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tag:theconversation.com,2011:/id/topics/software-1792/articles Software – The Conversation 2018-11-27T11:40:56Z tag:theconversation.com,2011:article/107319 2018-11-27T11:40:56Z 2018-11-27T11:40:56Z Why Twitter's cute, heart-shaped 'like' button is not so harmless

Researchers have found people use the 'like' button on social media posts for many reasons. Worawee Meepian

Jack Dorsey, Twitter's founder and CEO, was recently reported to have questioned how the site "incentivizes people to want (the number of likes on their posts) to go up." He also said that "he was not a fan of the heart-shaped ('like') button and that Twitter would be getting rid of it 'soon.'" Twitter has since released a statement indicating that there are no immediate plans to remove the "like" button.

Whatever the future of Twitter's "like" button may be, as a scholar of social media and religion I'd argue that the cute little heart-shaped button on Twitter and Facebook is far more impactful than it appears.

How people use the 'like' button

The "like" button is not there by accident. Instead, this one-click feature exists as an intentional design decision.

Like most big tech companies, Twitter has an entire department dedicated to understanding users. Every feature and font, based on their research, is there to maximize the overall user experience.

The design decisions furtively influence users' feelings and behaviors. Chris Nodder, a user experience researcher and the author of "Evil by Design," explains how designers must always ask the question, "How do we influence behavior through the medium of software?"

In other words, design decisions are made not only to improve a users' experience but also influence their behaviors. The "like" button is one example.

While the heart-shaped button is seemingly only for expressing appreciation for the content of a social media post, researchers have determined that people use the button for many other reasons.

A team of researchers, for example, found that users in the United States often chose to like something for bonding purposes rather than simply liking the content.

Another study of Facebook users found that the "like" button is used to maintain relationships with existing friends or to develop new relationships. People may use the "like" button as a way to publicly show closeness to another person, or even as an effort toward dating someone.

The point being that the "like" button does far more than just express how much a person likes a particular picture or post.

Not so harmless

Research has also shown that the "like" button is not entirely harmless.

Social networking sites are powerful tools for building relationships. Nevertheless, research has shown that certain social media features can adversely affect users.

For instance, a study found that impersonal gestures such as the one-click "like" communication may not promote user well-being. According to Facebook researcher Moira Burke and Robert Kraut, an emeritus professor at Carnegie Mellon University, "simply reading about friends, receiving text communication from weak ties, and receiving one-click communication did not affect well-being." On the other hand, more personal and direct communication such as a direct message or personalized comment can have an impact on user well-being.

 <img alt="" src="https://images.theconversation.com/files/247361/original/file-20181126-140534-5zx7zz.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/247361/original/file-20181126-140534-5zx7zz.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=394&fit=crop&dpr=1 600w, https://images.theconversation.com/files/247361/original/file-20181126-140534-5zx7zz.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=394&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/247361/original/file-20181126-140534-5zx7zz.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=394&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/247361/original/file-20181126-140534-5zx7zz.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=495&fit=crop&dpr=1 754w, https://images.theconversation.com/files/247361/original/file-20181126-140534-5zx7zz.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=495&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/247361/original/file-20181126-140534-5zx7zz.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=495&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 The number of 'likes' on a post can generate feelings of envy. 13_Phunkod

A particularly harmful byproduct of the "like" button is found in the way social networking sites foster negative social comparisons. A review of research on the topic has found that social media use correlates with measurable increases in envy and depression.

These feelings of envy can take two different forms: malicious envy and benign envy. Malicious envy involves resentment and a desire to harm the other person. Benign envy involves admiration and a desire to obtain what the other person possesses.

One of the studies in the review involved 194 college-aged Facebook users in Germany. In this study, researchers found that "the closer the relationship, the more a Facebook user will experience benign envy."

These digital showrooms allow people to present the best version of themselves for everyone else to see. Often, people use the number of likes to judge others and themselves. That little heart-shaped button becomes a publicly quantifiable measure of social support.

According to this research, the "like" button works as a "mechanism to compare oneself with others." The number of "likes," make social support quantifiable. It can then be easily viewed for making social comparisons.

Ripple effects of social networking sites

Given the impact of social networking sites on the feelings and behaviors of billions of users, I believe there needs to be an ethical component to designing these technologies.

As Twitter is busy "rethinking everything," the company would do well to think about how the platform is shaping the feelings and behaviors of its users.

Michigan Tech humanities scholar Robert Johnson, in his book, "User-Centered Technology," writes how technologies have "ripple effects" that "shape culture in defining ways."

The same argument is true for social networking sites. As such, every design feature – even that little heart-shaped button – must be carefully scrutinized.

A. Trevor Sutton does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 The 'like' button does far more than just express how much a person likes a particular picture or post. It could be used to make social comparisons. A. Trevor Sutton, Ph.D. Student in Doctrinal Theology, Concordia Seminary Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/96758 2018-05-22T10:48:13Z 2018-05-22T10:48:13Z Cheating workers out of wages is easier than ever

Labor laws are often based on how we worked decades ago.  AP Photo/Richard Sheinwald

Jara Neal Willis, a nurse at a hospital in Texas, usually clocked in a few minutes before the start of her shift and stayed late whenever her patients needed help. Her lunch breaks were often cut short by requests from doctors, patients or their families.

Willis and her colleagues, however, claimed they were not paid for those extra few minutes worked before and after their shifts. Or for working during lunch breaks.

It wasn't because of mischievous gremlins falsifying their time cards in a backroom, but settings in the software the hospital used to track comings and goings. Two features alone, "rounding" and "automatic break deductions," could result in the loss of up to 44 minutes a day – or US$1,382 a year at the federal minimum wage.

Timekeeping software was the focus of a study I co-authored last year documenting how it could be used to facilitate wage theft.

But it left a lingering question: Did companies actually use these features to shortchange workers? Based on my review of hundreds of lawsuits like Willis', the answer is yes – and it's just the tip of the iceberg.

Wage theft gets a tech upgrade

"Wage theft" is a shorthand term that refers to situations in which someone isn't paid for the work. In its simplest form, it might consist of a manager instructing employees to work off the clock. Or a company refusing to pay for overtime hours.

A report from the Economic Policy Institute estimated that employees lose $15 billion to wage theft every year, more than all of the property crime in the United States put together.

That report, however, focused on workers being paid less than the federal or state minimum wage. Our 2017 study, which was based on promotional materials, employer policies and YouTube videos, suggested that companies can now use software to avoid paying all sorts of hourly workers.

Hundreds and hundreds

When an employee clocks in for the day – using a computer login, ID badge or phone – that employee's time log becomes a form of data.

I wanted to know if there was any evidence that employers have ever used rounding and automatic break deductions to change that data, to their workers' detriment. So I did what law professors normally do in such situations: I ran a search of legal opinions to see if there were any cases in which workers sought to reclaim wages lost through digital wage theft.

Before our study, I hadn't even heard of this practice, so I expected to find only a handful of cases. Instead, I found hundreds and hundreds of legal opinions involving digital wage theft. And this suggests there are hundreds more because, typically, for every case that results in a legal opinion many more do not.

I decided to read a bunch to get a flavor for what employees were claiming and a window into how employers were using the software. I eventually stopped after wading through more than 300 cases, which are described in a study published in the American Business Law Journal.

The study's methodology does not support quantitative inferences about how often digital wage theft occurs or how much money U.S. workers have lost to these practices over time.

But what I can say is that this is not a theoretical problem. Real workers have lost real money to these practices.

Rounding away

Rounding – the functionality used to nickel and dime workers like Jara Willis – is a convenient way for companies to consistently reclaim employee hours.

 <img alt="" src="https://images.theconversation.com/files/219258/original/file-20180516-155564-187n88g.png?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/219258/original/file-20180516-155564-187n88g.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=503&fit=crop&dpr=1 600w, https://images.theconversation.com/files/219258/original/file-20180516-155564-187n88g.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=503&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/219258/original/file-20180516-155564-187n88g.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=503&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/219258/original/file-20180516-155564-187n88g.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=632&fit=crop&dpr=1 754w, https://images.theconversation.com/files/219258/original/file-20180516-155564-187n88g.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=632&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/219258/original/file-20180516-155564-187n88g.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=632&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 This illustrates how a rounding system to the quarter hour works.

Even though the software can precisely record the time an employee clocks in and out, the "rounding" functionality changes that time according to a preset increment. Companies argue they use it to increase payroll predictability.

The preferred rounding increment in the cases I reviewed appeared to be to the quarter hour. So arriving to work at 8:53 a.m. would be rounded to 9, while 8:52 would become 8:45.

In theory, employees could even the odds when it comes to rounding by carefully timing their arrivals and departures. They could show up late or leave early, or punch in extra early or leave extra late.

But companies have two extra weapons to corral employee punches to work in their favor: policies and discipline. Yes, you could show up late or leave early, but then you'd be flagged for discipline under the attendance policy.

 <img alt="" src="https://images.theconversation.com/files/219266/original/file-20180516-155579-1hyss0l.png?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/219266/original/file-20180516-155579-1hyss0l.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=778&fit=crop&dpr=1 600w, https://images.theconversation.com/files/219266/original/file-20180516-155579-1hyss0l.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=778&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/219266/original/file-20180516-155579-1hyss0l.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=778&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/219266/original/file-20180516-155579-1hyss0l.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=977&fit=crop&dpr=1 754w, https://images.theconversation.com/files/219266/original/file-20180516-155579-1hyss0l.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=977&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/219266/original/file-20180516-155579-1hyss0l.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=977&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 A hospital posted a 'Stop Mooching' sign to discourage people from coming too early or staying too late, submitted as an evidence in litigation.

Sometimes employers in these cases further stacked the deck by prohibiting workers from punching in more than seven minutes early. Others actually "invited" employees to punch in up to seven minutes early, labeling it a "grace period," as though it were an accommodation to workers.

Willis' hospital, however, took a highly unusual approach to persuading workers to clock in during periods that favored the hospital. According to testimony from that case, supervisors labeled any employee who clocked in too early or out too late, thereby gaining minutes under the rounding system, a "moocher."

One manager even posted "no mooching" signs with a picture of a cow and a clock in the hospital hallway.

Working the odds

Rounding works the odds, sort of like a casino. And in fact, some of the cases I reviewed actually involved casino workers, perhaps because they are especially attuned to statistics and realize they're on the wrong side of the equation.

In one case brought by casino workers, the plaintiff's expert estimated that the 2,100 employees who opted into the lawsuit lost 87,710 hours over a five-year period, or roughly $950,000 at their $10.80 average hourly rate.

 <img alt="" src="https://images.theconversation.com/files/219818/original/file-20180521-14970-flxhfe.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/219818/original/file-20180521-14970-flxhfe.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=399&fit=crop&dpr=1 600w, https://images.theconversation.com/files/219818/original/file-20180521-14970-flxhfe.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=399&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/219818/original/file-20180521-14970-flxhfe.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=399&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/219818/original/file-20180521-14970-flxhfe.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=501&fit=crop&dpr=1 754w, https://images.theconversation.com/files/219818/original/file-20180521-14970-flxhfe.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=501&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/219818/original/file-20180521-14970-flxhfe.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=501&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 This is a casino worker's time records, annotated by plaintiff's counsel in litigation.

But the company's rounding policy actually covered 28,000 employees. If those workers were similarly affected by the policy, that would have meant a loss of about 1.17 million hours, or $12.6 million in wages the company was able to reclaim through the rounding policy over five years.

The case settled for $450,000, about half of which went to attorneys' fees. In other words, even though this particular company was caught, dragged through litigation and forced to settle, it still would have made a hefty profit from its rounding policy. That's not exactly a deterrent.

Unpaid breaks

Employers also reclaim time through what is known as "automatic break deductions." The software assumes that you took your full meal break, even if you didn't.

In some workplaces, taking a lunch break can be difficult, especially for those providing patient care in hospitals and nursing homes. Studies of nurses suggest that they are completely unable to take breaks in about 10 percent of shifts and aren't relieved of duty for meals and breaks in about 40 percent.

In the cases I reviewed, companies didn't make it easy for workers to override the break deduction. Employees complained that they didn't have authorization to do so and instead had to fill out an extra paper form. Or ask their supervisor for approval. Or both.

Companies even discouraged workers from doing so. A nurse received an "action plan" from her hospital after requesting too many break overrides. Rather than fixing the staffing problems that led to the missed breaks, the hospital recommended that she "keep snacks in her office."

 <img alt="" src="https://images.theconversation.com/files/219834/original/file-20180521-14965-dwlazu.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/219834/original/file-20180521-14965-dwlazu.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/219834/original/file-20180521-14965-dwlazu.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/219834/original/file-20180521-14965-dwlazu.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/219834/original/file-20180521-14965-dwlazu.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/219834/original/file-20180521-14965-dwlazu.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/219834/original/file-20180521-14965-dwlazu.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Can't take a break? Well, at least you can keep snacks at your desk. The Marmot, CC BY

Outdated legal rules

So how did this problem come about in the first place?

These types of employer abuses are made possible by half-century-old rules that permitted rounding because at the time companies had to calculate hours by hand.

The outdated regulations assume that rounding will "average out" in the long term, essentially forcing workers to prove that they don't – as in the cases I reviewed.

That leaves employers free to use rounding because it's theoretically possible that it all might average out. And because collective litigation to recover lost wages requires affected workers to "opt in" to a class action suit, only a small fraction of workers ever get their money back.

While punch cards aren't that common anymore, many workplaces still use outdated systems for recording and processing employee time.

What's more, the outdated regulations don't even mention automatic break deductions. That leaves courts struggling to figure out what's fair in cases where there often isn't even an electronic record of the missed break.

This problem is not going away. As long as these regulatory loopholes exist, employers and software makers will find ways to exploit them. That means if you're paid an hourly wage, you may very well be losing out.

Elizabeth C. Tippett does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 Hundreds of court cases show that companies are using features of timekeeping software to shortchange workers, a few minutes at a time. Elizabeth C. Tippett, Associate Professor, School of Law, University of Oregon Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/94605 2018-04-08T19:46:11Z 2018-04-08T19:46:11Z Game of thrones: IT departments and the cloud

  Paul Harvey/Flickr, CC BY

Cloud computing, a buzzword in the tech and business worlds, became popular through the big leaders – Amazon, Microsoft, Google, Apple and Salesforce.com. It's mostly associated with Apple's iCloud, yet not easily defined. Technically, it represents a shared pool of computing resources, including networks, servers, applications, and services, that can be provisioned with minimal management effort.

'The climb'

Cloud computing exists under numerous categories. The most popular are software-as-a-service (SaaS), platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS). SaaS is the most common, and can be used by both individuals and enterprises. The former might use Microsoft Office 365, Dropbox and Google Apps, the latter Salesforce.com, Amazon Web Services, Zendesk and Slack. For companies, these types of services can allow a remote workforce to be supported more easily and operate more independently.

Cloud services became popular due to the benefits they can offer for individuals and enterprises of different sizes, sectors and industries. The low prices and the pay-per-use characteristic of cloud services make them attractive, plus they offer agility, flexibility and ubiquity. The adoption of cloud services has increased in the recent years, and spending is estimated to reach $383 billion by 2020.

'The wars to come'

Despite their advantages, cloud services are not yet widely adopted because of a range of threats. These can include security risks, including data confidentiality, insider and outsider attacks, and data loss. Every individual and enterprise highly values their data, making the need for protection paramount. With the cloud, however, data location is unknown for users and clients, and can lead to compliance issues. The data stored in public cloud environments must abide by national and international laws, yet its "location" is unclear.

Moreover, cloud services can be afflicted by poor connectivity, servers downtime, service unpredictability and bugs in large distributed systems. Vendor lock-in is also perceived as a risk, where user data is "locked" in one provider and can't easily be stored elsewhere. This list of threats forms subtext of the ongoing wars between the IT departments and cloud services.

'The old gods and the new'

Information technology departments have controlled of IT since the beginning of the digital era, reigning with full discretion – they're the kings and gods of the legacy hardware and software of enterprises. They built infrastructure, developed applications, made adjustments and adaptations and monitoring the security of companies' complex systems. Whenever business departments needed a service to achieve their goals, they've had to go through the gods of IT.

As in every fairy tale, the knight in shining armour will arrive on its strong white horse to save the day. In our case, however, the knight arrives on a scalable cloud. Sweeping business departments off their feet, cloud computing offers easily accessible, ubiquitous, economical and scalable services.

While cloud computing is the saviour of business departments, it is the foe of IT departments. Studies have shown a relationship between the adoption of cloud services and decreased IT employment. IT employees can thus feel threatened by their enterprises implementing cloud services, and can oppose to the new gods of the cloud.

'Unbowed, unbent, unbroken'

To keep their enterprises competitive, IT employees are nonetheless attention training sessions and replacing obsolete skills with valuable new abilities. Far from resisting the new gods of the cloud, such IT employees embrace them.

