Week 12

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This week I made some minor changes to the python script so now the generated file is in the right format. I also made some major changes to my paper and have an updated draft of it. Next week I’ll be able to get together a draft of my poster and then I should be pretty much set to start extending this work in my psychology capstone next semester.

Week 11

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This week I re-structured things again. I decided to look into other methods of generating a visualization and decided to separate that process out from NVDA. Under this design, the developer runs NVDA with my plugin installed and that generates a text file. Then the developer navigates to my web-based visualization tool, uploads the file, and then views the generated visualization. I have a working demo of the visualization tool now, but I’m still working on ironing out some of the issues in generating the text files (specifically coming up with a method for splitting the chunks pulled out appropriately).

Week 10

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At this point, since I’m still having issues with DrawString, I decided to start looking into alternative options. My adviser recommended that I look into Tkinter again, but after some playing around with it and digging into it, I learned that Tkinter is fundamentally incompatible with NVDA because it does not produce accessible content. wxPython is often used to make NVDA-related GUIs, so I  looked into that some but I don’t think it will let me make quite what I want to, so I’ve decided to back up and change plans slightly. I’m going back to the idea of just creating a clean-as-possible transcript of what information NVDA is able to gather when it’s running and developing a script completely outside of NVDA that can take that transcript and create a visual representation from it. I’ve started working on the transcription script and will hopefully have that done by the end of the week so that over the weekend I’ll be able to get a visualization together and ready to present.

Week 9

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I found this function to create a font and played around with it until I had a version that was recognized by the GdipDrawString function. Now I have code that compiles without errors and causes no runtime errors, but the text is still not displaying (or maybe just not displaying how & where I thought it should). So I still need to play around with that problem, but at least I know that I have found a function that produces the right type of thing to pass to GdipDrawString.

I spent quite a bit of time this week drafting my paper and sent a rough draft to my adviser Friday. He responded quickly enough that I was able to make revisions before submitting a draft for grading. Overall I’m happy with the way the paper is shaping up.

Week 8

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This week I’ve focused primarily on drafting my final paper. So far I have a general outline of my paper (abstract –> introduction –> related work –> framework and design –> implementation –> conclusions and future work), and I’ve fully drafted the abstract, introduction, and related work sections. I have a clear idea of what I want to go in each of the other sections, so I should be able to have those sections fully drafted by this time next week.

In terms of the software development component of the project, I almost have a complete (although not especially fancy or sophisticated) visualization tool, but I have spent most of the last week making lots of progress at getting nowhere. All I absolutely need at this point is to figure out how to make a call to the function GdipDrawText, but because gdip is the way python handles GDI+ — a component of .NET made to work for C, C++, and C#, the documentation on it is fuzzy and somewhat sparse. I still have a couple of ideas to try, though, and I’m trying to find people who have experience using it.

Week 7

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This week I started playing around with creating the visualization tool piece of my project. In doing that, I learned a lot of the more finicky details of NVDA. It uses an internal installation of Python, which makes sense for users because they don’t need to already have Python installed on their machine to use NVDA. However, this makes installing packages more difficult and requires basically copying and pasting the contents of the package into a directory within the NVDA set up so that its installation of python can use it. To test small changes I want to make, I’ve found the NVDA Python console to be really helpful, but it’s only accessible when NVDA is running which can sometimes slow down the testing process. Overall, I feel like this project has gotten me really into the weeds and I’ve begun identifying specific problems in the way NVDA tries to go about reading this type of content, but I’m not entirely sure of what solutions would best fit with the existing NVDA structure.

Week 6

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I found some quirks and issues with the way NVDA works in different settings. I can now get a transcript with the appropriate details by using object review mode in Firefox, but not by navigating the objects normally or while in Chrome. This gets at the issue of “not knowing what you don’t know” and screen reader users needing to have significant experience and ideas of how to work around poorly designed web layouts. Because at this point I don’t believe there’s an easy way to generalize the automatic transcript making and restructuring process, I think identifying these issues and barriers and creating example demonstrations may be the best end result for my project at this point.

