Computer Sciences Commons^™

Characterising The Knowledge About Primitive Variables In Java Code Comments, Mahfouth Alghamdi, Shinpei Hayashi, Takashi Kobayashi, Christoph Treude

Research Collection School Of Computing and Information Systems

Primitive types are fundamental components available in any programming language, which serve as the building blocks of data manipulation. Understanding the role of these types in source code is essential to write software. Little work has been conducted on how often these variables are documented in code comments and what types of knowledge the comments provide about variables of primitive types. In this paper, we present an approach for detecting primitive variables and their description in comments using lexical matching and advanced matching. We evaluate our approaches by comparing the lexical and advanced matching performance in terms of recall, precision, …

Corn Co-Product Logistics: An Application Of Linear Programming, Dmitry Kalashnikov Adams

Department of Agricultural Economics: Dissertations, Theses, and Student Research

The purpose of this thesis is not to explore new ways to apply or to study the general field of linear programming. Rather the emphasis is on applying a particular type of linear programming to a specific problem. In this thesis the classic case of linear programing - the transportation problem – is used to optimize corn co-product logistics between six ethanol producing facilities. At the core, the problem of corn germ logistics lies in transporting products from areas of excess supply to areas with excess demand. The challenge of optimizing corn germ logistics lies in managing transportation between producing …

Digital Twin Technology Applications For Transportation Infrastructure - A Survey-Based Study, Hector Cruz

Open Access Theses & Dissertations

In the past couple of decades, various industries have taken advantage of emerging advanced technologies, such as digital twin (DT), to find more effective solutions in their respective areas. In the transportation infrastructure sector, the concept and implementation of DT technologies are slowly gaining traction but lagging behind other major industries. To better understand the limitations, opportunities and challenges for the adoption of DT in this sector, a survey questionnaire was distributed to collect information from industry professionals involved in transportation infrastructure projects. The purpose of this study is to understand how DT technology is being perceived by the industry. …

On Using Demographic Data With Deprivation Index For Predicting Chronic Diseases, Olugbenga Temitope Iyiola

Open Access Theses & Dissertations

Researchers have worked on modeling and predicting the likelihood of developingchronic diseases, such as diabetes and high blood pressure, using medical data (e.g., heart-rate, blood sugar). However, many of these diseases demonstrate strong links with demographics and socio-economic status (e.g., race, gender, income). It is also less time-consuming to retrieve demographic and socio-economic data, some of which are publicly available through US Census Bureau, than to carry out medical tests. Hence, demographic data can give a quicker estimate of the susceptibility of a person to a chronic disease.

In this work, we study the effect of using medical vs. demographics …

Addressing Challenges In Aggregating And Analyzing Agroecological Data, Sarah E. Mccord

Open Access Theses & Dissertations

Agroecosystems face multiple threats including land degradation and climate change, changing and competing land uses, invasive species and disease spread, and biodiversity loss. While scientists seek to understand rapidly changing ecosystems, land managers are struggling to maintain ecosystem services amid transitions to novel ecosystem states. Understanding agroecosystem drivers and ensuing responses requires quality information about ecosystems that span biomes, trophic scales, ecological processes, spatiotemporal scales, land use, and land ownership. Yet, using multi-scale agroecosystem information can be frustrating for both scientific researchers and land managers as it is difficult to locate data that are trustworthy, easily accessible, standardized, and connected …

Making Valid Inferences With Decision Tree, George Ekow Quaye

Open Access Theses & Dissertations

HypoThesis testing and Confidence Interval (CI) estimates are key statistics in predicting future values in data analysis. Most often, CI estimates are directly obtained from the summary statistics of a particular statistical methodology output. However, when it comes to the summary of decision tree outputs, these CI estimates are not directly obtained. So a na\"{i}ve way of making node-level inference is to construct a $(1-\alpha) \times 100\%$ confidence interval for a node mean $\bar{y}_t$ using the relation: $\bar{y}_t \, \pm \, z_{1-\alpha/2} \, \frac{s_t}{\sqrt{n_t}}$, where $\bar{y}_t$ is the node mean and $s_t$ is the standard deviation estimates from the decision …

How Does Land Cover Classification In Google Earth Engine Compare With Traditional Methods Of Land Cover Classification? What Are The Tradeoffs?, Carlos Sebastian Reyes

Open Access Theses & Dissertations

The project focuses on comparing land cover classification of traditional methods such as ArcGIS with newer ones such as Google Earth Engine (GEE) as well as discussing any potential tradeoffs. Two studies were performed in both platforms, the first involved analyzing land cover change in the Middle Rio Grande (MRG) region of southern New Mexico, far west Texas, and northern Chihuahua, Mexico. The MRG study focused on urban and agricultural change in the region using two different classification methods. The second study focused on creating a post-hurricane damage assessment (PDA) with the goal of developing an automated method of estimating …

Effect Of Augmented Reality On Consumer Behavior In E-Commerce, Chibuke Uzoechina, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

AR technology has been referred to as the future of e-commerce. In this paper, we propose to investigate the effect of AR on consumer behavior in e-commerce. Based on the theory of telepresence, we generated a set of hypotheses on their relationships. An experimental study is proposed to test the hypotheses. Our findings will be of interest to e-commerce companies that are looking at incorporating AR into their e-commerce platforms.

