Firewalls

What do firewalls do?

At an abstract level, you can think of a firewall as a filter, or an intermediary between your device and the Internet (you could also think of it as a traffic officer). What a firewall does very generally is decide whether or not certain information from the internet should be allowed to enter your device, and then respectively grants or prohibits entrance to that information.

How does a firewall work - a basic model.

When information "knocks" on a device's firewall,  the firewall "questions" it to determine if it belongs to any of the following categories:

1. "traffic sent in response to a request of the computer (solicited traffic)" (1) or 
2. "unsolicited traffic that has been specified as allowed (excepted traffic)". (1)

If it does not belong to either category, i.e., if it has not been sent as a response to a request of the device and has also not been specifically permitted by the user, then the firewall denies entrance to the information.

References:
(1) https://technet.microsoft.com/en-us/network/bb545423.aspx
(2) https://www.microsoft.com/en-us/safety/pc-security/firewalls-whatis.aspx
(3) https://www.paloaltonetworks.com/documentation/glossary/what-is-a-firewall

Image References:
(a)https://www.microsoft.com/en-us/safety/pc-security/firewalls-whatis.aspx

Understanding PAC-Man

Back in the day, PAC-Man used to be one of the most-played games in the world! Game-play was pretty simple and straightforward - try to eat all the dots and fruit as fast as you can and without being eaten by a ghost. Everyone knew the rules, and people of all ages could play it.

Everyone also knew how the ghosts behaved more or less - they roamed around until they figured out your approximate location, and then started actively chasing you. How did they know however how to do that?

According to Chad Birch, "Each of the ghosts is programmed with an individual “personality”, a different algorithm it uses to determine its method of moving through the maze", but there is also a logic that all ghosts share (1).

More specifically, each ghost has what is called a 'target tile', which it tries to reach, and arriving to which is the basis for the ghost's behavior. "All of the ghosts use identical methods to travel towards their targets, but the different ghost personalities come about due to the individual way each ghost has of selecting its target tile." (1)


 References:

1. http://gameinternals.com/post/2072558330/understanding-pac-man-ghost-behavior

Image References:

1. https://www.google.com/search?q=pac+man&newwindow=1&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiI6_eQraLQAhUJ7mMKHT4BBmcQ_AUICCgB#imgrc=ukfIYq6AlYNIJM%3A

The emmersion of virtual reality (VR) in video games

Earlier today a friend of mine was showing me the reactions of professional video-game testers to VR video-games, and I was thinking about how awesome VR is as a concept, but also how dangerous and detrimental it could be to society.

What is VR?
"Virtual reality is a computer-generated environment that lets you experience a different reality. A VR headset fits around your head and over your eyes, and visually separates you from whatever space you're physically occupying. Images are fed to your eyes from two small lenses. Through VR you can virtually hike the Grand Canyon, tour the Louvre, experience a movie as if you are part of it, and immerse yourself in a video game without leaving your couch." [1]

When was it first conceived?
Starting in the 1950's, experts of all sorts of disciplines have talked about or alluded to the idea of virtual reality. "The first references to the concept of virtual reality came from science fiction. Stanley G. Weinbaum's 1935 short story "Pygmalion's Spectacles" describes a goggle-based virtual reality system with holographic recording of fictional experiences, including smell and touch." [2]
People from areas like theater, literature, and computer science have found interest in this and, one of the very first applications of it was in 1968, when "Ivan Sutherland, with the help of his student Bob Sproull, created what is widely considered to be the first virtual reality and augmented reality (AR) head-mounted display (HMD) system"[2] 



References:
[1] https://www.cnet.com/special-reports/vr101/
[2] https://en.wikipedia.org/wiki/Virtual_reality

Image Sources:
[3] https://www.google.com/search?q=virtual+reality&biw=2144&bih=1051&noj=1&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiO-rmO5pDQAhUEQiYKHYcGCfsQ_AUICygE&dpr=0.9#imgrc=COd8Rc2BkzorGM%3A

Generic Programming

Generic programming is defined in Musser & Stepanov (1989) as follows,

Generic programming centers around the idea of abstracting from concrete, efficient algorithms to obtain generic algorithms that can be combined with different data representations to produce a wide variety of useful software.