However, one might wonder about the impact the transition of old gods to new has on enterprises. How should enterprises deal with this confrontation? Should they oppose the cloud and stick to their traditional methods and be on the IT department side? Or should they face them down and adopt cloud services? And the most important question is: In a game of thrones, what would Jon Snow do?

Sabine Khalil does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 Cloud services became popular due to the benefits they can offer to users, but their adoption can be opposed by IT departments. Sabine Khalil, Assistant Professor in Management of Information Sytems, Propedia Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/91872 2018-03-27T07:54:23Z 2018-03-27T07:54:23Z Evolutionary computation has been promising self-programming machines for 60 years – so where are they?

  Shutterstock

What if computers could program themselves? Instead of the laborious job of working out how a computer could solve a problem and then writing precise coded instructions, all you would have to do is tell it what you want and the computer would generate an algorithm that solves your problem.

Enter evolutionary computation, which can be seen as a type of artificial intelligence and a branch of machine learning. First suggested in the 1950s, evolutionary computation is the idea that a computer can evolve its own solutions to problems, rather than humans having to go through a series of possibly complex steps to write the computer program ourselves. In theory, this would mean computer programs that might take weeks to program manually could be ready in a matter of minutes.

This idea enabled computers to solve complex problems that may not be well be understood and are difficult for humans to tackle. Computer scientists have used evolutionary computation on many problems, including formulating the best mix of ingredients for shrimp feed, portfolio optimisation, telecommunications, playing games and automated packing.

And researchers who have been studying evolutionary computation for over 60 years have made tremendous advances. It is even the subject of several scientific journals. Yet, as I noted in a recent paper, the idea still isn't used widely outside the research community. So why isn't evolutionary computing evolving faster?

How does evolutionary computation work?

 <img alt="" src="https://images.theconversation.com/files/206824/original/file-20180217-75984-2v3ojh.png?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/206824/original/file-20180217-75984-2v3ojh.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=639&fit=crop&dpr=1 600w, https://images.theconversation.com/files/206824/original/file-20180217-75984-2v3ojh.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=639&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/206824/original/file-20180217-75984-2v3ojh.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=639&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/206824/original/file-20180217-75984-2v3ojh.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=803&fit=crop&dpr=1 754w, https://images.theconversation.com/files/206824/original/file-20180217-75984-2v3ojh.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=803&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/206824/original/file-20180217-75984-2v3ojh.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=803&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Charles Darwin. Good Free Photos

Evolutionary computation draws on Charles Darwin's principles of natural evolution, commonly known as survival of the fittest. That is, the weakest (less well adapted) members of a species die off and the strongest survive. Over many generations, the species will evolve to become better adapted to its environment.

In evolutionary computation, the computer creates a population of potential solutions to a problem. These are often random solutions, so they are unlikely to solve the problem being tackled or even come close. But some will be slightly better than others. The computer can discard the worst solutions, retain the better ones and use them to "breed" more potential solutions. Parts of different solutions will be combined (this is often called "crossover") to create a new generation of solutions that can then be tested and the process begins again.

Another important element of evolutionary computation, as with natural selection, is mutation. Every so often a small, random change is made to one of the solutions being tested. This means new potential solutions can be created that wouldn't be possible from just using crossover.

Hopefully a combination of crossover and mutation will produce new potential solutions that are better than their "parents". This might not happen every time, but as more generations are produced, better solutions are more likely to emerge. It's not unusual for evolutionary computation to involve many millions of generations, just as natural selection can take many millions of years to noticeably alter a living species.

 <img alt="" src="https://images.theconversation.com/files/206826/original/file-20180217-75994-gwsbjj.png?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/206826/original/file-20180217-75994-gwsbjj.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=640&fit=crop&dpr=1 600w, https://images.theconversation.com/files/206826/original/file-20180217-75994-gwsbjj.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=640&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/206826/original/file-20180217-75994-gwsbjj.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=640&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/206826/original/file-20180217-75994-gwsbjj.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=805&fit=crop&dpr=1 754w, https://images.theconversation.com/files/206826/original/file-20180217-75994-gwsbjj.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=805&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/206826/original/file-20180217-75994-gwsbjj.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=805&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Genetic programming tree. Wikimedia

One of the most popular types of evolutionary computation is genetic programming. This involves one computer program evolving another working program to tackle a specific problem. The user provides some measure of what comprises a good program and then the evolutionary process takes over, hopefully returning a program that solves the problem.

We can trace genetic programming back to the late 1980s, with one of the main proponents being John Koza. But even though it has since made significant research advances, genetic programming is not used on a daily basis by commercial organisations or home computer users. Given how tricky it can be to develop software systems that work effectively and efficiently, it would seem sensible to get computers to help in the same way they are changing many other industries.

Selection, Mutation and Crossover.

Why hasn't evolutionary computation been adopted?

The commercial sector hasn't embraced evolutionary computation as it has other technologies developed by researchers. For example, 3D printing was invented in the 1980s and after a long period of development is now being used in industrial manufacturing and even by people in their homes. Similarly, augmented reality, virtual reality and artificial intelligence have emerged from the research community and become major products for big tech companies.

One of the key issues holding evolutionary computation back is the failure of researchers to focus on problems that the commercial sector would recognise. For example, computer scientists have intensively studied how evolutionary computation could be used to schedule exam timetables or working out routes for vehicles.

But researchers often only study simplified versions of problems that are of little use in the real world. For example, many vehicle routing simulations involve calculating the distance between two points using a straight line. Vehicle routes in the real world rarely follow straight lines, and have to contend with one way systems, breakdowns, legal issues (such as how long before a driver must rest), time constraints and a whole lot more. However, this complexity is actually where evolutionary computation could help. If you can adequately define the problem as it occurs in the real world, then the evolutionary algorithm should be able to deal with its complexity.

Another problem is that the solutions evolutionary computation generates are often hard to explain. For example, even though a genetic programming system might create a solution with a perfect outcome, how it actually works might be a mystery to a human programmer as the system may have produced complex code that is difficult to interpret and understand.

An evolutionary computation system is also complex to implement and support and this may put off some commercial organisations. It would help if there was an easy-to-use framework that hid much of the underlying complexity. While these frameworks exist in the scientific community, they are not easily accessible by the commercial sector, never mind home users.

IBM's famous computer architect Frederick Brooks said that you cannot tackle increasingly large software development projects simply by throwing more people at them. It would be an immense help to the software development industry if, instead of having to manually develop every piece of a system, developers could specify the requirements of its key parts and let an evolutionary process deliver the solutions.

Graham Kendall does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 A branch of AI research promises to deliver computers that evolve their own software but the tech industry has yet to catch on. Graham Kendall, Professor of Computer Science and Provost/CEO/PVC, University of Nottingham Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/92859 2018-03-12T14:56:52Z 2018-03-12T14:56:52Z How we're making the tools to connect isiXhosa and isiZulu to the digital age

Software tools can take multiple languages to entirely new spaces. Zubada/Shutterstock

We live in a world where around 7000 languages are spoken, and one where information and communication technologies are becoming increasingly ubiquitous. This puts increasing demands on more, and more advanced, Human Language Technologies (HLTs).

These technologies comprise computational methods, computer programmes and electronic devices that are specialised for analysing, producing or modifying texts and speech.

Engaging with a language like English is made easier thanks to the many tools to support you, such as spellcheckers in browsers and autocomplete for text messages. This is mainly because English has a relatively simple and well investigated grammar, more data that software can learn from, and substantial funding to develop tools. The situation is somewhat to very different for most language in the world.

This is beginning to change. Profit driven multinationals such as Google, Facebook and Microsoft, for instance, have invested in the development of HLTs also for African languages.

Researchers and scientists, myself included are also investigating and creating these technologies. It has a direct relevance for society: languages, and the identities and cultures intertwined with them, are a national resource for any country. In a country like South Africa, learning different languages can foster cohesion and inclusion.

Just learning a language, however, is not enough if there's no infrastructure to support it. For instance, what's the point of searching the Web in, say, isiXhosa when the search engine algorithms can't process the words properly anyway and so won't return the results you're looking for? Where are the spellcheckers to assist you in writing emails, school essays, or news articles?

That's why we have been laying both theoretical foundations and creating proof-of-concept tools for several South African languages. This includes spellcheckers for isiZulu and isiXhosa and the generation of text in mainly these languages from structured input.

Using rules of the language to develop tools

Tool development for the Nguni group of languages – and isiZulu and isiXhosa in particular – wasn't simply a case of copy-and-pasting tools from English. I had to develop novel algorithms that can handle the quite different grammar. I have also collaborated with linguists to figure out the details of each language.

For instance, even just automatically generating the plural noun in isiZulu from a noun in the singular required a new approach that combined syntax – how it is written – with semantics (the meaning) of the nouns by using its characteristic noun class system. In English, merely syntax-based rules can do the job.

Rule-based approaches are also preferred for morphological analysers, which split each word into its constituent parts, and for natural language generation. Natural language generation involves taking structured data, information or knowledge, such as the numbers in the columns in a spreadsheet, and creating readable text from them.

A simple way of realising that is to use templates where the software slots in the values given by the data or the logical theory. This is not possible for isiZulu, because the sentence constituents are context-dependent.

A grammar engine is needed to generate even the most basic sentences correctly. We have worked out the core aspects of the workflow in the engine. This is being extended with more details of the verbs.

Using lots of text to develop tools

The rules-based approach is resource intensive. This, in combination with global hype around "Big Data", has brought data-driven approaches to the fore.

The hope is that better quality tools may now be developed with less effort and that it will be easier to reuse those tools for related languages. This can work, provided one has a lot of good quality text, referred to as a corpus.

Such corpora are being developed, and the recently established South African Centre for Digital Language Resources (SADiLaR) aims to pool computational resources. We investigated the effects of a corpus on the quality of an isiZulu spellchecker, which showed that learning the statistics-driven language model on old texts like the bible does not transfer well to modern day texts such as news items from the Isolezwe newspaper, nor vice versa.

The spellchecker has about 90% accuracy in single-word error detection and it seems to contribute to the intellectualisation of isiZulu.

Its algorithms use trigrams and probabilities of their occurrence in the corpus to compute the probability that a word is spelled correctly, rather than a dictionary based approach that is impractical for agglutinating languages. The algorithms were reused for isiXhosa simply by feeding it a small isiXhosa corpus: it achieved about 80% accuracy already even without optimisations.

Data-driven approaches are also pursued in tools for finding information online, i.e., to develop search engines alike a 'Google for isiZulu'. Algorithms for data-driven machine translation, on the other hand, can easily be misled by out-of-domain training data from which it has to learn the patterns.

Relevance for South Africa

This sort of natural language generation could be incredibly useful in South Africa. The country has 11 official languages, with English as the language of business. That has resulted in the other 10 being sidelined, and in particular those that were already under resourced.

This trend runs counter to citizens' rights and the state's obligations as outlined in the Constitution. These obligations go beyond just promoting language. Take, for instance, the right to have access to the public health system. One study showed that only 6% of patient-doctor consultations was held in the patient's home language. The other 94% essentially didn't receive the quality care they deserved because of language barriers.

The sort of research I'm working on with my team can help. It could contribute to, among others, realising technologies such as automatically generating patient discharge notes in one's own language, text-based weather forecasts, and online language learning exercises.

A longer version of this article, along with figures, can be found on the author's blog.

Maria Keet received funding for this research from the National Research Foundation of South Africa.
 Software tools for South Africa's Nguni languages may assist with redress and effective communication. Maria Keet, Senior Lecturer in Computer Science, University of Cape Town Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/90629 2018-02-04T18:05:46Z 2018-02-04T18:05:46Z Insecure by design – lessons from the Meltdown and Spectre debacle

Spectre and Meltdown affected billions of devices. Shutterstock

The disclosure of the Meltdown and Spectre computer vulnerabilities on January 2, 2018 was in many ways unprecedented. It shocked – and scared – even the experts.

The vulnerabilities bypass traditional security measures in the computer and affect billions of devices, from mobile phones to massive cloud servers.

We have, unfortunately, grown used to attacks on computer systems that exploit the inevitable flaws resulting from vast conceptual complexity. Our computer systems are the most complex artefacts humans have ever built, and the growth of complexity has far outstripped our ability to manage it.

   Read more: What are software vulnerabilities, and why are there so many of them?

A new kind of vulnerability

Meltdown and Spectre are qualitatively different from previous computer vulnerabilities. Not only are they effective across a wide class of computer hardware and operating systems from competing vendors. And not only were the vulnerabilities hiding in plain sight for more than a decade. The really shocking realisation is that Meltdown and Spectre do not exploit flaws in the computer hardware or software.

As Intel stated in its press release, these attacks:

…gather sensitive data from computing devices that are operating as designed.

The ingenuity of the attacks lies in combining seemingly unrelated design features that were thought to be well understood – stuff we teach undergraduate computer science students. The vulnerability is not in any of the individual features, but in the complex interaction between them.

It turns out that computer systems are insecure not because of mistakes made in the implementation, but because of ill-conceived design.

As a community of computer systems experts, we have to ask ourselves how such a debacle is possible, and how a recurrence can be prevented.

We have known for a while that the established "wait for something to happen and then try to fix it" approach – better known as "patch and pray" – does not work even for more common implementation flaws, as witnessed by the proliferation of exploits. It works even less well for such insecure-by-design situations.

Automated evaluation of designs

The fundamental problem is that humans are unable to fully understand the conceptual complexity of modern computer systems and how its seemingly unrelated features might interact. There is no hope that this will change.

But solving complex problems is what machines are increasingly good at. So, the only real solution can be the automated evaluation of designs, with the aim of mathematically proving that under all circumstances a design will behave in a way that is considered secure – in particular by not leaking secret data.

In other words, a design must be considered insecure unless there is a rigorous mathematical proof to the contrary.

This is not an easy ask by any definition, and much more work across many areas of computer science and engineering is needed to make it a reality. But we need to start somewhere, and we need to start now.

   Read more: Artificial intelligence cyber attacks are coming – but what does that mean?

We will reap benefits of embarking on such a program long before we achieve the goal of rigorous end-to-end proof. Significant improvements will be achieved through partial results, both in the form of proving weaker properties, and by establishing desired properties in a less rigorous fashion.

For example, an incomplete evaluation may be more feasible than a complete one, and produce a probabilistic result, such as a greatly reduced likelihood of exploits.

Rewriting the hardware-software contract

A necessary, and overdue, first step is a new and improved hardware-software contract.

Computer systems are a combination of hardware and software. The people and companies that develop hardware are largely separate from those developing the software. Given the vastly different skills and experience required, this is inevitable.

To make development practical, both sides work to an interface, called the instruction-set architecture (ISA), which presents the contract between hardware and software functionality.

The problem, clearly exhibited by the Meltdown and Spectre attacks, is that the ISA is under-specified for security, or safety for that matter. It simply does not provide ways to isolate the speed of progress of a computation from other system activities.

   Read more: Apple, Android and PC chip problem – why your smartphone and laptop are so at risk

The ISA a functional specification, meaning it defines how the visible state of the machine will (eventually) change if an operation is triggered. It intentionally abstracts away anything to do with time. In particular, it hides how long operations take and how this time depends on the internal state of the machine. The problem is that this internal state depends on potentially confidential data processed by previous operations.

This means that by observing the exact timing of particular sequences of operations, it is possible to infer data that is supposed to be kept secret. This is exactly what happened with Meltdown and Spectre.

The abstraction is there for a good reason: It allows hardware designers to change things "under the hood", usually in order to improve performance. Consequently, there will be resistance from hardware manufacturers to a tighter contract. But we believe that the refined specifications can be kept abstract enough to retain manufacturers' ability to innovate, and to avoid exposing confidential IP.

The recent debacle has shown that the ISA is too abstract, making it impossible to tell whether a system is secure or if it will leak secrets. This must change, urgently.

Gernot Heiser receives funding from the Australian Department of Defence and has received funding from Google for research on timing-channel defence mechanisms. He has in the past received funding from Intel for unrelated research.

Yuval Yarom is a member of the international team that discovered the Meltdown and Spectre exploits.
 Preventing problems like Meltdown and Spectre from reocurring requires software developers to be given sufficient information about hardware to ensure security. Gernot Heiser, Chief Research Scientist at CSIRO's Data61; Scientia Professor and John Lions Chair, UNSW Yuval Yarom, Researcher, CSIRO's Data61; Senior Lecturer,, University of Adelaide Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/88674 2017-12-14T09:21:39Z 2017-12-14T09:21:39Z Drones, volcanoes and the 'computerisation' of the Earth

The Mount Agung volcano spews smoke, as seen from Karangasem, Bali. EPA-EFE/MADE NAGI

The eruption of the Agung volcano in Bali, Indonesia has been devastating, particularly for the 55,000 local people who have had to leave their homes and move into shelters. It has also played havoc with the flights in and out of the island, leaving people stranded while the experts try to work out what the volcano will do next.

