Physical Sciences and Mathematics Commons^™

Overrepresentation Of The Underrepresented: Gender Bias In Wikipedia, Anna Marinina

Honors College Theses

The goal of our research is to determine if gender bias exists in Wikipedia. Wikipedia is a very large dataset that has been used to train artificial intelligence models. If a dataset that is being used for this purpose is biased, then the artificial intelligence model that was trained with it will be biased as well, therefore making biased decisions. For this reason, it is important to explore large datasets for any potential biases before they are used in machine learning. Since Wikipedia is ontologically structured, we used graph theory to create a network of all of the website’s categories …

Local Radiance, Scott Peter Britell

Dissertations and Theses

Recent years have seen a proliferation of web applications based on content management systems (CMS). Using a CMS, non-technical content authors are able to define custom content types to support their needs. These content type names and the attribute names in each content type are typically domain-specific and meaningful to the content authors. The ability of a CMS to support a multitude of content types allows for endless creation and customization but also leads to a large amount of heterogeneity within a single application. While this meaningful heterogeneity is beneficial, it introduces the problem of how to write reusable functionality …

A Longitudinal Study Of Mammograms Utilizing The Automated Wavelet Transform Modulus Maxima Method, Brian C. Toner

Electronic Theses and Dissertations

Breast cancer is a disease which predominatly affects women. About 1 in 8 women are diagnosed with breast cancer during their lifetime. Early detection is key to increasing the survival rate of breast cancer patients since the longer the tumor goes undetected, the more deadly it can become. The modern approach for diagnosing breast cancer relies on a combination of self-breast exams and mammography to detect the formation of tumors. However, this approach only accounts for tumors which are either detectable by touch or are large enough to be observed during a screening mammogram. For some individuals, by the time …

Realtime Object Detection Via Deep Learning-Based Pipelines, James G. Shanahan, Liang Dai

Information Systems and Analytics Department Faculty Conference Proceedings

Ever wonder how the Tesla Autopilot system works (or why it fails)? In this tutorial we will look under the hood of self-driving cars and of other applications of computer vision and review state-of-the-art tech pipelines for object detection such as two-stage approaches (e.g., Faster R-CNN) or single-stage approaches (e.g., YOLO/SSD). This is accomplished via a series of Jupyter Notebooks that use Python, OpenCV, Keras, and Tensorflow. No prior knowledge of computer vision is assumed (although it will be help!). To this end we begin this tutorial with a review of computer vision and traditional approaches to object detection such …

Technical Report 2019-01: Pupil Labs Eye Tracking User Guide, Joan D. Gannon, Augustine Ubah, Chris Dancy

Other Faculty Research and Publications

No abstract provided.

Versatile Binary-Level Concolic Testing, Bo Chen

Dissertations and Theses

Computing systems are experiencing an explosive growth, both in complexities and diversities, ushered in by the proliferation of cloud computing, mobile computing, and Internet of Things. This growth has also exposed the consequences of unsafe, insecure, and unreliable computing systems. These all point to the great needs of sophisticated system validation techniques. Recent advances in research on symbolic execution has shown great promises for automated software analysis, e.g., generating test cases, finding bugs, and detecting security vulnerabilities. However, symbolic execution is mostly adopted to analyze user applications, while modern computing systems in practice consist of many components shipped by various …

Design And Experimental Evaluation Of Deepmarket: An Edge Computing Marketplace With Distributed Tensorflow Execution Capability, Soyoung Kim

Dissertations and Theses

There is a rise in demand among machine learning researchers for powerful computational resources to train complex machine learning models, e.g., deep learning models. In order to train these models in a reasonable amount of time, the training is often distributed among multiple machines; yet paying for such machines (either through renting them on cloud data centers or building a local infrastructure) is costly. DeepMarket attempts to reduce these costs by creating a marketplace that integrates multiple computational resources over a distributed TensorFlow framework. Instead of requiring users to rent expensive GPU/CPUs from a third-party cloud provider, DeepMarket allows users …

Inclusion Of Women In Computer Science, Naomi Johnson, Dr. Kevin Seppi

Journal of Undergraduate Research

Since the 1980’s, the percentage of computer science degrees awarded to women in the United States has fallen dramatically. There are growing numbers of men earning bachelor’s degrees in CS, and the numbers of women are increasing very slowly. For decades, researchers have been studying recruitment and retention of women and other minorities in CS, yet it is still not apparent what departments, professors, or students can do in order to get the numbers of women earning degrees in CS up again.

Cs + Sociology: Using Big Data To Identify And Understand Educational Inequality In America (1), Joseph Cleary, Elin Waring

Open Educational Resources

This is the first of two lessons/labs for teaching and learning of computer science and sociology. Either and be used on their own or they can be used in sequence, in which case this should be used first.

