Computer Sciences Commons^™

Custom T-Shirt Designs, Ranjan Khadka

Electronic Theses, Projects, and Dissertations

Custom T-shirt Designs is a web-based application. The purpose of this project is to provide a website that would allow customers to be able to customize T-shirt and place an order of custom T-shirt. Customers can sign up, sign in, select T-shirt color, add text, choose Font, choose Font color, upload an image, apply filters to images, transform text or images, choose T-shirt size and save designs for future references. Customers would be able to add the design to a cart, manage cart and checkout with their credit card to purchase for the order and view their previous orders. The …

Tackling The Interleaving Problem In Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

Activity discovery (AD) is the unsupervised process of discovering activities in data produced from streaming sensor networks that are recording the actions of human subjects. One major challenge for AD systems is interleaving, the tendency for people to carry out multiple activities at a time a parallel. Following on from our previous work, we continue to investigate AD in interleaved datasets, with a view towards progressing the state-of-the-art for AD.

Informing The Use Of Hyper-Parameter Optimization Through Meta-Learning, Samantha Corinne Sanders

Theses and Dissertations

One of the challenges of data mining is finding hyper-parameters for a learning algorithm that will produce the best model for a given dataset. Hyper-parameter optimization automates this process, but it can still take significant time. It has been found that hyperparameter optimization does not always result in induced models with significant improvement over default hyper-parameters, yet no systematic analysis of the role of hyper-parameter optimization in machine learning has been conducted. We propose the use of meta-learning to inform the decision to optimize hyper-parameters based on whether default hyper-parameter performance can be surpassed in a given amount of time. …

Accuracy And Racial Biases Of Recidivism Prediction Instruments, Julia J. Dressel

Dartmouth College Undergraduate Theses

Algorithms have recently become prevalent in the criminal justice system. Tools known as recidivism prediction instruments (RPIs) are being used all over the country to assess the likelihood that a criminal defendant will reoffend at some point in the future. In June of 2016, researchers at ProPublica published an analysis claiming an RPI called COMPAS was biased against black defendants. This claim sparked a nation-wide debate as to how fairness of an algorithm should be measured, and exposed the many ways that algorithms are not necessarily fair. Algorithms are used in the criminal justice system because they are regarded as …

Assemble.Live: Designing For Schisms In Large Groups In Audio/Video Calls, Benjamin P. Packer

Dartmouth College Undergraduate Theses

Although new communication technologies have compressed the space and latency between participants, leading to new forms of computer mediated interaction that scale with the number of participants [Klein, 1999], there still exist no audio/video calling solutions that can accommodate the type of group conversation that takes place in a group of four or more. Groups of this size frequently schism, forming two or more sub-conversations with their own independently operating turn taking systems [Egbert, 1997]. This paper proposes that traditional audio/video calling fails to accommodate schisms because a) there is no way to signal intended recipiency, b) there exists only …

Conference Schedule (2017), Association Of Christians In The Mathematical Sciences

ACMS Conference Proceedings 2017

No abstract provided.

Table Of Contents (2017), Association Of Christians In The Mathematical Sciences

ACMS Conference Proceedings 2017

No abstract provided.

Association Of Christians In The Mathematical Sciences Proceedings 2017, Association Of Christians In The Mathematical Sciences

ACMS Conference Proceedings 2017

The conference proceedings of the Association of Christians in the Mathematical Sciences biannual conference, May 31-June 2, 2017 at Charleson Southern University.

