6G will achieve terabits-per-second speeds
The school has been a major research partner in millimeter 5G development, alongside Nokia, and is now starting work on 6Genesis, its 6G development program. 6G is also sometimes called 5G Long Term Evolution. The University of Oulu has been promised funding for the program that is the equivalent of U.S. $290 million that will be supplied by the Finish government’s Academy of Finland and other sources, including partners. Collaborators in the eight-year program will include Nokia, BusinessOulu (my host, which paid some of my travel expenses to the UArctic Congress conference last week), and other universities. “Millisecond latency [found in 5G] is simply not sufficient,” Pouttu said. It’s “too slow.” One of the problems that will be encountered in 5G overall is related to required scalability, he said. The issue is that the entire network stack is going to be run on non-traditional, software-defined radio. That method inherently introduces network slowdowns. Each orchestration, connection or process decelerates the communication.
The solution to dysfunctional cybersecurity and network teams
The answer appears to be allowing the cybersecurity team complete access to the network. "The percentage of survey participants reporting a high level of trust between teams more than doubles at organizations providing complete visibility to cybersecurity staff," the report mentions. "Similarly, when the cybersecurity team has complete visibility, organizations have a higher level of confidence that they are well equipped to protect the network from future cybersecurity attacks." Besides resolving trust issues and promoting collaboration, there are the following additional benefits: Both teams have greater confidence that team members understand what's happening on the network; Each team's activity will complement, not overlap or interfere, with the other team's efforts; and Respondents (55%) believe integrating the teams will allow a faster, more-efficient response to security events.
Information pilfered includes “emails, retainer agreements, non-disclosure agreements, settlements, litigation strategies, liability analysis, defence formations, collection of expert witness testimonies, testimonies, communications with government officials in countries all over the world, voice mails, dealings with the FBI, USDOJ, DOD, and more, confidential communications, and so much more,” the group wrote, explaining that the law firm paid the initial ransom demand but then breached the terms of agreement by reporting to law enforcement. The group, which threatened to “bury” the company unless a second ransom demand was paid in bitcoin, said it would escalate the release of the law firm’s internal files, noting “each time a Layer is opened, a new wave of liability will fall upon you.” The hackers referred to Hiscox as one “of the biggest insurers on the planet,” referencing the World Trade Center, following up with a tweet promising to provide “many answers about 9.11 conspiracies through our 18.000 secret documents leak.”
Machine Learning in Excel
This article is written for you who is curious of the mathematics behind neural networks, NN. It might also be useful if you are trying to develop your own NN. It is a cell by cell walk through of a three layer NN with two neurons in each layer. Excel is used for the implementation. ... We are both curious about Machine Learning and Neural Networks. There are several frameworks and free api:s in this area and it might be smarter to use them than inventing something that is already there. But on the other hand, it does not hurt to know how machine learning works in depth. And we also think it is a lot more fun to dig down into things, don't we? My journey into machine learning have perhaps just started. And I started by googling, reading a lot of great stuff on the internet. I saw a few good YouTube videos also. But I found it hard to gain enough knowledge to start coding my own AI. Finally I found this article, that suited me, and which the rest of this text is based on.
An Introduction to CSS Shapes
Until the introduction of CSS Shapes, it was nearly impossible to design a magazine-esque layout with free flowing text for the web. On the contrary, web design layouts have traditionally been shaped with grids, boxes, and straight lines. CSS Shapes allow us to define geometric shapes that text can flow around. These shapes can be circles, ellipses, simple or complex polygons, and even images and gradients. A few practical design applications of Shapes might be displaying circular text around a circular avatar, displaying text over the simple part of a full-width background image, and displaying text flowing around drop caps in an article. Now that CSS Shapes have gained widespread support across modern browsers, it’s worth taking a look into the flexibility and functionality they provide to see if they might make sense in your next design project. The current implementation of CSS Shapes is CSS Shapes Module Level 1, which mostly revolves around the shape-outside property. shape-outside defines a shape that text can flow around.