But this has been a fascinating time for scholars like me who investigate the use of drones in social justice, environmental activism and crisis preparedness. The use of drones in this context is just the latest example of the "computerisation of nature" and raises questions about how reality is increasingly being constructed by software.

Amazon drone delivery is developing in the UK, drone blood delivery is happening in Rwanda, while in Indonesia people are using drones to monitor orangutan populations, map the growth and expansion of palm oil plantations and gather information that might help us predict when volcanoes such as Agung might again erupt with devastating impact.

In Bali, I have the pleasure of working with a remarkable group of drone professionals, inventors and hackers who work for Aeroterrascan, a drone company from Bandung, on the Indonesian island of Java. As part of their corporate social responsibility, they have donated their time and technologies to the Balinese emergency and crisis response teams. It's been fascinating to participate in a project that flies remote sensing systems high in the air in order to better understand dangerous forces deep in the Earth.

I've been involved in two different drone volcano missions. A third mission will begin in a few days. In the first, we used drones to create an extremely accurate 3D map of the size of the volcano – down to 20cm of accuracy. With this information, we could see if the volcano was actually growing in size – key evidence that it is about to blow up.

The second mission involved flying a carbon dioxide and sulphur dioxide smelling sensor through the plume. An increase in these gases can tell us if an eruption looms. There was a high degree of carbon dioxide and that informed the government to raise the threat warning to the highest level.

In the forthcoming third mission, we will use drones to see if anyone is still in the exclusion zone so they can be found and rescued.

What is interesting to me as an anthropologist is how scientists and engineers use technologies to better understand distant processes in the atmosphere and below the Earth. It has been a difficult task, flying a drone 3,000 meters to the summit of an erupting volcano. Several different groups have tried and a few expensive drones have been lost – sacrifices to what the Balinese Hindus consider a sacred mountain.

More philosophically, I am interested in better understanding the implications of having sensor systems such as drones flying about in the air, under the seas, or on volcanic craters – basically everywhere. These tools may help us to evacuate people before a crisis but it also entails transforming organic signals into computer code. We've long interpreted nature through technologies that augment our senses, particularly sight. Microscopes, telescopes and binoculars have been great assets for chemistry, astronomy and biology.

The internet of nature

But the sensorification of the elements is something different. This has been called the computationalisation of Earth. We've heard a lot about the internet of things but this is the internet of nature. This is the surveillance state turned onto biology. The present proliferation of drones is the latest step in wiring everything on the planet. In this case, the air itself, to better understand the guts of a volcano.

These flying sensors, it is hoped, will give volcanologists what anthropologist Stephen Helmreich called abduction – or a predictive and prophetic "argument from the future".

But the drones, sensors and software we use provide a particular and partial worldview. Looking back at today from the future, what will be the impact of increasing datafication of nature: better crop yield, emergency preparation, endangered species monitoring? Or will this quantification of the elements result in a reduction of nature to computer logic?

There is something not fully comprehended – or more ominously not comprehensible – about how flying robots and self-driving cars equipped with remote sensing systems filter the world through big data crunching algorithms capable of generating and responding to their own artificial intelligence.

These non-human others react to the world not as ecological, social, or geological processes but as functions and feature sets in databases. I am concerned by what this software view of nature will exclude, and as they remake the world in their database image, what the implications of those exclusions might be for planetary sustainability and human autonomy.

In this future world, there may be less of a difference between engineering towards nature and the engineering of nature.

Adam Fish receives funding from Leverhulme Trust.
 Robots, like drones, are filtering the natural world through algorithms and turning the world into data. Adam Fish, Senior Lecturer in Sociology and Media Studies, Lancaster University Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/85390 2017-10-22T19:01:09Z 2017-10-22T19:01:09Z I've always wondered: why do our computing devices seem to slow down?

Your gadgets might slow down if they're bloated with apps. Neirfy/Shutterstock

This is an article from I've Always Wondered, a series where readers send in questions they'd like an expert to answer. Send your question to email@example.com

"Why do phones, tablets and computers always slow down as they get older, to the point that they become unusable, but when I back it up and restore it onto a brand new device, it's fast again (despite not changing any of the installed software)?" - Jason Yosar

Plenty of misconceptions and conspiracy theories surround this topic.

Internet searches for "iPhone slow" spike after the release of a new-generation model, but there's no evidence to suggest that manufacturers deliberately degrade the performance of older devices with software updates.

Computer hardware does not typically slow down over its useful life. Instead, there are several other reasons why smartphones, tablets and PCs start to seem less snappy. The good news is that you can often take steps to improve your existing device's performance.

Memory bloat

Each time they update, apps typically become larger and more full of features. Visual pizzazz is also a major attraction, and so desktop and mobile operating systems periodically receive significant redesigns.

All that extra functionality and glitz requires your device to do more computation than it did when it arrived home from the store. Given that it doesn't magically speed up to compensate, it has less spare capacity available to respond to you quickly.

Newer apps not only tend to do more computation, they also usually take up more space in your device's storage.

Devices only have a limited amount of fast "Random Access Memory" (RAM) available. One of a device's data storage components, RAM is the rough equivalent of an office whiteboard - fast and convenient, but limited in capacity. Its contents are wiped every time you switch your device off.

When it runs out of space in RAM, your device can shift things to and from the much slower (and permanent until explicitly erased) data storage, flash memory, which takes considerable time.

In older PCs with mechanical hard disks, this used to be called "thrashing", as users heard the hard disk's read-write heads moving across the platters as they waited for data to be shifted in and out of the filled-up RAM.

Flash memory is silent and much faster than magnetic hard disks ever were, but it is still orders of magnitude slower than RAM.

 <img alt="" src="https://images.theconversation.com/files/190338/original/file-20171016-21935-6fikbd.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/190338/original/file-20171016-21935-6fikbd.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/190338/original/file-20171016-21935-6fikbd.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/190338/original/file-20171016-21935-6fikbd.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/190338/original/file-20171016-21935-6fikbd.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/190338/original/file-20171016-21935-6fikbd.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/190338/original/file-20171016-21935-6fikbd.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Random Access Memory is a form of data storage. Marcin Bajer/Flickr, CC BY-NC

Excessive cacheing

To make their apps run faster, some designers make them store copies of things in RAM that they think the user might want to see again to speed things up. For instance, a web browser might retain a copy of what the content in each tab looks like, even if only one tab is visible at a given moment.

Known as cacheing, this makes things work much faster – until your system starts to run out of memory. For cacheing to be effective, the amount of space devoted to it must be carefully managed by the application and the device's operating system.

Some app developers don't put the effort that they should into doing this well, and their applications not only slow down over time, but can drag the rest of the system down with them too.

More and more software

It's also not uncommon for useful software to be accompanied by "crapware" – less-than-useful add-ons like browser toolbars – that use system resources and impact performance.

Additional software can slow a system down in many ways: filling up permanent storage, using up more precious RAM, and using the computer's central processing unit "in the background" without you noticing. All these factors can result in the system having fewer resources available to respond to you promptly.

A new or factory-reset device tends to have less of this accumulated "cruft" (unwanted data and software) installed, and therefore has more resources available to do the tasks that a user actually wants.

Another unpleasant possibility is that some of the computing capabilities of your device are being used by malware - whether viruses, worms or other varieties of malicious software.

What can you do?

You're not going to be able to match the performance of the latest and greatest high-end smartphone, tablet, or PC with an older model, as newer devices generally have fundamentally faster components. But with a small amount of effort, you can get the most out of your existing device.

Whether you're using a phone, tablet, PC or Mac, the most useful zero-cost action you can take is to uninstall unnecessary apps and add-ons.

However, in some circumstances it may be easier - AFTER carefully backing up all your data - to simply perform the equivalent of a factory reset and reinstall the operating system from scratch, adding only the apps you actually need.

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Robert Merkel does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 There are a few reasons why smartphones, tablets and PCs start to seem less snappy over time. Robert Merkel, Lecturer in Software Engineering, Monash University Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/86027 2017-10-20T13:36:27Z 2017-10-20T13:36:27Z DNA techniques could transform facial recognition technology

  Shutterstock

When police in London recently trialled a new facial recognition system, they made a worrying and embarrassing mistake. At the Notting Hill Carnival, the technology made roughly 35 false matches between known suspects and members of the crowd, with one person "erroneously" arrested.

Camera-based visual surveillance systems were supposed to deliver a safer and more secure society. But despite decades of development, they are generally not able to handle real-life situations. During the 2011 London riots, for example, facial recognition software contributed to just one arrest out of the 4,962 that took place.

The failure of this technology means visual surveillance still relies mainly on people sitting in dark rooms watching hours of camera footage, which is totally inadequate to protect people in a city. But recent research suggests video analysis software could be dramatically improved thanks to software advances made in a completely different field: DNA sequence analysis. By treating video as a scene that evolves in the same way DNA does, these software tools and techniques could transform automated visual surveillance.

Since the Metropolitan Police installed the first CCTV cameras in London in 1960, up to 6m of them have now been deployed in the UK. And body-worn cameras are now being issued to frontline officers, creating not only even more video footage to analyse, but also more complex data due to constant camera motion.

Yet automated visual surveillance remains mostly limited to tasks in relatively controlled environments. Detecting trespass on a specific property, counting people passing through a given gate, or number-plate recognition can be completed quite accurately. But analysing footage of groups of people or identifying individuals in a public street is unreliable because outdoor scenes vary and change so much.

In order to improve automated video analysis, we need software that can deal with this variability rather than treating it as an inconvenience – a fundamental change. And one area that is used to dealing with large amounts of very variable data is genomics.

 <img alt="" src="https://images.theconversation.com/files/191211/original/file-20171020-13934-1d1ydzr.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/191211/original/file-20171020-13934-1d1ydzr.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=316&fit=crop&dpr=1 600w, https://images.theconversation.com/files/191211/original/file-20171020-13934-1d1ydzr.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=316&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/191211/original/file-20171020-13934-1d1ydzr.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=316&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/191211/original/file-20171020-13934-1d1ydzr.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=398&fit=crop&dpr=1 754w, https://images.theconversation.com/files/191211/original/file-20171020-13934-1d1ydzr.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=398&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/191211/original/file-20171020-13934-1d1ydzr.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=398&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Finding faces in the crowd. Shutterstock

Since the three billion DNA characters of the first human genome (the entire set of genetic data in a human) were sequenced in 2001, the production of this kind of genomic data has increased at an exponential rate. The sheer amount of this data and the degree to which it can vary means vast amounts of money and resources have been needed to develop specialised software and computing facilities to handle it.

Today it's possible for scientists to relatively easily access genome analysis services to study all sorts of things, from how to combat diseases and design personalised medical services, to the mysteries of human history.

Genomic analysis includes the study of the evolution of genes over time by investigating the mutations which have occurred. This is surprisingly similar to the challenge in visual surveillance, which relies on interpreting the evolution of a scene over time to detect and track moving pedestrians. By treating differences between the images that make up a video as mutations, we can apply the techniques developed for genomic analysis to video.

Early tests of this "vide-omics" principle have already demonstrated its potential. My research group at Kingston University has, for the first time, shown that videos could be analysed even when captured by a freely moving camera. By identifying camera motion as mutations, they can be compensated so that a scene appears as if filmed by a fixed camera.

Meanwhile, researchers at the University of Verona have demonstrated that image processing tasks can be encoded in such a way that standard genomics tools could be exploited. This is particularly important since such an approach reduces significantly the cost and time of software development.

Combining this with our strategy could eventually deliver the visual surveillance revolution that was promised many years ago. If the "vide-omics" principle were to be adopted, the coming decade could deliver much smarter cameras. In which case, we had better get used to being spotted on video far more often.

Jean-Christophe Nebel does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 Treating video like a mutating gene could improve surveillance software. Jean-Christophe Nebel, Associate Professor in Pattern Recognition, Kingston University Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/81435 2017-09-06T00:39:39Z 2017-09-06T00:39:39Z The 'internet of things' is sending us back to the Middle Ages

Is this our relationship to tech companies now? Queen Mary Master

Internet-enabled devices are so common, and so vulnerable, that hackers recently broke into a casino through its fish tank. The tank had internet-connected sensors measuring its temperature and cleanliness. The hackers got into the fish tank's sensors and then to the computer used to control them, and from there to other parts of the casino's network. The intruders were able to copy 10 gigabytes of data to somewhere in Finland.

By gazing into this fish tank, we can see the problem with "internet of things" devices: We don't really control them. And it's not always clear who does – though often software designers and advertisers are involved.

In my recent book, "Owned: Property, Privacy and the New Digital Serfdom," I discuss what it means that our environment is seeded with more sensors than ever before. Our fish tanks, smart televisions, internet-enabled home thermostats, Fitbits and smartphones constantly gather information about us and our environment. That information is valuable not just for us but for people who want to sell us things. They ensure that internet-enabled devices are programmed to be quite eager to share information.

Take, for example, Roomba, the adorable robotic vacuum cleaner. Since 2015, the high-end models have created maps of its users' homes, to more efficiently navigate through them while cleaning. But as Reuters and Gizmodo reported recently, Roomba's manufacturer, iRobot, may plan to share those maps of the layouts of people's private homes with its commercial partners.

Security and privacy breaches are built in

Like the Roomba, other smart devices can be programmed to share our private information with advertisers over back-channels of which we are not aware. In a case even more intimate than the Roomba business plan, a smartphone-controllable erotic massage device, called WeVibe, gathered information about how often, with what settings and at what times of day it was used. The WeVibe app sent that data back to its manufacturer – which agreed to pay a multi-million-dollar legal settlement when customers found out and objected to the invasion of privacy.

Those back-channels are also a serious security weakness. The computer manufacturer Lenovo, for instance, used to sell its computers with a program called "Superfish" preinstalled. The program was intended to allow Lenovo – or companies that paid it – to secretly insert targeted advertisements into the results of users' web searches. The way it did so was downright dangerous: It hijacked web browsers' traffic without the user's knowledge – including web communications users thought were securely encrypted, like connections to banks and online stores for financial transactions.

The underlying problem is ownership

One key reason we don't control our devices is that the companies that make them seem to think – and definitely act like – they still own them, even after we've bought them. A person may purchase a nice-looking box full of electronics that can function as a smartphone, the corporate argument goes, but they buy a license only to use the software inside. The companies say they still own the software, and because they own it, they can control it. It's as if a car dealer sold a car, but claimed ownership of the motor.

This sort of arrangement is destroying the concept of basic property ownership. John Deere has already told farmers that they don't really own their tractors but just license the software – so they can't fix their own farm equipment or even take it to an independent repair shop. The farmers are objecting, but maybe some people are willing to let things slide when it comes to smartphones, which are often bought on a payment installment plan and traded in as soon as possible.

How long will it be before we realize they're trying to apply the same rules to our smart homes, smart televisions in our living rooms and bedrooms, smart toilets and internet-enabled cars?

A return to feudalism?

The issue of who gets to control property has a long history. In the feudal system of medieval Europe, the king owned almost everything, and everyone else's property rights depended on their relationship with the king. Peasants lived on land granted by the king to a local lord, and workers didn't always even own the tools they used for farming or other trades like carpentry and blacksmithing.

Over the centuries, Western economies and legal systems evolved into our modern commercial arrangement: People and private companies often buy and sell items themselves and own land, tools and other objects outright. Apart from a few basic government rules like environmental protection and public health, ownership comes with no trailing strings attached.

This system means that a car company can't stop me from painting my car a shocking shade of pink or from getting the oil changed at whatever repair shop I choose. I can even try to modify or fix my car myself. The same is true for my television, my farm equipment and my refrigerator.

Yet the expansion of the internet of things seems to be bringing us back to something like that old feudal model, where people didn't own the items they used every day. In this 21st-century version, companies are using intellectual property law – intended to protect ideas – to control physical objects consumers think they own.

Intellectual property control

My phone is a Samsung Galaxy. Google controls the operating system and the Google Apps that make an Android smartphone work well. Google licenses them to Samsung, which makes its own modification to the Android interface, and sublicenses the right to use my own phone to me – or at least that is the argument that Google and Samsung make. Samsung cuts deals with lots of software providers which want to take my data for their own use.

But this model is flawed, in my view. We need the right to fix our own property. We need the right to kick invasive advertisers out of our devices. We need the ability to shut down the information back-channels to advertisers, not merely because we don't love being spied on, but because those back doors are security risks, as the stories of Superfish and the hacked fish tank show. If we don't have the right to control our own property, we don't really own it. We are just digital peasants, using the things that we have bought and paid for at the whim of our digital lord.

Even though things look grim right now, there is hope. These problems quickly become public relations nightmares for the companies involved. And there is serious bipartisan support for right-to-repair bills that restore some powers of ownership to consumers.