Housing Price Prediction Using Support Vector Regression, Jiao Yang Wu

Master's Projects

The relationship between house prices and the economy is an important motivating factor for predicting house prices. Housing price trends are not only the concern of buyers and sellers, but it also indicates the current economic situation. Therefore, it is important to predict housing prices without bias to help both the buyers and sellers make their decisions. This project uses an open source dataset, which include 20 explanatory features and 21,613 entries of housing sales in King County, USA. We compare different feature selection methods and feature extraction algorithm with Support Vector Regression (SVR) to predict the house prices in …

Evolutionary Game Theoretic Multi-Objective Optimization Algorithms And Their Applications, Yi Ren Cheng

Graduate Doctoral Dissertations

Multi-objective optimization problems require more than one objective functions to be optimized simultaneously. They are widely applied in many science fields, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conicting objectives. Most of the real world multi-objective optimization problems are NP-Hard problems. It may be too computationally costly to find an exact solution but sometimes a near optimal solution is sufficient. In these cases, Multi-Objective Evolutionary Algorithms (MOEAs) provide good approximate solutions to problems that cannot be solved easily using other techniques. However Evolutionary Algorithm is not …

Building Efficient Large-Scale Big Data Processing Platforms, Jiayin Wang

Graduate Doctoral Dissertations

In the era of big data, many cluster platforms and resource management schemes are created to satisfy the increasing demands on processing a large volume of data. A general setting of big data processing jobs consists of multiple stages, and each stage represents generally defined data operation such as ltering and sorting. To parallelize the job execution in a cluster, each stage includes a number of identical tasks that can be concurrently launched at multiple servers. Practical clusters often involve hundreds or thousands of servers processing a large batch of jobs. Resource management, that manages cluster resource allocation and job …

Using Computational Models To Understand Asd Facial Expression Recognition Patterns, Irene L. Feng

Dartmouth College Undergraduate Theses

Recent advances in computer vision have led to interest in studying how computer vision can simulate our own perception to better understand the intricacies of human neurobiology. Researchers have made strides in computer vision to imitate many facets of human perception, such as object detection, character recognition, and face identification. However, there have been fewer studies that try to model atypical human perception. My thesis focuses specifically on individuals with Autism Spectrum Disorder (ASD) and their deficit in the facial expression recognition (FER) task. I built multiple computer vision models using hand-crafted features and also convolutional neural network architectures to …

Improving Elementary Math Learning Through Ipad Games, Kaya M. Thomas

Dartmouth College Undergraduate Theses

Mathematics has proven to be challenging to many from a very young age. Young students are influenced by their teachers on how to feel about math and how well they can perform. Currently many methods of teaching mathematics do not encourage learning, but instead promote memorization which has been shown to increase students' anxiety about math. Math anxiety affects student performance as well as their ability to understand the material. Fractions are one of the most difficult concepts for young students to learn. Various techniques have been created in order to better instruct students on how to understand fractions. More …

Scene Classification From Degraded Images: Comparing Human And Computer Vision Performance, Tim M. Tadros

Dartmouth College Undergraduate Theses

People can recognize the context of a scene with just a brief glance. Visual information such as color, objects and their properties, and texture are all important in correctly determining the type of scene (e.g. indoors versus outdoors). Although these properties are all useful, it is unclear which features of an image play a more important role in the task of scene recognition. To this aim, we compare and contrast a state-of-the-art neural network and GIST model with human performance on the task of classifying images as indoors or outdoors. We analyze the impact of image manipulations, such as blurring …

Sliding Window Based Feature Extraction And Traffic Clustering For Green Mobile Cyberphysical Systems, Jiao Zhang, Li Zhou, Angran Xiao, Sai Zeng, Haitao Zhao, Jibo Wei

Publications and Research

Both the densification of small base stations and the diversity of user activities bring huge challenges for today’s heterogeneous networks, either heavy burdens on base stations or serious energy waste. In order to ensure coverage of the network while reducing the total energy consumption, we adopt a green mobile cyberphysical system (MCPS) to handle this problem. In this paper, we propose a feature extractionmethod using sliding window to extract the distribution feature of mobile user equipment (UE), and a case study is presented to demonstrate that the method is efficacious in reserving the clustering distribution feature. Furthermore, we present traffic …

Spam, Fraud, And Bots: Improving The Integrity Of Online Social Media Data, Amanda Jean Minnich

