1
Software Engineering / Re: গুগলের ১৩ টি সফটওয়্যার যা আপনার জীবনকে সহজ করে তুলবে
« on: October 15, 2018, 04:28:34 PM »
Thanks for sharing sir 
News:
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2
Software Engineering / Why Apple Watch link to Jamal Khashoggi 'killing' is unlikely
« on: October 15, 2018, 04:26:01 PM »
!!!!!!!!!!!!!!!!!!
There is a story going around that Jamal Khashoggi, the journalist missing presumed killed, recorded exactly what happened inside Saudi Arabia's Istanbul consulate on his Apple Watch. It strikes me as extremely unlikely.
Source:
https://www.bbc.com/news/technology-45857777
There is a story going around that Jamal Khashoggi, the journalist missing presumed killed, recorded exactly what happened inside Saudi Arabia's Istanbul consulate on his Apple Watch. It strikes me as extremely unlikely.
Source:
https://www.bbc.com/news/technology-45857777
3
Software Engineering / Harvard Printer Blasts Droplets with 100 Times Earth's Gravitational Force
« on: September 25, 2018, 12:01:51 PM »
A new printing technique designed at Harvard University's engineering school uses sound waves to control and fire droplets from a nozzle with incredible force, allowing researchers to print with liquids thicker and more viscous than ever before. Honey, stem-cell-based inks and liquid metals all printed fluidly using these vibrations, which come from acoustic techniques that researchers had previously used to levitate liquids, not print with them.
Source: https://www.livescience.com/63483-printer-sound-blast.html
Source: https://www.livescience.com/63483-printer-sound-blast.html
4
Software Engineering / Re: WHY DO WE HAVE TO STUDY NUMERICAL ANALYSIS?
« on: September 16, 2018, 11:29:56 AM »
This post is surely going to fulfill the utter desire of those students who ask "why should we read numerical analysis !!!!". Thank you for sharing madam.
5
Cyber and Software Security / Hackers Target 3rd Dimension of Cyberspace: Users' Minds
« on: September 09, 2018, 09:15:02 AM »
People...What else is not possible !!!!
The Russian attacks on the 2016 U.S. presidential election and the country's continuing election-related hacking have happened across all three dimensions of cyberspace — physical, informational and cognitive. The first two are well-known: For years, hackers have exploited hardware and software flaws to gain unauthorized access to computers and networks — and stolen information they've found. The third dimension, however, is a newer target — and a more concerning one.
This three-dimensional view of cyberspace comes from my late mentor, Professor Dan Kuehl of the National Defense University, who expressed concern about traditional hacking activities and what they meant for national security. But he also foresaw the potential — now clear to the public at large — that those tools could be used to target people's perceptions and thought processes, too. That's what the Russians allegedly did, according to federal indictments issued in February and July, laying out evidence that Russian civilians and military personnel used online tools to influence Americans' political views — and, potentially, their votes. They may be setting up to do it again for the 2018 midterm elections.
Some observers suggest that using internet tools for espionage and as fuel for disinformation campaigns is a new form of "hybrid warfare."Their idea is that the lines are blurring between the traditional kinetic warfare of bombs, missiles and guns, and the unconventional, stealthy warfare long practiced against foreigners' "hearts and minds" by intelligence and special forces capabilities.
However, I believe this isn't a new form of war at all: Rather, it is the same old strategies taking advantage of the latest available technologies. Just as online marketing companies use sponsored content and search engine manipulation to distribute biased information to the public, governments are using internet-based tools to pursue their agendas. In other words, they're hacking a different kind of system through social engineering on a grand scale.
Source: https://www.livescience.com/63214-hackers-target-your-mind.html
The Russian attacks on the 2016 U.S. presidential election and the country's continuing election-related hacking have happened across all three dimensions of cyberspace — physical, informational and cognitive. The first two are well-known: For years, hackers have exploited hardware and software flaws to gain unauthorized access to computers and networks — and stolen information they've found. The third dimension, however, is a newer target — and a more concerning one.
