CSCC46 Blog 2019

The Internet of Things(IoT) is a system relying on physical devices which, collecting information and sharing those information through the internet without any human efforts. Thanks to cheaper chips and the great power of internet, billions of physical devices around the world are able to be connected to the internet, as a result, IoT becomes more efficient.

The network constructed by the IoT devices looks like the graphs above, and it has many paths inside the network. Every path starts from the user input data(data sources) and passes the application logic services and gateways(intermediate nodes). Finally, the information in the network reaches the storage and display stage (upper-level nodes). Overall, it is evident that IoT creates a network developed by hardware devices in an effective way. Since it does not require any manual work, it speeds up the transmission of information.

Some of my concerns is whether the fast-speed IoT be a potential threat to the public’s privacy and whether the information in the network is recorded without the public’s permission.  The fact is that sensors in the network will collect some extremely sensitive data such as what you say and do in the private space. However, as many IoT devices cannot be patched effectively, they have become the target of many hackers. The hackers are now able to get private information from the device level, and the users’ information security is put in the weak position.

One of the ways that we express the relationship (of what)is like the graph above. Node B represents the user and Node C represents the IoT device. Node B does not trust Node C since IOT wants to record some parts of data and keeps the privacy. By the theory of structural balance, in order to keep the whole network balanced, we need to find another node A which can be another device, another person to be trusted or another group (decentralized).

At present, people think about combining the blockchain technology with the IoT to increase privacy security. The advantage of blockchain is that the block will be broadcasted to many parties to get the validation of information before adding it to the chain. The third parties works in the same way as node A which I want to find out previously. After the information gets validated, it is safe to go released.

IoT still has a few problems currently, but in the near future, these problems are likely to be solved since the public and the market pay much attention on it. Hopefully, IoT can be integrated into people’s life and can be applied in the business system someday.

References:

https://www.zdnet.com/article/what-is-the-internet-of-things-everything-you-need-to-know-about-the-iot-right-now/

https://datafloq.com/read/securing-internet-of-things-iot-with-blockchain/2228

One unique property technology and its concepts have is its applicability to different fields that may not share obvious connections. Consider the brain: arguably one of the most important organs in the body due to its complex structure and its role to control thought, speech, movements, and other involuntary processes such as breathing, circulation, etc., and Neuroscience: the study of the structure and functions of the nervous system. The brain, too, can be analyzed using graph theory and networks to observe brain network data, synaptic links, and various other properties.

This post addresses the correlations between the brain and graph theory. It is fascinating to be able to take concepts learned and find areas such as neuroscience to apply these ideas to. As someone with a desire to use my skills to help others and contribute to the advancement of medicine, this subject greatly reflects the types of activities I can contribute to with my knowledge. Furthermore, choosing this subject helps portray the idea that networks are a useful computational model that makes mapping out complex structures easier to understand.

The Brain and Graph Theory

On the surface, the brain looks like a complex structure with many different components. The brain includes millions of neurons that are interconnected through their different axons. But looking deeper, the brain can be looked at as one complex network where the nodes are the brain’s neurons, or entire brain regions depending on the study, and the edges represent the different connections between each neuron. These connections can either be binary or weighted, and directed or undirected depending on the study.

The two common brain mappings using graphs are primarily structural and functional mappings of the brain, and these are used to model different connections. Structural graphs are generally sparse since the most possible structural connections in a given nervous system do not exist. These, in turn, are temporally relative stable. Whereas, functional graphs more dense as they record statistical dependencies among neuronal time series.

The brain could be mapped using a simple graph (ie. nodes & edges are homogenous with each other). However, annotation of nodes/edges can address the simplicity of the model and allow an additional layer of data to be linked to network elements. This type of graph is useful for identifying biologically meaningful networks (ie. strong/weak communities). Additionally, simple graphs can be used to map multidimensional relationships expressed in multilayer networks. These different layers are used to simulate different types of interactions such as synaptic links, temporal correlations, gene expression, etc.

Lastly, a notable observation for mapping brain connectivity arises from the idea that connectivity drives the functional specialization of specific areas of the brain. This means that different areas of the brain have specific connectivity blueprints that indicate their community, resulting in the predicability of their functions. Furthermore, the degree of a neuron, which are the number of connections by a node, or the strength of its connections are a strong indicator of centrality in the brain.

