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The District Attorney’s Major Crimes Division announces the guilty verdict in the November 2020 Eve Street barber shop double murder.

2020 Augusta barbershop double slaying suspects found guilty

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The r/golf user went on to say that he’s planning to put them all together, in order to properly commemorate such valuable collectibles: “After laying them out like that I had a couple of ideas, arrange them how I had it and frame it on a green felt background, or buy an Augusta National ...

Golfer discovers grandpa's collection of vintage Masters badges, and even an Augusta National scorecard | This is the Loop | GolfDigest.com

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Augusta

augusta

Image: Regula's survey shows that, while average industry losses from deepfake fraud sit at $440,000, 37% of Crypto companies are losing over $500,000. RESTON, Va — A new “Deepfake Trends 2024” study by Regula, a global developer of forensic devices and identity verification solutions, highlights the pressing financial challenges posed by deepfakes to the Crypto industry. The study reveals that while the industry averages a loss of $440,000 due to advanced fraud techniques, a significant portion – 37% of Crypto companies – are facing losses exceeding half a million dollars each. Deepfakes, which manipulate audio and video to create convincing yet fraudulent content, have become a major concern for more than half of the organizations in the Crypto sector. According to the study, 53% of businesses in the industry have encountered incidents of video deepfakes. Notably, the Crypto firms are particularly vulnerable to audio deepfake fraud, with 57% of respondents reporting such incidents, compared to 50% in other sectors. This heightened susceptibility may stem from the industry’s reliance on specific verification methods. The study identifies multifactor authentication (MFA) as the most widely employed mitigation strategy, utilized by 57% of respondents. In contrast, other industries often prefer biometric verification as their top choice. Audio verification is one of the components within MFA, enabling users to confirm their identity through various methods, such as a password, text message code, and their voice. The impact of deepfake fraud on the crypto industry extends beyond immediate financial losses. Organizations face substantial costs, with 35% of respondents citing legal expenses as a significant burden. Penalties and fines were reported by 33% of organizations, reflecting financial repercussions from regulatory violations or legal settlements. Additionally, 27% of respondents acknowledge reputational risk as a key concern, highlighting the potential damage to trust and credibility that deepfake incidents can cause. “Losing $500,000 to a single type of fraud is staggering for any organization, especially in a fast-paced industry like Crypto,” said Henry Patishman, Executive Vice President of Identity Verification Solutions at Regula. “This finding underscores the need for robust security measures, grounded in best practice. As the threat landscape evolves, it’s crucial for companies to adopt a liveness-centric approach, focusing on real-time verification of people and only physical objects.” Find more insights on deepfake fraud in the survey report. Read the full version on our website. Additional resources: ●  	Identity Fraud Statistics 2023: How Businesses Respond to the Issue ●  	Data from 2022: One-Third of Global Businesses Already Hit by Voice and Video Deepfake Fraud ●  	Financial Organizations Challenged by the Growing Community of Digital Nomads ●  	The Anatomy of ID Document Liveness Detection The research was initiated by Regula and conducted by Sapio Research in August 2024 using an online survey of 575 business decision-makers across the Financial Services (including Traditional Banking and FinTech), Crypto, Technology, Telecommunications, Aviation, Healthcare, and Law Enforcement sectors. The respondent geography included Germany, Mexico, the UAE, the US, and Singapore. About Regula Regula is a global developer of forensic devices and identity verification solutions. With our 30+ years of experience in forensic research and the largest library of document templates in the world, we create breakthrough technologies in document and biometric verification. Our hardware and software solutions allow over 1,000 organizations and 80 border control authorities globally to provide top-notch client service without compromising safety, security or speed. Regula was repeatedly named a Representative Vendor in the Gartner® Market Guide for Identity Verification. Learn more at www.regulaforensics.com. Image : Regula's survey shows that, while average industry losses from deepfake fraud sit at $440,000, 37% of Crypto companies are losing over $500,000. Image Image Regula's survey shows that, while average industry losses from deepfake fraud sit at $440,000, 37% of Crypto companies are losing over $500,000. RESTON, Va — A new “ Deepfake Trends 2024 ” study by Regula , a global developer of forensic devices and identity verification solutions, highlights the pressing financial challenges posed by deepfakes to the Crypto industry. The study reveals that while the industry averages a loss of $440,000 due to advanced fraud techniques, a significant portion – 37% of Crypto companies – are facing losses exceeding half a million dollars each. RESTON, Va — — Deepfake Trends 2024 Deepfake Trends 2024 Regula Regula Deepfakes, which manipulate audio and video to create convincing yet fraudulent content, have become a major concern for more than half of the organizations in the Crypto sector. According to the study, 53% of businesses in the industry have encountered incidents of video deepfakes. Notably, the Crypto firms are particularly vulnerable to audio deepfake fraud, with 57% of respondents reporting such incidents, compared to 50% in other sectors. This heightened susceptibility may stem from the industry’s reliance on specific verification methods. The study identifies multifactor authentication (MFA) as the most widely employed mitigation strategy, utilized by 57% of respondents. In contrast, other industries often prefer biometric verification as their top choice. Audio verification is one of the components within MFA, enabling users to confirm their identity through various methods, such as a password, text message code, and their voice. The impact of deepfake fraud on the crypto industry extends beyond immediate financial losses. Organizations face substantial costs, with 35% of respondents citing legal expenses as a significant burden. Penalties and fines were reported by 33% of organizations, reflecting financial repercussions from regulatory violations or legal settlements. Additionally, 27% of respondents acknowledge reputational risk as a key concern, highlighting the potential damage to trust and credibility that deepfake incidents can cause. “Losing $500,000 to a single type of fraud is staggering for any organization, especially in a fast-paced industry like Crypto,” said Henry Patishman, Executive Vice President of Identity Verification Solutions at Regula. “This finding underscores the need for robust security measures, grounded in best practice. As the threat landscape evolves, it’s crucial for companies to adopt a liveness-centric approach, focusing on real-time verification of people and only physical objects.” “Losing $500,000 to a single type of fraud is staggering for any organization, especially in a fast-paced industry like Crypto,” said Henry Patishman, Executive Vice President of Identity Verification Solutions at Regula. “ This finding underscores the need for robust security measures, grounded in best practice. As the threat landscape evolves, it’s crucial for companies to adopt a liveness-centric approach, focusing on real-time verification of people and only physical objects.” “Losing $500,000 to a single type of fraud is staggering for any organization, especially in a fast-paced industry like Crypto,” This finding underscores the need for robust security measures, grounded in best practice. As the threat landscape evolves, it’s crucial for companies to adopt a liveness-centric approach, focusing on real-time verification of people and only physical objects.” Find more insights on deepfake fraud in the survey report. Read the full version on our website. Find more insights Additional resources: Additional resources: ● Identity Fraud Statistics 2023: How Businesses Respond to the Issue Identity Fraud Statistics 2023: How Businesses Respond to the Issue Identity Fraud Statistics 2023: How Businesses Respond to the Issue ●  	Data from 2022: One-Third of Global Businesses Already Hit by Voice and Video Deepfake Fraud One-Third of Global Businesses Already Hit by Voice and Video Deepfake Fraud One-Third of Global Businesses Already Hit by Voice and Video Deepfake Fraud ● Financial Organizations Challenged by the Growing Community of Digital Nomads Financial Organizations Challenged by the Growing Community of Digital Nomads Financial Organizations Challenged by the Growing Community of Digital Nomads ● The Anatomy of ID Document Liveness Detection The Anatomy of ID Document Liveness Detection The Anatomy of ID Document Liveness Detection The research was initiated by Regula and conducted by Sapio Research in August 2024 using an online survey of 575 business decision-makers across the Financial Services (including Traditional Banking and FinTech), Crypto, Technology, Telecommunications, Aviation, Healthcare, and Law Enforcement sectors. The respondent geography included Germany, Mexico, the UAE, the US, and Singapore. The research was initiated by Regula and conducted by Sapio Research in August 2024 using an online survey of 575 business decision-makers across the Financial Services (including Traditional Banking and FinTech), Crypto, Technology, Telecommunications, Aviation, Healthcare, and Law Enforcement sectors. The respondent geography included Germany, Mexico, the UAE, the US, and Singapore. The research was initiated by Regula and conducted by Sapio Research in August 2024 using an online survey of 575 business decision-makers across the Financial Services (including Traditional Banking and FinTech), Crypto, Technology, Telecommunications, Aviation, Healthcare, and Law Enforcement sectors. The respondent geography included Germany, Mexico, the UAE, the US, and Singapore. About Regula Regula is a global developer of forensic devices and identity verification solutions. With our 30+ years of experience in forensic research and the largest library of document templates in the world, we create breakthrough technologies in document and biometric verification. Our hardware and software solutions allow over 1,000 organizations and 80 border control authorities globally to provide top-notch client service without compromising safety, security or speed. Regula was repeatedly named a Representative Vendor in the Gartner® Market Guide for Identity Verification. Learn more at www.regulaforensics.com . www.regulaforensics.com www.regulaforensics.com

Over One-Third of Crypto Companies Lose More Than $500,000 Each to Deepfake Fraud

ከአንድ ሶስተኛ በላይ የሚሆኑ የCrypto ኩባንያዎች በጥልቅ ማጭበርበር እያንዳንዳቸው ከ500,000 ዶላር በላይ ያጣሉ

এক-তৃতীয়াংশেরও বেশি ক্রিপ্টো কোম্পানি ডিপফেক জালিয়াতির জন্য $500,000-এর বেশি হারায়

Más de un tercio de las empresas de criptomonedas pierden más de 500.000 dólares cada una por fraudes de deepfake

Yli kolmasosa salausalan yrityksistä menettää kukin yli 500 000 dollaria syväväärennöksistä

Máis dun terzo das empresas criptográficas perden máis de 500.000 dólares cada unha por unha fraude falsa

A kriptográfiai cégek több mint egyharmada egyenként több mint 500 000 dollárt veszít mélyhamisítás miatt

