwork for a powerful AI-app ecosystem. The technology is intended to serve as a foundation for the creation of an ideal AI data market, with an emphasis on assuring data authenticity and integrity—key qualities that are sometimes difficult to achieve in digital settings. The Team and Vision The Team and Vision Rivalz is guided by individuals that have strong roots in artificial intelligence, data management, and Web3 technologies. The team that guides Rivalz consists of approximately thirty professionals. They have been at the forefront of developing solutions that combine these sectors since the year 2019, with the intention of bringing about a new age of data-driven artificial intelligence applications. The initiative is bolstered by the assistance of well-known organisations in the industry, such as Magnus Capital and Delphi Ventures, amongst others. This provides an additional layer of legitimacy and financial support, which promises considerable growth and innovation. Key Components of Rivalz Key Components of Rivalz - RX - Rivalz Platform : Here, users can create AI Personas, essentially digital identities that can interact within the AI ecosystem, expanding into roles such as AI NPCs (Non-Player Characters), AI Companions, and AI Agents. RX - Rivalz Platform - RECO - Rivalz Ecosystem Collaboration : This segment focuses on fostering collaborations across various fields such as DID (Digital Identity), GameFi (Gaming Finance), and SocialFi (Social Finance), creating a network of partners leveraging the Rivalz infrastructure. RECO - Rivalz Ecosystem Collaboration - RNET - Rivalz Network : Dedicated to enhancing neural network infrastructure, RNET supports the development and operation of modular AI applications and agents. RNET - Rivalz Network At its core, the Rivalz network relies on rNodes and Validator zNodes, essential for maintaining network integrity and efficiency. These nodes are analogous to the foundations of a building, crucial for the stability and functionality of the entire structure. Why Follow Rivalz? Why Follow Rivalz? A window into the future of artificial intelligence and blockchain integration is provided by Rivalz, which is accessible to enthusiasts, developers, and casual spectators alike. Because of the ever-increasing complexity of the digital world, platforms such as Rivalz are absolutely necessary in order to guarantee the dependability and safety of the data that is used to power artificial intelligence applications. This dedication to the provenance of data is what distinguishes Rivalz as a pioneer in the field of artificial intelligence technologies for the future. 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

Unlock the Secrets of AI: How Rivalz Is Transforming Data Integrity Forever!

এআই-এর গোপনীয়তা আনলক করুন: কীভাবে রিভালজ ডেটা অখণ্ডতাকে চিরতরে রূপান্তরিত করছে!

Lüfte die Geheimnisse der KI: Wie Rivalz die Datenintegrität für immer verändert!

Descubra los secretos de la IA: ¡Cómo Rivalz está transformando la integridad de los datos para siempre!

Découvrez les secrets de l'IA : comment Rivalz transforme l'intégrité des données pour toujours !

एआई के रहस्यों को अनलॉक करें: कैसे रिवाल्ज़ डेटा अखंडता को हमेशा के लिए बदल रहा है!

AI の秘密を解き明かす: Rivalz がデータ整合性を永遠に変革する方法!

AI의 비밀을 풀어보세요: Rivalz가 데이터 무결성을 영원히 변화시키는 방법!

Desvende os segredos da IA: como a Rivalz está transformando a integridade dos dados para sempre!

Раскройте секреты искусственного интеллекта: как Rivalz навсегда меняет целостность данных!

Yapay Zekanın Sırlarını Açığa Çıkarın: Rivalz, Veri Bütünlüğünü Sonsuza Kadar Nasıl Dönüştürüyor?

Mở khóa bí mật của AI: Rivalz đang biến đổi tính toàn vẹn dữ liệu mãi mãi như thế nào!

