Modern computing devices allow users to enter information using keyboards, mice, or touch screens. Mobile devices have additional sensors like gyrometers and accelerometers that orient the screen. All of these input devices collect data at millisecond precision.
Recent research has focused on a person’s emotional state and how those emotions affect their fine motor movements. Your emotions, like happiness, sadness, anger or frustration, cause immediate, uncontrolled changes in how your hand moves a computer mouse, navigates a touch pad screen or holds a smartphone. Below are the results of a series of controlled laboratory studies that we conducted to determine the effects of negative emotions on a person’s mouse cursor movements.
Participants were told they were going to take a timed, scored intelligence test, then given a monetary reward depending on how many questions they answered correctly. They were divided into two groups.
While the participants in the first group read the instructions, a timer in the upper right-hand corner of the screen began to count down. The questions took a long time to load, while the timer continued to count down. By the time each question loaded, there was only fifteen seconds left to answer a difficult question.
The screen repeatedly advanced to the next question before the participants could answer, telling them their time had run out because they took too long; creating frustration. Then these participants were told that because of their slow reaction time and incorrect answers, their scores indicated a lower intelligence level than most of the people who had taken the test; increasing frustration.
The second group was not timed, had easy questions, and was congratulated for answering every question correctly at the end of the test.
Theory predicts that frustration impacts the mind, increasing a person’s mental effort. As people experience increased mental effort, they become more indecisive, and their mouse movements get longer and slower.
Studies 2 and 3
In Study 2, we asked two different groups to navigate a fictitious e-commerce website, pretend to buy the product and then fill out a survey rating their emotional level.
The first group had a website that loaded slowly, along with many other errors, in an attempt to frustrate them. The second group experienced no problems with the website. Mouse cursor movements were captured during the website navigation for each group and the results were similar to Study 1—frustrated participants had longer, slower mouse movements.
We were also able to predict which participants had the “frustrating” website, and which ones didn’t, at an accuracy rate of about 82 per cent.
In Study 3, participants were asked to use real, web-based, product configuration systems to build either a Dell computer or a Volkswagen automobile. Each participant navigated through five different configurations with random degrees of difficulty, and was asked to rate his or her emotions after each task. Without knowing which participants had which configuration, we were able to identify who was experiencing frustration, whose frustration was building over time, and ultimately who was working on an easy, medium or hard configuration task, just by the changes in their mouse movements.
The studies above show that unfairness and challenging content leads to frustration. Prior research shows that increased frustration leads to increased mental effort. Increased mental effort causes predictable changes in mouse movements, which can be captured by all modern computer input devices.
Read more here: blogs.lse.ac.uk/businessreview/2016/10/27/negative-emotions-influence-how-we-move-the-computer-mouse/
Today, GoodCall examines progress – or the lack thereof – made by women in the college classroom and in the workplace. Writer Terri Williams found one respected computer science program that has made great strides in recruiting women. Earlier, Terri examined a new report on the state of young women in the workplace. In both cases, a major key to success is having strong women mentors and role models.
Take a peek into a typical computer science program classroom around the country: 5 of every 6 students will be male. But Carnegie-Mellon’s never been typical – particularly when it comes to its highly rated computer science curriculum. The latest stride: Women make up 48.5% of the enrollment in the class this year.
Here’s why it’s a good thing, especially for women. A recent survey revealed computer science is one of the best majors for jobs of the future, and another survey found half of high-paying jobs required coding skills.
Other recent research, however, revealed that some female students might avoid STEM and business majors because of a perceived marriage market penalty. That’s not happening at Carnegie Mellon, where the enrollment increase comes on the heels of a 38% increase in the number of women who applied to the program. In addition, 43.3% of students in CMU’s 2016 College of Engineering are women.
The increases in the enrollment of women in both departments during the past four years have been impressive.
Read more here: www.goodcall.com/news/women-make-computer-science-gains-09126
K-12 educators in the U.S. are struggling. Like everyone else, they know that computer technology is a well-paying, in-demand field that’s desperate for a more diverse workforce. But many have had a hard time figuring out exactly how to prepare kids for tech careers and provide them with a basic understanding of computer science. Until now, that is.
A coalition of computer science organizations — led in part by the Seattle-based nonprofit Code.org — recently released the K–12 Computer Science Framework. The document provides a roadmap for educators eager to expand beyond lessons in how to use a spreadsheet or build a PowerPoint deck.
And perhaps most importantly, the framework aims to make computer science welcoming to all students — including female, black, Hispanic and other kids who have been disproportionately absent from these classes and, ultimately, the tech industry.
“We’re at that point where districts all around the country are looking around and thinking [computer science] is huge,” said Greg Bianchi, STEM Curriculum Developer for the Bellevue School District.
But without guidance, “how does someone in a K-12 system navigate that, and make the right choices to say, ‘Here is what we want to do at K, here is what we want to do in third grade?’” said Bianchi, who’s also a project consultant for Washington STEM, an educational nonprofit.
