AS the Philippines, her home country is prone to disasters and calamities, Filipino techpreneur Charmaine "Ishka" Villacisneros worked to bring the United States 911 emergency response technology to the country three years ago,

NGA 911 changing public safety for the better

For more information about Wrap Technologies ... is a leading global provider of advanced public safety solutions, integrating state-of-the-art technology, cutting-edge tools, and comprehensive services to address the complex, modern day challenges ...

Giles Tipsword Joins Wrap Technologies as Senior Vice President of North American Sales

Olu, the Lagos State Governor has reiterated his administration’s dedication to investing in advanced transportation solutions to align with global technological progress.A statement by Mrs. Bolanle Ogunlola,

Lagos Restates Commitment To Investment In Smart Transportation Solutions

“As the premier HVACR online training program globally, we are thrilled to partner with Finnovant to expand our advanced immersive ... financial services and the management of personal information through blockchain technology. • Biometric security ...

Online HVACR Training Program Now Offered in Spanish

Agencies will be on the hook for tapping chief AI officers, adding risk management practices to AI like independent evaluations and more.

On the heels of Biden's executive order, agencies get White House directive on implementing AI

It’s a partnership that’s generated lots of excitement, both within Verizon Business and HCL Tech and in the broader world of industry analysts and advisors. I recently sat down with Kyle Malady, Executive VP and CEO of Verizon Business, and C Vijayakumar, who goes by “CVK,” CEO and Managing Director of HCL Tech, to talk about the new partnership.

Inside The Verizon-HCL Tech Deal With Kyle Malady And C. Vijayakumar

sustainable food and beverage solutions through advanced ingredient, formulation, and packaging technology, proudly announces the results of a Life Cycle Assessment (LCA) study conducted by Planet FWD for Hope and Sesame® Sesamemilk in comparison to milk ...

Planting Hope’s Hope and Sesame(R) Sesamemilk has Lowest Water Usage Across Dairy and Plant Milks, per Life Cycle Assessment with Planet FWD

"Leading cancer centers are increasingly adopting the use of liquid-biopsy-based CGP, and with the TSO 500 ctDNA v2 assay we've aimed to make it easier for customers to integrate and enable deeper insights for cancer research," said Kevin Keegan, general manager of Oncology at Illumina.

Illumina launches advanced liquid biopsy assay to enable comprehensive genomic profiling of solid tumors

Veteran channel chief Todd Palmer has made the jump to cybersecurity vendor Illumio from Tanium, amid what he described as a massive growth opportunity for partners in zero-trust security.

Illumio Hires Veteran Of Tanium, Palo Alto Networks As Channel Chief

With Meta’s Llama 2 and the breadth of the Dell Generative AI Solutions technology and services portfolio, organizations of all sizes have access to more reliable tools to deliver GenAI solutions from desktops to core data centers, edge locations and ...

Dell Technologies and Meta to Drive Generative AI Innovation with Llama 2 On Premises

Advancio

advancio

LOS ANGELES, United States, October 30th, 2024/Chainwire/--Xsolla,a global video game commerce company, announces plans to launch Xsolla ZK and introduce a digital backpack of virtual items on the blockchain. Xsolla ZK is powered by ZKsync technology and will drive the continuous growth and expansion of Web3 technologies to further develop solutions on the blockchain for the video game industry. Xsolla ZK will become part of the Elastic Chain ecosystem- an expanding constellation of interconnected chains powered by Zksync, an Ethereum Layer 2 zero-knowledge roll-up technology. Xsolla ZK will also introduce its ‘digital backpack’ for game developers, item creators, and gaming infrastructure providers to store and manage in-game items. Xsolla has seen success in the gaming industry, with two decades of experience, over 2,500 games monetized with its products, and over 1,000 developers and publishers utilizing its technology for their games. Lee Jacobson, Senior Vice President of Business Development Web3 at Xsolla, expressed his enthusiasm for the project: "Xsolla ZK leverages Ethereum Layer 2 zk Rollup technology to create a digital backpack for game developers. By deploying our expertise on the ZKsync Elastic Chain, we provide game developers with a scalable and pioneering solution aligning with the economic models with which they are familiar. Xsolla ZK is not just about innovation; it's about creating real value for the gaming community."Xsolla will combine its expertise in in-game commerce with ZKsync’s cutting-edge blockchain technology. Rich Kim, Head of Gaming at Matter Labs, said, "We are thrilled to see companies like Xsolla launch pioneering projects for Web3 gaming. For gaming ecosystems to thrive, it’s critical to close the gap for both users and builders; builders need proven, plug-and-play infrastructure to launch rich features while handling the performance required by mass usage games. Additionally, users need easy-to-use and convenient in-game features and payment options that can be leveraged across a broad ecosystem of games. Xsolla ZK is carving a path for gaming to transition and thrive in Web3." About Xsolla Xsolla is a global video game commerce company with a robust and powerful set of tools and services designed specifically for the industry. Since its founding in 2005, Xsolla has helped thousands of game developers and publishers of all sizes fund, market, launch, and monetize their games globally and across multiple platforms. As an innovative leader in game commerce, Xsolla’s mission is to solve the inherent complexities of global distribution, marketing, and monetization to help their partners reach more geographies, generate more revenue, and create relationships with gamers worldwide. Headquartered and incorporated in Los Angeles, California, with offices in Montreal, London, Berlin, Beijing, Guangzhou, Seoul, Tokyo, Kuala Lumpur, Raleigh, and other cities around the world, Xsolla supports major gaming titles like Valve, Take-Two, KRAFTON, Nexters, NetEase, Playstudios, Playrix, miHoYo, and more. About the Elastic Chain The Elastic Chain is an ever-expanding cluster of ZK rollups, secured by cryptography and designed for native interoperability with a unified, seamless user experience. The Elastic Chain delivers the functionality of a multi-chain ecosystem with the simplicity of a single blockchain, enabling scalable, secure, and efficient transactions. These core components ensure that this cluster of ZK Chains can interact and transact with each other efficiently, inheriting the security of Ethereum and forming a network that can scale horizontally without compromising on the core properties that make blockchains so powerful. For more information about Xsolla ZK and how to get early access, users can visit: xsolla.pro/zk For additional information and to learn more, users can visit xsolla.com. For additional information and to learn more, users can visit matter-labs.io. Contact Global Director of Public Relations Derrick Stembridge Xsolla d.stembridge@xsolla.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here LOS ANGELES, United States, October 30th, 2024/Chainwire/--Xsolla,a global video game commerce company, announces plans to launch Xsolla ZK and introduce a digital backpack of virtual items on the blockchain. Xsolla ZK is powered by ZKsync technology and will drive the continuous growth and expansion of Web3 technologies to further develop solutions on the blockchain for the video game industry. Xsolla ZK will become part of the Elastic Chain ecosystem- an expanding constellation of interconnected chains powered by Zksync, an Ethereum Layer 2 zero-knowledge roll-up technology. Elastic Chain Elastic Chain Xsolla ZK will also introduce its ‘digital backpack’ for game developers, item creators, and gaming infrastructure providers to store and manage in-game items. Xsolla has seen success in the gaming industry, with two decades of experience, over 2,500 games monetized with its products, and over 1,000 developers and publishers utilizing its technology for their games. Lee Jacobson, Senior Vice President of Business Development Web3 at Xsolla, expressed his enthusiasm for the project: "Xsolla ZK leverages Ethereum Layer 2 zk Rollup technology to create a digital backpack for game developers. By deploying our expertise on the ZKsync Elastic Chain, we provide game developers with a scalable and pioneering solution aligning with the economic models with which they are familiar. Xsolla ZK is not just about innovation; it's about creating real value for the gaming community."Xsolla will combine its expertise in in-game commerce with ZKsync’s cutting-edge blockchain technology. Rich Kim, Head of Gaming at Matter Labs, said, "We are thrilled to see companies like Xsolla launch pioneering projects for Web3 gaming. For gaming ecosystems to thrive, it’s critical to close the gap for both users and builders; builders need proven, plug-and-play infrastructure to launch rich features while handling the performance required by mass usage games. Additionally, users need easy-to-use and convenient in-game features and payment options that can be leveraged across a broad ecosystem of games. Xsolla ZK is carving a path for gaming to transition and thrive in Web3." Rich Kim, Head of Gaming at Matter Labs, said, "We are thrilled to see companies like Xsolla launch pioneering projects for Web3 gaming. For gaming ecosystems to thrive, it’s critical to close the gap for both users and builders; builders need proven, plug-and-play infrastructure to launch rich features while handling the performance required by mass usage games. Additionally, users need easy-to-use and convenient in-game features and payment options that can be leveraged across a broad ecosystem of games. Xsolla ZK is carving a path for gaming to transition and thrive in Web3." About Xsolla Xsolla is a global video game commerce company with a robust and powerful set of tools and services designed specifically for the industry. Xsolla Xsolla Since its founding in 2005, Xsolla has helped thousands of game developers and publishers of all sizes fund, market, launch, and monetize their games globally and across multiple platforms. As an innovative leader in game commerce, Xsolla’s mission is to solve the inherent complexities of global distribution, marketing, and monetization to help their partners reach more geographies, generate more revenue, and create relationships with gamers worldwide. Headquartered and incorporated in Los Angeles, California, with offices in Montreal, London, Berlin, Beijing, Guangzhou, Seoul, Tokyo, Kuala Lumpur, Raleigh, and other cities around the world, Xsolla supports major gaming titles like Valve, Take-Two, KRAFTON, Nexters, NetEase, Playstudios, Playrix, miHoYo, and more. About the Elastic Chain The Elastic Chain is an ever-expanding cluster of ZK rollups, secured by cryptography and designed for native interoperability with a unified, seamless user experience. Elastic Chain Elastic Chain The Elastic Chain delivers the functionality of a multi-chain ecosystem with the simplicity of a single blockchain, enabling scalable, secure, and efficient transactions. These core components ensure that this cluster of ZK Chains can interact and transact with each other efficiently, inheriting the security of Ethereum and forming a network that can scale horizontally without compromising on the core properties that make blockchains so powerful. For more information about Xsolla ZK and how to get early access, users can visit: xsolla.pro/zk xsolla.pro/zk xsolla.pro/zk For additional information and to learn more, users can visit xsolla.com . xsolla.com xsolla.com For additional information and to learn more, users can visit matter-labs.io . matter-labs.io matter-labs.io Contact Global Director of Public Relations Derrick Stembridge Xsolla d.stembridge@xsolla.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here