解锁 AI 的秘密：Rivalz 如何永远改变数据完整性！

This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) H. Wen, Department of Economics, University of Bath; (2) T. Huang, Faculty of Business and Law, University of Roehampton; (3) D. Xiao, School of Mathematical Sciences. Table of Links Abstract and Introduction Relevant Blockchain Technologies Basic Model Advanced Models for Real-World Scenarios Monte Carlo Simulations Conclusions Future Investigations, and References Abstract In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry. While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets—where misleading practices like fake ratings are rampant—remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions. Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior, preserving the reputation system’s foundational sustainability. Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system’s privacy facets, rendering it particularly influential in the ever-evolving digital domain. Keywords: blockchain, reputation system, sustainable oracle, agent, credit point, integrity-driven rating, incentive mechanisms, Monte-Carlo simulation 1. Introduction Information asymmetry [1] introduces challenges for consumers trying to select products or services that best fit their needs. In traditional retail markets, consumers gravitate towards brands they trust based on personal experiences, the experiences of others, or advertisements [2]. Each brand embodies a certain value that can be monetized in open markets. Offering products or services that fall short of their targeted consumers’ expectations can diminish this brand value. These brand mechanisms [3] serve to benefit both consumers and producers in retail and even broader traditional market sectors [4]. With the increased convenience and diversity they provide, online products and services are becoming the preferred choice for many consumers. A notable trend in this domain is the rise of third-party P2P offerings on internet platforms [5]. This rapidly growing sector includes a wide array of services. Modern internet platforms such as Amazon, eBay, Taobao, Anyvan, ClickMechanic, and Mybuilder have transformed the way third-party service providers engage with consumers. Essentially, these platforms enable vendors to showcase their products or services, allowing customers to easily browse, compare, and purchase. Central to these operations is the role of customer reviews. A high number of positive reviews can significantly boost the attractiveness of a vendor, influencing other potential customers’ purchasing decisions. Conversely, negative reviews can deter future sales. This significant influence has, however, paved the way for manipulative tactics. The prevalence of deceptive practices, such as fake ratings and click farming, is notably higher online compared to traditional markets [6, 7, 8]. For instance, some vendors might engage in ’review farming,’ where they either purchase their products to leave positive reviews or hire individuals to write misleadingly favorable reviews for them. On the darker side, there might be instances of vendors leaving malicious negative reviews for competitors to tarnish their reputation. Hence, the establishment of reliable brand mechanisms presents substantial challenges. Given this scenario, verifying authentic consumer actions and crafting efficient reputation mechanisms become critical. While online platforms have the capability to employ algorithms to filter genuine from deceptive actions, thus allowing third-party producers to construct a legitimate reputation, the efficacy of such methods remains questionable. A persistent tension exists between the deceptive maneuvers of some third-party producers and the countermeasures introduced by platform verification algorithms. This dynamic essentially forms a cyclical battle. Present solutions might demand trade-offs, potentially compromising user privacy or other desirable attributes [9]. While machine learning offers a promising avenue [10], its implementation requires either an initial training phase with assured genuine user data or a substantial development period before it becomes truly effective. The advent of blockchain technology, pioneered by Satoshi Nakamoto [11], and augmented by Nick Szabo’s concept of the smart contract [12]—brought to fruition by Vitalik Buterin through Ethereum in 2014 [13]—has paved the way for innovative on-chain reputation systems.[1] Oracles play a pivotal role in bridging the gap between the real-world and blockchain technology, as they provide the crucial link by which off-chain data can be accessed and utilized by on-chain smart contracts. This function is imperative given that blockchains natively cannot access information outside their network. In 2017, Chainlink introduced its oracle mechanism [14], significantly amplifying the capabilities of decentralized systems. Its renowned use-case for price feeds laid the foundation for the blossoming of Decentralized Finance [15, 16]. Essentially, oracles act as third-party intermediaries, relaying external data to smart contracts. Chainlink’s decentralized oracle network assures the authenticity and integrity of data relayed into the blockchain. This is achieved by sourcing data from a plethora of independent oracles and consolidating their responses, mitigating risks associated with singular data points and enhancing the reliability and security of smart contracts dependent on external data. Yet, the functioning of these oracles primarily relies on incentive mechanisms, most notably vesting incentives—taking, for instance, the native asset ”Link” transitioning from non-circulation to circulation. Over a period, these incentives might have the potential to dilute the value for all participants. As the pool of these rewards nears depletion, apprehensions about the system’s enduring viability come to the fore. Successive enhancements, like Chainlink 2.0 [17], along with other contemporary solutions, have endeavored to surmount this challenge. Nonetheless, their effectiveness is a subject of continued discussion. While these refined models and strategies demonstrate potential, their ability to maintain prolonged and unwavering effectiveness remains under examination [18]. Hence, forging a truly sustainable oracle infrastructure becomes essential to fortifying the link between the real-world and blockchain technology. Recent studies have illuminated various dimensions of blockchain technology, notably the blockchainbased reputation systems [19, 20]. While these endeavors are commendable, some of them appear more theoretical and might encounter challenges when catering to all dynamic agent[2] interactions. Others have opted for centralized selection of trusted nodes, potentially introducing vulnerabilities associated with single points of failure [21, 22, 23, 24]. Mechanisms aiming to emulate real-world settings sometimes lean on intricate processes for identifying agent types and monitoring their on-chain activities. These processes occasionally overshadow more direct incentive mechanisms that have demonstrated spontaneous efficacy [25]. Some proposed economic incentive structures seem to lean on increasing blockchain asset ”numbers”, which might be susceptible to dilution over time [26], instead of furnishing concrete advantages. As mechanism designs grow in complexity, they might inadvertently elevate intervention costs or introduce unpredictabilities. Relying heavily on uncertainties akin to those proposed by chaos theory [27]—inspired by the renowned ”Butterfly Effect” [28]—might tread on delicate ground. While there appear to be promising solutions, such as ”Truth” [29]—a blockchain-aided reputation system aimed at obviating the need to trust e-commerce platforms and ensuring feedback authenticity and anonymity—the system is not without its potential shortcomings. ”Truth” introduces a hybrid model wherein the centralized e-commerce platform remains in charge of traditional trading services, but the blockchain oversees the authentication of feedback. Although ”Truth” presents an innovative approach to the age-old problem of authenticating e-commerce feedback, certain aspects warrant deeper scrutiny. For example, the paper does not adequately address the motivations that would drive consumers to engage in the feedback process. Understanding these incentives is vital for ensuring widespread adoption and sincere feedback. Furthermore, the system seems to overlook potential malicious behaviors by sellers, such as posting negative feedback against competitors or hiring fictitious consumers to generate positive reviews. While transactions may take place through the prescribed channels, there is a potential for compensating these ”hired” consumers via alternative methods, thereby circumventing the system’s safeguards. For ”Truth” to fully realize its potential and to demonstrate its effectiveness in real-world settings, these concerns need to be addressed in future versions or research Given these challenges, there’s merit in exploring a more streamlined, basic model that inherently incentivizes agent behavior integrity and fosters sustainability [30, 31, 32, 33, 34]. Initial evaluations of this model could employ Monte-Carlo simulations across vast parameter domains, capturing real-world intricacies. It’s essential to note that this rudimentary model need not capture every intricate detail of real-world scenarios. A basic reputation system, even if it offers only a singular credibility rating, can be invaluable in myriad applications. This value is realized when the reputation information it delivers is accurate and trustworthy, allowing users to rely on it rather than turning to other platforms that struggle to filter out low-cost fake ratings, as previously discussed. Building upon a well-defined basic model foundation allows for iteration and enhancement. By integrating corrective components to the foundational equations, we can preserve core incentives while refining the model towards an everlasting reputation system to mirror real-world scenarios more accurately. Ensuring this model’s competitive advantage over other platforms or blockchain solutions amplifies its resilience and sustainability. This strategy not only highlights the model’s practical utility but, as adoption grows, leverages the network effect. Widespread integration of our system fortifies commercial barriers against replication, making it increasingly arduous for potential competitors to emulate our unique model and its benefits. While the applications of the everlasting reputation system continue to expand across various domains and demographics, this system can function as a sustainable oracle network with demonstrated reliability. The integration of Ring Signatures [35, 36, 37, 38, 39] and Zero Knowledge Proofs (ZKPs) [40, 41, 42] can further enhance the privacy dimensions of such oracle mechanisms, especially in situations where privacy is paramount. Ring Signatures allow for digital signature from a group member without disclosing the identity of the specific signer. ZKPs represent an advanced cryptographic technique enabling agents to substantiate certain claims (like their assets or states) without disclosing the specifics. The structure of this paper is organized as follows: Section 2 introduces the relevant blockchain technologies. Following this, the basic model is presented in Section 3, which lays the foundation for understanding the incentive mechanisms that promote self-motivated integrity among agents. We then refine this basic model in Section 4 to mirror real-world scenarios more closely, ensuring that the incentives for integrity-driven ratings are preserved. After building a robust theoretical base, Section 5 uses Monte Carlo simulations to validate our propositions, followed by a detailed analysis and discussion of the results. Our findings from the simulations are summarized in Section 6, where we underscore that our reputation system functions effectively as a universal sustainable oracle. Finally, potential directions for future research are proposed in Section 7. [1] In our model, the term ”reputation system” is used generically to denote the alternative solution we propose. However, in practical application scenarios, it might also be referred to as a ”platform.” The two terms can be used interchangeably. [2] In our model, we employ the term ”agent” to represent entities involved in the system. Conversely, in real-world application scenarios, we refer to them as ”users”. Throughout this paper, the term ”agent” is used in the context of the model while ”user” is employed in the context of real-world applications. Both terms can be considered interchangeable in many scenarios.

Introducing an Intrinsic Integrity-Driven Rating Model for a Sustainable Reputation
System

একটি টেকসই খ্যাতির জন্য একটি অন্তর্নিহিত সততা-চালিত রেটিং মডেল প্রবর্তন করা হচ্ছে
পদ্ধতি

Einführung eines intrinsischen, integritätsorientierten Bewertungsmodells für eine nachhaltige Reputation
System

Presentación de un modelo de calificación basado en la integridad intrínseca para una reputación sostenible
Sistema

Présentation d’un modèle de notation intrinsèque axé sur l’intégrité pour une réputation durable
Système

स्थायी प्रतिष्ठा के लिए एक आंतरिक अखंडता-संचालित रेटिंग मॉडल का परिचय
प्रणाली

持続可能な評判を実現するための本質的な誠実性を重視した評価モデルの導入
システム

지속 가능한 평판을 위한 본질적인 청렴도 중심 평가 모델 도입
체계

Apresentando um modelo de classificação baseado na integridade intrínseca para uma reputação sustentável
Sistema

Представляем внутреннюю рейтинговую модель, основанную на честности, для устойчивой репутации
Система