Recent years have seen progress in reclaiming ownership from would-be digital barons. What is important is that we recognize and reject what these companies are trying to do, buy accordingly, vigorously exercise our rights to use, repair and modify our smart property, and support efforts to strengthen those rights. The idea of property is still powerful in our cultural imagination, and it won't die easily. That gives us a window of opportunity. I hope we will take it.

Joshua A.T. Fairfield does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 The companies that make our digital devices think – and act – like they still own them, even after we've bought them. Are we becoming digital serfs? Joshua A.T. Fairfield, Professor of Law, Washington and Lee University Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/80751 2017-08-03T20:16:22Z 2017-08-03T20:16:22Z Computer says no: robo-advice is growing but we still don't trust it

Those who got advice from the online calculator in the study showed a small, but statistically significant, increase in trust towards robo-advice. www.shutterstock.com

People are open to receiving financial advice from robots, our studies show, but there might be a way to go to in convincing people to trust them over a human.

We surveyed 138 people about their attitudes to, and preferences for, superannuation advice from a human or a computer. Unsurprisingly, most stated they would prefer to deal with a human across a broad range of financial decisions.

Some did prefer the computer – these tended to be younger people, and those on higher incomes. In a follow-up study we tested whether this would change after people actually used the technology.

We did this by exposing 101 people to an online calculator, in which they learned how increasing their superannuation contributions would change their income in retirement. We compared these to another 101 people in a control group who simply read some general information about retirement income.

A little over half of our sample indicated that they would trust robo-advice. Those who got advice from the online calculator showed a small, but statistically significant, increase in trust towards robo-advice. However, most still stated they preferred human advice, and were slightly less willing to pay for automated advice after trying out the calculator.

The openness of younger people is encouraging, as they tend to be the most disengaged from their superannuation, but also have the most to gain from getting it right (as the benefits will build up for longer).

How the robots could help

Automated financial advice systems (so-called "robo-advisors") have great potential to extend the reach of professional financial advice.

Digital technology has quietly revolutionised the world of banking and financial services. Automatic Teller Machines (ATMs) were an early example of a computer replacing a human worker. Interestingly banks responded to this increasing productivity by employing more people.

Financial advice is the latest frontier for automation, with a number of "robo-advisors" beginning to interact with customers. Even though we face a growing number of financial decisions, most Australians currently don't get formal financial advice. Cost is a significant barrier to this.

Like many digital products, robo-advisors are costly to design and build, but once up and running they can serve large numbers of people. These bots could extend low-cost and dependable advice to those who currently miss out.

Robo-advice is currently a small part of the financial services market, but it is forecast to grow rapidly. In the US, firms such as Betterment and Wealthfront have begun to disrupt the wealth management industry. In Australia robo-advice products are being developed both by startups and industry incumbents.

But financial advice is about more than numbers. While technology can easily handle the maths, people may also need the human touch. They may want emotional support and motivation, rather than just the cold hard facts, to get them to confidently engage with these difficult and important decisions. Trust requires good design.

Digital technology might also prove useful in getting people more engaged with their financial decisions. Most Australians pay remarkably little attention to their superannuation, even though it makes up a significant portion of our overall wealth (second only to the family home).

Robo-advice could help people learn, and try out different scenarios, without the worry of appearing ignorant to a person. Unfortunately, our experiment found little evidence for this – overall levels of motivation, and perceptions of autonomy and competence were unchanged.

Distinguishing between people who were initially more engaged or less engaged with their superannuation, our study showed that the online calculator had a greater impact on those who were initially less engaged. This suggests robo-advice might prove most useful to those who need it the most, making them feel more competent and in control.

So for those of us who don't pay enough attention to our financial decision-making, the robots are here to help.

This research was funded by the CSIRO-Monash Superannuation Research Cluster, which received financial support from AMP Capital, ANZ, BT, Cbus, Challenger, Mercer, Vanguard Investments and Colonial First State.

This research was funded by the CSIRO-Monash Superannuation Research Cluster, which received financial support from AMP Capital, ANZ, BT, Cbus, Challenger, Mercer, Vanguard Investments and Colonial First State.

Martin Lochner does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 Robo-advice might prove most useful to those who need it the most, making them feel more competent and in control. Andrew Reeson, Behavioural Economist, CSIRO Andreas Duenser, Research Scientist, CSIRO Martin Lochner, Post-Doctoral Fellow in Cognitive Psychology, University of Tasmania Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/81433 2017-08-02T01:21:06Z 2017-08-02T01:21:06Z Inside the fight against malware attacks

Taking a much closer look at what's going on inside malware. MuchMania/Shutterstock.com

When malicious software attacks, computer scientists and security researchers want to know how the attackers got into what was supposed to be a secure system, and what they're actually doing that's causing problems for users. It's a growing problem, affecting government projects, retail stores and individuals around the world.

However, fighting malware is a cyclical arms race: As defenders and analysts improve their methods, attackers step up their game, too. Today, as many as 80 percent of malware authors include elements in their attacks that specifically try to defeat malware-protection software.

My research group at the University of Texas at Arlington develops methods and tools professional malware analysts use to understand these attacks. One of our best-known efforts was led by alumna Shabnam Aboughadareh, who while she was working toward her Ph.D. developed a malware analysis tool that is particularly hard for malware authors to defend against.

Analyzing malware

When an attack is discovered or reported, malware analysts work to get a copy of any software that's being installed on target computers. When they begin examining it, an early topic of inquiry is how the malware managed to break into a computer network or system. That often uncovers security holes in commonly used operating systems or applications – which can then be disclosed to those programs' authors, who can fix the flaws.

In addition, analysts try to figure out what a piece of malware does once it breaks in – how it travels through a computer and throughout a network, and what actions it takes, such as altering files, copying data, running programs or even installing new software to assist itself in the attack. Those actions can be described in ways that help malware detection tools catch future attacks before they can do damage.

In observing a malware attack, we also try to determine which computers and which files have been manipulated, so they can be repaired. We also see what data – such as client lists, product plans or other sensitive business data – might have been read and copied by the malware. And we often try to infer the attackers' identity, or at least how advanced their skills are, to help prepare defenses against possible follow-up attacks.

Running malicious code

Doing any of that requires us to watch the malware in action. It would be nice if we could simply decode the software and dissect its instructions without actually running these malicious programs. But malware authors know we'll be looking, so they take steps to make our jobs harder, such as compressing or encrypting their malware programs before setting them loose.

So our best option is to run the malware on our own computers. To prevent our own machines from being taken over or corrupted, though, we have to be careful. Typically we create what's called a "virtual machine" – a program that simulates a fully functional computer but that does not have direct access to the computer's files and hardware. Ideally, that would let us observe all the actions the malware tries to take without actually harming our own computers.

So far, however, there has been no single piece of software that can analyze every attack. Some malware programs operate on a very low technological level, working directly with very specific areas of a computer's memory and hard drive storage systems, even changing how the computer works – so users can no longer trust the machines to do what is expected of them. Other malicious software works at higher levels, more like normal software that interacts with the operating system rather than the computer's hardware directly. The most advanced malware attacks on both levels.

Most analysis tools focus on one or the other of those types of attacks – but not both. So they can't catch everything, and – even for the malware they do detect – can't show every action the malware takes. (Some analysis techniques involve running some anti-malware software in the virtual machine, but those programs are vulnerable to manipulation from the malware itself.)

Taking a fuller look

The program Shabnam Aboughadareh created, called SEMU, is the first malware analysis system that addresses all these problems. It operates fully outside the virtual machine, and watches closely what goes on inside it, to detect and log malware actions. That helps SEMU provide a comprehensive log of malware operations, which in turn reduces the manual effort required for a malware analyst to understand what the malware writer's program was supposed to do.

That comprehensive log – recording events at the lowest levels of the virtual machine's operating system – is the key to SEMU's success, because it allows human analysts to track where and how malware manipulates aspects of the operating system.

When we tested SEMU against other malware analysis tools, we found that SEMU was the only publicly available tool that could detect all the activity – things like reading files, changing memory or file data, or sending information out over a network connection – needed to understand how the malware worked. By merging close examination of computer activity with detailed logging, and running in a safe environment where the malware couldn't tamper with its monitoring, SEMU shows a direction for future analysis methods.

Christoph Csallner is currently a member of the Association for Computing Machinery and an academic editor of PeerJ Computer Science. This material is based upon work supported by the National Science Foundation under Grants No. 1017305 and 1117369. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 How do malware analysts examine software that's designed to wreak havoc with computers? By using tools that watch software's inner workings very closely. Christoph Csallner, Associate Professor of Computer Science and Engineering, University of Texas Arlington Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/80800 2017-07-17T20:02:10Z 2017-07-17T20:02:10Z A brief history of GnuPG: vital to online security but free and underfunded

GnuPGP still has many important uses today. AAP Image/Mick Tsikas

Most people have never heard of the software that makes up the machinery of the internet. Outside developer circles, its authors receive little reward for their efforts, in terms of either money or public recognition.

One example is the encryption software GNU Privacy Guard (also known as GnuPG and GPG), and its authors are regularly forced to fundraise to continue the project.

GnuPG is part of the GNU collection of free and open source software, but its story is an interesting one, and it begins with software engineer Phil Zimmermann.

We do not know exactly what Zimmermann felt on January 11, 1996, but relief is probably a good guess. The United States government had just ended its investigation into him and his encryption software, PGP or "Pretty Good Privacy".

In the 1990s, the US restricted the export of strong cryptography, viewing it as sensitive technology that had once been the exclusive purview of the intelligence and military establishment. Zimmermann had been facing serious punishment for posting PGP on the internet in 1991, which could have been seen as a violation of the Arms Export Control Act.

To circumvent US export regulations and ship the software legally to other countries, hackers even printed the source code as a book, which would allow anyone to scan it at its destination and rebuild the software from scratch.

Zimmermann later worked with the PGP Corporation, which helped define PGP as an open internet standard, OpenPGP. A number of software packages implement this standard, of which GnuPG is perhaps the best-known.

What is PGP?

PGP implements a form of cryptography that is known as "asymmetric cryptography" or public-key cryptography.

The story of its discovery is itself worth telling. It was invented in the 1970s by researchers at the British intelligence service GCHQ and then again by Stanford University academics in the US, although GCHQ's results were only declassified in 1997.

Asymmetric cryptography gives users two keys. The so-called "public" key is meant to be distributed to everyone and is used to encrypt messages or verify a "signature". The "private" or "secret" key must be known only to the user. It helps decrypt messages or "sign" them - the digital equivalent of a seal to prove origin and authenticity.

Zimmermann published PGP because he believed that everybody has a right to private communication. PGP was meant to be used for email, but could be used for any kind of electronic communication.

 <img alt="" src="https://images.theconversation.com/files/178383/original/file-20170717-29369-1jpm2i3.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/178383/original/file-20170717-29369-1jpm2i3.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=401&fit=crop&dpr=1 600w, https://images.theconversation.com/files/178383/original/file-20170717-29369-1jpm2i3.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=401&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/178383/original/file-20170717-29369-1jpm2i3.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=401&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/178383/original/file-20170717-29369-1jpm2i3.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=504&fit=crop&dpr=1 754w, https://images.theconversation.com/files/178383/original/file-20170717-29369-1jpm2i3.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=504&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/178383/original/file-20170717-29369-1jpm2i3.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=504&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Phil Zimmermann is the creator of PGP. Jon Callas/Flickr, CC BY

The challenge facing security software

Despite Zimmermann's work, the dream of free encryption for everyone never quite came to full bloom.

Neither Zimmermann's original PGP nor the later GnuPG managed to become entirely user-friendly. Both use highly technical language, and the latter is still known for being accessible only by typing out commands - an anachronism even in the late 1990s, when most operating systems already used the mouse.

Many users did not understand why they should encrypt their email at all, and attempts to integrate the tools with email clients were not particularly intuitive.

Big corporations such as Microsoft, Google and Apple shunned it – to this day, they do not ship PGP with their products, although some are now implementing forms of end-to-end encryption.

Finally, there was the issue of distributing public keys - they had to be made available to other people to be useful. Private initiatives never gathered much attention. In fact, a number of academic studies in the early and late 2000s showed that these attempts never managed to attract widespread public usage.

The release of the Edward Snowden documents in 2013 spurred renewed interest in PGP. "Crypto parties" became a global phenomenon when people met in person to exchange their public keys, but this was ultimately short-lived.

PGP today

When I met Zimmermann in Silicon Valley in 2015, he admitted that he did not currently use PGP. In a more recent email, he said this is because it does not run on current versions of macOS or iOS. "I may soon run GnuPG," he wrote.

By today's standards, GnuPG – like all implementations of OpenPGP – lacks additional security features that are provided by chat apps such as WhatsApp or Signal. Both are spiritual descendants of PGP and unthinkable without Zimmermann's invention, but they go beyond what OpenPGP can do by protecting messages even in the case of a private key being lost.

What's more, email reveals the sender and receiver names anyway. In the age of data mining, this is often enough to infer the contents of encrypted communication.

Nevertheless, GnuPG (and hence OpenPGP) are alive and well. Relative to the increased computational power available today, their cryptography is as strong today as it was in 1991. GnuPG just found new use cases - very important ones.

Journalists use it to allow their sources to deposit confidential data and leaks. This is a vital and indispensable method of self-protection for the leaker and the journalist.

But even more importantly, digital signatures are where GnuPG excels today.

Linux is one of the world's most common operating system (it even forms the basis of Android). On internet servers that run Linux, software is downloaded and updated from software repositories - and most of them sign their software with GnuPG to confirm its authenticity and origin.

GnuPG works its magic behind closed curtains, once again.

Ralph Holz does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 Most people have never heard of the software that makes up the machinery of the internet - especially the tools that keep us safe. Ralph Holz, Lecturer in Networks and Security, University of Sydney Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/80615 2017-07-12T20:06:04Z 2017-07-12T20:06:04Z Resume robot wars: how employees could match employers' use of tech in job applications

Ironically, one of the biggest advantages of a bot-based application system is that it can come across as more personal than a human. www.shutterstock.com

An estimated 90% of large companies are using automated software to read and respond to resumes. From tracking software that reads a resume looking for keywords, to automated emails, phone interviews and skills testing – companies are doing less manual processing than ever before.

If companies are using automated software in hiring, then job applicants should be able to use the same technology.

In an increasingly competitive job market, with some advertisements attracting hundreds of thousands of applications, there is less chance that the time spent on putting together an application will be rewarded. As the number of applicants increases, the success of any one applicant decreases. In other words, job applications have become a numbers game.

In the hands of applicants, automated software would drastically cut down the time taken to apply for the requisite number of jobs to secure a position. Automating the process would reduce time spent reading job ads, uploading resumes and signing on to various job sites under the current, woefully inefficient system.

What a job application robot looks like

Several programmers have already attempted to create a job application robot. This type of online robot, or "bot", is a programmed piece of software that performs a variety of functions on behalf of the applicant.

The most common type of such software has three core components. The bot runs a trawling algorithm that searches the internet for relevant job ads and compiles them into a list. It then uses an email template to create a cover letter, using keywords from the advertisement and job description to tailor a resume for the position.

To cap it off, the bot sends an automated email applying for the job on your behalf to the relevant human resources manager, making sure to get their name right. In effect, this is a total automation of the entire job application process, from start to finish.

When will the robot wars start?

Despite the obvious appeal of fully automated job application bots to consumers, uptake has been relatively slow. Most of the automated bots thus far have been created by independent hobbyists, often as one-off experimental projects.

There have only been a few open source tools (that can be used by others to develop software) that cover part of the process. Consumers are also wary of using application bots, when human resources managers continue to insist that they manually spend time personalising each application.

Ironically, one of the biggest advantages of a bot-based application system is that it can come across as more personal than a human. If resumes are read initially by tracking software looking for particular keywords from job ads ("individualised" keywords), it makes sense to use bots that automatically include those key words in an application. A bot's application in this way can appear more "tailored" to each job ad than if written manually by a human being, who might overlook the importance of focusing on keywords.

In two test case scenarios, bots were found to outperform the manual applications of their human inventors. One programmer, Robert Coombs, found that his bot had an increased response rate of 11% compared with manual applications, out of a test of 538 jobs. Another programmer, Benjamin Derville, had a 50% response rate for 50 jobs using a bot. It was the best response rate he had ever achieved when applying for jobs.

Despite this measured success, both programmers warn that online applications are inherently rigged. With the rise of networking, nepotism and cronyism, companies are increasingly ignoring external applicants altogether.

Even though full automation might not have taken off just yet, partial automation of job applications has become increasingly common. Job applicant advice books now commonly include a section on automating one or two aspects of the job search.

Most commonly, these books will include the idea of "mail merging". This involves manually collecting data about various jobs in a spreadsheet, including job title, employer title, company name and so on. Data are then "merged" into several new cover letters in Microsoft Word, automatically.

