For cyber criminals, manufacturing makes a highly strategic target because in many cases these are operations that can't afford to be out of action for a long period of time, so they could be more likely to give in to the demands of the attackers and pay hundreds of thousands of dollars in bitcoin in exchange for getting the network back. "Manufacturing requires significant uptime in order to meet production and any attack that causes downtime can cost a lot of money. Thus, they may be more inclined to pay attackers," Selena Larson, intelligence analyst for Dragos, told ZDNet. "Additionally, manufacturing operations don't necessarily have the most robust cybersecurity operations and may make interesting targets of opportunity for adversaries," she added. The nature of manufacturing means industrial and networking assets are often exposed to the internet, providing avenues for hacking groups and ransomware gangs to gain access to the network via remote access technology such as remote desktop protocol (RDP) and VPN services or vulnerabilities in unpatched systems. As of October 2020, the company said there were at least 108 advisories containing 262 vulnerabilities impacting industrial equipment found in manufacturing environments during the course of this year alone.
“Instead of helping people who face daily threats from unaccountable surveillance agencies – including activists, journalists and people just looking for better lives – this ‘aid’ risks doing the very opposite,” said PI advocacy director Edin Omanovic. To overcome the issues related to “surveillance humanitarianism”, the report recommends that all UN humanitarian and related bodies “adopt and implement mechanisms for sustained and meaningful participation and decision-making of migrants, refugees and stateless persons in the adoption, use and review of digital border technologies”. Specifically, it added that migrants, refugees and others should have access to mechanisms that allow them to hold bodies like the UNHCR directly accountable for violations of their human rights resulting from the use of digital technologies, and that technologies should be prohibited if it cannot be shown to meet equality and non-discrimination requirements. It also recommends that UN member states place “an immediate moratorium on the procurement, sale, transfer and use of surveillance technology, until robust human rights safeguards are in place to regulate such practices”. A separate report on border and migration “management” technologies published by European Digital Rights (EDRi), which was used to supplement the UN report ...
Usually, software testing includes Unit tests, Regression tests and Integration tests. Moreover, there are certain rules that people follow: don’t merge the code before it passes all the tests, always test newly introduced blocks of code, when fixing bugs, write a test that captures the bug. Machine learning adds up more actions to your to-do list. You still need to follow ML’s best practices. Moreover, every ML model needs not only to be tested but evaluated. Your model should generalize well. This is not what we usually understand by testing, but evaluation is needed to make sure that the performance is satisfactory. ... First of all, you split the database into three non-overlapping sets. You use a training set to train the model. Then, to evaluate the performance of the model, you use two sets of data: Validation set - Having only a training set and a testing set is not enough if you do many rounds of hyper parameter-tuning (which is always). And that can result in over fitting. To avoid that, you can select a small validation data set to evaluate a model. Only after you get maximum accuracy on the validation set, you make the testing set come into the game; and Test set (or holdout set) - Your model might fit the training dataset perfectly well. ...
For deep learning, the model training stage is very similar to the initial learning stage of humans. During early stages, the model experiences a mass intake of data, which creates a significant amount of information to mine for each decision and requires significant processing time and power to determine the action or answer. But as training occurs, neural connections become stronger with each learned action and adapt to support continuous learning. As each connection becomes stronger, redundancies are created and overlapping connections can be removed. This is why continuously restructuring and sparsifying deep learning models during training time, and not after training is complete, is necessary. After the training stage, the model has lost most of its plasticity and the connections cannot adapt to take over additional responsibility, so removing connections can result in decreased accuracy. Current methods such as the one unveiled in 2020 by MIT researchers where attempts are made to make the deep learning model smaller post-training phase have reportedly seen some success. However, if you prune in the earlier stages of training when the model is most receptive to restructuring and adapting, you can drastically improve results.
If there is a quantum bubble, it’s inflated both by the new flurry of Sycamore-type academic work and a simultaneous push from private corporations to develop real-world quantum applications, like avoiding traffic jams, as a form of competitive advantage. We’ve known about the advantages that quantum physics can offer computing since at least the 1980s, when Argonne physicist Paul Benioff described the first quantum mechanical model of a computer. But the allure of the technology seems to have just now bitten enterprising businesspeople from the tiniest of startups to the largest of conglomerates. “My personal opinion is there’s never been a more exciting time to be in quantum,” says William Hurley. Strangeworks, the startup he founded in 2018, serves as a sort of community hub for developers working on quantum algorithms. Hurley, a software systems analyst who has worked for both Apple and IBM, says that more than 10,000 developers have signed up to submit their algorithms and collaborate with others. Among the collaborators—Austin-based Strangeworks refers to them as “friends and allies”—is Bay Area startup Rigetti Computing, which supplies one of the three computers that Amazon Web Services customers can access to test out their quantum algorithms.