Xsolla To Launch Xsolla ZK, Advancing Web3 Adoption For Video Games

Xsolla, Xsolla ZK-ni işə salır, Video Oyunlar üçün Web3 qəbulunu inkişaf etdirir

Xsolla ke spuštění Xsolla ZK, postupující přijetí Web3 pro videohry

Xsolla lanzará Xsolla ZK, impulsando la adopción de Web3 para videojuegos

Xsolla lanzará Xsolla ZK, avanzando na adopción de Web3 para videoxogos

Xsolla が Xsolla ZK をリリース、ビデオゲームへの Web3 導入を推進

Xsolla Ko Lancer Xsolla ZK, Ko Avancé Adoption Web3 Pona Ba Jeux Vidéo

Xsolla lanceert Xsolla ZK, wat de acceptatie van Web3 voor videogames bevordert

Xsolla lançará Xsolla ZK, promovendo a adoção do Web3 para videogames

Xsolla va lansa Xsolla ZK, promovând adoptarea Web3 pentru jocurile video

Xsolla Kuzindua Xsolla ZK, Kuendeleza Uasili wa Web3 kwa Michezo ya Video

Xsolla запускає Xsolla ZK, просуваючи впровадження Web3 для відеоігор

I-Xsolla iyakuQalisa i-Xsolla ZK, iPhuhlisa iWeb3 yoMthombo kwiMidlalo yeVidiyo

Advancing the Detection of Ultralight Vector Dark Matter through Novel Analytical Strategies

Authors:
(1) Seokil Ham, KAIST;
(2) Jungwuk Park, KAIST;
(3) Dong-Jun Han, Purdue University;
(4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms 2 Related Works Knowledge distillation for multi-exit networks. Multi-exit neural networks [9, 13, 26, 27, 28, 32] aim at making efficient inference via early exits in resource-constrained applications. In the multi-exit network literature, it is well-known that distilling the knowledge of the last exit to others significantly improves the overall performance on clean data without an external teacher network, i.e., via self-distillation [15, 20, 24, 27]. However, it is currently unclear how adversarial training can benefit from self-distillation in multi-exit networks. One challenge is that simply applying existing self-distillation techniques increases adversarial transferability across different submodels, since the same knowledge from the last exit is distilled to all other exits, increasing dependency among different submodels in the network. Compared to the existing ideas, our contribution is to develop a self-distillation strategy that does not increase the dependency of submodels as much; this helps reduce adversarial transferability of the multi-exit network for better robustness. Improving adversarial robustness. Most existing defense methods [6, 7, 31] have mainly focused on creating new adversarial training losses tailored to single-exit networks. Several other works have utilized the concept of knowledge distillation [8, 23, 33, 34] showing that distilling the knowledge of the teacher network can improve robustness of the student network. Especially in [8], given a teacher network, it is shown that robustness of the teacher network can be distilled to the student network during adversarial training. Compared to these works, our approach can be viewed as a new self-distillation strategy for multi-exit networks where teacher/student models are trained together. More importantly, adversarial transferability across different submodels has not been an issue in previous works as the focus there has been on the single-exit network. In contrast, in our multi-exit setup, all submodels sharing some model parameters require extra robustness against adversarial attacks; this motivates us to propose exit-wise orthogonal knowledge distillation, to reduce adversarial transferability among different submodels. Some prior works [19, 22, 29, 30] aim at improving adversarial robustness of the ensemble model, by reducing adversarial transferability across individual models. Specifically, the adaptive diversity-promoting regularizer proposed in [22] regularizes the non-maximal predictions of individual models to be mutually orthogonal, and the maximal term is used to compute the loss as usual. While the previous work focuses on reducing the transferability among different models having independent parameters, in a multi-exit network setup, the problem becomes more challenging in that all submodels have some shared parameters, making the models to be highly correlated. To handle this issue, we specifically take advantage of knowledge distillation in an exit-wise manner, which can further reduce the dependency among different submodels in the multi-exit network. Adversarial training for multi-exit networks. When focused on multi-exit networks, only a few prior works considered the adversarial attack issue in the literature [3, 10, 11, 12]. The authors of [10, 11] focused on generating slowdown attacks in multi-exit networks rather than defense strategies. In [12], the authors proposed an adversarial training strategy by generating adversarial examples targeting a specific exit (single attack) or multiple exits (average attack and max-average attack). However, (i) [12] does not take advantage of knowledge distillation during training and (ii) [12] does not directly handle the high correlations among different submodels, which can result in high adversarial transferability. Our solution overcomes these limitations by reducing adversarial transferability while correctly guiding the predictions of adversarial examples at each exit, via self knowledge distillation. This paper is available on arxiv under CC 4.0 license. Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Authors: Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Works 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References 5. Conclusion, Acknowledgement and References A. Experiment Details A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms 2 Related Works Knowledge distillation for multi-exit networks. Multi-exit neural networks [9, 13, 26, 27, 28, 32] aim at making efficient inference via early exits in resource-constrained applications. In the multi-exit network literature, it is well-known that distilling the knowledge of the last exit to others significantly improves the overall performance on clean data without an external teacher network, i.e., via self-distillation [15, 20, 24, 27]. However, it is currently unclear how adversarial training can benefit from self-distillation in multi-exit networks. One challenge is that simply applying existing self-distillation techniques increases adversarial transferability across different submodels, since the same knowledge from the last exit is distilled to all other exits, increasing dependency among different submodels in the network. Compared to the existing ideas, our contribution is to develop a self-distillation strategy that does not increase the dependency of submodels as much; this helps reduce adversarial transferability of the multi-exit network for better robustness. Multi-exit neural networks [9, 13, 26, 27, 28, 32] aim Improving adversarial robustness. Most existing defense methods [6, 7, 31] have mainly focused on creating new adversarial training losses tailored to single-exit networks. Several other works have utilized the concept of knowledge distillation [8, 23, 33, 34] showing that distilling the knowledge of the teacher network can improve robustness of the student network. Especially in [8], given a teacher network, it is shown that robustness of the teacher network can be distilled to the student network during adversarial training. Compared to these works, our approach can be viewed as a new self-distillation strategy for multi-exit networks where teacher/student models are trained together. More importantly, adversarial transferability across different submodels has not been an issue in previous works as the focus there has been on the single-exit network. In contrast, in our multi-exit setup, all submodels sharing some model parameters require extra robustness against adversarial attacks; this motivates us to propose exit-wise orthogonal knowledge distillation, to reduce adversarial transferability among different submodels. Improving adversarial robustness. Some prior works [19, 22, 29, 30] aim at improving adversarial robustness of the ensemble model, by reducing adversarial transferability across individual models. Specifically, the adaptive diversity-promoting regularizer proposed in [22] regularizes the non-maximal predictions of individual models to be mutually orthogonal, and the maximal term is used to compute the loss as usual. While the previous work focuses on reducing the transferability among different models having independent parameters, in a multi-exit network setup, the problem becomes more challenging in that all submodels have some shared parameters, making the models to be highly correlated. To handle this issue, we specifically take advantage of knowledge distillation in an exit-wise manner, which can further reduce the dependency among different submodels in the multi-exit network. Adversarial training for multi-exit networks. When focused on multi-exit networks, only a few prior works considered the adversarial attack issue in the literature [3, 10, 11, 12]. The authors of [10, 11] focused on generating slowdown attacks in multi-exit networks rather than defense strategies. In [12], the authors proposed an adversarial training strategy by generating adversarial examples targeting a specific exit (single attack) or multiple exits (average attack and max-average attack). However, (i) [12] does not take advantage of knowledge distillation during training and (ii) [12] does not directly handle the high correlations among different submodels, which can result in high adversarial transferability. Our solution overcomes these limitations by reducing adversarial transferability while correctly guiding the predictions of adversarial examples at each exit, via self knowledge distillation. Adversarial training for multi-exit networks. 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

Advancing Robustness in Multi-Exit Networks Through Exit-Wise Knowledge Distillation