Sürdürülebilir Bir İtibar için İçsel Dürüstlük Odaklı Derecelendirme Modelinin Tanıtılması
Sistem

Giới thiệu Mô hình xếp hạng dựa trên tính chính trực nội tại để có được danh tiếng bền vững
Hệ thống

引入内在诚信驱动的评级模型以实现可持续的声誉
系统

Every great innovation comes with some unique challenges. Automation can simplify your marketing processes, save time, and give insight you may otherwise miss. As helpful — even necessary — as automating content is, it carries some cybersecurity risks you should know about. That doesn’t mean these tools are strictly unsafe, but it does warrant attention. You need to ensure you’re using AI safely if you hope to experience its benefits to the fullest. You can do so by following these five content automation cybersecurity steps. 1. Determine Your Risk Level The first step to more secure content automation is deciding exactly what and how serious your cyber risks are. A more detailed picture of your risk landscape will make it easier to determine what kind of protections are necessary. Some generative AI models may require legal permission to use customers’ data. Other marketing automation tools don’t store any customer information, so breaches won’t be quite so severe. Review your tools and the data they access to see what type of risk you’re dealing with. In general, the more sensitive information you use in content automation, the more advanced security measures you’ll need. Your specific market may influence these decisions, too. At least 14 states have data privacy laws that may introduce unique requirements. Consider all these factors when determining your risk level and err on the side of going further than required to be safe. 2. Encrypt Everything Regardless of your risk level, you’ll want to encrypt all your content automation data. Encryption scrambles information so it doesn’t reveal any actual details even if a hacker accesses it. That should apply to everything from customer information to social media logins to AI training data. You may already encrypt data at rest, but it’s important to encrypt it in transit, too. You might even be able to encrypt it during use. That wasn’t always possible, but new standards like homomorphic encryption let you analyze data with AI without decrypting it. Refer to any applicable laws or industry standards to see if there’s a specific level of encryption you must use. The government has recently recognized quantum-proof encryption methods you could use if you want to go the extra mile. 3. Restrict Access Access controls are another big part of content automation cybersecurity. When fewer people or programs can access your content tools, scheduling software, and social media accounts, hackers have fewer options to break into. Zero-trust architecture takes access restrictions and user authentication to extremes, so it’s the safest way forward. While this approach takes a while to set up, it increases visibility and reduces downtime by double-checking everything before trusting it. Even if you don’t opt for the zero-trust model, you should restrict content automation tools so only the employees who need it for their daily work can access it. Strict access controls only work when you can reliably tell who’s who. Simple username-password combinations are too simple and easy to break past. Use multi-factor authentication (MFA) — ideally through SMS or cryptographic keys and not email — instead. 4. Choose Your Tools Carefully Next, you’ll want to take a closer look at the software you use to automate your content strategy. These tools could potentially have access to some sensitive information — including customer data and your social media accounts — so you should ensure they’re safe before trusting them. The first step in reducing these third-party risks is to consolidate your content automation tools as much as possible. Businesses use more than a hundred apps on average, and any one of these could have security flaws that jeopardize your data. Opting for a single provider to cover more services makes it easier to ensure you’re using a reliable, safe solution. You should also have a formal process for determining if a software provider meets your security needs. The Cybersecurity and Information Security Agency outlines software supply chain security steps you can follow to create such a system. 5. Train Everyone Make sure everyone on your content team understands security best practices. These include aspects like strong password management, why to use MFA, and how to spot phishing attempts. As many as 95% of cybersecurity issues stem in part from human error. Consequently, if you train everyone on your team to practice safer, more mindful practices, you’ll prevent a lot of damage. Of course, even well-trained employees can still make mistakes, but the more prepared your workforce is for these risks, the less likely those errors will become. Because people tend to forget things, periodic refresher training is a good idea. Retrain everyone in security best practices at least once annually to keep safety at the top of everybody’s mind. Cybersecurity Is Crucial for Content Automation Without reliable cybersecurity, your content automation tools could become a huge risk before you realize it. By contrast, you can use this technology to its fullest extent if you protect it first. These five steps will help you create a cybersecurity plan that works for your content automation strategy. Be sure to review your system regularly to ensure you’re on top of any developing security trends or new risks. Sources: https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://morefield.com/blog/implementing-zero-trust-architecture/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf Every great innovation comes with some unique challenges. Automation can simplify your marketing processes, save time, and give insight you may otherwise miss. As helpful — even necessary — as automating content is, it carries some cybersecurity risks you should know about . That doesn’t mean these tools are strictly unsafe, but it does warrant attention. you should know about You need to ensure you’re using AI safely if you hope to experience its benefits to the fullest. You can do so by following these five content automation cybersecurity steps. 1. Determine Your Risk Level The first step to more secure content automation is deciding exactly what and how serious your cyber risks are. A more detailed picture of your risk landscape will make it easier to determine what kind of protections are necessary. Some generative AI models may require legal permission to use customers’ data. Other marketing automation tools don’t store any customer information, so breaches won’t be quite so severe. Review your tools and the data they access to see what type of risk you’re dealing with. In general, the more sensitive information you use in content automation, the more advanced security measures you’ll need. Your specific market may influence these decisions, too. At least 14 states have data privacy laws that may introduce unique requirements. Consider all these factors when determining your risk level and err on the side of going further than required to be safe. 14 states have data privacy laws 2. Encrypt Everything Regardless of your risk level, you’ll want to encrypt all your content automation data. Encryption scrambles information so it doesn’t reveal any actual details even if a hacker accesses it. That should apply to everything from customer information to social media logins to AI training data. You may already encrypt data at rest, but it’s important to encrypt it in transit, too. You might even be able to encrypt it during use. That wasn’t always possible, but new standards like homomorphic encryption let you analyze data with AI without decrypting it. Refer to any applicable laws or industry standards to see if there’s a specific level of encryption you must use. The government has recently recognized quantum-proof encryption methods you could use if you want to go the extra mile. recognized quantum-proof encryption methods 3. Restrict Access Access controls are another big part of content automation cybersecurity. When fewer people or programs can access your content tools, scheduling software, and social media accounts, hackers have fewer options to break into. Zero-trust architecture takes access restrictions and user authentication to extremes, so it’s the safest way forward. While this approach takes a while to set up, it increases visibility and reduces downtime by double-checking everything before trusting it. Even if you don’t opt for the zero-trust model, you should restrict content automation tools so only the employees who need it for their daily work can access it. it increases visibility and reduces downtime Strict access controls only work when you can reliably tell who’s who. Simple username-password combinations are too simple and easy to break past. Use multi-factor authentication (MFA) — ideally through SMS or cryptographic keys and not email — instead. 4. Choose Your Tools Carefully Next, you’ll want to take a closer look at the software you use to automate your content strategy. These tools could potentially have access to some sensitive information — including customer data and your social media accounts — so you should ensure they’re safe before trusting them. The first step in reducing these third-party risks is to consolidate your content automation tools as much as possible. Businesses use more than a hundred apps on average, and any one of these could have security flaws that jeopardize your data. Opting for a single provider to cover more services makes it easier to ensure you’re using a reliable, safe solution. more than a hundred apps You should also have a formal process for determining if a software provider meets your security needs. The Cybersecurity and Information Security Agency outlines software supply chain security steps you can follow to create such a system. outlines software supply chain security steps 5. Train Everyone Make sure everyone on your content team understands security best practices. These include aspects like strong password management, why to use MFA, and how to spot phishing attempts. As many as 95% of cybersecurity issues stem in part from human error. Consequently, if you train everyone on your team to practice safer, more mindful practices, you’ll prevent a lot of damage. Of course, even well-trained employees can still make mistakes, but the more prepared your workforce is for these risks, the less likely those errors will become. 95% of cybersecurity issues Because people tend to forget things, periodic refresher training is a good idea. Retrain everyone in security best practices at least once annually to keep safety at the top of everybody’s mind. Cybersecurity Is Crucial for Content Automation Without reliable cybersecurity, your content automation tools could become a huge risk before you realize it. By contrast, you can use this technology to its fullest extent if you protect it first. These five steps will help you create a cybersecurity plan that works for your content automation strategy. Be sure to review your system regularly to ensure you’re on top of any developing security trends or new risks. Sources: Sources: https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://hackernoon.com/what-are-the-top-challenges-facing-cybersecurity-automation-adoption https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://iapp.org/resources/article/us-state-privacy-legislation-tracker/ https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms https://morefield.com/blog/implementing-zero-trust-architecture/ https://morefield.com/blog/implementing-zero-trust-architecture/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.statista.com/statistics/1233538/average-number-saas-apps-yearly/ https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www.cisa.gov/sites/default/files/publications/defending_against_software_supply_chain_attacks_508_1.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2022.pdf