יותר משליש מחברות הקריפטו מפסידות יותר מ-500,000 דולר כל אחת כתוצאה מהונאות Deepfake

仮想通貨企業の3分の1以上がディープフェイク詐欺で50万ドル以上を失っている

Крипто-компаниялардын үчтөн биринен көбү Deepfake алдамчылыгынан ар бири 500 000 доллардан ашык акча жоготушту

Daugiau nei trečdalis kriptovaliutų kompanijų praranda daugiau nei 500 000 USD dėl netikro sukčiavimo

Mahigit Isang-Ikatlo ng Mga Kumpanya ng Crypto ang Nawalan ng Higit sa $500,000 Bawat Isa sa Deepfake Fraud

超过三分之一的加密货币公司因 Deepfake 欺诈损失超过 50 万美元

ATLANTA, Georgia, October 30th, 2024/CyberNewsWire/--White House National Cyber Director, CEOs, Key Financial Services Companies, Congressional and Executive Branch Experts will discuss industry priorities for 2025 and beyond The American Transaction Processors Coalition (ATPC) Cyber Council will convene “The Tie that Binds: A 21st Century Cybersecurity Dialogue,” on October 31, 2024, at the Bank of America Financial Center Tower’s Convention Hall in Atlanta. This event will feature leading cyber experts from the financial services sector, Federal agencies, the White House, and Congress to focus on pressing cybersecurity issues and ways the financial services sector is addressing these issues. It will include discussions on evolving technologies that will influence the path forward, the role of AI, supply chain security needs, and more. “Cybersecurity is the backbone of the payment processing industry,” said H. West Richards, ATPC executive director. “The work of the ATPC Cyber Council is a testament to our commitment to safeguarding our financial ecosystem and fostering a collaborative approach to tackling the cybersecurity challenges of tomorrow.” Key Speakers and Highlights: The Honorable Harry Coker, Jr., White House National Cyber Director, will deliver the luncheon keynote.
The Honorable Rich McCormick (R-GA-06) will deliver a keynote address.
Moira Bergin, Subcommittee on Cybersecurity Staff Director, House Committee on Homeland Security, will discuss legislative priorities and global cybersecurity risks.
The Honorable Andre Dickens, Mayor of Atlanta, will provide a video address.
Barry McCarthy, CEO of Deluxe and Chair of the ATPC Board of Directors, will also deliver a keynote.
Bridgette Walsh, Executive Director of the Financial Services Sector Coordinating Council, and Josh Magri, Founder & CEO of Cyber Risk Institute, will participate in a fireside discussion on private sector best practices.
A panel on AI in financial services will feature Clarissa Banks (Deluxe), David Excell (Featurespace), David King (Mastercard), and Donna Teevens (ACI Worldwide), moderated by Rick Van Luvender.
A panel on cyber education will include Dr. Tony Coulson (CSUSB), Dr. Albena Asenova-Belal (Gwinnett Technical College), Dr. Humayun Zafar (Kennesaw State University), and Dr. Michael Nowatkowski (Augusta University).
H. West Richards, ATPC Executive Director, will open the event with a welcome address.
Rick Van Luvender, ATPC Cyber Council Chair & SVP, Head of Cybersecurity Client Trust & International Cybersecurity Service at Fiserv, will deliver the opening remarks.
Norma Krayem, ATPC Cyber Council Director & Vice President, Chair of the Cybersecurity, Privacy & Digital Innovation Practice Group at Van Scoyoc Associates, will provide insights on future cybersecurity trends. The forum will conclude with a fireside chat focused on “A Look to the Future: 2025: Top Cybersecurity and Critical Technology Priorities for the ATPC Cyber Council,” featuring Rick Van Luvender from Fiserv and Norma Krayem, the ATPC Cyber Council director, focusing on future cybersecurity and critical technology priorities. Conference details are available at https://atpcoalition.com/atpc-cyber-forum/. ATPC is a leading voice for America’s payments processors, consisting of the world’s largest, global payment processors, banks, credit card companies and financial services companies. ATPC member companies are uniquely positioned to ensure global payments move seamlessly across the world, while empowering broader and more diverse participation within the financial services system. In the race for a better tomorrow, technology solutions can advance faster than companies can keep up with cybersecurity risks. As a result, the ATPC is one of the few coalitions that created a standalone Cybersecurity Council to prioritize these key cybersecurity issues across its member companies. The ATPC Cyber Council is a unique group made up of only CISOs, CSOs, CIOs and CTOs who are on the front lines every day dealing with the operational impacts of cybersecurity. These U.S. based companies serve hundreds of millions of customer businesses across the globe daily and process hundreds of billions of transactions per year. About the ATPC The ATPC is a leading voice for America’s payments processors, driving awareness of the industry and its value to consumers, businesses, and the economy with legislators and regulators at federal, state, and international levels. The ATPC is rooted in Georgia’s Transaction Alley where electronic payments and the fintech industry began. Yet, our members enable payments in states across the nation and in every corner of the globe. The ATPC has a rich history of economic development, thought leadership, and engagement on legislative and regulatory topics like cybersecurity, privacy, financial inclusion, fraud, as well as emerging themes like open banking, AI, and stable coins. About the ATPC Cyber Council The American Transaction Processors Coalition (ATPC) established a dedicated Cyber Council to galvanize the efforts of the ATPC member companies in addressing cybersecurity risks. The Cyber Council’s mission is to identify best practices and areas of shared risk to help ATPC members address the evolving cyber threat across America’s payments processing system to strengthen industry’s ability to identify, protect, detect, respond to and recover from cyberattacks. Contact Alison Watson Golin awatson@golin.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here ATLANTA, Georgia, October 30th, 2024/CyberNewsWire/--White House National Cyber Director, CEOs, Key Financial Services Companies, Congressional and Executive Branch Experts will discuss industry priorities for 2025 and beyond The American Transaction Processors Coalition (ATPC) Cyber Council will convene “The Tie that Binds: A 21st Century Cybersecurity Dialogue,” on October 31, 2024, at the Bank of America Financial Center Tower’s Convention Hall in Atlanta. This event will feature leading cyber experts from the financial services sector, Federal agencies, the White House, and Congress to focus on pressing cybersecurity issues and ways the financial services sector is addressing these issues. It will include discussions on evolving technologies that will influence the path forward, the role of AI, supply chain security needs, and more. “Cybersecurity is the backbone of the payment processing industry,” said H. West Richards, ATPC executive director. “The work of the ATPC Cyber Council is a testament to our commitment to safeguarding our financial ecosystem and fostering a collaborative approach to tackling the cybersecurity challenges of tomorrow.” “Cybersecurity is the backbone of the payment processing industry,” said H. West Richards, ATPC executive director. “The work of the ATPC Cyber Council is a testament to our commitment to safeguarding our financial ecosystem and fostering a collaborative approach to tackling the cybersecurity challenges of tomorrow.” Key Speakers and Highlights: The Honorable Harry Coker, Jr., White House National Cyber Director, will deliver the luncheon keynote. The Honorable Rich McCormick (R-GA-06) will deliver a keynote address. Moira Bergin, Subcommittee on Cybersecurity Staff Director, House Committee on Homeland Security, will discuss legislative priorities and global cybersecurity risks. The Honorable Andre Dickens, Mayor of Atlanta, will provide a video address. Barry McCarthy, CEO of Deluxe and Chair of the ATPC Board of Directors, will also deliver a keynote. Bridgette Walsh, Executive Director of the Financial Services Sector Coordinating Council, and Josh Magri, Founder & CEO of Cyber Risk Institute, will participate in a fireside discussion on private sector best practices. A panel on AI in financial services will feature Clarissa Banks (Deluxe), David Excell (Featurespace), David King (Mastercard), and Donna Teevens (ACI Worldwide), moderated by Rick Van Luvender. A panel on cyber education will include Dr. Tony Coulson (CSUSB), Dr. Albena Asenova-Belal (Gwinnett Technical College), Dr. Humayun Zafar (Kennesaw State University), and Dr. Michael Nowatkowski (Augusta University). H. West Richards, ATPC Executive Director, will open the event with a welcome address. Rick Van Luvender, ATPC Cyber Council Chair & SVP, Head of Cybersecurity Client Trust & International Cybersecurity Service at Fiserv, will deliver the opening remarks. Norma Krayem, ATPC Cyber Council Director & Vice President, Chair of the Cybersecurity, Privacy & Digital Innovation Practice Group at Van Scoyoc Associates, will provide insights on future cybersecurity trends. The Honorable Harry Coker, Jr., White House National Cyber Director, will deliver the luncheon keynote. The Honorable Rich McCormick (R-GA-06) will deliver a keynote address. Moira Bergin, Subcommittee on Cybersecurity Staff Director, House Committee on Homeland Security, will discuss legislative priorities and global cybersecurity risks. The Honorable Andre Dickens, Mayor of Atlanta, will provide a video address. Barry McCarthy, CEO of Deluxe and Chair of the ATPC Board of Directors, will also deliver a keynote. Bridgette Walsh, Executive Director of the Financial Services Sector Coordinating Council, and Josh Magri, Founder & CEO of Cyber Risk Institute, will participate in a fireside discussion on private sector best practices. A panel on AI in financial services will feature Clarissa Banks (Deluxe), David Excell (Featurespace), David King (Mastercard), and Donna Teevens (ACI Worldwide), moderated by Rick Van Luvender. A panel on cyber education will include Dr. Tony Coulson (CSUSB), Dr. Albena Asenova-Belal (Gwinnett Technical College), Dr. Humayun Zafar (Kennesaw State University), and Dr. Michael Nowatkowski (Augusta University). H. West Richards, ATPC Executive Director, will open the event with a welcome address. Rick Van Luvender, ATPC Cyber Council Chair & SVP, Head of Cybersecurity Client Trust & International Cybersecurity Service at Fiserv, will deliver the opening remarks. Norma Krayem, ATPC Cyber Council Director & Vice President, Chair of the Cybersecurity, Privacy & Digital Innovation Practice Group at Van Scoyoc Associates, will provide insights on future cybersecurity trends. The forum will conclude with a fireside chat focused on “A Look to the Future: 2025: Top Cybersecurity and Critical Technology Priorities for the ATPC Cyber Council,” featuring Rick Van Luvender from Fiserv and Norma Krayem, the ATPC Cyber Council director, focusing on future cybersecurity and critical technology priorities. Conference details are available at https://atpcoalition.com/atpc-cyber-forum/. https://atpcoalition.com/atpc-cyber-forum/. https://atpcoalition.com/atpc-cyber-forum/. ATPC is a leading voice for America’s payments processors, consisting of the world’s largest, global payment processors, banks, credit card companies and financial services companies. ATPC member companies are uniquely positioned to ensure global payments move seamlessly across the world, while empowering broader and more diverse participation within the financial services system. In the race for a better tomorrow, technology solutions can advance faster than companies can keep up with cybersecurity risks. As a result, the ATPC is one of the few coalitions that created a standalone Cybersecurity Council to prioritize these key cybersecurity issues across its member companies. The ATPC Cyber Council is a unique group made up of only CISOs, CSOs, CIOs and CTOs who are on the front lines every day dealing with the operational impacts of cybersecurity. These U.S. based companies serve hundreds of millions of customer businesses across the globe daily and process hundreds of billions of transactions per year. About the ATPC The ATPC is a leading voice for America’s payments processors, driving awareness of the industry and its value to consumers, businesses, and the economy with legislators and regulators at federal, state, and international levels. ATPC ATPC The ATPC is rooted in Georgia’s Transaction Alley where electronic payments and the fintech industry began. Yet, our members enable payments in states across the nation and in every corner of the globe. The ATPC has a rich history of economic development, thought leadership, and engagement on legislative and regulatory topics like cybersecurity, privacy, financial inclusion, fraud, as well as emerging themes like open banking, AI, and stable coins. About the ATPC Cyber Council The American Transaction Processors Coalition (ATPC) established a dedicated Cyber Council to galvanize the efforts of the ATPC member companies in addressing cybersecurity risks. American Transaction Processors Coalition (ATPC) American Transaction Processors Coalition (ATPC) The Cyber Council’s mission is to identify best practices and areas of shared risk to help ATPC members address the evolving cyber threat across America’s payments processing system to strengthen industry’s ability to identify, protect, detect, respond to and recover from cyberattacks. Contact Alison Watson Golin awatson@golin.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here

