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Repsense

repsense

**TEL AVIV, Israel, November 11th, 2024/CyberNewsWire/--**Sweet Security today announced the availability of its cloud-native detection and response platform on the Amazon Web Services (AWS) marketplace. Sweet’s solution unifies threat detection across cloud infrastructure, network, workloads, and applications. It provides deep runtime context that enables security teams to quickly extract actual attack narratives from a sea of isolated incidents. Using Sweet, AWS Marketplace customers can detect active threats in real time and respond to them within minutes, enabling them to resolve threats with unprecedented speed - 2-5 minute MTTR - and maintain an agile and resilient environment. AWS customers visiting AWS re:Invent 2024 in Las Vegas can book a meeting to learn more here. “Cloud environments are noisy and complex, making them fertile grounds for attackers -- deterring them requires detection and response capabilities that, to date, have been aspirational, but we’ve made them table stakes,” said Eyal Fisher, Co-Founder and Chief Product Officer of Sweet Security and former head of the Cyber Operation Center, Unit 8200 (Col., retired). “We’re delighted that our solution is now available to AWS Marketplace. customers and look forward to helping them simplify the burden of cloud security and do their jobs faster and better.” What Makes Sweet So Sweet? Sweet Security offers detection and response for cloud native environments. Its approach is unique in how it unifies detection across cloud infrastructure, network, workloads, and applications, providing deep runtime context that cuts through the noise and delivers actual attack narratives. Key Features include: ●     Advanced threat detection and incident response (IR) across infrastructure, network, application, and workload levels. ●     Vulnerability management enriched with runtime insights, reducing CVEs by 99% and putting only the critical risks in front of security personnel. ●     Lean sensor technology that requires minimal resources (50 MB RAM, 0.20% CPU per node) and take only minutes to deploy ●     30+ out-of-the-box integrations with SIEM, SOAR, notification and ticketing systems, and mor Sweet empowers security teams to achieve a Mean Time to Detect of 30 seconds (MTTD) and a 2-5 minute Mean Time to Resolve (MTTR), transforming cloud security into a more proactive and effective discipline. For more information, users can visit Sweet Security on the AWS Marketplace. About Sweet Security Specializing in Cloud Native Detection & Response (D&R), Sweet Security protects cloud environments in real time. Founded by the IDF's former CISO, Sweet's solution focuses on the relationships between cloud infrastructure, workloads and applications , as well as network, and identity components. Leveraging a lean, eBPF-based sensor and deep behavioral analysis, Sweet analyzes anomalies, generating vital insights on incidents, vulnerabilities, and non-human identities. Its GenAI-infused technology cuts through the noise and delivers actionable recommendations on critical, real-time cloud risks. Privately funded, Sweet is backed by Evolution Equity Partners, Munich Re Ventures, Glilot Capital Partners, CyberArk Ventures and an elite group of angel investors. For more information, please visit http://sweet.security. Contact Account Director Chloe Amante Montner Tech PR camante@montner.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here. **TEL AVIV, Israel, November 11th, 2024/CyberNewsWire/--**Sweet Security today announced the availability of its cloud-native detection and response platform on the Amazon Web Services (AWS) marketplace. Sweet’s solution unifies threat detection across cloud infrastructure, network, workloads, and applications. It provides deep runtime context that enables security teams to quickly extract actual attack narratives from a sea of isolated incidents. Using Sweet, AWS Marketplace customers can detect active threats in real time and respond to them within minutes, enabling them to resolve threats with unprecedented speed - 2-5 minute MTTR - and maintain an agile and resilient environment. AWS customers visiting AWS re:Invent 2024 in Las Vegas can book a meeting to learn more here . here here “Cloud environments are noisy and complex, making them fertile grounds for attackers -- deterring them requires detection and response capabilities that, to date, have been aspirational, but we’ve made them table stakes,” said Eyal Fisher, Co-Founder and Chief Product Officer of Sweet Security and former head of the Cyber Operation Center, Unit 8200 (Col., retired). “We’re delighted that our solution is now available to AWS Marketplace. customers and look forward to helping them simplify the burden of cloud security and do their jobs faster and better.” “Cloud environments are noisy and complex, making them fertile grounds for attackers -- deterring them requires detection and response capabilities that, to date, have been aspirational, but we’ve made them table stakes,” said Eyal Fisher, Co-Founder and Chief Product Officer of Sweet Security and former head of the Cyber Operation Center, Unit 8200 (Col., retired). “We’re delighted that our solution is now available to AWS Marketplace. customers and look forward to helping them simplify the burden of cloud security and do their jobs faster and better.” What Makes Sweet So Sweet? Sweet Security offers detection and response for cloud native environments. Its approach is unique in how it unifies detection across cloud infrastructure, network, workloads, and applications, providing deep runtime context that cuts through the noise and delivers actual attack narratives. Key Features include: Key Features include: ●     Advanced threat detection and incident response (IR) across infrastructure, network, application, and workload levels. ●     Vulnerability management enriched with runtime insights, reducing CVEs by 99% and putting only the critical risks in front of security personnel. ●     Lean sensor technology that requires minimal resources (50 MB RAM, 0.20% CPU per node) and take only minutes to deploy ●     30+ out-of-the-box integrations with SIEM, SOAR, notification and ticketing systems, and mor Sweet empowers security teams to achieve a Mean Time to Detect of 30 seconds (MTTD) and a 2-5 minute Mean Time to Resolve (MTTR), transforming cloud security into a more proactive and effective discipline. For more information, users can visit Sweet Security on the AWS Marketplace. Sweet Security on the AWS Marketplace. Sweet Security on the AWS Marketplace. About Sweet Security Specializing in Cloud Native Detection & Response (D&R), Sweet Security protects cloud environments in real time. Founded by the IDF's former CISO, Sweet's solution focuses on the relationships between cloud infrastructure, workloads and applications , as well as network, and identity components. Sweet Security Sweet Security Leveraging a lean, eBPF-based sensor and deep behavioral analysis, Sweet analyzes anomalies, generating vital insights on incidents, vulnerabilities, and non-human identities. Its GenAI-infused technology cuts through the noise and delivers actionable recommendations on critical, real-time cloud risks. Privately funded, Sweet is backed by Evolution Equity Partners, Munich Re Ventures, Glilot Capital Partners, CyberArk Ventures and an elite group of angel investors. For more information, please visit http://sweet.security . http://sweet.security http://sweet.security Contact Account Director Chloe Amante Montner Tech PR camante@montner.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here. here . here

Sweet Security Launches Its Cloud Native Detection And Response Platform On The AWS Marketplace

Sweet Security lancerer sin Cloud Native Detection and Response Platform på AWS Marketplace

Sweet Security lanza su plataforma de detección y respuesta nativa de la nube en el mercado de AWS

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Sweet Security が AWS Marketplace でクラウドネイティブ検出および対応プラットフォームを開始

Sweet Security AWS tirgū izlaiž savu mākoņa sākotnējās noteikšanas un reaģēšanas platformu

Sweet Security lanceert zijn cloud-native detectie- en responsplatform op de AWS Marketplace

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Sweet Security wprowadza na rynek AWS swoją platformę wykrywania i reagowania w chmurze

Sweet Security Puyu Nativo Detección chaymanta Kutichiy Plataforma AWS qhatupi qallarichin

Sweet Security spúšťa svoju cloudovú natívnu platformu detekcie a odozvy na trhu AWS

Sweet Security Inotangisa Yayo Cloud Native Detection uye Mhinduro Platform PaIyo AWS Musika.