Computer Science ETDs

Online data contains a wealth of information, but as with most user-generated content, it is full of noise, fraud, and automated behavior. The prevalence of "junk" and fraudulent text affects users, businesses, and researchers alike. To make matters worse, there is a lack of ground truth data for these types of text, and the appearance of the text is constantly changing as fraudsters adapt to pressures from hosting sites. The goal of my dissertation is therefore to extract high-quality content from and identify fraudulent and automated behavior in large, complex social media datasets in the absence of ground truth data. …

An Open Source Discussion Group Recommendation System, Sarika Padmashali

Master's Projects

A recommendation system analyzes user behavior on a website to make suggestions about what a user should do in the future on the website. It basically tries to predict the “rating” or “preference” a user would have for an action. Yioop is an open source search engine, wiki system, and user discussion group system managed by Dr. Christopher Pollett at SJSU. In this project, we have developed a recommendation system for Yioop where users are given suggestions about the threads and groups they could join based on their user history. We have used collaborative filtering techniques to make recommendations and …

Neural Net Stock Trend Predictor, Sonal Kabra

Master's Projects

This report analyzes new and existing stock market prediction techniques. Traditional technical analysis was combined with various machine-learning approaches such as artificial neural networks, k-nearest neighbors, and decision trees. Experiments we conducted show that technical analysis together with machine learning can be used to profitably direct an investor’s trading decisions. We are measuring the profitability of experiments by calculating the percentage weekly return for each stock entity under study. Our algorithms and simulations are developed using Python. The technical analysis methodology combined with machine learning algorithms show promising results which we discuss in this report.

Predicting Pancreatic Cancer Using Support Vector Machine, Akshay Bodkhe

Master's Projects

This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that …

Adding Differential Privacy In An Open Board Discussion Board System, Pragya Rana

Master's Projects

This project implements a privacy system for statistics generated by the Yioop search and discussion board system. Statistical data for such a system consists of various counts, sums, and averages that might be displayed for groups, threads, etc. When statistical data is made publicly available, there is no guarantee of preserving the privacy of an individual. Ideally, any data extracted should not reveal any sensitive information about an individual. In order to help achieve this, we implemented a Differential Privacy mechanism for Yioop. Differential privacy preserves privacy up to some controllable parameters of the number of items or individuals being …

Path-Finding Methodology For Visually-Impaired Patients Based On Image-Processing, Abhilash Goyal

Master's Projects

The objective of this project is to propose and develop the path-finding methodology for the visually impaired patients. The proposed novel methodology is based on image-processing and it is targeted for the patients who are not completely blind. The major problem faced by visually impaired patients is to walk independently. It is mainly because these patients can not see obstacles in front of them due to the degradation in their eye sight. Degradation in the eye-sight is mainly because either the light doesn't focus on the retina properly or due to the malfunction of the photoreceptor cells on the retina, …

A Survey Of Trustworthy Computing On Mobile & Wearable Systems, Travis Peters

Computer Science Technical Reports

Mobile and wearable systems have generated unprecedented interest in recent years, particularly in the domain of mobile health (mHealth) where carried or worn devices are used to collect health-related information about the observed person. Much of the information - whether physiological, behavioral, or social - collected by mHealth systems is sensitive and highly personal; it follows that mHealth systems should, at the very least, be deployed with mechanisms suitable for ensuring confidentiality of the data it collects. Additional properties - such as integrity of the data, source authentication of data, and data freshness - are also desirable to address other …

Credit Scoring Using Logistic Regression, Ansen Mathew

Master's Projects

This report presents an approach to predict the credit scores of customers using the Logistic Regression machine learning algorithm. The research objective of this project is to perform a comparative study between feature selection and feature extraction, against the same dataset using the Logistic Regression machine learning algorithm. For feature selection, we have used Stepwise Logistic Regression. For feature extraction, we have used Singular Value Decomposition (SVD) and Weighted Singular Value Decomposition (SVD). In order to test the accuracy obtained using feature selection and feature extraction, we used a public credit dataset having 11 features and 150,000 records. After performing …

