Everything old is new again. In the 1960s, IBM’s OS/650 mainframe operating system had two modes, MFT and MVT, in which process got its own address space and couldn’t interface with others processes running parallel with it. This was the ancestor to both virtual machines (VM) and containers. Fast forward to now, and IBM Secure Service Container for IBM Cloud Private enables you to run an operating system and applications in containers with their own address space and walls to keep them from interacting with other programs.
Of course, you can do a wee bit more with Secure Service Containers (SSC) on IBM LinuxONE and Z mainframes than you could on a 360 mainframe with a maximum of 1MB of memory. IBM Cloud Private is a Platform as a Service (PaaS) environment for developing and managing containerized applications. It’s built on top of the Kubernetes container orchestrator Kubernetes.
The physical ports on your computer allow communicate with peripheral devices such as your keyboard and mouse and to connect with internet devices via Ethernet cables.
Within computer networking, ports serve a similar purpose. When a computer system seeks to connect to another computer, the port serves as a communication endpoint. It is also possible for different services running on the same computer to expose various ports and communicate with one another using these ports. In simple terms, if a software application or service needs to communicate with others, it will expose a port. Ports are identified with positive 16-bit unsigned integers, ranging from 0 to 65535. Other services use this port number to communicate with the service or app. Port numbers are divided into three ranges: well-known ports, registered ports, and dynamic or private ports.
Well-known ports (also known as system ports) are numbered from 0 through 1023. For example, to connect to the host example.com via SSH, I would use this command:
It’s odd that printk() would pose so many problems for kernel development, given that it’s essentially just a replacement for printf() that doesn’t require linking the standard C library into the kernel.
And yet, it’s famously a mess, full of edge cases, corner cases, deadlocks, race conditions and a variety of other tough-to-solve problems. The reason for this is, unlike printf(), the printk() system call has to produce reasonable behavior even when the entire system is in the midst of crashing. That’s really the whole point—printk() needs to report errors and warnings that can be used to debug whatever strange and unexpected catastrophe has just hit a running system.
Trying to fix all the deadlocks and other problems at the same time would be too large a task for anyone, especially since each one is a special case defined by the particular context in which the printk() call appeared. But, sometimes a bunch of instances in a particular region of code can be addressed all together.
Oracle Linux images available on Oracle Cloud Infrastructure are frequently updated to help ensure access to the latest software. The latest Oracle Linux images provided in Oracle Cloud Infrastructure now include Oracle Linux 7 Update 5 with the Unbreakable Enterprise Kernel Release 5 (UEK R5). UEK R5 is an extensively tested and optimized Linux kernel designed for 64-bit (Intel x86_64) and ARM (aarch64)…
Click to Read More at Oracle Linux Kernel Development
Oracle Linux images available on Oracle Cloud Infrastructure are frequently updated to help ensure access to the latest software. The latest Oracle Linux images provided in Oracle Cloud Infrastructure now include Oracle Linux 7 Update 5 with the Unbreakable Enterprise Kernel Release 5 (UEK R5). UEK R5 is an extensively tested and optimized Linux kernel designed for 64-bit (Intel x86_64) and ARM (aarch64)…
Click to Read More at Oracle Linux Kernel Development
Oracle Linux images available on Oracle Cloud Infrastructure are frequently updated to help ensure access to the latest software. The latest Oracle Linux images provided in Oracle Cloud Infrastructure now include Oracle Linux 7 Update 5 with the Unbreakable Enterprise Kernel Release 5 (UEK R5). UEK R5 is an extensively tested and optimized Linux kernel designed for 64-bit (Intel x86_64) and ARM (aarch64)…
Click to Read More at Oracle Linux Kernel Development
The Linux Foundation strives to make Open Source Summit one of the most inclusive tech events in a variety of ways, offering activities such as the “Women in Open Source” lunch, a diversity social, a first-time attendees get-together, and more. The have activities focused on children, too. Not only does Open Source Summit offer free on-site childcare for attendees’ children, they also sponsor a Kid’s Day.
At this year’s Kid’s Day in Vancouver, the primary goal was to introduce the kids to coding via HTML, and very little computer knowledge or experience was required to participate. “The basics, typing, browsing the Internet and minor computer operation, are all your child needs to participate,” according to the website.
For this event, The Linux Foundation collaborated with Banks Family Tech, who organized the 4-hour long workshop. This workshop was geared toward children ages 9–18 and was open to children from the community as well as those of event attendees. The kids that participated actually ranged in age from 5-13 years of age, and, many already had some coding experience. Some had tried Scratch, and others had written scripts for games.
“We are going to teach how to go from nothing and become coders,” said Phillip Banks, founder of Banks Family Tech.
