Month: December 2020

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The first article in a series on how software companies adapted and continue to adapt to enhance their resilience starts by laying a foundation for thinking about organizational resilience. It looks at what organizations can do structurally during surprising and disruptive events to establishes conditions that help engineering teams adapt in practice and real time as disruptive events occur.

By Laura Maguire

Article originally posted on Data Science Central. Visit Data Science Central

An interesting application of Benford’s and Zipf’s Laws to fraudulent Covid-19 data was published recently on the peer-reviewed PLOS-ONE website. The study, titled On the authenticity of COVID-19 case figures [1] showed how the two laws could be used to identify fraudulent Covid-19 data.

The “Usual” Applications of Benford’s and Zipf’s Laws

• Benford’s law  [no term] is a probability distribution [no term] for the likelihood of the first digit in a set of numbers [2]. The idea is that there are set probabilities for certain numbers (1, 2, 3,…) occurring in certain lists of numbers. The probability of any particular number occurring is given by a formula: As well as the fairly mundane application of analyzing data in written texts like The Times or Reader’s Digest, it can be applied to analyze large sets of naturally occurring numbers like stock market prices, income distributions, or river drainage rates.
• Zipf’s law, which gives rise to the Zeta probability distribution, [no term] states that, given a list of the most frequent words in a random book, the most common word will appear twice as often as the second most common, which will appear twice as often as the third most common, and so on.  Practical applications include analysis of  data that has large steps down from the top, like the drop from executive salaries to management salaries or the number of books ales by top authors (like Stephen King or J.K. Rowling) compared to lesser know authors.

Interestingly, both distributions can also be employed to detect fraud. Benford’s law detects the probability of a particular number appearing first, so any large set of numbers should follow set probabilities. If fraudsters are constantly inflating figures (e.g. increasing their average bill from $100 to  $900), the data set will upset the natural order of data– violating Benford’s law and producing an anomaly that can be picked up by an algorithm. The law has a long history of detecting both financial and non-financial fraud, including electoral fraud [3], scientific fraud [4], and fake law enforcement statistics [5].  Kennedy and Yam, the authors of  this new study took an unusual step forward and applied Zipf’s law (in addition to Benford’s) to finding fraud in published Covid-19 data.

Fraud in Covid-19 Data

In the early days of the pandemic, it was difficult (if not impossible) to ascertain how serious the Covid-19 outbreak actually was. For example, Chinese authorities downplayed the outbreak’s threat, allowing countless numbers of carriers to travel globally [6]. But how can we be certain that the case numbers coming out of China (and indeed, every other country) were fraudulent? Were countries deliberately dampening figures in order to keep tourism alive? One way to answer these questions is to apply the two abovementioned laws to the data, and see if any anomalies crop up. This is exactly what Kennedy and Yam did in their study.

One of the questions the authors tried to address is that when they found fraudulent data, did it come from a central government or local authorities feeding incorrect data to the top? The authors found that Zipf’s law can help answer this question:  significant deviations from Zipf’s law might indicate fraud by figures reported by a central government. However, if a country’s figures largely follow Zipf’s law with a few deviations on the sub-national level, that might indicate fraud by regional authorities. 

The authors also noted that during the early days of an epidemic, where there are few cases per capita, the number of confirmed cases seems to obey Benford’s law to a “large degree.” This gives a very simple and practical way to ascertain whether reported numbers of cases in any pandemic–especially in the early days–are likely to be fraudulent.

Applying these laws and identifying which data is false and which is true can result in a much faster, better response to future pandemics. It’s worth noting though that at this stage the study’s result are purely theoretical as they have been applied to the current Covid-19 pandemic, which has resulted in government action on an unprecedented scale. Further study of past and future pandemics is necessary to confirm the validity of applying these statistical laws the data from pandemics.

References

[1] On the authenticity of COVID-19 case figures

[2] Frunza, M. (2015). Solving Modern Crime in Financial Markets: Analytics and Case Studies. Academic Press.

