This is outrageous but not surprising at all. In my opinion, more than a car safety issue, this is a money-making issue.
EU pushes car manufacturers to build safer cars but this has a high cost. European customers respond well to marketing campaigns promoting safety. Therefore, those extra safety costs have a good return on investment.
In the US, car safety is not considered as important as in EU because companies don't think there will be a good ROI. Because car manufacturers don't believe is viable enough, they tried to hide this report.
Hahaha. I had the exact same reflection. I don't know much about Baseball either. When I was listening people talking about Baseball and referring to Yogi Bear, I thought it was a mascot of one of the teams.
Anyway, RIP.
This article is so true.
We evolved/improved in so many ways, especially technologically speaking but when it comes to parenting, we should go back to the basics. We know how to work, create an app, market a product... but when it comes to raising a child, this is another issue.
Parenting is a basic knowledge, which is getting lost. The importance of parenting and the ways to raise a child could be more taught at school.
The history of parenting suggests the bar is very low. As long as you don't spend every day beating your children or completely neglecting them they will be OK.
Very nice article. I wasn't aware of most of those projects.
I am especially interested to see the results of "World Wonders Project", "Solve for X", "Ingress" and "Google Sky". I am sure Google is also secretly working on other projects that they don't want to communicate on yet (Same as Apple).
Note: all those projects really remind me of Hailo's project XYZ in the Silicon Valley Series.
Thanks for those links (Especially the last one). Resize.ly is just beta, so let's see what they come up with in the near future.
On another hand, I hope there will be a resizing/compressing service inspired by Pied Piper (Silicon Valley series) sometime soon.
I am not surprised. Last time I was in New York, I wasn't in downtown and it was so hard for me to get a taxi. There are so many taxis in NYC but most of them stay in the same area. As soon as you are not in a populated area, it's close to impossible to grab a taxi. I am pleased that Uber is making things move.
Very interesting approach. Choosing a company name is very important. I especially like the name-association part. I would just add 'meaning in foreign languages' because sometimes a company name can sound well in English but could mean something bad in a foreign language.
Actually, I don't think anyone can blog well [You need to be able to write well, to express your ideas well and so on.].
The article says that everyone can start learning how to cook or to program by practicing but this doesn't mean everyone can do it well. This is why not everyone can program. The author emphasises on the fact that not everyone can become a good programmer.
This is true for almost everything: some people are just more gifted than others. For instance, everyone can learn how to play music, play sports, market a product, learn about marine biology, sciences... but this doesn't mean they will be good at it.
So cool and so clever. It's well explained and it looks so easy. It proves that you don't always need great instruments/material to make something awesome. In your case, you didn't need to get a weatherproof inspection machine, you did your inspection by yourself. Congrats!
It sounds great and much more protective than passwords. You can't copy/imitate behaviors.
However, I am wondering if the system still works if you are tired or sick. Your behavior might change in this case and therefore the system would not recognise you.
I think you misunderstood the intention of this feature. The goal is to identify and/or profile users that themselves use just a regular log-in. This can be then used to improve targeted marketing, selling that information to third-parties for example.
Note how the article mentions that the gender can be determined after a few keystrokes, even though the user never entered that specific information. This is certainly not the only metric that can be identified. The point of the article is to develop a solution to prevent leakage of private/personal information.
> Note how the article mentions that the gender can be determined after a few keystrokes, even though the user never entered that specific information.
Research got median 88% accuracy testing subsets of 98 males and 35 females.
Note that I got 74% accuracy on that data set by guessing male, male, male, male, male...
The original researcher knows in advance what the ratio is, yes, that's my point. I'm illustrating that the research is not very good. They couldn't even identify women to take part in the study. Given the numbers involved, it certainly isn't Facebook-ready.
In general, I don't believe it is possible to distinguish male and female typing patterns.
What you might be recognising is how people learned to type combined with the size of their hands - that might partly but not exactly break along gender lines. Bucketing people on that basis is just a recipe for awkwardness.
Fabricating facts and using ad-hominem is not a very good way of backing up your arguments.
Quote from the paper:
We use the public GREYC keystroke benchmark database for this work. It is one of the largest databases (in term of number of users and sessions) in keystroke dynamics. To out knowledge, no existing database contains more individuals. In order to reduce the bias due to this high quantity of male information, we only kept the first n male samples( where n is the number of female samples).
( Don't bother with your response, I won't be reading it. )
>We use the public GREYC keystroke benchmark database
Yes. That's their own database which they're talking up, the one that they made to do this research. That's what I was talking about.
>In order to reduce the bias due to this high quantity of male information, we only kept the first n male samples( where n is the number of female samples).
It happens that I didn't read this part.
On reflection, what I understand now is far worse than what I originally understood:
- They have 35 females and 98 males, they take many handwriting samples from each.
- Since the participants provided many samples, these samples appear both in the training set data and in the test set data.
- I use the training set data to figure out if I can recognise the handwriting of the 35 female participants.
- Then I look through the test data to see if I can identify those participants again.
Basically what you've shown is you can identify the handwriting of 35 people if you've already seen it - 88% of the time.
Splitting groups into 'female' and 'male' is a red herring. This method would presumably work, even if I split them into two random groups.
That's the first thing I thought of, tired or sick. Or how many times I've used one hand to type in my password because I had a drink or food in my other.
How would mobile work with this? Sometimes I use both fingers, sometimes just my thumb on one hand. Would it just create multiple behavior profiles for me that are accepted?
In combination of the right password and the behavior match, it seems like this would actually be pretty strong. I'm looking forward to trying to break it tomorrow with a friend.