Image Analysis Of Charged Bimodal Colloidal Systems In Microgravity., Adam J. Cecil

Electronic Theses and Dissertations

Colloids are suspensions of two or more phases and have been topics of research for advanced, tunable materials for decades. Stabilization of colloids is typically attributed to thermodynamic mechanisms; however, recent studies have identified transport or entropic mechanisms that can potentially stabilize a thermodynamically unstable colloidal system. In this study, suspensions of silsesquioxane microparticles and zirconia nanoparticles were dispersed in a nitric acid solution and allowed to aggregate for 8-12 days in microgravity aboard the International Space Station. The suspensions were subsequently imaged periodically at 2.5x magnification. Due to the inadequacy of existing image analysis programs, the python package “Colloidspy” …

Incorporating Demographic Structure And Variable Interaction Types Into Community Assembly Models, Akhil Reddy Alasandagutti, Nayan Chawla

Honors Theses

Theoretical studies of ecological food webs have allowed ecologists to remove the constraints of specific location and timescales from their study of ecological communities; food webs are generally complex and thus empirical study is difficult. Further, this theoretical approach allows ecologists to compare ecological processes and outcomes across any possible food web structures. However, these simulated communities are only as useful as the model from which they were constructed. Modifying existing considerations in these models, and generating new ones, are the jobs of theoretical ecologists that seek to achieve the shared goal of a majority of simulations: representation of real …

Temporal Convolutional Neural Network For Intrusion Detection, Luis Javier Romo Jr.

Theses and Dissertations

Intrusion detection is an important endeavor for large organizations who are constantly targeted by malicious actors. The nature of network traffic data creates many challenges for researchers that want to create an accurate and efficient system for detecting attacks on networks. Many machine learning algorithms have been developed to take on this task. In this paper, we will review some of these techniques, some data sets used to test these techniques, and an experiment where we developed an intrusion detection system that uses a convolution neural network that can perform sequence modeling. This convolutional neural network outperformed a long-shorted term …

Semantic Adversarial Attack On Support Vector Machine, Yessica Rodriguez

Theses and Dissertations

Despite the breakthroughs in machine learning, most classifiers are not robust against adversarial attacks. They can be easily fooled by adversarial examples. These examples can be created in a variety of ways. In this thesis, the ideas of detecting edges or critical pixels in an image are investigated that could be used for fooling classifiers. Identifying those critical pixels in an image can lead the way to fix the vulnerabilities and thus making it robust against cyber-attacks. For testing, a Support Vector Machine (SVM) is used to see the success of the adversarial examples generated.

Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos

Electronic Theses and Dissertations

Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification …

The Shifting Sands Of Motivation: Revisiting What Drives Contributors In Open Source, Marco Gerosa, Igor Wiese, Bianca Trinkenreich, Georg Link, Gregorio Robles, Christoph Treude, Igor Steinmacher, Anita Sarma

Research Collection School Of Computing and Information Systems

Open Source Software (OSS) has changed drastically over the last decade, with OSS projects now producing a large ecosystem of popular products, involving industry participation, and providing professional career opportunities. But our field's understanding of what motivates people to contribute to OSS is still fundamentally grounded in studies from the early 2000s. With the changed landscape of OSS, it is very likely that motivations to join OSS have also evolved. Through a survey of 242 OSS contributors, we investigate shifts in motivation from three perspectives: (1) the impact of the new OSS landscape, (2) the impact of individuals' personal growth …

A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu

Research Collection School Of Computing and Information Systems

This paper studies the integration of the vehicle routing problem with cross-docking (VRPCD). The aim is to find a set of routes to deliver products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic based on column generation is proposed. The first phase focuses on generating a set of feasible candidate routes in both pickup and delivery processes by implementing an adaptive large neighborhood search algorithm. A set of destroy and repair operators are …

