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Linksight

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Generative AI: Expert Insights on Evolution, Challenges, and Future Trends

জেনারেটিভ এআই: বিবর্তন, চ্যালেঞ্জ এবং ভবিষ্যত প্রবণতা সম্পর্কে বিশেষজ্ঞের অন্তর্দৃষ্টি

Generative KI: Experteneinblicke in Entwicklung, Herausforderungen und zukünftige Trends

IA generativa: opiniones de expertos sobre la evolución, los desafíos y las tendencias futuras

IA générative : avis d'experts sur l'évolution, les défis et les tendances futures

जनरेटिव एआई: विकास, चुनौतियों और भविष्य के रुझानों पर विशेषज्ञ अंतर्दृष्टि

生成型 AI: 進化、課題、将来の動向に関する専門家の見解

생성적 AI: 진화, 과제 및 미래 동향에 대한 전문가의 통찰력

IA generativa: insights de especialistas sobre evolução, desafios e tendências futuras

Генеративный ИИ: экспертные взгляды на эволюцию, проблемы и будущие тенденции

Üretken Yapay Zeka: Gelişim, Zorluklar ve Gelecekteki Eğilimler Hakkında Uzman Görüşleri

AI sáng tạo: Thông tin chuyên sâu của chuyên gia về sự tiến hóa, thách thức và xu hướng tương lai

生成式人工智能：专家对演进、挑战和未来趋势的见解

I have 9 years of experience as a venture investor, and I can confirm that one of the most important things in a startup’s early stages is the team. Therefore, whether or not I decide to invest in a project largely depends on my meeting with its founder. In addition to convincing me with figures, he/she needs to build trust. While I might feel ready to invest after the first meeting, I often take some weeks to reflect. These are the qualities that influenced my decision. Industry Knowledge and Experience As a founder, you must know the ins and outs of any industry you want to venture into. For example, a fintech entrepreneur with prior experience in the banking sector will offer invaluable expertise. This is a quality I appraise, as it means you have a firsthand understanding of the industry's pain points and nuances. As an investor, I also consider the educational background of the founder. I inquire about their academic achievements, including grades and thesis topics. A solid educational foundation adds credibility to the founder's qualifications. Entrepreneurial Background Entrepreneurship is a rare talent; from my experience, only about 2% of individuals inherently possess it. In most cases, experience is the best teacher. Therefore, I value founders who have gained experience in large corporations before embarking on their own ventures. Additionally, I have greater confidence in founders who have undergone 1-2 unsuccessful projects. In my view, success alone doesn't provide valuable lessons; it's the mistakes and setbacks that offer profound learning opportunities. It's crucial that a founder evaluates these experiences and learns from them. Willingness to Admit Mistakes and Accept Change Periodically, I meet founders who don’t take criticism well. They find it difficult to admit mistakes and often accept a single hypothesis as the truth. But to make an idea work, you need to test 5-10 hypotheses, adapt the model, and possibly make a pivot. Instead, they're stuck on one idea and keep forcing it; even if it's not improving. Beyond the product itself, there are external factors necessary for growth. Hiring the right team, building a marketing strategy, finding enablers, and so on. Experienced investors with foresight can discover potential pitfalls and highlight blind spots, but not every founder is an open listener. As much as I value entrepreneurial experience and industry expertise, founders need to remain flexible so that their experience helps, and not hinders, the startup's growth. Honesty I value honesty right from early communication with the founder. My confidence in a project grows when founders openly discuss challenges, setbacks, and whatever isn’t working. Sometimes, initial hypotheses don't pan out, but determined founders continue to explore and iterate. Other times, customer acquisition costs may be prohibitively high, prompting them to experiment with new acquisition or investment channels. Being candid about what's not working demonstrates integrity, resilience, and a commitment to finding solutions. On the other hand, if founders withhold information about their challenges, it diminishes my confidence in the project. As long as I believe in the vision, I’m always open to offering solutions where possible. Passion Building a startup often spans a decade-long journey. Passion is needed to drive the commitment to see this long path through. Therefore, I look at how passionate a founder is about his/her project, and how deeply immersed they are in the topic. I also prioritize understanding a founder's commitment level and endgame aspirations — whether they are ready to dedicate themselves fully to the project, aiming for a profitable exit, or seeking to establish a lasting legacy for future generations. For instance, one founder candidly expressed a desire to exit the startup within 3-5 years. In response, I adjusted my expectations accordingly and focused on realistic growth targets rather than aiming for astronomical returns. Conclusion Successful tech entrepreneurs have a fine blend of unique skills, industry expertise, and unwavering passion. In my view, to achieve success, a founder must be 120% committed to their startup, whether it’s for the short or long term. They must also be adaptable and not overlook potential challenges. A final word to the upcoming innovators and industry disruptors: When engaging with investors, demonstrate your expertise and unwavering motivation. To build trust, be transparent in your dealings and forthright about the obstacles you face. Substantiate your claims with solid data to crown your efforts. Adopting this approach will significantly enhance your prospects of securing funding. I have 9 years of experience as a venture investor, and I can confirm that one of the most important things in a startup’s early stages is the team. Therefore, whether or not I decide to invest in a project largely depends on my meeting with its founder. In addition to convincing me with figures, he/she needs to build trust. While I might feel ready to invest after the first meeting, I often take some weeks to reflect. These are the qualities that influenced my decision. Industry Knowledge and Experience Industry Knowledge and Experience As a founder, you must know the ins and outs of any industry you want to venture into. For example, a fintech entrepreneur with prior experience in the banking sector will offer invaluable expertise. This is a quality I appraise, as it means you have a firsthand understanding of the industry's pain points and nuances. As an investor, I also consider the educational background of the founder. I inquire about their academic achievements, including grades and thesis topics. A solid educational foundation adds credibility to the founder's qualifications. Entrepreneurial Background Entrepreneurial Background Entrepreneurship is a rare talent; from my experience, only about 2% of individuals inherently possess it. In most cases, experience is the best teacher. Therefore, I value founders who have gained experience in large corporations before embarking on their own ventures. Additionally, I have greater confidence in founders who have undergone 1-2 unsuccessful projects. In my view, success alone doesn't provide valuable lessons; it's the mistakes and setbacks that offer profound learning opportunities. It's crucial that a founder evaluates these experiences and learns from them. Willingness to Admit Mistakes and Accept Change Willingness to Admit Mistakes and Accept Change Periodically, I meet founders who don’t take criticism well. They find it difficult to admit mistakes and often accept a single hypothesis as the truth. But to make an idea work, you need to test 5-10 hypotheses, adapt the model, and possibly make a pivot. Instead, they're stuck on one idea and keep forcing it; even if it's not improving. Beyond the product itself, there are external factors necessary for growth. Hiring the right team, building a marketing strategy, finding enablers, and so on. Experienced investors with foresight can discover potential pitfalls and highlight blind spots, but not every founder is an open listener. As much as I value entrepreneurial experience and industry expertise, founders need to remain flexible so that their experience helps, and not hinders, the startup's growth. Honesty Honesty I value honesty right from early communication with the founder. My confidence in a project grows when founders openly discuss challenges, setbacks, and whatever isn’t working. Sometimes, initial hypotheses don't pan out, but determined founders continue to explore and iterate. Other times, customer acquisition costs may be prohibitively high, prompting them to experiment with new acquisition or investment channels. Being candid about what's not working demonstrates integrity, resilience, and a commitment to finding solutions. On the other hand, if founders withhold information about their challenges, it diminishes my confidence in the project. As long as I believe in the vision, I’m always open to offering solutions where possible. Passion Passion Building a startup often spans a decade-long journey. Passion is needed to drive the commitment to see this long path through. Therefore, I look at how passionate a founder is about his/her project, and how deeply immersed they are in the topic. I also prioritize understanding a founder's commitment level and endgame aspirations — whether they are ready to dedicate themselves fully to the project, aiming for a profitable exit, or seeking to establish a lasting legacy for future generations. For instance, one founder candidly expressed a desire to exit the startup within 3-5 years. In response, I adjusted my expectations accordingly and focused on realistic growth targets rather than aiming for astronomical returns. Conclusion Conclusion Successful tech entrepreneurs have a fine blend of unique skills, industry expertise, and unwavering passion. In my view, to achieve success, a founder must be 120% committed to their startup, whether it’s for the short or long term. They must also be adaptable and not overlook potential challenges. A final word to the upcoming innovators and industry disruptors: A final word to the upcoming innovators and industry disruptors: When engaging with investors, demonstrate your expertise and unwavering motivation. To build trust, be transparent in your dealings and forthright about the obstacles you face. Substantiate your claims with solid data to crown your efforts. Adopting this approach will significantly enhance your prospects of securing funding.

Which Founders Do Investors Trust? Insights From a Business Angel Who Invested in 52 Startups

EthCC 2024: Impressions and Insights about the Biggest European Blockchain Conference

EthCC 2024: বৃহত্তম ইউরোপীয় ব্লকচেইন সম্মেলন সম্পর্কে ইমপ্রেশন এবং অন্তর্দৃষ্টি

EthCC 2024: Eindrücke und Einblicke zur größten europäischen Blockchain-Konferenz

EthCC 2024: Impresiones y conocimientos sobre la mayor conferencia europea sobre blockchain

EthCC 2024 : impressions et aperçus de la plus grande conférence européenne sur la blockchain

EthCC 2024: सबसे बड़े यूरोपीय ब्लॉकचेन सम्मेलन के बारे में इंप्रेशन और अंतर्दृष्टि

EthCC 2024: ヨーロッパ最大のブロックチェーンカンファレンスについての印象と考察

EthCC 2024: 유럽 최대 블록체인 컨퍼런스에 대한 인상과 통찰

EthCC 2024: Impressões e Insights sobre a Maior Conferência Europeia de Blockchain

EthCC 2024: впечатления и идеи о крупнейшей европейской конференции по блокчейну

EthCC 2024: Avrupa'nın En Büyük Blockchain Konferansı Hakkında İzlenimler ve İçgörüler

EthCC 2024: Những ấn tượng và hiểu biết sâu sắc về Hội nghị Blockchain lớn nhất châu Âu