And while the framework could better prep kids for tech careers, it’ll do more than that, supporters said.
“The reason for kids to learn computer science is not that they’ll enter the software industry workforce — although certainly some will,” said Ed Lazowska, Bill & Melinda Gates Chair in Computer Science & Engineering at the University of Washington, via email. “It’s that no matter what career you choose, knowledge of computer science is increasingly essential — it’s a life skill in the 21st century, not vocational training.”
Ed Lazowska, Bill & Melinda Gates Chair in Computer Science & Engineering at the University of Washington, endorsed the framework.
But it’s an area of academics that has been dominated by white and Asian male students.
A study just released by Gallup and Google found that middle and high school girls say they are less interested in computer science and less confident that they can learn it than boys. The survey also found that black students were less likely to have a computer science class at school than white peers, and that both black and Hispanic students spend less time on computers at home.1
Read more here: www.geekwire.com/2016/kids-really-need-know-computer-science-new-roadmap-wins-widespread-support/
Google’s parent company is halting operations and laying off staff in a number of cities where it once hoped to bring high-speed internet access by installing new fiber-optic networks.
The company also announced that Craig Barratt, a veteran tech executive who led the ambitious – and expensive – Google Fiber program, is stepping down as CEO of Access, the division of Google’s parent company, Alphabet, that operates the five-year-old program.
In a statement, Barratt said Google Fiber would continue to provide service in a handful of cities where it’s already operating, including Atlanta, Austin and Charlotte.
But it will pause further plans in at least eight more metropolitan areas where it has been holding exploratory talks with local officials. Those include Oklahoma City, Phoenix, Dallas, Tampa and Jacksonville in Florida, Portland in Oregon and Los Angeles and San Jose in California.
Barratt didn’t say how many jobs would be cut. His statement described the Access business as “solid”, but said it would make “changes to focus our business and product strategy” and incorporate new technology.
Read more here: www.theguardian.com/technology/2016/oct/26/google-fiber-internet-stops-alphabet-layoffs?CMP=edit_2221
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Read more here: www.microsoft.com/en-us/surface/devices/surface-studio
Chinese firm Hangzhou Xiongmai Technology Co Ltd said it will recall some of its products sold in the United States after it was identified by security researchers as having made parts for devices that were targeted in a major hacking attack on Friday.
Hackers unleashed a complex attack on the Internet through common devices like webcams and digital recorders, and cut access to some of the world's best known websites in a stunning breach of global internet stability.
The electronics components firm, which makes parts for surveillance cameras, said in a statement on its official microblog that it would recall some of its earlier products sold in the United States, strengthen password functions and send users a patch for products made before April last year.
It said the biggest issue was users not changing default passwords, adding that, overall, its products were well protected from cyber security breaches. It said reports that its products made up the bulk of those targeted in the attack were false.
"Security issues are a problem facing all mankind. Since industry giants have experienced them, Xiongmai is not afraid to experience them once, too," the company statement said.
Friday's cyber attack alarmed security experts because it represented a new type of threat rooted in the proliferation of simple digital devices such as webcams. These often lack proper security, and hackers found a way to harness millions of them to flood a target with so much traffic that it couldn't cope.
Read more here: www.reuters.com/article/us-cyber-attacks-manufacturers-idUSKCN12O0MS
One of the most potent tools in a cyber criminal's arsenal is the 'distributed denial of service' attack - commonly known as a DDoS attack. These prolific hacks can take even the most protected computers offline.
Notable DDoS attacks include the Christmas day campaign that brought the PlayStation and Xbox live networks down and, most recently, taken most of the internet offline, including popular sites such as Twitter, eBay and the Telegraph.
Here's everything you need to know about DDoS attacks.
How does a DDoS attack work?
DDoS attacks harness the power of a network of tens of thousands of compromised computers, known as a "botnet", to flood a website's servers with page view requests, leaving legitimate traffic unable to get through.
In a similar way that sites such as Ticketmaster crash when one-off or hugely popular event tickets are released, DDoS attacks overwhelm the target server, flooding it with traffic so that it's unable to function properly.
The huge amount of connection requests can, in some cases, cause entire websites to crash.
How does a computer become part of a botnet?
In order to add computers to a botnet, a hacker must first gain control of the machine. They achieve this by exploiting vulnerabilities within the computer's operating system to install malicious software on the computer that provides them with always-on, remote access to the PC.
This means that attackers can exploit your computer to use it as part of the DDoS attack, or access it remotely and retrieve usernames, passwords, financial information and other sensitive data.
Laptops, computers and other internet-connected devices can be turned into a botnet army
Could my computer be affected?
Any computer is vulnerable to being added to a botnet. The malware necessary to exploit devices can be installed without your knowledge if you click on a malicious link, or visit a website that is serving infected adverts.
