Computer Sciences Commons^™

Proactive Empirical Assessment Of New Language Feature Adoption Via Automated Refactoring: The Case Of Java 8 Default Methods, Raffi Khatchadourian, Hidehiko Masuhara

Publications and Research

Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations …

Proactive Empirical Assessment Of New Language Feature Adoption Via Automated Refactoring: The Case Of Java 8 Default Methods, Raffi Khatchadourian, Hidehiko Masuhara

Publications and Research

Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations …

Multiple Sclerosis Identification Based On Fractional Fourier Entropy And A Modified Jaya Algorithm, Shui-Hua Wang, Hong Cheng, Preetha Phillips, Yu-Dong Zhang

Publications and Research

Aim: Currently, identifying multiple sclerosis (MS) by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, …

Csc 59940: Topics In Front End Web Application Development (Syllabus), David Moon, Michelle Shu, Nyc Tech-In-Residence Corps

Open Educational Resources

Syllabus for the course "CSC 59940 - Front-End Web Application Development" delivered at the City College of New York in Spring 2018 by Michelle Shu and David Moon as part of the Tech-in-Residence Corps program.

Csci 49900 Advanced Applications: A Capstone Course For Csci Majors, Alejandro Gonzalez Sole, Jesse Greenberg, James Lin, Sabeena Lalwani, Sean Laude, Sid Wighe, Nyc Tech-In-Residence Corps

Open Educational Resources

No abstract provided.

Poster: Towards Safe Refactoring For Intelligent Parallelization Of Java 8 Streams, Yiming Tang, Raffi Khatchadourian, Mehdi Bagherzadeh, Syed Ahmed

Publications and Research

The Java 8 Stream API sets forth a promising new programming model that incorporates functional-like, MapReduce-style features into a mainstream programming language. However, using streams correctly and efficiently may involve subtle considerations. In this poster, we present our ongoing work and preliminary results to- wards an automated refactoring approach that assists developers in writing optimal stream code. The approach, based on ordering and typestate analysis, determines when it is safe and advantageous to convert streams to parallel and optimize a parallel streams.

On The Use Of Semantic-Based Aig To Automatically Generate Programming Exercises, Laura Zavala, Benito Mendoza

Publications and Research

In introductory programming courses, proficiency is typically achieved through substantial practice in the form of relatively small assignments and quizzes. Unfortunately, creating programming assignments and quizzes is both, time-consuming and error-prone. We use Automatic Item Generation (AIG) in order to address the problem of creating numerous programming exercises that can be used for assignments or quizzes in introductory programming courses. AIG is based on the use of test-item templates with embedded variables and formulas which are resolved by a computer program with actual values to generate test-items. Thus, hundreds or even thousands of test-items can be generated with a single …

Relating Justification Logic Modality And Type Theory In Curry–Howard Fashion, Konstantinos Pouliasis

Dissertations, Theses, and Capstone Projects

This dissertation is a work in the intersection of Justification Logic and Curry--Howard Isomorphism. Justification logic is an umbrella of modal logics of knowledge with explicit evidence. Justification logics have been used to tackle traditional problems in proof theory (in relation to Godel's provability) and philosophy (Gettier examples, Russel's barn paradox). The Curry--Howard Isomorphism or proofs-as-programs is an understanding of logic that places logical studies in conjunction with type theory and -- in current developments -- category theory. The point being that understanding a system as a logic, a typed calculus and, a language of a class of categories constitutes …

Vision-Based Assistive Indoor Localization, Feng Hu

Dissertations, Theses, and Capstone Projects

An indoor localization system is of significant importance to the visually impaired in their daily lives by helping them localize themselves and further navigate an indoor environment. In this thesis, a vision-based indoor localization solution is proposed and studied with algorithms and their implementations by maximizing the usage of the visual information surrounding the users for an optimal localization from multiple stages. The contributions of the work include the following: (1) Novel combinations of a daily-used smart phone with a low-cost lens (GoPano) are used to provide an economic, portable, and robust indoor localization service for visually impaired people. (2) …

Multimodal Sensing And Data Processing For Speaker And Emotion Recognition Using Deep Learning Models With Audio, Video And Biomedical Sensors, Farnaz Abtahi

Dissertations, Theses, and Capstone Projects

The focus of the thesis is on Deep Learning methods and their applications on multimodal data, with a potential to explore the associations between modalities and replace missing and corrupt ones if necessary. We have chosen two important real-world applications that need to deal with multimodal data: 1) Speaker recognition and identification; 2) Facial expression recognition and emotion detection.