EthCC 2024：欧洲最大的区块链会议印象与见解

Authors:
(1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu);
(2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Table of Links Abstract and I. Introduction II. Overview of PING System and Gridlock III. Design Insights and Educational Impact IV. Conclusion and References III. DESIGN INSIGHTS AND EDUCATIONAL IMPACT A. Design Insights and Student Feedback When discussing design changes made in the game with regards to student feedback, the iterations focused on three main areas: Interface design, student testing, and tutorials/student assistance. 1) Interface Design For interface design, we had a few main considerations. First, the target audience for the game is highly generalized, and thus includes players who are and are not familiar with virtual games. For players who are familiar with virtual games, certain aspects such as a standard control scheme and familiar interface elements are enough for players to understand the goals, controls, and objective of the game. For example, the updated game features a system of objective markers shown in Figure 4, where students receive a visual indication of their current objective. Even among students who were less familiar with virtual games, a visual indicator marking their objective helps to significantly improve their ability to navigate the virtual environment. Outside of marking objectives, it was also very important to have both a stated objective and a guide on the control scheme of the game. For Gridlock, students can open up a “how to play” screen at any time that indicates the control scheme of the game. Additionally, students can also access a “current objectives” screen that has a written statement of their current objective. Among players experienced with virtual games, these features were largely ignored. However, their inclusion is crucial to help players who are unfamiliar with navigating virtual game environments. One final aspect that also helped students navigate the environment was “notification” markers on certain in-game utilities. For example, when students start the game, they have a notification on one of the menu options. This incentivizes them to click this option, thus taking them to the assistance menu, as shown in Figure 5. This way, students are already familiarized with this menu before it becomes relevant, allowing them to have a smoother experience in the latter parts of the game. 2) Student Testing Another modification that was well-received among students was the replacement of some testing methods with interactive mini-games or puzzles. In each of these basic games, students receive an instructional prompt before participating. These games are designed in such a way that we can gather data on the students’ knowledge while they play. For example, as shown in Figure 6, students are tested on their ability to convert binary numbers through a game where they are tasked with matching binary numbers to their decimal counterparts as quickly as possible. 3) Student Assistance Finally, the in-game tutorials were also modified as per student feedback. In prior iterations, accessing the personalized assistance provided to students was required for students to progress. Furthermore, the assistance was specifically tailored to a student’s issues. In the updated version of the game, the assistance is general-purpose, but students are directed to a specific area that addresses their issues. This way, students have the option to manually explore additional help materials. Additionally, students now must manually access the help materials, which prevents the help prompts from breaking the flow of the game. If a student is performing poorly, the game can also put a notification marker on the help menu to encourage the student to request help. With all these changes, students have much more freedom to either accept the help as given, explore additional content at their own pace, or to ignore the help entirely and attempt to solve problems on their own. This gives students guidance if they need it, and the freedom to explore and solve problems without being limited or interrupted by the system. B. Educational Impact Preliminary results for an evaluation of educational efficacy use a pre-test and a post-test administered before and after students played the game, respectively. These tests asked students to design a state machine for a vending machine; a task which has a similar required skill set as the traffic light problem in the game. Figure 7 shows a comparison of score improvement among students from our pre-test to the post-test. Students were graded on both assignments on a scale from 0 to 1, and the graph compares four scenarios: 1) The updated game with PING system integration, modified based on student feedback as per the design statements outlined in this paper (𝑁 = 13); 2) The prior version of the game with PING system integration (𝑁 = 20); 3) An old version of Gridlock that did not use the PING system (𝑁 = 14); and 4) Students who did not play the game (𝑁 = 8). As shown, students who played the updated game had greater improvement over students who played the prior version of the game. Additionally, there were a fewer number of students who regressed or showed little improvement compared to past classroom evaluations. This is likely due to the new quality of life features and easier-to-access help documentation making it both easier and less frustrating for students to learn with Gridlock. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Authors: Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); Dept. of Electrical and Computer Engineering, Rowan University, (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Dept. of Electrical and Computer Engineering, Rowan University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of PING System and Gridlock II. Overview of PING System and Gridlock III. Design Insights and Educational Impact III. Design Insights and Educational Impact IV. Conclusion and References IV. Conclusion and References III. DESIGN INSIGHTS AND EDUCATIONAL IMPACT A. Design Insights and Student Feedback A. Design Insights and Student Feedback When discussing design changes made in the game with regards to student feedback, the iterations focused on three main areas: Interface design, student testing, and tutorials/student assistance. 1) Interface Design 1) Interface Design For interface design, we had a few main considerations. First, the target audience for the game is highly generalized, and thus includes players who are and are not familiar with virtual games. For players who are familiar with virtual games, certain aspects such as a standard control scheme and familiar interface elements are enough for players to understand the goals, controls, and objective of the game. For example, the updated game features a system of objective markers shown in Figure 4, where students receive a visual indication of their current objective. Even among students who were less familiar with virtual games, a visual indicator marking their objective helps to significantly improve their ability to navigate the virtual environment. Outside of marking objectives, it was also very important to have both a stated objective and a guide on the control scheme of the game. For Gridlock, students can open up a “how to play” screen at any time that indicates the control scheme of the game. Additionally, students can also access a “current objectives” screen that has a written statement of their current objective. Among players experienced with virtual games, these features were largely ignored. However, their inclusion is crucial to help players who are unfamiliar with navigating virtual game environments. One final aspect that also helped students navigate the environment was “notification” markers on certain in-game utilities. For example, when students start the game, they have a notification on one of the menu options. This incentivizes them to click this option, thus taking them to the assistance menu, as shown in Figure 5. This way, students are already familiarized with this menu before it becomes relevant, allowing them to have a smoother experience in the latter parts of the game. 2) Student Testing 2) Student Testing Another modification that was well-received among students was the replacement of some testing methods with interactive mini-games or puzzles. In each of these basic games, students receive an instructional prompt before participating. These games are designed in such a way that we can gather data on the students’ knowledge while they play. For example, as shown in Figure 6, students are tested on their ability to convert binary numbers through a game where they are tasked with matching binary numbers to their decimal counterparts as quickly as possible. 3) Student Assistance 3) Student Assistance Finally, the in-game tutorials were also modified as per student feedback. In prior iterations, accessing the personalized assistance provided to students was required for students to progress. Furthermore, the assistance was specifically tailored to a student’s issues. In the updated version of the game, the assistance is general-purpose, but students are directed to a specific area that addresses their issues. This way, students have the option to manually explore additional help materials. Additionally, students now must manually access the help materials, which prevents the help prompts from breaking the flow of the game. If a student is performing poorly, the game can also put a notification marker on the help menu to encourage the student to request help. With all these changes, students have much more freedom to either accept the help as given, explore additional content at their own pace, or to ignore the help entirely and attempt to solve problems on their own. This gives students guidance if they need it, and the freedom to explore and solve problems without being limited or interrupted by the system. B. Educational Impact B. Educational Impact Preliminary results for an evaluation of educational efficacy use a pre-test and a post-test administered before and after students played the game, respectively. These tests asked students to design a state machine for a vending machine; a task which has a similar required skill set as the traffic light problem in the game. Figure 7 shows a comparison of score improvement among students from our pre-test to the post-test. Students were graded on both assignments on a scale from 0 to 1, and the graph compares four scenarios: 1) The updated game with PING system integration, modified based on student feedback as per the design statements outlined in this paper (𝑁 = 13); 2) The prior version of the game with PING system integration (𝑁 = 20); 3) An old version of Gridlock that did not use the PING system (𝑁 = 14); and 4) Students who did not play the game (𝑁 = 8). As shown, students who played the updated game had greater improvement over students who played the prior version of the game. Additionally, there were a fewer number of students who regressed or showed little improvement compared to past classroom evaluations. This is likely due to the new quality of life features and easier-to-access help documentation making it both easier and less frustrating for students to learn with Gridlock. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Enhancing Learning with Gamified Instruction: Design Insights and Educational Impact

Unlocking the Power of Crypto APIs: Transforming Data Into Insight and Opportunity

ক্রিপ্টো API-এর শক্তি আনলক করা: তথ্যকে অন্তর্দৃষ্টি এবং সুযোগে রূপান্তর করা

Die Leistungsfähigkeit von Krypto-APIs freisetzen: Daten in Erkenntnisse und Chancen umwandeln

Liberando el poder de las cripto API: transformando los datos en conocimientos y oportunidades

Libérer la puissance des API cryptographiques : transformer les données en informations et en opportunités

क्रिप्टो एपीआई की शक्ति को अनलॉक करना: डेटा को अंतर्दृष्टि और अवसर में बदलना

暗号 API のパワーを解き放つ: データを洞察と機会に変える

암호화 API의 힘 활용: 데이터를 통찰력과 기회로 전환

Desbloqueando o poder das APIs criptográficas: transformando dados em insights e oportunidades

Раскрытие возможностей криптографических API: преобразование данных в понимание и возможности

Kripto API'lerinin Gücünün Kilidini Açmak: Verileri İçgörüye ve Fırsata Dönüştürmek

Khai phá sức mạnh của API tiền điện tử: Chuyển đổi dữ liệu thành thông tin chuyên sâu và cơ hội

释放加密 API 的力量：将数据转化为洞察力和机遇

**DUBAI, UAE, July 15th, 2024/CyberNewsWire/--**On May 31, 2024, as a result of the hacking 4502.9 BTC(worth approximately $308M)were stolen from the Japanese exchange Bitcoin.DMM.com. The cybersecurity agency Match Systems conducted the current situation of the case. Japanese cryptocurrency exchange DMM Bitcoin was recently hacked, resulting in the theft of over $300 million dollars in Bitcoins. The hackers were able to access a private key, which allowed them to transfer 4,502.9 bitcoins from the exchange's main wallet. The incident occurred on May 30, 2024, and DMM Bitcoin announced the hack the following day. The reasons for the hack of the exchange are still unclear. The attack could have occurred as a result of compromised private keys. The keys of a hot wallet connected to the Internet could have been compromised either through an internal threat or an external hack, which could have allowed hackers to initiate a transfer of funds. Whether social engineering techniques or malware were used to get exchange employees to sign a transaction authorizing the transfer of funds to their wallets, we have yet to learn. An insider attack scenario cannot be ruled out, in which someone with legitimate access to the system contributed to the hack by giving the hackers the necessary data or initiating the attack. The investigation revealed that the hackers used cryptocurrency mixer JoinMarket to launder the stolen funds. More than 2,000 BTC was sent to addresses associated with JoinMarket, while the remaining 2,500 BTC remained at the hackers' original addresses. The Match Systems team was able to identify the first large withdrawal from the mixer in the amount of 223.38 BTC, as well as more than 50 withdrawals over 10 BTC that may be relevant to this case. The full cycle of money laundering can take months to a year, and Match Systems will continue to monitor the movement of stolen funds, as well as looking into the cause of the attack. About Match Systems Match Systems, is a leading company specializing in AML services, blockchain investigations, and implementation of compliance procedures for cryptocurrency projects around the world. By leveraging advanced technology and expertise in financial crime detection, the company is poised to help organizations to navigate the complex regulatory landscape as well as minimize the risks associated with digital currencies. Contact Joseph Match Systems mediacoverage@matchsystems.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **DUBAI, UAE, July 15th, 2024/CyberNewsWire/--**On May 31, 2024, as a result of the hacking 4502.9 BTC(worth approximately $308M)were stolen from the Japanese exchange Bitcoin.DMM.com. The cybersecurity agency Match Systems conducted the current situation of the case. Japanese cryptocurrency exchange DMM Bitcoin was recently hacked, resulting in the theft of over $300 million dollars in Bitcoins. The hackers were able to access a private key, which allowed them to transfer 4,502.9 bitcoins from the exchange's main wallet. The incident occurred on May 30, 2024, and DMM Bitcoin announced the hack the following day. The reasons for the hack of the exchange are still unclear. The attack could have occurred as a result of compromised private keys. The keys of a hot wallet connected to the Internet could have been compromised either through an internal threat or an external hack, which could have allowed hackers to initiate a transfer of funds. Whether social engineering techniques or malware were used to get exchange employees to sign a transaction authorizing the transfer of funds to their wallets, we have yet to learn. An insider attack scenario cannot be ruled out, in which someone with legitimate access to the system contributed to the hack by giving the hackers the necessary data or initiating the attack. The investigation revealed that the hackers used cryptocurrency mixer JoinMarket to launder the stolen funds. More than 2,000 BTC was sent to addresses associated with JoinMarket, while the remaining 2,500 BTC remained at the hackers' original addresses. The Match Systems team was able to identify the first large withdrawal from the mixer in the amount of 223.38 BTC, as well as more than 50 withdrawals over 10 BTC that may be relevant to this case. The full cycle of money laundering can take months to a year, and Match Systems will continue to monitor the movement of stolen funds, as well as looking into the cause of the attack. About Match Systems Match Systems , is a leading company specializing in AML services, blockchain investigations, and implementation of compliance procedures for cryptocurrency projects around the world. By leveraging advanced technology and expertise in financial crime detection, the company is poised to help organizations to navigate the complex regulatory landscape as well as minimize the risks associated with digital currencies. Match Systems Match Systems Contact Joseph Match Systems mediacoverage@matchsystems.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Match Systems' CEO Andrei Kutin Provides Insight On DMM Bitcoin Breach

Transforming Mobile Maps with Jetpack Compose: Insights from Google I/O for Developers

জেটপ্যাক কম্পোজের সাহায্যে মোবাইল মানচিত্র রূপান্তর: বিকাশকারীদের জন্য Google I/O থেকে অন্তর্দৃষ্টি

Mobile Karten mit Jetpack Compose transformieren: Erkenntnisse von Google I/O für Entwickler

Transformando mapas móviles con Jetpack Compose: información de Google I/O para desarrolladores

Transformer les cartes mobiles avec Jetpack Compose : informations de Google I/O pour les développeurs