Prototypical Contrastive Learning Of Unsupervised Representations, Junnan Li, Pan Zhou, Caiming Xiong, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that bridges contrastive learning with clustering. PCL not only learns low-level features for the task of instance discrimination, but more importantly, it encodes semantic structures discovered by clustering into the learned embedding space. Specifically, we introduce prototypes as latent variables to help find the maximum-likelihood estimation of the network parameters in an Expectation-Maximization framework. We iteratively perform E-step as finding the distribution of prototypes via clustering and M-step as optimizing the network via contrastive learning. We propose ProtoNCE loss, a generalized version of the InfoNCE loss for contrastive …

Dialogue State Tracking With Incremental Reasoning, Lizi Liao, Le Hong Long, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-theart methods in terms of joint …

Immigrant Families' Health-Related Information Behavior On Instant Messaging Platforms: Health-Related Information Exchange In Immigrant Family Groups On Instant Messaging Platforms, Lev Poretski, Taamannae Taabassum, Anthony Tang

Research Collection School Of Computing and Information Systems

For immigrant families, instant messaging family groups are a common platform for sharing and discussing health-related information. Immigrants often maintain contact with their family abroad and trust information in shared IM family groups more than the information from local authorities and sources. In this study, we aimed to understand health-related information behaviors of immigrant families in their IM family groups. Based on the interviews with 6 participants from immigrant families to Canada, we found that immigrant families’ discourse on IM platforms is motivated by love and care for other family members. The families used local and international sources of information, …

Cross-Modal Food Retrieval: Learning A Joint Embedding Of Food Images And Recipes With Semantic Consistency And Attention Mechanism;, Hao Wang, Doyen Sahoo, Chenghao Liu, Ke Shu, Achananuparp Palakorn, Ee Peng Lim, Steven Hoi

Research Collection School Of Computing and Information Systems

Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc. In this paper, we investigate cross-modal retrieval between food images and cooking recipes. The goal is to learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. Two major challenges in addressing this problem are 1) large intra-variance and small inter-variance across cross-modal food data; and 2) difficulties in obtaining discriminative recipe representations. To address these …

Learning Index Policies For Restless Bandits With Application To Maternal Healthcare, Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe

Research Collection School Of Computing and Information Systems

In many community health settings, it is crucial to have a systematic monitoring and intervention process to ensure that the patients adhere to healthcare programs, such as periodic health checks or taking medications. When these interventions are expensive, they can be provided to only a fixed small fraction of the patients at any period of time. Hence, it is important to carefully choose the beneficiaries who should be provided with interventions and when. We model this scenario as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention …

Mining And Managing Big Data Refactoring For Design Improvement: Are We There Yet?, Eman Abdullah Alomar, Mohamed Wiem Mkaouer, Ali Ouni

Articles

Refactoring is a set of code changes applied to improve the internal structure of a program, without altering its external behavior. With the rise of continuous integration and the awareness of the necessity of managing technical debt, refactoring has become even more popular in recent software builds. Recent studies indicate that developers often perform refactorings. If we consider all refactorings performed across all projects, this consists of the refactoring knowledge that represents a rich source of information that can be useful for both developers and practitioners to better understand how refactoring is being applied in practice. However, mining, processing, and …

Refactoring Practices In The Context Of Modern Code Review: An Industrial Case Study At Xerox, Eman Abdullah Alomar, Hussein Alrubaye, Mohamed Wiem Mkaouer, Ali Ouni, Marouane Kessentini

Articles

Modern code review is a common and essential 2 practice employed in both industrial and open-source projects 3 to improve software quality, share knowledge, and ensure con4 formance with coding standards. During code review, developers 5 may inspect and discuss various changes including refactoring 6 activities before merging code changes in the code base. To date, 7 code review has been extensively studied to explore its general 8 challenges, best practices and outcomes, and socio-technical 9 aspects. However, little is known about how refactoring activities 10 are being reviewed, perceived, and practiced. 11 This study aims to reveal insights into …

Finding The Needle In A Haystack: On The Automatic Identification Of Accessibility User Reviews, Eman Abdullah Alomar, Wajdi Aljedaani, Murtaza Tamjeed, Mohamed Wiem Mkaouer, Yasime Elglaly

Articles

In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many limitations. User reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this paper is to support the automated identification of accessibility in user reviews, to help technology professionals in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user …

Using Deep Learning To Automate The Diagnosis Of Skin Melanoma, Akhil Reddy Alasandagutti

Honors Theses

Machine learning and image processing techniques have been widely implemented in the field of medicine to help accurately diagnose a multitude of medical conditions. The automated diagnosis of skin melanoma is one such instance. However, a majority of the successful machine learning models that have been implemented in the past have used deep learning approaches where only raw image data has been utilized to train machine learning models, such as neural networks. While they have been quite effective at predicting the condition of these lesions, they lack key information about the images, such as clinical data, and features that medical …