ATPC Cyber Forum To Focus On Next Generation Cybersecurity And Artificial Intelligence Issues

ATPC Kiberforumu növbəti nəsil kibertəhlükəsizlik və süni intellekt məsələlərinə diqqət yetirəcək

Το ATPC Cyber Forum θα επικεντρωθεί σε ζητήματα κυβερνοασφάλειας και τεχνητής νοημοσύνης επόμενης γενιάς

El Foro Cibernético ATPC se centrará en cuestiones de ciberseguridad e inteligencia artificial de próxima generación

ATPC Cyber Forum para centrarse en cuestións de ciberseguridade e intelixencia artificial de próxima xeración

ATPC Cyber Forum fokusirat će se na kibernetičku sigurnost sljedeće generacije i pitanja umjetne inteligencije

ATPC Cyber Forum Pou Konsantre sou Pwochen Jenerasyon Sibèsekirite ak Pwoblèm Entèlijans Atifisyèl

ATPC サイバーフォーラム、次世代サイバーセキュリティと人工知能の問題に焦点を当てる

វេទិកា ATPC Cyber Forum ដើម្បីផ្តោតលើបញ្ហាសុវត្ថិភាពតាមអ៊ីនធឺណិតជំនាន់ក្រោយ និងបញ្ហាបញ្ញាសិប្បនិម្មិត

ATPC kibernetinis forumas, skirtas naujos kartos kibernetinio saugumo ir dirbtinio intelekto problemoms

د ATPC سایبر فورم به د راتلونکي نسل سایبر امنیت او مصنوعي استخباراتو مسلو باندې تمرکز وکړي

ATPC Cyber Foramu Yekutarisa Pane Inotevera Generation Cybersecurity Uye Artificial Intelligence Nyaya

ATPC Cyber Forum ኣብ ቀፃሊ ወለዶ ሳይበር ሴኩሪቲን ኣርቲፊሻል ኢንተለጀንስን ጉዳያት ትኹረት ክገብር እዩ።

 Passes VS Patreon: A User's Review

Metamorphosis Commenced, vol2: Crecimiento Aleph Review & Q4 in Lisbon and Thailand

Metamorphosis Commended, vol2: Crecimiento Aleph Review & Q4 v Lisabonu a Thajsku

Metamorphosis Commenced, τόμος 2: Crecimiento Aleph Review & Q4 στη Λισαβόνα και την Ταϊλάνδη

Metamorfosis iniciada, vol. 2: Revisión y cuarto trimestre de Crecimiento Aleph en Lisboa y Tailandia

Metamorphosis Commenced, vol2: Crecimiento Aleph Review & Q4 en Lisboa e Tailandia

変身開始、第2巻：クレシミエント・アレフのレビューとリスボンとタイでの第4四半期

Метаморфоз басталды, 2-том: Crecimiento Aleph шолуы және Лиссабондағы және Таиландтағы 4-тоқсан

Metamorphosis ເລີ່ມຕົ້ນ, vol2: Crecimiento Aleph Review & Q4 ໃນ Lisbon ແລະປະເທດໄທ

میټامورفوسس پیل شوی، vol2: Crecimiento Aleph Review & Q4 په لیزبون او تایلینډ کې

Metamorfosis qallarisqa, vol2: Crecimiento Aleph Revisión & Q4 Lisboapi, Tailandiapipas

Metamorphosis Yakatangwa, vol2: Crecimiento Aleph Ongororo & Q4 muLisbon neThailand

Metamorfoza filloi, vëll 2: Crecimiento Aleph Review & Q4 në Lisbonë dhe Tajlandë

Metamorphosis bắt đầu, tập 2: Đánh giá Crecimiento Aleph & Q4 tại Lisbon và Thái Lan