जेटपैक कंपोज के साथ मोबाइल मैप्स को बदलना: डेवलपर्स के लिए Google I/O से जानकारी

Jetpack Compose でモバイル マップを変革: 開発者向け Google I/O からの洞察

Jetpack Compose로 모바일 지도 혁신: 개발자를 위한 Google I/O의 유용한 정보

Transformando mapas móveis com Jetpack Compose: insights do Google I/O para desenvolvedores

Преобразование мобильных карт с помощью Jetpack Compose: выводы Google I/O для разработчиков

Jetpack Compose ile Mobil Haritaları Dönüştürme: Geliştiriciler için Google I/O'dan Analizler

Chuyển đổi bản đồ di động bằng Jetpack Compose: Thông tin chi tiết từ Google I/O dành cho nhà phát triển

使用 Jetpack Compose 改造移动地图：Google I/O 开发者大会上的见解

Cybersecurity and AI: Meetings and Insights from the Nexus 2050 Conference

Uncovering Gender Bias in Talent Shows: Insights from The Voice

Author:
(1) Anuar Assamidanov, Department of Economics, Claremont Graduate University, 150 E 10th St, Claremont, CA 91711. (Email: anuar.assamidanov@cgu.edu). Table of Links Abstract Background Data Methodology Results Discussion and Conclusion, and References A Appendix Tables and Figures 2 Data I used the blind audition part of the show as a natural experiment in this work. The data on a blind audition was compiled from historical Voice TV shows on Wikipedia. I web-scraped the results of the blind audition for four countries: the UK, France, Germany, and Australia. Each country held approximately ten seasons each year, starting from the year 2012. Each country’s Voice Wiki page is divided by season, each season is divided by the episodes, and the order of the performance splits each episode. The page includes the singer and coach’s information, the singer’s age, the order of performance, and the song. From the Wiki page, I constructed the outcome variable based on the coaches who pressed the button if the coach chose the specific artist. Thus, I can build the result of the four coaches for each artist. Similarly, the page provides the actual order of the performance. Since the primary goal of the analysis is to identify gender-specific preferences, I needed to determine the gender of the artist. I infer the gender of the artists based on their first names. I used an online application programming interface (API) called Genderize.io. The application has pre-trained machine learning algorithms that are used to predict gender. The API gives a confidence level in probabilistic terms and the exact name count in their already used datasets. Utilizing a threshold of 90% probability, I am able to attach a gender to approximately 97% of the whole dataset. For the remaining 3% of the first names, I hardcoded by checking the performance on the YouTube webpage. The gender of the coaches can easily be identified as all of them are well-established artists. I also used the genre of the song the artist performs. Using song information from the Wikipedia page, I extracted the genre of the music from the Spotify API. The API gives multiple genres that correspond to the given song. By doing frequency distribution, I determined the most frequent genres and came up with the eleven unique genres. Summary statistics are shown at the coach level in Table 1. Column (1) includes all coaches in the sample, while Columns (2) and (3) disaggregate to coaches who are female or male, respectively. The data has 11,972 observations, 58 unique coaches, and 3,009 unique artists. 31.6 percent of the coaches were female, though there is considerable heterogeneity across the team gender composition and failure rates for female and male artists. Similarly, about 46.3 percent of artists are men, providing decent statistical power for an own-gender bias analysis. My primary analysis includes all the coaches’ choices, with approximately 40 percent of the artists being chosen in the blind audition round. Female artists are chosen about 21.6 percent of the time. The difference between female coaches and male coaches choosing female artists is a 1 percent difference. This pattern of having a 1% difference continues to hold for the male artists who are chosen 18 percent of the time. Thus, a naive comparison of the raw data for female and male gender would yield a conclusion of no gender bias. Since the preference of female and male artists across female and male coaches is similar, the selection difference lies at about one percent. To justify this premise, I examine the order of each performance at the artist level and how TV show-related specification affects decision-making. Table 1 also provides summary statistics for TV show-related specifications, considering them heterogeneous in the analysis. Each coach and artist has a unique specification, such as the order of their performance, their preference towards a specific coach, and the genre of the song. Ultimately, 16.8 percent of the artists ended up on a team. From that team configuration, we can determine the gender composition of the team, which is the number of women and men on the team during the blind audition stage. The average difference between the number of males and females for each order on the team is -0.533, meaning that the number of female artists is 0.5 people higher on average compared with the male artists. Additional information is obtained when two or more coaches pursue the same artist, providing the artist with the final choice. I estimated the failure rate for these cases, and each coach has about a 0.27 failure rate for female artists and 0.24 for male artists. This is the rate at which a coach wanted an artist of a particular gender on their team, and the artist did not choose the coach. Both of these variables have considerable differences across the gender of the coach, which enriches our study to identify own-gender bias in the selection process. Notes: This table presents summary statistics for the estimation sample. The unit of observation is performance in the blind audition stage. The table shows statistics of all performances for which artists and drivers are men and women. The sample includes four countries, and each country has ten seasons. Standard deviations in parentheses. All values reported in this table are unconditional average rates of the variables indicated in rows. As discussed and shown in the Section 1, the institutional setting provides for the exogenous assignment of gender encounters between coach and artist. Table 2 examines this exogeneity assumption by estimating differences in the observed characteristics of male and female artists. The first column shows mean characteristics for all artists, while the second and third columns present average outcomes for male and female artists. The fourth column reports the difference between the two means. Comparing overall raw means is consistent with the assignment procedure described above. There is no significant difference between male and female artists in any show-related specifications, such as being chosen by coaches, selecting a team, being chosen by a male coach, and the order of their performance. This supports our identification strategy: in a given show setting, there is little evidence to suggest that male artists’ performances systematically differ from those performed by female ones. However, I also find some statistically significant differences. For example, male artists are 5.2 percent more likely to perform pop genre songs. Similarly, the share of classic style and the share of rock style is lower, while the share of country style is higher among male artists. All these significant observables will be estimated in the setting as confounding factors. Notes: Standard deviations in parentheses in columns 1,2 and 3. Each entry in column 4 is derived from a separate OLS regression where explanatory variable is indicator for male artist. * p < 0.1, ** p < 0.05, *** p < 0.01 This paper is available on arxiv under CC 4.0 license. Author: (1) Anuar Assamidanov, Department of Economics, Claremont Graduate University, 150 E 10th St, Claremont, CA 91711. (Email: anuar.assamidanov@cgu.edu). Author: Author: (1) Anuar Assamidanov, Department of Economics, Claremont Graduate University, 150 E 10th St, Claremont, CA 91711. (Email: anuar.assamidanov@cgu.edu). Table of Links Abstract Abstract Background Background Data Data Methodology Methodology Results Results Discussion and Conclusion, and References Discussion and Conclusion, and References A Appendix Tables and Figures A Appendix Tables and Figures 2 Data I used the blind audition part of the show as a natural experiment in this work. The data on a blind audition was compiled from historical Voice TV shows on Wikipedia. I web-scraped the results of the blind audition for four countries: the UK, France, Germany, and Australia. Each country held approximately ten seasons each year, starting from the year 2012. Each country’s Voice Wiki page is divided by season, each season is divided by the episodes, and the order of the performance splits each episode. The page includes the singer and coach’s information, the singer’s age, the order of performance, and the song. From the Wiki page, I constructed the outcome variable based on the coaches who pressed the button if the coach chose the specific artist. Thus, I can build the result of the four coaches for each artist. Similarly, the page provides the actual order of the performance. Since the primary goal of the analysis is to identify gender-specific preferences, I needed to determine the gender of the artist. I infer the gender of the artists based on their first names. I used an online application programming interface (API) called Genderize.io. The application has pre-trained machine learning algorithms that are used to predict gender. The API gives a confidence level in probabilistic terms and the exact name count in their already used datasets. Utilizing a threshold of 90% probability, I am able to attach a gender to approximately 97% of the whole dataset. For the remaining 3% of the first names, I hardcoded by checking the performance on the YouTube webpage. The gender of the coaches can easily be identified as all of them are well-established artists. I also used the genre of the song the artist performs. Using song information from the Wikipedia page, I extracted the genre of the music from the Spotify API. The API gives multiple genres that correspond to the given song. By doing frequency distribution, I determined the most frequent genres and came up with the eleven unique genres. Summary statistics are shown at the coach level in Table 1. Column (1) includes all coaches in the sample, while Columns (2) and (3) disaggregate to coaches who are female or male, respectively. The data has 11,972 observations, 58 unique coaches, and 3,009 unique artists. 31.6 percent of the coaches were female, though there is considerable heterogeneity across the team gender composition and failure rates for female and male artists. Similarly, about 46.3 percent of artists are men, providing decent statistical power for an own-gender bias analysis. My primary analysis includes all the coaches’ choices, with approximately 40 percent of the artists being chosen in the blind audition round. Female artists are chosen about 21.6 percent of the time. The difference between female coaches and male coaches choosing female artists is a 1 percent difference. This pattern of having a 1% difference continues to hold for the male artists who are chosen 18 percent of the time. Thus, a naive comparison of the raw data for female and male gender would yield a conclusion of no gender bias. Since the preference of female and male artists across female and male coaches is similar, the selection difference lies at about one percent. To justify this premise, I examine the order of each performance at the artist level and how TV show-related specification affects decision-making. Table 1 also provides summary statistics for TV show-related specifications, considering them heterogeneous in the analysis. Each coach and artist has a unique specification, such as the order of their performance, their preference towards a specific coach, and the genre of the song. Ultimately, 16.8 percent of the artists ended up on a team. From that team configuration, we can determine the gender composition of the team, which is the number of women and men on the team during the blind audition stage. The average difference between the number of males and females for each order on the team is -0.533, meaning that the number of female artists is 0.5 people higher on average compared with the male artists. Additional information is obtained when two or more coaches pursue the same artist, providing the artist with the final choice. I estimated the failure rate for these cases, and each coach has about a 0.27 failure rate for female artists and 0.24 for male artists. This is the rate at which a coach wanted an artist of a particular gender on their team, and the artist did not choose the coach. Both of these variables have considerable differences across the gender of the coach, which enriches our study to identify own-gender bias in the selection process. Notes: This table presents summary statistics for the estimation sample. The unit of observation is performance in the blind audition stage. The table shows statistics of all performances for which artists and drivers are men and women. The sample includes four countries, and each country has ten seasons. Standard deviations in parentheses. All values reported in this table are unconditional average rates of the variables indicated in rows. As discussed and shown in the Section 1, the institutional setting provides for the exogenous assignment of gender encounters between coach and artist. Table 2 examines this exogeneity assumption by estimating differences in the observed characteristics of male and female artists. The first column shows mean characteristics for all artists, while the second and third columns present average outcomes for male and female artists. The fourth column reports the difference between the two means. Comparing overall raw means is consistent with the assignment procedure described above. There is no significant difference between male and female artists in any show-related specifications, such as being chosen by coaches, selecting a team, being chosen by a male coach, and the order of their performance. This supports our identification strategy: in a given show setting, there is little evidence to suggest that male artists’ performances systematically differ from those performed by female ones. However, I also find some statistically significant differences. For example, male artists are 5.2 percent more likely to perform pop genre songs. Similarly, the share of classic style and the share of rock style is lower, while the share of country style is higher among male artists. All these significant observables will be estimated in the setting as confounding factors. Notes: Standard deviations in parentheses in columns 1,2 and 3. Each entry in column 4 is derived from a separate OLS regression where explanatory variable is indicator for male artist. * p < 0.1, ** p < 0.05, *** p < 0.01 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