A Game Theoretical Analysis Of Non-Linear Blockchain System, Lin Chen, Lei Xu, Zhimin Gao, Ahmed Sunny, Keshav Kasichainula, Weidong Shi

Computer Science Faculty Publications

Recent advances in the blockchain research have been made in two important directions. One is refined resilience analysis utilizing game theory to study the consequences of selfish behavior of users (miners), and the other is the extension from a linear (chain) structure to a non-linear (graphical) structure for performance improvements, such as IOTA and Graphcoin. The first question that comes to mind is what improvements that a blockchain system would see by leveraging these new advances. In this paper, we consider three major properties for a blockchain system: 𝛼-partial verification, scalability, and finality-duration. We establish a formal framework and prove …

Improving Treatment Of Local Liver Ablation Therapy With Deep Learning And Biomechanical Modeling, Brian Anderson, Kristy Brock, Laurence Court, Carlos Eduardo Cardenas, Erik Cressman, Ankit Patel

Dissertations and Theses (Open Access)

In the United States, colorectal cancer is the third most diagnosed cancer, and 60-70% of patients will develop liver metastasis. While surgical liver resection of metastasis is the standard of care for treatment with curative intent, it is only avai lable to about 20% of patients. For patients who are not surgical candidates, local percutaneous ablation therapy (PTA) has been shown to have a similar 5-year overall survival rate. However, PTA can be a challenging procedure, largely due to spatial uncertainties in the localization of the ablation probe, and in measuring the delivered ablation margin.

For this work, we hypothesized …

Implementation Of Uniform Interpolationalgorithms, Jose A. Castellanos Joo

Computer Science ETDs

This thesis discusses algorithms for the uniform interpolation problem and presents their implementation for the following theories: (quantifier-free) equality with uninterpreted functions (EUF), unit two-variable per inequality (UTVPI), and theoretic aspects for the combination of the two previous theories. The uniform interpolation algorithms implemented in this thesis were originally proposed in \cite{KAPUR2017}. Refutational proof-based solutions are the usual approach of many interpolation algorithms \cite{10.1007/978-3-642-00768-2_34, mcmillan2011interpolants, 10.1007/978-3-540-24730-2_2}. The approach taken in \cite{KAPUR2017} relies on quantifier-elimination heuristics to construct a uniform interpolant using one of the two formulas involved in the interpolation problem. The latter makes it possible to study the complexity …

Ship-Gan: Generative Modeling Based Maritime Traffic Simulator, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar

Research Collection School Of Computing and Information Systems

Modeling vessel movement in a maritime environment is an extremely challenging task given the complex nature of vessel behavior. Several existing multiagent maritime decision making frameworks require access to an accurate traffic simulator. We develop a system using electronic navigation charts to generate realistic and high fidelity vessel traffic data using Generative Adversarial Networks (GANs). Our proposed Ship-GAN uses a conditional Wasserstein GAN to model a vessel's behavior. The generator can simulate the travel time of vessels across different maritime zones conditioned on vessels' speeds and traffic intensity. Furthermore, it can be used as an accurate simulator for prior decision …

Research Artifact: The Potential Of Meta-Maintenance On Github, Hideaki Hata, Raula Kula, Takashi Ishio, Christoph Treude

Research Collection School Of Computing and Information Systems

This is a research artifact for the paper “Same File, Different Changes: The Potential of Meta-Maintenance on GitHub”. This artifact is a data repository including a list of studied 32,007 repositories on GitHub, a list of targeted 401,610,677 files, the results of the qualitative analysis for RQ2, RQ3, and RQ4, the results of the quantitative analysis for RQ5, and survey material for RQ6. The purpose of this artifact is enabling researchers to replicate our mixed-methods results of the paper, and to reuse the results of our exploratory study for further software engineering research. This research artifact is available at https://github.com/NAIST-SE/MetaMaintenancePotential …

On Decentralization Of Bitcoin: An Asset Perspective, Ling Cheng, Feida Zhu, Huiwen Liu, Chunyan Miao

Research Collection School Of Computing and Information Systems

Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer digital payment system, has been gaining increasing attention from both academia and industry. An effort designed to overcome a cluster of bottlenecks inherent in existing centralized financial systems, Bitcoin has always been championed by the crypto community as an example of the spirit of decentralization. While the decentralized nature of Bitcoin's Proof-of-Work consensus algorithm has often been discussed in great detail, no systematic study has so far been conducted to quantitatively measure the degree of decentralization of Bitcoin from an asset perspective -- How decentralized is Bitcoin as a financial asset? …