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 13 Acknowledgements This paper developed through extensive discussions over several years with “Team ELIZA”, including Anthony Hay, Art Schwarz, Sarah Ciston, Peggy Weil, Peter Millican, David Berry, and Mark Marino. I am especially indebted to Anthony Hay and Art Schwarz for detailed discussion about technical aspects of ELIZA and SLIP, such as how symbol manipulation worked in SLIP. Terry Winograd and John Markoff participated in helpful background discussions, and Anne Rawls and Clemens Eisenmann provided access to the Garfinkel archives, and Andrei Korbut introduced us to them. Many anonymous commenters in the Hacker News community helped clarify concepts, and provided additional pointers to information that I could not have easily discovered on my own. I am especially indebted to the MIT Archivists, Myles Crowley, Mikki Macdonald, and Allison Schmitt. References [1] https://grammarist.com/new-words/chatbot/. Accessed 2024-04-18. [2] https://sites.google.com/view/elizagen-org/news#h.ykbzq5nuiccs. Accessed 2024-04-18. [3] https://plato.stanford.edu/entries/church-turing/. Accessed 2024-04-18. [4] https://en.wikipedia.org/wiki/Von_Neumann_architecture. Accessed 2024-04-18. [5] https://en.wikipedia.org/wiki/Information_Processing_Language. Accessed 2024-04-18. [6] https://en.wikipedia.org/wiki/Dartmouth_workshop. Accessed 2024- 04-18. [7] https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist). Accessed 2024-04-18. [8] https://plato.stanford.edu/entries/meaning/. Accessed 2024-04-18. [9] https://en.wikipedia.org/wiki/Conceptual_blending. Accessed 2024- 04-18. [10] https://sites.google.com/view/elizagen-org/news#h.74lv9le8r29u. Accessed 2024-04-18. [11] https://en.wikipedia.org/wiki/ARPANET. Accessed 2024-04-18. [12] https://en.wikipedia.org/wiki/History_of_personal_computers. Accessed 2024-04-18. [13] https://en.wikipedia.org/wiki/HAL_9000. Accessed 2024-04-18. [14] https://sites.google.com/view/elizagen-org/. Accessed 2024-04-18. [15] https://en.wikipedia.org/wiki/HyperCard. Accessed 2024-04-18. [16] https://sites.google.com/view/elizaarchaeology/. Accessed 2024- 04-18. [17] D. G. Bobrow. “Natural language input for a computer problem solving system”. PhD Dissertation. Cambridge, MA: Massachusetts Institute of Technology, May 1964. [18] L. Cameron. What to know about the scientist who invented the term ”software engineering”. https://www.computer.org/publications/tech-news/events/what-to-know-aboAccessed: 2024-04-18. Oct. 2018. [19] D. Chisnall. Influential programming languages, Part 4: Lisp. https://www.informit.com/articles/artAccessed 2024-04-18. 2011. [20] Noam Chomsky and George A. Miller. “Introduction to the formal analysis of natural languages”. In: Handbook of Mathematical Psychology. Ed. by R. Duncan Luce, Robert R. Bush, and Eugene Galanter. Vol. 2. Hoboken, NJ: John Wiley and Sons, 1963, pp. 269–321. [21] K. M. Colby. “Modeling a paranoid mind”. In: The Behavioral and Brain Sciences 4 (1981), pp. 515–560. [22] K. M. Colby and J. P. Gilbert. “Programming a computer model of neurosis”. In: J. Math Psych 1 (1964), pp. 405–416. [23] Ada Augusta Countess of Lovelace. Notes on L. F. Menabrea’s “Sketch of the analytical engine invented by Charles Babbage”. http://imaginaryinstruments.org/lovelace-anaAccessed 2024-04-18. Sept. 1843. [24] G. Dyson. Turing’s cathedral. Vintage Books, 2012. [25] C. Eisenmann et al. ““Machine Down”: making sense of human–computer interaction—Garfinkel’s research on ELIZA and LYRIC from 1967 to 1969 and its contemporary relevance”. In: AI & Soc (2023). doi: 10.1007/s00146-023-01793-z. [26] E. Feigenbaum. An information processing theory of verbal learning. Paper P-1817. The RAND Corporation, 1959. [27] H. Gelernter, J. R. Hansen, and L. L. Gerberich. “A Fortran-compiled list-processing language”. In: JACM 7.2 (Apr. 1960), pp. 87–101. [28] D. Gentner and A. L. Stevens. Mental models. Hillsdale, NJ: Lawrence Erlbaum Associates, 1983. [29] B. F. Green et al. “Baseball: An automatic question-answerer”. In: Name of Journal or Conference Volume Number.Issue Number (1961). Additional Information, Page Numbers. [30] F. J. Gruenberger. The history of the JOHNNIAC. RAND memorandum RM-5654-PR. 1968. [31] P. Hayward. Flexible discussion under student control in the ELIZA computer program. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1967. [32] D. R. Hofstadter. G¨odel, Escher, Bach: An eternal golden braid. New York: Basic Books, 1979. [33] P. McCorduck. Machines who think. W.H. Freeman and Co., 1979. [34] M. T. McGuire, S. Lorch, and G. C. Quarton. “Man-machine natural language exchanges based on selected features of unrestricted input—II. The use of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [35] A. Newell and H. A. Simon. “Computer science as empirical inquiry: Symbols and search”. In: Communications of the ACM 19.3 (1976), pp. 113– 126. [36] S. North. “ELIZA”. In: Creative Computing (1977), pp. 100–103. [37] C. Petzold. The annotated turing. Wiley, 2008. [38] G. C. Quarton, M. T. McGuire, and S. Lorch. “Man-machine natural language exchanges based on selected features of unrestricted input—I. The development of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [39] R. C. Schank and R. P. Abelson. Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum Associates, 1977. [40] J. R. Searle. “Minds, brains, and programs”. In: Behavioral and Brain Sciences 3.3 (1980), pp. 417–457. [41] P. Seibel. Coders at Work. APress, 2009. [42] SLIP: University of Michigan executive system for the IBM 7090 computer. Vol. 2. University of Michigan Computing Center, 1965. [43] E. F. Taylor. ELIZA: A skimmable report on the ELIZA conversational tutoring system. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1968. [44] A. Turing. “Computing machinery and intelligence”. In: Mind 49 (1950), pp. 433–460. [45] D. Walden and R. Nickerson, eds. A culture of innovation: Insider accounts of computing and life at BBN. A Sixty Year Report 18 October 1948 to 1 July 2010. Waterside Publishing, 201. [46] J. Weizenbaum. Computer power and human reason. W.H. Freeman and Co., 1976. [47] J. Weizenbaum. “ELIZA—a computer program for the study of natural language communication between man and machine”. In: Communications of the ACM 9.1 (1966), pp. 36–45. doi: 10.1145/365153.365168. [48] J. Weizenbaum. “How to make a computer appear intelligent”. In: Datamation (Feb. 1962), pp. 24–26. [49] J. Weizenbaum. “Symmetric list processor”. In: Comm. ACM 6.9 (1963), pp. 524–536. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 13 Acknowledgements This paper developed through extensive discussions over several years with “Team ELIZA”, including Anthony Hay, Art Schwarz, Sarah Ciston, Peggy Weil, Peter Millican, David Berry, and Mark Marino. I am especially indebted to Anthony Hay and Art Schwarz for detailed discussion about technical aspects of ELIZA and SLIP, such as how symbol manipulation worked in SLIP. Terry Winograd and John Markoff participated in helpful background discussions, and Anne Rawls and Clemens Eisenmann provided access to the Garfinkel archives, and Andrei Korbut introduced us to them. Many anonymous commenters in the Hacker News community helped clarify concepts, and provided additional pointers to information that I could not have easily discovered on my own. I am especially indebted to the MIT Archivists, Myles Crowley, Mikki Macdonald, and Allison Schmitt. References [1] https://grammarist.com/new-words/chatbot/. Accessed 2024-04-18. [2] https://sites.google.com/view/elizagen-org/news#h.ykbzq5nuiccs. Accessed 2024-04-18. [3] https://plato.stanford.edu/entries/church-turing/. Accessed 2024-04-18. [4] https://en.wikipedia.org/wiki/Von_Neumann_architecture. Accessed 2024-04-18. [5] https://en.wikipedia.org/wiki/Information_Processing_Language. Accessed 2024-04-18. [6] https://en.wikipedia.org/wiki/Dartmouth_workshop. Accessed 2024- 04-18. [7] https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist). Accessed 2024-04-18. [8] https://plato.stanford.edu/entries/meaning/. Accessed 2024-04-18. [9] https://en.wikipedia.org/wiki/Conceptual_blending. Accessed 2024- 04-18. [10] https://sites.google.com/view/elizagen-org/news#h.74lv9le8r29u. Accessed 2024-04-18. [11] https://en.wikipedia.org/wiki/ARPANET. Accessed 2024-04-18. [12] https://en.wikipedia.org/wiki/History_of_personal_computers. Accessed 2024-04-18. [13] https://en.wikipedia.org/wiki/HAL_9000. Accessed 2024-04-18. [14] https://sites.google.com/view/elizagen-org/. Accessed 2024-04-18. [15] https://en.wikipedia.org/wiki/HyperCard. Accessed 2024-04-18. [16] https://sites.google.com/view/elizaarchaeology/. Accessed 2024- 04-18. [17] D. G. Bobrow. “Natural language input for a computer problem solving system”. PhD Dissertation. Cambridge, MA: Massachusetts Institute of Technology, May 1964. [18] L. Cameron. What to know about the scientist who invented the term ”software engineering”. https://www.computer.org/publications/tech-news/events/what-to-know-aboAccessed: 2024-04-18. Oct. 2018. [19] D. Chisnall. Influential programming languages, Part 4: Lisp. https://www.informit.com/articles/artAccessed 2024-04-18. 2011. [20] Noam Chomsky and George A. Miller. “Introduction to the formal analysis of natural languages”. In: Handbook of Mathematical Psychology. Ed. by R. Duncan Luce, Robert R. Bush, and Eugene Galanter. Vol. 2. Hoboken, NJ: John Wiley and Sons, 1963, pp. 269–321. [21] K. M. Colby. “Modeling a paranoid mind”. In: The Behavioral and Brain Sciences 4 (1981), pp. 515–560. [22] K. M. Colby and J. P. Gilbert. “Programming a computer model of neurosis”. In: J. Math Psych 1 (1964), pp. 405–416. [23] Ada Augusta Countess of Lovelace. Notes on L. F. Menabrea’s “Sketch of the analytical engine invented by Charles Babbage”. http://imaginaryinstruments.org/lovelace-anaAccessed 2024-04-18. Sept. 1843. [24] G. Dyson. Turing’s cathedral. Vintage Books, 2012. [25] C. Eisenmann et al. ““Machine Down”: making sense of human–computer interaction—Garfinkel’s research on ELIZA and LYRIC from 1967 to 1969 and its contemporary relevance”. In: AI & Soc (2023). doi: 10.1007/s00146-023-01793-z. [26] E. Feigenbaum. An information processing theory of verbal learning. Paper P-1817. The RAND Corporation, 1959. [27] H. Gelernter, J. R. Hansen, and L. L. Gerberich. “A Fortran-compiled list-processing language”. In: JACM 7.2 (Apr. 1960), pp. 87–101. [28] D. Gentner and A. L. Stevens. Mental models. Hillsdale, NJ: Lawrence Erlbaum Associates, 1983. [29] B. F. Green et al. “Baseball: An automatic question-answerer”. In: Name of Journal or Conference Volume Number.Issue Number (1961). Additional Information, Page Numbers. [30] F. J. Gruenberger. The history of the JOHNNIAC. RAND memorandum RM-5654-PR. 1968. [31] P. Hayward. Flexible discussion under student control in the ELIZA computer program. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1967. [32] D. R. Hofstadter. G¨odel, Escher, Bach: An eternal golden braid. New York: Basic Books, 1979. [33] P. McCorduck. Machines who think. W.H. Freeman and Co., 1979. [34] M. T. McGuire, S. Lorch, and G. C. Quarton. “Man-machine natural language exchanges based on selected features of unrestricted input—II. The use of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [35] A. Newell and H. A. Simon. “Computer science as empirical inquiry: Symbols and search”. In: Communications of the ACM 19.3 (1976), pp. 113– 126. [36] S. North. “ELIZA”. In: Creative Computing (1977), pp. 100–103. [37] C. Petzold. The annotated turing. Wiley, 2008. [38] G. C. Quarton, M. T. McGuire, and S. Lorch. “Man-machine natural language exchanges based on selected features of unrestricted input—I. The development of the time-shared computer as a research tool in studying dyadic communication”. In: J. Psychiat. Res. 5.2 (1967). [39] R. C. Schank and R. P. Abelson. Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum Associates, 1977. [40] J. R. Searle. “Minds, brains, and programs”. In: Behavioral and Brain Sciences 3.3 (1980), pp. 417–457. [41] P. Seibel. Coders at Work. APress, 2009. [42] SLIP: University of Michigan executive system for the IBM 7090 computer. Vol. 2. University of Michigan Computing Center, 1965. [43] E. F. Taylor. ELIZA: A skimmable report on the ELIZA conversational tutoring system. Educational Research Center, Massachusetts Institute of Technology, Cambridge. 1968. [44] A. Turing. “Computing machinery and intelligence”. In: Mind 49 (1950), pp. 433–460. [45] D. Walden and R. Nickerson, eds. A culture of innovation: Insider accounts of computing and life at BBN. A Sixty Year Report 18 October 1948 to 1 July 2010. Waterside Publishing, 201. [46] J. Weizenbaum. Computer power and human reason. W.H. Freeman and Co., 1976. [47] J. Weizenbaum. “ELIZA—a computer program for the study of natural language communication between man and machine”. In: Communications of the ACM 9.1 (1966), pp. 36–45. doi: 10.1145/365153.365168. [48] J. Weizenbaum. “How to make a computer appear intelligent”. In: Datamation (Feb. 1962), pp. 24–26. [49] J. Weizenbaum. “Symmetric list processor”. In: Comm. ACM 6.9 (1963), pp. 524–536. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

Who Made ELIZA Possible? 