Revealing Gender Bias: Insights from Blind Auditions on 'The Voice

Author:
(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es);
(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es);
(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Related work The proposed pipeline Evaluation Conclusion & Acknowledgements References & Appendix 3. The proposed pipeline The purpose of the proposed pipeline is, given the text reviews of an Amazon product, to classify them as normal if they refer to it, and as anomalous if they describe different products or if they are so generic that they do not provide useful information to consumers. These classifications will be accompanied by a normality score and explanations that justify them. Figure 1 shows the modules that are part of the proposed pipeline to solve this task. In the first phase, we encode the text reviews of the target product using the pretrained MPNet transformer [25]. In the second phase, a DAEF [26] autoencoder is trained using these embeddings. Using the reconstruction errors issued by the network and a predefined threshold error, we can classify reviews as normal (error greater than the threshold) or anomalous (error less than the threshold). For the last phase, we propose a method based on the most frequent normal terms to generate an explanation associated with each classification. Therefore, the proposed pipeline is composed of three main modules: (1) Text encoding; (2) Anomaly detection; (3) Explainability. The following sections describe each of them in detail. 3.1. Text encoding Although there are models capable of dealing directly with images or text, machine learning models are usually designed to be trained using numerical vectors that represent the sample data (tabular data). This input format of the data is the usual one for classic anomaly detection methods. In the NLP area, there are multiple techniques to represent texts using vectors of real numbers, which are known as embeddings. These techniques allow the generation of vector spaces that try to represent the relationships and semantic similarities of the language, so that, for example, two synonymous words will be found at a shorter distance in the vector space than two unrelated words. Embeddings can be calculated independently for each word of the language (word embeddings), which led to models such as word2vec [27] or Global Vectors (GloVe) [28]. The representation of a sentence (sentence embedding) or a document (document embedding) will therefore be the sum of all the individual representations of the terms that make it up. To obtain representations of fixed length, it is usual to perform operations such as the mean. In certain cases, these operations between embeddings can worsen or even invalidate the final embedding, so specific models have been developed capable of understanding and representing a text as a whole, instead of just encoding it word by word. Among these models are those based on transformers [29], such as BERT [30], XLNet [31], GPT-3 [32] or GPT-4 [33], which have been trained over large-scale datasets and can solve different tasks, including sentence embedding. Since it inherits the advantages of the BERT and XLNet models while overcoming their limitations, we will use the pre-trained MPNet [25] model to calculate the embeddings of the reviews. MPNet combines Masked Language Modeling (MLM) and Permuted Language Modeling (PLM) to predict token dependencies, using auxiliary position information as input to enable the model to view a complete sentence and reduce position differences. Models like GPT-3 or GPT-4 are more advanced, but in addition to not being open source, they demand much higher computational resources, unaffordable for a significant part of the scientific community, as is our particular case. MPNet maps sentences and paragraphs to a 768 dimensional dense vector space e ∈ R 768×1 , providing fast and quality encodings. Computing time is a critical aspect in the area in which this work is framed, since in e-commerce platforms (and online reviews in general) we can find a huge number of products and reviews to deal with. Therefore, the proposed pipeline uses MPNet to calculate the embeddings of the reviews, which allows them to be represented as numeric vectors of fixed size. This model and others are available in the Hugging Face repository [34]. 3.2. Anomaly detection Anomaly detection is a field with a large number of algorithms that solve the problem of distinguishing between normal and anomalous instances in a wide variety of ways [4, 35]. Depending on the assumptions made and the processes they employ, we can distinguish between five main types of methods: probabilistic, distance-based, information theory-based, boundarybased, and reconstruction-based methods. Among the latter are autoencoder networks (AE) [36], one of the most widely used models. AE are a type of self-associative neural network whose output layer seeks to reproduce the data presented to the input layer after having gone through a dimensional compression phase. In this way, they manage to obtain a representation of the input data in a space with a dimension smaller than the original, learning a compact representation of the data, retaining the important information, and compressing the redundant one. One of the requirements of our proposed pipeline is to generate a normality score for each review. This score will allow us, among other things, to order the different reviews by their level of normality. When AE networks are used in anomaly detection scenarios, the classification is usually carried out based on the reconstruction error that they emit to reproduce in its output the embeddings of the reviews it receives as inputs, which represents the level of normality of the evaluated instance. This reconstruction error can be used as the normality score we are looking for. Due to the speed of its training, we have decided to use DAEF (Deep Autoencoder for Federated learning) [26] as our anomaly detection model. Unlike traditional neural networks, DAEF trains a deep AE network in a non-iterative way, which drastically reduces its training time without losing the performance of traditional (iterative) AEs. The proposed pipeline uses DAEF to calculate the embeddings of the reviews, being able to issue a normality score associated with each of the review classifications. 3.3. Explainability As black-box ML models are mainly being employed to make important predictions in critical contexts, the demand for transparency is increasing. The danger is in creating and using decisions that are not justifiable, legitimate, or that do not allow obtaining detailed explanations of their behaviour. Explanations supporting the output of a model are crucial, moreover in fields such as medicine, autonomous vehicles, security, or finance. When ML models do not meet any of the criteria imposed to declare them explainable, a separate method must be applied to the model to explain its decisions. This is the purpose of post-hoc explainability techniques [37], which aim at communicating understandable information about how an already developed model produces its predictions for any given input. Within these techniques, the so-called model-agnostic techniques are those designed to be plugged into any model with the intent of extracting some information from its prediction procedure. To generate the explanations we have implemented an approach based on a statistical analysis of the dataset. Based on the definitions of normal and anomalous review presented in Section 2, Our hypothesis assumes that normal reviews will always refer directly or indirectly to the target product so that there will be a list of terms used very frequently among normal texts. The appearance of one or more of these “normal” terms in a review would justify its classification by the system as normal. In the same way, anomalous reviews may be justified with the non-presence of said terms. The original text reviews classified as normal using the AD model will be processed (tokenization, lemmatization and stopword removal) and analyzed to obtain the list of the top-n most frequent terms. This list of terms will be compared with the lists of frequent terms of other products in order to remove the terms they have in common, so that the final list for a product does not contain generic terms. This final list of terms will be used to generate the explanations. New reviews classified as normal will be searched for the presence of any of these terms. To make this search more flexible, it will also be analyzed if there are terms in the review semantically close to any of the words on the list. To quantify the closeness of two words, the cosine similarity emitted by the MPNet transformer model itself will be used as a measure of distance. Reviews classified as anomalous will be explained by the non-occurrence of terms from the list. Figure 2 represents an overall view of the pipeline considering the product “chocolate bars” as the normal class. In summary, in this work, we propose the use of this statistical analysis of the terms to generate the explanations associated with the classification of the reviews. This paper is available on arxiv under CC 4.0 license. Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Author: Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Abstract and Introduction Related work Related work The proposed pipeline The proposed pipeline Evaluation Evaluation Conclusion & Acknowledgements Conclusion & Acknowledgements References & Appendix References & Appendix 3. The proposed pipeline The purpose of the proposed pipeline is, given the text reviews of an Amazon product, to classify them as normal if they refer to it, and as anomalous if they describe different products or if they are so generic that they do not provide useful information to consumers. These classifications will be accompanied by a normality score and explanations that justify them. Figure 1 shows the modules that are part of the proposed pipeline to solve this task. In the first phase, we encode the text reviews of the target product using the pretrained MPNet transformer [25]. In the second phase, a DAEF [26] autoencoder is trained using these embeddings. Using the reconstruction errors issued by the network and a predefined threshold error, we can classify reviews as normal (error greater than the threshold) or anomalous (error less than the threshold). For the last phase, we propose a method based on the most frequent normal terms to generate an explanation associated with each classification. Therefore, the proposed pipeline is composed of three main modules: (1) Text encoding; (2) Anomaly detection; (3) Explainability. The following sections describe each of them in detail. 3.1. Text encoding 3.1. Text encoding Although there are models capable of dealing directly with images or text, machine learning models are usually designed to be trained using numerical vectors that represent the sample data (tabular data). This input format of the data is the usual one for classic anomaly detection methods. In the NLP area, there are multiple techniques to represent texts using vectors of real numbers, which are known as embeddings. These techniques allow the generation of vector spaces that try to represent the relationships and semantic similarities of the language, so that, for example, two synonymous words will be found at a shorter distance in the vector space than two unrelated words. Embeddings can be calculated independently for each word of the language (word embeddings), which led to models such as word2vec [27] or Global Vectors (GloVe) [28]. The representation of a sentence (sentence embedding) or a document (document embedding) will therefore be the sum of all the individual representations of the terms that make it up. To obtain representations of fixed length, it is usual to perform operations such as the mean. In certain cases, these operations between embeddings can worsen or even invalidate the final embedding, so specific models have been developed capable of understanding and representing a text as a whole, instead of just encoding it word by word. Among these models are those based on transformers [29], such as BERT [30], XLNet [31], GPT-3 [32] or GPT-4 [33], which have been trained over large-scale datasets and can solve different tasks, including sentence embedding. Since it inherits the advantages of the BERT and XLNet models while overcoming their limitations, we will use the pre-trained MPNet [25] model to calculate the embeddings of the reviews. MPNet combines Masked Language Modeling (MLM) and Permuted Language Modeling (PLM) to predict token dependencies, using auxiliary position information as input to enable the model to view a complete sentence and reduce position differences. Models like GPT-3 or GPT-4 are more advanced, but in addition to not being open source, they demand much higher computational resources, unaffordable for a significant part of the scientific community, as is our particular case. MPNet maps sentences and paragraphs to a 768 dimensional dense vector space e ∈ R 768×1 , providing fast and quality encodings. Computing time is a critical aspect in the area in which this work is framed, since in e-commerce platforms (and online reviews in general) we can find a huge number of products and reviews to deal with. Therefore, the proposed pipeline uses MPNet to calculate the embeddings of the reviews, which allows them to be represented as numeric vectors of fixed size. This model and others are available in the Hugging Face repository [34]. 3.2. Anomaly detection 3.2. Anomaly detection Anomaly detection is a field with a large number of algorithms that solve the problem of distinguishing between normal and anomalous instances in a wide variety of ways [4, 35]. Depending on the assumptions made and the processes they employ, we can distinguish between five main types of methods: probabilistic, distance-based, information theory-based, boundarybased, and reconstruction-based methods. Among the latter are autoencoder networks (AE) [36], one of the most widely used models. AE are a type of self-associative neural network whose output layer seeks to reproduce the data presented to the input layer after having gone through a dimensional compression phase. In this way, they manage to obtain a representation of the input data in a space with a dimension smaller than the original, learning a compact representation of the data, retaining the important information, and compressing the redundant one. One of the requirements of our proposed pipeline is to generate a normality score for each review. This score will allow us, among other things, to order the different reviews by their level of normality. When AE networks are used in anomaly detection scenarios, the classification is usually carried out based on the reconstruction error that they emit to reproduce in its output the embeddings of the reviews it receives as inputs, which represents the level of normality of the evaluated instance. This reconstruction error can be used as the normality score we are looking for. Due to the speed of its training, we have decided to use DAEF (Deep Autoencoder for Federated learning) [26] as our anomaly detection model. Unlike traditional neural networks, DAEF trains a deep AE network in a non-iterative way, which drastically reduces its training time without losing the performance of traditional (iterative) AEs. The proposed pipeline uses DAEF to calculate the embeddings of the reviews, being able to issue a normality score associated with each of the review classifications. 3.3. Explainability 3.3. Explainability As black-box ML models are mainly being employed to make important predictions in critical contexts, the demand for transparency is increasing. The danger is in creating and using decisions that are not justifiable, legitimate, or that do not allow obtaining detailed explanations of their behaviour. Explanations supporting the output of a model are crucial, moreover in fields such as medicine, autonomous vehicles, security, or finance. When ML models do not meet any of the criteria imposed to declare them explainable, a separate method must be applied to the model to explain its decisions. This is the purpose of post-hoc explainability techniques [37], which aim at communicating understandable information about how an already developed model produces its predictions for any given input. Within these techniques, the so-called model-agnostic techniques are those designed to be plugged into any model with the intent of extracting some information from its prediction procedure. To generate the explanations we have implemented an approach based on a statistical analysis of the dataset. Based on the definitions of normal and anomalous review presented in Section 2, Our hypothesis assumes that normal reviews will always refer directly or indirectly to the target product so that there will be a list of terms used very frequently among normal texts. The appearance of one or more of these “normal” terms in a review would justify its classification by the system as normal. In the same way, anomalous reviews may be justified with the non-presence of said terms. The original text reviews classified as normal using the AD model will be processed (tokenization, lemmatization and stopword removal) and analyzed to obtain the list of the top-n most frequent terms. This list of terms will be compared with the lists of frequent terms of other products in order to remove the terms they have in common, so that the final list for a product does not contain generic terms. This final list of terms will be used to generate the explanations. New reviews classified as normal will be searched for the presence of any of these terms. To make this search more flexible, it will also be analyzed if there are terms in the review semantically close to any of the words on the list. To quantify the closeness of two words, the cosine similarity emitted by the MPNet transformer model itself will be used as a measure of distance. Reviews classified as anomalous will be explained by the non-occurrence of terms from the list. Figure 2 represents an overall view of the pipeline considering the product “chocolate bars” as the normal class. In summary, in this work, we propose the use of this statistical analysis of the terms to generate the explanations associated with the classification of the reviews. 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