Students will develop CS skills and behaviors including but not limited to: learning what an API is, learning how to access and utilize data on an API, and developing their R coding skills and knowledge. Students will also learn basic, but important, sociological principles such as how poverty is related to educational opportunities in America. Although prior knowledge of CS and sociology …

Single Image Reflection Removal Beyond Linearity, Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Due to the lack of paired data, the training of image reflection removal relies heavily on synthesizing reflection images. However, existing methods model reflection as a linear combination model, which cannot fully simulate the real-world scenarios. In this paper, we inject non-linearity into reflection removal from two aspects. First, instead of synthesizing reflection with a fixed combination factor or kernel, we propose to synthesize reflection images by predicting a non-linear alpha blending mask. This enables a free combination of different blurry kernels, leading to a controllable and diverse reflection synthesis. Second, we design a cascaded network for reflection removal with …

Crumpled And Abraded Encryption: Implementation And Provably Secure Construction, Scott Sherlock Griffy

Dissertations and Theses

Abraded and crumpled encryption allows communication software such as messaging platforms to ensure privacy for their users while still allowing for some investigation by law enforcement. Crumpled encryption ensures that each decryption is costly and prevents law enforcement from performing mass decryption of messages. Abrasion ensures that only large organizations like law enforcement are able to access any messages. The current abrasion construction uses public key parameters such as prime numbers which makes the abrasion scheme difficult to analyze and allows possible backdoors. In this thesis, we introduce a new abrasion construction which uses hash functions to avoid the problems …

Building Of Mira’S Ghost Cleansing Services, Ashley Vargas

Honors College Theses

In this thesis, I built a PC game called Mira’s Cleansing Services. It is a sidescroller game that follows the story of a female exorcist for-hire who is also a high school student. Using ABC Scrum, a modified version of Scrum for game development, I developed the game using Game Maker. Additionally, I created all the art and music in different software. Because of time constraints and because I was working alone, I only developed the first level of the game. However, I plan to continue developing the game after this semester to eventually complete and publish it. My ultimate …

Mobile Solar Energy Environmental Control System With Remote Accessibility, Luis S. Garay

Theses and Dissertations

The proposed system, Mobile Solar Energy Environmental Control System with Remote Accessibility (mSEECS w/ RA), is based on the utilization of an already available Energy Storage Unit (ESU) in a Transport Unit System (TUS) to harness solar energy to provide a system to enhance the environment, safety and security of a TUS in a stationary or a mobile state by monitoring and controlling the operation of its onboard systems, and provide remote accessibility to know the state of affairs at any time. To relate to a real world application, the battery (ESU) of a vehicle (TUS) is underutilized …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

Csci 380-04 Selected Topics In Computer Science: Mobile Application And Product Development, Bhargava Chinthirla, Eric Spector, Nyc Tech-In-Residence Corps

Open Educational Resources

No abstract provided.

Csci 381/780 Data Analytics, Kumar Ramansenthil, Nyc Tech-In-Residence Corps

Open Educational Resources

No abstract provided.

Csc 21700 Probability And Statistics For Computer Science, Evan Agovino, Nyc Tech-In-Residence Corps

Open Educational Resources

No abstract provided.

Interview Of Stephen Andrilli, Ph.D., Stephen Francis Andrilli Ph.D., Jane Highley

All Oral Histories

Stephen Francis Andrilli was born in August 1952 in Bryn Mawr, PA. He was born to Francis and Leatrice Andrilli. Dr. Andrilli is the oldest of four children; his three sisters are Carol (now Carol Strosser), Patricia (now Patricia Kempczynski), and Barbara (now Barbara Parkes). Aside from a few years of living in Gettysburg, Dr. Andrilli has lived in the Philadelphia area for most of his life. He attended St. Jerome School, where he finished 8th grade. He then attended LaSalle College High School, where he graduated in 1969 at age 16. He entered La Salle University (formerly La Salle …

Concept Building Web Application, Jaime Becker

Computer Science

In this thesis, I have ported a python software application that is used for the collection of information for functional learning classifications into a web application. I have analyzed the problems in the previous software and created the new web application to address these problems using Flask, AppEngine, and Bootstrap. This web application mirrors the original software look and feel but adds several functionalities and improvements like support for multiple input modalities and a separate administrative and user view. I also present a proposed evaluation of the web application.

The Evolution Of Data Science: A New Mode Of Knowledge Production, Jennifer Lewis Priestley, Robert J. Mcgrath

Faculty and Research Publications

Is data science a new field of study or simply an extension or specialization of a discipline that already exists, such as statistics, computer science, or mathematics? This article explores the evolution of data science as a potentially new academic discipline, which has evolved as a function of new problem sets that established disciplines have been ill-prepared to address. The authors find that this newly-evolved discipline can be viewed through the lens of a new mode of knowledge production and is characterized by transdisciplinarity collaboration with the private sector and increased accountability. Lessons from this evolution can inform knowledge production …

Cis 356 Digital Trends And Transformations, Douglas Ng, Nyc Tech-In-Residence Corps

Open Educational Resources

No abstract provided.