Week 5

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This week I’ve gotten minorly stuck. I know that NVDA is able to grab — and speak — certain information about the data visualizations I’ve created(it might say, for example, “graphic New York” when I navigate to a point representing the population of New York). But when I try to grab information about those navigator objects, I end up grabbing objects whose roles are either “unknown” or “section.” These objects also don’t have names, descriptions, basic text, or any useful attribute containing the text information NVDA is able to provide, but isTextEmpty also consistently returns False. At this point I’m not entirely sure where I should be looking to keep digging — I’ve looked into the code for NVDAObjects, TextInfo, speech.py, etc. I’ve also dug into forums and things and found an NVDA development mailing list, so I posted a response to a forum thread asking for clarification about something that might be relevant to this issue and sent a more detailed account of the issue I’m facing and request for guidance to the mailing list. Hopefully I’ll get this sorted out by this time next week.

Weekly Update (09/19/2018)

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I’ve been researching Voronoi graph regions and their usage in recommendation system. I’m particularly interested in finding open-source implementation of Voronoi graph regions usage. I’ve also been reading and researching FiveThirtyEight’s gerrymandering maps and how they created their maps of different districting schemes. I am especially interested in the algorithm they used for one of the districting plans. Lastly, I decided on a mapping tool, Leaflet, which is an open-source JavaScript library for interactive maps. I started building the visualization tool using Leaflet and the algorithm taken from FiveThirtyEight.

Weekly update 4

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  • Met with Andy Moore and talked about projects regarding natural disasters.
    • Realized that many of the ideas were too big for a semester, and started researching in Earthquake Early Warning systems
  • Worked on Quiz 2, and collected papers for each topic.
  • Worked on Annotated bibliography.

Weekly update 3

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  • Read the papers about how to read a paper.
    • Met with Michael Lerner regarding one of the strategies talked about in his research last year.
  • Found 5-7 papers related to each of the three topic areas.
  • Attempted the Quiz 1 for CS388.

Weekly Update 2

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  • Met with Ajit to filter ideas regarding parallel computing, and machine learning.
  • Emailed Andy Moore in Geology to talk about Earthquake and Tsunami predictions.
  • Emailed Charlie for suggestions regarding my Structure From Motion idea.
  • Searched for more specific details on work done in similar areas.

Week 2

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In this past week, I’ve found and simplified 2 types of data visualizations that I think would be useful for testing purposes: a slopegraph and a bubble graph. I’ve run some preliminary tests with NVDA on them to see how it’s trying to process them and developed basic plans on how to process that information in a way that makes more sense to NVDA.


In the upcoming week, I plan to finish gathering my data visualization test sets and coming up with those early transformation ideas and begin looking into how to get NVDA to transcribe what it’s looking at and output that to a file so it’s easier to track and document its process.

Flannery Currin Week 1

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This past week I reached out to Dave and he agreed to be my mentor for this project and prepared my presentation slides based on my project proposal. I dug into the NVDA developer’s guide and walked through some basic examples of creating and testing NVDA add-ons, so now I have a better understanding of what developing an add-on looks like. I met with Dave to go over my general approach and strategies to adapt that approach as necessary as time goes on. I also reached out to the director of the program where I worked over the summer to ask her to connect me with a colleague of hers or one of his students who have done more work in this space and who could help me especially at this stage of my project.

In the upcoming week I hope to develop criteria for the types of data visualizations I’ll be focusing on and begin creating examples of those and playing preliminarily with how NVDA digests them. I’d also like to go back over my literature review and begin working on paring it down some and shaping it into a Related Works section of my final paper.

2/7 Weekly Progress Report

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This past week I set up a git repo for my senior research, and am now working to create a system diagram for my project.

In addition, this week I will be working through the source code of scikit-learn to better understand how they create their decision trees, and identify which parts of the extensive source code are required for my project, so as to reduce the space required for the research.

Project Update

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So this week I went to get DOCK, found out that I need a license to use it, and that licenses are free for academic purposes.  I’ve got a DOCK license and I’m working on learning the ins and outs of it, but I’ve got a pretty good handle on it already.