SEC v. Ripple Labs, Inc., Court Filing, retrieved on August 05, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 4 of 5. III. Civil Penalties A. Legal Standard The SEC is also authorized to seek the imposition of civil monetary penalties, which serve “the dual goals of punishment of the individual violator and deterrence of future violations.” Off. Comm. of Unsecured Creditors of WorldCom, Inc. v. SEC, 467 F.3d 73, 81 (2d Cir. 2006) (quotation marks and citation omitted). “Courts can impose penalties in civil injunctive actions not to exceed the greater of: (i) the gross pecuniary gain to a defendant as a result of the violation, or (ii) a specified amount per violation, depending on whether the violation falls in the first, second[,] or third penalty tier.” SEC v. Bajic, No. 20 Civ. 07, 2023 WL 6289953, at *4 (S.D.N.Y. Sept. 27, 2023) (citing 15 U.S.C. § 77t(d)(2); 15 U.S.C. § 78u(d)(3)(B)). A court may impose a first-tier penalty for any violation of the Exchange or Securities Acts; a second-tier penalty if the violation “involved fraud, deceit, manipulation, or deliberate or reckless disregard of a regulatory requirement”; and a third-tier penalty if, in addition to meeting the second-tier requirements, the “violation directly or indirectly resulted in substantial losses or created a significant risk of substantial losses to other persons.” Id. (citations omitted). Because “[t]he term ‘violation’ is not defined by the statutory scheme,” courts have discretion to determine the unit of violation for the purposes of imposing civil penalties under the tier method. SEC v. Fowler, 6 F.4th 255, 264 (2d Cir. 2021); see id. at 265. Although gross pecuniary gain or “the tier determines the maximum penalty, . . . the actual amount of the penalty [is] left up to the discretion of the district court.” SEC v. Kern, 425 F.3d 143, 153 (2d Cir. 2005). In determining the appropriate penalty, courts may consider factors including: (1) the egregiousness of the defendant’s conduct; (2) the degree of the defendant’s scienter; (3) whether the defendant’s conduct created substantial losses or the risk of substantial losses to other persons; (4) whether the defendant’s conduct was isolated or recurrent; and (5) whether the penalty should be reduced due to the defendant’s demonstrated current and future financial condition. SEC v. Rajaratnam, 918 F.3d 36, 44 (2d Cir. 2019) (citation omitted); see also id. at 45 (noting that the list is not exhaustive); cf. Fowler, 6 F.4th at 266 (noting that the Circuit has not held that “the civil penalty for a securities fraud offense needs to be proportional to the disgorgement amount”). The Court may also consider “the extent to which other aspects of the relief and/or judgment issued in this matter will have the desired punitive effect.” SEC v. Universal Exp., Inc., 646 F. Supp. 2d 552, 568 (S.D.N.Y. 2009), aff’d, 438 F. App’x 23 (2d Cir. 2011). B. Application The SEC asks the Court to impose a penalty of $876,308,712, equal to the net profits it calculates for the Institutional Sales. SEC Mem. at 23. Ripple argues that a penalty of no more than $10 million, or “about [redacted] of Ripple’s actual gross revenues . . . from pre-Complaint Institutional Sales,” is appropriate. Ripple Opp. at 30 (citation omitted). The Court finds that a first-tier penalty is appropriate in this case, which involves no allegations of “fraud, deceit, [or] manipulation,” and no conclusively established “deliberate or reckless disregard of a regulatory requirement.” 15 U.S.C. § 77t(d)(2)(B); see supra Part I.B. Although Ripple’s gross pecuniary gain from the Institutional Sales sets a higher statutory ceiling, the Court finds that the tier-analysis method more closely tailors the penalty to the scope of Ripple’s actual wrongdoing than the parties’ (virtually) all-or-nothing requests. As to the amount of the penalty, the Court has discussed the second and fourth Rajaratnam factors, which overlap with the factors relevant to injunctive relief, above. See supra Part I.B. On the first factor, the egregiousness of Ripple’s conduct, there is no question that the recurrent, highly lucrative violation of Section 5 is a serious offense. However, this case does not involve allegations of fraud, misappropriation, or other more culpable conduct. Relatedly, as to the third factor, the SEC has not established that Ripple’s failure to register the Institutional Sales caused substantial losses (or the risk thereof) to investors. See supra Part II.B. And, on the fifth factor, Ripple does not contest that its current financial condition does not merit a reduced penalty. See Ripple Opp. at 28–29. Beyond the Rajaratnam factors, the Court considers that Ripple will not be ordered to pay disgorgement, which dictates in favor of a larger penalty to accomplish “the desired punitive effect.” Universal Exp., Inc., 646 F. Supp. 2d at 568. The Court finds, therefore, that a perviolation penalty at the first-tier maximum is appropriate. The final step is to define and count the “violations” involved in the Institutional Sales. Because Section 5’s registration requirement is “transaction-specific,” it follows that each unregistered transaction constitutes a separate violation of the statute. Cavanagh, 155 F.3d at 133; see also SEC v. Colonial Inv. Mgmt. LLC, 381 F. App’x 27, 29 (2d Cir. 2010) (summary order) (affirming per-transaction penalties calculation). The SEC suggests that each of Ripple’s “1,700 relevant contracts” constitutes a separate violation. SEC Mem. at 23–24 n.8. Ripple responds that the “1,700 relevant contracts” figure includes “hundreds of contracts related to Programmatic Sales and Other Distributions,” which should be excluded. Ripple Opp. at 19 n.20. But, Ripple does not offer its own tabulation of the relevant contracts, and the SEC does not respond to Ripple’s contention in its reply brief. Based on the Court’s independent analysis of Ripple’s expert report summarizing the relevant contracts, see Schwarz Rep., ECF No. 582-7, the Court finds that 1,278 transactions violated Section 5, resulting in a civil penalty of $125,035,150. [10] Accordingly, the SEC’s request for a civil monetary penalty is GRANTED in part, and the Court will impose a penalty of $125,035,150. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on August 07, 2024, dropbox.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. [10]  The Court arrived at this figure by first tabulating the 1,278 “Sales Contracts” listed in Exhibit C of the expert’s report, which excluded “Programmatic Contracts,” “Service Contracts,” and “Other Contracts.” Schwarz Rep. at 80–124; see id. ¶¶ 18–55. The Court then determined the applicable penalty for each contract based on its date. See 17 C.F.R. § 201.1001, Tbl. I; Adjustments to Civ. Monetary Penalty Amounts, Release No. 6521, 2024 WL 111023 (Jan. 5, 2024) (“Release No. 6521”). For contracts entered into on or after November 2, 2015, the penalty is currently $115,231 per contract. Release No. 6521. For contracts entered into between March 6, 2013 and November 2, 2015, the penalty is $80,000 per contract. 17 C.F.R. § 201.1001, Tbl. I. And, for contracts entered into between March 4, 2009 and March 5, 2013, the penalty is $75,000 per contract. Id. For the four undated contracts, the Court applied the current inflation-adjusted penalty amount. Finally, the Court totaled the per-contract penalties to arrive at a civil penalty of $125,035,150. This sum is concededly an estimate of the maximum first-tier penalty; it is possible that the contracts listed in Exhibit C are either an incomplete or overinclusive list of Ripple’s Institutional Sales during the relevant period. Because neither party provided a more specific calculation, however, the Court believes that its estimate is an adequate approximation. SEC v. Ripple Labs, Inc., Court Filing, retrieved on August 05, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 4 of 5. SEC v. Ripple Labs, Inc., Court Filing, retrieved on August 05, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 4 of 5. HackerNoon’s Legal PDF Series here III. Civil Penalties A. Legal Standard A. Legal Standard The SEC is also authorized to seek the imposition of civil monetary penalties, which serve “the dual goals of punishment of the individual violator and deterrence of future violations.” Off. Comm. of Unsecured Creditors of WorldCom, Inc. v. SEC, 467 F.3d 73, 81 (2d Cir. 2006) (quotation marks and citation omitted). “Courts can impose penalties in civil injunctive actions not to exceed the greater of: (i) the gross pecuniary gain to a defendant as a result of the violation, or (ii) a specified amount per violation, depending on whether the violation falls in the first, second[,] or third penalty tier.” SEC v. Bajic, No. 20 Civ. 07, 2023 WL 6289953, at *4 (S.D.N.Y. Sept. 27, 2023) (citing 15 U.S.C. § 77t(d)(2); 15 U.S.C. § 78u(d)(3)(B)). A court may impose a first-tier penalty for any violation of the Exchange or Securities Acts; a second-tier penalty if the violation “involved fraud, deceit, manipulation, or deliberate or reckless disregard of a regulatory requirement”; and a third-tier penalty if, in addition to meeting the second-tier requirements, the “violation directly or indirectly resulted in substantial losses or created a significant risk of substantial losses to other persons.” Id. (citations omitted). Because “[t]he term ‘violation’ is not defined by the statutory scheme,” courts have discretion to determine the unit of violation for the purposes of imposing civil penalties under the tier method. SEC v. Fowler, 6 F.4th 255, 264 (2d Cir. 2021); see id. at 265. Although gross pecuniary gain or “the tier determines the maximum penalty, . . . the actual amount of the penalty [is] left up to the discretion of the district court.” SEC v. Kern, 425 F.3d 143, 153 (2d Cir. 2005). In determining the appropriate penalty, courts may consider factors including: (1) the egregiousness of the defendant’s conduct; (2) the degree of the defendant’s scienter; (3) whether the defendant’s conduct created substantial losses or the risk of substantial losses to other persons; (4) whether the defendant’s conduct was isolated or recurrent; and (5) whether the penalty should be reduced due to the defendant’s demonstrated current and future financial condition. SEC v. Rajaratnam, 918 F.3d 36, 44 (2d Cir. 2019) (citation omitted); see also id. at 45 (noting that the list is not exhaustive); cf. Fowler, 6 F.4th at 266 (noting that the Circuit has not held that “the civil penalty for a securities fraud offense needs to be proportional to the disgorgement amount”). The Court may also consider “the extent to which other aspects of the relief and/or judgment issued in this matter will have the desired punitive effect.” SEC v. Universal Exp., Inc., 646 F. Supp. 2d 552, 568 (S.D.N.Y. 2009), aff’d, 438 F. App’x 23 (2d Cir. 2011). B. Application B. Application The SEC asks the Court to impose a penalty of $876,308,712, equal to the net profits it calculates for the Institutional Sales. SEC Mem. at 23. Ripple argues that a penalty of no more than $10 million, or “about [redacted] of Ripple’s actual gross revenues . . . from pre-Complaint Institutional Sales,” is appropriate. Ripple Opp. at 30 (citation omitted). The Court finds that a first-tier penalty is appropriate in this case, which involves no allegations of “fraud, deceit, [or] manipulation,” and no conclusively established “deliberate or reckless disregard of a regulatory requirement.” 15 U.S.C. § 77t(d)(2)(B); see supra Part I.B. Although Ripple’s gross pecuniary gain from the Institutional Sales sets a higher statutory ceiling, the Court finds that the tier-analysis method more closely tailors the penalty to the scope of Ripple’s actual wrongdoing than the parties’ (virtually) all-or-nothing requests. As to the amount of the penalty, the Court has discussed the second and fourth Rajaratnam factors, which overlap with the factors relevant to injunctive relief, above. See supra Part I.B. On the first factor, the egregiousness of Ripple’s conduct, there is no question that the recurrent, highly lucrative violation of Section 5 is a serious offense. However, this case does not involve allegations of fraud, misappropriation, or other more culpable conduct. Relatedly, as to the third factor, the SEC has not established that Ripple’s failure to register the Institutional Sales caused substantial losses (or the risk thereof) to investors. See supra Part II.B. And, on the fifth factor, Ripple does not contest that its current financial condition does not merit a reduced penalty. See Ripple Opp. at 28–29. Beyond the Rajaratnam factors, the Court considers that Ripple will not be ordered to pay disgorgement, which dictates in favor of a larger penalty to accomplish “the desired punitive effect.” Universal Exp., Inc., 646 F. Supp. 2d at 568. The Court finds, therefore, that a perviolation penalty at the first-tier maximum is appropriate. The final step is to define and count the “violations” involved in the Institutional Sales. Because Section 5’s registration requirement is “transaction-specific,” it follows that each unregistered transaction constitutes a separate violation of the statute. Cavanagh, 155 F.3d at 133; see also SEC v. Colonial Inv. Mgmt. LLC, 381 F. App’x 27, 29 (2d Cir. 2010) (summary order) (affirming per-transaction penalties calculation). The SEC suggests that each of Ripple’s “1,700 relevant contracts” constitutes a separate violation. SEC Mem. at 23–24 n.8. Ripple responds that the “1,700 relevant contracts” figure includes “hundreds of contracts related to Programmatic Sales and Other Distributions,” which should be excluded. Ripple Opp. at 19 n.20. But, Ripple does not offer its own tabulation of the relevant contracts, and the SEC does not respond to Ripple’s contention in its reply brief. Based on the Court’s independent analysis of Ripple’s expert report summarizing the relevant contracts, see Schwarz Rep., ECF No. 582-7, the Court finds that 1,278 transactions violated Section 5, resulting in a civil penalty of $125,035,150. [10] Accordingly, the SEC’s request for a civil monetary penalty is GRANTED in part, and the Court will impose a penalty of $125,035,150. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 07, 2024, dropbox.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 07, 2024, dropbox.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. dropbox.com [10]  The Court arrived at this figure by first tabulating the 1,278 “Sales Contracts” listed in Exhibit C of the expert’s report, which excluded “Programmatic Contracts,” “Service Contracts,” and “Other Contracts.” Schwarz Rep. at 80–124; see id. ¶¶ 18–55. The Court then determined the applicable penalty for each contract based on its date. See 17 C.F.R. § 201.1001, Tbl. I; Adjustments to Civ. Monetary Penalty Amounts, Release No. 6521, 2024 WL 111023 (Jan. 5, 2024) (“Release No. 6521”). For contracts entered into on or after November 2, 2015, the penalty is currently $115,231 per contract. Release No. 6521. For contracts entered into between March 6, 2013 and November 2, 2015, the penalty is $80,000 per contract. 17 C.F.R. § 201.1001, Tbl. I. And, for contracts entered into between March 4, 2009 and March 5, 2013, the penalty is $75,000 per contract. Id. For the four undated contracts, the Court applied the current inflation-adjusted penalty amount. Finally, the Court totaled the per-contract penalties to arrive at a civil penalty of $125,035,150. This sum is concededly an estimate of the maximum first-tier penalty; it is possible that the contracts listed in Exhibit C are either an incomplete or overinclusive list of Ripple’s Institutional Sales during the relevant period. Because neither party provided a more specific calculation, however, the Court believes that its estimate is an adequate approximation.