This three-dimensional view of cyberspace comes from my late mentor, Professor Dan Kuehl of the National Defense University, who expressed concern about traditional hacking activities and what they meant for national security. But he also foresaw the potential — now clear to the public at large — that those tools could be used to target people's perceptions and thought processes, too. That's what the Russians allegedly did, according to federal indictments issued in February and July, laying out evidence that Russian civilians and military personnel used online tools to influence Americans' political views — and, potentially, their votes. They may be setting up to do it again for the 2018 midterm elections.
Some observers suggest that using internet tools for espionage and as fuel for disinformation campaigns is a new form of "hybrid warfare."Their idea is that the lines are blurring between the traditional kinetic warfare of bombs, missiles and guns, and the unconventional, stealthy warfare long practiced against foreigners' "hearts and minds" by intelligence and special forces capabilities.
However, I believe this isn't a new form of war at all: Rather, it is the same old strategies taking advantage of the latest available technologies. Just as online marketing companies use sponsored content and search engine manipulation to distribute biased information to the public, governments are using internet-based tools to pursue their agendas. In other words, they're hacking a different kind of system through social engineering on a grand scale.
Source: https://www.livescience.com/63214-hackers-target-your-mind.html
6
Software Engineering / Re: Nasa names astronauts for first commercial flights
« on: September 09, 2018, 09:11:07 AM »
Really !!! good to know...
7
Software Engineering / How Scientists Created the Purest Drop of Water on Earth
« on: September 09, 2018, 09:09:36 AM »
And here it goes:
If cleanliness is next to godliness, then this is one divine droplet.
Researchers at the Vienna University of Technology announced yesterday (Aug. 23) that they have created the cleanest drop of water in the world.
This ultrapure water could help explain how self-cleaning surfaces, such as those coated with titanium dioxide (TiO2), become covered with a mysterious layer of molecules when they come into contact with air and water.
Researchers at the Vienna University of Technology announced yesterday (Aug. 23) that they have created the cleanest drop of water in the world.
This ultrapure water could help explain how self-cleaning surfaces, such as those coated with titanium dioxide (TiO2), become covered with a mysterious layer of molecules when they come into contact with air and water.
"We had four labs [around the world] studying this and four different explanations for it," said study co-author Ulrike Diebold, a chemist at the Vienna University of Technology.
In the light of day
When TiO2 surfaces are exposed to ultraviolet light, they react in ways that "eat up" any organic compounds on them, Diebold told Live Science. This gives these surfaces a number of useful properties; for example, a TiO2-coated mirror will repel water vapor even in a steamy bathroom.
But leave them in a dark room too long, Diebold said, and the mysterious dirt forms.
Most of the proposed explanations for this involve some sort of chemical reaction with ambient water vapor. But Diebold and her colleagues applied the ultraclean water droplet to the surface and showed that water alone doesn't cause the film to appear.
Creating that superclean drop was a challenge, though. As Live Science previously reported, water very easily becomes contaminated with trace impurities, and perfectly pure water does not exist.
To get as close to perfectly pure as possible, Diebold said, her team had to design a specialized gadget that pushed water to its limits.
In one chamber of the device was a vacuum, with a "finger" hanging from its ceiling cooled to minus 220 degrees Fahrenheit (minus 140 Celsius). The researchers then released a thin, purified sample of water vapor from an adjacent chamber into the vacuum, so that the water formed an icicle at the tip of that finger. The researchers then allowed the icicle to warm up and melt, so that it dripped onto a piece of TiO2 below before quickly evaporating into the ultra-low-pressure chamber. Afterward, the TiO2 showed no sign of the molecular film that some researchers suspected came from water, the researchers reported today (Aug. 23) in the journal Science.
"The key is that neither the water nor the titanium dioxide had ever been exposed to air before," Diebold said.
Follow-up scans of TiO2 using microscopes and spectroscopes showed that the film wasn't made up of water or water-related compounds at all. Instead, acetic acid (which gives vinegar its sour taste) and formic acid, a similar compound, turned up on the surface. Both are byproducts of plant growth and are present in only tiny quantities in the air — but, apparently, there's enough of this material floating around to dirty a self-cleaning surface.