Sweet Security, Bulut Yerel Algılama ve Yanıt Platformunu AWS Pazarında Başlattı

Authors:
(1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier;
(2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier;
(3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier;
(4) Stefano Ermon, CZ Biohub;
(5) Christopher D. Manning, Stanford University;
(6) Chelsea Finn, Stanford University. Table of Links Abstract and 1. Introduction 2 Related Work 3 Preliminaries 4 Direct Preference Optimization 5 Theoretical Analysis of DPO 6 Experiments 7 Discussion, Acknowledgements, and References Author Contributions A Mathematical Derivations A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.6 Proof of Theorem 1 B DPO Implementation Details and Hyperparameters C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C.2 GPT-4 prompts for computing summarization and dialogue win rates C.3 Unlikelihood baseline D Additional Empirical Results D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.3 Human study details Additional Empirical Results D.1 Performance of Best of N baseline for Various N We find that the Best of N baseline is a strong (although computationally expensive, requiring sampling many times) baseline in our experiments. We include an evaluation of the Best of N baseline for various N for the Anthropic-HH dialogue and TL;DR summarization; the results are shown in Figure 4. D.2 Sample Responses and GPT-4 Judgments In this section, we present examples of comparisons between DPO and the baseline (PPO temp 0. for summarization, and the ground truth chosen response for dialogue). See Tables 4-6 for summarization examples, and Tables 7-10 for dialogue examples. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier; (2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier; (3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier; (4) Stefano Ermon, CZ Biohub; (5) Christopher D. Manning, Stanford University; (6) Chelsea Finn, Stanford University. Authors: Authors: (1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier; (2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier; (3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier; (4) Stefano Ermon, CZ Biohub; (5) Christopher D. Manning, Stanford University; (6) Chelsea Finn, Stanford University. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Direct Preference Optimization 4 Direct Preference Optimization 5 Theoretical Analysis of DPO 5 Theoretical Analysis of DPO 6 Experiments 6 Experiments 7 Discussion, Acknowledgements, and References 7 Discussion, Acknowledgements, and References Author Contributions Author Contributions A Mathematical Derivations A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.6 Proof of Theorem 1 A.6 Proof of Theorem 1 B DPO Implementation Details and Hyperparameters B DPO Implementation Details and Hyperparameters C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C.2 GPT-4 prompts for computing summarization and dialogue win rates C.2 GPT-4 prompts for computing summarization and dialogue win rates C.3 Unlikelihood baseline C.3 Unlikelihood baseline D Additional Empirical Results D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.3 Human study details D.3 Human study details Additional Empirical Results D.1 Performance of Best of N baseline for Various N We find that the Best of N baseline is a strong (although computationally expensive, requiring sampling many times) baseline in our experiments. We include an evaluation of the Best of N baseline for various N for the Anthropic-HH dialogue and TL;DR summarization; the results are shown in Figure 4. D.2 Sample Responses and GPT-4 Judgments In this section, we present examples of comparisons between DPO and the baseline (PPO temp 0. for summarization, and the ground truth chosen response for dialogue). See Tables 4-6 for summarization examples, and Tables 7-10 for dialogue examples. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Performance of Best of N Baseline for Various N and Sample Responses and GPT-4 Judgments