— Musser, David R.; Stepanov, Alexander A., Generic Programming

or, more simply put,

[Generic programming is the process of lifting] algorithms and data structures from concrete examples to their most general and abstract form.

— Bjarne Stroustrup, Evolving a language in and for the real world: C++ 1991-2006

This is achieved by writing algorithms in terms of "types to-be-specified-later".

Generic programming is a characteristic of computer science in general, as opposed to a characteristic of a specific programming language. It deals with algorithms, and can be applied to most languages, with some differences. For example, these "types to-be-specified-later" are called generics in languages such as Java, C#, F#, Objective-C, and Swift; parametric polymorphism in ones such as ML, Scala, Haskell; and templates in languages like C++ and D;

Each language also has its own way of treating these types, with whatever advantages and disadvantages those ways may entail (there is no such thing as perfection).

Below are some more details about, and an example of Generic Programming.

 

References:

https://en.wikipedia.org/wiki/Generic_programming#Generics_in_Java

Image References:

Image 1: https://www.google.com/imgres?imgurl=http%3A%2F%2Fimages.slideplayer.com%2F16%2F5079496%2Fslides%2Fslide_1.jpg&imgrefurl=http%3A%2F%2Fslideplayer.com%2Fslide%2F5079496%2F&docid=9BEYxVSEtYQM1M&tbnid=qlXd3qo9VaSJEM%3A&w=960&h=720&noj=1&bih=875&biw=1637&ved=0ahUKEwjo7Na0qP7PAhUEOyYKHRHvAPcQMwhcKDMwMw&iact=mrc&uact=8

Image 2: https://www.google.com/imgres?imgurl=http%3A%2F%2Fimages.locanto.in%2F1195428230%2FLearn-use-of-Generic-Programming-in-Java-5_5.jpg&imgrefurl=http%3A%2F%2Fghaziabad.locanto.in%2FID_497963856%2FLearn-use-of-Generic-Programming-in-Java-5.html&docid=kKEqqPItgOTJwM&tbnid=Ov6i86DzN9gOJM%3A&w=283&h=178&noj=1&bih=875&biw=1637&ved=0ahUKEwjo7Na0qP7PAhUEOyYKHRHvAPcQMwhgKDcwNw&iact=mrc&uact=8

MRI's to be used in fetal health tests

So far, ultrasounds have been the main tool for fetal health examinations.
Ultrasounds are affordable and easy to transport, but it doesn't generate as much information as an MRI scan, which could potentially measure the volume of specific chemicals in the placenta and in fetal organs.

This method would be especially helpful for prevention purposes, to catch potential malfunctions and/or issues, before they actually pose a threat to the fetus's health. For example, as found by Ellen Grant’s group, "measurements of oxygen absorption rates in the placenta can indicate placental disorders that might endanger the fetus". The timely measurement of these rates could prove to be the determining factor for the survival or not of the fetus, and they cannot be performed using ultrasound scans.

Hence it is very fortunate that MIT's Computer Science and Artificial Intelligence research department was able to generate "a new algorithm for tracking organs through sequences of MRI scans".

Picture1 - Fetal Ultrasound

Picture 2 - Fetal MRI

 Picture 3 - Placental MRI

References:
http://news.mit.edu/2016/algorithm-mri-scans-fetal-health-1021
Image Sources:
1 - https://www.google.com/imgres?imgurl=https%3A%2F%2Fupload.wikimedia.org%2Fwikipedia%2Fcommons%2F2%2F2f%2FCRL_Crown_rump_lengh_12_weeks_ecografia_Dr._Wolfgang_Moroder.jpg&imgrefurl=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FUltrasound&docid=6Kozj1SQ0QlfvM&tbnid=gAWPT2uSwNGlCM%3A&w=976&h=735&bih=1039&biw=1070&ved=0ahUKEwi0-PnMme3PAhXLLSYKHRKDBMEQMwgwKAAwAA&iact=mrc&uact=8
2 - https://www.google.com/search?q=MRI+SCan&source=lnms&tbm=isch&sa=X&ved=0ahUKEwjtityome3PAhXHwiYKHR3oC18Q_AUICCgB&biw=1070&bih=1039&dpr=0.9#tbm=isch&q=fetus+mri&imgrc=nLMWN29OmcR9DM%3A
3 - https://www.google.com/search?q=placental+mri&biw=1070&bih=1039&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiRvILxme3PAhXM5iYKHXqeB5wQ_AUIBigB&dpr=0.9#imgrc=_S7UIA0OzgzrDM%3A