"The future of cybersecurity involves not just protecting data but predicting threats before they occur," says ShivaDutt Jangampeta, senior manager of security engineering at JP Morgan Chase. As digital threats grow, advanced and proactive approaches become crucial. Jangampeta's work focuses on anticipating and neutralizing dangers before they can cause harm. A Journey Rooted in Excellence Jangampeta’s story begins in Medchal, Telangana, India, where he developed an interest in technology at a young age. This passion led him to pursue higher education in the United States, where he earned a master’s degree from Florida International University in 2009. His early academic success laid the foundation for a career in cybersecurity. At JP Morgan Chase, Jangampeta manages teams responsible for delivering a strong threat intelligence platform and security information and event management (SIEM) data delivery. He is crucial in maintaining the financial institution's security by detecting insider and external threats in real-time. Recognized for Advancing Industry Standards in Cybersecurity Jangampeta has received numerous accolades, including the Global Recognition Award for his work in artificial intelligence (AI)-driven threat detection, the ISSN Award for his contributions to industry standards, the Titan Award for improving cybersecurity measures globally, the Best Researcher Award, and the Industry Innovation Research Award from International Research Excellence, as well as multiple Best Paper Awards. These recognitions highlight his important contributions and the respect he has earned from his peers. In addition to his professional achievements, Jangampeta is a respected thought leader. He has written multiple articles on cybersecurity, AI, machine learning (ML), and threat intelligence, sharing his insights with the broader community. His role as a judge for the Globee Awards in three categories demonstrates his proficiency and influence. Research and Contributions Jangampeta has made an impact on cybersecurity through his work on SIEM systems and data security. In April 2020, Jangampeta and co-author Sai Krishna Reddy Khambam published a study in the Turkish Journal of Computer and Mathematics Education. They explored how SIEM solutions help businesses follow regulations while spotting and reducing cyber risks. Their work shows how SIEM provides a complete view of IT systems and helps meet regulatory standards. In April 2021, Jangampeta and co-authors Sukender Reddy Mallreddy and Jaipal Reddy Padamati published a seminal study on data security that other cybersecurity professionals have widely referenced. The paper identifies critical data security risks and suggests best practices to mitigate them, emphasizing the importance of robust security measures across all data systems. This research has become an essential reference in the field, influencing academic discussions and practical implementations in various industries. In February 2022, Jangampeta published research on financial data security in fintech and banking systems in the same journal. The study highlights the role of SIEM systems in protecting sensitive financial information and emphasizes the importance of strong cybersecurity measures. It also explored how SIEM solutions can enhance data security and trust in the fintech sector. Jangampeta has helped improve the understanding and use of SIEM systems and data security through these studies. His collaborations with other experts show his dedication to creating secure and compliant IT systems in various industries. Strengthening Defenses in a Digital World Despite progress in cybersecurity, the industry still faces many challenges. Using AI and ML brings new risks and ethical issues, but Jangampeta remains hopeful. He says, "We must stay ahead, keep developing, and adapt to new threats." Jangampeta is dedicated to improving cybersecurity to tackle these growing threats. As the world becomes increasingly digital, the demand for robust cybersecurity measures has never been greater. Jangampeta's work has fortified JP Morgan Chase's defenses and shaped industry standards now adopted by financial institutions worldwide. His forward-looking research continues to influence the development of next-generation cybersecurity strategies, ensuring that the industry remains resilient despite evolving threats." Sharing his philosophy, Jangampeta says, "In cybersecurity, complacency is the enemy. We must always be vigilant, learning, and ready to face the next challenge." His dedication to proactive defense strategies continues to set new standards in the industry, guiding the future of cybersecurity toward more adaptive and resilient frameworks. As threats grow, so will Jangampeta's contributions, ensuring that digital security remains robust and forward-thinking. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. "The future of cybersecurity involves not just protecting data but predicting threats before they occur," says ShivaDutt Jangampeta , senior manager of security engineering at JP Morgan Chase. As digital threats grow, advanced and proactive approaches become crucial. "The future of cybersecurity involves not just protecting data but predicting threats before they occur," ShivaDutt Jangampeta ShivaDutt Jangampeta Jangampeta's work focuses on anticipating and neutralizing dangers before they can cause harm. A Journey Rooted in Excellence A Journey Rooted in Excellence Jangampeta’s story begins in Medchal, Telangana, India, where he developed an interest in technology at a young age. This passion led him to pursue higher education in the United States, where he earned a master’s degree from Florida International University in 2009. His early academic success laid the foundation for a career in cybersecurity. At JP Morgan Chase, Jangampeta manages teams responsible for delivering a strong threat intelligence platform and security information and event management (SIEM) data delivery. He is crucial in maintaining the financial institution's security by detecting insider and external threats in real-time. Recognized for Advancing Industry Standards in Cybersecurity Jangampeta has received numerous accolades, including the Global Recognition Award for his work in artificial intelligence (AI)-driven threat detection, the ISSN Award for his contributions to industry standards, the Titan Award for improving cybersecurity measures globally, the Best Researcher Award, and the Industry Innovation Research Award from International Research Excellence, as well as multiple Best Paper Awards. These recognitions highlight his important contributions and the respect he has earned from his peers. In addition to his professional achievements, Jangampeta is a respected thought leader. He has written multiple articles on cybersecurity, AI, machine learning (ML), and threat intelligence, sharing his insights with the broader community. His role as a judge for the Globee Awards in three categories demonstrates his proficiency and influence. Research and Contributions Jangampeta has made an impact on cybersecurity through his work on SIEM systems and data security. In April 2020, Jangampeta and co-author Sai Krishna Reddy Khambam published a study in the Turkish Journal of Computer and Mathematics Education. They explored how SIEM solutions help businesses follow regulations while spotting and reducing cyber risks. Their work shows how SIEM provides a complete view of IT systems and helps meet regulatory standards. study study In April 2021, Jangampeta and co-authors Sukender Reddy Mallreddy and Jaipal Reddy Padamati published a seminal study on data security that other cybersecurity professionals have widely referenced. The paper identifies critical data security risks and suggests best practices to mitigate them, emphasizing the importance of robust security measures across all data systems. This research has become an essential reference in the field, influencing academic discussions and practical implementations in various industries. In February 2022, Jangampeta published research on financial data security in fintech and banking systems in the same journal. The study highlights the role of SIEM systems in protecting sensitive financial information and emphasizes the importance of strong cybersecurity measures. It also explored how SIEM solutions can enhance data security and trust in the fintech sector. Jangampeta has helped improve the understanding and use of SIEM systems and data security through these studies. His collaborations with other experts show his dedication to creating secure and compliant IT systems in various industries. Strengthening Defenses in a Digital World Despite progress in cybersecurity, the industry still faces many challenges. Using AI and ML brings new risks and ethical issues, but Jangampeta remains hopeful. He says, "We must stay ahead, keep developing, and adapt to new threats." Jangampeta is dedicated to improving cybersecurity to tackle these growing threats. "We must stay ahead, keep developing, and adapt to new threats." As the world becomes increasingly digital, the demand for robust cybersecurity measures has never been greater. Jangampeta's work has fortified JP Morgan Chase's defenses and shaped industry standards now adopted by financial institutions worldwide. His forward-looking research continues to influence the development of next-generation cybersecurity strategies, ensuring that the industry remains resilient despite evolving threats." Sharing his philosophy, Jangampeta says, " In cybersecurity, complacency is the enemy. We must always be vigilant, learning, and ready to face the next challenge." His dedication to proactive defense strategies continues to set new standards in the industry, guiding the future of cybersecurity toward more adaptive and resilient frameworks. As threats grow, so will Jangampeta's contributions, ensuring that digital security remains robust and forward-thinking. In cybersecurity, complacency is the enemy. We must always be vigilant, learning, and ready to face the next challenge." This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here . here here

ShivaDutt Jangampeta's Role in Advancing Cybersecurity Practices

সাইবার নিরাপত্তা অনুশীলনের অগ্রগতিতে শিবদত্ত জঙ্গমপেতার ভূমিকা

ShivaDutt Jangampetas Rolle bei der Weiterentwicklung von Cybersicherheitspraktiken