Explainable AI in Action: Generating Insights from Review Anomalies

Author:
(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es);
(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es);
(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Related work The proposed pipeline Evaluation Conclusion & Acknowledgements References & Appendix 5. Conclusion In this work, we have proposed a pipeline for detecting anomalous reviews associated with Amazon products, which can be directly extrapolated to other online review platforms or scenarios with similar characteristics. The representation of the reviews using MPNet embeddings has enabled the training of classical anomaly detection algorithms that have achieved a very good performance. These have been evaluated using reviews from different products and categories, and the score they emit allows us to sort the reviews based on their normality. A technique based on the occurrence of frequent terms has been proposed to generate explanations associated with the classifications of the reviews. This technique has been compared with SHAP, one of the reference post-hoc techniques in the field of explainability, and with GPT-3, due to its high power and versatility. To evaluate this aspect of the pipeline, we conducted a two-part survey in which 241 members of the university community participated. From the first part of the explainability test we can conclude that, in general terms, the effect of the explanations has not been beneficial for the users. In any case, these tests allow us to reflect on the difficulty of using explainability and evaluation techniques in borderline scenarios where subjectivity plays an important role, such as the one presented in this article or in other fields of NLP, as well as in areas such as image or audio generation. Regarding the second part of the explainability test, we have been able to conclude that respondents preferred explanations that presented a more natural and familiar appearance over more condensed and concise explanations such as those provided by SHAP, regardless of the explanation effect they provide. Explanations based on term frequency analysis have been preferred by respondents along with GPT-3, however, our approach presents a significantly lower computational costs and both its use and the explanations produced are simpler for the users. As future work, it would be interesting to evaluate GPT-3 or other large models carrying out the complete process followed by the pipeline proposed in this work, instead of being tested only in the explainability module. We have not carried out this test due to the high computational cost that would be involved in processing the thousands of reviews to be evaluated using GPT3. Another interesting possible line of future work would be to broaden the scope of the survey, both in terms of the number of products involved and the number of reviews, in order to clarify the conclusions reached at the forward simulation stage. Lastly, it would be very useful to try presenting the explanations issued by SHAP in a more familiar or natural format for the end user, so that we can see if their level of preference is increased for the general public. Acknowledgements This work was supported in part by grant Machine Learning on the Edge - Ayudas Fundación BBVA a Equipos de Investigación Científica 2019 ; the Spanish National Plan for Scientific and Technical Research and Innovation (PID2019-109238GB-C22 and TED2021-130599A-I00); the Xunta de Galicia (ED431C 2022/44) and ERDF funds. CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia, Spain through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). This paper is available on arxiv under CC 4.0 license. Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Author: Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Abstract and Introduction Related work Related work The proposed pipeline The proposed pipeline Evaluation Evaluation Conclusion & Acknowledgements Conclusion & Acknowledgements References & Appendix References & Appendix 5. Conclusion In this work, we have proposed a pipeline for detecting anomalous reviews associated with Amazon products, which can be directly extrapolated to other online review platforms or scenarios with similar characteristics. The representation of the reviews using MPNet embeddings has enabled the training of classical anomaly detection algorithms that have achieved a very good performance. These have been evaluated using reviews from different products and categories, and the score they emit allows us to sort the reviews based on their normality. A technique based on the occurrence of frequent terms has been proposed to generate explanations associated with the classifications of the reviews. This technique has been compared with SHAP, one of the reference post-hoc techniques in the field of explainability, and with GPT-3, due to its high power and versatility. To evaluate this aspect of the pipeline, we conducted a two-part survey in which 241 members of the university community participated. From the first part of the explainability test we can conclude that, in general terms, the effect of the explanations has not been beneficial for the users. In any case, these tests allow us to reflect on the difficulty of using explainability and evaluation techniques in borderline scenarios where subjectivity plays an important role, such as the one presented in this article or in other fields of NLP, as well as in areas such as image or audio generation. Regarding the second part of the explainability test, we have been able to conclude that respondents preferred explanations that presented a more natural and familiar appearance over more condensed and concise explanations such as those provided by SHAP, regardless of the explanation effect they provide. Explanations based on term frequency analysis have been preferred by respondents along with GPT-3, however, our approach presents a significantly lower computational costs and both its use and the explanations produced are simpler for the users. As future work, it would be interesting to evaluate GPT-3 or other large models carrying out the complete process followed by the pipeline proposed in this work, instead of being tested only in the explainability module. We have not carried out this test due to the high computational cost that would be involved in processing the thousands of reviews to be evaluated using GPT3. Another interesting possible line of future work would be to broaden the scope of the survey, both in terms of the number of products involved and the number of reviews, in order to clarify the conclusions reached at the forward simulation stage. Lastly, it would be very useful to try presenting the explanations issued by SHAP in a more familiar or natural format for the end user, so that we can see if their level of preference is increased for the general public. Acknowledgements This work was supported in part by grant Machine Learning on the Edge - Ayudas Fundación BBVA a Equipos de Investigación Científica 2019 ; the Spanish National Plan for Scientific and Technical Research and Innovation (PID2019-109238GB-C22 and TED2021-130599A-I00); the Xunta de Galicia (ED431C 2022/44) and ERDF funds. CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia, Spain through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). 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

Effective Anomaly Detection Pipeline for Amazon Reviews: Insights and Future Directions

On-Device AI Models and Core ML Tools: Insights From WWDC 2024

As digital assets and blockchain technologies reshape industries, understanding the legal landscape is crucial for businesses and innovators. Steve Wang, an associate with Caldwell Law specializing in intellectual property and technology transactions, shares his insights on the multifaceted legal challenges in this rapidly evolving domain. The regulatory landscape for blockchain technologies and digital assets is continually evolving. Companies must stay informed about the latest regulations and guidelines to ensure compliance and avoid legal pitfalls. Current challenges include the complexity of rules and regulations and the lack of clear guidelines at state, federal, and global levels. Inconsistent interpretations of legal frameworks, such as the Howey test for cryptocurrencies, can deter investors and disrupt markets. Intellectual property issues also arise with NFTs, impacting their creation, reproduction, sale, and transfer. "Inconsistent interpretations of existing legal frameworks, such as the Howey test for cryptocurrencies, can deter investors and innovators and cause market disruption," Steve explains. "Additionally, intellectual property issues often arise with NFTs in terms of their creation, reproduction, sale, and transfer." Industry-Specific Legal Considerations In the sustainable and green tech sectors, unique legal aspects include government regulations for creating and trading tokenized carbon credits, assessing environmental impacts, verifying permits, and the enforceability of smart contracts. Companies in supply chain, transportation, and waste management are increasingly adopting blockchain to monitor and reduce their carbon footprint in exchange for tokenized carbon credits. Legal expertise in blockchain, environmental law, intellectual property law, and contract law is crucial for these companies. "Companies in supply chain, transportation, and waste management have started to adopt blockchain technology to monitor and reduce their carbon footprint in exchange for tokenized carbon credits," Steve explains. "Consulting with legal experts in blockchain, environmental law, intellectual property law, and contract law is crucial for these companies." Additionally, in satellite communication and remote sensing, regulations related to dual-use technology, intellectual property rights, data privacy, cybersecurity, and export restrictions pose unique challenges. Dual-use technology, which can be used for both civilian and military applications, often requires export licenses. Navigating intellectual property and cybersecurity regulations ensures compliance with federal and international laws. Advanced Technologies and IP Patent subject matter eligibility under 35 U.S.C. Sec. 101 presents significant challenges for companies working in digital image processing, AI, and ML. The Alice/Mayo test, established by the U.S. Supreme Court, is the framework for determining patent eligibility of inventions. The success of obtaining patent protection for AI/ML technologies depends on how the patent application describes significant technical improvements or non-conventional steps for a technical solution. Companies in big data analytics and environmental and molecular biology must secure their intellectual property and comply with evolving regulations, addressing challenges related to data privacy, IP ownership, and regulatory compliance. "The likelihood of successfully obtaining patent protection for AI/ML technologies depends on how the patent application describes significant technical improvements or non-conventional steps for a technical solution," Steve advises. Transaction Due Diligence and Compliance When engaging in transactions involving digital assets, companies with significant IP portfolios must maintain a complete list of all IP assets and related documents. This includes verifying the validity of IP assets and ensuring proper chain of title and ownership. Conducting a freedom-to-operate analysis to avoid infringing third-party IP rights and reviewing any litigation history or settlement agreements is essential. "Conducting a freedom-to-operate analysis to avoid infringing third-party IP rights and reviewing any litigation history or settlement agreements is also essential," Steve adds. Legal pitfalls to be aware of when purchasing company assets with software or acquiring tech-focused companies include verifying IP ownership, addressing open-source software issues, and complying with export control restrictions. Patenting and IP Protection Patenting AI and ML technologies is challenging due to patent subject matter eligibility issues. Describing unique technical improvements in the patent application and staying updated with USPTO guidelines are key strategies. Working with an experienced patent attorney, conducting a patent landscape search, formulating a patent filing strategy, and utilizing the USPTO's prioritized patent examination program (Track One) can help secure patent protection quickly and create a robust IP portfolio. Having a patent family with multiple members can also add significant value to an IP portfolio. "Having a patent family with multiple members can also be an effective way to bring additional value to an IP portfolio," said Steve. Compliance and Risk Management Ensuring regulatory compliance and developing effective risk management strategies are crucial for mitigating potential legal risks associated with digital assets. Companies must stay informed about relevant IP, data privacy, employment, and contract laws in all jurisdictions where they operate. By understanding these legal challenges and leveraging expert advice, companies can navigate the complex digital asset landscape more effectively, fostering innovation while ensuring compliance and protecting their intellectual property. As digital assets and blockchain technologies reshape industries, understanding the legal landscape is crucial for businesses and innovators. Steve Wang, an associate with Caldwell Law specializing in intellectual property and technology transactions, shares his insights on the multifaceted legal challenges in this rapidly evolving domain. Caldwell Law The regulatory landscape for blockchain technologies and digital assets is continually evolving. Companies must stay informed about the latest regulations and guidelines to ensure compliance and avoid legal pitfalls. Current challenges include the complexity of rules and regulations and the lack of clear guidelines at state, federal, and global levels. Inconsistent interpretations of legal frameworks, such as the Howey test for cryptocurrencies, can deter investors and disrupt markets. Intellectual property issues also arise with NFTs, impacting their creation, reproduction, sale, and transfer. "Inconsistent interpretations of existing legal frameworks, such as the Howey test for cryptocurrencies, can deter investors and innovators and cause market disruption," Steve explains. "Additionally, intellectual property issues often arise with NFTs in terms of their creation, reproduction, sale, and transfer." "Inconsistent interpretations of existing legal frameworks, such as the Howey test for cryptocurrencies, can deter investors and innovators and cause market disruption," Steve explains. "Additionally, intellectual property issues often arise with NFTs in terms of their creation, reproduction, sale, and transfer." Industry-Specific Legal Considerations Industry-Specific Legal Considerations In the sustainable and green tech sectors, unique legal aspects include government regulations for creating and trading tokenized carbon credits, assessing environmental impacts, verifying permits, and the enforceability of smart contracts. Companies in supply chain, transportation, and waste management are increasingly adopting blockchain to monitor and reduce their carbon footprint in exchange for tokenized carbon credits. Legal expertise in blockchain, environmental law, intellectual property law, and contract law is crucial for these companies. "Companies in supply chain, transportation, and waste management have started to adopt blockchain technology to monitor and reduce their carbon footprint in exchange for tokenized carbon credits," Steve explains. "Consulting with legal experts in blockchain, environmental law, intellectual property law, and contract law is crucial for these companies." "Companies in supply chain, transportation, and waste management have started to adopt blockchain technology to monitor and reduce their carbon footprint in exchange for tokenized carbon credits," Steve explains. "Consulting with legal experts in blockchain, environmental law, intellectual property law, and contract law is crucial for these companies." Additionally, in satellite communication and remote sensing, regulations related to dual-use technology, intellectual property rights, data privacy, cybersecurity, and export restrictions pose unique challenges. Dual-use technology, which can be used for both civilian and military applications, often requires export licenses. Navigating intellectual property and cybersecurity regulations ensures compliance with federal and international laws. Advanced Technologies and IP Advanced Technologies and IP Patent subject matter eligibility under 35 U.S.C. Sec. 101 presents significant challenges for companies working in digital image processing, AI, and ML. The Alice/Mayo test, established by the U.S. Supreme Court, is the framework for determining patent eligibility of inventions. The success of obtaining patent protection for AI/ML technologies depends on how the patent application describes significant technical improvements or non-conventional steps for a technical solution. Companies in big data analytics and environmental and molecular biology must secure their intellectual property and comply with evolving regulations, addressing challenges related to data privacy, IP ownership, and regulatory compliance. "The likelihood of successfully obtaining patent protection for AI/ML technologies depends on how the patent application describes significant technical improvements or non-conventional steps for a technical solution," Steve advises. "The likelihood of successfully obtaining patent protection for AI/ML technologies depends on how the patent application describes significant technical improvements or non-conventional steps for a technical solution," Steve advises. Transaction Due Diligence and Compliance Transaction Due Diligence and Compliance When engaging in transactions involving digital assets, companies with significant IP portfolios must maintain a complete list of all IP assets and related documents. This includes verifying the validity of IP assets and ensuring proper chain of title and ownership. Conducting a freedom-to-operate analysis to avoid infringing third-party IP rights and reviewing any litigation history or settlement agreements is essential. "Conducting a freedom-to-operate analysis to avoid infringing third-party IP rights and reviewing any litigation history or settlement agreements is also essential," Steve adds. "Conducting a freedom-to-operate analysis to avoid infringing third-party IP rights and reviewing any litigation history or settlement agreements is also essential," Steve adds. Legal pitfalls to be aware of when purchasing company assets with software or acquiring tech-focused companies include verifying IP ownership, addressing open-source software issues, and complying with export control restrictions. Patenting and IP Protection Patenting and IP Protection Patenting AI and ML technologies is challenging due to patent subject matter eligibility issues. Describing unique technical improvements in the patent application and staying updated with USPTO guidelines are key strategies. Working with an experienced patent attorney, conducting a patent landscape search, formulating a patent filing strategy, and utilizing the USPTO's prioritized patent examination program (Track One) can help secure patent protection quickly and create a robust IP portfolio. Having a patent family with multiple members can also add significant value to an IP portfolio. USPTO's prioritized patent examination program (Track One) "Having a patent family with multiple members can also be an effective way to bring additional value to an IP portfolio," said Steve. "Having a patent family with multiple members can also be an effective way to bring additional value to an IP portfolio," said Steve. Compliance and Risk Management Compliance and Risk Management Ensuring regulatory compliance and developing effective risk management strategies are crucial for mitigating potential legal risks associated with digital assets. Companies must stay informed about relevant IP, data privacy, employment, and contract laws in all jurisdictions where they operate. By understanding these legal challenges and leveraging expert advice, companies can navigate the complex digital asset landscape more effectively, fostering innovation while ensuring compliance and protecting their intellectual property.