Author:
(1) Mingda Chen. Table of Links Abstract


Acknowledgements


1 INTRODUCTION


1.1 Overview


1.2 Contributions


2 BACKGROUND


2.1 Self-Supervised Language Pretraining


2.2 Naturally-Occurring Data Structures


2.3 Sentence Variational Autoencoder


2.4 Summary


3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING


3.1 Improving Language Representation Learning via Sentence Ordering Prediction


3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training


3.3 Summary


4 LEARNING SEMANTIC KNOWLEDGE FROM WIKIPEDIA


4.1 Learning Entity Representations from Hyperlinks


4.2 Learning Discourse-Aware Sentence Representations from Document Structures


4.3 Learning Concept Hierarchies from Document Categories


4.4 Summary


5 DISENTANGLING LATENT REPRESENTATIONS FOR INTERPRETABILITY AND CONTROLLABILITY


5.1 Disentangling Semantics and Syntax in Sentence Representations


5.2 Controllable Paraphrase Generation with a Syntactic Exemplar


5.3 Summary


6 TAILORING TEXTUAL RESOURCES FOR EVALUATION TASKS


6.1 Long-Form Data-to-Text Generation


6.2 Long-Form Text Summarization


6.3 Story Generation with Constraints


6.4 Summary


7 CONCLUSION


APPENDIX A - APPENDIX TO CHAPTER 3


APPENDIX B - APPENDIX TO CHAPTER 6


BIBLIOGRAPHY CHAPTER 1 - INTRODUCTION Written language is ubiquitous. Humans use writing to communicate ideas and store knowledge in their daily lives. These activities naturally produce traces of human intelligence, resulting in abundant, freely-available textual data: e.g., Wikipedia,[1] Reddit,[2] and Fandom,[3] among others. These data often contain sophisticated knowledge expressed with complex language structures. For example, for encyclopedias, there usually are dedicated structures for connecting pieces of information scattered around different places for the convenience of readers (e.g., hyperlinks that point the same person or events mentioned in other documents to the same place for disambiguation). Aside from explicit structures, corpora have rich implicit structures. For example, the data pair in bilingual text shares the same semantic meaning but differs in syntactic forms. The implicit difference between the data pair allows us to disentangle the semantic and syntactic information implied in the data structure. Despite the rich structures, recent advances in NLP have been driven by deep neural models trained on a massive amount of plain text, which often strips away the knowledge and structure from the input. This thesis research approaches to better drive supervision from various naturally-occurring textual resources. In particular, we (1) improve ways of transforming plain text into training signals; (2) propose approaches to exploit the rich structures in Wikipedia and paraphrases; and (3) create evaluation benchmarks from fan-contributed websites to reflect real-world challenges. Below we briefly introduce these three areas and summarize our contributions. This paper is available on arxiv under CC 4.0 license. [1] https://www.wikipedia.org/, an online collaborative encyclopedia. [2] https://www.reddit.com/, an online forum for discussion and web content rating. [3] https://www.fandom.com/, a fan-contributed encyclopedia of movies, films, and other media. Author: (1) Mingda Chen. Author: Author: (1) Mingda Chen. Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training 3.3 Summary 4 LEARNING SEMANTIC KNOWLEDGE FROM WIKIPEDIA 4.1 Learning Entity Representations from Hyperlinks 4.2 Learning Discourse-Aware Sentence Representations from Document Structures 4.3 Learning Concept Hierarchies from Document Categories 4.4 Summary 5 DISENTANGLING LATENT REPRESENTATIONS FOR INTERPRETABILITY AND CONTROLLABILITY 5.1 Disentangling Semantics and Syntax in Sentence Representations 5.2 Controllable Paraphrase Generation with a Syntactic Exemplar 5.3 Summary 6 TAILORING TEXTUAL RESOURCES FOR EVALUATION TASKS 6.1 Long-Form Data-to-Text Generation 6.2 Long-Form Text Summarization 6.3 Story Generation with Constraints 6.4 Summary 7 CONCLUSION APPENDIX A - APPENDIX TO CHAPTER 3 APPENDIX B - APPENDIX TO CHAPTER 6 BIBLIOGRAPHY Abstract Abstract Abstract Acknowledgements Acknowledgements Acknowledgements 1 INTRODUCTION 1 INTRODUCTION 1 INTRODUCTION 1 INTRODUCTION 1.1 Overview 1.1 Overview 1.1 Overview 1.2 Contributions 1.2 Contributions 1.2 Contributions 2 BACKGROUND 2 BACKGROUND 2 BACKGROUND 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.1 Self-Supervised Language Pretraining 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.2 Naturally-Occurring Data Structures 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.3 Sentence Variational Autoencoder 2.3 Sentence Variational Autoencoder 2.4 Summary 2.4 Summary 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training 3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training 3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training 3.3 Summary 3.3 Summary 3.3 Summary 4 LEARNING SEMANTIC KNOWLEDGE FROM WIKIPEDIA 4 LEARNING SEMANTIC KNOWLEDGE FROM WIKIPEDIA 4 LEARNING SEMANTIC KNOWLEDGE FROM WIKIPEDIA 4 LEARNING SEMANTIC KNOWLEDGE FROM WIKIPEDIA 4.1 Learning Entity Representations from Hyperlinks 4.1 Learning Entity Representations from Hyperlinks 4.1 Learning Entity Representations from Hyperlinks 4.2 Learning Discourse-Aware Sentence Representations from Document Structures 4.2 Learning Discourse-Aware Sentence Representations from Document Structures 4.2 Learning Discourse-Aware Sentence Representations from Document Structures 4.3 Learning Concept Hierarchies from Document Categories 4.3 Learning Concept Hierarchies from Document Categories 4.3 Learning Concept Hierarchies from Document Categories 4.4 Summary 4.4 Summary 4.4 Summary 5 DISENTANGLING LATENT REPRESENTATIONS FOR INTERPRETABILITY AND CONTROLLABILITY 5 DISENTANGLING LATENT REPRESENTATIONS FOR INTERPRETABILITY AND CONTROLLABILITY 5 DISENTANGLING LATENT REPRESENTATIONS FOR INTERPRETABILITY AND CONTROLLABILITY 5 DISENTANGLING LATENT REPRESENTATIONS FOR INTERPRETABILITY AND CONTROLLABILITY 5.1 Disentangling Semantics and Syntax in Sentence Representations 5.1 Disentangling Semantics and Syntax in Sentence Representations 5.1 Disentangling Semantics and Syntax in Sentence Representations 5.2 Controllable Paraphrase Generation with a Syntactic Exemplar 5.2 Controllable Paraphrase Generation with a Syntactic Exemplar 5.2 Controllable Paraphrase Generation with a Syntactic Exemplar 5.3 Summary 5.3 Summary 5.3 Summary 6 TAILORING TEXTUAL RESOURCES FOR EVALUATION TASKS 6 TAILORING TEXTUAL RESOURCES FOR EVALUATION TASKS 6 TAILORING TEXTUAL RESOURCES FOR EVALUATION TASKS 6 TAILORING TEXTUAL RESOURCES FOR EVALUATION TASKS 6.1 Long-Form Data-to-Text Generation 6.1 Long-Form Data-to-Text Generation 6.1 Long-Form Data-to-Text Generation 6.2 Long-Form Text Summarization 6.2 Long-Form Text Summarization 6.2 Long-Form Text Summarization 6.3 Story Generation with Constraints 6.3 Story Generation with Constraints 6.3 Story Generation with Constraints 6.4 Summary 6.4 Summary 6.4 Summary 7 CONCLUSION 7 CONCLUSION 7 CONCLUSION 7 CONCLUSION APPENDIX A - APPENDIX TO CHAPTER 3 APPENDIX A - APPENDIX TO CHAPTER 3 APPENDIX A - APPENDIX TO CHAPTER 3 APPENDIX B - APPENDIX TO CHAPTER 6 APPENDIX B - APPENDIX TO CHAPTER 6 APPENDIX B - APPENDIX TO CHAPTER 6 BIBLIOGRAPHY BIBLIOGRAPHY BIBLIOGRAPHY CHAPTER 1 - INTRODUCTION Written language is ubiquitous. Humans use writing to communicate ideas and store knowledge in their daily lives. These activities naturally produce traces of human intelligence, resulting in abundant, freely-available textual data: e.g., Wikipedia,[1] Reddit,[2] and Fandom,[3] among others. These data often contain sophisticated knowledge expressed with complex language structures. For example, for encyclopedias, there usually are dedicated structures for connecting pieces of information scattered around different places for the convenience of readers (e.g., hyperlinks that point the same person or events mentioned in other documents to the same place for disambiguation). Aside from explicit structures, corpora have rich implicit structures. For example, the data pair in bilingual text shares the same semantic meaning but differs in syntactic forms. The implicit difference between the data pair allows us to disentangle the semantic and syntactic information implied in the data structure. Despite the rich structures, recent advances in NLP have been driven by deep neural models trained on a massive amount of plain text, which often strips away the knowledge and structure from the input. This thesis research approaches to better drive supervision from various naturally-occurring textual resources. In particular, we (1) improve ways of transforming plain text into training signals; (2) propose approaches to exploit the rich structures in Wikipedia and paraphrases; and (3) create evaluation benchmarks from fan-contributed websites to reflect real-world challenges. Below we briefly introduce these three areas and summarize our contributions. 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 [1] https://www.wikipedia.org/, an online collaborative encyclopedia. [2] https://www.reddit.com/, an online forum for discussion and web content rating. [3] https://www.fandom.com/, a fan-contributed encyclopedia of movies, films, and other media.

Leveraging Natural Supervision for Language Representation Learning and Generation: Introduction

