Over a period of only a couple months, entire workforces were required to familiarize themselves with digital tools which never were needed in a traditional work environment. At the same time, financial institutions were required to connect with customers using mobile apps, online tools and digital engagement capabilities that were foreign to many. The impact of these changes was felt most by the employees who had been with their financial institution the longest or were in areas of an organization that had not adjusted to recent marketplace realities. Many financial institutions responded to internal and external digital needs with mid-term solutions, understanding that significantly more is needed. The impact of COVID-19 has forced banks and credit unions to quickly assess the digital competency of their teams, while looking to internal training and the marketplace to provide longer term solutions. This comes at a time when every industry is looking to address a massive digital and technology skills gap. The research from the Digital Banking Report found that 72% of financial services executives believed there was either a moderate (37%) or significant (35%) skills gap. Less than three in ten thought there was only a minor or no threat.
A machine infrastructure encompasses almost every stage of the machine learning workflow. To train, test, and deploy machine learning models you need services from data scientists, data engineers, software prog engineers, and DevOps engineers. The infrastructure allows people from all these domains to collaborate and empower them to associate for an end to end execution of the project. Some examples of tools and platforms are AWS(amazon web services, Google Cloud, Microsoft Azure machine learning studio, Kubeflow: Machine-Learning Toolkit for Kubernetes. Architecture deals with the arrangement of these components(things discussed above) and also takes care of how they must interact with them. Think of it as building a machine learning home where bricks, concrete, iron, are integral to the infrastructure, applications, etc. The architecture shapes our home by using these materials. Similarly, the architecture here provides that interaction among these components. ... In machine learning, for the given data different models are built and we keep track through version control tools like DVC and Git. Version control will keep track of changes made to the model at each stage and keep a repository.
Conventional approaches usually set up two separate clusters, one dedicated to Big Data processing, and the other dedicated to deep learning (e.g., a GPU cluster), with “connector” (or glue code) deployed in between. Unfortunately, this “connector approach” not only introduces a lot of overheads (e.g., data copy, extra cluster maintenance, fragmented workflow, etc.), but also suffers from impedance mismatches that arise from crossing boundaries between heterogeneous components (more on this in the next section). To address these challenges, we have developed open source technologies that directly support new AI algorithms on Big Data platforms. ... Before diving into the technical details of BigDL and Analytics Zoo, I shared a motivating example in the tutorial. JD is one of the largest online shopping websites in China; they have stored hundreds of millions of merchandise pictures in HBase, and built an end-to-end object feature extraction application to process these pictures (for image-similarity search, picture deduplication, etc.). While object detection and feature extraction are standard computer vision algorithms, this turns out to be a fairly complex data analysis pipeline when scaling to hundreds of millions pictures in production, as shown in the slide below.
While it was not immediately obvious that the trojan was present on the device, researchers were able to detect it given its similarity to another malware downloader. “Proof of infection is based on several similarities to other variants of Downloader Wotby,” Collier explained. “Although the infected Settings app is heavily obfuscated, we were able to find identical malicious code. Additionally, it shares the same receiver name: com.sek.y.ac; service name: com.sek.y.as; and activity names: com.sek.y.st, com.sek.y.st2, and com.sek.y.st3.” The app did not trigger any malicious activity when researchers analyzed the device, which they expected; however, the smartphone they examined also did not have a SIM card installed, which also could affect how the malware behaves, he said. “Nevertheless, there is enough evidence that this Settings app has the ability to download apps from a third-party app store,” he wrote. “This is not okay.” The other malware variant came preinstalled in the UL40’s Wireless Update app, which functions as the device’s main way of updating security patches, the operating system and other apps.
Refactoring, Improving the design of existing code: This book is written in Java as it’s the principal language, but the concept and idea are applicable to any Object-oriented language, like C++ or C#. This book will teach you how to convert a mediocre code into a great code that can stand production load and real-world software development nightmare, the CHANGE. The great part is that Martin literally walks you the steps by taking a code you often see and then step by step converting into more flexible, more usable code. You will learn the true definition of clean code by going through his examples. ... The Art of Unit Testing: If there is one thing I would like to improve on projects, as well as programmers, are their ability to unit test. After so many years or recognition that Unit testing is must have practiced for a professional developer, you will hardly find developers who are a good verse of Unit testing and follows TDD. Though I am not hard on following TDD, at a bare minimum, you must write the Unit test for the code you wrote and also for the code you maintain. Projects are also not different, apart from open source projects, many commercial in-house enterprise projects suffer from the lack of Unit test.