****Originally published on Live Science.
If cleanliness is next to godliness, then this is one divine droplet.
Researchers at the Vienna University of Technology announced yesterday (Aug. 23) that they have created the cleanest drop of water in the world.
This ultrapure water could help explain how self-cleaning surfaces, such as those coated with titanium dioxide (TiO2), become covered with a mysterious layer of molecules when they come into contact with air and water.
Researchers at the Vienna University of Technology announced yesterday (Aug. 23) that they have created the cleanest drop of water in the world.
This ultrapure water could help explain how self-cleaning surfaces, such as those coated with titanium dioxide (TiO2), become covered with a mysterious layer of molecules when they come into contact with air and water.
"We had four labs [around the world] studying this and four different explanations for it," said study co-author Ulrike Diebold, a chemist at the Vienna University of Technology.
In the light of day
When TiO2 surfaces are exposed to ultraviolet light, they react in ways that "eat up" any organic compounds on them, Diebold told Live Science. This gives these surfaces a number of useful properties; for example, a TiO2-coated mirror will repel water vapor even in a steamy bathroom.
But leave them in a dark room too long, Diebold said, and the mysterious dirt forms.
Most of the proposed explanations for this involve some sort of chemical reaction with ambient water vapor. But Diebold and her colleagues applied the ultraclean water droplet to the surface and showed that water alone doesn't cause the film to appear.
Creating that superclean drop was a challenge, though. As Live Science previously reported, water very easily becomes contaminated with trace impurities, and perfectly pure water does not exist.
To get as close to perfectly pure as possible, Diebold said, her team had to design a specialized gadget that pushed water to its limits.
In one chamber of the device was a vacuum, with a "finger" hanging from its ceiling cooled to minus 220 degrees Fahrenheit (minus 140 Celsius). The researchers then released a thin, purified sample of water vapor from an adjacent chamber into the vacuum, so that the water formed an icicle at the tip of that finger. The researchers then allowed the icicle to warm up and melt, so that it dripped onto a piece of TiO2 below before quickly evaporating into the ultra-low-pressure chamber. Afterward, the TiO2 showed no sign of the molecular film that some researchers suspected came from water, the researchers reported today (Aug. 23) in the journal Science.
"The key is that neither the water nor the titanium dioxide had ever been exposed to air before," Diebold said.
Follow-up scans of TiO2 using microscopes and spectroscopes showed that the film wasn't made up of water or water-related compounds at all. Instead, acetic acid (which gives vinegar its sour taste) and formic acid, a similar compound, turned up on the surface. Both are byproducts of plant growth and are present in only tiny quantities in the air — but, apparently, there's enough of this material floating around to dirty a self-cleaning surface.
****Originally published on Live Science.
8
Artificial Intelligence / Dueling Neural Networks
« on: September 09, 2018, 09:03:43 AM »
Something really interesting and thought provoking!!!!!
Artificial intelligence is getting very good at identifying things: show it a million pictures, and it can tell you with uncanny accuracy which ones depict a pedestrian crossing a street. But AI is hopeless at generating images of pedestrians by itself. If it could do that, it would be able to create gobs of realistic but synthetic pictures depicting pedestrians in various settings, which a self-driving car could use to train itself without ever going out on the road.
The solution first occurred to Ian Goodfellow, then a PhD student at the University of Montreal, during an academic argument in a bar in 2014. The approach, known as a generative adversarial network, or GAN, takes two neural networks—the simplified mathematical models of the human brain that underpin most modern machine learning—and pits them against each other in a digital cat-and-mouse game.
Both networks are trained on the same data set. One, known as the generator, is tasked with creating variations on images it’s already seen—perhaps a picture of a pedestrian with an extra arm. The second, known as the discriminator, is asked to identify whether the example it sees is like the images it has been trained on or a fake produced by the generator—basically, is that three-armed person likely to be real?