How Can You Integrate Cybersecurity Into Your Content Automation Process?

Unlike traditional centralized exchanges, instant crypto exchanges allow users to trade cryptocurrencies directly from their wallets, without the need to deposit funds into an exchange account. Also, many of them help to exchange crypto between different blockchains. Integrating instant crypto exchange functionality into applications through opens up new possibilities for developers. By using these APIs, developers can offer their users the ability to seamlessly swap between cryptocurrencies without leaving the application. This eliminates the need for users to navigate multiple platforms and chains providing access to a wide range of trading pairs. And of course APIs allow developers to benefit from exchange fees, creating a new revenue stream for their applications. APIs In this article, I will explore the process of integrating a ChangeNow API into an application. I will cover the essential steps, including obtaining an API key, authenticating requests, retrieving available trading pairs, executing trades, and monitoring transaction status. By the end of this guide, you'll have a clear understanding of how to leverage such APIs to add powerful trading capabilities to your application. So, let's dive in? Step 1: Sign Up for a ChangeNOW API Key To get started with the ChangeNOW API, you first need to sign up for an API key. This key will be used to authenticate your API requests and ensure secure access to the ChangeNOW exchange services. Navigate to the ChangeNOW page and click on the "Start your integration" button. crypto exchange API Fill in the required information, such as your email address and password, to create a new account. Once you have successfully created an account, log in to your ChangeNOW account dashboard. In the account dashboard, locate the "API" section. This is where you can manage your API keys and access the necessary information for integrating the ChangeNOW API into your application. Click on the "Generate New API Key" button to create a new API key. You may be prompted to provide additional information or verify your account before proceeding. After successfully generating a new API key, you will see it displayed in the API section of your account dashboard. Make sure to copy and store this API key securely, as you will need it for authenticating your API requests. With your ChangeNOW API key generated, you are now ready to proceed to the next step of setting up your development environment and starting to integrate the ChangeNOW API into your application. Remember to keep your API key confidential and avoid sharing it with others. If you suspect that your API key has been compromised, you can always generate a new one from your account dashboard and update your application accordingly. Step 2: Set Up Your Development Environment Before you start integrating the ChangeNOW API into your application, you need to set up your development environment. This involves choosing a programming language and an Integrated Development Environment (IDE) that you are comfortable with, and installing the necessary libraries for making HTTP requests. For this guide, I will provide examples in . Python Install Python on your machine if you haven't already. You can download the latest version of Python from the official and follow the installation instructions for your operating system. website Choose an for Python development. Some popular options include PyCharm, Visual Studio Code, and Sublime Text. IDE Install the requests library, which is a popular library for making HTTP requests in Python. You can install it using pip, the package installer for Python, by running the following command in your terminal or command prompt: pip install requests Create a new Python file in your IDE and import the requests library at the top of your file: import requests With your development environment set up and the necessary libraries installed, you are now ready to start making API requests to the ChangeNOW API. Step 3: Authenticate Your API Requests To authenticate your API requests and ensure secure communication with the ChangeNOW API, you need to include your API key in the x-changenow-api-key header of each request. This header tells the ChangeNOW API that you are an authorized user and grants you access to the API endpoints. Here's how you can set the API key header in Python: import requests

api_key = 'YOUR_API_KEY'  # Replace with your actual API key

headers = { 'x-changenow-api-key': api_key }

response = requests.get('https://api.changenow.io/v1/endpoint', headers=headers) Replace 'YOUR_API_KEY' with the actual API key you obtained in Step 1. The headers object is created with the x-changenow-api-key header set to your API key. This header is then included in the API request using the headers parameter of the requests.get() method. By including the API key header in each request, you authenticate yourself as an authorized user of the ChangeNOW API. The API server will validate your API key and grant access to the requested endpoints if the key is valid. Step 4: Retrieve Available Currency Pairs To start using the ChangeNOW API, you need to know which currency pairs are available for exchange. The ChangeNOW API provides an endpoint to retrieve the list of available currency pairs. To retrieve the available currency pairs, you need to make a GET request to the /v1/market-info/available-pairs/ endpoint. Here's how you can send the request and parse the response: import requests

api_key = 'YOUR_API_KEY'

headers = {

'x-changenow-api-key': api_key }

response = requests.get('https://api.changenow.io/v1/market-info/available-pairs/', headers=headers)

if response.status_code == 200:

available_pairs = response.json()

print("Available currency pairs:")

for pair in available_pairs: print(pair) 

else: print(f"Error: {response.status_code}") The code sends a GET request to the /v1/market-info/available-pairs/ endpoint, including the API key in the x-changenow-api-key header for authentication. If the request is successful (status code 200), the response will contain a JSON array of available currency pairs. The code then parses the response and extracts the list of currency pairs. The response.json() method is used to parse the JSON response into a Python list. The code then iterates over the list and prints each currency pair. If the request fails, the code prints an error message along with the status code. By retrieving the available currency pairs, you can provide your users with the options to choose from when performing an exchange. Step 5: Check Minimum and Maximum Exchange Amounts Before performing an exchange, it's important to know the minimum and maximum amounts allowed for a specific currency pair. The ChangeNOW API provides an endpoint to retrieve this information. To check the minimum and maximum exchange amounts, you need to make a GET request to the /v2/exchange/range endpoint, providing the required parameters: fromCurrency and toCurrency. Here's how you can construct the request URL with query parameters and parse the response: import requests

api_key = 'YOUR_API_KEY'

headers = { 'x-changenow-api-key': api_key }

from_currency = 'btc'  # Replace with the source currency

to_currency = 'eth'    # Replace with the target currency

url = f'https://api.changenow.io/v2/exchange/range?fromCurrency={from_currency}&toCurrency={to_currency}'

response = requests.get(url, headers=headers)

if response.status_code == 200:

data = response.json() 