As of September 2020, C++ is the fourth most popular programming language globally behind C, Java and Python, and – according to the latest TIOBE index – is also the fastest growing. C++ is a general-purpose programming language favored by developers for its power and flexibility, which makes it ideal for operating systems, web browsers, search engines (including Google's), games, businesses applications and more. Stroustrup summarizes: "If you have a problem that requires efficient use of hardware and also to handle significant complexity, C++ is an obvious candidate. If you don't have both needs, either a low-level efficient language or a high-level wasteful language will do." Yet even with its widespread popularity, Stroustrup notes that it is difficult to pinpoint exactly where C++ is used, and for what. "A first estimate for both questions is 'everywhere'," he says. "In any large system, you typically find C++ in the lower-level and performance-critical parts. Such parts of a system are often not seen by end-users or even by developers of other parts of the system, so I sometimes refer to C++ as an invisible foundation of everything."
Cybercrime has hit the U.S. so hard that in 2018 a supervisory special agent with the FBI who investigates cyber intrusions told The Wall Street Journal that every American citizen should expect that all of their data (personally identifiable information) has been stolen and is on the dark web — a part of the deep web — which is intentionally hidden and used to conceal and promote heinous activities. Some estimates put the size of the deep web (which is not indexed or accessible by search engines) at as much as 5,000 times larger than the surface web, and growing at a rate that defies quantification. The dark web is also where cybercriminals buy and sell malware, exploit kits, and cyberattack services, which they use to stirke victims — including businesses, governments, utilities, and essential service providers on U.S. soil. A cyberattack could potentially disable the economy of a city, state or our entire country. In his 2016 New York Times bestseller — Lights Out: A Cyberattack, A Nation Unprepared, Surviving the Aftermath — Ted Koppel reveals that a major cyberattack on America’s power grid is not only possible but likely, that it would be devastating, and that the U.S. is shockingly unprepared.
As the global economy recovers from COVID-19, one particular area of focus for FinTech is financial inclusion. According to the World Bank, there are currently around 1.7 billion unbanked individuals worldwide, and FinTechs will be central to efforts to integrate these people into the global banking system. Doing so will help to mitigate the economic and social impact of the pandemic. According to Deloitte, FinTechs, in strategic partnerships with financial institutions, retailers and government sectors across jurisdictions, can help democratise financial services by providing basic financial services in a fair and transparent way to economically vulnerable populations. Digital finance is also expanding in other areas. Health concerns in the COVID-19 era have made physical cash payments less practical, opening the door to an increase in digital payments and e-wallets. Though cash use was predicted to decline in any case, COVID-19 has hurried that decline, due to concerns that handing over money can cause human to human transmission of the virus. According to a Mastercard survey looking at the implications of the coronavirus pandemic, 82 percent of respondents worldwide viewed contactless as the cleaner way to pay, and 74 percent said they will continue to use contactless payment post-pandemic.
Here's how it works: First, DNS is the internet's master address list. With it, instead of writing out an IPv4 address like "18.104.22.168," or an IPv6 address such as "2400:cb00:2048:1::c629:d7a2," one of Cloudflare's many addresses, you simply type in "http://www.cloudflare.com," DNS finds the right IP address for you, and you're on your way. With DNS cache poisoning, however, your DNS requests are intercepted and redirected to a poisoned DNS cache. This rogue cache gives your web browser or other internet application a malicious IP address. Instead of going to where you want to go, you're sent to a fake site. That forged website can then upload ransomware to your PC or grab your user name, password, and account numbers. In a word: Ouch! Modern defense measures -- such as randomizing both the DNS query ID and the DNS request source port, DNS-based Authentication of Named Entities (DANE), and Domain Name System Security Extensions (DNSSE) -- largely stopped DNS cache poisoning. These DNS security methods, however, have never been deployed enough, so DNS-based attacks still happen. Now, though researchers have found a side-channel attack that can be successfully used against the most popular DNS software stacks, SAD DNS.
The composable healthcare organization is a healthcare organization that can reconfigure its capabilities -- both its business and operating model -- at the pace of market change. We have lived in a world and in an industry where there's been stable business and operational models. If you're a provider organization or a payer organization or a life sciences company, those heritage business models have been pretty stable. That's in terms of how organizations think, their culture, the way their business is architected -- so the organizational structures, the way they collaborate, all the way down to the way we've architected technology. They've really done that in service of a relatively stable business and operating model. What we're marking here are three main points. On a very simple level it's this: Adaptability is more important than ever, adaptability is more possible than ever, adaptability can be done by the people who you and I are speaking to -- the people you're reporting for and the people we work with on the Gartner health team. The idea of adaptability is nothing new to CIOs, in general. If you go back to when many of today's CIOs were in high school or even in college, there was reusable code, object-oriented programming -- we've just gone through a decade-and-a-half of more data services and agile development.
Quote for the day:
"If you genuinely want something, don't wait for it--teach yourself to be impatient." -- Gurbaksh Chahal