In this way, form data is used to fill in hundreds of cover letters in a matter of minutes, rather than hours. On the institutional side, companies like LinkedIn are making it easier than ever for applicants to fill in their application data automatically from their existing profile, rather than starting from scratch in each application.

Considering the time saved, the efficiency gained, and the current asymmetry of automated technology, it seems clear that job applicants should be given access to automated job application bots. This would not only save everyone time, but would also drastically reduce the amount of human error in the system – leading to an employment system that uses new technology to free up our lives.

Joshua Krook does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 If companies are using automated software in hiring, then job applicants should be able to use the same technology. Joshua Krook, Doctoral Candidate in Law, University of Adelaide Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/78036 2017-05-30T07:12:56Z 2017-05-30T07:12:56Z When it comes to ransomware, it's sometimes best to pay up

Businesses struck by ransomware have to make some hard decisions. Christiaan Colen/Flickr, CC BY-SA

Companies hit by ransomware are faced with an ethical dilemma: pay up to save their now-encrypted data, or hold the moral high ground and lose it all.

This is a question many companies may have to face. The recent WannaCry cyber-attack, which targeted the data of organisations including UK hospitals, is part of a growing and lucrative "industry".

In most cases, the perpetrators attempt to encrypt a business's data and then refuse to share the decryption key unless a ransom is paid. WannaCry reportedly demanded that companies pay upwards of US$300 in Bitcoin.

Of course, there are ways to protect yourself. Up-to-date software and effective backups are good controls for ransomware, but many people fail to keep up. For examples, an estimated 7% of computers globally still use Windows XP software, despite Microsoft having ended support for the platform. In the case of WannaCry, this was an important vulnerability.

Paying up may be the rational choice for an individual business, but given that cybercriminals go where the money is, the repercussions for others could be significant.

The case for paying up

Pop-culture morality tells us a ransom should not be paid; movies tell us that paying the ransom means the bad guys win.

In the real world, however, businesses faces a serious dilemma. Paying the ransom could save the business and keep staff employed, but the cybercriminal will probably feel encouraged to continue their attacks.

Ultimately, businesses held to ransom have at least four choices:

Refuse to pay the ransom and risk the possibility that the criminals will carry out their threats

Call authorities to launch a criminal investigation, although whether the data will be decrypted is uncertain

Attempt to use decryption tools to access the data. One such method is "brute force" – a trial and error computational method to guess all possible variants of the decryption key – but some mathematicans estimate that's beyond the processing power of most computers.

Pay the ransom and hope you get your data back.

Ransomware 'WannaCry' attack explained.

Several factors may affect the decision, including whether the victim expects that the encrypted data will be returned once payment is made, or how embarrassing it will be to inform clients about the incident.

The value of the data is also important. If the data held hostage is not integral, then the business is obviously less likely to pay.

Thinking about others

The victim's consideration of the consequences of their choice for others is also important.

Economically, rather like the decision of an individual trawler to over-fish the seas or a factory to pollute the air, paying the ransom creates a "negative externality".

That is, paying the ransom may benefit the cybercriminal as well as the business and its survival, but it's a sub-optimal choice from the perspective of the wider community. The business that pays the ransom obtains all the benefits of their choice, but much of the cost is borne by others, who may become the victim of emboldened cybercriminals.

The moral dilemma is difficult: paying the ransom saves the business but hurts others. However, not paying the ransom is to feel morally superior while waiting in the unemployment line.

How to fight ransomware

Avoiding such a dilemma entirely requires businesses to prepare for ransomware attacks.

There are several key actions and responses a company can take to blunt the impact of a cyber-attack. Chani Simms, co-founder of Meta Defence Labs, has suggested, among other things:

Implementing preventive controls to make attacks less successful, such as regularly "patching" software and training staff in good information security practices.

Ensuring data is backed up offline and business continuity plans are in place.

If an attack is still successful, quickly isolating infected computers to limit losses.

Such simple strategies are estimated to mitigate most cyber intrusions as well as ransomware. Yet the risk remains that ransomware creators will find a vulnerability, encrypt important data and leave the business with a sticky choice.

Until someone creates a ransomware-proof software system, some might decide that paying up is the rational choice.

Micheal Axelsen is a fellow of CPA Australia and a member of ISACA.
 Movies tell us that paying a ransom means the bad guys win, but in the real world it's not that simple. Micheal Axelsen, Lecturer (Business Information Systems), The University of Queensland Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/77717 2017-05-15T07:16:27Z 2017-05-15T07:16:27Z After 'WannaCrypt', should governments stockpile software vulnerabilities? Experts respond
The "WannaCrypt" malware has disrupted vital infrastructure in almost 100 countries so far. Security analysts are concerned it may be part of a dump of security flaws a group called the Shadow Brokers claims to have stolen from the United States' National Security Agency.

In a blog post Sunday, Microsoft's president and chief legal officer, Brad Smith, decried government stockpiling of software vulnerabilities.

"We have seen vulnerabilities stored by the CIA show up on WikiLeaks, and now this vulnerability stolen from the NSA has affected customers around the world," he wrote. "The governments of the world should treat this attack as a wake-up call. They need to take a different approach and adhere in cyberspace to the same rules applied to weapons in the physical world."

We asked a panel of experts to weigh in: Should governments be allowed to stockpile exploits, or should they be made to disclose them to vendors, including Microsoft?

Greg Austin, professor, Australian Centre for Cyber Security, University of New South Wales

Vulnerabilities in commercially available software provide an easy way in for spy agencies and criminals to access adversary computer systems. I agree with the Microsoft proposition that the refusal of US agencies to publicise vulnerabilities can be compared with the US armed forces losing a Tomahawk Cruise missile. There are circumstances where it could be that serious.

Of course, our cyber intelligence agency, Australian Signals Directorate, may also be using vulnerabilities in software that Australian citizens rely on. So we need to ask the government about its policy in this regard. I doubt we will stop that practice in the current climate of global cyber escalation. But, in the medium term, Australia must commit to new "highly secure" systems instead of using inherently vulnerable software and machines.

We must also commit to diplomatic agreement on the disclosure of vulnerabilities in commercially available systems. Countries with high levels of pirated software, like China and Russia, are vulnerable because patches sent by Microsoft to repair vulnerabilities only go to registered IP addresses with a licensed copy of the software. If a user has installed an unlicensed pirated version, they never get the patches.

The Australian government needs to have a more mature conversation with its citizens about what is really going on in cyber space. So far we have not had it, even though the Turnbull government deserves credit for starting down that path with its cyber security strategy.

Monique Mann, lecturer, School of Justice, Faculty of Law, Crime and Justice Research Centre, Queensland University of Technology

In an escalating cryptowar with widespread uptake of end-to-end encryption, governments contend that they cannot always disclose cyber security vulnerabilities. This is because they use zero-day vulnerabilities to spy.

But state-sponsored programs of cyber warfare go beyond stockpiling vulnerabilities. Countries are actively developing digital weapons to hack into, infect, monitor and disrupt computer systems.

The Vault 7 disclosures, published by WikiLeaks in March, revealed both the extent of the Central Intelligence Agency's hacking capabilities and its inability to keep them secure. An arsenal of malware, viruses, trojans and zero day exploits was taken and leaked.

Now Wikileaks says it wants to work with technology companies to address vulnerabilities and disarm these digital weapons. Yet, these could also have been sold or used for sinister purposes.

Digital weapons can fall into the wrong hands. The consequences, like "WannaCrypt", can be disastrous.

Governments should be promoting cyber security rather than undermining it. Failing to disclose and address vulnerabilities weakens cybersecurity. There should also be limits on the development and deployment of digital weapons.

It is time for a digital Geneva Convention to protect the internet: the critical infrastructure and the citizens who depend on it.

Robert Merkel, lecturer in software engineering, Monash University

I've argued previously that Western governments should be far more careful with their use and distribution of stockpiled vulnerabilities in commercial software. But I wouldn't hold my breath for it to happen.

For better or worse, intelligence agencies seem to have persuaded governments that the intelligence they gain from exploiting such vulnerabilities outweighs the risks when those exploits leak. Whether they are right is something only historians will be able to answer, given the decades it will take for contemporary intelligence operations to be declassified.

Even if Western intelligence agencies were required to cease stockpiling vulnerabilities and instead report them to vendors, their counterparts in Russian and Chinese intelligence agencies seem unlikely to follow suit. They face little domestic political pressure to behave ethically.

Indeed, while the vulnerability used by the "WannaCrypt" ransomware is thought to have come originally from the National Security Agency, the public disclosure of the vulnerabilities is believed by some US officials to have been the work of the Russian intelligence services.

Russian IT systems were among the most heavily affected by "WannaCrypt". If the release was in fact the work of Russian intelligence, the blowback, in terms of inconvenience and expense for Russian companies and other branches of the Russian government, has been substantial. It was also foreseeable – and they did it anyway.

While Microsoft's call for governments to think harder about the real-world costs of their espionage techniques is admirable, it's hard to imagine it actually happening any time soon.

Monique Mann is a director of the Australian Privacy Foundation. While at the Australian Institute of Criminology, she consulted for the Australian Criminal Intelligence Commission on information systems and cybercrime. The views expressed here are those of the author and do not represent the views of any Commonwealth agency.

Greg Austin and Robert Merkel do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.
 "It is time for a digital Geneva Convention to protect the internet." Greg Austin, Professor, Australian Centre for Cyber Security, UNSW Monique Mann, Lecturer, School of Justice, Faculty of Law, Crime and Justice Research Centre, Queensland University of Technology Robert Merkel, Lecturer in Software Engineering, Monash University Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/67971 2016-11-10T01:19:05Z 2016-11-10T01:19:05Z Sexual assault enters virtual reality
Although various forms of online sexual harassment have been with us since the dawn of the internet, recent news suggests that it's moving into another dimension – the third, to be precise. Gropers are now finding a way to target women through the fully immersive headsets of virtual reality.

Writer Jordan Belamire recently wrote of her experience of virtual sexual assault. The man's disembodied hands, in the "QuiVr" virtual reality archery game, simulated constant groping of Belamire's virtual body – specifically, rubbing at her avatar's chest – and chased her through the game world, heedless to her cries of "Stop!" over the game's voice chat.

Some of the response – not least from the game's developers – was encouraging. But the internet's id manifested itself in the comments on stories about the incident, heaping imprecations, slander and abuse against Belamire. If we analyze the content of these comments, we gain insight into why these assaults – and online harassment more broadly – are occurring, and what might be done to stop them.

We'll have to grapple with some of the most toxic parts of our communities, and find new ways of creating and enforcing social norms in all the virtual worlds we're creating. As a scholar of online harassment, I know that most fundamentally, we must address the false belief that online harm isn't real, because the internet itself isn't real. When human beings are involved and interacting with each other, it's very real indeed. And in VR, it's even more so.

Into the pit of online comments

At the bottom of an emotional article written by QuiVR's developers, apologizing for what happened to Belamire and promising reform, is the following comment: "You weren't a victim of anything. The VR community has just become a victim of the outrage brigade."

Another commenter adds: "here's some advice for you. TURN OFF THE F—ING GAME YOU STUPID B–CH!"

A third writes, "I gotta say, you don't have a frigging clue what sexual assault is if THIS is what you consider sexual assault."

Several others, meanwhile, noted that Belamire writes romance novels and suggested she should "be above" the abuse, or claimed that she's just seeking publicity. "She writes an adult lesbian romance novel and feels harassed by digital gloves," mocks one commenter. T'was ever thus: If a woman evinces any sexual sensibility whatsoever, she must give blanket consent to any and all sexual contact.

What's virtual, and what's reality?

But by far the overriding theme of the angry comments is that they accused Belamire of making a mountain out of a molehill because it was an online experience. These were "floating hands" in a "virtual world" that she could easily turn off, or just "take off her headset" to escape from.

These outraged players never seem to ask why men do not worry about encountering hands-y people with boundary issues when they play games, or why such people should determine who plays and who doesn't. Yes, Belamire chose to play the game, but that doesn't mean she signed up to be sexually assaulted.

These notions illustrate the core mentality of both the abuser and their legions of apologists in the world's comment sections: What happens online is not real, therefore it's all okay.

It's not serious, except when it is

In this abuser-apologist world, people who complain about harassment are at fault themselves, and at times demonized as the actual problem. It's an inherently contradictory idea: The "games aren't real" argument doesn't seem to dissuade angry commenters from taking Belamire's complaints personally.

"Games are supposed to be a place to mentally get entirely away from this world, these rules, with a character in another one," laments one commenter, arguing that anti-harassment efforts will interfere with his escapism. "Feminists basically want it to be a crime for men to even approach a woman in the street, and now they want to do the same in virtual reality?" says another.

Often, in a single comment, someone yells at Belamire for complaining about an "unreal" groping and then caterwauls about some forthcoming Orwellian regime in gaming. One commenter actually tells Belamire to turn off the game, right before likening the idea of tracking repeat offenders to the Third Reich. (One wonders why he doesn't turn off his computer for a while, if her story so offends him.)

 <img alt="" src="https://images.theconversation.com/files/144284/original/image-20161102-27215-8oeuhp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/144284/original/image-20161102-27215-8oeuhp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=372&fit=crop&dpr=1 600w, https://images.theconversation.com/files/144284/original/image-20161102-27215-8oeuhp.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=372&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/144284/original/image-20161102-27215-8oeuhp.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=372&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/144284/original/image-20161102-27215-8oeuhp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=468&fit=crop&dpr=1 754w, https://images.theconversation.com/files/144284/original/image-20161102-27215-8oeuhp.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=468&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/144284/original/image-20161102-27215-8oeuhp.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=468&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Mobius strip: a piece of paper with only one side. David Benbennick, CC BY-SA

He's articulating a Mobius strip of thought, folding two contradictory notions into a single idea: The offending action wasn't real and should be ignored, but any remedy would be real enough that we have to worry about the impending Nazification of our games and get very, very angry about it.

Online experiences are real ones

Video games are not just unreal playthings. The mediating interface of a game does not make abusive behavior between two or more real people any less abusive. Slurs are still slurs; unwanted sexual advances are still both unwanted and sexual. The addition of computer graphics, a game controller, or an unfashionable headset does not render human interaction unreal.

 <img alt="" src="https://images.theconversation.com/files/144285/original/image-20161102-27212-1b1pkkg.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/144285/original/image-20161102-27212-1b1pkkg.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=313&fit=crop&dpr=1 600w, https://images.theconversation.com/files/144285/original/image-20161102-27212-1b1pkkg.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=313&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/144285/original/image-20161102-27212-1b1pkkg.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=313&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/144285/original/image-20161102-27212-1b1pkkg.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=394&fit=crop&dpr=1 754w, https://images.theconversation.com/files/144285/original/image-20161102-27212-1b1pkkg.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=394&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/144285/original/image-20161102-27212-1b1pkkg.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=394&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 This interaction experience is as real as friends sitting on an actual couch together. HyacintheLuynes, CC BY-SA

In VR specifically we confront another contradiction. The entire selling point of VR is its unparalleled simulation of reality. It presents a physical, embodied experience that surrounds you, fills your senses, and is tactile in ways unlike any other video game.

This has been a holy grail of game design since the dawn of the industry: fooling a player's body into feeling like it's really in the game world. We should not be surprised if a simulated sexual assault, then, feels real enough in all the ways that matter.

This point was addressed head-on in a discussion about designing safer VR games at the Game Connect Asia Pacific conference in Melbourne in late October. One VR developer, Justine Colla, cofounder of the Alta VR studio, argued that the "visceral" nature of immersion in VR can give abusers more power. "Users retain memories in VR as if they experienced them in real life," she said.

This, she said, combines with an inability for players to physically push away offender to ensure that attackers have "all the power with none of the consequences." Assaults feel real, and the target has no way to fight back.

We cannot have it both ways, touting VR's realness while casting aspersions on people who complain of abuse in VR. Trying to do so would be laughable if the consequences weren't so dire. Virtual reality is virtually real.

Game developers respond

Fortunately, QuiVr's developers are modeling good behavior for the whole industry. They wrote a pointed article that explains why they not only believe Belamire but take personal responsibility for what happened to her. They also explain what steps they're taking to improve the experience. Foremost among them is a move they call a "power gesture:"

putting your hands together, pulling both triggers, and pulling them apart as if you are creating a force field. No matter how you activate it, the effect is instantaneous and obvious – a ripple of force expands from you, dissolving any nearby player from view, at least from your perspective, and giving you a safety zone of personal space.

This is a bold step in the right direction. It not only provides an instant reprieve for harassment victims but allows them to actually embody their strength through a gesture that feels empowering. It's an elegant solution, but this one solution may not work for every VR environment. We need something more: a change of mindset.

As games are being developed, quality assurance testers often try to "break" the game, finding ways that inventive players might unexpectedly use game systems that the developers did not intend. Testers should include in this ongoing process efforts to identify ways players could harm each other. Developers should deal with them the same way they do other problems in the game's design. It's not "just a game" anymore.