That's why it's crucial to ensure your antivirus software is up-to-date, that you have downloaded and installed the latest security patches for your computer, and that you are using a firewall to control what programs can and cannot gain access to their machine via the internet. If you don't have this protection, you're an easy target for hackers.
Read more here: www.telegraph.co.uk/technology/0/what-is-a-ddos-attack-and-could-my-computer-be-a-weapon/
Ever wonder what it would be like if a device could decode your thoughts into actual speech or written words? While this might enhance the capabilities of already existing speech interfaces with devices, it could be a potential game-changer for those with speech pathologies, and even more so for "locked-in" patients who lack any speech or motor function.
"So instead of saying 'Siri, what is the weather like today' or 'Ok Google, where can I go for lunch?' I just imagine saying these things," explains Christian Herff, author of a review recently published in the journal Frontiers in Human Neuroscience.
While reading one's thoughts might still belong to the realms of science fiction, scientists are already decoding speech from signals generated in our brains when we speak or listen to speech.
In their review, Herff and co-author, Dr. Tanja Schultz, compare the pros and cons of using various brain imaging techniques to capture neural signals from the brain and then decode them to text.
The technologies include functional MRI and near infrared imaging that can detect neural signals based on metabolic activity of neurons, to methods such as EEG and magnetoencephalography (MEG) that can detect electromagnetic activity of neurons responding to speech. One method in particular, called electrocorticography or ECoG, showed promise in Herff's study.
This study presents the Brain-to-text system in which epilepsy patients who already had electrode grids implanted for treatment of their condition participated. They read out texts presented on a screen in front of them while their brain activity was recorded. This formed the basis of a database of patterns of neural signals that could now be matched to speech elements or "phones."
Read more here: https://www.sciencedaily.com/releases/2016/10/161025114035.htm
One by one, the skills that separate us from machines are falling into the machines’ column. First there was chess, then Jeopardy!, then Go, then object recognition, face recognition, and video gaming in general. You could be forgiven for thinking that humans are becoming obsolete.
But try any voice recognition software and your faith in humanity will be quickly restored. Though good and getting better, these systems are by no means perfect. Are you ordering “ice cream” or saying “I scream”? Probably both, if it’s a machine you are talking to.
So it ought to be reassuring to know that ordinary conversational speech recognition is something machines still struggle at—that humans are still masters of their own language.
That view may have to change. Quickly. Today, Geoff Zweig and buddies at Microsoft Research in Redmond, Washington, say they’ve cracked this kind of speech recognition and that their machine-learning algorithms now outperform humans for the first time in recognizing ordinary conversational speech.
Speech recognition research has a long history. In the 1950s, early computers could recognize up to 10 words spoken clearly by a single speaker. In the 1980s, researchers built machines that could transcribe simple speech with a vocabulary of 1,000 words. In the 1990s they progressed to recordings of a person reading the Wall Street Journal, and then onto broadcast news speech.
These scenarios are all increasingly ambitious. But they are also simpler than ordinary speech because of various constraints. The vocabulary in the Wall Street Journal is limited to business and finance, and the sentences are well structured and grammatically correct, which is not necessarily true of ordinary speech. Broadcast news speech is less formal but still high structured and clearly pronounced. All of these examples have eventually been conquered by machines.
But the most difficult task—transcribing ordinary conversational speech—has steadfastly resisted the onslaught.
Ordinary speech is significantly more difficult because of the vocabulary size and also because of the noises other than words that people make when they speak. Humans use a range of noises to manage turn-taking in conversation, a type of communication that linguists call a backchannel.
For example, uh-huh is used to acknowledge the speaker and signal that he or she should keep talking. But uh is a hesitation indicating that the speaker has more to say, a warning that there is more to come. In turn management, uh plays the opposite role to uh-huh.
Humans have little difficulty parsing these sounds and understanding their role in a conversation. But machines have always struggled with them.
In 2000, the National Institute of Standards and Technology released a data set to help researchers tackle this problem. The data consisted of recordings of ordinary conversations on the telephone. Some of these were conversations between individuals on an assigned topic. The rest were conversations between friends and relatives on any topic.
Most of the data was to help train a machine-learning algorithm to recognize speech. The rest was a test that the machines had to transcribe.
The measure of performance was the number of words that the machine got wrong, and the ultimate goal was to do the task better than humans.
So how good are humans? The general consensus is that when it comes to transcription, humans have an error rate of about 4 percent. In other words, they incorrectly transcribe four words in every hundred. In the past, machines have got nowhere near this benchmark.
Now Microsoft says it has finally matched human performance, albeit with an important caveat. The Microsoft researchers began by reassessing human performance in transcription tasks. They did this by sending the telephone recordings in the NIST data set to a professional transcription service and measuring the error rate.
Read more here: www.technologyreview.com/s/602714/first-computer-to-match-humans-in-conversational-speech-recognition/