[3] Klimek P, Jiménez R, Hidalgo M, Hinteregger A, Thurner S. Forensic analysis of Turkish elections in 2017-2018. PLOS ONE. 2018;13(10). pmid:30289899

[4] Diekmann A. Not the first digit! Using Benford’s law to detect fraudulent scientific data. Journal of Applied Statistics. 2007;34(3):321–329.

[5] Deckert J, Myagkov M, Ordeshook PC. Benford’s law and the detection of election fraud. Political Analysis. 2011;19(3):245–268.

[6] 8 times world leaders downplayed the coronavirus and put their countries at greater risk for infection

Covid-19 image: CDC (Public Domain).

Article originally posted on InfoQ. Visit InfoQ

Qt 6 is a new major release of Qt, the free and open-source, cross-platform toolkit for creating GUI apps that powers Linux’s KDE desktop environment. While striving to keep full source compatibility, Qt 6 brings many changes, including improved QML, a new graphic architecture, C++17 support, improved tooling, and more.

By Sergio De Simone

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A data science model is a statistical black box; testing it requires an understanding of mathematical techniques like algorithms, randomness, and statistics. To validate data science models you can use thresholds to handle output variance.

By Ben Linders

Article originally posted on InfoQ. Visit InfoQ

The 2020 Association for Computing Machinery (ACM) Gordon Bell Prize was given to a team of researchers from institutions in the USA and China for their project titled: “Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning”

By Patrick Kelly

Article originally posted on InfoQ. Visit InfoQ

Go was started more than a decade ago in the Engineering department from Google. It was designed with the purpose of providing a programming language easy to learn that will allow to develop Google’s systems at the next level. In the past decade, the language became more and more stable, currently being used for implementing some of the most popular tools on the web (Kubernetes, Terraform etc.)

By Olimpiu Pop, Steve Francia

Article originally posted on InfoQ. Visit InfoQ

In this podcast, Mario Platt, VP Head of Information Security at CloudMargin, sat down with InfoQ podcast co-host Daniel Bryant. Topics discussed included: the differences and similarities between DevSecOp and DevOps; the role of a platform in relation to system security; and the value of threat modelling.

By Mario Platt

Article originally posted on InfoQ. Visit InfoQ

What are the questions you should be asking and what behaviours should you be measuring within your Scrum teams in order to improve overall dependability and delivery efficiency? We explore how you can transform your Sprints into the building blocks for success and ensure you can continue to meet (and even surpass) long-term user and business expectations.

By Will Lytle

Article originally posted on InfoQ. Visit InfoQ

In a recent blog post, Cloudflare announced a fast, secure, and free way to build and host JAMstack sites with Cloudflare Pages. It seamlessly integrates with a Git repository and existing JAMstack frameworks and is in beta now.

By Steef-Jan Wiggers

Article originally posted on Data Science Central. Visit Data Science Central

The 5G technology return on investment market registering a CAGR of 132.8% from 2020 to 2026. 5G technology is the next generation of wireless communication technology. 5G technology networks are anticipated to allow telecom providers to expand consumer services such as video streaming, virtual reality applications; support new industrial uses such as industrial monitoring systems, industrial sensors; support growing number of internet of things (IoT) devices such as smart homes, medical devices; enable the use of advanced technologies such as smart city applications and autonomous vehicles; and perform advance data analytics.

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5G technology is estimated to revolutionize various industry verticals offering a wide scope of advancements in respective industries. Enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultrareliable and low latency communications (URLLC) are the multiple wireless connectivity features driving the 5G technology advancements. Moreover, international standardization of 5G technologies is expected to fuel the adoption of industrial automation and connected cars. 5G technology is expected to pave the way toward hyper-connected society and provide socio-economic value addition.

Increase in demand for low latency connectivity in industrial automation, rise in number of IoT devices, surge in content streaming services, and growing adoption of edge computing are some of the prominent factors driving the growth of 5G technology. Besides, emergence of 5G technology represents lucrative economic opportunities through improved access to social services such as education, employment, healthcare, transportation, and energy.

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