Over time, the generator can become so good at producing images that the discriminator can’t spot fakes. Essentially, the generator has been taught to recognize, and then create, realistic-looking images of pedestrians.
The technology has become one of the most promising advances in AI in the past decade, able to help machines produce results that fool even humans.
GANs have been put to use creating realistic-sounding speech and photorealistic fake imagery. In one compelling example, researchers from chipmaker Nvidia primed a GAN with celebrity photographs to create hundreds of credible faces of people who don’t exist. Another research group made not-unconvincing fake paintings that look like the works of van Gogh. Pushed further, GANs can reimagine images in different ways—making a sunny road appear snowy, or turning horses into zebras.
The results aren’t always perfect: GANs can conjure up bicycles with two sets of handlebars, say, or faces with eyebrows in the wrong place. But because the images and sounds are often startlingly realistic, some experts believe there’s a sense in which GANs are beginning to understand the underlying structure of the world they see and hear. And that means AI may gain, along with a sense of imagination, a more independent ability to make sense of what it sees in the world. —Jamie Condliffe
Source: https://www.wired.com/2017/04/googles-dueling-neural-networks-spar-get-smarter-no-humans-required/
Artificial intelligence is getting very good at identifying things: show it a million pictures, and it can tell you with uncanny accuracy which ones depict a pedestrian crossing a street. But AI is hopeless at generating images of pedestrians by itself. If it could do that, it would be able to create gobs of realistic but synthetic pictures depicting pedestrians in various settings, which a self-driving car could use to train itself without ever going out on the road.
The solution first occurred to Ian Goodfellow, then a PhD student at the University of Montreal, during an academic argument in a bar in 2014. The approach, known as a generative adversarial network, or GAN, takes two neural networks—the simplified mathematical models of the human brain that underpin most modern machine learning—and pits them against each other in a digital cat-and-mouse game.
Both networks are trained on the same data set. One, known as the generator, is tasked with creating variations on images it’s already seen—perhaps a picture of a pedestrian with an extra arm. The second, known as the discriminator, is asked to identify whether the example it sees is like the images it has been trained on or a fake produced by the generator—basically, is that three-armed person likely to be real?
Over time, the generator can become so good at producing images that the discriminator can’t spot fakes. Essentially, the generator has been taught to recognize, and then create, realistic-looking images of pedestrians.
The technology has become one of the most promising advances in AI in the past decade, able to help machines produce results that fool even humans.
GANs have been put to use creating realistic-sounding speech and photorealistic fake imagery. In one compelling example, researchers from chipmaker Nvidia primed a GAN with celebrity photographs to create hundreds of credible faces of people who don’t exist. Another research group made not-unconvincing fake paintings that look like the works of van Gogh. Pushed further, GANs can reimagine images in different ways—making a sunny road appear snowy, or turning horses into zebras.
The results aren’t always perfect: GANs can conjure up bicycles with two sets of handlebars, say, or faces with eyebrows in the wrong place. But because the images and sounds are often startlingly realistic, some experts believe there’s a sense in which GANs are beginning to understand the underlying structure of the world they see and hear. And that means AI may gain, along with a sense of imagination, a more independent ability to make sense of what it sees in the world. —Jamie Condliffe
Source: https://www.wired.com/2017/04/googles-dueling-neural-networks-spar-get-smarter-no-humans-required/
9
Data Mining and Big Data / Re: Google launches new search engine to help scientists find the datasets they need
« on: September 09, 2018, 08:48:26 AM »
Wonderful initiative...thanks for sharing sir
10
Software Engineering / No Vision, No Problem for This 'Blind' Cheetah Robot
« on: August 28, 2018, 10:12:23 AM »
What a wonderful effort by the Engineers and roboticists at MIT. They are are clearly doing everything in their power to ease our transition into a full-on robot takeover. Their latest achievement in "blind locomotion" — robots that can navigate without the benefit of vision sensors — is the 90-lb. (41 kilograms) Cheetah 3. This four-limbed mechanical beast can stomp its way up debris-littered stairs, sprint over uneven terrain, and recover after being pummeled or pushed.