El papel de ShivaDutt Jangampeta en el avance de las prácticas de ciberseguridad

Le rôle de ShivaDutt Jangampeta dans l'avancement des pratiques de cybersécurité

साइबर सुरक्षा प्रथाओं को आगे बढ़ाने में शिवदत्त जंगमपेटा की भूमिका

サイバーセキュリティ実践の推進におけるシヴァダット・ジャンガンペタの役割

사이버 보안 관행을 발전시키는 ShivaDutt Jangampeta의 역할

O papel de ShivaDutt Jangampeta no avanço das práticas de segurança cibernética

Роль ШиваДутта Джангампеты в продвижении практики кибербезопасности

ShivaDutt Jangampeta'nın Siber Güvenlik Uygulamalarını Geliştirmedeki Rolü

Vai trò của ShivaDutt Jangampeta trong việc thúc đẩy các hoạt động an ninh mạng

ShivaDutt Jangampeta 在推进网络安全实践中发挥的作用

Authors:
(1) Tinghui Ouyang, National Institute of Informatics, Japan (thouyang@nii.ac.jp);
(2) Isao Echizen, National Institute of Informatics, Japan (iechizen@nii.ac.jp);
(3) Yoshiki Seo, Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Japan (y.seo@aist.go.jp). Table of Links Abstract and Introduction Description and Related Work of OOD Detection Methodology Experiments and Results Conclusions, Acknowledgement and References V. CONCLUSIONS In this paper, we addressed the issue of out-of-distribution (OOD) data in AI quality management by proposing a framework that combines deep learning and statistical measures for OOD detection. Initially, we leveraged the strong feature representation and dimensionality reduction capabilities of AE to extract activation traces from hidden neurons as input features for OOD analysis. Subsequently, we employed five statistical measures, namely KD, LOF, MD, kNN, and the proposed LCP, using these representative features for OOD detection. Our findings revealed that the proposed LCP, which incorporates both neighbor information and data reconstruction error, outperforms the other measures in OOD detection. Thus, it was proved to be valuable for describing and detecting OOD data. Furthermore, this research extracted reasonable corner case data with high OOD scores from the given datasets, such as MNIST, CIFAR10, and GTSRB datasets. These corner case data have high OOD scores and exhibit abnormal characteristics compared to normal data. Therefore, detecting such data using the proposed method is helpful in future AI quality assurance, particularly for quality analysis related to data security. VI. ACKNOWLEDGEMENT This research is supported by the New Energy and Industrial Technology Development Organization (NEDO) project ’JPNP20006’, and JSPS Grant-in-Aid for Early-Career Scientists (Grant Number 22K17961). REFERENCES [1] The National Institute of Advanced Industrial Science and Technology (AIST). Machine learning quality management guideline, 2023. https://www.digiarc.aist.go.jp/en/publication/aiqm/. [2] Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. Deepxplore: Automated whitebox testing of deep learning systems. In proceedings of the 26th Symposium on Operating Systems Principles, pages 1–18, 2017. [3] Tinghui Ouyang, Yoshinao Isobe, Saima Sultana, Yoshiki Seo, and Yutaka Oiwa. Autonomous driving quality assurance with data uncertainty analysis. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE, 2022. [4] Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, and Thomas G Dietterich. Anomaly and novelty detection, explanation, and accommodation (andea). In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 4145–4146, 2021. [5] Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu. Generalized out-of-distribution detection: A survey. arXiv preprint arXiv:2110.11334, 2021. [6] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):1–58, 2009. [7] Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko. A review of novelty detection. Signal processing, 99:215–249, 2014. [8] Chuanxing Geng, Sheng-jun Huang, and Songcan Chen. Recent advances in open set recognition: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(10):3614–3631, 2020. [9] R´emi Domingues, Maurizio Filippone, Pietro Michiardi, and Jihane Zouaoui. A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern recognition, 74:406–421, 2018. [10] Jing Li, Pengbo Lv, Huijun Li, and Wanghu Chen. Outlier detection based on stacked autoencoder and gaussian mixture model. In 2022 IEEE International Conference on Big Data (Big Data), pages 3763–3769. IEEE, 2022. [11] Michael E Tipping and Christopher M Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 61(3):611–622, 1999. [12] John A Quinn and Masashi Sugiyama. A least-squares approach to anomaly detection in static and sequential data. Pattern Recognition Letters, 40:36–40, 2014. [13] Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner. Detecting adversarial samples from artifacts. arXiv preprint arXiv:1703.00410, 2017. [14] Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems, 31, 2018. [15] Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel. On the (statistical) detection of adversarial examples. arXiv preprint arXiv:1702.06280, 2017. [16] Ahmed Abusnaina, Yuhang Wu, Sunpreet Arora, Yizhen Wang, Fei Wang, Hao Yang, and David Mohaisen. Adversarial example detection using latent neighborhood graph. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7687–7696, 2021. [17] Bruno Andriamanalimanana, Ali Tekeoglu, Korkut Bekiroglu, Saumendra Sengupta, Chen-Fu Chiang, Michael Reale, and Jorge Novillo. Symmetric kullback-leibler divergence of softmaxed distributions for anomaly scores. In 2019 IEEE Conference on Communications and Network Security (CNS), pages 1–6. IEEE, 2019. [18] Jianbo Yu, Xiaoyun Zheng, and Shijin Wang. A deep autoencoder feature learning method for process pattern recognition. Journal of Process Control, 79:1–15, 2019. [19] Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel M¨uller, and Marius Kloft. Deep one-class classification. In International conference on machine learning, pages 4393–4402. PMLR, 2018. [20] Jinhan Kim, Robert Feldt, and Shin Yoo. Guiding deep learning system testing using surprise adequacy. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), pages 1039–1049. IEEE, 2019. [21] Zenan Li, Xiaoxing Ma, Chang Xu, and Chun Cao. Structural coverage criteria for neural networks could be misleading. In 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), pages 89–92. IEEE, 2019. [22] Shiqing Ma, Yingqi Liu, Guanhong Tao, Wen-Chuan Lee, and Xiangyu Zhang. Nic: Detecting adversarial samples with neural network invariant checking. In 26th Annual Network And Distributed System Security Symposium (NDSS 2019). Internet Soc, 2019. [23] Tarem Ahmed. Online anomaly detection using kde. In GLOBECOM 2009-2009 IEEE Global Telecommunications Conference, pages 1–8. IEEE, 2009. [24] Siying Xu, Huiyi Liu, Liting Duan, andWenjingWu. An improved lof outlier detection algorithm. In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pages 113–117. IEEE, 2021. [25] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008. [26] Yann LeCun. The mnist database of handwritten digits. http://yann. lecun. com/exdb/mnist/, 1998. [27] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. [28] Tom Fawcett. An introduction to roc analysis. Pattern recognition letters, 27(8):861–874, 2006. [29] Tinghui Ouyang, Vicent Sanz Marco, Yoshinao Isobe, Hideki Asoh, Yutaka Oiwa, and Yoshiki Seo. Corner case data description and detection. In 2021 IEEE/ACM 1st Workshop on AI Engineering-Software Engineering for AI (WAIN), pages 19–26. IEEE, 2021. [30] Stallkamp, Johannes, Marc Schlipsing, Jan Salmen, and Christian Igel. The German traffic sign recognition benchmark: a multi-class classification competition. In The 2011 international joint conference on neural networks, pages 1453-1460. IEEE, 2011. This paper is available on arxiv under CC 4.0 license. Authors: (1) Tinghui Ouyang, National Institute of Informatics, Japan (thouyang@nii.ac.jp); (2) Isao Echizen, National Institute of Informatics, Japan (iechizen@nii.ac.jp); (3) Yoshiki Seo, Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Japan (y.seo@aist.go.jp). Authors: Authors: (1) Tinghui Ouyang, National Institute of Informatics, Japan (thouyang@nii.ac.jp); (2) Isao Echizen, National Institute of Informatics, Japan (iechizen@nii.ac.jp); (3) Yoshiki Seo, Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Japan (y.seo@aist.go.jp). Table of Links Abstract and Introduction Abstract and Introduction Description and Related Work of OOD Detection Description and Related Work of OOD Detection Methodology Methodology Experiments and Results Experiments and Results Conclusions, Acknowledgement and References Conclusions, Acknowledgement and References V. CONCLUSIONS In this paper, we addressed the issue of out-of-distribution (OOD) data in AI quality management by proposing a framework that combines deep learning and statistical measures for OOD detection. Initially, we leveraged the strong feature representation and dimensionality reduction capabilities of AE to extract activation traces from hidden neurons as input features for OOD analysis. Subsequently, we employed five statistical measures, namely KD, LOF, MD, kNN, and the proposed LCP, using these representative features for OOD detection. Our findings revealed that the proposed LCP, which incorporates both neighbor information and data reconstruction error, outperforms the other measures in OOD detection. Thus, it was proved to be valuable for describing and detecting OOD data. Furthermore, this research extracted reasonable corner case data with high OOD scores from the given datasets, such as MNIST, CIFAR10, and GTSRB datasets. These corner case data have high OOD scores and exhibit abnormal characteristics compared to normal data. Therefore, detecting such data using the proposed method is helpful in future AI quality assurance, particularly for quality analysis related to data security. VI. ACKNOWLEDGEMENT This research is supported by the New Energy and Industrial Technology Development Organization (NEDO) project ’JPNP20006’, and JSPS Grant-in-Aid for Early-Career Scientists (Grant Number 22K17961). REFERENCES [1] The National Institute of Advanced Industrial Science and Technology (AIST). Machine learning quality management guideline, 2023. https://www.digiarc.aist.go.jp/en/publication/aiqm/. [2] Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. Deepxplore: Automated whitebox testing of deep learning systems. In proceedings of the 26th Symposium on Operating Systems Principles, pages 1–18, 2017. [3] Tinghui Ouyang, Yoshinao Isobe, Saima Sultana, Yoshiki Seo, and Yutaka Oiwa. Autonomous driving quality assurance with data uncertainty analysis. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE, 2022. [4] Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, and Thomas G Dietterich. Anomaly and novelty detection, explanation, and accommodation (andea). In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 4145–4146, 2021. [5] Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu. Generalized out-of-distribution detection: A survey. arXiv preprint arXiv:2110.11334, 2021. [6] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):1–58, 2009. [7] Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko. A review of novelty detection. Signal processing, 99:215–249, 2014. [8] Chuanxing Geng, Sheng-jun Huang, and Songcan Chen. Recent advances in open set recognition: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(10):3614–3631, 2020. [9] R´emi Domingues, Maurizio Filippone, Pietro Michiardi, and Jihane Zouaoui. A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern recognition, 74:406–421, 2018. [10] Jing Li, Pengbo Lv, Huijun Li, and Wanghu Chen. Outlier detection based on stacked autoencoder and gaussian mixture model. In 2022 IEEE International Conference on Big Data (Big Data), pages 3763–3769. IEEE, 2022. [11] Michael E Tipping and Christopher M Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 61(3):611–622, 1999. [12] John A Quinn and Masashi Sugiyama. A least-squares approach to anomaly detection in static and sequential data. Pattern Recognition Letters, 40:36–40, 2014. [13] Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner. Detecting adversarial samples from artifacts. arXiv preprint arXiv:1703.00410, 2017. [14] Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems, 31, 2018. [15] Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel. On the (statistical) detection of adversarial examples. arXiv preprint arXiv:1702.06280, 2017. [16] Ahmed Abusnaina, Yuhang Wu, Sunpreet Arora, Yizhen Wang, Fei Wang, Hao Yang, and David Mohaisen. Adversarial example detection using latent neighborhood graph. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7687–7696, 2021. [17] Bruno Andriamanalimanana, Ali Tekeoglu, Korkut Bekiroglu, Saumendra Sengupta, Chen-Fu Chiang, Michael Reale, and Jorge Novillo. Symmetric kullback-leibler divergence of softmaxed distributions for anomaly scores. In 2019 IEEE Conference on Communications and Network Security (CNS), pages 1–6. IEEE, 2019. [18] Jianbo Yu, Xiaoyun Zheng, and Shijin Wang. A deep autoencoder feature learning method for process pattern recognition. Journal of Process Control, 79:1–15, 2019. [19] Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel M¨uller, and Marius Kloft. Deep one-class classification. In International conference on machine learning, pages 4393–4402. PMLR, 2018. [20] Jinhan Kim, Robert Feldt, and Shin Yoo. Guiding deep learning system testing using surprise adequacy. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), pages 1039–1049. IEEE, 2019. [21] Zenan Li, Xiaoxing Ma, Chang Xu, and Chun Cao. Structural coverage criteria for neural networks could be misleading. In 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), pages 89–92. IEEE, 2019. [22] Shiqing Ma, Yingqi Liu, Guanhong Tao, Wen-Chuan Lee, and Xiangyu Zhang. Nic: Detecting adversarial samples with neural network invariant checking. In 26th Annual Network And Distributed System Security Symposium (NDSS 2019). Internet Soc, 2019. [23] Tarem Ahmed. Online anomaly detection using kde. In GLOBECOM 2009-2009 IEEE Global Telecommunications Conference, pages 1–8. IEEE, 2009. [24] Siying Xu, Huiyi Liu, Liting Duan, andWenjingWu. An improved lof outlier detection algorithm. In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pages 113–117. IEEE, 2021. [25] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008. [26] Yann LeCun. The mnist database of handwritten digits. http://yann. lecun. com/exdb/mnist/, 1998. [27] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. [28] Tom Fawcett. An introduction to roc analysis. Pattern recognition letters, 27(8):861–874, 2006. [29] Tinghui Ouyang, Vicent Sanz Marco, Yoshinao Isobe, Hideki Asoh, Yutaka Oiwa, and Yoshiki Seo. Corner case data description and detection. In 2021 IEEE/ACM 1st Workshop on AI Engineering-Software Engineering for AI (WAIN), pages 19–26. IEEE, 2021. [30] Stallkamp, Johannes, Marc Schlipsing, Jan Salmen, and Christian Igel. The German traffic sign recognition benchmark: a multi-class classification competition. In The 2011 international joint conference on neural networks, pages 1453-1460. IEEE, 2011. 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