SEC Hands Ripple a $125M Reality Check

SEC হ্যান্ডস রিপল $125M রিয়েলিটি চেক পায়

SEC übergibt Ripple einen Realitätscheck über 125 Millionen Dollar

La SEC le da a Ripple una dosis de realidad de 125 millones de dólares

La SEC remet à Ripple un chèque de 125 millions de dollars

एसईसी ने रिपल को 125 मिलियन डॉलर का वास्तविकता परीक्षण दिया

SECがリップル社に1億2500万ドルの現実を突きつける

SEC Hands Ripple이 1억 2,500만 달러의 현실 확인을 받다

SEC Hands Ripple recebe uma verificação da realidade de US$ 125 milhões

SEC вручает Ripple проверку на реальность на сумму 125 млн долларов

SEC, Ripple'a 125 Milyon Dolarlık Gerçeklik Kontrolü Sağladı

SEC trao Ripple khoản tiền kiểm tra thực tế trị giá 125 triệu đô la

SEC 向 Ripple 发出 1.25 亿美元现实检查

A few months ago when I first started learning about Cryptocurrencies, It felt like I had been thrown into a whole new world with people using complex terms I didn't understand anything about. One concept in particular I struggled to understand was that of APIs. It took hours of tutorials and personal research to finally understand what APIs are and what they do. So, if you're a trainee web developer, crypto enthusiast, or a newbie to crypto trading, and you currently have a similar problem that I had, then this is the one tutorial you are going to need. Using simple language and easy-to-understand analogies, at the end of this tutorial, you'll save yourself hours of research time, and you will be able to answer the basic questions: What are Cryptocurrency APIs and how do they work?
Why are Crypto Currency APIs important?
What are the top APIs out there? Now let's dive in! What Are Cryptocurrency APIs, and How Do They Work? To understand this, we must first understand what APIs are. API is an acronym that stands for Application Programming Interface. Sounds complex? let's break it down. An application is software that helps people obtain information about specific things or carry out particular tasks like how the Expedia app on your phone helps you book flights on the go or the Amazon weather app helps you check weather forecasts. An interface in this context, is a program that helps 2 applications or websites communicate with each other, kinda like a middleman. So an API is basically a program or a “link” that helps two apps (or websites) “talk” to each other about a particular thing. So, when an app (let's call it app A) needs information about a particular thing or needs access to a service rendered by another app B, an API of app B is used. This API for app B serves as a link, a connection via which the specific information app A requires from app B is obtained. Wait … What?! Okay, okay. Let's break it down further using this analogy: Imagine you're opening a bakery, and you decide to sell cupcakes, muffins, and sourdough bread. For some reason, however,  you don't have the capacity to make sourdough bread of your own. What do you do? You reach out to another bakery that sells sourdough bread. Make an agreement for them to sell you sourdough bread when you request it. So, every time you need bread, you reach out to an employee of that bakery, and he brings you the bread. Now, bread isn't the only thing this second bakery sells, but it's the only thing you need from them. That's basically how an API works. When an app needs information that it cannot provide on its own, it uses the API of another app to gain access to that information from the other app. For a more technical analogy. Let's use a flight booking app to illustrate how APIs work: Imagine you're using a travel app like Expedia to book a flight. When you search for flights, the app doesn't have all the flight information stored locally. Instead, it uses specific APIs to fetch data from airlines' systems or other flight databases. Here's basically what happens behind the scenes: The travel app sends a request to the airline's API (e.g., "Get flights from Nigeria to New York on August 26th").


The airline's API processes the request and returns the relevant data (e.g., flight numbers, departure times, prices).


The travel app receives the data and displays it in a user-friendly format. So you see: The travel app is the "client" or "consumer" of the API. The airline's system is the "provider" of the API. The API acts as an intermediary, allowing the travel app to access the airline's data without having to replicate it locally. So, if I’m an app developer and the app I'm working on serves to provide specific information about the flight schedules from Qatar Airways for instance, I will use an API of Qatar Airways to get me and my future users access to that information. Now that we understand what an API is, applying that knowledge directly, we can say that cryptocurrency APIs are basically programs that enable different apps or websites to get access to specific data about Cryptocurrencies (i.e., chart updates, transactions, etc.) from other websites that directly monitor and interact with the Crypto Market. So, if I want information about the current status of Bitcoin on the Crypto Market, I open a website like Dervi. This website cannot monitor the Crypto market directly, so it uses an API to get information about current Bitcoin status from the Trading View website. Why Are Crypto Currency APIs Important? From what we've discussed earlier, we can say some of the importance of these APIs to Cryptocurrency are: Real-time data access: APIs provide up-to-date information on market prices, trading volumes, and blockchain data, enabling informed decisions.