The first part of our work assesses the effectiveness of speech-related sensory data modalities and their combinations in speaker recognition using deep learning models. First, the role of electromyography (EMG) is highlighted as a unique biometric sensor in improving audio-visual speaker …

Object Localization, Segmentation, And Classification In 3d Images, Allan Zelener

Dissertations, Theses, and Capstone Projects

We address the problem of identifying objects of interest in 3D images as a set of related tasks involving localization of objects within a scene, segmentation of observed object instances from other scene elements, classifying detected objects into semantic categories, and estimating the 3D pose of detected objects within the scene. The increasing availability of 3D sensors motivates us to leverage large amounts of 3D data to train machine learning models to address these tasks in 3D images. Leveraging recent advances in deep learning has allowed us to develop models capable of addressing these tasks and optimizing these tasks jointly …

Cryptosystems Using Subgroup Distortion, Indira Chatterji, Delaram Kahrobaei, Ni Yen Lu

Publications and Research

In this paper we propose cryptosystems based on subgroup distortion in hyperbolic groups. We also include concrete examples of hyperbolic groups as possible platforms.

Gradient Estimation For Attractor Networks, Thomas Flynn

Dissertations, Theses, and Capstone Projects

It has been hypothesized that neural network models with cyclic connectivity may be more powerful than their feed-forward counterparts. This thesis investigates this hypothesis in several ways. We study the gradient estimation and optimization procedures for several variants of these networks. We show how the convergence of the gradient estimation procedures are related to the properties of the networks. Then we consider how to tune the relative rates of gradient estimation and parameter adaptation to ensure successful optimization in these models. We also derive new gradient estimators for stochastic models. First, we port the forward sensitivity analysis method to the …

Predicting The Next Us President By Simulating The Electoral College, Boyan Kostadinov

Publications and Research

We develop a simulation model for predicting the outcome of the US Presidential election based on simulating the distribution of the Electoral College. The simulation model has two parts: (a) estimating the probabilities for a given candidate to win each state and DC, based on state polls, and (b) estimating the probability that a given candidate will win at least 270 electoral votes, and thus win the White House. All simulations are coded using the high-level, open-source programming language R. One of the goals of this paper is to promote computational thinking in any STEM field by illustrating how probabilistic …

An Information Theory Approach To Hypothesis Testing In Criminological Research, Gohar A. Petrossian, Mike Maxfield

Publications and Research

Background: This research demonstrates how the Akaike information criterion (AIC) can be an alternative to null hypothesis significance testing in selecting best fitting models. It presents an example to illustrate how AIC can be used in this way.

Methods: Using data from Milwaukee, Wisconsin, we test models of place-based predictor variables on street robbery and commercial robbery. We build models to balance explanatory power and parsimony. Measures include the presence of different kinds of businesses, together with selected age groups and social disadvantage.

Results: Models including place-based measures of land use emerged as the best models among the set of …

Component Tree Analysis Of Cystovirus Φ6 Nucleocapsid Cryo-Em Single Particle Reconstructions, Lucas Oliveira, Ze Ye, Al Katz, Alexandra Alimova, Hui Wei, Gabor T. Herman, Paul Gottlieb

Publications and Research

The 3-dimensional structure of the nucleocapsid (NC) of bacteriophage φ6 is described utilizing component tree analysis, a topological and geometric image descriptor. The component trees are derived from density maps of cryo-electron microscopy single particle reconstructions. Analysis determines position and occupancy of structure elements responsible for RNA packaging and transcription. Occupancy of the hexameric nucleotide triphosphorylase (P4) and RNA polymerase (P2) are found to be essentially complete in the NC. The P8 protein lattice likely fixes P4 and P2 in place during maturation. We propose that the viral procapsid (PC) is a dynamic structural intermediate where the P4 and P2 …

The Legacy Of Multics And Secure Operating Systems Today, John Schriner

Publications and Research

This paper looks to the legacy of Multics from 1963 and its influence on computer security. It discusses kernel-based and virtualization-based containment in projects like SELinux and Qubes, respectively. The paper notes the importance of collaborative and research-driven projects like Qubes and Tor Project.

Design Of Cloud Based Robots Using Big Data Analytics And Neuromorphic Computing, Ashwin Satyanarayana, Janusz Kusyk, Yu-Wen Chen

Publications and Research

Understanding the brain is perhaps one of the greatest challenges facing twenty-first century science. While a traditional computer excels in precision and unbiased logic, its abilities to interact socially lags behind those of biological neural systems. Recent technologies, such as neuromorphic engineering, cloud infrastructure, and big data analytics, have emerged that can narrow the gap between traditional robots and human intelligence. Neuromorphic robotics mimicking brain functions can contribute in developing intelligent machines capable of learning and making autonomous decisions. Cloud-based robotics take advantage of remote resources for parallel computation and sharing large amounts of information while benefiting from analysis of …

Cis 4400 Data Warehousing For Analytics, Royce Kok, Nyc Tech-In-Residence Corps

Open Educational Resources

No abstract provided.

Deep Convolutional Neural Networks Enable Discrimination Of Heterogeneous Digital Pathology Images, Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha

Publications and Research

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error.

In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer.

In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry …

Proceedings Of The Cuny Games Conference 4.0: The Interactive Course, Robert O. Duncan, Joe Bisz, Julie Cassidy, Kathleen Offenholley, Maura A. Smale, Carolyn Stallard, Deborah Sturm, Anders A. Wallace, Cuny Games Network

Publications and Research

Proceedings of the CUNY Games Conference, held from January 22-23, 2018, at the CUNY Graduate Center and Borough of Manhattan Community College.

