Using Complex Networks to improve Machine Learning methods
Let’s start by defining what a complex network is: a collection of entities
called nodes connected between themselves by edges that represent some kind of
relationship. If you’re thinking: this is a graph! Well, you are correct, most
complex networks can be considered a graph. However, complex networks usually
scale up to thousands or millions of nodes and edges, which can make them pretty
hard to analyze with standard graph algorithms. There is a lot of synergy
between complex networks and the data science field because we have tools to try
and understand how the network is built and what behavior we can expect from the
entire system. Because of that, if you can model your data as a complex network,
you have a new set of tools to apply to it. In fact, there are many machine
learning algorithms that can be applied to complex networks and also algorithms
that can leverage network information for prediction. Even though this
intersection is relatively new, we can already play around with it a bit.
How to Find a Mentor and Get Started in Open Source
What separates open source from its proprietary counterpart is the open source
community, made up of a mix of volunteers, super-fans and über-users of a
product or suite of products. So while it’s reasonably overwhelming to think
where to start, there’s the unique benefit of built-in communities to support
you. It’s good to start with an idea of what you want to get out of your
contribution — a job, a mentor, experience in a methodology, service, interest
or coding language. Use the CNCF project landscape to search by your interest —
monitoring, securing, or deploying, for example — or by organization or
skillset. Next, think if you want to be part of one of the biggest, horizontal
communities or if you’re feel more comfortable in a smaller niche. And then it’s
about deciding what you want to put in to achieve that goal. For Mohan,
contributing to open source projects gives her experience in a wider breadth of
technologies outside of her job, including in Kubernetes and chaos
engineering.
Securing a New World: Navigating Security in the Hybrid Work Era
Security doesn’t get any easier with some workers returning to the office,
others staying home and quite a few doing a bit of both. That’s because the
office, which was once the company’s security standard, is often full of devices
that have been sitting idle since early last year. Security patches, which are
issued all the time, are important to install at the point they’re published.
But a computer that has been turned off for a year, unable to download patches,
is a vulnerable device. And there may be dozens or even hundreds of patches
waiting in the queue that are needed to bring a device up to par. There are, not
surprisingly, a host of recommendations that experts have offered to help
security teams in their work. Educating employees on the threats that people and
companies face is one of their top suggestions. A survey from Proofpoint’s State
of the Phish report emphasizes the need for a people-centric approach to
cybersecurity protections and awareness training that accounts for changing
conditions, like those constantly experienced throughout the pandemic.
Now’s the time for more industries to adopt a culture of operational resilience
When you think about resiliency and doing work in operational models, it’s a
verb-based system, right? How are you going to do it? How are you going to
serve? How are you going to manage? How are you going to change, modify, and
adjust to immediate recovery? All of those verbs are what make resiliency
happen. What differentiates one business sector from another aren’t those verbs.
Those are immutable. It’s the nouns that change from sector to sector. So,
focusing on all the same verbs, that same perspective we looked at within
financial services, is equally as integratable when you think about
telecommunications or power. ... We’re seeing resiliency in the top five
concerns for board-level folks. They need a solution that can scale up and down.
You cannot take a science fair project and impact an industry nor provide value
in the quick way these firms are looking for. The idea is to be able to try it
out and experiment. And when they figure out exactly how to calibrate the
solution for their culture and level of complexity, then they can rinse, repeat,
and replicate to scale it out.
AWS's new quantum computing center aims to build a large-scale superconducting quantum computer
The launch of the AWS Center for Quantum Computing sees Amazon reiterating its
ambition to take a leading role in the field of quantum computing, which is
expected to one day unleash unprecedented amounts of compute power. Experts
predict that quantum computers, when they are built to a large enough scale,
will have the potential to solve problems that are impossible to run on
classical computers, unlocking huge scientific and business opportunities in
fields like materials science, transportation or manufacturing. There are
several approaches to building quantum hardware, all relying on different
methods to control and manipulate the building blocks of quantum computers,
called qubits. AWS has announced that the company has chosen to focus its
efforts on superconducting qubits -- the same method used by rival quantum teams
at IBM and Google, among others. AWS reckons that superconducting processors
have an edge on alternative approaches: "Superconducting qubits have several
advantages, one of them being that they can leverage microfabrication techniques
derived from the semiconductor industry," Nadia Carlsten tells ZDNet.