Research and analysis: APIs provide data for researchers, analysts, and investors to study market trends and make predictions.


Integration with other services: APIs enable integration with payment systems, e-commerce platforms, and other services, expanding cryptocurrency adoption. In essence, cryptocurrency APIs facilitate innovation, automation, and accessibility in the cryptocurrency and blockchain ecosystem. What Are the Top Cryptocurrency APIs? Now that we understand what APIs are and can see why they are so important, let's look at some of the top Crypto CryptoCurrency APIs: Coin Market Cap API Coin Market Cap is one of the best cryptocurrency data APIs. So as a developer, incorporating it into your app or website will be really beneficial. You see it offers real-time cryptocurrency data and market information, such as: Historical cryptocurrency data since 2013
Live price updates for cryptocurrency assets
Market data, volume, and exchange information
Trading data and analytics
Data on non-fungible tokens (NFTs) Enterprise, professional, and free API plans This is an API that is used all over the world by cryptocurrency traders, developers, and financial firms. CoinGeko API CoinGeko is a big name amongst web developers! It offers many exciting features and has an easy-to-use interface. It has its own charts which it uses to track several coins in the Crypto market. Some of the important features it offers are these: Real-time and historical crypto prices,
Market capitalization data,
Trading volume and exchange data,
Metadata,
NFT collection floor prices. Coin Gecko API offers a free plan! As well as several paid plans with additional features and support. Binance: Binance is considered one of the largest cryptocurrency exchange platforms globally. It allows users to buy and sell digital assets. Its free API allows web developers to interact with the Binance platform and create custom trading applications like: Executing trades and checking their account balances.
Viewing their trading history using the API.
Enables developers to create custom trading strategies and automate their trading activities. The world of cryptocurrency is constantly changing, evolving, and expanding. APIs make it easier for web developers to build cryptocurrency-related apps, wallets, exchanges, and analytics tools. Which makes monitoring of trends within the Crypto market much easier for traders. A few months ago when I first started learning about Cryptocurrencies, It felt like I had been thrown into a whole new world with people using complex terms I didn't understand anything about. One concept in particular I struggled to understand was that of APIs. It took hours of tutorials and personal research to finally understand what APIs are and what they do. So, if you're a trainee web developer, crypto enthusiast, or a newbie to crypto trading, and you currently have a similar problem that I had, then this is the one tutorial you are going to need. Using simple language and easy-to-understand analogies, at the end of this tutorial, you'll save yourself hours of research time, and you will be able to answer the basic questions: What are Cryptocurrency APIs and how do they work? Why are Crypto Currency APIs important? What are the top APIs out there? What are Cryptocurrency APIs and how do they work? Why are Crypto Currency APIs important? What are the top APIs out there? Now let's dive in! What Are Cryptocurrency APIs, and How Do They Work? What Are Cryptocurrency APIs, and How Do They Work? To understand this, we must first understand what APIs are. API is an acronym that stands for Application Programming Interface. Sounds complex? let's break it down. An application is software that helps people obtain information about specific things or carry out particular tasks like how the Expedia app on your phone helps you book flights on the go or the Amazon weather app helps you check weather forecasts. An interface in this context, is a program that helps 2 applications or websites communicate with each other, kinda like a middleman. So an API is basically a program or a “link” that helps two apps (or websites) “talk” to each other about a particular thing. So, when an app (let's call it app A) needs information about a particular thing or needs access to a service rendered by another app B, an API of app B is used. This API for app B serves as a link, a connection via which the specific information app A requires from app B is obtained. Wait … What?! Wait … What?! Okay, okay. Let's break it down further using this analogy: Imagine you're opening a bakery, and you decide to sell cupcakes, muffins, and sourdough bread. For some reason, however,  you don't have the capacity to make sourdough bread of your own. What do you do? What do you do? You reach out to another bakery that sells sourdough bread. Make an agreement for them to sell you sourdough bread when you request it. So, every time you need bread, you reach out to an employee of that bakery, and he brings you the bread. Now, bread isn't the only thing this second bakery sells, but it's the only thing you need from them. That's basically how an API works. When an app needs information that it cannot provide on its own, it uses the API of another app to gain access to that information from the other app. For a more technical analogy. Let's use a flight booking app to illustrate how APIs work: Imagine you're using a travel app like Expedia to book a flight. When you search for flights, the app doesn't have all the flight information stored locally. Instead, it uses specific APIs to fetch data from airlines' systems or other flight databases. Here's basically what happens behind the scenes: The travel app sends a request to the airline's API (e.g., "Get flights from Nigeria to New York on August 26th"). The airline's API processes the request and returns the relevant data (e.g., flight numbers, departure times, prices). The travel app receives the data and displays it in a user-friendly format. The travel app sends a request to the airline's API (e.g., "Get flights from Nigeria to New York on August 26th"). The travel app sends a request to the airline's API (e.g., "Get flights from Nigeria to New York on August 26th"). The airline's API processes the request and returns the relevant data (e.g., flight numbers, departure times, prices). The airline's API processes the request and returns the relevant data (e.g., flight numbers, departure times, prices). The travel app receives the data and displays it in a user-friendly format. The travel app receives the data and displays it in a user-friendly format. So you see: The travel app is the "client" or "consumer" of the API. The travel app is the "client" or "consumer" of the API. The airline's system is the "provider" of the API. The airline's system is the "provider" of the API. The API acts as an intermediary, allowing the travel app to access the airline's data without having to replicate it locally. The API acts as an intermediary, allowing the travel app to access the airline's data without having to replicate it locally. So, if I’m an app developer and the app I'm working on serves to provide specific information about the flight schedules from Qatar Airways for instance, I will use an API of Qatar Airways to get me and my future users access to that information. Now that we understand what an API is, applying that knowledge directly, we can say that cryptocurrency APIs are basically programs that enable different apps or websites to get access to specific data about Cryptocurrencies (i.e., chart updates, transactions, etc.) from other websites that directly monitor and interact with the Crypto Market. So, if I want information about the current status of Bitcoin on the Crypto Market, I open a website like Dervi. This website cannot monitor the Crypto market directly, so it uses an API to get information about current Bitcoin status from the Trading View website. Why Are Crypto Currency APIs Important? Why Are Crypto Currency APIs Important? From what we've discussed earlier, we can say some of the importance of these APIs to Cryptocurrency are: Real-time data access: APIs provide up-to-date information on market prices, trading volumes, and blockchain data, enabling informed decisions. Research and analysis: APIs provide data for researchers, analysts, and investors to study market trends and make predictions. Integration with other services: APIs enable integration with payment systems, e-commerce platforms, and other services, expanding cryptocurrency adoption. Real-time data access: APIs provide up-to-date information on market prices, trading volumes, and blockchain data, enabling informed decisions. Real-time data access: APIs provide up-to-date information on market prices, trading volumes, and blockchain data, enabling informed decisions. Research and analysis: APIs provide data for researchers, analysts, and investors to study market trends and make predictions. Research and analysis: APIs provide data for researchers, analysts, and investors to study market trends and make predictions. Integration with other services: APIs enable integration with payment systems, e-commerce platforms, and other services, expanding cryptocurrency adoption. Integration with other services: APIs enable integration with payment systems, e-commerce platforms, and other services, expanding cryptocurrency adoption. In essence, cryptocurrency APIs facilitate innovation, automation, and accessibility in the cryptocurrency and blockchain ecosystem. What Are the Top Cryptocurrency APIs? What Are the Top Cryptocurrency APIs? Now that we understand what APIs are and can see why they are so important, let's look at some of the top Crypto CryptoCurrency APIs: Coin Market Cap API Coin Market Cap API Coin Market Cap is one of the best cryptocurrency data APIs. So as a developer, incorporating it into your app or website will be really beneficial. You see it offers real-time cryptocurrency data and market information, such as: Historical cryptocurrency data since 2013 Live price updates for cryptocurrency assets Market data, volume, and exchange information Trading data and analytics Data on non-fungible tokens (NFTs) Enterprise, professional, and free API plans Historical cryptocurrency data since 2013 Live price updates for cryptocurrency assets Market data, volume, and exchange information Trading data and analytics Data on non-fungible tokens (NFTs) Enterprise, professional, and free API plans This is an API that is used all over the world by cryptocurrency traders, developers, and financial firms. CoinGeko API CoinGeko API CoinGeko is a big name amongst web developers! It offers many exciting features and has an easy-to-use interface. It has its own charts which it uses to track several coins in the Crypto market. Some of the important features it offers are these: Real-time and historical crypto prices, Market capitalization data, Trading volume and exchange data, Metadata, NFT collection floor prices. Real-time and historical crypto prices, Market capitalization data, Trading volume and exchange data, Metadata, NFT collection floor prices. Coin Gecko API offers a free plan! As well as several paid plans with additional features and support. Binance: Binance: Binance is considered one of the largest cryptocurrency exchange platforms globally. It allows users to buy and sell digital assets. Its free API allows web developers to interact with the Binance platform and create custom trading applications like: Executing trades and checking their account balances. Viewing their trading history using the API. Enables developers to create custom trading strategies and automate their trading activities. Executing trades and checking their account balances. Viewing their trading history using the API. Enables developers to create custom trading strategies and automate their trading activities. The world of cryptocurrency is constantly changing, evolving, and expanding. APIs make it easier for web developers to build cryptocurrency-related apps, wallets, exchanges, and analytics tools. Which makes monitoring of trends within the Crypto market much easier for traders.