Critical Play with History (Panel) - Composition & Storytelling - Health & Cognitive Sciences - Gaming Anthropology: Teaching Culture and Power Through Games and Design (Panel) - Twine & Writing Games - Easy Ideas II - STEM Games - Global Games for Change Catalog (Panel) - Comics & Active Learning - Fact Checking & Research - Computer Science & Game Design - SimGlobal: Building a Serious Roleplay Course for the Social Sciences (Panel) - Role Playing Games, Narrative, …

Two-Dimensional Maximal Repetitions, Amihood Amir, Gad M. Landau, Shoshana Marcus, Dina Sokol

Publications and Research

Maximal repetitions or runs in strings have a wide array of applications and thus have been extensively studied. In this paper, we extend this notion to 2-dimensions, precisely defining a maximal 2D repetition. We provide initial bounds on the number of maximal 2D repetitions that can occur in a matrix. The main contribution of this paper is the presentation of the first algorithm for locating all maximal 2D repetitions in a matrix. The algorithm is efficient and straightforward, with runtime O(n^2 log n log log n+ rho log n), where n^2 is the size of the input, and rho is …

Breadcrumbs: Privacy As A Privilege, Prachi Bhardwaj

Capstones

Breadcrumbs: Privacy as a Privilege Abstract

By: Prachi Bhardwaj

In 2017, the world saw more data breaches than in any year prior. The count was more than the all-time high record in 2016, which was 40 percent more than the year before that.

That’s because consumer data is incredibly valuable today. In the last three decades, data storage has gone from being stored physically to being stored almost entirely digitally, which means consumer data is more accessible and applicable to business strategies. As a result, companies are gathering data in ways previously unknown to the average consumer, and hackers are …

Real-Time Indoor Assistive Localization With Mobile Omnidirectional Vision And Cloud Gpu Acceleration, Feng Hu, Zhigang Zhu, Jeury Mejia, Hao Tang

Publications and Research

In this paper we propose a real-time assistive localization approach to help blind and visually impaired people in navigating an indoor environment. The system consists of a mobile vision front end with a portable panoramic lens mounted on a smart phone, and a remote image feature-based database of the scene on a GPU-enabled server. Compact and elective omnidirectional image features are extracted and represented in the smart phone front end, and then transmitted to the server in the cloud. These features of a short video clip are used to search the database of the indoor environment via image-based indexing to …

The Ability Of Different Imputation Methods To Preserve The Significant Genes And Pathways In Cancer, Rosa Aghdam, Taban Baghfalaki, Pegah Khosravi, Elnaz Saberi Ansari

Publications and Research

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase …

Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen

Student Theses

The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the …

Study Of Self-Similarity In Brain Data, Jennifer Holst

Student Theses

In the area of computer science, past research has found that the concept of self-similarity is present in local and Internet-based network traffic. This study considers the possibility that data traveling through the neuronal network in the human brain is also self-similar. By analyzing publicly available raw EEG data and estimating its Hurst parameter, we find indications that brain data traffic may in fact be self-similar.

On Improvised Music, Computational Creativity And Human-Becoming, Arto Artinian, Adam James Wilson

Publications and Research

Music improvisation is an act of human-becoming: of self-expression—an articulation of histories and memories that have molded its participants—and of exploration—a search for unimagined structures that break with the stale norms of majoritarian culture. Given that the former objective may inhibit the latter, we propose an integration of human musical improvisers and deliberately flawed creative software agents that are designed to catalyze the development of human-ratified minoritarian musical structures.

Defaultification Refactoring: A Tool For Automatically Converting Java Methods To Default, Raffi Khatchadourian, Hidehiko Masuhara

Publications and Research

Enabling interfaces to declare (instance) method implementations, Java 8 default methods can be used as a substitute for the ubiquitous skeletal implementation software design pattern. Performing this transformation on legacy software manually, though, may be non-trivial. The refactoring 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. All of this is necessary to preserve type-correctness and confirm semantics preservation. We demonstrate an automated refactoring tool called Migrate Skeletal Implementation to Interface for transforming legacy Java code to use the new default construct. The …

Exploring The Internal Statistics: Single Image Super-Resolution, Completion And Captioning, Yang Xian

Dissertations, Theses, and Capstone Projects

Image enhancement has drawn increasingly attention in improving image quality or interpretability. It aims to modify images to achieve a better perception for human visual system or a more suitable representation for further analysis in a variety of applications such as medical imaging, remote sensing, and video surveillance. Based on different attributes of the given input images, enhancement tasks vary, e.g., noise removal, deblurring, resolution enhancement, prediction of missing pixels, etc. The latter two are usually referred to as image super-resolution and image inpainting (or completion).

Image super-resolution and completion are numerically ill-posed problems. Multi-frame-based approaches make use of the …