Katherine Cross does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 Underlying online harassment is the false idea that events that happen on internet aren't real. But whenever people are interacting, it's all real. Katherine Cross, Ph.D. Student in Sociology, City University of New York Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/67947 2016-10-31T15:24:43Z 2016-10-31T15:24:43Z From dinosaurs to crime scenes – how our new footprint software can bring the past to life

New software could help to reveal the story behind the imprint. Shutetrstock

A fossil footprint is one of the most evocative insights into the past. It can tell you not only about presence, but also about the biomechanics of the track-maker. We have studied ancient footprints from around the world for more than a decade – and perhaps most notably we were part of the team that discovered the second oldest human footprints at Ileret in northern Kenya in 2009. They date back 1.5m years and were likely made by Homo erectus.

Over the past ten years, we have witnessed unparalleled technological advances. We used to take a large, expensive and delicate optical laser scanner into the field, encased in a shroud specially made by a sail maker. I remember with horror how it exploded on the first day of one particular field trip.

 <img alt="" src="https://images.theconversation.com/files/143914/original/image-20161031-15783-lv5q9z.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/143914/original/image-20161031-15783-lv5q9z.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=896&fit=crop&dpr=1 600w, https://images.theconversation.com/files/143914/original/image-20161031-15783-lv5q9z.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=896&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/143914/original/image-20161031-15783-lv5q9z.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=896&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/143914/original/image-20161031-15783-lv5q9z.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=1126&fit=crop&dpr=1 754w, https://images.theconversation.com/files/143914/original/image-20161031-15783-lv5q9z.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=1126&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/143914/original/image-20161031-15783-lv5q9z.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=1126&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 The world's second oldest human footprint. Author provided

It had been flown at great expense first to the Kenyan capital, Nairobi, and then on a small plane which landed on a dirt strip. But then, on its first day out, it was connected to a generator supposedly that had been repaired in a back street Nairobi shop – and the sparks flew. My colleagues had to infill the excavation and it was another six months before I was back out with a repaired scanner and a new generator.

These days, thankfully, we need nothing more than a digital camera in the field. We take 20 pictures from different angles and I have a 3D model to rival those I once created by physically scanning the footprint. And with far fewer headaches. In fact, in October, Microsoft demonstrated a 3D scanner that works on a smart phone – technology is changing fast.

Great leaps forward

Despite this, 3D models have yet to make an impact in some areas. One of these is in the analysis of footwear evidence at crime scenes. Here, traditional methods of photography and casting still prevail and footwear evidence is no longer routinely collected at many crime scenes. Yet it has real power, especially when brought into 3D – and the 21st century.

Footwear impressions provide an important source of evidence from crime scenes. They can help to determine the sequence of events and – if distinctive due to the wear patterns – can link a suspect to multiple crime scenes. The value is not only as a tool in prosecution, but crucially in intelligence gathering often around petty crime. Unfortunately, it isn't being routinely used – at least until now.

 <img alt="" src="https://images.theconversation.com/files/143915/original/image-20161031-15934-1l0wggs.png?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/143915/original/image-20161031-15934-1l0wggs.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=600&fit=crop&dpr=1 600w, https://images.theconversation.com/files/143915/original/image-20161031-15934-1l0wggs.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=600&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/143915/original/image-20161031-15934-1l0wggs.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=600&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/143915/original/image-20161031-15934-1l0wggs.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=754&fit=crop&dpr=1 754w, https://images.theconversation.com/files/143915/original/image-20161031-15934-1l0wggs.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=754&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/143915/original/image-20161031-15934-1l0wggs.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=754&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Footprint: modelled digitally. Author provided

Working with a talented team of software developers, we have now translated academic know-how and software developed for research into a freeware package that puts 3D tools into the hands of everyone. Over the past year, with funding from a Natural Environment Research Council Innovation Award and with the help of the Home Office and the National Crime Agency, we have developed DigTrace a bespoke software tool for footprint analysis. This tool is now freely available to police forces and forensic services both in the UK and overseas.

DigTrace is the first integrated freeware product that allows crime scene officers to capture 3D images of footwear impressions with nothing more than a digital camera and then to visualise, analyse and compare these traces digitally. It integrates fully with existing databases and approaches and we hope it will change the cost benefit equation of footwear analysis.

From the extraordinary to the everyday

The use of 3D modelling of this kind need not only be used in high capital crimes but can also be used to tackle the petty crime that often plagues society. Take, for example, a series of grass verges on the edges of car parks of ill repute. Using this tool, you can easily scan footprints in these areas. And if you can link the same piece of footwear to several of these disreputable areas, you know you are looking for one individual or group rather than several. The power lies in improving intelligence.

We've been developing the software for the better part of a decade. What started as a collection of tools for addressing specific research tasks, built using a variety of technologies, has now been consolidated with the help of NERC into a standalone software suite. There is some neat mathematics that underlies the various 3D transformations required. Keeping things simple, however, has been the key to building trust with end-users who are not computer scientists.

At the heart of the approach is the idea of digital photogrammetry – take a series of images, identify common pixels in each and triangulate the pixels to define their location in 3D space. The result is a 3D pixel cloud that can be scaled, transformed and surfaced. We are actively researching the enabling technology here at Bournemouth University to develop new tools from this basic premise. Creating 3D models from video and even CCTV footage is in our sights. Our aim is to provide tools that make society safer, not just from high profile-crime, but that which affects everyday lives.

While DigTrace may help fight crime, it is also there for geologists and archaeologists to help them study dinosaur or ancient human tracks. The use of 3D data is a brave new world from printing in 3D to developing tools that visualise and analyse such data – and it is helping to bring the recent, and most distant, past to life.

Matthew Robert Bennett receives funding from the UK, Natural Environment Research Council.

Marcin Budka receives funding from the UK Natural Environment Research Council.
 How to solve mysteries with an accessible computer program. Matthew Robert Bennett, Professor of Environmental and Geographical Sciences, Bournemouth University Marcin Budka, Principal Academic in Data Science, Bournemouth University Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/66370 2016-10-04T04:19:37Z 2016-10-04T04:19:37Z Has auto-translation software finally stopped being so useless?

Will our digital phrasebook finally be able to handle more than just simple snippets? cybrain/Shutterstock.com

If you've ever put a phrase into an online translator and then laughed at the garbled results, your fun might be coming to an end. Google claimed last week to have eradicated 80% of the errors made by its translation software.

Translating text from one language into another is a simple proposition but a fiendishly complicated problem. Of course, it has traditionally been a job for human translators, but over the past half-century or so automated machine translation has become an important sub-field of artificial intelligence.

Auto-translation systems, including Google Translate, were already pretty good at translating single words or even short sentences. But people are well aware of the limitations of this technology when it comes to translating longer, more complex passages, and hence are cautious about relying on them for important tasks.

 <img alt="" src="https://images.theconversation.com/files/140222/original/image-20161004-27269-g84dxs.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/140222/original/image-20161004-27269-g84dxs.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=167&fit=crop&dpr=1 600w, https://images.theconversation.com/files/140222/original/image-20161004-27269-g84dxs.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=167&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/140222/original/image-20161004-27269-g84dxs.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=167&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/140222/original/image-20161004-27269-g84dxs.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=210&fit=crop&dpr=1 754w, https://images.theconversation.com/files/140222/original/image-20161004-27269-g84dxs.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=210&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/140222/original/image-20161004-27269-g84dxs.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=210&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Google Translate: good for short phrases, and about to get better at long ones too. Google Translate

Machine translation systems work by analysing an input text from one language and creating an equivalent representation in the target language. This can be as simple as word substitution, but such a system cannot guarantee high-quality output. That's because it is difficult to program a computer to understand the text as humans do, and then to translate it to another language while keeping the meaning and semantics intact.

This is partly because different languages originated at different times and have different evolutionary histories. This gives each language a set of unique subtleties that can be difficult for humans to learn, let alone a computer attempting to go from simple word substitutions to intelligible sentences.

Not all words in one language have a direct equivalent in another, so several words might be needed to convey the meaning of a single word in the original language (a classic example being the German schadenfreude). The grammatical structure can also be different. Not all languages use the same subject-verb-object format found in most English phrases.

Auto-translation software can also struggle with words that have different definitions depending on their context. This means that the program will need to analyse the entire sentence or paragraph as a whole to deduce what it means.

Clearly, understanding the broader meaning is critical for producing useful translations. But teaching a machine to derive the subtle meanings of language is no easy task, as the often comical results of older translation software make clear.

Human translators rely on knowledge, experience and common sense, but we don't really know precisely what is going on when the brain synthesises language. If we don't know how it really works, how do we go about teaching a computer to do it?

The machine learning approach

As described above, the real challenge lies in moving beyond individual words or short phrases to translating large pieces of text such as entire websites or novels.

At the simpler end of the spectrum the technology already does a pretty good job. If you're travelling in a foreign country you can use an augmented reality app such as Word Lens to decipher street signs in real time. Simple tourist phrases are easily conjured up using programs that have simple language rules hard-coded into their programming.

But say you want to read a novel, or browse a foreign-language website, or translate a PowerPoint presentation in real time at a conference. This needs a new approach – one that recognises and reproduces the flow and meaning of the whole.

Google's new approach involves what it calls "Neural Machine Translation (NMT)". It relies on an artificial neural network which attempts to simulate the human brain's approach to translation. Crucially, it can "learn" as it becomes more experienced, gradually improving its accuracy as it translates more text.

As NMT algorithms do not rely on human logic (that is, hand-coded algorithms), they can modify themselves as they go. In theory, they should be able to find ways to translate text that the human coders might not have conceived when designing the system.

The future

Reaching 100% accuracy will not be easy, but we can expect tech companies like Google to devote a lot of energy to trying. It is likely to be an evolutionary process, not a one-off breakthrough, and it will take huge amounts of time, data and processing power to improve the results until they are effectively flawless.

The latest development nevertheless represents a huge step forward – finally propelling machine translation to a standard that is acceptable for most tasks. For now, if you need 100% accuracy you will still need to hire a human translator, but with every day that passes computers are honing their skills.

This raises the question of how seamlessly auto-translation will become a part of our everyday experience in the future. In time, we may browse websites that automatically open up in our preferred language based on our profile, or listen to lectures in whatever language we choose, or engage in real-time discussions with people speaking a different language without having a human translator listening in. The opportunities are limitless.

If we can improve the accuracy to almost 100%, language barriers will begin to disappear. We would belong to one global village, where anyone can share their knowledge and expertise with anyone else.

In a world where computers are multilingual, will anyone need to bother learning another language? It's too early to say. But just as mapping software has all but eradicated the feeling of being lost in a strange place, we're heading for a world where you can be anywhere on the planet and never be lost for words.

Vidyasagar Potdar does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 Auto-translation software has been pretty frustrating to use. But news of vast improvements to Google's translation software raises the prospect that websites will soon be browsable in any language. Vidyasagar Potdar, Senior Research Fellow, School of Information Systems, Curtin University Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/56967 2016-08-09T00:23:02Z 2016-08-09T00:23:02Z Why save a computer virus?

Hex code from the Blaster worm reveals the potential motivations of the worm's creator. Ward Moerman

On average, 82,000 new malware threats are created each day. These include all sorts of malicious software – like computer viruses, computer worms and ransomware. Some are pranks or minor annoyances; others seek to pilfer data or extort money. Malware has been used to steal sensitive emails from political parties, or even as weapons directed at civilian, government or military targets.

Malware has been called a "pervasive feature of the internet" by the head of the British Library's digital preservation team. A multi-billion-dollar industry exists to control its spread. Though it is part of the texture of digital life, libraries, museums and archives tasked with preserving the past are not saving malware for future generations. They are likely (and rightly) afraid: It can destroy data, which librarians and archivists are bound to protect.

Without long-term preservation, though, viruses and worms themselves will be difficult to analyze, research or write about. Cultural heritage institutions should seek to archive malware in ways that render it safely accessible to researchers and historians.

Our research has addressed two separate but connected concerns: First, how would an institution create a malware archive? And second, how should archivists, who have already encountered malware-infected hard drives and disks in their collections, handle these items? If an archivist chooses to remove the infection, what might we lose? And if the malware is not removed, how can the infected data be stored and accessed safely?

<audio preload="metadata" controls="controls" data-duration="2978" data-image="" data-title="An interview with Jonathan Farbowitz about preserving malware, from All Sides With Ann Fisher/WOSU." data-size="23830692" data-source="All Sides With Ann Fisher/WOSU" data-source-url="http://radio.wosu.org/post/tech-tuesday-archiving-viruses-smart-home-renters-and-tattoo-design-iphone-photography" data-license="All rights reserved" data-license-url=""> <source src="https://cdn.theconversation.com/audio/472/081616b.mp3" type="audio/mpeg"> </source></audio>
 An interview with Jonathan Farbowitz about preserving malware, from All Sides With Ann Fisher/WOSU. All Sides With Ann Fisher/WOSU22.7 MB (download)

Studying e-infections

A recent history of malware appears in the new film "Zero Days," a documentary about the Stuxnet worm that destroyed Iranian nuclear equipment. "Zero Days" reveals that researchers not only examined Stuxnet's code to discover how it worked, but also looked at current geopolitics to determine why it was created.

Without efforts to save code and other items that add context, researchers may lose the ability to conduct similar analysis in the future – and to check the work of the past. Information related to historical malware can disappear from the internet. For example, anti-virus firms have removed publicly accessible information about malware from their websites.

In 1988, Robert Morris, a Cornell graduate student, released the first worm to draw widespread attention. Morris' motivations remain unclear, but some suspect curiosity, hubris or the desire to demonstrate network vulnerabilities.

 <img alt="" src="https://images.theconversation.com/files/133000/original/image-20160803-12211-xf2iq8.png?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/133000/original/image-20160803-12211-xf2iq8.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=296&fit=crop&dpr=1 600w, https://images.theconversation.com/files/133000/original/image-20160803-12211-xf2iq8.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=296&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/133000/original/image-20160803-12211-xf2iq8.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=296&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/133000/original/image-20160803-12211-xf2iq8.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=373&fit=crop&dpr=1 754w, https://images.theconversation.com/files/133000/original/image-20160803-12211-xf2iq8.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=373&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/133000/original/image-20160803-12211-xf2iq8.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=373&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 The screen of a computer infected with Worms Against Nuclear Killers (WANK). WANK targeted computers at NASA to protest the launch of the Galileo Space Probe. Author-provided screenshot, CC BY-SA

Since then, malware has been used for many purposes:

As a political statement, such as the WANK worm, released to express political dissent. Today the hacktivist movement includes groups like Anonymous (which has carried out online actions in support of Black Lives Matter and the Occupy movement) and Cult of the Dead Cow (which attempted to interfere with China's internet censorship technologies).

To disrupt the rhythms of everyday life, such as ILOVEYOU, which in 2000 infected more than 50 million computers over 10 days. It cost an estimated US$5.5 billion to $8.7 billion in lost time and data recovery expenses. It prompted the Pentagon, the CIA and many corporations to temporarily shut down their email systems.

As artistic expression, such as the Rebel! virus, part of an Italian art installation. Since then, artists like Eva and Franco Mattes (with hacker group Epidemic) and James Hoff have created malware or used malware code in their work.

To affect world conflicts, such as the 2015 Ukrainian conflict, when malware took down part of Ukraine's electric grid. The Egyptian government monitored political dissidents' communications with spyware during the 2011 Arab Spring.

As internet connectivity becomes a feature of home heating and security devices, medical devices and even baby monitors, security experts worry about increasing numbers of malware attacks on this equipment.

An important resource for research

As digital culture scholar Jussi Parikka wrote recently, malware reflects the society in which it arose. In the 1990s, for example, not only were several computer viruses named for AIDS, but computer security professionals used "safe sex" analogies to explain how to keep electronics virus-free.

 <img alt="" src="https://images.theconversation.com/files/130100/original/image-20160711-9277-116lsqy.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/130100/original/image-20160711-9277-116lsqy.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=375&fit=crop&dpr=1 600w, https://images.theconversation.com/files/130100/original/image-20160711-9277-116lsqy.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=375&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/130100/original/image-20160711-9277-116lsqy.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=375&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/130100/original/image-20160711-9277-116lsqy.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=471&fit=crop&dpr=1 754w, https://images.theconversation.com/files/130100/original/image-20160711-9277-116lsqy.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=471&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/130100/original/image-20160711-9277-116lsqy.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=471&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Screenshot of a computer infected with the AIDS virus (released circa 1990). Trlkly/Wikipedia, CC BY-NC-ND

The interactions between malware, culture and history should not be lost. Just as historians have examined FBI wiretaps on Martin Luther King Jr.'s phone, researchers will want to know if a prominent activist had spyware on their computer and who likely installed it. Dissecting the spyware itself may prove crucial in understanding how the surveillance worked and its ultimate goal.