Analyzing Audience Responses to Ukrainian Memetic Warfare Tactics

Authors: (1) Yuwen Lu, he contributed equally to this work, University of Notre Dame, USA; (2) Chao Zhang, he contributed equally to this work and work done as a visiting researcher at the University of Notre Dame; (3) Yuewen Yang, Work done as a visiting researcher at the University of Notre Dame; (4) Yaxin Yao, Virginia Tech, USA; (5) Toby Jia-Jun Li, University of Notre Dame, USA. Table of Links Introduction Background and Related Work Co-Design Workshops Technology Probe Study Results Scaling Up: A Research Agenda Limitations and Future Work Conclusions and References Appendix 3 CO-DESIGN WORKSHOPS In the first phase of our study, we conducted 5 in-person exploratory co-design workshops[6] to achieve the following goals: (1) Exploring users’ perceived disruptiveness and annoyance of different dark patterns in various usage contexts; (2) Learning the existing measures that users have developed or adopted, consciously or subconsciously, to cope with dark patterns; (3) Investigating users’ needs and expectations regarding dark pattern intervention techniques. 3.1 Participants We recruited 12 participants (PA1–PA12; 5 men, 7 women) through word of mouth, email mailing list, and flyer distribution. Our participants represent diverse backgrounds in occupational domains (e.g., health, education, social assistance, and information services), Internet usage (ranging from 2–5 hours to 8+ hours per day), and knowledge of dark patterns (7 had heard of the concept, the remaining 5 had not). Detailed demographics can be found in Appendix A.1. We conducted 5 in-person workshops with 2–4 participants in each session. The groups were divided based on participants’ time availability. Each participant was compensated $30 for their time. 3.2 Workshop Activities Each workshop lasted 2 hours and started with a brief introduction to the concept and examples of dark patterns. The participants completed three activities together: a focus group discussion, a storyboard fill-in session, and a tangible website redesign activity. In a focus group, participants were first introduced to the concept of dark patterns, then reflected on and shared dark patterns examples they previously encountered in everyday lives. During the discussion, researchers provided feedback and clarifications to help them understand the boundary and varied “darkness” level of dark patterns [38]. The participants were then asked to rank their examples by the level of perceived annoyance and disruptiveness and provide explanations. Researchers followed up with questions to find out the current strategies adopted by participants to address the impacts of dark patterns. 3.2.1 Focus Group Discussion. We designed this activity to help participants ground their understanding of dark patterns’ prevalence and impact in their concrete personal experiences. Reflecting on dark patterns and the associated level of annoyance also acted as a stimulus, prompting participants to contemplate countermeasures in subsequent activities. The format was intentionally less structured, with an emphasis on encouraging speak-up and fostering a comfortable environment that would promote open dialogue in subsequent activities. The second activity was a storyboard fill-in. Storyboards are commonly used HCI tools to visually communicate user experience scenarios to the audience [119, 122, 134]. In co-design workshops, storyboards help contextualize participants and prompt them to think about their needs, goals, and constraints in the scenarios described [9, 90]. In our study, we adopted “fill-in-the-blanks” storyboards [88, 129] to explore the users’ desired solutions to dark patterns. For each storyboard, we left one or two frames blank, encouraging participants to provide insights into their understanding of dark patterns and their preferred abilities to counter these patterns [7]. 3.2.2 Storyboard Fill-in. Specifically, we presented participants with 6 storyboards (Fig. 1) depicting scenarios in which a tool helped them mitigate the negative impacts of dark patterns. Of these, 3 focused on improving users’ awareness of dark patterns (Fig. 1a), and 3 (Fig. 1b) others on helping users take act against dark patterns. Each storyboard contained 4 frames, representing the background of the scenario, the tool used, how the tool helped, and the desired outcome respectively. We left the second or/and the third frames blank and asked participants to brainstorm their desired tools and their interactions. The last activity was a tangible user interface redesign. While storyboard fill-in focused on the design of intervention scenarios (e.g. the user flow of interventions), this activity targeted intervention at a lower, more granular level—the specific alternatives of dark patterns on UI design. We designed this activity to be tangible, in the form of paper prototyping instead of digital UI mockup modification, to encourage participants to make bold changes and think outside the box [113]. It also avoids the learning process for a new digital design tool. 3.2.3 Tangible Website Redesign. We curated 13 representative dark pattern examples on 7 websites for online shopping, flight booking, video streaming, and social media as a diverse set of scenarios. These examples were selected from previous research literature [11, 38], online discussions of dark patterns [15], and the researchers’ own experiences, with the goal of triggering discussions among participants. In each workshop, participants selected around 5 dark patterns that they were most concerned about to work on. For each dark pattern instance, we provided a printout of the website interface and a set of cut-out UI widgets from the same interface. During the activity, participants edited the cut-out widgets and drew new UI widgets using a variety of provided stationery[8], and re-assembled them into a more desired design of the original website interface. The interfaces created by the participants were collected for analysis [9]. 3.3 Workshop Findings Following the open coding methods [14, 63], two researchers conducted a thematic analysis of audio transcriptions for workshops and materials produced in the three activities. The researchers conducted three rounds of iterative labeling, in which descriptive labels on relevant transcript pieces were created, grouped, and generalized into higher-level themes. The analysis was discussion-based, with no necessity for inter-rater reliability due to the aim of discovering emergent themes [84]. Comparative analysis was not the focus of the study, but a comparison of responses from tech industry users and other users showed no significant difference in their perceptions or coping mechanisms concerning dark patterns. This is in line with previous work [55] which showed no significant differences between end users and experts in perceptions of dark patterns. Our workshop findings (WF) are described below in response to our workshop goals. Participants expressed their desire to know more about the potential impacts of dark patterns. Although many were able to detect dark patterns, most participants only developed a vague assumption about the impacts, falling short of articulating the specifics. The particular mechanisms and impact remain as a “blackbox”, confirming findings in [11, 35]. WF1: Users would like to learn more about the impact of dark patterns. Participants often asked about the detailed mechanisms and impact of dark patterns and felt the knowledge was useful. Importantly, clear knowledge of dark patterns’ impact can help users choose services more consciously and potentially reduce the irritation of seeing dark patterns. PA6 and PA7 mentioned that for disguised ads on Instagram, “those are annoying at first, but once you know that (its impact) and come to expect it, it’s like okay (less annoying) (PA6)”. These findings complement existing research by demonstrating users’ autonomy—they are not merely passive consumers of dark patterns. They are interested in actively learning, and the acquired knowledge can change their usage behavior and connections with online platforms. Despite the prevalent negative perception of manipulative UX design patterns, not all participants viewed them unfavorably. In fact, responses varied widely from negative to positive, echoing findings from studies on online behavioral advertising [124]. Users often perceived a persuasive pattern as helpful when it aligns user goals with stakeholder profits. PA2 liked the autoplay feature on Netflix, even when understanding it used forced continuation, because “it is useful when I am away from my mouse”. Similarly, for disguised ads on social media, PA12 expressed that “I won’t block them. I usually don’t engage or buy stuff... Maybe I’ll see something in the future I like”. We summarized 3 most common perceptions from participants: disruptiveness (the user experience was disrupted by the dark pattern), indifference (the dark pattern was neither harmful nor useful), and helpfulness (the pattern was helpful in the current context). WF2: Users’ perceptions of dark patterns are personal and dynamically changing, which are formed based on user preferences, types of dark pattern instances, and usage contexts. Users encountering a dark pattern typically evaluate its potential pros and cons subconsciously, influenced by factors like perceived convenience, potential consequences, and the pattern’s apparent malicious intent. Accordingly, three factors—the user, the dark pattern, and the usage context— determine this perception. An example is the autoplay feature for the next episode on Netflix. PA1, PA10, and PA12 expressed their dislike of this feature, while PA2, PA5, and PA11 generally thought it was convenient. Furthermore, even the same user’s perception can shift with different usage contexts and changing goals. PA11 found auto-play harmful when using Netflix during work because of the short break time, but useful when casually browsing after work just for fun. These findings are in parallel with results in [35] and enhance previous findings by highlighting the highly individualized and contextualized nature of users’ perceptions of dark patterns. This finding reveals more details regarding how end users react when facing dark patterns’ perceived influences, in addition to the conclusions in [11]. For disruptive dark patterns, many users actively seek solutions to mitigate the impact. PA12 used a calendar to track the end of free subscription trials as a reminder to unsubscribe. On Instagram, PA6 developed a habit to avoid disguised ads when tapping through all stories. “Funnily enough, every time I watch a story, I have developed... an unconscious habit, I close out (by swiping down) and I click the next one. (PA6)” If the user can successfully find a solution, it becomes a “muscle memory” for them. For the Instagram habit, PA6 expressed that “I didn’t know why I do that, but I guess that’s a dark pattern and I am unconsciously adapted.” PA1 also developed the habit of reaching their mouse before an episode ends on Netflix, to wiggle the cursor in time and avoid forced continuation. WF3: Users develop varied coping mechanisms based on their different dark pattern perceptions. For dark patterns that users feel indifferent to, the most common strategy is ignoring them. PA6 mentioned that they gradually got used to “confirmshaming” dark patterns and “just don’t care anymore”. When asked about a disguised advertisement on a flight booking website during the redesign activity, PA8, PA9, and PA10 reported that they did not even notice it. They considered it “too colorful” to be relevant and therefore simply ignored it. 3.4 Design Implications The thematic analysis results of our co-design workshops offered several design implications for dark pattern intervention techniques and user empowerment. When encountering disruptive dark patterns, users often feel manipulated but have no ability to resist (WF1). To help participants regain self-autonomy [82], we can empower end users with the ability to change the interfaces of dark patterns. It would help users take the initiative to mitigate the negative impact. DI1: Empower users with the ability to make changes on dark patterns. During our workshops, participants expressed the need for information on the potential influences of dark patterns and envisioned a ranking of their severity. With such information, users can make better informed evaluations of the impact of a dark pattern on themselves (WF2) and develop their coping mechanism accordingly (WF3). DI2: Provide information on the potential consequences of dark patterns. Perceptions of dark patterns may shift with users, types of dark pattern instances, and usage contexts. Even for the same dark pattern, users may act differently (WF3). As a result, it is necessary to have multiple intervention options for users to choose from. In this way, users can have more flexibility in personalization and autonomy. DI3: Offer users multiple intervention options for each dark pattern. In our redesign activity, participants proposed intervention techniques for dark patterns that can be categorized into three approaches: (1) modifying interface components and layouts to eliminate malicious design, (2) adjusting user flows to prevent users from falling into behavioral traps, and (3) evoking reflection by uncovering the outcomes of dark patterns for long-term self-change. Future intervention designs can take inspiration from these strategies and apply them in appropriate scenarios. DI4: Design dark pattern interventions with three strategies: interface design change, user flow adjustment, and behavioral outcome reflection. For example, on a flight booking website, while the website highlighted the more expensive first-class and main-cabin options over the basic economy, PA8, PA9, and PA10 changed them to the same size and color to pursue a fair style. PA1, PA2, and PA3 designed an agent to provide an appropriate action guide with dark patterns on Amazon to help them save money. PA6 and PA7 wanted to know, in the long term, how many times dark patterns on a certain website affected their behavior, to reflect on their relationship with the platform. We used these findings and design implications from our co-design workshop to guide our second co-design phase—a technology probe study. [6] The protocol of workshops has been reviewed and approved by the IRB at our institution. [7] Examples of participant-filled storyboards are available in the supplemental materials. [8] The provided stationery included but was not limited to pencils, colored pens, scissors, highlighters, and glue sticks [9] Examples of participant-redesigned interfaces are available in the supplemental materials. This paper is under CC 4.0 license. available on arxiv