min_amount = data['minAmount'] 

max_amount = data['maxAmount'] 

print(f"Minimum exchange amount: {min_amount}") 

print(f"Maximum exchange amount: {max_amount}") 

else: print(f"Error: {response.status_code}") Set the fromCurrency and toCurrency variables to the desired source and target currencies, respectively. The code constructs the request URL by appending the fromCurrency and toCurrency parameters as query parameters to the /v2/exchange/range endpoint URL. It then sends a GET request to the constructed URL, including the API key in the x-changenow-api-key header for authentication. If the request is successful (status code 200), the response will contain a JSON object with the minAmount and maxAmount fields. The code parses the response and extracts these values. Tthe response.json() method is used to parse the JSON response into a Python dictionary. The minAmount and maxAmount values are then accessed and printed. By checking the minimum and maximum exchange amounts, you can ensure that the user's requested exchange amount falls within the allowed range for the selected currency pair. Step 6: Estimate Exchange Amounts Before proceeding with an exchange, you may want to provide an estimate of the exchange amount to the user based on their input. The ChangeNOW API offers an endpoint to estimate the exchange amount for a given currency pair and amount. To estimate the exchange amount, you need to make a GET request to the /v2/markets/estimate endpoint, providing the required parameters: fromCurrency, toCurrency, fromAmount or toAmount, and type. Here's how you can build the request URL, send the request, and handle the response: import requests

api_key = 'YOUR_API_KEY'

headers = { 'x-changenow-api-key': api_key }

from_currency = 'btc'   # Replace with the source currency

to_currency = 'eth'     # Replace with the target currency 

from_amount = 1.5       # Replace with the source amount

url = f'https://api.changenow.io/v2/markets/estimate?fromCurrency={from_currency}&toCurrency={to_currency}&fromAmount={from_amount}&type=direct'

response = requests.get(url, headers=headers)

if response.status_code == 200:

data = response.json() 

estimated_amount = data['toAmount'] 

print(f"Estimated exchange amount: {estimated_amount} {to_currency}") 

else: print(f"Error: {response.status_code}") Set the fromCurrency, toCurrency, and fromAmount variables to the desired source currency, target currency, and source amount. The code builds the request URL by appending the fromCurrency, toCurrency, fromAmount, and type parameters as query parameters to the /v2/markets/estimate endpoint URL. It then sends a GET request to the constructed URL, including the API key in the x-changenow-api-key header for authentication. If the request is successful (status code 200), the response will contain a JSON object with the estimated exchange amount in the toAmount field. The code parses the response and extracts the estimated amount. The response.json() method is used to parse the JSON response into a Python dictionary. The toAmount value is then accessed and printed along with the target currency. If the request fails, the code prints an error message along with the status code. By estimating the exchange amount, you can provide the user with an idea of how much they will receive in the target currency based on their input amount in the source currency. Step 7: Create an Exchange Transaction Once the user has provided the necessary information, such as the source and target currencies, the amount to exchange, and the destination address, you can create an exchange transaction using the ChangeNOW API. To create an exchange transaction, you need to make a POST request to the /v2/exchange/refund endpoint, including the required parameters in the request body: id, address, and extraId (if applicable). Here's how you can construct the request payload, send the POST request, and handle the API response: import requests

api_key = 'YOUR_API_KEY'

headers = { 'x-changenow-api-key': api_key, 'Content-Type': 'application/json' }

transaction_id = 'YOUR_TRANSACTION_ID'  # Replace with the actual transaction ID

refund_address = 'YOUR_REFUND_ADDRESS'  # Replace with the actual refund address 

extra_id = 'YOUR_EXTRA_ID'  # Replace with the actual extra ID (if applicable)

payload = {

'id': transaction_id, 

'address': refund_address, 

'extraId': extra_id 

}

response = requests.post('https://api.changenow.io/v2/exchange/refund', headers=headers, json=payload)

if response.status_code == 200:

data = response.json() 

refund_status = data['result'] 

print(f"Refund status: {refund_status}") 

else: print(f"Error: {response.status_code}") Set the transactionId, refundAddress, and extraId variables to the appropriate values for the exchange transaction. The code constructs the request payload as a JSON object containing the id, address, and extraId fields. It then sends a POST request to the /v2/exchange/refund endpoint, including the API key in the x-changenow-api-key header and the payload in the request body. If the request is successful (status code 200), the response will contain a JSON object indicating the refund status in the result field. The code parses the response and extracts the refund status. The response.json() method is used to parse the JSON response into a Python dictionary. The result value is then accessed and printed. By creating an exchange transaction, you initiate the process of exchanging the user's funds from the source currency to the target currency. The API will handle the exchange process and send the exchanged funds to the specified destination address. Step 8: Monitor Transaction Status After creating an exchange transaction, you can monitor its status to keep track of the progress and provide updates to the user. The ChangeNOW API allows you to retrieve the status and details of transactions using the /v2/exchanges endpoint. To monitor the status of a transaction, you need to make a GET request to the /v2/exchanges endpoint. You can optionally include query parameters to filter and sort the transactions. Here's how you can build the request URL with query parameters, send the request, and parse the response to retrieve transaction details and status: import requests

api_key = 'YOUR_API_KEY'  # Replace with your actual API key

headers = { 'x-changenow-api-key': api_key }

url = 'https://api.changenow.io/v2/exchanges?limit=10&offset=0&sortField=createdAt&sortDirection=DESC'

response = requests.get(url, headers=headers)

if response.status_code == 200:

data = response.json() 

transactions = data['exchanges'] 

for transaction in transactions: 

transaction_id = transaction['requestId'] 

status = transaction['status'] 

print(f"Transaction ID: {transaction_id}, Status: {status}") 

else: print(f"Error: {response.status_code}") The code builds the request URL with optional query parameters. In this example, the limit parameter is set to 10 to retrieve a maximum of 10 transactions, the offset parameter is set to 0 to start from the beginning, the sortField parameter is set to 'createdAt' to sort the transactions by creation date, and the sortDirection parameter is set to 'DESC' to sort in descending order. It then sends a GET request to the /v2/exchanges endpoint, including the API key in the x-changenow-api-key header for authentication. If the request is successful (status code 200), the response will contain a JSON object with an array of transactions in the exchanges field. The code parses the response and iterates over the transactions. The response.json() method is used to parse the JSON response into a Python dictionary. The code then iterates over the exchanges array and prints the transaction ID and status for each transaction. By monitoring the transaction status, you can keep track of the progress of the exchange and provide relevant updates to the user. You can periodically poll the API to fetch the latest status of the transaction until it reaches a final state (e.g., "finished" or "failed"). Congratulations! You have successfully learned how to set up and use the ChangeNOW API to integrate cryptocurrency exchange functionality into your application. If you have any problems or need further assistance while implementing the ChangeNOW API, please feel free to leave a comment below. There are on HackerNoon, but you'd like to see a similar step-by-step guide for integrating any other API into your application, let me know in the comments section. many guides Keep exploring, keep learning, and happy coding!