Assessing The Effectiveness Of Computer Science Rpps: The Case Of Cafecs, Erin Henrick, Steven Mcgee, Ronald I. Greenberg, Lucia Dettori, Andrew M. Rasmussen, Don Yanek, Dale F. Reed

Computer Science: Faculty Publications and Other Works

Research Practice Partnerships (RPPs) are a relatively

recent development as a potential strategy to address the

complex challenges in computer science education. Consequently,

there is little guidance available for assessing the effectiveness of

RPPs. This paper describes the formative evaluation approach

used to assess the progress of the first year of the formalized RPP,

Chicago Alliance for Equity in Computer Science (CAFE´CS).

This paper contributes to the RPP literature by providing a case

study of how an RPP effectiveness framework can be adapted

and used to inform partnership improvement efforts in computer

science education.

Knowing Without Knowing: Real-Time Usage Identification Of Computer Systems, Leila Mohammed Hawana

Dissertations and Theses

Contemporary computers attempt to understand a user's actions and preferences in order to make decisions that better serve the user. In pursuit of this goal, computers can make observations that range from simple pattern recognition to listening in on conversations without the device being intentionally active. While these developments are incredibly useful for customization, the inherent security risks involving personal data are not always worth it. This thesis attempts to tackle one issue in this domain, computer usage identification, and presents a solution that identifies high-level usage of a system at any given moment without looking into any personal data. …

A Semester Long Classroom Course Mimicking A Software Company And A New Hire Experience For Computer Science Students Preparing To Enter The Software Industry, David A. Chamberlain

Master’s Theses and Projects

Students in a Computer Science degree programs must learn to code before they can be taught Software Engineering skills. This core skill set is how to program and consists of the constructs of various languages, how to create short programs or applications, independent assignments, and arrive at solutions that utilize the skills being covered in the language for that course (Chatley & Field, 2017). As an upperclassman, students will often be allowed to apply these skills in newer ways and have the opportunity to work on longer, more involved assignments although frequently still independent or in small groups of two …

Adaptive Strategies Of Multi-Objective Optimization For Greener Networks, Hatem Yazbek

CCE Theses and Dissertations

Increasing energy costs and environmental issues related to the Internet and wired networks continue to be a major concern. Energy-efficient or power-aware networks continue to gain interest in the research community. Existing energy reduction approaches do not fully address all aspects of the problem. We consider the problem of reducing energy by turning off network links, while achieving acceptable load balance, by adjusting link weights. Changing link weights frequently can cause network oscillation or instability in measuring the resulting traffic load, which is a situation to be avoided. In this research, we optimize two objectives, which are minimizing network power …

Reduction Of False Positives In Intrusion Detection Based On Extreme Learning Machine With Situation Awareness, Donald A. Burgio

CCE Theses and Dissertations

Protecting computer networks from intrusions is more important than ever for our privacy, economy, and national security. Seemingly a month does not pass without news of a major data breach involving sensitive personal identity, financial, medical, trade secret, or national security data. Democratic processes can now be potentially compromised through breaches of electronic voting systems. As ever more devices, including medical machines, automobiles, and control systems for critical infrastructure are increasingly networked, human life is also more at risk from cyber-attacks. Research into Intrusion Detection Systems (IDSs) began several decades ago and IDSs are still a mainstay of computer and …

Assessing The Presence Of Mindfulness Within Cyber And Non-Cybersecurity Groups, Christopher Wilder

CCE Theses and Dissertations

Corporations and individuals continue to be under Phishing attack. Researchers categorizes methods corporations and individuals can employ to reduce the impact of being caught in a Phishing scheme. Corporation enable technical mechanisms such as automated filtering, URL blacklisting, and manipulation of browser warning messages to reduce phishing susceptibility costing billions of dollars annually. However, even with robust efforts to educate employees about phishing techniques through security awareness training the abundance of attacks continues to plague organizations. This study aims to identify whether a correlation exists between mindfulness and phishing susceptibility. The goal of this research is to determine if mindful …

Improving Random Forests By Feature Dependence Analysis, Silu Zhang

Electronic Theses and Dissertations

Random forests (RFs) have been widely used for supervised learning tasks because of their high prediction accuracy good model interpretability and fast training process. However they are not able to learn from local structures as convolutional neural networks (CNNs) do when there exists high dependency among features. They also cannot utilize features that are jointly dependent on the label but marginally independent of it. In this dissertation we present two approaches to address these two problems respectively by dependence analysis. First a local feature sampling (LFS) approach is proposed to learn and use the locality information of features to group …

Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework, Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, Lee Giles

Computer Science Faculty Publications

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library …

A Survey Of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data, S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders among children, that affects different areas in the brain that allows executing certain functionalities. This may lead to a variety of impairments such as difficulties in paying attention or focusing, controlling impulsive behaviours and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper explores the ADHD identification studies using eye movement data and functional Magnetic Resonance Imaging (fMRI). This study discusses different machine learning techniques, existing models and analyses the existing literature. We have identified the current challenges and possible future directions …