Tóm tắt nội dung--A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but researches on smart Ponzi scheme detection is still very limited. The existing smart Ponzi scheme detection methods have the problems of requiring many human resources in feature engineering and poor model portability. To solve these problems, we propose a datadriven smart Ponzi scheme detection system in this paper. The system uses dynamic graph embedding technology to automatically learn the representation of an account based on multi-source and multi-modal data related to account transactions. Compared with traditional methods, the proposed system requires very limited human-computer interaction. To the best of our knowledge, this is the first work to implement smart Ponzi scheme detection through dynamic graph embedding. Ponzi schemes require a constant flow of funds from new investors. The detection method based on source contributed by new investors to pay off the returns of existing code inspection detects the smart Ponzi scheme by manually investors (Figure 1).
Israel's tech and innovation ecosystem has had a monumental year so far in 2021, breaking funding records and yielding 10 new unicorns – private companies valued at $1 billion or more -- just in the first three months of the year, more than any country in Europe. Israeli high-tech activity on public markets also increased significantly this year, a trend reflected in the number of IPOs, SPAC (special-purpose acquisition company) transactions, and follow-on offerings. "Year in and year out, Tel Aviv's startup community has proven that it can achieve more than whole countries within its 52km2, thanks to investment in world-class research facilities, robust government support, and an ever-reliable influx of investment," writes WIRED UK Contributor Allyssia Alleyne in a new post this week highlighting 10 "hottest startups from Tel Aviv as part of the UK edition of the American tech publication's annual round-up (except in 2020) of Europe's 100 hottest startups. They include startups and companies from London, Amsterdam, Stockholm, Barcelona, Dublin, Helsinki, Berlin, Paris, and Lisbon. These 100 companies "are a cohort like no other," says Greg Williams, the deputy global editorial director of WIRED. "They survived an unprecedented year, embodying what entrepreneurial spirit is all about." The companies, featured in the September/October issue on newsstands this month, are not necessarily "the largest, best-known or most-funded," but they "are generating buzz" and they are organizations "people are talking about and inspired by," added Williams. The Tel Aviv entry is a mix of established companies with prominent backers, high-flying unicorns, and determined startups. Many operate in the deep tech sector. "Tel Aviv has long been known as a place where founders have built innovative companies in verticals such as fintech and cybersecurity.
TORONTO, ON / ACCESSWIRE / July 28, 2021 / DigiMax Global Inc. (the'Company' or'DigiMax') (CSE:DIGI)(OTC:DBKSF), a company that provides artificial intelligence ("AI") and cryptocurrency technology solutions, is pleased to announce that it has signed its first collaboration agreement to expand CryptoHawk services into Hong Kong and surrounding areas. CryptoHawk is an Artificial Intelligence driven, price-trend prediction tool that can be profitably used by any investor interested in trading Bitcoin or Ethereum. The tool is different as it uses AI and machine learning to capture profit from the volatility of crypto currencies, rather than incur the risk of buy-and-hold investments. As previously announced by the Company, in its first full month of operation in June 2021, CryptoHawk signals achieved a 1-month, long-short return on BTC of more than 25% compared to a buy-and-hold return for the same period of a loss of 10%. In both up and down markets, CryptoHawk has the potential to deliver subscribers much higher returns when trading.
We live in the age of artificial intelligence, machine learning and robots. Now, one company is combining all these next-generation technologies, and it's doing it on Cardano. Charles Hoskinson, the Cardano founder recently met Grace, a humanoid robot that will be integrated into Cardano in the near future. Grace was developed by SingularityNet, a decentralized AI company that has chosen to migrate to Cardano from Ethereum. Sophia is world-famous for being the world's first'lifelike' social humanoid robot, going as far as being granted citizenship in Saudi Arabia (she even sold an NFT for close to $700,000, so she is very pro-crypto).