The causes of technical debt, and how to mitigate it
There is no single silver bullet that will fix technical debt. Instead, it needs
to be addressed in a multi-faceted way. First, there needs to be a better
cultural understanding across the entire business regarding precisely what it
is. Importantly, stakeholders, including product owners, must also understand
how their actions and decisions may be contributing. Going back to the credit
card analogy, it helps if stakeholders can bear in mind that they could be
dealing with 22% or higher annual interest. In such a case, the temptation to
‘spend’ beyond the team’s limits and live with minimum payments is less
tempting. To pay off existing architectural and other types of technical debt,
teams should compare their current minimum payments and the impact of those on
overall velocity and team morale with the staggering expense of re-architecting
part or all of a solution. Moving from a monolith to microservices is a good
example. As mentioned, however, there is no one-size-fits-all solution.
Long-term maintenance and ‘expenses’ need to be considered as well.
Why aren’t optical disks the top choice for archive storage?
Optical media is also designed with full backwards compatibility, meaning future
BD-R and ODA drives will be able to read disks written in today’s drives. For
example, you can read a CD-R disk written in 1991 in a current BD-R drive. In
contrast, LTO-8 tape drives cannot read LTO-5 tape although they can read LTO-6
tapes. BD-R drives advertise a lifetime of 50 years and Sony advertises 100
years, both of which are longer than tape (30 years) and magnetic hard drives
(five years). If you wanted a 50-year archive on LTO, you would be forced to
migrate data at least once to avoid bit rot but not, as some optical marketing
material suggests, every 10 years. Many people do this anyway to allow them to
retire older tape drives and achieve greater storage density. There is also no
current requirement to re-tension the tapes every so often. There is some debate
about the bit error rate of optical versus tape, but that is a complex issue
beyond the scope of this article.
How to develop a high-impact team
Innovation is increasingly becoming a team sport, requiring diverse perspectives
and collective intelligence. These innovation-focused teams tend to be
ephemeral. They form, collaborate, and disband quickly. Team members need to be
able to step up and step back with equal ease. To participate in this fast,
fluid model of leadership, less assertive employees (and those uninterested in
careers in management) will likely need help stepping up. To get these reluctant
leaders to step up and then step back, provide a path of retreat. Show them that
being a designated leader can be a temporary assignment, existing for the
duration of a project or even for just a single meeting. Some team members will
need encouragement and support to become “step-up” leaders, but others will do
so with ease. It can take work to then get them to step back and support others.
You can help these people develop a more fluid leadership style by modeling
healthy followership practices. Let them see you collaborating with a peer
organization or contributing to a project led by someone below you in the
management hierarchy.
Why automation progress stalls: 3 hidden culture challenges
“A general challenge with putting automation in place is that IT culture often
focuses on heroic problem-solving rather than more mundane processes that
prevent problems from happening in the first place,” says Red Hat technology
evangelist Gordon Haff. “Automation has long been part of the picture – think
system admins writing Bash scripts – but it’s also been reactive rather than
proactive.” If your organization has treated automation mostly as a reactive
problem-solver in the past, people may be less inclined to instinctively grasp
its greater value. That’s where leaders have work to do in terms of
communicating your big-picture plan and the role that automation – and everyone
on the team – plays in it. This is also a mindset that must shift over time with
experience and results: Automation should be as much (or more) about improvement
and optimization as it is about dousing production fires or cutting costs.
Ideally, automation should be boring, in the best possible sense of the word.
“Modern automation practices, such as we often see in SRE roles, make automating
systems and workflows part of the daily routine,” Haff says.
Regulation fatigue: A challenge to shift processes left
President Biden’s recent executive order asks government vendors to attest “to the extent practicable, to the integrity and provenance of open source software used within any portion of a product.” The president’s recent order, and the potential actions of legislators to follow, could lead to burdensome regulations that interfere with shift left practices, and ultimately slow down the pace of software development. The challenge with the directive is that nearly 60 percent of software developers have little to no secure coding training. Developers are traditionally focused on pushing out innovative, stable products, not triaging security alerts. They want to use open-source code without thinking about its possible security risks. Developers rely on open-source components because these are ready-made pieces of code that allow them to keep up with competitive release time frames. They often leave it to their security teams to identify mistakes at the end of the development process. Developers’ reliance on open-source components often presents a challenge to the cautious attitude of security teams.
Quote for the day:
"Leaders, be mindful that there is a
tendency to become arrogant. Such hubris blinds even the best intentions.
Lead with humility." -- S Max Brown
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