A Beginner's Introduction to Cryptocurrency APIs: The Basics You Need as a Trainee Web Developer

A Comparative Algorithm Audit of Conspiracies on the Net: Conclusion and Bibliography

This paper is available on arxiv under CC 4.0 license. Authors: (1) Qin Wang, CSIRO Data61, Australia; (2) Shiping Chen, CSIRO Data61, Australia. Table of Links Abstract & Introduction Ethereum Accounts Account Abstraction Security Analysis Further Discussions Conclusion & References Abstract Ethereum recently unveiled its upcoming roadmap’s Splurge phase, highlighting the integration of EIP-4337 as a foundational standard for account abstraction (AA). AA aims to enhance user accessibility and facilitate the expansion of functionalities. Anticipatedly, the deployment of AA is poised to attract a broad spectrum of new users and ignite further innovation in DApps. In this paper, we elucidate the underlying operating mechanisms of this new concept, as well as provide a review of concurrent advancements in accounts, wallets, and standards related to its development. We step further by conducting a preliminary security evaluation to qualitatively assess the extent of security enhancements achieved through AA updates. —Ethereum, Account Abstraction, EOA Index Terms I. INTRODUCTION Accounts within the Ethereum ecosystem [1] serve as the bedrock for asset querying, storage, and transactions, constituting a pivotal element of its infrastructure. However, the present account design poses challenges for Web2 users due to its complicated design. Ethereum classifies accounts into two types [2], namely, externally owned accounts (EOA) and contract accounts (CA)[1]. EOA is controlled by private keys held by its creators or users. Contract accounts are controlled by codes without the involvement of private keys (Fig.1). The reliance on private keys makes these two accounts act in different roles: EOA can prove the validity of a transaction and trigger the state transition in CAs but with limitations: • Expense: The functioning of the contract wallet necessitates initiation by an EOA, essentially entailing a contract invocation. Every transaction within this process incurs an extra 21,000 Gas cost, which includes charges for ECDSA signature verification, Nonce value verification, and ensuring adequate account balance. • Elevated barrier: EOAs must possess a substantial ether balance to cover Gas expenses (involving management of two separate accounts), or alternatively depend on a Relayer to manage Gas payments, potentially introducing centralization concerns. • Flunction: In addition to the fee in ether (ETH), users are consequently required to hold ether, which exposes them to the potential volatility of its price. • User perception: From a user’s perspective, grasping the nuances of gas price, gas limit, and transaction congestion is far from straightforward. The effort to integrate two distinct types of accounts while preserving their core functionalities has been a longstanding subject of deliberation within Ethereum communities. Two primary technical pathways have come to the forefront. • EOA delegates control to smart contracts, in which the contract logic can inplement the core functionalities from EOA transactions such as gas payment (e.g., EIP[2] -3074). • EOA accounts are designed to be armed with several key functionalities from smart contracts (EIP-4337). The concept of account abstraction (AA), indexed by EIP4337 [3], aligns with the second approach, aiming to bestow EOA with the programmable functionality akin to CAs. The incorporation of EIP-4337 into the present roadmap [4] signifies Ethereum’s definitive stance in these dual directions. By adopting AA updates, Ethereum sidesteps the potential hurdle of transitioning existing users. This is attributed to its implementation on the application layers rather than the consensus layer, which inherently offers robust backward compatibility capabilities. Ethereum transactions are required to emanate from EOAs, which are typically accessed through various wallets like MetaMask, Phantom, and Rainbow. The account abstraction approach ensures the retention of current users while empowering them with an effortless means of engaging with smart contracts. Beyond that, the functionalities elucidated in EIP-4337 can be further applied across Ethereum-compatible blockchains, including platforms such as BNB Chain (formerly BSC), Polygon, Avalanche, Optimism, Arbitrum, and Base (also refer to Sec.III-D). As of August 2023, a sequence of indicators[3] highlights the escalating attention garnered by this impactful concept. The cumulative count of active accounts has reached 739,295, and the aggregate count of successfully executed UserOperations (detailed explanations refer to Sec.III) has reached 1,307,197. In addition, the total number of bundled transactions has amounted to 1,197,871. Besides, in August, a remarkable upswing is observed across various market indicators compared to previous months (starting from March 2023, which marks the launch of EIP-4337): The count of monthly active EIP4337 smart accounts has surged to an impressive 420k (inclusive of all platforms). The earnings from monthly revenues and UserOperation fees have surpassed 24k and 220k respectively. Notably, the monthly tally of successfully executed UserOperations stands at 710k, while the gas expenses covered by paymasters have exceeded $360k USD Given its nascent stage, only a limited number of studies have delved into this emerging concept. We are aligned with the growing momentum surrounding account abstraction and further expand the boundaries of its research landscape. In particular, Our attempts. (Sec.II&III). We conduct a thorough exploration of diverse resources, including academic literature, blogs, forum discussions, and Git repositories. Drawing from such analyses, we offer a coherent and succinct elucidation of the fundamental concepts and operational mechanisms of account abstraction. • Concept refinement (Sec.IV). We further delve into the security risks that may exist in AA. Delving into historical contract and account-related vulnerabilities, we construct a framework (Tab.III) to engage in a proper discussion about the security implications of this account paradigm. • Security framework (Sec.V). Additionally, we provide more discussion about the principle of account abstraction designs and potential opportunities inspired. • Further discussion Our security discussion (majorly Sec.IV) led to the following insights. It is evident that integrating AA into current account systems can alleviate a multitude of vulnerabilities in both contract usage and block creation domains. However, AA’s effectiveness remains limited when addressing intrinsic aspects of solidity language and design intricacies, such as structural elements, configuration constraints, and verification mechanisms. Upon further analysis, we have identified that AA’s strength in fortifying security arises from its decoupling of key account abstraction components. By reallocating functions previously encompassed within accounts, AA can protect against numerous vulnerabilities in Ethereum’s application layer: critical functions like gas payment mechanisms are seamlessly transferred to the paymaster, avoiding vulnerabilities related to gas fees; the intricate process of transaction packaging now benefits from the management of bundler; and complex trading and swapping operations, previously reliant on CEX or DEX, are elegantly executed within the same transaction. The decoupling process has reduced the threat that was heavily dependent on contract functions. thereby increasing the overall security quotient. Security results. The Ethereum official documentation [4] has introduced formal documentation detailing its account abstraction concept and roadmap that is accompanied by a suite of relevant standards, encompassing EIP-2771, 4337, 2938, and 3047. Notably, EIP-4337 encapsulates the central concept driving this transition. In a study by Singh et al. [6], an initial exploration into the Ethereum account abstraction is provided, outlining its distinctive features and functional methodologies. Binance Research [7] has released an insightful analysis that delves into recent market trends and noteworthy advancements within this area. Based on these contributions, a range of media sources [8]–[11] have embarked on elucidating this intricate concept, offering surrounding insights and explanations. Besides, several studies have initiated the development of on-top solutions aimed at addressing various challenges within the Web3 ecosystem [12], such as address incompatibility [13]. Available resources. [1] Across several documents, the term smart contract account has been used interchangeably to denote the same concept within the context of this paper. [2] Ethereum Request for Comment (ERC) is one type of Ethereum Improvement Proposals (EIP) that requires on-chain agreements. Throughout this paper, we use EIP to refer to all standards for the sake of simplicity. [3] Dune Analytics https://dune.com/niftytable

Account Abstraction, Analysed: Abstract & Introduction

This paper is under CC 4.0 license. available on arxiv Authors: (1) Mounika Vanamala, Department of Computer Science, University of Wisconsin-Eau Claire, United States; (2) Keith Bryant, Department of Computer Science, University of Wisconsin-Eau Claire, United States; (3) Alex Caravella, Department of Computer Science, University of Wisconsin-Eau Claire, United States. Table of Links Abstract & Introduction Discussions Conclusions, Acknowledgment, and References Conclusion Upon recognizing the significance of cyber security vulnerability controls during the software requirement phase, the CAPEC software vulnerability repository emerged as the most practical repository for this study. The arrangement of attack patterns thus facilitates precise identification and seamless referral back to CAPEC for recommended defense strategies. We define and elaborate on topic modeling, as well as unsupervised and supervised ML methods, showcasing recent research instances and the applicability of these approaches. As our research continues, our efforts will involve the implementation of supervised machine learning. The CAPEC repository provides a prelabeled dataset, a valuable asset for training data set implementation. Supervised ML offers the added benefit of proficiently utilizing metrics to fine-tune the ML process, thus enabling thorough evaluation and process enhancement. A training set for the SRS document must either be crafted or located for supervised ML execution. Given the absence of a comparable research framework employing supervised ML, our future endeavors will assess and compare results stemming from Naïve Bayes and RF ML methodologies. Naïve Bayes showcases statistical prowess across both large and small data sets, making it suitable for the modest data set of SRS documents as well as the larger data set encompassing CAPEC Vulnerabilities. RF's capacity to counteract overfitting aligns well with the intricate data from CAPEC. The algorithm returning the most accurate recommendations for CAPEC attack patterns from an SRS document will be harnessed to deploy an automated tool for result processing and visualization. Acknowledgment Funding Information Author’s Contributions Acquisition of data and analysis and interpretation of data and content written. Keith Bryant and Alex Caravella: Conception and design of the article, intellectual content generation, critically reviewed the article. Keith Bryant, Alex Caravella, and Mounika Vanamala: Contribution to intellectual content ideation and reviewed the article along with the coordination for publication. Mounika Vanamala: Ethics This article is original and contains unpublished material. The corresponding author confirms that all of the other authors have read and approved the manuscript and that no ethical issues are involved. References Al-Sabahi, K., Zuping, Z., & Kang, Y. (2018). Latent semantic analysis approach for document summarization based on word embeddings. arXiv preprint arXiv:1807.02748. https://doi.org/10.3837/tiis.2019.01.015 Alyami, H., Nadeem, M., Alharbi, A., Alosaimi, W., Ansari, M. T. J., Pandey, D., ... & Khan, R. A. (2021). The evaluation of software security through quantum computing techniques: A durability perspective. Applied Sciences, 11(24), 11784. https://doi.org/10.3390/app112411784 Asim, M. N., Ghani, M. U., Ibrahim, M. A., Mahmood, W., Dengel, A., & Ahmed, S. (2021). Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification. 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