Navigating Legal Challenges in the Digital Asset Space: Insights from Steve Wang

For investors, developers, and analysts, staying current with real-time data, on-chain activity, NFT metadata, and other essential crypto-related data is important in the rapidly evolving world of cryptocurrencies. This is where APIs come into play. These APIs give users easy access to a variety of cryptocurrency data, enabling them to build robust applications, make well-informed decisions, and carry out thorough market analysis. This article explores the definition of crypto data APIs, their role in the cryptocurrency ecosystem, and how they make it easier to obtain different kinds of crucial crypto data. Crypto Data APIs are interfaces that allow applications to interact with cryptocurrency data providers. These APIs enable users to retrieve and manipulate data related to cryptocurrencies, such as price information, market statistics, trading volumes, blockchain transactions, and more. APIs make it easier to integrate Bitcoin data into apps and services by providing standardized ways to access data. Accessing Crypto Data Crypto data APIs are software interfaces that allow developers to programmatically access and retrieve a wide range of data related to cryptocurrencies, blockchain networks, and the broader digital asset ecosystem. Aggregating Crypto Information These APIs consolidate and standardize data from multiple sources, providing a unified way for applications to fetch real-time pricing, trading volume, on-chain metrics, and other essential cryptocurrency market intelligence. Powering Crypto Innovations Crypto data APIs are the building blocks for a wide range of cryptocurrency-related applications, from trading platforms and portfolio trackers to decentralized finance (DeFi) protocols and non-fungible token (NFT) marketplaces. Significance of Crypto Data APIs in the Cryptocurrency Ecosystem Data Access Crypto data APIs provide seamless access to a wealth of cryptocurrency data, empowering users to make informed decisions and gain valuable market insights. Market Analysis By aggregating and analyzing real-time price, volume, and other market data, crypto data APIs enable investors and developers to track trends and identify opportunities. Automation Integrating crypto data APIs into applications and tools allows for automated data processing, triggering alerts, and making data-driven decisions at scale. /Aggregated Price and Market Data Crypto data APIs offer access to comprehensive, real-time price and market data for cryptocurrencies. This includes global volume, market capitalization, trading pairs, price history, and other key metrics aggregated from major exchanges. These insights are essential for analyzing market trends, developing trading strategies, and powering portfolio management tools. On-chain DEX Data Decentralized exchanges (DEXes) on blockchain networks provide crucial on-chain data that offers valuable insights into the decentralized finance (DeFi) ecosystem. This data includes trading volumes, liquidity pools, swap fees, and other key metrics that help analyze the activity and health of these decentralized platforms. Daily Volume The total daily trading volume across leading DEXes can reach hundreds of millions of dollars, reflecting the growth and importance of decentralized exchange platforms. Liquidity Pools DEXes maintain thousands of liquidity pools that allow for seamless token swaps and access to a wide range of cryptocurrencies and digital assets. Average Swap Fees The typical swap fees on DEXes are significantly lower than centralized exchanges, often in the range of 0.1% to 1%, enabling more affordable trading. Metadata and NFT Collection Data Crypto data APIs provide access to valuable metadata and NFT collection data. This includes details on individual NFTs, such as their rarity, attributes, and sales history. APIs also enable retrieval of aggregated data on entire NFT collections, including total volume, floor prices, and trading activity. By integrating these data sources, developers can build powerful applications that showcase NFT analytics, enable NFT discovery, and facilitate secondary market trading. Access to this rich data empowers users to make informed decisions when buying, selling, or investing in NFTs. Other Essential Crypto Information Crypto Wallets Crypto wallets are digital tools that allow users to securely store, send, and receive cryptocurrencies. They provide a gateway to the blockchain network and are essential for interacting with decentralized applications. Crypto Staking Crypto staking is the process of holding cryptocurrencies in a wallet to support the operation of a blockchain network. By staking, users can earn rewards in the form of additional cryptocurrency. Accessing Crypto Data APIs Accessing crypto data APIs typically involves three key steps: choosing a reliable provider, obtaining the necessary API keys, and then integrating the API into your application or platform. By following these steps, you can unlock a wealth of real-time cryptocurrency market data, on-chain analytics, and other essential information to power your crypto-related projects and decisions. Use Cases and Applications Cryptocurrency Trading Crypto data APIs provide vital real-time price and market data, enabling traders to make informed decisions and develop sophisticated trading strategies. Blockchain Analytics These APIs unlock on-chain data, allowing researchers and analysts to uncover insights into transaction patterns, wallet activity, and network health. Portfolio Management Investors can leverage crypto data APIs to track their digital asset holdings, monitor portfolio performance, and stay up-to-date on the latest market trends. Conclusion In conclusion, crypto data APIs have revolutionized the way we access and leverage essential information in the cryptocurrency ecosystem. They provide invaluable insights into market trends, on-chain activity, NFT metadata, and more, empowering developers, analysts, and investors to make informed decisions. Thanks for reading this article. Want to connect? Send me a direct message on X, If you're curious or have any questions on the subject; I'd be pleased to chat with you! For investors, developers, and analysts, staying current with real-time data, on-chain activity, NFT metadata, and other essential crypto-related data is important in the rapidly evolving world of cryptocurrencies. This is where APIs come into play. These APIs give users easy access to a variety of cryptocurrency data, enabling them to build robust applications, make well-informed decisions, and carry out thorough market analysis. This article explores the definition of crypto data APIs, their role in the cryptocurrency ecosystem, and how they make it easier to obtain different kinds of crucial crypto data. Crypto Data APIs are interfaces that allow applications to interact with cryptocurrency data providers. These APIs enable users to retrieve and manipulate data related to cryptocurrencies, such as price information, market statistics, trading volumes, blockchain transactions, and more. APIs make it easier to integrate Bitcoin data into apps and services by providing standardized ways to access data. Accessing Crypto Data Crypto data APIs are software interfaces that allow developers to programmatically access and retrieve a wide range of data related to cryptocurrencies, blockchain networks, and the broader digital asset ecosystem. Aggregating Crypto Information These APIs consolidate and standardize data from multiple sources, providing a unified way for applications to fetch real-time pricing, trading volume, on-chain metrics, and other essential cryptocurrency market intelligence. Powering Crypto Innovations Crypto data APIs are the building blocks for a wide range of cryptocurrency-related applications, from trading platforms and portfolio trackers to decentralized finance (DeFi) protocols and non-fungible token (NFT) marketplaces. Significance of Crypto Data APIs in the Cryptocurrency Ecosystem Data Access Crypto data APIs provide seamless access to a wealth of cryptocurrency data, empowering users to make informed decisions and gain valuable market insights. Market Analysis By aggregating and analyzing real-time price, volume, and other market data, crypto data APIs enable investors and developers to track trends and identify opportunities. Automation Integrating crypto data APIs into applications and tools allows for automated data processing, triggering alerts, and making data-driven decisions at scale. /Aggregated Price and Market Data Crypto data APIs offer access to comprehensive, real-time price and market data for cryptocurrencies. This includes global volume, market capitalization, trading pairs, price history, and other key metrics aggregated from major exchanges. These insights are essential for analyzing market trends, developing trading strategies, and powering portfolio management tools. On-chain DEX Data Decentralized exchanges (DEXes) on blockchain networks provide crucial on-chain data that offers valuable insights into the decentralized finance (DeFi) ecosystem. This data includes trading volumes, liquidity pools, swap fees, and other key metrics that help analyze the activity and health of these decentralized platforms. Daily Volume The total daily trading volume across leading DEXes can reach hundreds of millions of dollars, reflecting the growth and importance of decentralized exchange platforms. Liquidity Pools DEXes maintain thousands of liquidity pools that allow for seamless token swaps and access to a wide range of cryptocurrencies and digital assets. Average Swap Fees The typical swap fees on DEXes are significantly lower than centralized exchanges, often in the range of 0.1% to 1%, enabling more affordable trading. Metadata and NFT Collection Data Crypto data APIs provide access to valuable metadata and NFT collection data. This includes details on individual NFTs, such as their rarity, attributes, and sales history. APIs also enable retrieval of aggregated data on entire NFT collections, including total volume, floor prices, and trading activity. By integrating these data sources, developers can build powerful applications that showcase NFT analytics, enable NFT discovery, and facilitate secondary market trading. Access to this rich data empowers users to make informed decisions when buying, selling, or investing in NFTs. Other Essential Crypto Information Crypto Wallets Crypto wallets are digital tools that allow users to securely store, send, and receive cryptocurrencies. They provide a gateway to the blockchain network and are essential for interacting with decentralized applications. Crypto Staking Crypto staking is the process of holding cryptocurrencies in a wallet to support the operation of a blockchain network. By staking, users can earn rewards in the form of additional cryptocurrency. Accessing Crypto Data APIs Accessing crypto data APIs typically involves three key steps: choosing a reliable provider, obtaining the necessary API keys, and then integrating the API into your application or platform. By following these steps, you can unlock a wealth of real-time cryptocurrency market data, on-chain analytics, and other essential information to power your crypto-related projects and decisions. Use Cases and Applications Cryptocurrency Trading Crypto data APIs provide vital real-time price and market data, enabling traders to make informed decisions and develop sophisticated trading strategies. Blockchain Analytics These APIs unlock on-chain data, allowing researchers and analysts to uncover insights into transaction patterns, wallet activity, and network health. Portfolio Management Investors can leverage crypto data APIs to track their digital asset holdings, monitor portfolio performance, and stay up-to-date on the latest market trends. Conclusion In conclusion, crypto data APIs have revolutionized the way we access and leverage essential information in the cryptocurrency ecosystem. They provide invaluable insights into market trends, on-chain activity, NFT metadata, and more, empowering developers, analysts, and investors to make informed decisions. Thanks for reading this article. Want to connect? Send me a direct message on X, If you're curious or have any questions on the subject; I'd be pleased to chat with you! Want to connect? Send me a direct message on X, If you're curious or have any questions on the subject; I'd be pleased to chat with you! X, X , X X

