The rampant fraudulent activities on Ethereum hinder the healthy development of the blockchain ecosystem, necessitating the reinforcement of regulations. However, multiple imbalances involving account interaction frequencies and interaction types in the Ethereum transaction environment pose significant challenges to data mining-based fraud detection research. To address this, we first propose the concept of meta-interactions to refine interaction behaviors in Ethereum, and based on this, we present a dual self-supervision enhanced Ethereum fraud detection framework, named Meta-IFD. This framework initially introduces a generative self-supervision mechanism to augment the interaction features of accounts, followed by a contrastive self-supervision mechanism to differentiate various behavior patterns, and ultimately characterizes the behavioral representations of accounts and mines potential fraud risks through multi-view interaction feature learning. Extensive experiments on real Ethereum datasets demonstrate the effectiveness and superiority of our framework in detecting common Ethereum fraud behaviors such as Ponzi schemes and phishing scams. Additionally, the generative module can effectively alleviate the interaction distribution imbalance in Ethereum data, while the contrastive module significantly enhances the framework's ability to distinguish different behavior patterns. The source code will be released on GitHub soon.

The use of AI in B2B marketing can help businesses identify their ideal prospects, convert them into leads, and seamlessly guide them through the sales funnel in a more expedited and efficient manner than is allowed by traditional processes. AI has already become an integral part of modern-day marketing. However, the greatest leaps of AI in marketing have been largely limited to Business-To-Consumer (B2C) operations. That's because AI's basic ability to automate repetitive tasks has found many applications in B2C endeavors, where scale is the biggest challenge. Be it automated mass emailer distribution or chatbot-based customer service, the uses of AI are largely dependent on its ability to handle a high volume and fairly low variety of tasks.

It's been estimated that 96% of companies consider Account Based Marketing (ABM) as the key driver to increase revenue in B2B sales. ABM enables organizations, such as Reuters, to modify their marketing budgets by focusing on the customers' likelihood of purchase in order to optimize their marketing strategies and target customers who are more prone to convert. Account-based marketing is a data-driven customized B2B marketing approach. It focuses on high-value accounts and delivers messages based on particular traits and needs of these accounts. The sales, marketing, and executive teams sustain and improve their relations with these customers to direct them to buy more expensive or additional products.

In Part 1 of this series of posts, we offered step-by-step guidance for using Amazon SageMaker, SageMaker projects and Amazon SageMaker Pipelines, and AWS services such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, AWS Key Management Service (AWS KMS), and AWS Identity and Access Management (IAM) to implement secure architectures for multi-account enterprise machine learning (ML) environments. In this second and final part, we provide instructions for deploying the solution from the source code GitHub repository to your account or accounts and experimenting with the delivered SageMaker notebooks. The provided CloudFormation templates provision all the necessary infrastructure and security controls in your account. An Amazon SageMaker Studio domain is also created by the CloudFormation deployment process. The following diagram shows the resources and components that are created in your account.

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model deployment process. This process must fulfill your organization's operational and security requirements. Amazon SageMaker and Studio provide a wide range of specialized functionality for building highly secure, scalable, and flexible MLOps platforms to cover your model deployment use cases and requirements. Three SageMaker services, SageMaker Pipelines, SageMaker Projects, and SageMaker Model Registry, build a foundation to implement enterprise-grade secure multi-account model deployment workflow.

Account-Based Marketing (ABM) is a strategy that's been around for over 100 years, yet only lately has gained the interest of business marketers. In this article, you'll find everything you need to know about Account-Based Marketing and how it can work for your business. So without any further ado, let's get straight into it. For those not familiar with the terms, Account-Based Marketing (ABM) is an approach to marketing that focuses on identifying and prioritizing accounts or contacts within a target market. In most cases, ABM will also involve creating personalized value propositions for each account or contact, building individualized campaigns based around those unique value propositions, and then delivering customized content to each account in order to generate the best possible ROI.

In B2B marketing, Account-Based Marketing has become one of the most talked about strategies. Advertising automation tools can make your Account-Based Marketing strategy easier and more scalable. Following the adoption of ABM, interest in Account-based Advertising (ABA) is no surprise. Thanks to new technology, targeting highly relevant advertising to the target accounts is now possible. The account as a whole can be targeted as well as an individual's buyer persona and role in their organization.

According to some estimates, worldwide spending on marketing automation software will hit $32 billion this year, and that number is expected to increase in the years to come as more companies begin embracing marketing automation and the tools become more sophisticated. Thanks to products from companies like Hubspot, Marketo and Act-On Software, marketers now have the ability to create highly effective digital marketing programs that were unthinkable just a few years ago. Technological improvements related to machine learning, chatbots and analytics will drive a number of notable developments in the field of marketing automation. Here's a look at four trends to watch in 2018. A recent MarketingSherpa study found that 58 percent of social media users follow brands on social media.

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"There are too many risks and technical barriers to overcome in trying to stitch together an ABM strategy using disparate technologies that don't use a common data model to follow and measure a business audience throughout the entire funnel and lifecycle," said Chris Golec, CEO of Demandbase. "What's been missing from ABM technology is an integrated platform that makes it easier to implement, execute and grow your ABM efforts seamlessly. I'm confident the new AI-based platform introduced today with fully integrated targeting, engagement and sales conversion solutions will set us apart for many years to come." The Demandbase ABM Platform makes it easy to build target accounts from CRM data, by uploading a pre-existing account list, or by leveraging buyer intent signals generated by the only AI-powered ABM platform that learns from 50 billion B2B interactions every month. Marketers can further segment and manage these audiences by a number of firmographic variables such as industry, size, location and others.