Advancing OOD Detection: A Deep Learning Approach

Scene Graphs & WikiData in Meme Analysis: Advancing Hate Speech Detection

**TALLINN, Estonia, November 20th, 2024/Chainwire/--**Following its global unveiling on the main stage of Binance’s Blockchain Week  conference in Dubai, AI platform bitGPT is experiencing sizable growth across every vertical of the project, from active partnership conversations to the number of users currently testing its technology. bitGPT’s X and Telegram communities grew in size by more than 1,000% in the days following its presentation at Binance in which hundreds of onlookers watched as its AI platform was launched and tested for the first time in front of a public audience. The demonstration replay surpassed a quarter-million views on Binance’s website within hours of being posted – the most popular replay event of the conference. bitGPT’s live demo and subsequent growth have been covered by several major new outlets, including reporting from Decrypt that predicted bitGPT’s AI platform “will help onboard the next billion Web3 users and kickstart the AI Agent economy.” https://youtu.be/Lm8hI_ZuiI8 https://www.youtube.com/embed/Lm8hI_ZuiI8 Backed by former SingularityNET co-founders and a growing team of AI and Web3 experts, bitGPT bridges the mass acceleration and adoption of generative AI technologies with the entire crypto ecosystem, providing users – both humans and AI agents – with a unified UX to connect and transact across any decentralized network or service using natural language commands. Available in more than 25 languages, its platform overcomes language, literacy, and interface barriers in what appears to be the Web3 industry’s most legitimate effort yet to onboard its first billion Web3 users, all while positioning the platform as a central node for kickstarting the AI Agent economy. Developed in stealth for more than two years, bitGPT’s AI platform has already been tested by a wide audience, with a growing community of dedicated users generating valuable data and insights that have been continually used to refine and expand the capabilities of its model. After advancing from Alpha to Beta earlier this year, bitGPT is preparing for full launch just as interest and attention for AI-enabled Web3 technologies take off. Despite achieving rapid significant adoption, centralized AI model providers present critical challenges, including data privacy risks, restricted accessibility, systemic vulnerabilities, and the emergence of techno-feudalism due to reliance on dominant third-party entities. This centralization compromises user autonomy, undermines digital sovereignty, and fosters a dependency cycle that stifles innovation and equitable access. A growing ecosystem of crypto projects including NEAR Protocol, bittensor, and The Graph have entered the space to introduce Web3’s privacy and security benefits to the ever-growing AI category. Yet usability and adoption challenges continue to limit the industry’s long-term potential. And many of these networks and services still do not connect directly with the end user, meaning users may fail to realize the security and privacy benefits of decentralized options. bitGPT’s breakthrough platform brings to reality the potential for an on-chain economy with its first billion human users, plus billions upon billions of AI Agents. Its platform goes beyond conventional interfaces, offering an intuitive, AI-driven ecosystem for all Web3 activities. By seamlessly integrating cutting-edge technologies with user-centric design, bitGPT provides an unparalleled suite of intelligent tools that simplify and enhance every aspect of blockchain engagement. With bitGPT, users can instantly connect with and transact across any network or service, creating a universally interoperable user experience. Use cases are truly endless, including: Cross-chain Swap Optimization — AI algorithms analyze liquidity across multiple DEXs and chains to find the most efficient swap routes.
Gas Fee Prediction — Machine learning models forecast gas prices, allowing users to optimize transaction timing.
Sentiment Analysis — Real-time analysis of social media, news, and on-chain data to gauge market sentiment.
Trend Prediction — Advanced machine learning models for short and medium-term price trend forecasting.
Alpha Discovery — Identification of potential high-growth opportunities based on on-chain metrics and market data.
Proposal Summarization — AI-generated summaries of complex governance proposals.
Voting Recommendations — Personalized suggestions based on user's previous voting patterns and stated preferences.
Impact Assessment — AI-driven analysis of potential outcomes for different chain governance voting scenarios.
24/7 Market Monitoring — Agents track user-specified metrics and events across multiple chains and platforms. Users can set specific criteria for alerts and research focus.
External Data Integration — Agents can access and analyze data from approved external sources for comprehensive research. In the spirit of Web3 technologies and the wider universe of decentralized networks and services, bitGPT enables the mass transition away from Large Language Models (LLMs) to privacy-preserving Small Language Models (SLMs), ensuring user data protection without compromising functionality. bitGPT allows for on-device AI processing – significantly enhancing security and reducing latency for a seamless user experience – and AI-powered cross-chain operations and market analysis, enabling users to navigate the multi-chain landscape with unprecedented ease. The platform is capable of hosting a virtually unlimited number of AI Agents that can perform any on-chain task automatically and autonomously, adding significant demand for nearly every decentralized project while fueling innovation across the entire crypto universe. After its successful unveiling on the main stage of Binance’s Blockchain Week conference – a demonstration that was followed immediately by Binance founder Changpeng Zhao – the bitGPT team is now focused on organizing its final Beta releases before full launch, including a fully featured mobile app, with a growing list of enterprise and Web3 partners positioned to assist its launch. About bitGPT Backed by a strong team of Web3 and AI industry leaders — including the cofounders of several major crypto projects like SingularityNET — bitGPT represents a revolutionary transformation in blockchain interaction, redefining the user experience through advanced artificial intelligence. Its platform goes beyond conventional interfaces, offering an intuitive, AI-driven ecosystem for all Web3 activities. By seamlessly integrating cutting-edge technology with user-centric design, bitGPT provides an unparalleled suite of intelligent tools that simplify and enhance every aspect of blockchain engagement. The result is an AI-driven platform capable of onboarding the next billion Web3 users as well as kickstarting the AI Agent economy. Learn more — www.BitGPT.network Join the BitGPT community: Telegram Discord X Contact bitGPT info@bitgpt.network This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here **TALLINN, Estonia, November 20th, 2024/Chainwire/--**Following its global unveiling on the main stage of Binance’s Blockchain Week  conference in Dubai, AI platform bitGPT is experiencing sizable growth across every vertical of the project, from active partnership conversations to the number of users currently testing its technology. bitGPT’s X and Telegram communities grew in size by more than 1,000% in the days following its presentation at Binance in which hundreds of onlookers watched as its AI platform was launched and tested for the first time in front of a public audience. X X Telegram Telegram The demonstration replay surpassed a quarter-million views on Binance’s website within hours of being posted – the most popular replay event of the conference. replay replay bitGPT’s live demo and subsequent growth have been covered by several major new outlets, including reporting from Decrypt that predicted bitGPT’s AI platform “will help onboard the next billion Web3 users and kickstart the AI Agent economy.” Decrypt Decrypt https://youtu.be/Lm8hI_ZuiI8 https://youtu.be/Lm8hI_ZuiI8 https://youtu.be/Lm8hI_ZuiI8 https://www.youtube.com/embed/Lm8hI_ZuiI8 https://www.youtube.com/embed/Lm8hI_ZuiI8 https://www.youtube.com/embed/Lm8hI_ZuiI8 Backed by former SingularityNET co-founders and a growing team of AI and Web3 experts, bitGPT bridges the mass acceleration and adoption of generative AI technologies with the entire crypto ecosystem, providing users – both humans and AI agents – with a unified UX to connect and transact across any decentralized network or service using natural language commands. SingularityNET SingularityNET Available in more than 25 languages, its platform overcomes language, literacy, and interface barriers in what appears to be the Web3 industry’s most legitimate effort yet to onboard its first billion Web3 users, all while positioning the platform as a central node for kickstarting the AI Agent economy. Developed in stealth for more than two years, bitGPT’s AI platform has already been tested by a wide audience, with a growing community of dedicated users generating valuable data and insights that have been continually used to refine and expand the capabilities of its model. After advancing from Alpha to Beta earlier this year, bitGPT is preparing for full launch just as interest and attention for AI-enabled Web3 technologies take off. Despite achieving rapid significant adoption, centralized AI model providers present critical challenges, including data privacy risks, restricted accessibility, systemic vulnerabilities, and the emergence of techno-feudalism due to reliance on dominant third-party entities. This centralization compromises user autonomy, undermines digital sovereignty, and fosters a dependency cycle that stifles innovation and equitable access. A growing ecosystem of crypto projects including NEAR Protocol, bittensor, and The Graph have entered the space to introduce Web3’s privacy and security benefits to the ever-growing AI category. Yet usability and adoption challenges continue to limit the industry’s long-term potential. And many of these networks and services still do not connect directly with the end user, meaning users may fail to realize the security and privacy benefits of decentralized options. bitGPT’s breakthrough platform brings to reality the potential for an on-chain economy with its first billion human users, plus billions upon billions of AI Agents. Its platform goes beyond conventional interfaces, offering an intuitive, AI-driven ecosystem for all Web3 activities. By seamlessly integrating cutting-edge technologies with user-centric design, bitGPT provides an unparalleled suite of intelligent tools that simplify and enhance every aspect of blockchain engagement. With bitGPT, users can instantly connect with and transact across any network or service, creating a universally interoperable user experience. Use cases are truly endless, including: Cross-chain Swap Optimization — AI algorithms analyze liquidity across multiple DEXs and chains to find the most efficient swap routes. Gas Fee Prediction — Machine learning models forecast gas prices, allowing users to optimize transaction timing. Sentiment Analysis — Real-time analysis of social media, news, and on-chain data to gauge market sentiment. Trend Prediction — Advanced machine learning models for short and medium-term price trend forecasting. Alpha Discovery — Identification of potential high-growth opportunities based on on-chain metrics and market data. Proposal Summarization — AI-generated summaries of complex governance proposals. Voting Recommendations — Personalized suggestions based on user's previous voting patterns and stated preferences. Impact Assessment — AI-driven analysis of potential outcomes for different chain governance voting scenarios. 24/7 Market Monitoring — Agents track user-specified metrics and events across multiple chains and platforms. Users can set specific criteria for alerts and research focus. External Data Integration — Agents can access and analyze data from approved external sources for comprehensive research. Cross-chain Swap Optimization — AI algorithms analyze liquidity across multiple DEXs and chains to find the most efficient swap routes. Gas Fee Prediction — Machine learning models forecast gas prices, allowing users to optimize transaction timing. Sentiment Analysis — Real-time analysis of social media, news, and on-chain data to gauge market sentiment. Trend Prediction — Advanced machine learning models for short and medium-term price trend forecasting. Alpha Discovery — Identification of potential high-growth opportunities based on on-chain metrics and market data. Proposal Summarization — AI-generated summaries of complex governance proposals. Voting Recommendations — Personalized suggestions based on user's previous voting patterns and stated preferences. Impact Assessment — AI-driven analysis of potential outcomes for different chain governance voting scenarios. 24/7 Market Monitoring — Agents track user-specified metrics and events across multiple chains and platforms. Users can set specific criteria for alerts and research focus. External Data Integration — Agents can access and analyze data from approved external sources for comprehensive research. In the spirit of Web3 technologies and the wider universe of decentralized networks and services, bitGPT enables the mass transition away from Large Language Models (LLMs) to privacy-preserving Small Language Models (SLMs), ensuring user data protection without compromising functionality. bitGPT allows for on-device AI processing – significantly enhancing security and reducing latency for a seamless user experience – and AI-powered cross-chain operations and market analysis, enabling users to navigate the multi-chain landscape with unprecedented ease. The platform is capable of hosting a virtually unlimited number of AI Agents that can perform any on-chain task automatically and autonomously, adding significant demand for nearly every decentralized project while fueling innovation across the entire crypto universe. After its successful unveiling on the main stage of Binance’s Blockchain Week conference – a demonstration that was followed immediately by Binance founder Changpeng Zhao – the bitGPT team is now focused on organizing its final Beta releases before full launch, including a fully featured mobile app, with a growing list of enterprise and Web3 partners positioned to assist its launch. About bitGPT Backed by a strong team of Web3 and AI industry leaders — including the cofounders of several major crypto projects like SingularityNET — bitGPT represents a revolutionary transformation in blockchain interaction, redefining the user experience through advanced artificial intelligence. Its platform goes beyond conventional interfaces, offering an intuitive, AI-driven ecosystem for all Web3 activities. bitGPT bitGPT By seamlessly integrating cutting-edge technology with user-centric design, bitGPT provides an unparalleled suite of intelligent tools that simplify and enhance every aspect of blockchain engagement. The result is an AI-driven platform capable of onboarding the next billion Web3 users as well as kickstarting the AI Agent economy. Learn more — www.BitGPT.network www.BitGPT.network www.BitGPT.network Join the BitGPT community: Telegram Telegram Discord Discord X X Contact bitGPT info@bitgpt.network This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