HTML workshop
The workshop focused squarely on HTML, one of the easiest computing languages. “It’s close to English and it’s not hard text and syntax to learn. It allows us to squeeze a lot of things into a day and get them excited so that they can go home and learn more,” said Banks. “After that, maybe, you can go to Python but HTML is so easy as they get a quick return by manipulating objects, text color and other things on a web-page immediately.”
This Kid’s Day event had a great mix of participants. While some of the kids accompanied their parents who were attending the conference, the majority were from the local community, whose parents learned about the workshop from social networks like Facebook. Khristine Carino, Director for Communications of SCWIST (Society for Canadian Women In Science and Technology), not only brought her own kids but also invited families from underrepresented minorities in Vancouver.
In the workshop, the children learned HTML basics like font tags, how to use fonts and colors, how to add images and videos, and how choose a background for their website. They also had the opportunity to share what they created with the whole group and learn from each other.
“It’s not so much about learning to code, just to be a coder; it’s learning to understand how things work,” said Banks. You can hear more in the video below.
Check out the full list of activities coming up at Open Source Summit in Europe and sign up to receive updates:
The Linux command line offers many tools that are helpful for software developers. One among them is strace, basics of which we’ll be discussing in this tutorial using some easy to understand examples.
But before we do that, it’s worth mentioning that all examples in this article have been tested on an Ubuntu 18.04 LTS machine.
Linux strace command
The strace command in Linux lets you trace system calls and signals. Following is its syntax:
Advances in technology have instigated a substantial shift in consumer expectations. Today’s financial services customers demand access to a range of services, real-time updates and a seamless customer experience. At Open FinTech Forum, I will provide some insight into Spotcap’s approach to credit risk assessment using text mining and machine learning.
Bruce Brenkus, Chief Risk Officer, Spotcap
A recent survey by Oracle found, that although customers are generally satisfied with basic banking services, their satisfaction drops when attempting more complex transactions such as securing a loan.We have observed the same sentiment across the business community. This is why, at Spotcap, we’ve turned tradition on its head and created a more efficient take on business loans.
We undertake cash flow based, rather than credit-score based underwriting, and use technology to speed up the process. Combining tried and tested credit assessment principles with innovative technology such as our automated data scraping services, machine learning credit models, and skilled human analysts enables us to offer a more efficient take on business loans.
Machine learning credit algorithms
Our risk assessment utilizes numerous sources but relies heavily on three main sources – borrower profile, bank account, and business profile – and is supported by a set of machine learning credit algorithms. This approach allows us to accurately and fairly assess how a business is performing today, and make a prediction about its future performance.
Whilst we feed our models with hundreds of data points sourced from credit bureaus, tax agencies, business records and the applicants themselves, it is bank account transactional data that often paints the most accurate picture.
Spotcap’s Bank Account Model incorporates more than 200 numerical variables. Business bank account data, when structured correctly, is one of the strongest sources of predictive information for short-term lending and risk mitigation. We construct the raw data found in a bank account into a form of variables enabling us to derive meaningful insights.
We have also developed bank account text mining tools to identify key negative factors such as payment reversals, late fees and collections transactions.However, this requires a supervised approach to minimize the risk of false positives.
The more data you feed into your machine learning models, the more accurate will be your results. But it’s not only about quantity, it’s primarily the quality of data that matters. Well specified machine learning models can help lenders make faster and more informed decisions. However, even the most powerful machine learning algorithm will fail if applied to data with measurement error. The better your understanding of your data, the more accurate and insightful your results. Our underwriters and data scientists continuously add new knowledge and risk drivers to our models to get even more precise outcomes.
It’s all about automating the right parts of your analysis and remembering that human interaction is important at every stage of the model life cycle because we’re dealing with real people and real businesses, which are by nature complex. Human expertise combined with advanced technology enables us to make accurate, yet flexible credit decisions within one day.
The amount of data we generate every day is staggering—currently estimated at 2.6 quintillion bytes—and it’s the resource that makes deep learning possible. Since deep-learning algorithms require a ton of data to learn from, this increase in data creation is one reason that deep learning capabilities have grown in recent years. In addition to more data creation, deep learning algorithms benefit from the stronger computing power that’s available today as well as the proliferation of Artificial Intelligence (AI) as a Service. AI as a Service has given smaller organizations access to artificial intelligence technology and specifically the AI algorithms required for deep learning without a large initial investment.
Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The more deep learning algorithms learn, the better they perform.
8 practical examples of deep learning
Now that we’re in a time when machines can learn to solve complex problems without human intervention, what exactly are the problems they are tackling? Here are just a few of the tasks that deep learning supports today and the list will just continue to grow as the algorithms continue to learn via the infusion of data.