All in all: as complex as a structure like the brain is, using familiar techniques such as graph theory can be used to break it down into specific communities or regions that can be further analyzed. And ironically, the premise of computer science is prevalently evident in these ideas, where you must look at a complex problem, break it down to smaller, easier to manage subproblems in order to complete the full picture.

Sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136126/

Humans are social animals that are constantly looking for social acceptance and approval. It has been hardwired into our brains evolution since being in a group dramatically increases the chances that the individual will survive. It’s why we try to constantly track the responses of those around us, and is why people who don’t notice subtle social cues or body language have difficulty communicating. The difference between interactions in a physical and online environment is that, while out brains still function the same way, some of the stimulus required to help moderate our behaviour is blocked, and in some cases, our tribal behaviour is even intensified.

The primary goal of any site on the internet is to maximize user engagement, which is to say, make sure the user stays on their site for as long as possible. This is especially true for social media and networking sites that depend on selling ad space for profit. Given a human’s tribal brain and a platform where people share information and content, social media’s best bet to keep people on their site is to show them what they want to see.

Take for example Facebook. Facebook tries to keep their users online by making them feel comfortable and accepted, and what better way than to connect a user with people that already accept them, their in-person friendships. Given Fb can’t be entirely sure who a user’s every friend is, it extrapolates that information from the people they’re already friends with, exploring mutual acquaintances using the social networking concept of triadic closure. Triadic closure here is the idea that if a user has strong bonds with two other users, two close friends, then it is likely that the two other users may also be friends, or at the very least, know or be interested in knowing each other. For a user on Fb, this means they are generally connected with people who are at least acquainted with the people they already know.

Another aspect of Fb beyond friending someone is the sharing of content. As Fb wants to keep users on their site for as long as possible, it is in their best interests to show a user things they will like and want to see more of. At first, this information may come from the user’s friends as they may, most likely, have common interests. But as the user interacts with the system, liking or disliking, commenting and sharing, Fb gets a better idea of what appeals directly to the user in question and directs similar content their way, so as to keep them engaged.

In the physical world, people moderate themselves due to a fear of rejection from the social structures they are a part of, even if they hold opposing views from others in their community. People who have to live and work with each other are more willing to communicate and discuss their views in hopes of reaching an agreement, due to the social consequences of antagonistic relationships in-person. 

On the other hand, the social cost of angering someone or garnering disapproval is comparatively smaller online, where if a user disagrees with another on a certain topic, there may be no obligation on either side to try to convince the other of their perspective. As such, users can easily distance themselves from those they don’t have much in common with, while seeking the acceptance of groups they already identify with. In online communities where the cost of entering and leaving is nothing, it is more work for a user to try to advocate for moderation than to simply leave and find their own echo chamber. 

This homophily, the tendency of individuals to associate with similar others, is an inherent part of human biology that social media and social networking sites can use to keep the attention of their users while also quickly delineating individuals into groups they can advertise to. This is beneficial for the platform, but turns any online interaction for the user into an echo chamber that bounces their own views and opinions back to them, which can be intensely harmful as it creates an environment of approval for all behaviours, including ones that are prejudiced or destructive.

Preventing groups with self-affirming content requires members to have relationships with communities that may have directly opposing views that the original group. This should theoretically be possible, at the very least for people undecided on their opinions, but is not a sustainable state in the long term, as per the Theory of Structural Balance for networks. It suggests that there are certain stable configurations that networks may morph, into over time: specifically, the idea of ‘the enemy of my enemy is my friend’ and ‘the friend of my friend is my friend.’

The latter is what is applied alongside triadic closure to suggest friendships on Fb, while the former forces the idea that, if there is friction or enmity between the two groups, anyone caught in between would at some point have to make a choice. Which is to say, if a user if friends with two people or groups that hate each other, at some point it is likely they will pick a side. This denotes the isolationism of these networks as their most stable form within the current system.

In an effort to keep users on their platform, social media strives to create accepting communities. But as a result of human tribal mentality, social network interactions and the platforms’ own goals, this acceptance comes at the cost of isolation in a cycle of approval without mediation from outside voices.