AI Platform bitGPT Experiences Significant Growth Following Binance Demonstration

AI platforma bitGPT doživljava značajan rast nakon Binance demonstracije

La plataforma de inteligencia artificial bitGPT experimenta un crecimiento significativo tras la demostración de Binance

پلتفرم هوش مصنوعی bitGPT پس از نمایش بایننس، رشد قابل توجهی را تجربه می کند

Platfòm AI bitGPT fè eksperyans yon kwasans enpòtan apre demonstrasyon Binance

AIプラットフォームbitGPT、Binanceのデモンストレーション後に大幅な成長を記録

AI პლატფორმა bitGPT განიცდის მნიშვნელოვან ზრდას Binance-ის დემონსტრირების შემდეგ

AI platforma bitGPT patyrė reikšmingą augimą po „Binance“ demonstravimo

AI платформ bitGPT нь Binance-ийн үзүүлбэрийн дараа мэдэгдэхүйц өсөлттэй байна

د AI پلیټ فارم bitGPT د بائنانس مظاهرې وروسته د پام وړ وده تجربه کوي

AI Platform bitGPT Zvakaitika Kukura Kwakakosha Kutevera Binance Kuratidzira

แพลตฟอร์ม AI bitGPT ประสบการเติบโตอย่างมีนัยสำคัญหลังการสาธิตของ Binance

AI platformasi bitGPT Binance namoyishidan keyin sezilarli o'sishni boshdan kechirmoqda