Most top global companies have investments in Artificial Intelligence and the market for AI is expected to grow to $267 billion by 2027. Understanding AI and its applications is increasingly important. The University of Hong Kong (HKU) Business School MBA program offers a specialist AI module, which introduces students to the technology and its uses within various industries. With an understanding of cutting-edge tech topics like AI, HKU MBA students go on to land jobs in a variety of high-tech industries, many benefiting from Hong Kong's vibrant tech startup scene. HKU MBA alums, Aakriti Jain (class of 2020) and Geeseok Oh (class of 2019), used what they learned about AI from the MBA to secure exciting jobs in fintech.
Scientists have developed a device modelled on the human brain that can learn by association in the same way as Pavlov's dog. In the famous experiment, Russian physiologist Ivan Pavlov conditioned a dog to associate a bell with food. In order to replicate this way of learning, researchers from Northwestern University in the US and the University of Hong Kong developed so-called "synaptic transistors" capable of simultaneously processing and storing information in the same way as a brain. Instead of a bell and food, the researchers conditioned the circuit to associate light with pressure by pulsing an LED lightbulb and then immediately applying pressure with a finger press. The organic electrochemical material allowed the device to build memories and after five training cycles, the circuit associated light with pressure in such a way that light alone was able to trigger a signal for the pressure.
Blockchain & AI are the major architecture techs of our time. Its convergence is a key factor for the present & future of tech. These emerging & foundation technologies deal with data, value storage creation and lead the digital transformation of the 4IR. The history of Artificial Intelligence AI began in antiquity, with the power of imagination – myths, stories, rumours making artificial beings endowed with intelligence or consciousness by master craftsmen, magic. The History of Blockchain & Ledgers start when the first recorded ledgers systems were found in Mesopotamia, today's Iraq, 7000 years ago.
Bitcoin, as one of the most popular cryptocurrency, is recently attracting much attention of investors. Bitcoin price prediction task is consequently a rising academic topic for providing valuable insights and suggestions. Existing bitcoin prediction works mostly base on trivial feature engineering, that manually designs features or factors from multiple areas, including Bticoin Blockchain information, finance and social media sentiments. The feature engineering not only requires much human effort, but the effectiveness of the intuitively designed features can not be guaranteed. In this paper, we aim to mining the abundant patterns encoded in bitcoin transactions, and propose k-order transaction graph to reveal patterns under different scope. We propose the transaction graph based feature to automatically encode the patterns. A novel prediction method is proposed to accept the features and make price prediction, which can take advantage from particular patterns from different history period. The results of comparison experiments demonstrate that the proposed method outperforms the most recent state-of-art methods.
The global Artificial Intelligence (AI) in Fintech market focuses on encompassing major statistical evidence for the Artificial Intelligence (AI) in Fintech industry as it offers our readers a value addition on guiding them in encountering the obstacles surrounding the market. A comprehensive addition of several factors such as global distribution, manufacturers, market size, and market factors that affect the global contributions are reported in the study. In addition the Artificial Intelligence (AI) in Fintech study also shifts its attention with an in-depth competitive landscape, defined growth opportunities, market share coupled with product type and applications, key companies responsible for the production, and utilized strategies are also marked. This intelligence and 2026 forecasts Artificial Intelligence (AI) in Fintech industry report further exhibits a pattern of analyzing previous data sources gathered from reliable sources and sets a precedented growth trajectory for the Artificial Intelligence (AI) in Fintech market. The report also focuses on a comprehensive market revenue streams along with growth patterns, analytics focused on market trends, and the overall volume of the market. Moreover, the Artificial Intelligence (AI) in Fintech report describes the market division based on various parameters and attributes that are based on geographical distribution, product types, applications, etc.
The temporal nature of modeling accounts as nodes and transactions as directed edges in a directed graph -- for a blockchain, enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning (ML) algorithms and identify the algorithm that performs the best in detecting which accounts are malicious. We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For Ethereum blockchain, we identify that for the entire dataset - the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect 554 more suspicious accounts. Further, using behavior change analysis for accounts, we identify 814 unique suspicious accounts across different temporal granularities.