I learn by doing, and I learn from others. So first of all, I don't think anyone is born with these skills. I mean, some people are better communicators than other people, but a lot of the things that you actually have to learn like how to manage somebody, how to... the good news is it can be learned and the way I learned it is by doing and getting better every time I did it. But I was also fortunate that I was able to surround myself with really great people every step along the way, both in Drupal and at Acquia frankly. So surrounding yourself with experienced managers, or experienced leaders is very helpful and fast tracks that learning, right? ... I think about it almost everyday actually. But I prioritize it lower than a lot of other things that I do. Literally, when I wake up I try to think, "What should I do today that has the biggest impact on Drupal and Acquia?" It's almost never coding for me, unfortunately. I secretly hope it would be one day it's like, "Wow, go code. Go write this piece of code." But it usually involves unblocking other people or teams, or helping to fundraise for the Drupal Association right now. So the coding is often reserved for evenings and weekends. I like to dabble with code still.
It is Architecture pattern which is introduced by Jeffrey Palermo in 2008, which will solve problems in maintaining application. In traditional architecture, where we use to implement by Database centeric architecture. Onion Architecture is based on the inversion of control principle. It's composed of domain concentric architecture where layers interface with each other towards the Domain (Entities/Classes). Main benefit of Onion architecture is higher flexibility and de-coupling. In this approach, we can see that all the Layers are dependent only on the Domain layer (or sometimes, it called as Core layer). ... Testability: As it decoupled all layers, so it is easy to write test case for each Components; Adaptability/Enhance: Adding new way to interact with application is very easy; Sustainability: We can keep all third party libraries in Infrastructure layer and hence maintainence will be easy; Database Independent: Since database is separated from data access, it is quite easy switch database providers; Clean code: As business logic is away from presentation layer, it is easy to implement UI;
In an age of dynamic disruption, IT is increasingly challenged to maintain optimal service delivery, while implementing remote working at an unprecedented scale. It’s not surprising, then, that nearly 60 percent of study respondents cite the need for greater visibility into remote user experiences. The top challenge for troubleshooting applications is the ability to understand end-user experience (nearly 47 percent). “As remote working becomes the new norm, IT teams are challenged to find and adapt technologies, such as flow-based reporting to manage bandwidth consumption, VPN oversubscription and troubleshooting applications. To guarantee the best performance and reduce cybersecurity threats, increasing network visibility is now a must for all businesses,” said Charles Thompson, Senior Director, Enterprise and Cloud, VIAVI. “By empowering NetOps, as well as application and security teams with network visibility, IT can mitigate the impact of disruptive migrations, incidents and new technologies like SD-WAN to achieve consistent operational excellence.”
“Deep neural networks are very computationally expensive,” says Song Han, an assistant professor at MIT who specializes in developing more efficient forms of deep learning and is not an author on Thompson’s paper. “This is a critical issue.” Han’s group has created more efficient versions of popular AI algorithms using novel neural network architectures and specialized chip architectures, among other things. But he says there is a “still a long way to go,” to make deep learning less compute-hungry. Other researchers have noted the soaring computational demands. The head of Facebook’s AI research lab, Jerome Pesenti, told WIRED last year that AI researchers were starting to feel the effects of this computation crunch. Thompson believes that, without clever new algorithms, the limits of deep learning could slow advances in multiple fields, affecting the rate at which computers replace human tasks. “The automation of jobs will probably happen more gradually than expected, since getting to human-level performance will be much more expensive than anticipated,” he says.
Ransomware is a type of malware that prevents users from accessing their system or personal files and demands a “ransom payment” in order to regain access. There are two types of campaigns for ransomware “Human-operated” and “Auto-spreading”, this article focusing on the human-operated campaigns. Human-operated campaigns tend to have common attack patterns which include: Gaining initial access, credential theft, lateral movement and persistence. For many of the human-operated campaigns, typical access comes from RDP brute force, a vulnerable internet-facing system, or weak application settings. Once attackers have gained access they can deploy a plethora of tools to get user credentials. After gaining credentials lateral movement takes place with either deploying a widely known commercial penetration testing suite called Cobalt Strike, changing settings of the WMI (Windows Management Instrument) or abusing management tools with low-level privilege. Finally, attackers want to keep a connection and make it persistent; this is done by creating new accounts, making GPO (Group Policy Object) changes, creating scheduled tasks, manipulating service registration, or by deploying shadow tools.
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