The DePIN sector is experiencing a surge, drawing attention as more people recognize its potential in reshaping the digital landscape. Recently, a viral poll, conducted by CoinList, asked users, “What category of projects do you want to see most on CoinList?” DePIN emerged as the top choice, a testament to its rising prominence. Following this, U2U Network - a DePIN-focused Layer 1, announced a collaboration with CoinList for an exclusive incentivized mainnet campaign. This means U2U Network is the first DePIN project to launch a growth campaign on CoinList in Q4 2024. DePIN - Beyond a Passing Trend DePIN (Decentralized Physical Infrastructure Network) stands out as one of today’s promising technologies, challenging the skepticism often directed at the blockchain industry. Unlike fleeting trends, DePIN aims to represent a foundational shift in managing infrastructure. By decentralizing essential services such as computing, energy, and telecommunications, DePIN delivers scalability, resilience, and cost-efficiency that surpass the usual capabilities of centralized systems. This innovation could mark the next wave of technological progress - one that democratizes access and unlocks wealth-generation opportunities for communities historically excluded from economic advancement. DePIN’s impact is poised to be more than incremental, serving as a catalyst for widespread change. The DePIN sector is gaining attention but also substantial investment as well, with a market capitalization exceeding $33.6 billion (Coingecko). Esteemed venture capitalists have begun investing in innovative DePIN projects, underscoring the sector’s vast potential. According to a 2023 report by Messari, the market potential for DePIN is valued at $2.2 trillion, with projections to surge to $3.5 trillion by 2028. To date, leading DePIN projects have collectively secured over $1 billion in funding, illustrating the growing confidence of investors in this space. Acknowledging the potential thrive of DePIN with the successful listing from peaq, U2U Network, a leading DePIN Layer1 in Asia, has just announced its new collaboration with CoinList, a top token launch platform in crypto with many successful DePIN projects such as Filecoin, NATIX, Koii, peaq, etc., for a new pre-listing campaign. CoinList x U2U Network: U2U Incentivized Mainnet Saga Campaign U2U Network, backed by Kucoin Ventures, Chain Capital, IDG Blockchain, JDI, Cointelegraph, Crypto Assets Japan, and V3V Ventures, is a modular Layer 1 network that uses advanced directed acyclic graph (DAG) technology and Ethereum Virtual Machine (EVM) compatibility to provide fast transactions and quick finality. U2U Network aims to address the fragmented nature of the DePIN market, where full integration across applications is rare. Its vision is to create a complete blockchain solution for DePIN. The network’s unique subnet technology allows for the creation of customizable, independent sub-networks that offer scalability and flexibility tailored for DePIN applications. This design provides high performance, strong security, and adaptability, giving developers the tools to build and launch DePINs efficiently while enabling users to connect, contribute, and potentially earn from various DePIN projects. CoinList’s collaboration with U2U Network reflects their shared commitment to advancing the DePIN sector. This collaboration is highlighted by the U2U Incentivized Mainnet Saga Campaign, marking a key step in driving the growth and adoption of DePIN technology. This campaign could serve as gateway to earning $U2U tokens before they hit the market. Participants can gain access to $U2U tokens through Owlto Finance by bridging $USDT to receive $pUSDT, which can then be staked in the U2U Incentivized Staking Pool. The program offers a reward pool of 10,000,000 $U2U tokens for eligible participants. Timeline: 90 days, from 12th Nov 2024 to 10th Feb 2025 For more information, users can read here: <https://mainnetsaga.u2u.xyz/ ](https://mainnetsaga.u2u.xyz/) Beyond its collaboration with CoinList, U2U Network is set to embark on an ambitious campaign with Bitget, a world-leading cryptocurrency exchange and Web3 ininnovaton. This collaboration marks another step in U2U Network’s mission to expand its reach and influence within the DePIN ecosystem. Users can stay connected with U2U Network’s social channels for upcoming details about this exciting venture. The Future Goes On Momentum is growing within the community from U2U Network’s pre-listing campaigns; the anticipated listing is set to launch in Q4 2024. With its vision to be a comprehensive blockchain solution for DePIN, U2U Network is positioned to make a significant impact on blockchain space, bringing robust innovation and value to its ecosystem and users alike. As the listing approaches, the path forward for U2U Network indicates significant opportunities and impactful contributions to the blockchain industry and beyond. The U2U incentivized mainnet campaign is not available to residents in the U.S. (and its territories), Canada and certain other jurisdictions. The content on this page has been provided by U2U Network and is for general informational purposes only. CoinList does not make any representations or warranties, express or implied, regarding the accuracy, completeness or timeliness of the information provided. Additionally, CoinList does not endorse or recommend any products, or services, mentioned on this page, nor does anything contained herein constitute financial, investment, legal or other professional advice. Users are encouraged to consult their own independent advisors before making any decisions based on the content of this site. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here The DePIN sector is experiencing a surge, drawing attention as more people recognize its potential in reshaping the digital landscape. Recently, a viral poll, conducted by CoinList , asked users, “What category of projects do you want to see most on CoinList?” DePIN emerged as the top choice, a testament to its rising prominence. CoinList CoinList Following this, U2U Network - a DePIN-focused Layer 1, announced a collaboration with CoinList for an exclusive incentivized mainnet campaign. This means U2U Network is the first DePIN project to launch a growth campaign on CoinList in Q4 2024. U2U Network U2U Network CoinList CoinList DePIN - Beyond a Passing Trend DePIN (Decentralized Physical Infrastructure Network) stands out as one of today’s promising technologies, challenging the skepticism often directed at the blockchain industry. Unlike fleeting trends, DePIN aims to represent a foundational shift in managing infrastructure. By decentralizing essential services such as computing, energy, and telecommunications, DePIN delivers scalability, resilience, and cost-efficiency that surpass the usual capabilities of centralized systems. This innovation could mark the next wave of technological progress - one that democratizes access and unlocks wealth-generation opportunities for communities historically excluded from economic advancement. DePIN’s impact is poised to be more than incremental, serving as a catalyst for widespread change. The DePIN sector is gaining attention but also substantial investment as well, with a market capitalization exceeding $33.6 billion ( Coingecko ). Esteemed venture capitalists have begun investing in innovative DePIN projects, underscoring the sector’s vast potential. Coingecko Coingecko According to a 2023 report by Messari , the market potential for DePIN is valued at $2.2 trillion, with projections to surge to $3.5 trillion by 2028. To date, leading DePIN projects have collectively secured over $1 billion in funding , illustrating the growing confidence of investors in this space. a 2023 report by Messari a 2023 report by Messari secured over $1 billion in funding secured over $1 billion in funding Acknowledging the potential thrive of DePIN with the successful listing from peaq, U2U Network, a leading DePIN Layer1 in Asia, has just announced its new collaboration with CoinList , a top token launch platform in crypto with many successful DePIN projects such as Filecoin , NATIX , Koii , peaq , etc., for a new pre-listing campaign. CoinList CoinList Filecoin Filecoin NATIX NATIX Koii Koii peaq peaq CoinList x U2U Network: U2U Incentivized Mainnet Saga Campaign U2U Network, backed by Kucoin Ventures , Chain Capital , IDG Blockchain , JDI , Cointelegraph , Crypto Assets Japan, and V3V Ventures , is a modular Layer 1 network that uses advanced directed acyclic graph (DAG) technology and Ethereum Virtual Machine (EVM) compatibility to provide fast transactions and quick finality. Kucoin Ventures Kucoin Ventures Chain Capital Chain Capital IDG Blockchain IDG Blockchain JDI JDI Cointelegraph Cointelegraph V3V Ventures V3V Ventures U2U Network aims to address the fragmented nature of the DePIN market, where full integration across applications is rare. Its vision is to create a complete blockchain solution for DePIN. The network’s unique subnet technology allows for the creation of customizable, independent sub-networks that offer scalability and flexibility tailored for DePIN applications. This design provides high performance, strong security, and adaptability, giving developers the tools to build and launch DePINs efficiently while enabling users to connect, contribute, and potentially earn from various DePIN projects. CoinList’s collaboration with U2U Network reflects their shared commitment to advancing the DePIN sector. This collaboration is highlighted by the U2U Incentivized Mainnet Saga Campaign, marking a key step in driving the growth and adoption of DePIN technology. This campaign could serve as gateway to earning $U2U tokens before they hit the market. Participants can gain access to $U2U tokens through Owlto Finance by bridging $USDT to receive $pUSDT, which can then be staked in the U2U Incentivized Staking Pool . The program offers a reward pool of 10,000,000 $U2U tokens for eligible participants. Owlto Finance Owlto Finance U2U Incentivized Staking Pool U2U Incentivized Staking Pool Timeline: 90 days, from 12th Nov 2024 to 10th Feb 2025 For more information, users can read here: <https://mainnetsaga.u2u.xyz/ ](https://mainnetsaga.u2u.xyz/) For more information, users can read here: <https://mainnetsaga.u2u.xyz/ ](https://mainnetsaga.u2u.xyz/) <https://mainnetsaga.u2u.xyz/ ](https://mainnetsaga.u2u.xyz/) Beyond its collaboration with CoinList, U2U Network is set to embark on an ambitious campaign with Bitget, a world-leading cryptocurrency exchange and Web3 ininnovaton. This collaboration marks another step in U2U Network’s mission to expand its reach and influence within the DePIN ecosystem. Users can stay connected with U2U Network’s social channels for upcoming details about this exciting venture. The Future Goes On Momentum is growing within the community from U2U Network’s pre-listing campaigns; the anticipated listing is set to launch in Q4 2024. With its vision to be a comprehensive blockchain solution for DePIN, U2U Network is positioned to make a significant impact on blockchain space, bringing robust innovation and value to its ecosystem and users alike. As the listing approaches, the path forward for U2U Network indicates significant opportunities and impactful contributions to the blockchain industry and beyond. The U2U incentivized mainnet campaign is not available to residents in the U.S. (and its territories), Canada and certain other jurisdictions . other jurisdictions The content on this page has been provided by U2U Network and is for general informational purposes only. CoinList does not make any representations or warranties, express or implied, regarding the accuracy, completeness or timeliness of the information provided. Additionally, CoinList does not endorse or recommend any products, or services, mentioned on this page, nor does anything contained herein constitute financial, investment, legal or other professional advice. Users are encouraged to consult their own independent advisors before making any decisions based on the content of this site. The content on this page has been provided by U2U Network and is for general informational purposes only. CoinList does not make any representations or warranties, express or implied, regarding the accuracy, completeness or timeliness of the information provided. Additionally, CoinList does not endorse or recommend any products, or services, mentioned on this page, nor does anything contained herein constitute financial, investment, legal or other professional advice. Users are encouraged to consult their own independent advisors before making any decisions based on the content of this site. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

U2U Network - The First DePIN Project To Launch Growth Campaign On CoinList In Q4 2024

Síť U2U – první projekt DePIN, který ve 4. čtvrtletí 2024 spustí kampaň růstu na CoinList

Red U2U: el primer proyecto DePIN que lanzará una campaña de crecimiento en CoinList en el cuarto trimestre de 2024

U2U Network – Ensimmäinen DePIN-projekti, joka käynnistää kasvukampanjan CoinListissa vuoden 2024 viimeisellä neljänneksellä

U2U Network: o primeiro proxecto DePIN en lanzar unha campaña de crecemento en CoinList no cuarto trimestre de 2024

רשת U2U - פרויקט ה-DePIN הראשון להשיק קמפיין צמיחה ברשימת CoinList ברבעון הרביעי של 2024

U2U Ցանց - Առաջին DePIN նախագիծը, որը գործարկում է աճի արշավ CoinList-ում 2024 թվականի 4-րդ եռամսյակում

U2U ネットワーク - 2024 年第 4 四半期に CoinList で成長キャンペーンを開始する最初の DePIN プロジェクト

បណ្តាញ U2U - គម្រោង DePIN ដំបូងដើម្បីចាប់ផ្តើមយុទ្ធនាការកំណើននៅលើ CoinList នៅក្នុង Q4 2024

U2U नेटवर्क - Q4 2024 मा CoinList मा वृद्धि अभियान सुरु गर्न पहिलो DePIN परियोजना।

Sieć U2U — pierwszy projekt DePIN, który uruchomi kampanię wzrostu na CoinList w czwartym kwartale 2024 r.