Harnessing Crypto Data APIs for Real-Time Market Insights and Blockchain Analytics

**ABU DHABI, UAE, June 4th, 2024/Chainwire/--**Solnarize is thrilled to announce that the presale of its $SRIZE tokens will commence on Friday, June 7th, 2024. This presale provides early participants the chance to engage with a project that combines innovative meme culture with the mission of promoting clean energy before listing on Raydium 48 hours after presale. Solnarize aims to innovate within the meme space by merging the viral appeal of meme culture with the vital mission of promoting clean energy. The team's approach integrates the $SRIZE token, which plays a pivotal role in Solnarize's ecosystem and the forthcoming Play-to-Earn (P2E) game, Solar Defender. A Unique Blend of Meme Culture and Sustainability Solnarize is a project designed to create a vibrant community that values both potential financial growth and environmental impact. The $SRIZE token serves as the cornerstone of Solnarize's ecosystem, driving transactions and rewards within their P2E game, Solar Defender. Solar Defender: The Heart of Solnarize Solar Defender is an engaging and educational game where players protect a solar-powered city from environmental threats. Players earn $SRIZE tokens as they navigate through various challenges, placing and upgrading solar panels to maintain the city's energy supply. This game is not only entertaining but also raises awareness about renewable energy and the importance of sustainability. Andzo, the developers team lead at Solnarize, explains, “Solar Defender is designed to offer an immersive experience that combines fun with education. By integrating clean energy concepts into gameplay, we aim to inspire a new generation of environmentally conscious gamers and crypto enthusiasts.” Utility and Potential of $SRIZE Token The $SRIZE token facilitates transactions within the Solar Defender game and is part of Solnarize’s commitment to sustainability and community-driven innovation. As the utility token at the heart of the Solnarize ecosystem, $SRIZE facilitates all in-game purchases, potential rewards, and upgrades, making it an integral part of the project’s success. Recently, Solnarize secured $350,000 in seed funding led by Sunivations Ventures, with additional contributions from angel investors dedicated to clean energy and sustainability. About Solnarize Solnarize is a meme project on the Solana blockchain, merging meme culture with sustainable energy initiatives. Their mission is to create a vibrant, engaged community that drives both technological innovation and environmental stewardship. Contact: Website: https://solnarize.com X: https://x.com/SolnarizeSrize Telegram: https://t.me/solnarize Medium: https://medium.com/@solnarize Contact Andzo Xemberzi Solnarize enquiries@solnarize.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **ABU DHABI, UAE, June 4th, 2024/Chainwire/--**Solnarize is thrilled to announce that the presale of its $SRIZE tokens will commence on Friday, June 7th, 2024. This presale provides early participants the chance to engage with a project that combines innovative meme culture with the mission of promoting clean energy before listing on Raydium 48 hours after presale. Solnarize aims to innovate within the meme space by merging the viral appeal of meme culture with the vital mission of promoting clean energy. The team's approach integrates the $SRIZE token, which plays a pivotal role in Solnarize's ecosystem and the forthcoming Play-to-Earn (P2E) game, Solar Defender. $SRIZE $SRIZE A Unique Blend of Meme Culture and Sustainability Solnarize is a project designed to create a vibrant community that values both potential financial growth and environmental impact. The $SRIZE token serves as the cornerstone of Solnarize's ecosystem, driving transactions and rewards within their P2E game, Solar Defender. Solar Defender: The Heart of Solnarize Solar Defender is an engaging and educational game where players protect a solar-powered city from environmental threats. Players earn $SRIZE tokens as they navigate through various challenges, placing and upgrading solar panels to maintain the city's energy supply. This game is not only entertaining but also raises awareness about renewable energy and the importance of sustainability. Andzo, the developers team lead at Solnarize, explains, “Solar Defender is designed to offer an immersive experience that combines fun with education. By integrating clean energy concepts into gameplay, we aim to inspire a new generation of environmentally conscious gamers and crypto enthusiasts.” Andzo, the developers team lead at Solnarize, explains, “Solar Defender is designed to offer an immersive experience that combines fun with education. By integrating clean energy concepts into gameplay, we aim to inspire a new generation of environmentally conscious gamers and crypto enthusiasts.” Utility and Potential of $SRIZE Token The $SRIZE token facilitates transactions within the Solar Defender game and is part of Solnarize’s commitment to sustainability and community-driven innovation. As the utility token at the heart of the Solnarize ecosystem, $SRIZE facilitates all in-game purchases, potential rewards, and upgrades, making it an integral part of the project’s success. Recently, Solnarize secured $350,000 in seed funding led by Sunivations Ventures, with additional contributions from angel investors dedicated to clean energy and sustainability. About Solnarize Solnarize is a meme project on the Solana blockchain, merging meme culture with sustainable energy initiatives. Their mission is to create a vibrant, engaged community that drives both technological innovation and environmental stewardship. Contact: Website: https://solnarize.com https://solnarize.com https://solnarize.com X: https://x.com/SolnarizeSrize https://x.com/SolnarizeSrize https://x.com/SolnarizeSrize Telegram: https://t.me/solnarize https://t.me/solnarize https://t.me/solnarize Medium: https://medium.com/@solnarize https://medium.com/@solnarize https://medium.com/@solnarize Contact Andzo Xemberzi Solnarize enquiries@solnarize.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 here

Solnarize's Upcoming Presale: Insights Into The Sustainability-Focused Meme Coin And P2E Game

When it comes to machine learning (ML), speed is the name of the game. The faster you can prepare your data, train your models, and deploy them into production, the quicker you can unlock insights and drive value for your business. Achieving this speed will require more from your company than just raw computing power. You’ll need a strategic approach to data pipeline development, cloud integration, and infrastructure planning. Your goal is to expedite the readiness of your ML models, and you can’t go wrong with some advice from an industry leader. Abhijeet Rajwade is the senior customer engineer at Google, where he spearheads the development of cloud, data, and digital workplace solutions for enterprise clients in the USA. He’s also contributed to the development of AI infrastructure and cloud technologies for years. If anyone in the industry knows how to get this right, it’s Abhijeet. The Importance of Simplifying Data Pipeline Development to Transform Data At the heart of any ML endeavor lies the data. But, preparing data for analysis and model training can be a complex and time-consuming process. That’s where you could use Google Dataflow to build a data transformation pipeline to help data readiness for Enterprise AI Workloads. Abhijeet focuses a great deal on the importance of streamlining dataflow development to enhance data engineers’ productivity. He was the product manager in charge of developing a Cloud Code Plugin for Dataflow that reduced the learning curve and ramp-up time for building dataflow streaming pipelines. This product offered several key features to enhance the developer experience, mostly accelerating the development cycle and mitigating errors more efficiently. By streamlining the creation and execution of data pipelines, organizations can accelerate the process of ingesting, transforming, and preparing data for ML tasks like feature engineering, model training, etc. Whether cleaning messy datasets, extracting relevant features, or aggregating information from multiple sources, simplified dataflow development tools empower data scientists and data engineers to focus on what they do best: analyzing data and building models. Strategic Cloud Capacity Planning: Optimizing Resources for ML Workloads In tandem with streamlined development processes, strategic cloud capacity planning plays a pivotal role in expediting ML model readiness. “Cloud capacity management is a key part of an effective IT strategy,” Abhijeet has said. “Cloud capacity planning not only ensures workloads have the required resources but also reduces the cloud bill due to overprovisioned workloads.” By evaluating capacity requirements, reviewing historical usage patterns, and strategizing capacity planning based on business needs, organizations can optimize resource allocation for ML workloads. This approach not only enhances performance but also reduces costs by ensuring optimal resource utilization. Accelerating ML Model Readiness with Integrated Solutions The convergence of dataflow development simplification, cloud code plugin integrations, and strategic cloud capacity planning offers a comprehensive solution for expediting ML model readiness. As organizations embrace these integrated solutions, they can navigate the complexities of ML model development with greater efficiency and agility. With tools and strategies designed to streamline development processes and optimize resource utilization, the journey from concept to deployment becomes a seamless and accelerated endeavor. The Industry is Changing—You Can Change With It “Reinvention is the fuel of resilience,” says Abhijeet. “But the ability to reinvent yourself ensures you’re not left stranded. You can adapt, learn new skills, and emerge stronger and more adaptable.” Is your company ready for the AI revolution? Many enterprises are on the brink of transformation, but without the right data and infrastructure strategy, they risk being left behind. This is where Abhijeet Rajwade can help. As a seasoned expert in designing solutions to transform data and leverage cloud infrastructure for AI workloads, he’s ready to design solutions that transform data and leverage cloud infrastructure to its greatest potential. The future is here, so it’s time to make sure your plans are ready for it. When it comes to machine learning (ML), speed is the name of the game. The faster you can prepare your data, train your models, and deploy them into production, the quicker you can unlock insights and drive value for your business. Achieving this speed will require more from your company than just raw computing power. You’ll need a strategic approach to data pipeline development, cloud integration, and infrastructure planning. Your goal is to expedite the readiness of your ML models, and you can’t go wrong with some advice from an industry leader. Abhijeet Rajwade is the senior customer engineer at Google, where he spearheads the development of cloud, data, and digital workplace solutions for enterprise clients in the USA. He’s also contributed to the development of AI infrastructure and cloud technologies for years. If anyone in the industry knows how to get this right, it’s Abhijeet. Abhijeet Rajwade Abhijeet Rajwade The Importance of Simplifying Data Pipeline Development to Transform Data The Importance of Simplifying Data Pipeline Development to Transform Data At the heart of any ML endeavor lies the data. But, preparing data for analysis and model training can be a complex and time-consuming process. That’s where you could use Google Dataflow to build a data transformation pipeline to help data readiness for Enterprise AI Workloads. Abhijeet focuses a great deal on the importance of streamlining dataflow development to enhance data engineers’ productivity. He was the product manager in charge of developing a Cloud Code Plugin for Dataflow that reduced the learning curve and ramp-up time for building dataflow streaming pipelines. This product offered several key features to enhance the developer experience, mostly accelerating the development cycle and mitigating errors more efficiently. By streamlining the creation and execution of data pipelines, organizations can accelerate the process of ingesting, transforming, and preparing data for ML tasks like feature engineering, model training, etc. Cloud Code Plugin for Dataflow Cloud Code Plugin for Dataflow Whether cleaning messy datasets, extracting relevant features, or aggregating information from multiple sources, simplified dataflow development tools empower data scientists and data engineers to focus on what they do best: analyzing data and building models. Strategic Cloud Capacity Planning: Optimizing Resources for ML Workloads Strategic Cloud Capacity Planning: Optimizing Resources for ML Workloads In tandem with streamlined development processes, strategic cloud capacity planning plays a pivotal role in expediting ML model readiness. “Cloud capacity management is a key part of an effective IT strategy,” Abhijeet has said. “Cloud capacity planning not only ensures workloads have the required resources but also reduces the cloud bill due to overprovisioned workloads.” By evaluating capacity requirements, reviewing historical usage patterns, and strategizing capacity planning based on business needs, organizations can optimize resource allocation for ML workloads. This approach not only enhances performance but also reduces costs by ensuring optimal resource utilization. strategic cloud capacity planning strategic cloud capacity planning Accelerating ML Model Readiness with Integrated Solutions Accelerating ML Model Readiness with Integrated Solutions The convergence of dataflow development simplification, cloud code plugin integrations, and strategic cloud capacity planning offers a comprehensive solution for expediting ML model readiness. As organizations embrace these integrated solutions, they can navigate the complexities of ML model development with greater efficiency and agility. With tools and strategies designed to streamline development processes and optimize resource utilization, the journey from concept to deployment becomes a seamless and accelerated endeavor. The Industry is Changing—You Can Change With It The Industry is Changing—You Can Change With It “Reinvention is the fuel of resilience,” says Abhijeet. “But the ability to reinvent yourself ensures you’re not left stranded. You can adapt, learn new skills, and emerge stronger and more adaptable.” Is your company ready for the AI revolution? Many enterprises are on the brink of transformation, but without the right data and infrastructure strategy, they risk being left behind. This is where Abhijeet Rajwade can help. As a seasoned expert in designing solutions to transform data and leverage cloud infrastructure for AI workloads, he’s ready to design solutions that transform data and leverage cloud infrastructure to its greatest potential. The future is here, so it’s time to make sure your plans are ready for it.