Things I have to do for the project to be finished:

[X] – Not done

[O] – Done

[I] – In progress


[O] Get a DOCK license

[I] Familiarize myself with DOCK

[I] Get a linux VM set up that can run Singularity

[X] Install Singularity

[X] Create Singularity Virtual Machine

[X] Write Paper

Adhish Adhikari Senior Project Proposal

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As a student I have realized that being able properly understand my personal academic data is very
important. From grades and transcripts to majors and courses, I need to be able to clearly acquire the information about my academic standing. We can take for example the data for a student’s degree progression. While a college degree has it’s hierarchy (a degree can be broken down into majors, minors and general requirements, majors can further be broken down into first year courses, major requirements and electives and so on), the course progression each semester is also chronological. Thus, I believe that the idea of using both treemaps and timeline data visualization for such data seems to be an idea worth exploring.

The Idea

Studying at Earlham College, it seems only natural that I’ll work with Earlham College’s degree progression data. As of right now, Earlham College provides information on academic standing (including, majors, grades, courses taken, etc) using a web based platform called Degreeworks. While Degreeworks does have all the relevant information, it lacks presentation. Thus, many students can’t really see the big picture. The interface is very traditional and major chunks are divided into lists. It’s difficult to imagine where you are in the road map to graduation by just staring at the list. A student can see that they have taken x credits out of 120 credits for their graduation requirement and y credits out of z requirement (for majors) credit. However, there is no relativity. These two things seem very disjointed even though they are deeply connected.

My goal for the senior project is therefore, to create a visual version of the Degreeworks which I call Degreeworks V2. By providing this visual interface for Earlham Degreeworks, I want to help Earlham College students to effectively visualize their academic standing. Like I discussed earlier, I will be using treemaps and timelines in order to visualize the data. Like I said, just being able to know how manycredits I have taken or how many are left does not give me a good sense of where I am. Neither does looking at a list of electives. If we can visualize this data, I think it would
hugely benefit the students as well as institution.


Like I discussed in my survey, D3 (short for Data driven documents) is one of the frameworks that provides processing capabilities . D3 is a domain-specific programming language that uses these algorithms to provide a library of classes and objects to create data visualizations including treemaps and timelines. D3 is a JavaScript based framework. However, if I go with the web-based option, I might need a little bit of php to connect to the database.

D3 does most of the rendering for us so the frontend work is limited to styling the visuals. I will useHTML for page content, CSS for aesthetics and SVG for vector graphics while a further JavaScript layer can be added for interactions.

1/24 – Weekly Progress Report

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For this week, I reviewed the source material on decision tree creation, attempting to further my understanding, and think about where to begin editing.

For next week, I will be setting up a git repo with the source code from sci-kit learn, and begin to work on the design of how to add more data.

Niraj Senior Research Paper & Software

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Ever since their introduction in the late 2000s, smartphones have been changing the landscape of people’s daily lives. As these devices get more popular, they are also getting more powerful. Today, we can hold in our hand a device that is more powerful than a room full of computers in the 1960s. Not only this, smartphones today come with sensors like accelerometers, gyroscope, magnetometer, GPS, ambient light sensor and so on. Human activity recognition utilizes the increased computational power of smartphones and their diverse array of sensors to collect raw data from a subset of the phone sensors, use the computational power of the phone to detect motion patterns and recognize the activity that the user is engaged in.

Fitness monitoring trackers like fitbit and android watch have also steadily gained popularity worldwide. This reflects the increasing demand for ways to monitor fitness. Activity recognition also presents us with a marvelous prospect when it comes to fitness monitoring. Using techniques employed in activity recognition, not only will users be able to track the number of step taken, the number of calories spent, the number of stairs climbed, the number of hours slept, their quality of sleep and distance traveled but smartphones can also be used to alert idle users to move around if it notices that they have been sitting for too long. Since no extra sensors are required and they are accessed through the smartphone, these applications are zero cost and easy to use. Therefore, my motivation behind this project is to provide an affordable means of monitoring fitness through an Android device.


The final product of my project will be an Android (possibly cross-platform) application that comes with a trained classification model (possibly based on decision trees) capable of classifying activities into separate classes based on the current input data stream from sensors like accelerometer, magnetometer, gyroscope, etc. Furthermore, the application will also keep track of how many steps the user has taken, stairs climbed, hours slept, distance travelled and so on. I plan to build a suitable visualization within the application to allow the users to better understand the data.


My paper will contain a detailed description of the framework I used to build the application, as well as the techniques I used to extract features from training and test datasets. Also, in this paper, I will justify my choice of the machine learning algorithm used and the visualization techniques used. More importantly, I will evaluate the accuracy of the current model and suggest further ways to improve on it.