Step by Step Guide on How to Integrate ChangeNOW API

Weather tracking is a common requirement for many web applications, ranging from personal projects to commercial services. PHP, a server-side scripting language, can be a powerful tool for retrieving and displaying weather information on websites. In this beginner's guide, we'll explore how you can use PHP to track weather data and integrate it into your web applications. Getting Started with Weather APIs To track Weather using PHP, we'll need access to weather data from a reliable source. Fortunately, there are several weather APIs available that provide developers with access to real-time and forecast weather information. One popular weather API is OpenWeatherMap. It offers a wide range of weather data, including current weather conditions, forecasts, and historical weather data. To get started, you'll need to sign up for an API key, which you can obtain by registering on the OpenWeatherMap website. Retrieving Weather Data with PHP Once you have obtained your API key, you can use PHP to retrieve weather data from the OpenWeatherMap API. Here's a simple example of how you can make a request to the API and display the current weather conditions: phpCopy code<?php

// Replace 'YOUR_API_KEY' with your actual OpenWeatherMap API key
$apiKey = 'YOUR_API_KEY';

// City and country code for the location you want to retrieve weather data for
$city = 'London';
$countryCode = 'UK';

// API endpoint URL
$url = "http://api.openweathermap.org/data/2.5/weather?q={$city},{$countryCode}&appid={$apiKey}";

// Make a request to the API
$response = file_get_contents($url);

// Decode the JSON response
$data = json_decode($response, true);

// Check if the request was successful
if ($data && $data['cod'] === 200) {
    // Extract relevant weather information
    $weatherDescription = $data['weather'][0]['description'];
    $temperature = round($data['main']['temp'] - 273.15, 1); // Convert temperature from Kelvin to Celsius

    // Display weather information
    echo "<h2>Current Weather in {$city}, {$countryCode}</h2>";
    echo "<p><strong>Temperature:</strong> {$temperature} °C</p>";
    echo "<p><strong>Description:</strong> {$weatherDescription}</p>";
} else {
    // Display error message if request fails
    echo 'Failed to retrieve weather data.';
}

?> In this example, we construct a URL with the desired city and country code, along with our API key. We then make a request to the OpenWeatherMap API using and decode the JSON response using . Finally, we extract relevant from the response and display it on the webpage. file_get_contents() json_decode() weather information Enhancements and Considerations : It's important to implement error handling to gracefully handle situations where the API request fails or returns unexpected data. Error Handling : Consider implementing caching mechanisms to reduce the number of API requests and improve performance. Caching : You can enhance the display of weather information by incorporating CSS styling and additional details such as wind speed, humidity, and atmospheric pressure. Display Formatting : Make your weather-tracking application accessible to users worldwide by supporting multiple languages and units of measurement. Localization : Keep your API key secure by avoiding hardcoding it directly into your PHP files. Consider using environment variables or configuration files to store sensitive information. Security Conclusion Tracking weather using PHP can be a valuable addition to your web applications, providing users with up-to-date weather information for their desired locations. By leveraging weather APIs such as OpenWeatherMap and incorporating PHP to retrieve and display weather data, you can create dynamic and engaging experiences for your website visitors. With the foundational knowledge provided in this guide, you can explore further customization and integration possibilities to meet the specific needs of your projects.

How to Integrate Weather Tracking into Web Applications with PHP

OFX, aka , is an open standard for creating 2D visual effects and video compositing. It operates in a plugin-like application development model. Essentially, it serves as both a Host - an application providing a set of methods, and a Plug-in - an application or module implementing this set. OFX Image Processing API This configuration offers the potential for unlimited expansion of the host application's functionality. DaVinci Resolve and Metal Applications such as Final Cut X and DaVinci Resolve Studio, starting from version 16, fully support Apple Metal pipelines. Similar to OpenCL and Cuda, in the case of OFX, you can obtain a descriptor or handler of a platform-specific command queue. The host system also takes responsibility for allocating a pool of such queues and balancing calculations on them. Moreover, it places the source and target image clip data in GPU memory, significantly simplifying the development of extensible functionality. OFX Version Support in Resolve With Resolve, things are slightly more complicated. DaVinci announces support for OFX v1.4, albeit with some limitations. Specifically, some methods for working with interface functions are not available for use. To determine which method is available, OFX allows you to examine the supported suite through key/value queries. Publishing methods in the plugin code are based on . But we will use the OpenFXS C++ shell adapted for C++17. For convenience, I've compiled everything into one repository: taken from the open-source . C calls dehancer-external Dehancer project OFXS Concept In this project, I will be using OpenFXS, a C++ extension to OpenFX that was originally written by and has become popular over time in commercial and open-source video processing projects. Bruno Nicoletti The original was not adapted to modern C++ dialects, so I updated it to make it compatible with . OpenFXS C++17 OFX, and consequently OFXS, is a standalone software module that is dynamically loaded by the host program. Essentially, it is a dynamic library that is loaded when the main application starts. OpenFXS, like OFX, must publish method signatures. Hence, we use one C method from the code. To start developing in OpenFXS, you need to agree to a few common sets of classes that are used to create new functionality in your application. Typically, in a new project, you need to inherit from these classes and implement or override some virtual methods. To create your own plugin on the host system, let's start by familiarizing ourselves with the following public classes and the same method: is a basic template for creating a plugin's data structure suite and control panel (although it can be left empty). The inherited class creates a singleton object that registers a set of parameters and presets in the host system, with which the developer registers his module; OFX::PluginFactoryHelper - base container class for creating and storing structure properties; OFX::ParamSetDescriptor - a container of properties used when manipulating graphic data when calling data processing procedures. Used by the host application to save the context of processing parameters in the internal database and work with plugin properties defined for each of its instances; OFX::ImageEffectDescriptor - a set of settings that allows you to manipulate the registered data structure; OFX::ParamSet - a set of settings for effects on graphical data, inherited from OFX::ParamSet; OFX::ImageEffect - in the child class, it is necessary to implement data stream processing: images or videos; OFX::MultiThread::Processor - method of registering a plugin (factory) in the host application; OFX::Plugin::getPluginIDs False Color One feature that distinguishes the process of shooting video from simply capturing an image in a photo is the dynamic change of scenes and lighting of both scenes as a whole and areas in the image. This determines the way exposure is controlled during the shooting process. In digital video, there is a control monitor mode for operators in which the exposure level of areas is mapped into a limited set of zones, each tinted with its own color. This mode is sometimes called "predator" or False Color mode. The scales are usually referenced to the IRE scale. Such a monitor allows you to see the exposure zones and avoid significant mistakes when setting camera shooting parameters. Something similar in meaning is used when exposing in photography - zoning according to Adams, for example. You can measure a specific target with an exposure meter and see in which zone it is located, and in real-time we see the zones, neatly tinted for ease of perception. The number of zones is determined by the objectives and capabilities of the control monitor. For instance, a monitor used with cameras can incorporate up to 6 zones. Arri Alexa Adding Extensions Before proceeding with the example, we need to add some simple proxy classes to implement OpenFXS as a platform for processing source data, such as Metal textures. These classes include: : A proxy class for OFX clip data. imetalling::Image : A functor for transferring data from the clip buffer into a Metal texture. From DaVinci, you can extract a buffer of any structure and packaging of image channel values into the plugin, and it should be returned in a similar form. imetalling::Image2Texture To make working with the stream format in OFX easier, you can request the host to prepare data of a specific type in advance. I will use floats packed in RGBA - red/green/blue/alpha. : A reverse functor for transforming a stream into a host system buffer. As you can see, there is potential for significant optimization of calculations if you teach the Metal computing cores to work not with the texture, but directly with the buffer. imetalling::ImageFromTexture We inherit the OFXS base classes and write our functionality without going into the details of how the Metal core works: : Here, we implement the stream transformation and initiate the processing. imetalling::falsecolor::Processor : This will be our specific part of the suite description for the plugin. We need to implement several mandatory calls related to setting up the structure and create an instance of the OFX::ImageEffect class with specific functionality, which we divide into two subclasses in the implementation: Interaction and Plugin. imetalling::falsecolor::Factory : Implementation of the interactive part of working with effects. Essentially, this is the implementation of only virtual methods from OFX::ImageEffect related to processing changes in plugin parameters. imetalling::falsecolor::Interaction : Implementation of thread rendering, that is, launching imetalling::Processor. imetalling::falsecolor::Plugin Additionally, we will need several utility classes built on top of Metal to logically separate the host code and the kernel code on MSL. These include: : A base class that obscures work with the Metal command queue. The main parameter will be the name of the kernel in the MSL code, and the executor of the kernel call. imetalling::Function : A general class for transforming a source texture into a target texture, extending Function to simply set the parameters for calling the MSL kernel. imetalling:Kernel : Bypass kernel. imetalling::PassKernel : Our main functional class, a "predator" emulator that posterizes (downsamples) to a specified number of colors. imetalling::FalseColorKernel The kernel code for the "predator" mode could look like this: static constant float3 kIMP_Y_YUV_factor = {0.2125, 0.7154, 0.0721};
constexpr sampler baseSampler(address::clamp_to_edge, filter::linear, coord::normalized);
 