Navigating User Responses to Dark Patterns in UX Design 

Authors: (1) Dominic Petrak, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany; (2) Nafise Sadat Moosavi, Department of Computer Science, The University of Sheffield, United Kingdom; (3) Ye Tian, Wluper, London, United Kingdom; (4) Nikolai Rozanov, Wluper, London, United Kingdom; (5) Iryna Gurevych, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany. Table of Links Abstract & Introduction Related Work Datasets Examined Manual Error Type Analysis and Taxonomies Automatic Filtering for Potentially Relevant Dialogs Statistical Analysis Evaluation and Experiments Discussion Conclusion, Limitation, Acknowledgments, and References A Integrated Error Taxonomy – Details B Error-Indicating Sentences And Phrases C Automatic Filtering – Implementation D Automatic Filtering – Sentence-Level Analysis E Task-Oriented Dialogs – Examples F Effectiveness Of Automatic Filtering – A Detailed Analysis G Inter-Annotator Agreement – Detailed Analysis H Annotation Guidelines I Hyperparameters and Baseline Experiments J Human-Human Dialogs – Examples 6 Statistical Analysis In this section, we conduct a statistical analysis of the distribution of error and user response types and their relations in the dialogs of the datasets examined. For this, we manually annotate 555 of the filtered dialogs (100 from each dataset, if available) with error and user response types, using the taxonomies proposed in Section 4.1 and 4.2. To avoid bias from our filtering procedure in Section 5, we also consider 600 randomly selected dialogs (100 from each dataset) that were not identified during this process (similarity <50%) in this analysis. Hereinafter, these dialogs are referred to as random dialogs. In Section 6.4, we assess the impact of our filtering procedure on this analysis. Overall, we manually annotate 1,155 dialogs with error and user response types. For annotation, we always consider the entire dialog (the context). 6.1 Error Type Distribution Overall, we identified 188 errors across all dialogs. Table 7 shows the distribution. As expected, the filtered dialogs contain a larger number of errors (130 overall) compared to the random dialogs (58 overall), especially for opendomain and knowledge-grounded dialogs, such as SFC (Hancock et al., 2019) and WoW (Dinan et al., 2019). Table 8 shows the most common error types and their frequency for both the filtered and random dialogs, which already accounts for 172 of all identified errors4 . In the case of open-domain dialogs, the most frequent error types are Ignore Question (E1) and Topic Transition Error (E6). This is particularly the case in the SFC dataset (Hancock et al., 2019), where we find the system utterances to be often out of context. In the case of task-oriented dialogs, Ignore Request (E2) and Ignore Expectation (E3) are the most common error types. We observe these errors when requests are only partially processed, e.g., when the user requests to book a hotel room and a train, but the system only books the hotel room. Moreover, we find that there is only little variety in language in task-oriented dialogs, regardless of the number of tasks reflected in the dataset (see Appendix E for examples). In the case of WoW (Dinan et al., 2019), the knowledgegrounded dataset, the Factually Incorrect (E5) error is the most frequently observed error type. 6.2 User Response Type Distribution Table 9 shows the distribution of user response types to errors in system utterances for both the random dialogs (R) and the filtered dialogs (F). As described in Section 4.2, UR2 (Repeat or Rephrase), UR3 (Make Aware with Correction), and UR5 (Ask for Clarification) are likely to contain free-text human feedback. In the case of the filtered dialogs, we find that UR3 and UR5 are more often observed in open-domain and knowledgegrounded dialogs, such as SFC (Hancock et al., 2019) or WoW (Dinan et al., 2019). UR2 is only rarely observed, and only in task-oriented dialogs. However, UR1 (Ignore and Continue) is also frequently observed, especially in SFC. For randomly selected dialogs, this is the most frequent user response type (it occurs 42 times). 6.3 Relation Between Error and User Response Types Figure 1 illustrates the relation between the most common error types (the 172 errors presented in Table 8) and user response types (Table 9) in both the filtered and random dialogs. We find that UR1, UR4, and UR5 are the most frequently observed user response types, particu- larly in the case of Ignore Question (E1) and Topic Transition Error (E6), which is mostly observed in open-domain datasets (Table 8). Along with UR3, UR5 is also a frequent response type in the case of Factually Incorrect (E5), which is mostly observed in WoW (Dinan et al., 2019). UR2 is only rarely observed. It sometimes occurs in the context of Ignore Request (E2) and Ignore Expectation (E3), which are mostly found in task-oriented dialogs. 6.4 Impact of Automatic Filtering As Table 7 shows, a total of 188 dialogs was identified to contain errors in this analysis. 130 of them were found in filtered dialogs and 58 in random dialogs (the ones that were missed by our automatic filtering procedure in Section 5). Considering this at the level of user response types (Table 9), 46 of these 58 errors were ignored by users or did not provide any additional information (UR1 or UR4, the ones marked in bold red in the table), meaning that they are irrelevant because they do not contain free-text human feedback. For the remaining 12 missed errors (UR3 or UR5, the ones marked in bold green), we find that they are not reflected in the set of 67 error-indicating sentences used for filtering. Although this limits the effectiveness, we find that our procedure for automatic filtering itself has no negative impact on the results of our analysis, but rather improved annotation efficiency. An approximated recall of 0.72 supports this assumption. The recall was approximated with respect to the ratio between the size of the filtered subsets and the original datasets5 (Table 6) and only considers the 12 missed relevant errors (when considering all missed errors, the recall is 0.35). We provide a more detailed analysis in Appendix F. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Statistical Analysis of Dialog Datasets for Error and Response Patterns  

Transak has announced its integration with Zerion Wallet, becoming the exclusive on-ramp provider for the platform's new ZERϴ Network. The partnership aims to facilitate cryptocurrency purchases through local payment methods for Zerion's 550,000 users. The integration enables users to purchase cryptocurrencies using credit cards, debit cards, and bank transfers directly within the Zerion Wallet interface. Transak's infrastructure operates under FinCEN registration as a Money Services Business, maintaining ISO/IEC 27001 certification and SOC II compliance. ZERϴ Network, Zerion's Layer 2 rollup solution, implements zero-knowledge proofs for Ethereum scalability. The network utilizes a sponsorship model to eliminate gas fees for users. The system operates through Zerion Wallet's verification of user eligibility via Onchain DNA, a reputation system that determines transaction sponsorship based on user activity. "The elimination of gas fees represents a significant change in transaction accessibility," states Evgeny Yurtaev, CEO of Zerion. The integration positions Transak to support ZERϴ Network from its initial launch. To mark the partnership, both companies have initiated a promotion where Zerion Loyalty Program users outside the UK can purchase ETH on ZERϴ Network without fees through Transak. However, standard card processing and bank fees may still apply depending on the payment method. Sami Start, CEO of Transak, commented on the partnership's focus on reducing barriers to Web3 participation through the elimination of gas fees. Technical details of the integration include: Support for 170+ cryptocurrencies