Vitalii Senior Research Paper and Software

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Cities are complicated systems that consist of numerous interconnected citizens, businesses, various transportation modes, services, and utilities. Scientists expect around 70% of the population to be located in cities and surrounding areas by 2050. Hence, the demand for smart cities which would provide everyone with high-quality services and ensure a suitable environment for economic and social well-being has appeared. Smart cities are mostly driven by the Internet of Things (IoT). A major part of costs that city municipalities face come from data collection. IoT applications in smart cities are argued to have a significant positive impact on efficiency and cost saving.

Garbage is a direct source of spreading diseases in underdeveloped countries and it contributes to the overall estimation of how clean an environment is. Since garbage collection process has to be repeated continuously, some countries simply cannot afford it which leads to some portion of garbage not being picked up. Studies have shown that garbage directly influences life expectancy which makes it a very important issue to be considered by governments all over the world. That is where the question arises of how to get a good implementation of waste collection system at a price that government can afford. The aim of my project is to create an affordable waste monitoring system that takes advantage of IoT, historic data, and various routing techniques.

Software and Hardware

The final product of my project is a waste monitoring system that consists of Arduino board mounted directly in the garbage bin along with ultrasonic sensor and RF transmitter, Arduino board acting as a control center with RF receiver, and software that connects all of the specified components. An ultrasonic sensor is capable of detecting the distance to objects it points to. Therefore, it can be used to measure garbage can fill level. Arduino board within a trash bin will continuously receive this information and send it using RF transmitter directly to the control center at every specified period for further analysis. The control center will receive this data using RF receiver. Later on, the software will analyze received information and make adjustments to routing plans for near future if a given value falls within a warning or critical zone. Such a system will create routing and scheduling policies that reflect a real-time data collected at physical locations of garbage bins.

I acknowledge that testing a full-scale monitoring system will be impossible with an amount of time and hardware components available to me. Therefore, after coming up with a prototype of the monitoring system, I will run simulations based on available data from Dr. Charles Peck of waste collection services within Richmond, IN. This approach will let me evaluate the effectiveness of a proposed system without necessarily setting up a complete system and complete a result section of my paper.


This paper will provide good background information about IoT and it’s applications in smart city. The primary focus of the paper will be to evaluate the effectiveness of the created waste monitoring system, various routing policies etc. Additionally, this paper will suggest optimal techniques that minimize the garbage spread based on the available resources.

Zeyang Gao senior research project

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Aim of the project

More and more large-scale Monte Carlo simulations are now run on parallel systems like networked workstations or clusters. In a parallel environment, the quality of a PRNG is even more important, to some extent because feasible sample sizes are easily 1015 times larger than on a sequential machine [2]. The main problem is the parallelization of the PRNG itself. Some generators with good quality that do not run on parallel sacrifice their efficiency. Those parallelized generators cannot ensure their quality. This problem

becomes even difficult for TRNGs, due to their nature of instability of quality and complex implementation for parallelization and them- selves. Therefore, I think it is important to resolve this problem with a stable RNG design runs in parallel that can generate random numbers on a large scale. If applicable, I will definitely go for a TRNG based design. However, given the difficulties of imple- mentation and limited hardware knowledge, I will not feel guilty if I end up with a PRNG design.


Software and paper

The software of this project will be divided into two parts. The first part will the interface of TRNGs or the source code of a PRNG design. The second part will be a set of statistical test to certificate the randomness of output stream. My paper will introduce my implementation and design in great detail, including how to bring ordinary RNGs into parallel and how to optimize them for large scale purpose. It will also include a result analysis part where I run statistical test against sample streams. The success of a design is dependent on whether the design is able to generate random numbers on large scale and how successful (i.e. how many tests can the output pass) the output stream is.

Jeremy Swerdlow Senior Research Paper & Software

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In machine learning, there has been a shift away from focusing on the creation complex algorithms to solve problems. Instead, a large focus has been on the application of simpler algorithms which learn from datasets. This shift has been made possible through the ever-increasing computational power of modern computers, and the massive amounts of data generated and gathered through the internet of things. However, even given the power and storage cheaply available for creating these models, it can still be quite time and space intensive to make a useful machine learning model. Datasets can vary in size, but can range from hundreds of thousands, millions, or even billions of unique data points. Due to the copious amount of data, training even a relative fast machine learning model can take hours or days. Because of how time and resource intensive that process is, companies often wait to recreate the model, even though they may lose some performance due to it. Some companies even wait to do it on a monthly basis, such as CoverMyMeds, who update their models every 28 days.