inline float when_eq(float x, float y) {
  return 1.0 - abs(sign(x - y));
}
 
static inline float4 sampledColor(
        texture2d<float, access::sample> inTexture,
        texture2d<float, access::write> outTexture,
        uint2 gid
){
  float w = outTexture.get_width();
  return mix(inTexture.sample(baseSampler, float2(gid) * float2(1.0/(w-1.0), 1.0/float(outTexture.get_height()-1))),
             inTexture.read(gid),
             when_eq(inTexture.get_width(), w) // whe equal read exact texture color
  );
}
 
kernel void kernel_falseColor(
        texture2d<float, access::sample> inTexture [[texture(0)]],
        texture2d<float, access::write> outTexture [[texture(1)]],
        device float3* color_map [[ buffer(0) ]],
        constant uint& level [[ buffer(1) ]],
        uint2 gid [[thread_position_in_grid]])
{
  float4  inColor = sampledColor(inTexture,outTexture,gid);
  float luminance = dot(inColor.rgb, kIMP_Y_YUV_factor);
  uint      index = clamp(uint(luminance*(level-1)),uint(0),uint(level-1));
  float4    color = float4(1);
 
  if (index<level)
    color.rgb = color_map[index];
 
  outTexture.write(color,gid);
} Initialization of the OFX Plugin We will begin by defining the class In this class, we will set a single parameter - the status of the monitor (either on or off). This is necessary for our example. imetalling::falsecolor::Factory. We will inherit from and overload five methods: OFX::PluginFactoryHelper : This method is invoked to globally configure the instance when the plugin is first loaded. Overloading this method is optional. load() : This method is invoked when an instance is unloaded, for instance, to clear memory. Overloading this method is also optional. unload() : This is the second method that the OFX host calls when the plugin is loaded. It is virtual and must be defined in our class. In this method, we need to set all the properties of the plugin, regardless of its context type. For more details about the properties, refer to the code. describe(ImageEffectDescriptor&) ImageEffectDescriptor : Similar to the method, this method is also called when the plugin is loaded and must be defined in our class. It should define properties associated with the current context. describeInContext(ImageEffectDescriptor&,ContextEnum) describe The context determines the type of operations the application works with, such as filter, paint, transition effect, or frame retimer in a clip. : This is the most crucial method that we overload. We return a pointer to an object of type . In other words, our in which we have defined all the functionalities, both with regard to user events in the host program and rendering (transforming) the source frame into the target one: createInstance(OfxImageEffectHandle, ContextEnum) ImageEffect imetalling::falsecolor::Plugin OFX::ImageEffect *Factory::createInstance(OfxImageEffectHandle handle,OFX::ContextEnum) {
     return new Plugin(handle);
   } Handling Events At this stage, if you compile a bundle with the OFX module, the plugin will already be available in the host application, and in DaVinci, it can be loaded onto the correction node. However, to work fully with a plugin instance, you need to define at least the interactive part and the part associated with processing the incoming video stream. To do this, we inherit from the class and overload virtual methods: OFX::ImageEffect - This method allows us to define the logic for handling the event. The event type is determined by the value of OFX::InstanceChangedArgs::reason and can be: eChangeUserEdit, eChangePluginEdit, eChangeTime - the event occurred as a result of a property being edited by the user, changed in a plugin or host application, or as a result of a change in the timeline. changedParam(const OFX::InstanceChangedArgs&, const std::string&) The second parameter specifies the string name that we defined at the plugin initialization stage, in our case, it is one parameter: . false_color_enabled_check_box - This method allows us to define the logic for reacting to an event, and return a state that determines whether something has changed and whether rendering makes sense. The method must return false or true. This is a way to optimize and reduce the number of unnecessary calculations. isIdentity(...) You can read the implementation of interactive interaction with OFX in the code. As you can see, we receive pointers to the clips: the source one and the memory area in which we will put the target transformation. Interaction.cpp Implementation of Rendering Launch We will add another logical layer on which we will define all the logic for launching the transformation. In our case, this is the only method for overriding so far: - Here, you can find out the properties of the clips and decide how to render them. Also, at this stage, the Metal command queue and some useful attributes associated with the current timeline properties become available to us. render(const OFX::RenderArguments& args) Processing At the launch stage, an object with useful properties became available to us: we have at least a pointer to the video stream (more precisely, a memory area with frame image data), and, most importantly, a queue of Metal commands. Now, we can construct a generic class that will bring us closer to a simple form of reusing kernel code. The OpenFXS extension already has such a class: OFX::ImageProcessor; we just need to overload it. In the constructor, it has the OFX::ImageEffect parameter, i.e., in it, we will receive not only the current state of the plugin parameters but everything necessary for working with the GPU. At this stage, we just need to overload the method and initiate the processing of kernels already implemented on Metal. processImagesMetal() Processor::Processor(
            OFX::ImageEffect *instance,
            OFX::Clip *source,
            OFX::Clip *destination,
            const OFX::RenderArguments &args,
            bool enabled
    ) :
            OFX::ImageProcessor(*instance),
            enabled_(enabled),
            interaction_(instance),
            wait_command_queue_(false),
            /// grab the current frame of a clip from OFX host memory
            source_(source->fetchImage(args.time)),
            /// create a target frame of a clip with the memory area already specified in OFX
            destination_(destination->fetchImage(args.time)),
            source_container_(nullptr),
            destination_container_(nullptr)
    {
 
      /// Set OFX rendering arguments to GPU
      setGPURenderArgs(args);
 
      /// Set render window
      setRenderWindow(args.renderWindow);
 
      /// Place source frame data in Metal texture
      source_container_ = std::make_unique<imetalling::Image2Texture>(_pMetalCmdQ, source_);
 