Literary scholars will want to know if a virus damaged an early draft of an important novel. Malware on a corporate executive's computer could be evidence of espionage or a protest against the company.

Who is saving malware?

Computer security companies and security organizations hold the most comprehensive and well-organized collections of malware. However, these collections are not easy for researchers or the general public to browse – and were never designed for that use. And these organizations are not required to preserve their collections long-term. Their primary mission is to fight current malware threats. No organization has discarded unique samples yet. But what if the company with the best malware collection suddenly shuttered its doors?

Cultural heritage institutions, on the other hand, exist to keep objects for centuries, sometimes millennia, and make them broadly accessible. They can preserve a historical sample of malware for the future and ensure patrons can easily find and view the items they want to learn about.

Some efforts have already been taken to exhibit malware, including the Malware Museum, Daniel White's YouTube channel and exhibits at swissnex San Francisco and Frankfurt's Museum of Applied Arts. While these endeavors are commendable, they were mostly undertaken as side projects by individuals with other primary responsibilities. And they have displayed only a small number of viruses or worms and focused on their visual effects. None have committed to systematically collecting items that would give the malware further context.

Challenges of preserving malware

Saving and analyzing software often designed to wreak havoc – deleting files or launching internet-based attacks – presents unique challenges and requires complex solutions.

Even with special precautions (like simulated networks that fool malware into thinking it's online), studying malware will become increasingly difficult. Like all software, malware eventually becomes obsolete: One day, no contemporary computers will be able to demonstrate how these programs functioned without emulation or virtualization.

Institutions have not yet begun to face the question of which malware to preserve. Should viruses and worms that infected massive numbers of computers be the primary goal for preservationists? What about malware displaying novel programming techniques, or released in conjunction with offline protest? How can they even begin to evaluate candidates for preservation, given the massive amounts of malware constantly being created?

The work ahead

Working collaboratively, archivists can learn how to appraise the historical value of malware, assess and mitigate the risks of storing it, and document its existence or potential removal.

Any malware collection should aim beyond saving code. It should capture the process of development (which will be difficult as most malware creators prefer to remain anonymous), as well as the sometimes short-lived effects of the infection. For example, archivists can collect oral histories of computer security professionals and, if possible, malware coders. They can also save websites, emails and log files pertaining to an infection.

Already, scholars like Jussi Parikka, Finn Brunton, Eugene Thacker and Alexander Galloway have explored the importance of malware in contemporary communications. Archives, museums and libraries can support future research with well-curated collections.

Highlighting a seldom recognized aspect of computing history, a malware archive could spark the creation of new cultural histories. By preserving malware, we can understand how we got from the Morris worm in 1988 to Stuxnet to July's Democratic National Committee email hack – and beyond.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.
 How can archivists properly preserve computer programs often written specifically to destroy data? Howard Besser, Professor of Cinema Studies, New York University Jonathan Farbowitz, Graduate Student in Moving Image Archiving and Preservation, New York University Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/61812 2016-07-17T20:08:23Z 2016-07-17T20:08:23Z How a little mathematics can help create some beautiful music

Incredible rhythms when mathematics meets music. Shutterstock/carlos castilla

Since the time of Pythagoras around 500 BCE, music and mathematics have had an intimate and mutually supportive relationship.

Mathematics has been used to tune musical scales, to design musical instruments, to understand musical form and to generate novel music. But what can mathematics say about one of the most common features of contemporary music – rhythmic loops?

Repeated rhythmic loops are an essential component of most electronic dance music and hip-hop, and also play an important role in rock, jazz, Latin and non-Western music.

Now, two mathematical models of rhythmic loops – made in a free software application called XronoMorph – can be used to generate exciting new musical structures that would otherwise be hard to compose or perform.

XronoMorph: An Introduction.

Rhythmic loops: Circles and polygons

A natural geometrical characterisation of a periodic structure, such as a rhythmic loop, is as a circular arrangement of points. You can travel clockwise around a circle but inevitably you come back to where you started.

A common feature of rhythmic loops is that they are multilevel. For example, in Latin percussion, different instruments play different interlocking patterns that may or may not coincide. Such rhythms can be depicted by multiple polygons on the same circle.

A simple geometrical representation is to draw lines that make each of these independent levels into an independent polygon. In this way, a multilevel rhythm becomes a collection of inscribed polygons.

 <img alt="" src="https://images.theconversation.com/files/129603/original/image-20160706-12750-1a6ue0r.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/129603/original/image-20160706-12750-1a6ue0r.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=147&fit=crop&dpr=1 600w, https://images.theconversation.com/files/129603/original/image-20160706-12750-1a6ue0r.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=147&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/129603/original/image-20160706-12750-1a6ue0r.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=147&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/129603/original/image-20160706-12750-1a6ue0r.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=185&fit=crop&dpr=1 754w, https://images.theconversation.com/files/129603/original/image-20160706-12750-1a6ue0r.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=185&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/129603/original/image-20160706-12750-1a6ue0r.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=185&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Clave (top) + Conga (bottom) rhythm in score notation. Andrew Milne, Author provided

 <img alt="" src="https://images.theconversation.com/files/131348/original/image-20160721-8663-1ez9rxp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/131348/original/image-20160721-8663-1ez9rxp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=602&fit=crop&dpr=1 600w, https://images.theconversation.com/files/131348/original/image-20160721-8663-1ez9rxp.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=602&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/131348/original/image-20160721-8663-1ez9rxp.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=602&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/131348/original/image-20160721-8663-1ez9rxp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=756&fit=crop&dpr=1 754w, https://images.theconversation.com/files/131348/original/image-20160721-8663-1ez9rxp.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=756&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/131348/original/image-20160721-8663-1ez9rxp.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=756&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Clave (red) + Conga (blue) rhythm as polygons. Andrew Milne, Author provided

But which polygons?

There are more than 17 trillion different rhythms, and that is only counting rhythms with three levels where every beat occurs at one of 16 distinct time locations (16 being a very common temporal subdivision in music).

But, realistically, only a small proportion of these are of musical interest. The trick is to find them.

Two mathematical principles – well-formedness and perfect balance – allow us to easily navigate two distinct rhythmic sub-spaces that are of musical interest, but hard to explore with traditional computational tools or notation.

Well-formed polygons

Well-formedness elegantly generalises three properties commonly found in real-world multilevel rhythms:

each rhythmic level comprises only a small number of distinct beat lengths, often only one or two;

each level's beats are fairly evenly spaced in time – there aren't sudden clusters of events followed by long gaps; and

the rhythmic levels are hierarchical – there is a slow and metrically dominant level; above this is a faster and weaker level that splits the previous level's beats; above this is an even faster and weaker level that splits the previous level's beats; and so on.

Every level of a well-formed rhythm has two beat lengths: a long beat and a short beat. The length of a given beat is the time between its onset and the onset of the following beat.

A multilevel well-formed rhythm can then be fully defined by three numerical parameters: the numbers of long and short beats in the lowest level rhythm, and the ratio of the sizes of its long and short beats.

From these three numbers, an entire rhythmic hierarchy can be calculated, such that each level has no more than two beat lengths, each level arranges these beat lengths to make them as evenly spaced as possible and each successive level in the hierarchy is created by splitting the long beats of the level below.

Using XronoMorph, the above three parameters can be freely manipulated. The rhythmic hierarchy emerging from them often has great aesthetic appeal. Every level is related to every other level and is also intrinsically well-formed. Together, they create a somewhat self-similar and interwoven structure reminiscent of fractals.

A demonstration of how a well-formed hierarchy is built, and the effect of the lowest level's beat-size ratio.

Perfectly balanced polygons

Perfect balance is a mathematical principle that can generalise polyrhythms, a type of rhythm commonly used in sub-Saharan African music.

Unlike well-formed rhythms and most Western rhythms, polyrhythms are not hierarchical. They are more like an alliance of different rhythmic levels, each of equal status.

In a polyrhythm, two or more levels, each comprising evenly spaced beats, are superimposed.

Geometrically, they are combinations of regular polygons like equilateral triangles, squares, regular pentagons, and so forth. In real-world polyrhythms, beats from all levels typically coincide at a single location in the period.

A standard 2:3:6 polyrhythm. For clarity, the hexagonal level is not shown.

A standard 3:4:12 polyrhythm. For clarity, the 12-gon level is not shown.

A standard 3:5:15 polyrhythm. For clarity, the 15-gon level is not shown.

A notable feature of these polyrhythms is that the average position, or centre of gravity, of the circularly arranged beats is precisely at the centre of the circle. This property is defined as perfect balance.

All regular polygons are perfectly balanced, so any combination of regular polygons is also perfectly balanced, and this is true regardless of the individual rotations of the polygons.

This allows an immediate generalisation of standard polyrhythms. The regular polygons can be independently rotated (time-shifted) so they never coincide. This is a musical feature that is, as far as we know, rarely exploited but which produces exciting rhythmic grooves.

Perfectly balanced music constructed with independently rotated regular polygons: 2 digons, 3 squares, 1 heptagon. The polygons have been independently rotated so – unlike a standard polyrhythm – there is no single location at which they all coincide.

But perfect balance opens up yet another fascinating generalisation of standard polyrhythms. There are certain perfectly balanced shapes – irregular elemental polygons – that are not produced by a simple combination of regular polygons.

 <img alt="" src="https://images.theconversation.com/files/129521/original/image-20160706-12727-7puub2.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/129521/original/image-20160706-12727-7puub2.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=480&fit=crop&dpr=1 600w, https://images.theconversation.com/files/129521/original/image-20160706-12727-7puub2.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=480&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/129521/original/image-20160706-12727-7puub2.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=480&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/129521/original/image-20160706-12727-7puub2.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=603&fit=crop&dpr=1 754w, https://images.theconversation.com/files/129521/original/image-20160706-12727-7puub2.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=603&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/129521/original/image-20160706-12727-7puub2.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=603&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 An irregular elemental polygon: 1 triangle + 1 pentagon - 1 digon. Only the labelled vertices are sounded. Andrew Milne, Author provided

 <img alt="" src="https://images.theconversation.com/files/129522/original/image-20160706-12722-q3apcs.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/129522/original/image-20160706-12722-q3apcs.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=479&fit=crop&dpr=1 600w, https://images.theconversation.com/files/129522/original/image-20160706-12722-q3apcs.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=479&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/129522/original/image-20160706-12722-q3apcs.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=479&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/129522/original/image-20160706-12722-q3apcs.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=602&fit=crop&dpr=1 754w, https://images.theconversation.com/files/129522/original/image-20160706-12722-q3apcs.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=602&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/129522/original/image-20160706-12722-q3apcs.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=602&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 An irregular elemental polygon: 2 digons + 3 pentagons - 3 digons - 2 triangles. Only the labelled vertices are sounded. Andrew Milne, Author provided

Intriguingly, such polygons are constructed by summing differently rotated positively-weighted and negatively-weighted regular polygons. When a positively-weighted vertex and a negatively-weighted vertex coincide they cancel out.

Legal patterns are produced when no negative weights are left behind. So, although we never hear these negative beats directly, they have a ghostly impact by cancelling out some of the positive beats.

In XronoMorph, a wide choice of regular and irregular elemental polygons can be combined and independently rotated, creating a fascinating subspace of generalised polyrhythms.

Perfectly balanced music constructed with regular and irregular elemental polygons.

XronoMorph

We developed the rhythmic loop generator XronoMorph to demonstrate these principles, hoping it might inspire musicians and music enthusiasts to create novel and interesting rhythms that would be hard to play manually or to otherwise compose.

Simple and attractive software interfaces can also transform a need for expert knowledge into a willingness to intuitively interrogate the interface. In this way, we also hope it will encourage musical engagement, with potential application in music education by allowing visual exploration of complex rhythmic patterns.

Early reaction from musicians and composers has been highly enthusiastic with comments such as "This is an inspired design – truly musical", "the most interesting and inventive new app around" and "this really helps me better understand and create beats".

This lends support to the long-held notion that there is a profound connection between mathematics and music, and that a little mathematics can help create some beautiful music.

 <img alt="" src="https://images.theconversation.com/files/129525/original/image-20160706-12708-zkml1o.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/129525/original/image-20160706-12708-zkml1o.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=371&fit=crop&dpr=1 600w, https://images.theconversation.com/files/129525/original/image-20160706-12708-zkml1o.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=371&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/129525/original/image-20160706-12708-zkml1o.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=371&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/129525/original/image-20160706-12708-zkml1o.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=466&fit=crop&dpr=1 754w, https://images.theconversation.com/files/129525/original/image-20160706-12708-zkml1o.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=466&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/129525/original/image-20160706-12708-zkml1o.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=466&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 XronoMorph screenshot. Andrew Milne, Author provided

Andrew J. Milne is affiliated with Dynamic Tonality, a loosely organised collective of researchers and fellow travellers who host a website and build software for facilitating interaction with experimental music. The software application described in this article is free, but users can make a donation to support the costs of hosting the website and to support further software development.

Steffen A. Herff does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
 Mathematics can help musicians and music enthusiasts create rhythms that would be hard to play manually or to otherwise compose. Andrew J. Milne, Postdoctoral Research Fellow in Music Cognition and Computation, Western Sydney University Steffen A. Herff, PhD candidate, Memory and Music Perception, Western Sydney University Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/56708 2016-05-10T08:58:23Z 2016-05-10T08:58:23Z Why we need to tackle the growing mountain of 'digital waste'

Video streaming services such as iPlayer need 'green' software, too. dantaylor, CC BY

We are very aware of waste in our lives today, from the culture of recycling to the email signatures that urge us not to print them off. But as more and more aspects of life become reliant on digital technology, have we stopped to consider the new potential avenues of waste that are being generated? It's not just about the energy and resources used by our devices – the services we run over the cloud can generate "digital waste" of their own.

Current approaches to reducing energy use focus on improving the hardware: better datacentre energy management, improved electronics that provide more processing power for less energy, and compression techniques that mean images, videos and other files use less bandwidth as they are transmitted across networks. Our research, rather than focusing on making individual system components more efficient, seeks to understand the impact of any particular digital service – one delivered via a website or through the internet – and re-designing the software involved to make better, more efficient use of the technology that supports it.

We also examine what aspects of a digital service actually provide value to the end user, as establishing where resources and effort are wasted – digital waste – reveals what can be cut out. For example, MP3 audio compression works by removing frequencies that are inaudible or less audible to the human ear – shrinking the size of the file for minimal loss of audible quality.

This is no small task. Estimates have put the technology sector's global carbon footprint at roughly 2% of worldwide emissions – almost as much as that generated by aviation. But there is a big difference: IT is a more pervasive, and in some ways more democratic, technology. Perhaps 6% or so of the world's population will fly in a given year, while around 40% have access to the internet at home. More than a billion people have Facebook accounts. Digital technology and the online services it provides are used by far more of us, and far more often.

It's true that the IT industry has made significant efficiency gains over the years, far beyond those achieved by most other sectors: for the same amount of energy, computers can carry out about 100 times as much work as ten years ago. But devices are cheaper, more powerful and more convenient than ever and they're used by more of us, more of the time, for more services that are richer in content such as video streaming. And this means that overall energy consumption has risen, not fallen.

Some companies design their products and services with the environment in mind, whether that's soap powder or a smartphone. This design for environment approach often incorporates a life-cycle assessment, which adds up the overall impact of a product – from resource extraction, to manufacture, use and final disposal – to get a complete picture of its environmental footprint. However, this approach is rare among businesses providing online digital services, although some make significant efforts to reduce the direct impact of their operations – Google's datacentres harness renewable energy, for example.

 <img alt="" src="https://images.theconversation.com/files/121871/original/image-20160510-20746-fmgijf.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/121871/original/image-20160510-20746-fmgijf.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/121871/original/image-20160510-20746-fmgijf.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/121871/original/image-20160510-20746-fmgijf.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/121871/original/image-20160510-20746-fmgijf.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/121871/original/image-20160510-20746-fmgijf.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/121871/original/image-20160510-20746-fmgijf.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 It may seem like data costs nothing, but it how software is coded affects the energy electronics consumes. 3dkombinat/shutterstock.com

We were asked to understand the full life-cycle cost of a digital operation by Guardian News and Media, who wanted to include this in their annual sustainability report. We examined the impact of the computers in the datacentres, the networking equipment and transmission network, the mobile phone system, and the manufacture and running costs of the smartphones, laptops and other devices through which users receive the services the company provides.

In each case, we had to determine, through a combination of monitoring and calculation, what share of overall activity in each component should be allocated to the firm. As a result of this, Guardian News and Media became the first organisation to report the end-to-end carbon footprint of its digital services in its sustainability report.