Coverage across 160 countries


Integration with 75+ blockchain networks


EVM compatibility for the rollup solution The partnership reflects ongoing developments in blockchain infrastructure: Expansion of zero-fee transaction capabilities


Integration of traditional payment systems


Implementation of reputation-based transaction validation


Development of scalable Layer 2 solutions Transak's expansion continues with offices in: Miami (Headquarters)


Bengaluru (Tech Hub)


London


Milan


Dubai


Hong Kong Market analysts note that such integrations potentially influence broader cryptocurrency adoption patterns. The implementation of zero-fee structures and traditional payment methods may affect user behavior and transaction volumes across blockchain networks. Zerion Wallet currently provides access to over 60 DeFi protocols through its non-custodial platform. The wallet's integration with Transak expands payment options for users while maintaining security protocols and regulatory compliance. Industry observers suggest that partnerships between infrastructure providers and wallet services could impact the development of Layer 2 solutions and user onboarding processes in the cryptocurrency sector. The success of such integrations may influence future developments in blockchain payment systems and network scalability solutions. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Transak has announced its integration with Zerion Wallet, becoming the exclusive on-ramp provider for the platform's new ZERϴ Network. The partnership aims to facilitate cryptocurrency purchases through local payment methods for Zerion's 550,000 users. The integration enables users to purchase cryptocurrencies using credit cards, debit cards, and bank transfers directly within the Zerion Wallet interface. Transak's infrastructure operates under FinCEN registration as a Money Services Business, maintaining ISO/IEC 27001 certification and SOC II compliance. Transak ZERϴ Network, Zerion's Layer 2 rollup solution, implements zero-knowledge proofs for Ethereum scalability. The network utilizes a sponsorship model to eliminate gas fees for users. The system operates through Zerion Wallet's verification of user eligibility via Onchain DNA, a reputation system that determines transaction sponsorship based on user activity. "The elimination of gas fees represents a significant change in transaction accessibility," states Evgeny Yurtaev, CEO of Zerion. The integration positions Transak to support ZERϴ Network from its initial launch. To mark the partnership, both companies have initiated a promotion where Zerion Loyalty Program users outside the UK can purchase ETH on ZERϴ Network without fees through Transak. However, standard card processing and bank fees may still apply depending on the payment method. Sami Start, CEO of Transak, commented on the partnership's focus on reducing barriers to Web3 participation through the elimination of gas fees. Technical details of the integration include: Support for 170+ cryptocurrencies Coverage across 160 countries Integration with 75+ blockchain networks EVM compatibility for the rollup solution Support for 170+ cryptocurrencies Support for 170+ cryptocurrencies Coverage across 160 countries Coverage across 160 countries Integration with 75+ blockchain networks Integration with 75+ blockchain networks EVM compatibility for the rollup solution EVM compatibility for the rollup solution The partnership reflects ongoing developments in blockchain infrastructure: Expansion of zero-fee transaction capabilities Integration of traditional payment systems Implementation of reputation-based transaction validation Development of scalable Layer 2 solutions Expansion of zero-fee transaction capabilities Expansion of zero-fee transaction capabilities Integration of traditional payment systems Integration of traditional payment systems Implementation of reputation-based transaction validation Implementation of reputation-based transaction validation Development of scalable Layer 2 solutions Development of scalable Layer 2 solutions Transak's expansion continues with offices in: Miami (Headquarters) Bengaluru (Tech Hub) London Milan Dubai Hong Kong Miami (Headquarters) Miami (Headquarters) Bengaluru (Tech Hub) Bengaluru (Tech Hub) London London Milan Milan Dubai Dubai Hong Kong Hong Kong Market analysts note that such integrations potentially influence broader cryptocurrency adoption patterns. The implementation of zero-fee structures and traditional payment methods may affect user behavior and transaction volumes across blockchain networks. Zerion Wallet currently provides access to over 60 DeFi protocols through its non-custodial platform. The wallet's integration with Transak expands payment options for users while maintaining security protocols and regulatory compliance. Industry observers suggest that partnerships between infrastructure providers and wallet services could impact the development of Layer 2 solutions and user onboarding processes in the cryptocurrency sector. The success of such integrations may influence future developments in blockchain payment systems and network scalability solutions. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Transak Partners with Zerion Wallet, Integrates with ZERϴ Network

The NikolAI project has announced the release of its first NFT collection on the TON blockchain, coinciding with mathematician Nikolai Durov's 44th birthday. The collection, comprising 1,000 unique profile pictures (PFPs), represents the project's expansion into digital collectibles. The initiative targets existing token holders, with eligibility restricted to the top 44% of $NIKO holders on the TON blockchain. The qualification period extends until November 28, 2024, at 23:59 UTC, after which eligible holders can participate in the free mint. Market data indicates significant growth for the project, with the $NIKO token reaching a fully diluted valuation of $180 million within two weeks of launch. The project has gained attention for its AI integration, including an automated social media presence across X.com and Telegram platforms. The NFT collection introduces several features for holders: Participation rights in governance decisions


Priority access to future project developments


Potential yield opportunities


Integration with upcoming AI tools The collection's launch occurs amid increasing interest in AI-blockchain integrations within the cryptocurrency sector. Industry analysts note that such projects often aim to combine technological innovation with community engagement mechanisms. Technical implementation details include: AI-generated artwork for each NFT


Smart contract integration on TON blockchain


Automated eligibility verification system


Decentralized distribution mechanism Participating users must hold their tokens on-chain, as centralized exchange positions do not qualify for the NFT distribution. This requirement aligns with broader industry trends encouraging on-chain asset custody. The development represents a significant milestone in TON blockchain's NFT ecosystem, potentially influencing future projects in the space. Market observers suggest that successful implementation could establish new standards for AI-integrated blockchain applications. For cryptocurrency markets, this launch demonstrates continued evolution in NFT utility beyond simple collectibles. The integration of governance rights and future benefits indicates a shift toward more complex token economics in digital asset projects. The collection's timing coincides with increased attention on AI applications in blockchain technology, as projects explore combinations of artificial intelligence and distributed ledger systems. Industry participants suggest such integrations could influence future developments in both sectors. The project's expansion into NFTs reflects broader market trends toward multi-faceted cryptocurrency platforms combining various technological elements. Success metrics will likely depend on community engagement and technological implementation quality. For TON blockchain ecosystem participants, this development potentially signals increasing platform capabilities in NFT and AI integrations. The project's performance could influence future development directions on the network. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR The NikolAI project has announced the release of its first NFT collection on the TON blockchain, coinciding with mathematician Nikolai Durov's 44th birthday. The collection, comprising 1,000 unique profile pictures (PFPs), represents the project's expansion into digital collectibles. The initiative targets existing token holders, with eligibility restricted to the top 44% of $NIKO holders on the TON blockchain. The qualification period extends until November 28, 2024, at 23:59 UTC, after which eligible holders can participate in the free mint. Market data indicates significant growth for the project, with the $NIKO token reaching a fully diluted valuation of $180 million within two weeks of launch. The project has gained attention for its AI integration, including an automated social media presence across X.com and Telegram platforms. X.com The NFT collection introduces several features for holders: Participation rights in governance decisions Priority access to future project developments Potential yield opportunities Integration with upcoming AI tools Participation rights in governance decisions Participation rights in governance decisions Priority access to future project developments Priority access to future project developments Potential yield opportunities Potential yield opportunities Integration with upcoming AI tools Integration with upcoming AI tools The collection's launch occurs amid increasing interest in AI-blockchain integrations within the cryptocurrency sector. Industry analysts note that such projects often aim to combine technological innovation with community engagement mechanisms. Technical implementation details include: AI-generated artwork for each NFT Smart contract integration on TON blockchain Automated eligibility verification system Decentralized distribution mechanism AI-generated artwork for each NFT AI-generated artwork for each NFT Smart contract integration on TON blockchain Smart contract integration on TON blockchain Automated eligibility verification system Automated eligibility verification system Decentralized distribution mechanism Decentralized distribution mechanism Participating users must hold their tokens on-chain, as centralized exchange positions do not qualify for the NFT distribution. This requirement aligns with broader industry trends encouraging on-chain asset custody. The development represents a significant milestone in TON blockchain's NFT ecosystem, potentially influencing future projects in the space. Market observers suggest that successful implementation could establish new standards for AI-integrated blockchain applications. For cryptocurrency markets, this launch demonstrates continued evolution in NFT utility beyond simple collectibles. The integration of governance rights and future benefits indicates a shift toward more complex token economics in digital asset projects. The collection's timing coincides with increased attention on AI applications in blockchain technology, as projects explore combinations of artificial intelligence and distributed ledger systems. Industry participants suggest such integrations could influence future developments in both sectors. The project's expansion into NFTs reflects broader market trends toward multi-faceted cryptocurrency platforms combining various technological elements. Success metrics will likely depend on community engagement and technological implementation quality. For TON blockchain ecosystem participants, this development potentially signals increasing platform capabilities in NFT and AI integrations. The project's performance could influence future development directions on the network. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