      /// Create empty target frame texture in Metal
      destination_container_ = std::make_unique<imetalling::Image2Texture>(_pMetalCmdQ, destination_);
 
      /// Get parameters for packing data in the memory area of the target frame
      OFX::BitDepthEnum dstBitDepth = destination->getPixelDepth();
      OFX::PixelComponentEnum dstComponents = destination->getPixelComponents();
 
      /// and original
      OFX::BitDepthEnum srcBitDepth = source->getPixelDepth();
      OFX::PixelComponentEnum srcComponents = source->getPixelComponents();
 
      /// show a message to the host system that something went wrong
      /// and cancel rendering of the current frame
      if ((srcBitDepth != dstBitDepth) || (srcComponents != dstComponents)) {
        OFX::throwSuiteStatusException(kOfxStatErrValue);
      }
 
      /// set in the current processor context a pointer to the memory area of the target frame
      setDstImg(destination_.get_ofx_image());
    }
 
    void Processor::processImagesMetal() {
 
      try {
 
        if (enabled_)
          FalseColorKernel(_pMetalCmdQ,
                           source_container_->get_texture(),
                           destination_container_->get_texture()).process();
        else
          PassKernel(_pMetalCmdQ,
                           source_container_->get_texture(),
                           destination_container_->get_texture()).process();

        ImageFromTexture(_pMetalCmdQ,
                         destination_,
                         destination_container_->get_texture(),
                         wait_command_queue_);
 
      }
      catch (std::exception &e) {
        interaction_->sendMessage(OFX::Message::eMessageError, "#message0", e.what());
      }
    } Building the Project To build the project, you will need CMake, and it must be at least version 3.15. Additionally, you will require Qt5.13, which aids in the easy and convenient assembly of the bundle with the plugin installer in the system directory. To initiate cmake, you must first create a build directory. After creating the build directory, you can execute the following command: cmake -DPRINT_DEBUG=ON -DQT_INSTALLER_PREFIX=/Users/<user>/Develop/QtInstaller -DCMAKE_PREFIX_PATH=/Users/<user>/Develop/Qt/5.13.0/clang_64/lib/cmake -DPLUGIN_INSTALLER_DIR=/Users/<user>/Desktop -DCMAKE_INSTALL_PREFIX=/Library/OFX/Plugins .. && make install Afterward, the installer, called , will appear in the directory that you specified in the parameter. Let's go ahead and launch it! Once the installation is successful, you can start DaVinci Resolve and begin using our new plugin. IMFalseColorOfxInstaller.app PLUGIN_INSTALLER_DIR You can find and select it in the OpenFX panel on the color correction page, and add it as a node. External links False Color OFX Plugin Code The Open Effects Association - OFX header file version and OFXS library code under Resolve + examples Download DaVinci Resolve

An Easy Way to Develop Your Own Apple Metal Plugin and Integrate It Into DaVinci Resolve

আপনার নিজস্ব অ্যাপল মেটাল প্লাগইন বিকাশ করার এবং এটিকে DaVinci সমাধানে সংহত করার একটি সহজ উপায়

Eine einfache Möglichkeit, Ihr eigenes Apple Metal Plugin zu entwickeln und es in DaVinci Resolve zu integrieren

Una forma sencilla de desarrollar su propio complemento Apple Metal e integrarlo en DaVinci Resolve

Un moyen simple de développer votre propre plugin Apple Metal et de l'intégrer dans DaVinci Resolve

अपना स्वयं का Apple मेटल प्लगइन विकसित करने और इसे DaVinci रिज़ॉल्यूशन में एकीकृत करने का एक आसान तरीका

独自の Apple Metal プラグインを開発して DaVinci Resolve に統合する簡単な方法

나만의 Apple Metal 플러그인을 개발하고 DaVinci Resolve에 통합하는 쉬운 방법

Uma maneira fácil de desenvolver seu próprio plug-in Apple Metal e integrá-lo ao DaVinci Resolve

Простой способ разработать собственный плагин Apple Metal и интегрировать его в DaVinci Resolve

Kendi Apple Metal Eklentinizi Geliştirmenin ve DaVinci Resolve'ye Entegre Etmenin Kolay Yolu

Một cách dễ dàng để phát triển Plugin Apple Metal của riêng bạn và tích hợp nó vào DaVinci Resolve

开发您自己的 Apple Metal 插件并将其集成到 DaVinci Resolve 中的简单方法

How to Integrate React and AngularJS via Webpack Module Federation

**NEW YORK CITY, New York, February 16th, 2024/Chainwire/-- Introducing Kadena SpireKey, a human-friendly and secure way to seamlessly interact with any application by removing complex signing processes. Kadena Spirekey “Unlike complex Web3 wallets today where you have to remember every wallet you’ve ever created, Kadena’s SpireKey uses WebAuthn, a technology that has been developed by Google and Apple over the last 20 years, to help anyone securely digital assets directly on your phone or computer. It’s as easy as receiving a prompt on your mobile device and providing a fingerprint as a signature. Even that uncle who asks you about crypto every family holiday dinner can do it. With Kadena, we've made using applications accessible to everyone, no matter if you’re an experienced "degen" or using blockchain for the first time,” Said Kadena CMO, Mike Herron. SpireKey creates a seamless interaction between humans and technology, providing a Web2 experience with Web3 innovation. With SpireKey, users can sign transactions and Web3 applications, just like how it works on Apple Pay or Google Pay. However, it can be done directly on the user's device without opening multiple windows or copying and pasting keypairs, which eliminates potential vulnerabilities seen in traditional wallets. Added Security with Built-in Multi-sig “Kadena's built-in multi-sig signing enables an additional layer of security for SpireKey that only we can provide through our original language, Pact. With multi-sig, SpireKey allows for multiple signatures to be required for certain transaction types. For example, if you want to send over $10,000, you can set up parameters to require signatures from three different devices – your phone, laptop, and cold storage wallet. The multi-sig feature reduces the risk of compromised accounts because a bad actor would need access to three devices. It mitigates the risk of another attack vector, and adds to the overall security,” Said Stuart Popejoy, Kadena Co-Founder and CEO. SpireKey displays how Kadena thinks about the level of usability that Web3 technology should be at. At its core, SpireKey connects humans to interact with the blockchain and with one another. SpireKey believes that showing working examples will inspire all humans alike to see how Spirekey can impact the rest of the world beyond Web3. https://www.youtube.com/watch?v=paweE0nxAZU&embedable=true About Kadena is a blockchain technology protocol that was founded in 2017 by Stuart Popejoy and Will Martino. Kadena is the industry’s only scalable Layer-1 Proof of Work (PoW) blockchain. This scalability enables Kadena to deliver infrastructure-grade performance for any blockchain project. Kadena Along with Kadena's own smart contract language Pact, Kadena’s platform provides the world with the tools and environment to turn ideas and ambitions into reality. Founded by Stuart Popejoy and William Martino, who created JP Morgan’s first blockchain and led the SEC’s Crypto Committee, Kadena aims to allow for true blockchain mass adoption. For more information, users can follow Kadena's: | | | Twitter Telegram Discord YouTube Contact Kadena Press press@kadena.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Kadena SpireKey Integrates With WebAuthn To Provide Seamless Web3 Interactions

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