Part of why updating models is so intensive is that many do not allow data to be added after they are initially trained. This means each time you want to add data, you must create a new version from scratch, using the old set and the new points. Other types of models do allows this though, so it is possible to add it. The aim of my research focuses on learning how to add data dynamically to the model from neural networks, a machine learning algorithm based off of how the brain works with neurons, and apply similar logic to classification decision trees. The hypothesis of my research is that the time intensity of updating a decision tree can be decreased by adding data incrementally, with little loss to the tree’s effectiveness.

For the paper associated with this research, I will focus on the theory behind neural networks and their dynamic data addition, how decision trees are created, and how I will be adapting their training to mimic the behavior of neural networks when it comes to training. However, it may not be the case that decision trees can be changed to act as neural networks do, but can be edited in some other manner. To confirm or discredit my hypothesis, the resulting software will be tested on a series of datasets which range in size, type, and topic, and recorded in the paper.

For the software component of this research, I will be reviewing, editing, and testing the sci-kit learn package in Python, which comes with well-tested and documented implementations of both decision trees and neural networks. These will be gathered into a Git repository, along with the relevant datasets, my edited version of the code, and the necessary files to run to test the results.

Victor Zuniga Senior Project Idea

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The technology of blockchain has reached the mainstream conscience as a result of the popularity of Bitcoin and other cryptocurrencies. Blockchain as implemented in Bitcoin is a distributed ledger system in which transaction history is maintained by a consensus of participant nodes. These participant nodes also compete in using proof of work to decide the block of transaction added to the chain. This system has the benefits of being totally decentralized and creating a nearly unalterable transaction history. However, the proof of work method is resource intensive and slow. Another cryptocurrency, Etherium uses a consortium variation on blockchain in which a subset of participant nodes are selected to determine the next transaction block added to the chain. It is this consortium blockchain which my project will be based on.

In the healthcare industry of today data privacy is a major concern. There are numerous examples available of healthcare providers failing to maintain the security of their patients’ data and being hacked. As Internet of Things devices become more commonplace they will play an ever grMy senior project will focus on using blockchain technology to connect Internet of Things devices, specifically in a healthcare context where patient data is of high security concern. The implementation will make use of the consortium blockchain concepteater role in healthcare and form the basis of smart healthcare. Of primary concern with this fact is being able to secure these devices of low computational power. My project will use the consortium blockchain previously mentioned to secure such devices and improve the security of the data being transfered.


My paper will delve into the technology of blockchain and specifically focus on consortium blockchain. It will explain what the Internet of Things is and how these devices pertain to healthcare. And it will bring the two together explaining how a blockchain will provide increased security to an IoT network and how it allows providers to remain HIPAA compliant.


The hardware component of my project will utilize affordable single board computers (SBCs) like CHIP to model healthcare IoT devices. These SBCs will be set up in a network similar to one that could feasibly be found in smart healthcare. Additionally, another SBC or a more powerful computer, if need be, will be used as a sort of aggregator. For my blockchain implementation I will use the University of Sydney’s Red Belly Blockchain with the Linux Foundation’s Hyperledger as a backup. My code will use the blockchain framework which is currently geared towards currency and tweak it for communication. My prediction is that this repurposing will present the bulk of the challenge and time commitment for the project.

Minh Vo Senior Project

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With the non-stop improvements in technology, more and more fields are trying to apply computer science to achieve their goals in a more efficient and less time consuming way. Sports are no outsiders to this group of fields.

In sports, especially in soccer, technology has become an essential part. Soccer experts now make use of technology to evaluate a player’s or a team’s performances. Other than using their experience and their management abilities after many years being parts of the game, the soccer coaches also use statistics from data providers to improve their knowledge of their own players and teams so that they can come up with different strategies/tactics that bring them closer to the wins. Besides coaches, soccer analysts also make use of the data to predict results in the future as well as evaluate new talents emerging from the scene.