Deep Dream by Google

What it is: "A computer vision program created by Google which uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dreamlike hallucinogenic appearance in the deliberately over-processed images"

How it works: 

• detects patterns in images to enable the automatic classification of these images
• Like GP(Gennetic programming) programs, it can be trained
•  once that is done, it can be run in reverse, which facilitates the better understanding of the structure of the neural network.
• After enough reiterations, imagery will be adjusted so that it results in a form of pareidolia, which is what is used to algorithmically generate psychedelic and surreal image.

Why is this helpful: It invokes the need and possibilities for further study, because its resemblance to LSD- and psilocybin-induced hallucinations "is suggestive of a functional resemblance between artificial neural networks and particular layers of the visual cortex."

Examples of DeepDream images:

Here is a really cool sample of a DeepDream video:
https://www.youtube.com/watch?v=DgPaCWJL7XI

References:
https://en.wikipedia.org/wiki/DeepDream

Image references:

• https://www.google.com/imgres?imgurl=https%3A%2F%2Fkver.files.wordpress.com%2F2015%2F07%2Fspaghetti-meatballs-become-really-frightening.jpg&imgrefurl=https%3A%2F%2Fkver.wordpress.com%2F2015%2F07%2F08%2Fgoogle-deep-dream-ruins-food-forever%2F&docid=4Gs8Ro8qmVjLXM&tbnid=E7SgFL-0trC7pM%3A&w=1150&h=766&bih=912&biw=1920&ved=0ahUKEwi_7IPj59vPAhWs7YMKHZpMAYUQMwgiKAQwBA&iact=mrc&uact=8#h=766&imgdii=E7SgFL-0trC7pM%3A%3BE7SgFL-0trC7pM%3A%3B4KvKo7X_zU9TuM%3A&w=1150

• https://www.google.com/imgres?imgurl=https%3A%2F%2Fi.ytimg.com%2Fvi%2F0svabYFWXek%2Fmaxresdefault.jpg&imgrefurl=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3D0svabYFWXek&docid=9lK-i24lZv1BAM&tbnid=6-G21Z2Zyu0LQM%3A&w=1280&h=720&bih=912&biw=1920&ved=0ahUKEwi_7IPj59vPAhWs7YMKHZpMAYUQMwgmKAgwCA&iact=mrc&uact=8

GP - Fitness & Breeding

In my previous previous blog 'An Overview of Genetic Programming', I went over the general idea of genetic programming. In its basis, GP is testing certain smaller programs, determining whether they are "fit" based on a model of natural selection, and then breeding those programs. There are various ways of  breeding/creating new programs from the old one; Let's look at that in more depth:

Reproduction: The computer literally reproduces the program - i.e. creates a duplicate of it - and copies it to the new population.
Crossover: The computer takes two programs(that have passed the fitness test) and creates a new offspring program by recombining randomly chosen parts from those two selected programs.
Mutation: the computer takes an existing 'fit' program randomly, and changes a chosen part of it.
Lastly,
Architecture-altering operations: in this case, the computer chooses an architecture-altering operation from an available list of operations and applies it to one selected program , hence creating the new offspring program for the new population

References:
http://geneticprogramming.com/tutorial/

Image References:
http://previews.123rf.com/images/iqoncept/iqoncept0905/iqoncept090500016/4813642-An-illustration-of-a-DNA-double-helix-with-one-highlighted-in-red-Stock-Illustration.jpg