Data Ingestion Best Practices
In the good old days, when data was small and resided in a few-dozen tables at most, data ingestion could be performed manually. A human being defined a global schema and then assigned a programmer to each local data source to understand how it should be mapped into the global schema. Individual programmers wrote mapping and cleansing routines in their favorite scripting languages and then ran them accordingly. Today, data has gotten too large, both in size and variety, to be curated manually. You need to develop tools that automate the ingestion process wherever possible. For example, rather than manually defining a table’s metadata, e.g., its schema or rules about minimum and maximum valid values, a user should be able to define this information in a spreadsheet, which is then read by a tool that enforces the specified metadata.
Experian exec says biometrics won’t save you from mobile hacks
"There are a number of ways every security system, not limited to biometrics, can be duped. And most of it, as we have found in post breach research, is due to some form of human error. Biometrics themselves may be very strong, just like malware protection or device security, but the hackers look for a [human] weakness. For example, biometrics may have different levels of sensitivity, and if the person setting up the biometrics doesn't turn up the sensitivity high enough, more people are easily able to get in. If you turn it up too high, you have too many people rejected. "Point I'm making is 80% to 85% of all breaches we service have a root cause in employees not doing the right thing, making a mistake, doing stupid stuff. It's not necessarily that the hackers are so smart that they have all these different attack vectors that are so much better than the company's security; they're looking for the weakest link, and generally employees are the weakest link."
Artificial Intelligence is an engineering problem, not magic!
From an engineering point of view with no serious mathematics background, its very encouraging to see how accessible this field can be for folk like myself, dealing with applied technology solutions on a daily basis. This is to be the first of a series of articles I intend to write on the subject, a brief introduction. The aim is to build up knowledge of different AI areas and give just enough background to enable you to understand how things work, and how to implement them on a practical level. If you have a reasonable grasp of the fundamentals, there is no reason why you cannot get to a position quickly where you will: know how to approach different engineering problems with AI solutions
identify which category of AI will be most suitable for a given problem; and know what libraries to use, and what you need to chain together to build out a solid professional solution. Before we get stuck in, lets draw a line in the sand regarding AI .... the type of AI that we have nowadays, that does we must admit some wonderful (yet limited) things, is referred to as 'Narrow AI'.
Cloud computing gets a second look from health execs
While it’s certainly possible to manage data from disparate sources with on premise solutions, the services developed by cloud vendors—including the liberal use of APIs—are already available for this purpose. “This is not a nice to have anymore. It is very quickly becoming an institutional imperative,” says George Gardner-Serra, partner at Clarity Insights, a consulting firm specializing in data analytics. “The leading organizations are moving very quickly in that respect.” Vendors, eager to ink contracts in the healthcare sector, are working to address providers’ needs. First, the major cloud vendors—such as Amazon Web Services, Microsoft Azure, and Google Cloud—have invested heavily in developing solutions that address security and privacy issues. “They are all willing to sign business associate agreements and maintain HIPAA compliant structures,” notes Jeff Becker, a senior analyst at Forrester Research.
Debunking Low-Code Myths to Empower App Modernization
Using a low-code platform, citizen developers can develop very simple applications that can offer basic functionalities. Power builders can build applications with more functionalities than that offered by citizen developers. Professional developers, on the other hand, can deliver complex applications with multiple functionalities and automation processes. A low-code platform lets a professional developer build application swiftly by reducing the amount of manual coding required. In short, a low-code platform enhances the capabilities of all types of developers by letting them do more than what they are capable of in app development. ... Low-Code and No-Code terminology itself is misleading, as the distinction isn’t about whether people need to code or not. The distinction is more about the types of people using these platforms to build applications.” This sums up the required differentiation between low-code and no code platforms.
Quote for the day:
"Problem-solving leaders have one thing in common: a faith that there's always a better way." -- Gerald M. Weinberg
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