Web - Based Office Market, Manodivya Kathiravan

Master's Projects

People who work in an office often have different pools of resources that they want to exchange. They want to trade their resources/work(seller) with a person who wants that particular resource(buyer) and in return get another resource the buyer offers. These kind of exchanges are often called Barter-exchanges where an item is traded for another item without the involvement of actual money. An exchange is set to be complete when there is a match between an available item and a desired item. This exchange is called direct exchange. When an item desired by one user is made available through a …

Ai For Classic Video Games Using Reinforcement Learning, Shivika Sodhi

Master's Projects

Deep reinforcement learning is a technique to teach machines tasks based on trial and error experiences in the way humans learn. In this paper, some preliminary research is done to understand how reinforcement learning and deep learning techniques can be combined to train an agent to play Archon, a classic video game. We compare two methods to estimate a Q function, the function used to compute the best action to take at each point in the game. In the first approach, we used a Q table to store the states and weights of the corresponding actions. In our experiments, this …

Document Classification Using Machine Learning, Ankit Basarkar

Master's Projects

To perform document classification algorithmically, documents need to be represented such that it is understandable to the machine learning classifier. The report discusses the different types of feature vectors through which document can be represented and later classified. The project aims at comparing the Binary, Count and TfIdf feature vectors and their impact on document classification. To test how well each of the three mentioned feature vectors perform, we used the 20-newsgroup dataset and converted the documents to all the three feature vectors. For each feature vector representation, we trained the Naïve Bayes classifier and then tested the generated classifier …

Automated Refactoring Of Legacy Java Software To Default Methods, Raffi Khatchadourian, Hidehiko Masuhara

Publications and Research

Java 8 default methods, which allow interfaces to contain (instance) method implementations, are useful for the skeletal implementation software design pattern. However, it is not easy to transform existing software to exploit default methods as it requires analyzing complex type hierarchies, resolving multiple implementation inheritance issues, reconciling differences between class and interface methods, and analyzing tie-breakers (dispatch precedence) with overriding class methods to preserve type-correctness and confirm semantics preservation. In this paper, we present an efficient, fully-automated, type constraint-based refactoring approach that assists developers in taking advantage of enhanced interfaces for their legacy Java software. The approach features an extensive …

Community Detection In Social Networks, Ketki Kulkarni

Master's Projects

The rise of the Internet has brought people closer. The number of interactions between people across the globe has gone substantially up due to social awareness, the advancements of the technology, and digital interaction. Social networking sites have built societies, communities virtually. Often these societies are displayed as a network of nodes depicting people and edges depicting relationships, links. This is a good and e cient way to store, model and represent systems which have a complex and rich information. Towards that goal we need to nd e ective, quick methods to analyze social networks. One of the possible solution …

Communicating At Terahertz Frequencies, Farnoosh Moshirfatemi

Dissertations and Theses

The number of users who get access to wireless links is increasing each day and many new applications require very high data rates. The increasing demand for higher data rates has led to the development of new techniques to increase spectrum efficiency to achieve this goal. However, the limited bandwidth of the frequency bands that are currently used for wireless communication bounds the maximum data rate possible.

In the past few years, researchers have developed new devices that work as Terahertz (THz) transmitters and receivers. The development of these devices and the large available bandwidth of the THz band is …

Reducing Query Latency For Information Retrieval, Swapnil Satish Kamble

Master's Projects

As the world is moving towards Big Data, NoSQL (Not only SQL) databases are gaining much more popularity. Among the other advantages of NoSQL databases, one of their key advantage is that they facilitate faster retrieval for huge volumes of data, as compared to traditional relational databases. This project deals with one such popular NoSQL database, Apache HBase. It performs quite efficiently in cases of retrieving information using the rowkey (similar to a primary key in a SQL database). But, in cases where one needs to get information based on non-rowkey columns, the response latency is higher than what we …