NikolAI Launches AI-Generated NFT Collection on TON Blockchain

1. Forget about hardware; the real money is in software For months, headlines have fixated on Huawei's limitations in producing advanced 7nm chips, while Apple excels with its cutting-edge 3nm technology. However, this focus on hardware overlooks a critical lesson from history: software, not hardware, drives  profits. Bill Gates built the Microsoft empire not by building hardware—outsourced to IBM—but by dominating the software business. Today, Microsoft’s software business dwarfs IBM's historical combined hardware and software projects. Huawei’s HarmonyOS NEXT, already claimed to be the world’s third-largest mobile OS with over a billion installations, represents a significant milestone. While currently present mostly in China, the system has the potential to expand to other markets, particularly in the Global South. HarmonyOS could appeal to nations seeking to reduce reliance on U.S.-based technology. A large user base often translates into substantial revenue streams over time. Moreover, Huawei is working on extending its OS to personal computers under the name HarmonyOS PC, with a target release in Q3 2025. Even if this initiative gains traction solely within China, particularly in government systems, it could reduce the dominance of Microsoft Windows and Apple macOS, cutting into their lucrative revenue streams. 2. Know-How and Engineering Workforce Unlike hardware chip technology, software OS tools and expertise are widely accessible, starting with the open-source availability of Android OS. This means the necessary know-how is either readily available or easy to acquire. China also boasts a large skilled engineering workforce—reportedly four times larger than that of the U.S.—providing Huawei with ample talent at a competitive cost to develop its new OS. With sufficient time and resources, Huawei is likely to succeed in its HarmonyOS project. 3. Marketing or Reality? Reports suggest that HarmonyOS is facing some challenges. According to a media report, even the migration of China’s own WeChat app to the platform has encountered difficulties. This is not entirely surprising, as building a stable operating system is a highly complex and demanding task. Even Steve Jobs famously staged parts of his iPhone presentation during its early development stages. It required time and engineering effort for Apple to refine and perfect the product. Huawei faces a similar trajectory. If it commits to the project with enough talent, funding, and patience, HarmonyOS could mature into a serious alternative to Microsoft and Apple’s software dominance. 4 References [1] HarmonyOS [2] HarmonyOS NEXT [3] China's first fully home-grown mobile operating system HarmonyOS NEXT launched [4] https://www.huaweicentral.com/category/harmonyos/harmonyos-next/ [5] HarmonyOS NEXT becomes the world’s third major mobile OS, joining Android and iOS [6] Huawei’s HarmonyOS NEXT faces major test with WeChat: Will it pass? [7] Huawei’s China-Made 7nm Chip ‘Years Behind US’, Raimondo Says [8] In Huawei’s battle against Apple, chips are still a weakness, says analyst [9] HuaweiNext-gen Huawei PCs will ditch Windows for HarmonyOS: Chairman [10] Huawei HarmonyOS PC rumored to delay till Q3 2025 [11] HarmonyOS PC UI design allegedly surfaced on Huawei Developer website [12] The Global Distribution of STEM Graduates: Which Countries Lead the Way? [13] How Steve Jobs Faked His Way Through Unveiling the iPhone 1. Forget about hardware; the real money is in software For months, headlines have fixated on Huawei's limitations in producing advanced 7nm chips, while Apple excels with its cutting-edge 3nm technology. However, this focus on hardware overlooks a critical lesson from history: software, not hardware, drives  profits. Bill Gates built the Microsoft empire not by building hardware—outsourced to IBM—but by dominating the software business. Today, Microsoft’s software business dwarfs IBM's historical combined hardware and software projects. Huawei’s HarmonyOS NEXT , already claimed to be the world’s third-largest mobile OS with over a billion installations, represents a significant milestone. While currently present mostly in China, the system has the potential to expand to other markets, particularly in the Global South. HarmonyOS could appeal to nations seeking to reduce reliance on U.S.-based technology. A large user base often translates into substantial revenue streams over time. HarmonyOS NEXT world’s third-largest mobile OS Moreover, Huawei is working on extending its OS to personal computers under the name HarmonyOS PC , with a target release in Q3 2025 . Even if this initiative gains traction solely within China, particularly in government systems, it could reduce the dominance of Microsoft Windows and Apple macOS, cutting into their lucrative revenue streams. extending its OS to personal computers HarmonyOS PC Q3 2025 2. Know-How and Engineering Workforce Unlike hardware chip technology, software OS tools and expertise are widely accessible, starting with the open-source availability of Android OS. This means the necessary know-how is either readily available or easy to acquire. China also boasts a large skilled engineering workforce —reportedly four times larger than that of the U.S.—providing Huawei with ample talent at a competitive cost to develop its new OS. With sufficient time and resources, Huawei is likely to succeed in its HarmonyOS project. large skilled engineering workforce 3. Marketing or Reality? Reports suggest that HarmonyOS is facing some challenges. According to a media report, even the migration of China’s own WeChat app to the platform has encountered difficulties. This is not entirely surprising, as building a stable operating system is a highly complex and demanding task. Even Steve Jobs famously staged parts of his iPhone presentation during its early development stages. It required time and engineering effort for Apple to refine and perfect the product. Huawei faces a similar trajectory. If it commits to the project with enough talent, funding, and patience, HarmonyOS could mature into a serious alternative to Microsoft and Apple’s software dominance. 4 References [1] HarmonyOS HarmonyOS [2] HarmonyOS NEXT HarmonyOS NEXT [3] China's first fully home-grown mobile operating system HarmonyOS NEXT launched China's first fully home-grown mobile operating system HarmonyOS NEXT launched [4] https://www.huaweicentral.com/category/harmonyos/harmonyos-next/ https://www.huaweicentral.com/category/harmonyos/harmonyos-next/ [5] HarmonyOS NEXT becomes the world’s third major mobile OS, joining Android and iOS HarmonyOS NEXT becomes the world’s third major mobile OS, joining Android and iOS [6] Huawei’s HarmonyOS NEXT faces major test with WeChat: Will it pass? Huawei’s HarmonyOS NEXT faces major test with WeChat: Will it pass? [7] Huawei’s China-Made 7nm Chip ‘Years Behind US’, Raimondo Says Huawei’s China-Made 7nm Chip ‘Years Behind US’, Raimondo Says [8] In Huawei’s battle against Apple, chips are still a weakness, says analyst In Huawei’s battle against Apple, chips are still a weakness, says analyst [9] HuaweiNext-gen Huawei PCs will ditch Windows for HarmonyOS: Chairman HuaweiNext-gen Huawei PCs will ditch Windows for HarmonyOS: Chairman [10] Huawei HarmonyOS PC rumored to delay till Q3 2025 Huawei HarmonyOS PC rumored to delay till Q3 2025 [11] HarmonyOS PC UI design allegedly surfaced on Huawei Developer website HarmonyOS PC UI design allegedly surfaced on Huawei Developer website [12] The Global Distribution of STEM Graduates: Which Countries Lead the Way? The Global Distribution of STEM Graduates: Which Countries Lead the Way? [13] How Steve Jobs Faked His Way Through Unveiling the iPhone How Steve Jobs Faked His Way Through Unveiling the iPhone