This is where Machine Learning techniques can become useful. Machine Learning is one of the intelligent methodologies that have shown promising results in the domains of classification and prediction. Therefore, Machine Learning can be used as the learning strategy to provide a sport prediction framework for experts. The end goal of this project is to produce a program/script that will automatically execute the complete procedure of results prediction.

Paper Plan:

For the paper, there should be several sections that explain the framework of using Machine Learning to predict results in the English Premier League. This will include basic knowledge about soccer and the League, data preparation and understanding, feature extraction (Scikit-learn, Machine Learning algorithms/models, Selections), training and testing. Other than those sections, I will also discuss the results of my program/script in my paper from predictions for the upcoming matches. Finally, I will talk about the difficulties/obstacles of the project, the conclusions, and a few possible directions for further development of this field/topic.

Software Plan:

For the programming part, my plan is to just create a basic program/script that can carry out every step needed in the process of predicting the future results. This starts from writing code that retrieves the data from the source stated above and preprocesses the data in an usable format. As said above, during the course of the project and after training and testing have been performed, the best performing features and Machine Learning algorithms (provided and tested by using Scikit-learn) will be determined. I would then set those features and algorithms to be used in the program.

My current idea is that the program will first ask the user for the home team and the away team. Then, it will use the decided Machine Learning algorithms to predict the result between the specified teams and print that result out to the screen. However, the user can also choose to predict the 38 matches of one team for the whole season. The program will then write the predictions to an output file which can be accessed by the user.

Utilizing cloud storage for realistic augmented reality on mobile

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As for today, augmented reality technologies are shifting towards small devices with a focus on user interaction. As more techniques in rendering AR objects are developed, more computing powers are needed to keep up. Mobile AR technology has all functions built in, in addition to GPS and compass for realistic AR rendering technology. However, mobile devices lack storage and the raw power for 3D rendering of complex objects. The paper discusses the possibility of integrating cloud to fix these problems, and conclude that using cloud for performance is difficult while using cloud for storage is possible. Results show that performance drop when utilizing cloud storage for 3D objects are minimal. As for now, cloud fetched objects are rendered without textures, leading to a reduce in realism compared to local fetched objects. Thus, the next step of the pro ject is implementing textures fetch from cloud DB on top of the 3D object file fetch.

Links: GithubPaper


Examine different neural networks’ efficiency in predicting stock prices

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There has been a lot of attempts in building predictive models that
can correctly predict the stock price. However, most of these models
only focus on different in-market factors such as the prices of other
similar stocks. This paper discusses the efficiency/accuracy
of three different neural network models (feedforward, recurrent,
and convolutional) in predicting stock prices based on external
dependencies such as oil price, weather indexes, etc.

Software architecture


Bringing Innovative Load Balancing to NGINX

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Senior Capstone: Bringing Innovative Load Balancing to NGINX


Load balancing remains an important area of study in computer science largely due to the increasing demand on data centers and webservers. However, it is rare to see improvements in load balancing algorithms implemented outside of expensive specialized hardware. This research project is an attempt to bring these innovative techniques to NGINX, the industry leading open source load balancer and webserver. In addition to implementing a new, native NGINX module, I have developed a simple workflow to benchmark and compare the performance of available load balancing algorithms in any given production environment. My benchmarks indicate that it is possible to take advantage of more sophisticated load distribution techniques without paying a significant performance cost in additional overhead.

My Software Architecture


Survey Paper

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Topic 1 : Data Mining Analysis and Prediction 

Week 1: March 12- 18

Went into more detail on the annotated bibliographies, organized the order in which would will help my paper and best fit the flow of ideas.

Week 2:   March 19 – 25

Looked into what the data mining tools and application that are mentioned in the papers. Checked if they could fall within the scope of the work that I want to do. Created the overall outline for my paper including how the major topics and methodology would progress. Created the workflow diagram and looked at other previously done Survey paper. Latex seems like a good tool to use in addition to Zotero to create templates.

Watched some TED talk videos on the topic:

Aaron Koblin: Visualizing ourselves … with crowd-sourced data

Hans Rosling: The best stats you’ve ever seen

Mathias Lundø Nielsen :How to Monetize Big Data

Week 3: March 26 – 31

Started connecting major topics in terms of how they fit the block structures for my paper and compiled paragraphs on some topics. Looked into other previously done works mentioned in the papers regarding Data Mining and the tools used in those research.