Source: https://www.livescience.com/63016-cheetah-3-robot-runs-blind.html
Source: https://www.livescience.com/63016-cheetah-3-robot-runs-blind.html
11
Software Engineering / Re: What is Design Pattern and why it's needed?
« on: August 28, 2018, 10:08:51 AM »
Helpful topic
12
Data Mining and Big Data / Re: Discovering knowledge from mobile application users:Fuzzy Rule Based Approach
« on: August 28, 2018, 10:05:26 AM »
Thank you for sharing sir
13
Robotics and Embedded Systems / Ex-NASA Engineer Made the Perfect Rock Skipping Robot
« on: August 28, 2018, 10:02:06 AM »
Ex-NASA engineer and Youtube inventor Mark Rober has made a perfect rock-skipping robot. Not only can the robot perform impressively, but it can help you learn how to skip rocks better too.
Source: https://www.popularmechanics.com/technology/robots/a22643872/ex-nasa-engineer-made-perfect-rock-sipping-robot/
Source: https://www.popularmechanics.com/technology/robots/a22643872/ex-nasa-engineer-made-perfect-rock-sipping-robot/
14
Image Processing Computer Networks / Research work on Deep Convolutional Neural Netwok
« on: August 14, 2018, 09:23:54 AM »
I was looking for an effective work on deep convolutional Neural Network and came across this paper. Sharing the link as it might be useful those working on it:
Abstract: Deep Convolutional Neural Networks (CNNs)
have shown superior performance on the task of single-label
image classification. However, the applicability of CNNs to multilabel
images still remains an open problem, mainly because
of two reasons. First, each image is usually treated as an
inseparable entity and represented as one instance, which mixes
the visual information corresponding to different labels. Second,
the correlations amongst labels are often overlooked. To address
these limitations, we propose a deep Multi-Modal CNN for
Multi-Instance Multi-Label image classification, called MMCNNMIML.
By combining CNNs with Multi-Instance Multi-Label
(MIML) learning, our model represents each image as a bag of
instances for image classification and inherits the merits of both
CNNs and MIML. In particular, MMCNN-MIML has three main
appealing properties: i) It can automatically generate instance
representations for MIML by exploiting the architecture of
CNNs. ii) It takes advantage of the label correlations by grouping
labels in its later layers. iii) It incorporates the textual context
of label groups to generate multi-modal instances, which are
effective in discriminating visually similar objects belonging to
different groups. Empirical studies on several benchmark multilabel
image datasets show that MMCNN-MIML significantly
outperforms the state-of-the-art baselines on multi-label image
The whole paper is available here: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8432496
Abstract: Deep Convolutional Neural Networks (CNNs)
have shown superior performance on the task of single-label
image classification. However, the applicability of CNNs to multilabel
images still remains an open problem, mainly because
of two reasons. First, each image is usually treated as an
inseparable entity and represented as one instance, which mixes
the visual information corresponding to different labels. Second,
the correlations amongst labels are often overlooked. To address
these limitations, we propose a deep Multi-Modal CNN for
Multi-Instance Multi-Label image classification, called MMCNNMIML.
By combining CNNs with Multi-Instance Multi-Label
(MIML) learning, our model represents each image as a bag of
instances for image classification and inherits the merits of both
CNNs and MIML. In particular, MMCNN-MIML has three main
appealing properties: i) It can automatically generate instance
representations for MIML by exploiting the architecture of
CNNs. ii) It takes advantage of the label correlations by grouping
labels in its later layers. iii) It incorporates the textual context
of label groups to generate multi-modal instances, which are
effective in discriminating visually similar objects belonging to
different groups. Empirical studies on several benchmark multilabel
image datasets show that MMCNN-MIML significantly
outperforms the state-of-the-art baselines on multi-label image
The whole paper is available here: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8432496
15
Image Processing Computer Networks / Re: What is image processing used for in the real world?
« on: August 14, 2018, 09:17:02 AM »
Sure sir, Please let me know when you get the work started and we can also go for a meeting in this regard.