Citations:

• https://www.nyu.edu/about/news-publications/news/2018/june/jay-van-bavel-on-fake-news.html
• https://arxiv.org/pdf/1809.06057.pdf
• http://www.cs.toronto.edu/~ashton/cscc46/lectures/lecture3.pdf
• http://www.cs.toronto.edu/~ashton/cscc46/lectures/lecture4.pdf
• https://www.wikiwand.com/en/Balance_theory

Rail network is a crucial network which has to be made efficient. The increasing density of population needs to be considered as a key factor in the entire railways network. The train timetable scheduling can have defects because of large number of trains and passengers which cause real time traffic delays. Such delays can be prevented with a better evaluation and identification of the rail system performance. This can be done by considering a Train State Graph (TSG).


A train state graph is a directed graph with horizontal axis as time stamp and vertical axis as train index. The time stamp shows an occurrence of an event such as arrival, departure, delay, braking. Each nodes represents a unique state of a train. The vertices in the graph shows the transition of train.
As suggested in the paper, we can set a constant time step (e.g. 50 s), so that the longest time interval between two discrete events is the constant time step. This would help to analyze events but will not be effective if the time constant is too small or too big. A solution to this problem is to mark the delays caused by major events and then remove them and decrease the delay for future. In other words, simply identify and try to eliminate the root causes of the delay.

Source:
https://doi.org/10.1080/0305215X.2017.1284832

Throughout the last few decades, the United States has been working to stop the illegal cocaine trade. Most of the cocaine is produced in Colombia where traffickers send them through various routes in Central America and into the United States. The United States use interdiction to seize cocaine shipments at several checkpoints in Central America and to make arrests. However, the NarcoLogic model is proposed to showcase that interdiction may not be working as intended and instead, it may end up opening up new routes in Central America that are more well hidden. Currently, the United States is spending as much as $18 billion from its drug control budget towards interdiction. However, over the last 20 years or so, cocaine prices have dropped and deaths by cocaine overdoses have increased. This leads to people thinking that interdiction may not be effective.

As you can see from the graph, the red nodes indicate active checkpoints while the gray nodes indicate inactive checkpoints that may have been interdicted. Dashed edges indicate trafficking routes between checkpoints. Important red nodes are the one at the bottom right in Colombia which is the main source of cocaine as well as the ones near the Mexican border which is the main destination. There are a lot of inactive nodes in Costa Rica and Panama where there used to be a lot of checkpoints. But interdiction has rendered them inactive so in turn, Colombia gets a higher degree in the network by connecting edges all the way to Honduras and Nicaragua. This decreases the overall diameter of the network as well as average path length which means less checkpoints and harder for law enforcement to track cocaine shipments. Most edges in the graph are local bridges with relatively low betweenness which showcase the many paths that cocaine can get trafficked across Central America. So even if one local government decides to take a stand against cocaine trafficking, people can easily use other routes which shows how ineffective interdiction can be. There is barely any triadic closure which makes sense when you’re mapping routes because routes are all about taking shortcuts. This proves that cocaine trafficking can manage to be very efficient without needing extra routes since law enforcement in these countries often do not have to resources to stop it.

In conclusion, interdiction has not been very effective at stopping cocaine trafficking since it has instead formed shorter routes and those that are more spread out into either remote regions of areas of high poverty.

We know that graphs exist everywhere in the world and on any scale. Unsurprising, even minerals have networks and graphs at a cellular level. We are going to explore graphs of a particular mineral, zeolite, and how an MIT team managed to apply graph theory to predict the transformation of zeolite types.

Zeolites are microporous, aluminosilicate minerals. It is a very powerful mineral, and it is most commonly used as adsorbents, which hold molecules of a gas or liquid, and catalysts, which increases the rate of a chemical reaction. Examples include speeding up the “cracking” of petroleum in refineries, as well as freshening up your cat’s litter box.

Zeolites eventually turn into quartz. However, before that happens, this mineral is always in a metastable state. It can transform into other metastable states, some of which are already known. They can be produced with organic chemical compounds as well, but because organic material is expensive, it would be more economical to produce it through transformations.