Experts Skeptical of Huawei Silicon May Be Missing the Forest for the Trees

الخبراء المتشككون في السيليكون الخاص بشركة هواوي ربما يغفلون عن حقيقة الأمر

“Huawei Silicon”a şübhə ilə yanaşan ekspertlər ağaclar üçün meşədən məhrum ola bilərlər

Stručnjaci skeptični prema Huawei Silicijum možda nedostaje šuma za drveće

Los expertos escépticos sobre el silicio de Huawei podrían estar perdiendo de vista el bosque detrás de los árboles

מומחים הספקנים לגבי סיליקון Huawei עשויים להחמיץ את היער בשביל העצים

ファーウェイのシリコンに懐疑的な専門家は、木を見て森を見ずかもしれない

Pakar Ragu-ragu Huawei Silicon Mungkin Hilang Hutan untuk Pokok

Ditsebi tše di Belaelago Silicon ya Huawei e ka ba e Hlolwa Sethokgwa bakeng sa Dihlare

Стручњаци скептични према Хуавеи силицијуму можда недостаје шума за дрвеће

Hünärmenler Huawei Silikonyna şübheli garaýarlar, agaçlar üçin tokaýy ýitirip bilerler

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Chain4Travel hosted its second Decode Travel hackathon in Antalya, Turkey, bringing together over 30 specialists from five countries to develop blockchain-based solutions for the travel industry. The event, held at SAN TSG & Paximum headquarters, attracted participants from established travel companies including SunExpress, A3M Global Monitoring, and AERTiCKET Group. The two-day event focused on addressing technical challenges in the travel sector through blockchain technology, particularly using the Camino Network infrastructure. Seven teams worked on various projects targeting industry-specific issues, from payment systems to data privacy. Two teams concentrated on micropayment solutions, developing systems for automated conditional payments and multi-party disbursement. Their proposals aimed to streamline settlement processes and dispute resolution in travel transactions through smart contract implementation. The hackathon also addressed data privacy concerns, with one team creating a blockchain protocol for selective data sharing among travel businesses. Another team developed a platform based on Self-Sovereign Identity principles, focusing on GDPR compliance while maintaining personalization capabilities. Major industry players SunExpress and Paximum participated with teams working on distribution optimization and B2B payment automation. The winning team, from TripX Travel in Macedonia, received 10,000 CAM tokens for their AI-integrated solution for travel packaging, which utilized the Camino Messenger system for personalized booking experiences. "We aim to support the digital transformation in the sector by combining technology and tourism," said Erol Türk, CEO of SAN TSG. The event marked Turkey's first Web3 travel hackathon, representing a significant step in regional travel technology development. Pablo Castillo, CEO of Chain4Travel, emphasized the hackathon's role in demonstrating blockchain's practical applications: "The solutions created during Decode in Turkey prove that innovation can be realized in a matter of days." The event highlighted several key developments in travel technology: Integration of blockchain with existing travel infrastructure


Development of automated payment distribution systems


Implementation of privacy-preserving data sharing protocols


Creation of AI-enhanced booking systems


Establishment of cross-border collaboration frameworks The hackathon's international participation reflected growing interest in blockchain applications within the travel industry. Participants from Turkey, Germany, Macedonia, Spain, and Georgia contributed diverse perspectives to technical solutions. The Camino Network Foundation announced its continuing support for travel technology development through a grant program, encouraging developers and businesses to build on its open-source infrastructure. Industry analysts note that such events signal increasing adoption of blockchain technology in travel operations, particularly in areas of payments, data management, and booking systems. The solutions developed during the hackathon demonstrate potential applications for reducing operational costs and improving service delivery in the travel sector. The event's success in attracting established industry players suggests growing recognition of blockchain's role in travel technology development. Future hackathons are planned to continue fostering innovation and collaboration between travel companies and technology developers. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Chain4Travel hosted its second Decode Travel hackathon in Antalya, Turkey, bringing together over 30 specialists from five countries to develop blockchain-based solutions for the travel industry. The event, held at SAN TSG & Paximum headquarters, attracted participants from established travel companies including SunExpress, A3M Global Monitoring, and AERTiCKET Group. Chain4Travel The two-day event focused on addressing technical challenges in the travel sector through blockchain technology, particularly using the Camino Network infrastructure. Seven teams worked on various projects targeting industry-specific issues, from payment systems to data privacy. Two teams concentrated on micropayment solutions, developing systems for automated conditional payments and multi-party disbursement. Their proposals aimed to streamline settlement processes and dispute resolution in travel transactions through smart contract implementation. The hackathon also addressed data privacy concerns, with one team creating a blockchain protocol for selective data sharing among travel businesses. Another team developed a platform based on Self-Sovereign Identity principles, focusing on GDPR compliance while maintaining personalization capabilities. Major industry players SunExpress and Paximum participated with teams working on distribution optimization and B2B payment automation. The winning team, from TripX Travel in Macedonia, received 10,000 CAM tokens for their AI-integrated solution for travel packaging, which utilized the Camino Messenger system for personalized booking experiences. "We aim to support the digital transformation in the sector by combining technology and tourism," said Erol Türk, CEO of SAN TSG. The event marked Turkey's first Web3 travel hackathon, representing a significant step in regional travel technology development. Pablo Castillo, CEO of Chain4Travel, emphasized the hackathon's role in demonstrating blockchain's practical applications: "The solutions created during Decode in Turkey prove that innovation can be realized in a matter of days." The event highlighted several key developments in travel technology: Integration of blockchain with existing travel infrastructure Development of automated payment distribution systems Implementation of privacy-preserving data sharing protocols Creation of AI-enhanced booking systems Establishment of cross-border collaboration frameworks Integration of blockchain with existing travel infrastructure Integration of blockchain with existing travel infrastructure Development of automated payment distribution systems Development of automated payment distribution systems Implementation of privacy-preserving data sharing protocols Implementation of privacy-preserving data sharing protocols Creation of AI-enhanced booking systems Creation of AI-enhanced booking systems Establishment of cross-border collaboration frameworks Establishment of cross-border collaboration frameworks The hackathon's international participation reflected growing interest in blockchain applications within the travel industry. Participants from Turkey, Germany, Macedonia, Spain, and Georgia contributed diverse perspectives to technical solutions. The Camino Network Foundation announced its continuing support for travel technology development through a grant program, encouraging developers and businesses to build on its open-source infrastructure. Industry analysts note that such events signal increasing adoption of blockchain technology in travel operations, particularly in areas of payments, data management, and booking systems. The solutions developed during the hackathon demonstrate potential applications for reducing operational costs and improving service delivery in the travel sector. The event's success in attracting established industry players suggests growing recognition of blockchain's role in travel technology development. Future hackathons are planned to continue fostering innovation and collaboration between travel companies and technology developers. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

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