U2U Umuyoboro - Umushinga wambere DePIN wo gutangiza ubukangurambaga bwo Gukura Kurutonde rwibiceri Muri Q4 2024

U2U نیٹ ورک - Q4 2024 میں CoinList پر ترقی کی مہم شروع کرنے والا پہلا DePIN پروجیکٹ

Partnership to provide decentralized computing resources, enhancing platform scalability, security and high availability for distributed services.
Mizzle Partners with InFlux Technologies to Power DePIN Platform with Decentralized Cloud Infrastructure and Advanced Computing Resources InFlux Technologies (Flux), a leading global decentralized technology company specializing in cloud infrastructure, artificial intelligence, and decentralized cloud computing services, today announced a partnership with Mizzle, a pioneering decentralized physical infrastructure network (DePIN) platform. Under the partnership agreement, Flux will provide decentralized computing resources including CPU, GPU, storage and network capacity as required by Mizzle for its platform operations. This includes support for distributed applications and services, ensuring high availability, scalability and security. The agreement also includes monitoring and management of Mizzle’s infrastructure to ensure optimal performance along with maintenance and upgrades of the infrastructure as needed. Mizzle will work toward an estimated spend of $500,000-plus per year post-launch, with an estimated launch of January 2025. “This partnership represents a key step in our commitment to delivering decentralized computing solutions at scale. By supplying Mizzle with essential resources, we are ensuring the platform’s ability to maintain high availability, scalability, and security. This agreement highlights the growing demand for decentralized infrastructure and demonstrates its practical applications in supporting distributed services," said InFlux Technologies CEO and Co-founder, Daniel Keller. Mizzle is a hyper-efficient CI/CD with no-code development operations which simplifies server management allowing teams to innovate and scale without operational hurdles. Its confidential computing experience carries unmatched security with TEEs, eBPF and decentralized cloud compute, keeping data and operations fully protected. Mizzle has advanced storage and benefits from decentralized cloud storage enhanced with zero knowledge proofs and fully homomorphic encryption. The company is quantum ready with edge computing, is IoT-ready and committed to green computing. Flux ensures a minimum uptime of 99.99% of decentralized infrastructure services, barring any outages or maintenance windows and offers technical support to integrate and manage the compute resources. Flux offers data security and compliance and complies with all relevant data and security regulations, ensuring the infrastructure is designed to meet regulation standards. “We are excited to partner with InFlux Technologies, taking a key step toward advancing decentralized cloud solutions. By combining Mizzle's technology with Flux’s expertise, we will drive greater value for enterprises and governments worldwide. Together, we are shaping the future of decentralized applications and empowering innovation across the ecosystem,” said Founder of Mizzle Arjun Mishra. About Mizzle Mizzle is a DePIN platform designed to empower developers with no-code DevOps. We enable atomic and horizontal scaling of compute and storage, ensuring unparalleled flexibility and performance. Our platform combines advanced AI-driven infrastructure management with trusted execution environments (TEEs), leveraging eBPF technology for real-time protection and monitoring. We also incorporate  state-of-the-art cryptographic techniques, including Fully Homomorphic Encryption and Zero-Knowledge Proofs, to guarantee maximum data privacy and security. As we move into the quantum era, with a strong commitment to Green computing (ESG), Mizzle is your trusted partner for scalable, secure, and efficient decentralized infrastructure. For more information, visit the company’s website at www.mizzle.io. About InFlux Technologies InFlux Technologies (Flux) is powering a decentralized Web3 cloud infrastructure composed of user-operated, scalable, and globally distributed computational nodes. Flux provides the critical, high-availability infrastructure for the New Internet. The Flux service offers a  fully decentralized alternative to some of the world’s largest cloud infrastructure providers while offering competitive pricing. Flux is committed to developing disruptive solutions that empower individuals and businesses in the blockchain industry, emerging technologies like AI, and the broader technology space worldwide. For more information, visit the company’s website at www.runonflux.com. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. Partnership to provide decentralized computing resources, enhancing platform scalability, security and high availability for distributed services. Mizzle Partners with InFlux Technologies to Power DePIN Platform with Decentralized Cloud Infrastructure and Advanced Computing Resources Partnership to provide decentralized computing resources, enhancing platform scalability, security and high availability for distributed services. Mizzle Partners with InFlux Technologies to Power DePIN Platform with Decentralized Cloud Infrastructure and Advanced Computing Resources InFlux Technologies (Flux), a leading global decentralized technology company specializing in cloud infrastructure, artificial intelligence, and decentralized cloud computing services, today announced a partnership with Mizzle , a pioneering decentralized physical infrastructure network (DePIN) platform. InFlux Technologies InFlux Technologies Mizzle Mizzle Under the partnership agreement, Flux will provide decentralized computing resources including CPU, GPU, storage and network capacity as required by Mizzle for its platform operations. This includes support for distributed applications and services, ensuring high availability, scalability and security. The agreement also includes monitoring and management of Mizzle’s infrastructure to ensure optimal performance along with maintenance and upgrades of the infrastructure as needed. Mizzle will work toward an estimated spend of $500,000-plus per year post-launch, with an estimated launch of January 2025. “This partnership represents a key step in our commitment to delivering decentralized computing solutions at scale. By supplying Mizzle with essential resources, we are ensuring the platform’s ability to maintain high availability, scalability, and security. This agreement highlights the growing demand for decentralized infrastructure and demonstrates its practical applications in supporting distributed services," said InFlux Technologies CEO and Co-founder, Daniel Keller. Mizzle is a hyper-efficient CI/CD with no-code development operations which simplifies server management allowing teams to innovate and scale without operational hurdles. Its confidential computing experience carries unmatched security with TEEs, eBPF and decentralized cloud compute, keeping data and operations fully protected. Mizzle has advanced storage and benefits from decentralized cloud storage enhanced with zero knowledge proofs and fully homomorphic encryption. The company is quantum ready with edge computing, is IoT-ready and committed to green computing. Flux ensures a minimum uptime of 99.99% of decentralized infrastructure services, barring any outages or maintenance windows and offers technical support to integrate and manage the compute resources. Flux offers data security and compliance and complies with all relevant data and security regulations, ensuring the infrastructure is designed to meet regulation standards. “We are excited to partner with InFlux Technologies, taking a key step toward advancing decentralized cloud solutions. By combining Mizzle's technology with Flux’s expertise, we will drive greater value for enterprises and governments worldwide. Together, we are shaping the future of decentralized applications and empowering innovation across the ecosystem,” said Founder of Mizzle Arjun Mishra. About Mizzle About Mizzle Mizzle is a DePIN platform designed to empower developers with no-code DevOps. We enable atomic and horizontal scaling of compute and storage, ensuring unparalleled flexibility and performance. Our platform combines advanced AI-driven infrastructure management with trusted execution environments (TEEs), leveraging eBPF technology for real-time protection and monitoring. We also incorporate  state-of-the-art cryptographic techniques, including Fully Homomorphic Encryption and Zero-Knowledge Proofs, to guarantee maximum data privacy and security. As we move into the quantum era, with a strong commitment to Green computing (ESG), Mizzle is your trusted partner for scalable, secure, and efficient decentralized infrastructure. For more information, visit the company’s website at www.mizzle.io . www.mizzle.io www.mizzle.io About InFlux Technologies About InFlux Technologies InFlux Technologies (Flux) is powering a decentralized Web3 cloud infrastructure composed of user-operated, scalable, and globally distributed computational nodes. Flux provides the critical, high-availability infrastructure for the New Internet. The Flux service offers a  fully decentralized alternative to some of the world’s largest cloud infrastructure providers while offering competitive pricing. Flux is committed to developing disruptive solutions that empower individuals and businesses in the blockchain industry, emerging technologies like AI, and the broader technology space worldwide. For more information, visit the company’s website at www.runonflux.com . www.runonflux.com www.runonflux.com This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. Business Blogging

Mizzle Partners with InFlux Technologies to Power DePIN Platform

Mizzle werk saam met InFlux Technologies om DePIN-platform aan te dryf

Mizzle se asocia con InFlux Technologies para impulsar la plataforma DePIN

Mizzle kumppanuus InFlux Technologies Power DePIN Platform

Mizzle partnerek az InFlux Technologies segítségével a Power DePIN platformon

Mizzle が InFlux Technologies と提携して DePIN プラットフォームを強化

Mizzle, DePIN 플랫폼 구동을 위해 InFlux Technologies와 협력

DePIN платформасын иштетүү үчүн InFlux Technologies менен Mizzle өнөктөштөрү

Mizzle dia miara-miasa amin'ny InFlux Technologies amin'ny Power DePIN Platform

Mizzle партнери со InFlux Technologies to Power DePIN платформата

Mizzle Partners neInFlux Technologies kune Power DePIN Platform

Mizzle samarbetar med InFlux Technologies för att driva DePIN-plattformen

ሚዝል ምስ ኢንፍሉክስ ቴክኖሎጂስ ብምትሕብባር ንዲፒን ፕላትፎርም ሓይሊ ይህብ

How to Manage Permissions in a Langflow Chain for LLM Queries Using Permit.io

Stories, News & Mentions about ADVANCIO

Advancio’s Stories on HackerNoon

Advancio's News & Mentions