What the researchers wanted to find was pairs of zeolites that are readily able to transform into one another. They used AI to read 70,000 research papers on zeolites. The results from this research and analysis were that a topological description based on graph theory identifies the relevant zeolite pairings. The graph-based descriptions were based on locations and numbers of chemical bonds. These descriptions not only confirmed existing pairs but also helped discover unknown pairs, and it is proven to predict forms of zeolites that can intergrow.

The findings can also help explain theoretical transformations that do not seem to exist.

It is amazing that graph theory could find itself in the obscurest of places. These findings could lead to the production of a whole new set of zeolites, expanding its ever-growing practical usage.

References

https://phys.org/news/2019-10-mathematical-approach-zeolites.html

https://web.archive.org/web/20090215184310/http://www.grace.com/EngineeredMaterials/MaterialSciences/Zeolites/ZeoliteStructure.aspx

Hong Kong has been on global headlines since June, with 2 million (that’s a quarter of the population) marching on the streets demanding the withdrawal of the extradition bill, which has since evolved into anti-government protests . With just over 7 million people in the semi-autonomous city, some people wonder – why hasn’t the entire population turned against the government yet? Everybody has seen the police brutality and the ignorance of the government – yet, everyday on social media, there are people who choose to ignore the police-state like and authoritarian actions by the government and instead focus their attention on protesters fighting for Hong Kong’s freedoms?

It can simply be explained with the Theory of Structural Balance. It became clear through the intuition [Heider ‘46] that the “enemy of my friend, is my enemy”. Consider the following scenarios:

Protesters are blocking my way to work. I dislike them. The protesters dislike the government. Thus I support the government’s actions. (Example of exactly one edge labeled +)

The police force has stepped on my freedom (negative edge). The government controls the police force. (positive edge), thus I dislike the government.

These two intuitive examples show exactly that the Balance Theorem [Cartwright-Harary] can be applied to the situation in Hong Kong. Take note of the underlined sections of the scenarios – we can use local view to fill in this edge (without previously knowing) to achieve balance. Overall, the network is clearly separated into two notable factions – people who are in support of democracy, and people who are supporters of the Beijing controlled government.

Sources:

https://www.pori.hk/police_performance_eng

https://www.scmp.com/news/hong-kong/politics/article/3017622/public-increasingly-backing-radical-hong-kong-protesters

Gaming has become the most popular form of entertainment, and in today’s time, there are so many different and unique ways to experience it. There are many different gaming consoles to play on, and many different platforms to broadcast your experience, one of them being Twitch, the most commonly known and popular live streaming service.

Twitch streamers can broadcast their games to viewers in real time to entertain, much like watching television or a movie. Viewers can even chat with the streamer, as well as donate money to show their support for the stream. Additionally, viewers can follow streamers to not only show support, but to be notified when they go live, similar to how people subscribe to YouTube channels.

The growth of a Twitch streamer can be shown to follow Power Laws, which state that in a network, majority of common nodes have very little connections, and few important nodes have a lot of connections. This is exactly how a twitch streamer grows in popularity, as the average viewer may follow a streamer, which increases the connections the streamer has, but the connection between that average viewer to anyone else has not changed.

In this picture, suppose node A is a twitch streamer, and nodes B – F are viewers that follow streamer A. Even though B – F are following streamer A, they can interact with each other through the community that has been built, and in this picture, B and C are connected. This is also seen through viewer X, where X is connected to A and became a part of this community, and ended up connected to B.

Overall, Twitch is more than just a website to watch a person broadcast themselves playing a video game. It is a community of people who share the same passion, and the larger the streamer is, the larger their built up community is as well.

References:

https://www.futurelearn.com/courses/social-media/0/steps/16046

As the rapid development of social media especially in the mobile platform, various homogenous social application has been developed and widely being used(Instagram, Twitter, Facebook, e-mail). Users can share information on the web anytime, anywhere, resulting in large amount of user data. Social network actually have a lot of practical value under the era of big data. Therefore, the data in many fields can be detected by the special instruments such as police departments.