Week 4: April 1- 7 

Building on the outline and creating diagrams mentioned. Mostly going through papers to build on the brief few sentences mentioned for each topic.

Week 5: April 7 – 14

Worked on the second draft. Added more content to the paper, removed a couple of subtopic.

Week 6: April 14 -21 

Finished up the survey paper with all necessary topics and figures and diagrams as well as the conclusion.

Survey Paper

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Week 1 (3/29):

  1. I have found a reasonable amount of papers to read.
  2. I have refined the topic. The previous topic was about analyzing and find the relationship between weather pattern and oil stock price. I have decided to broaden the topic to find the relationship between two general entities (so not just weather pattern and oil stock price) by developing an algorithm for similarity scoring and matching.
  3. I have a basic outline for the survey paper jotted down.

Week 2 (4/5):

  1. A lot of readings, especially about SVM (Support Vector Machine)
  2. Work on adding some more text in the introduction part
  3. Read, read, and read.
  4. Explore the term artificial neural networks, etc.

Week 3 (4/12):

  1. Read about different approach to pattern matching problem in other area of study (BLAST, etc.)
  2. Read about AI neural networks, it’s confusing.
  3. Start adding meat to the outline of the survey paper.

Week 4 (4/19):

  1. Watch the MIT’s Opencourseware Intro to AI
  2. Modular Neural Network – Reading
  3. SVM readings

Week 5 (4/26):

  1. Continue the course
  2. Re-learn calculus

Annotated Bibliographies

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I/ Econ Simulation Game
1) “Educational Video Game Design: A Review of the Literature – Semantic Scholar.” N.p., n.d. Web. 16 Feb. 2017.
2) Squire, Kurt. “Changing the Game: What Happens When Video Games Enter the Classroom.” Innovate: journal of online education 1.6 (2005): n. pag. www.bibsonomy.org. Web. 16 Feb. 2017.
3) —. “Video Games in Education.” International Journal of Intelligent Simulations and Gaming 2 (2003): 49–62. Print.
II/ Social Network Data Mining
1) Cheong, France, and Christopher Cheong. “Social Media Data Mining: A Social Network Analysis Of Tweets During The 2010-2011 Australian Floods.” (2011): n. pag. works.bepress.com. Web. 16 Feb. 2017.
2) Kempe, David, Jon Kleinberg, and Éva Tardos. “Maximizing the Spread of Influence Through a Social Network.” Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2003. 137–146. ACM Digital Library. Web. 16 Feb. 2017. KDD ’03.
3) Wu, X. et al. “Data Mining with Big Data.” IEEE Transactions on Knowledge and Data Engineering 26.1 (2014): 97–107. IEEE Xplore. Web.
III/ Connection between stocks
Zager, Laura A., and George C. Verghese. “Graph Similarity Scoring and Matching.” Applied Mathematics Letters 21.1 (2008): 86–94. ScienceDirect. Web.

Capstone Abstracts – v1

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I/ Sometimes lectures and text books can be too “dry” for students to get excited about a subject, specifically economics. At the same time, researchers have found the potential of games in education, especially when used as an introduction to new concepts. EconBuild is a game that simulates different aspects of economics that we normally encounter in our economics intro classes, proving students a platform to practice what they learn in class. The game can help students to enforce the most fundamental elements of economics such as demand and supply, stock market, etc.

II/ In this day and age, more and more businesses choose to expand their brand using social networks, thus leading to the fact that social media users continue to provide advertisement, positive and negative. In order to become competitive, it is necessary for a company to establish its online present as well as analyze its component’s dominance. Using a Hadoop based approach to reduce the size of database, we can gather and analyze information about a company on social media and predict certain trends to help with its growth.

III/ Stock market is usually unpredictable. There is no particular rule that it obeys to, which is why investing in stock is considered a risky business. Many people have tried to analyze particular trends in order to guess whether the stock price would rise or not. However there hasn’t been a lot of software that analyze the relationship between different related stocks. Using support vector machine approach, combining with graph similarity scoring and matching algorithm, we can establish relationships between different stocks, thus open the possibility of being able to predict particular stock trends.