Expediting ML Model Readiness: Industry Expert Abhijeet Rajwade’s Insights

এমএল মডেল প্রস্তুতি ত্বরান্বিত করা: শিল্প বিশেষজ্ঞ অভিজিৎ রাজওয়াদের অন্তর্দৃষ্টি

Beschleunigung der ML-Modellbereitschaft: Einblicke des Branchenexperten Abhijeet Rajwade

Acelerar la preparación del modelo de aprendizaje automático: opiniones del experto de la industria Abhijeet Rajwade

Accélération de la préparation au modèle ML : les idées de l'expert du secteur Abhijeet Rajwade

एमएल मॉडल की तैयारी में तेजी लाना: उद्योग विशेषज्ञ अभिजीत राजवाड़े की अंतर्दृष्टि

ML モデルの準備の迅速化: 業界の専門家 Abhijeet Rajwade の洞察

ML 모델 준비 가속화: 업계 전문가 Abhijeet Rajwade의 통찰력

Agilizando a preparação do modelo de ML: insights do especialista do setor Abhijeet Rajwade

Ускорение готовности моделей машинного обучения: мнение отраслевого эксперта Абхиджита Раджваде

ML Modelinin Hazır Olmasını Hızlandırma: Endüstri Uzmanı Abhijeet Rajwade'in İçgörüleri

Đẩy nhanh quá trình sẵn sàng cho mô hình ML: Thông tin chi tiết của chuyên gia trong ngành Abhijeet Rajwade

加速 ML 模型准备：行业专家 Abhijeet Rajwade 的见解

Hey Hackers!Have you ever heard or seen the popular quote “There’s nothing new under the sun”?  How about this one “Ideas are unfaithful partners”? Both quotes point to the fact that, for the most part, everything we think, feel, or do has been thought, felt, or done by someone else at some point in the eternal crawl of time. This also applies to blog posts. Whether you’re writing a guide on how to create effective integration tests or an opinion piece on the AI bias problem, chances are that someone, somewhere has discussed a similar idea. This brings us to an important question: How do you float a unique POV in a sea of saturated subject matter? 1. Research, Research, and Some More Research for Good Measure This goes without saying, but just in case you need a reminder, research is arguably the most important part of a solid article. It’s an exercise that takes your ideas from mere speculation to valid theories. Before putting pen to paper (or fingers to keyboard), you must first research the topic at hand extensively. Find out what existing articles, on credible platforms, have to say about the topic. Look out for gaps, recent developments or emerging trends that haven’t been extensively covered yet. Fill those gaps! 2. Niche Down! Instead of trying to take on an entirely saturated subject matter like “What is AI?”, focus, instead, on a specific aspect or subtopic within it, like “How are Underdeveloped nations adjusting to the AI revolution?” This can help you find a unique perspective that hasn’t been addressed yet and position you as a thought leader in the field. Not to mention, it’s great for SEO purposes! 3. Lean on Stats and Data Nothing helps you stand out better than supporting your ideas with cold, hard data and statistics from credible sources. What’s more? The same data can tell different stories. For example, in a sample size of 10 people, if 50% like pineapples on pizza, you could frame the story in two ways. Story one: "5 out of 10 People Enjoy Pineapples on Pizza: Here’s Why." Story two: "Why 5 out of 10 People Dislike Pineapples on Pizza." Same data, two different narratives. 4. Analyze Contradictions or Controversies Sometimes, exploring contradictory viewpoints or controversial aspects of a topic can lead to interesting insights and a new angle. Here’s an example of two contradictory opinion pieces on the elusive identity of Satoshi Nakamoto: Craig Wright is Satoshi Nakamoto
Craig Wright is Not Satoshi Nakamoto 5. Think Outside the Box I know, I know. This is cliché advice. But, it is also important advice. When working on an idea that has been discussed extensively, always challenge yourself to look at it with a different lens. For example, if the topic is usually approached from a technical standpoint, consider exploring its social or ethical implications. And vice versa. Ready to breathe new life into an “old” story? Tell us what people aren’t saying about AI. The #bitcoin Writing Contest The #bitcoin Writing Contest, presented by Rootstock and HackerNoon, is now live with a total prize pool of $17,500 up for grabs!This once, you don’t have to worry about a new angle. Start writing today!! We’ve got some ideas laid out for you in this writing template. Summarily, for this contest, we welcome submissions on various Bitcoin topics like Runes, ordinals, layer 2 solutions, sidechains, and more. We're also interested in technical insights on SDKs, APIs, smart contract development frameworks, and tutorials for building applications on Rootstock. And that’s not all! There are more contests where that came from like The Crypto API Writing Contest by CoinGecko. You can find more details at contests.hackernoon.com Discover All HackerNoon Features on One Page We're constantly adding new features to help our writers create and share content effortlessly. Now, you can explore all these functionalities in one place! Visit our new Functionalities page! https://hackernoon.com/p/publish/functionality?embedable=true That’s all for today, Hackers.Until next time, keep reinventing the wheel! Hey Hackers! Have you ever heard or seen the popular quote “There’s nothing new under the sun”?  How about this one “Ideas are unfaithful partners”? Both quotes point to the fact that, for the most part, everything we think, feel, or do has been thought, felt, or done by someone else at some point in the eternal crawl of time. This also applies to blog posts. Whether you’re writing a guide on how to create effective integration tests or an opinion piece on the AI bias problem , chances are that someone, somewhere has discussed a similar idea. This brings us to an important question: create effective integration tests create effective integration tests AI bias problem AI bias problem How do you float a unique POV in a sea of saturated subject matter? How do you float a unique POV in a sea of saturated subject matter? How do you float a unique POV in a sea of saturated subject matter? 1. Research, Research, and Some More Research for Good Measure This goes without saying, but just in case you need a reminder, research is arguably the most important part of a solid article. It’s an exercise that takes your ideas from mere speculation to valid theories. Before putting pen to paper (or fingers to keyboard), you must first research the topic at hand extensively. Find out what existing articles, on credible platforms, have to say about the topic. Look out for gaps, recent developments or emerging trends that haven’t been extensively covered yet. Fill those gaps! 2. Niche Down! Instead of trying to take on an entirely saturated subject matter like “What is AI?”, focus, instead, on a specific aspect or subtopic within it, like “How are Underdeveloped nations adjusting to the AI revolution?” This can help you find a unique perspective that hasn’t been addressed yet and position you as a thought leader in the field. Not to mention, it’s great for SEO purposes! 3. Lean on Stats and Data Nothing helps you stand out better than supporting your ideas with cold, hard data and statistics from credible sources. What’s more? The same data can tell different stories. For example, in a sample size of 10 people, if 50% like pineapples on pizza, you could frame the story in two ways. Story one: "5 out of 10 People Enjoy Pineapples on Pizza: Here’s Why." Story two: "Why 5 out of 10 People Dislike Pineapples on Pizza." Same data, two different narratives. 4. Analyze Contradictions or Controversies 4. Analyze Contradictions or Controversies Sometimes, exploring contradictory viewpoints or controversial aspects of a topic can lead to interesting insights and a new angle. Here’s an example of two contradictory opinion pieces on the elusive identity of Satoshi Nakamoto: Craig Wright is Satoshi Nakamoto Craig Wright is Not Satoshi Nakamoto Craig Wright is Satoshi Nakamoto Craig Wright is Satoshi Nakamoto Craig Wright is Satoshi Nakamoto Craig Wright is Not Satoshi Nakamoto Craig Wright is Not Satoshi Nakamoto Craig Wright is Not Satoshi Nakamoto 5. Think Outside the Box 5. Think Outside the Box I know, I know. This is cliché advice. But, it is also important advice. When working on an idea that has been discussed extensively, always challenge yourself to look at it with a different lens. For example, if the topic is usually approached from a technical standpoint, consider exploring its social or ethical implications. And vice versa. Ready to breathe new life into an “old” story? Tell us what people aren’t saying about AI. Ready to breathe new life into an “old” story? Tell us what people aren’t saying about AI. Tell us what people aren’t saying about AI. Tell us what people aren’t saying about AI. The #bitcoin Writing Contest The #bitcoin Writing Contest The #bitcoin Writing Contest The #bitcoin Writing Contest , presented by Rootstock and HackerNoon, is now live with a total prize pool of $17,500 up for grabs! This once, you don’t have to worry about a new angle. #bitcoin Writing Contest #bitcoin Writing Contest Start writing today!! We’ve got some ideas laid out for you in this writing template. Start writing today!! We’ve got some ideas laid out for you in this writing template . writing template writing template Summarily, for this contest, we welcome submissions on various Bitcoin topics like Runes, ordinals, layer 2 solutions, sidechains, and more. We're also interested in technical insights on SDKs, APIs, smart contract development frameworks, and tutorials for building applications on Rootstock. And that’s not all! There are more contests where that came from like The Crypto API Writing Contest by CoinGecko . The Crypto API Writing Contest by CoinGecko The Crypto API Writing Contest by CoinGecko You can find more details at contests.hackernoon.com contests.hackernoon.com contests.hackernoon.com Discover All HackerNoon Features on One Page Discover All HackerNoon Features on One Page We're constantly adding new features to help our writers create and share content effortlessly. Now, you can explore all these functionalities in one place! Visit our new Functionalities page ! Functionalities page Functionalities page https://hackernoon.com/p/publish/functionality?embedable=true https://hackernoon.com/p/publish/functionality?embedable=true That’s all for today, Hackers. Until next time, keep reinventing the wheel!

How to Make Old Stories New Again: Insights from HackerNoon Editors

কীভাবে পুরানো গল্পগুলি আবার নতুন করা যায়: হ্যাকারনুন সম্পাদকদের কাছ থেকে অন্তর্দৃষ্টি

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Comment recréer de vieilles histoires : aperçus des éditeurs de HackerNoon

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古いストーリーを再び新しくする方法: HackerNoon 編集者からの洞察

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