The classic drama PERSON OF INTEREST tells a story that using a backdoor of a “machine” to detect the possibility of the happening of crime, and figure out who might be the potential victim or criminal. The mechanism beneath the “machine” is the analysis of the big data comes from social network of the victim or criminal, for example, if a married man has a mistress, they will try to text each other, the information of their communication will express what are they thinking and planning, so the wife of the married man might become a potential victim.

The big data from personal social network can be used to:

• To reveal hidden relationships, detect any criminal patterns, and prevent security threats.
• Link personal real-time social account to discover unusual user behaviour and suspicious transactions to expose fraud base on the his historical account activities.
• Test new sources of information and various data as evidence of criminal activity, such as the Internet, mobile devices, transaction processing, email, and social media.

In 2010, LAPD (Los Angles Police Department ) become the first employ data technology and information about past crimes to predict future unlawful activity. The technologies they were using are designed to predict where and when the crimes are likely to occur in next 12hours by examining the 10 years of data.

reference:

The Marvel Cinematic Universe is one of the most successful franchises in the world, earning billions but what about other more unknown cinematic universes. I am not talking about well known cinematic universes like the DC cinematic universe nor the Star Wars cinematic universe but rather a several clusters of films and television shows that are all unique with different actors and directors that become interconnected narratively through the roles and stories in these mediums. These cinematic universes can be viewed as a graph where each movie/show is a node on the graph whereas the edges would be the narrative threads or characters that connect the films together.

An example of this is would be the United States Space Program cinematic universe (USSPCU) which contains the films The Right Stuff, First Man, Hidden Figures, and Apollo 13. The Right Stuff is a film made in 1983 about the first 15 years of the NASA space program and has an actor who portrays Gus Grisson who we see him become Mercury Astronaut his character connects to both First Man and Apollo 13. In the 2018 film the First Man which follows Neil Armstrong and the Apollo 11 mission, we are reintroduced to Gus Grisson who becomes an Apollo 1 pilot who died during one of its launch tests. Both of these films are connected to the 1995 Apollo 13 film through a brief scene where the Apollo 13 astronauts meet Neil Armstrong and a key conversation between Jim Lovell (portrayed by Tom Hanks) and his son about the dangers of their mission which mentions the death of Gus Grisson. Another connection is the character Deke Slayton who is an astronaut recruit in The Right Stuff who becomes an executive who oversees the NASA space program in The First Man and Apollo. Hidden Figures, a 2016 film can be seen as a side story connected The Right Stuff overseeing the Mercury 7 launch which also shares 4 characters with each other. These links show how films can unintentionally become interconnected by the story or narrative threads and can tell a multi-movie story that we might not otherwise see to create a cinematic universe. Films based on historical events such as the USSPCU is an easier example to find as many of these historical pieces can be linked through real people that the actors’ portray. However, there are two other theories that feature only fictional entertainment to create new universes.

There is a theory that is based on television shows, it is called the “The Tommy Westphall Universe” (refer to the image below), which states that there are more than 400 interconnected TV shows and that they are all part of a dream from the character named Tommy Westphall, who is from a TV show called St. Elsewhere. This universe was constructed through the various crossovers and spin-offs with St. Elsewhere and other shows thus creating an ever-growing universe where any show that collaborates with anyone shows from the existing universe would then be added to the overall universe.

The other theory is the Pixar Animated Cinematic Universe which states that each Pixar Movie is related through several easter eggs that include cameos from other franchise characters such that they can create an overarching narrative.

References

Gott, Davey. “The Pixar Cinematic Universe!” NCI FM, Native Communications Inc, 2 Apr. 2019, www.ncifm.com/the-pixar-cinematic-universe/.

Hughes, William. “Do Historical Movies about the Same Event Count as the ‘Original Cinematic Universes’?” AV Club, AV Club, 23 Nov. 2018, news.avclub.com/do-historical-movies-about-the-same-event-count-as-the-1830625342.

Luling, Todd Van. “Fan Theory Proves Almost All TV Shows Exist Within Same Universe.” HuffPost Canada, HuffPost Canada, 16 Sept. 2015, www.huffingtonpost.ca/entry/tv-show-fake-tommy-westphall-universe_n_55f84ba1e4b00e2cd5e8118a?ri18n=true.

“The Pixar Theory an Interactive Story.” The Pixar Theory, 97th Floor, www.pixartheory.com/.