But what design approaches can be used to reduce the impact of the digital services we use? It will vary. For a web search service such as Google, for example, most of the energy will be used in the datacentre, with only a small amount transmitted through the network. So the approach to design should focus on making the application's software algorithms running in the datacentre as efficient as possible, while designing the user interaction so that it is simple and quick and avoids wasting time (and therefore energy) on smartphones or laptops.

On the other hand, a video streaming service such as BBC iPlayer or YouTube requires less work in the datacentre but uses the network and end-user's device far more intensively. The environmental design approach here should involve a different strategy: make it easier for users to preview videos so they can avoid downloading content they don't want; seek to avoid digital waste that stems from sending resource-intensive video when the user is only interested in the audio, and experiment with "nudge" approaches that provide lower resolution audio/video as the default.

With the explosive growth of digital services and the infrastructure needed to support them it's essential that we take their environmental impact seriously and strive to reduce it wherever possible. This means designing the software foundations of the digital services we use with the environment in mind.

Chris Preist receives funding from the UK EPSRC Digital Economy Program. He is affiliated with Forum for the Future.
 Software is eating everything in this online, digital world. We need to design code that uses as little energy as possible. Chris Preist, Reader in Sustainability and Computer Systems, University of Bristol Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/50664 2015-11-19T04:24:48Z 2015-11-19T04:24:48Z The big data challenge and how Africa can benefit

The Large Hadron Collider is playing a key role in enabling the collection of big data.  Supplied

Big data has become some sort of celebrity. Everybody talks about it, but it is not clear what it is. To unpack its relevance to society it is important to backtrack a bit to understand why and how it came to be this ubiquitous problem.

Big data is about processing large amounts of data. It is associated with multiplicities of data formats stored somewhere, say in a cloud or in distributed computing systems.

But the ability to generate data systematically outpaces the ability to store it. The amount of data is becoming so big and is produced so fast that it cannot be stored with current technologies in a cost effective way. What happens when big data becomes too big and too fast?

How fundamental science contributes to society

The big data problem is yet another example of how the methods and techniques developed by scientists to study nature have had an impact on society. The techno-economic fabric that underlies modern society would be unthinkable without these contributions.

There are numerous examples of how findings intended to probe nature ended up revolutionising life. Big data is intimately intertwined with fundamental science and continues to evolve with it.

Consider just a few examples: what would life be without electricity or electromagnetic waves? Without the fundamental studies of Maxwell, Hertz and other physicists on the nature of electromagnetism we would not have radio, television or other forms of wave mediated communication, for that matter.

Modern electronics is based on materials called semi-conductors. What would life today be without electronics? The invention of transistors and eventually of integrated circuits is based entirely on the work scientists have done by thoroughly studying semi-conductors.

Modern medicine relies on countless techniques and applications. These range from x-rays, medical imaging physics and nuclear magnetic resonance to other techniques such as radiation therapeutic and nuclear medicine physics. Modern medicine and research would be unthinkable without techniques that were initially conceived for scientific research purposes.

How the information age came about

The big data problem initially emerged as a result of the need for scientists to communicate and exchange data.

At the European laboratory CERN in 1990, internet pioneer Tim Berners-Lee suggested a browser called WorldWideWeb, leading to the first web server. The internet was born.

The internet has magnified the ability to exchange information and learn, leading to a proliferation of data.

The problem isn't only about volume. The time lapsing between the generation and processing of information has also been greatly reduced.

The Large Hadron Collider has pushed the boundaries of data collection to limits never seen before.

When the project, and its experiments, were being conceived in the late 1980s scientists realised that new concepts and techniques needed to be developed to deal with streams of data that were bigger than had ever been seen before.

It was then that concepts that contributed to cloud and distributed computing were developed.

One of the main tasks of the Large Hadron Collider is to observe and explore the Higgs boson, a particle connected with the generation of mass of fundamental particles, by means of colliding protons at high energy.

The probability of finding a Higgs boson in a high-energy proton-proton collision is extremely small. For this reason it is necessary to collide many protons many times every second.

The Large Hadron Collider produces data flows of the order of petabytes every second. To give an idea of how big a petabyte is, the entire written works of mankind from beginning of written history, in all languages, can be stored in about 50 petabytes. An experiment at the Large Hadron Collider generates that much data in less than one minute.

Only a small fraction of the data produced is stored. But even this has already reached the exabyte scale (one thousand times a petabyte) leading to new challenges in distributed and cloud computing.

The Square Kilometre Array (SKA) in South Africa will start generating data in the 2020s. SKA will have the processing power of about 100 million PCs. The data it collects in a single day would take nearly two million years to play back on an iPod.

This will produce new challenges for the correlation of vast amounts of data.

Big data and Africa

The African continent often lags behind the rest of the world when it comes to embracing innovation. Nevertheless big data is increasingly being seen as a solution to tackling poverty on the continent.

The private sector has been the first to get out of the starting blocks. The bigger African firms are, naturally, more likely to have big data projects. In Nigeria and Kenya at least 40% of businesses are in the planning stages of a big data project compared with the global average of 51%. Only 24% of medium companies in the two countries are planning big data projects.

Rich rewards can be reaped from harnessing big data. For example, healthcare organisations can benefit from digitising, combining and effectively using big data. This could enable a range of players, from single-physician offices and multi-provider groups to large hospital networks, to deliver better and more effective services.

Grasping the challenge of managing big data could have big economic spin-offs too. With economies becoming more and more sophisticated and complex the amount of data generated increases rapidly. As a result, in order to improve these complex processes it is necessary to process and understand increasing volumes of data. With this labour productivity is enhanced.

But for any of these benefits to become reality, Africa needs specialists who are proficient in big data techniques. Universities on the continent need to start teaching how big data can be used to find solutions to scientific problems. A sophisticated economy requires specialists who are skilled in big data techniques.

Bruce Mellado receives funding from the DST, NRF and the University of the Witwatersrand.
 Big data is about processing large amounts of data. It is often associated with multiplicities of data. But the ability to generate data outpaces the ability to store it. Bruce Mellado, Professor of Physics, University of the Witwatersrand Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/49804 2015-10-29T19:29:09Z 2015-10-29T19:29:09Z It's back to school for Facebook, and it's personal

Has Facebook found a potential new business venture? Flickr/Franklin Heijnen, CC BY-SA

The Facebook legend has it that Mark Zuckerberg got his big social network idea from the school Year Books of the same name: "The Facebook". But now he is going full-circle and returning to school with software to run the whole show.

The Personalised Learning Platform (PLP) is the social network giant's latest product. It's a software system for running a school's program-per-student, to customise the learning experience for each individual student.

Mark and his wife Priscilla have had a personal interest in improving the US public education system for some time now. In May 2014, they donated US$120-million to surprisingly needy Bay Area schools right in the shadow of the technology giants in Silicon Valley.

Even before Mark married Priscilla he had donated US$100-million to Newark public schools in 2010, so this is no new better man direction for him. The low-key Facebook announcement of PLP labours several points:

Facebook is doing it in partnership with a public school provider, (Summit Public Schools)

it will make the software free for other public schools, over time

it is committed to not selling student data nor using it to advertise to them.

Mike Sego, who is leading the project, says:

[…] a business plan for PLP is a long time away […] it's not our priority right now.

But that's what Zuckerberg used to say about your Facebook data before its initial public offering (IPO) in 2012. Now, Facebook is the most significant rival to Google for the global rivers-of-gold that flow from analytically targeted, person-specific advertising.

When Facebook entered its relationship with Summit Schools in late 2014, it put eight programmers to work on PLP, where Summit previously had just one.

Personalised Learning explained

So, what is Personalised Learning and did Facebook and/or Summit Schools invent the Personal Learning Environment (PLE) or Platform?

No, Personalised Learning and PLEs have been with us for some time. Personalised Learning involves adapting the curriculum, teaching approaches and learning materials to suit the individual student. This personalisation of the learning experience includes the student selecting and generating some of their own content. The teacher plays the role of mentor more than before.

It's been praised by some for allowing students to:

[…] learn whatever, wherever and whenever they desire, usually intermingled with other non-learning activities.

Some early conscripts to Personalised Learning believed the tools were already available, and that it merely needed a change of mindset by both students and teachers.

Whereas others thought that traditional Learning Management Systems (LMS) (such as Blackboard or Moodle, used by many universities and schools) needed to be replaced or refashioned towards personalisation and intelligent adaptiveness.

For time-poor teachers and academics an LMS can be inconvenient to learn and use. To students, an LMS is more a passive portal to content than a site for active learning. They are often seen as a system that best supports the administrative functions of a school. Apart from Moodle, most LMSs are paid-for systems.

Keep it simple … and familiar

Social network platforms have a high degree of user-friendliness. The ease in creating simple content and the speed that it can be uploaded and shared, has spawned a generation of learners that expect nothing less in usability.

With that in mind, our research late last year uncovered and demonstrated the close alignment between the data structures behind the Facebook platform, and those behind advanced LMSs.

The Events functionality in Facebook manages the What? When? Where? Who's invited? Who's coming, and Who's not? of an event. The Events sub-system in Facebook is a generalisation of events that happen in schools and universities (and beyond) such as classes, lectures, labs, creative studios and performance sessions.

The Groups sub-system in Facebook is a generalisation of the sorts of groupings of people found in schools and universities such as classes, reading groups, tutorial groups, project teams, all-middle-school teachers and more.

Facebook Pages are created through a set of built-in stylesheets. Those easily created Posts – the text, links, photos and videos – on your personal Facebook newsfeed, are also available for each Event, Group or Page.

In the early days Facebook started out with just Posts to lists of Friends, but after years of incremental additions and design generalisations, the Facebook platform matured and stabilised just prior to its IPO.

Another business plan for Facebook?

Our research into those data structures found that the Facebook platform was primed for entry into the lucrative Personalised Learning space.

At his September Townhall Q&A session, Zuckerberg revealed that Facebook had 20 people working on PLP. This growing commitment demonstrates significant product plans for the Personalised Learning Platform to the company.

This should be a wake up call to LMS vendors and their customers, that highly usable, low cost Personal Learning Environments will soon be in high demand.

Steve Goschnick has received research funding from the Australian Research Council, Ericsson Australia Ltd (1998-2000), The University of Melbourne, and a Telstra Broadband Development Grant (2004). He has been the Managing Director of Solid Software Pty Ltd, a data modelling and software development consultancy, since 1998.
 The social network giant is developing new software that could be used to help personalise learning in schools. It's a move that should have some worried they could lose business. Steve Goschnick, Adjunct Professor, Swinburne University of Technology Licensed as Creative Commons – attribution, no derivatives. tag:theconversation.com,2011:article/48485 2015-10-09T12:17:43Z 2015-10-09T12:17:43Z Toxic innovation: Volkswagen is the tip of a destructive iceberg

Consumer baiting? Tom Simpson, CC BY-NC-ND

The recent case of "cheating" at Volkswagen is still reverberating around the globe and threatens to reveal similar antics in other corporations.

Innovation takes new ideas and creatively applies them in the real world in ways which create value. But what if value is created for some in the short term but the impact for others is negative? The VW story points towards innovation that is toxic – either to customers, the offending organisation itself, or in the end, to both.

Toxic innovation develops and changes products, services or backroom business processes that harm customers. It can be deliberate and born of greed; it can be the result of benign but misguided intent; it can be an utterly unforseen and indirect effect. Ultimately, it can damage or destroy the organisation behind it.

Clumsy companies

The VW case is just one among other innovation stories where the same skills that produce genuinely successful products are put to the service of narrow financial goals that result in damaging impacts. Not all of these impacts are deliberate or conscious or known in advance.

 <img alt="" src="https://images.theconversation.com/files/97235/original/image-20151005-28786-4z4b8v.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/97235/original/image-20151005-28786-4z4b8v.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=399&fit=crop&dpr=1 600w, https://images.theconversation.com/files/97235/original/image-20151005-28786-4z4b8v.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=399&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/97235/original/image-20151005-28786-4z4b8v.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=399&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/97235/original/image-20151005-28786-4z4b8v.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=502&fit=crop&dpr=1 754w, https://images.theconversation.com/files/97235/original/image-20151005-28786-4z4b8v.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=502&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/97235/original/image-20151005-28786-4z4b8v.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=502&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Seen better days. Nomad Tales, CC BY-NC-ND

This phenomenon is often based on an endemic troubled business culture. When banks were recently outed for rigging the LIBOR interbank lending rate, this kind of rotten culture was uncovered as well. The LIBOR rigging process itself was remarkably innovative and involved plenty of creative activity. It was also illegal and its impact on banks and the world economy is still felt today.

VW's environmental "cheat" – deploying software that could trick emissions tests – allowed the car to pollute and no one to be any the wiser. It appears VW was warned not to do this, but the extent to which this deception was known in the higher echelons of the company is yet to be discovered.

So a potentially innovative bit of in-car software became toxic – for the environment in terms of emissions and ultimately in terms of poisoning the Volkswagen brand. What the case has also thrown up is that it is the emissions tests themselves that would benefit from some real (and non-toxic) innovation.

Designed to fail

Another example can be found in the concept of "planned obsolescence". Part of all good innovation involves testing products and knowing in advance how and when they will begin to, and ultimately totally fail. That's vital for safety, for maintenance and product replacement. So, when companies began to be accused of knowingly engineering products to fail or deliberately making them harder to repair, then it doesn't feel like their ingenuity is devoted to us lowly consumers.

 <img alt="" src="https://images.theconversation.com/files/97232/original/image-20151005-28744-vq5qat.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/97232/original/image-20151005-28744-vq5qat.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/97232/original/image-20151005-28744-vq5qat.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/97232/original/image-20151005-28744-vq5qat.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/97232/original/image-20151005-28744-vq5qat.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/97232/original/image-20151005-28744-vq5qat.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/97232/original/image-20151005-28744-vq5qat.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
  Håkan Dahlström, CC BY

We saw the impact of this idea recently when a Harvard study raised questions over whether an operating system update by Apple was slowing down iPhones at a time when product upgrades became available. Now, new software often loads new features which in turn demand more resources and can cause systems to slow, in fact it is a major part of the technology sector's appeal that companies are constantly seeking to attract customers towards enhanced products. Customers who felt the iOS9 update was slowing down their older phone may have been wrong, but the suspicion alone can be enough to damage trust in a brand.

There is certainly growing evidence that consumer electronics firms are focusing their innovation efforts on making it harder to repair products. If it ever does emerge that clever design by smartphone manufacturers is used to hobble older phones and bully us by stealth us into upgrading, the scandal could at least partly match that which has befallen VW.

Innovation or intrusion?

Facebook saw 4% wiped off its share price when its platform went down for several hours (and not for the first time). One reason cited for this was engineers engaging in incremental, small-scale innovation which crashed the whole network. This was unintended but the result was a lot of damage to Facebook's reputation as an always-on social media platform.

Perhaps more significantly, its attempts to change a core part of its platform, the "timeline", and introduce a supposed innovation known as the Year in Review, resulted in significant negative impact on its brand. Users started seeing photos they would rather have forgotten suddenly appearing under their noses. A phrase – "inadvertent algorithimic cruelty" – was even coined to describe this, and the firm was forced to issue an apology.

Not so Sunny Delight

 <img alt="" src="https://images.theconversation.com/files/97236/original/image-20151005-28732-tnyvma.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/97236/original/image-20151005-28732-tnyvma.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/97236/original/image-20151005-28732-tnyvma.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/97236/original/image-20151005-28732-tnyvma.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/97236/original/image-20151005-28732-tnyvma.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/97236/original/image-20151005-28732-tnyvma.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/97236/original/image-20151005-28732-tnyvma.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
 Showing some bottle. Gerald Angeles, CC BY-SA

Facebook's clunky attempt to spice up its offering might have been a failure of technology, but perhaps the most damaging innovations happen in marketing.

Kids' drink Sunny Delight was marketed to parents as a healthy fresh orange juice product. It turned into a brand disaster when parents discovered it was actually artificially coloured sugar water – and sales crashed.

The back room designers of this product had engaged in all kinds of product innovation activity to fool consumers into believing this drink was healthy and full of orange juice goodness. From pictures of oranges on the bright and optimistic packaging, to colour and taste – what we really had was a cheat product.

In short, VW is only the most current and most prominent example of toxic innovation. If corporations want to keep the trust of their consumers they will have to design toxic innovation out of their broader innovation activity. Sadly, stories are emerging in increasing frequency in which companies are applying the same skills they use to brings us wonderful products and services, to cheating, deceiving or in simply trying to be clumsily smart. The result is damage for everyone along the supply chain.

As the Volkswagen case shows, these attempts – or at least the ones we find out about – are a massively false economy. Innovation is always more successful in the long run when it is conscious, connected to real customer benefit, and generates trust and confidence.

Paul Levy owns shares in CATS3000 Ltd.
 Why do companies devote so much energy to ingenuity that causes harm? Paul Levy, Senior Researcher in Innovation Management, University of Brighton Licensed as Creative Commons – attribution, no derivatives.