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Articles 23101 - 23130 of 63342

Full-Text Articles in Computer Sciences

Semi-Supervised Learning Using Triple-Siamese Network, Debapriya Banerjee May 2020

Semi-Supervised Learning Using Triple-Siamese Network, Debapriya Banerjee

Computer Science and Engineering Theses - Archive

Missing data problem is inevitable in mostly all research areas including Artificial Intelligence, Machine Learning and Computer Vision where we have modicum knowledge about the complete dataset. One of the key reasons of missing data in AI is insufficiency of accurately labeled data. To solve a classification problem using ML or training a Deep Neural Network model, we need a huge amount of labeled data. It is difficult to get labeled data but unlabeled data is inexpensive and available easily. It is usual that we get no more than a single element per class to train our models due to …

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Development Of Text Analytics For Debriefing Reflection Essays, Md Shadekur Rahman May 2020

Development Of Text Analytics For Debriefing Reflection Essays, Md Shadekur Rahman

Computer Science and Engineering Theses - Archive

Evaluating and providing feedback to hundreds of free text assignments in an online environment is a challenging task for an instructor where he has to scan through essays to identify perspectives that are expected to appear in those essays. Reading large number of essays and then finding themes and providing customized feedback are time-consuming process. We have proposed a text analytics system named EssayIQ that aids course instructor in identifying assignment themes, providing theme presence statistics and giving feedback to learners. To the best of our knowledge, this is the first system that analyzes free text assignments in line with …

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Tapin: A Two-Factor User Authentication Scheme For Smartwatches Through Secret Finger Tapping, Akash Lohani May 2020

Tapin: A Two-Factor User Authentication Scheme For Smartwatches Through Secret Finger Tapping, Akash Lohani

Computer Science and Engineering Theses - Archive

Nowadays, smartwatches have become one of the most common wearable gadgets as they are small and portable. As more and more personal information is managed and processed inside smartwatches, it is important to have a secure user authentication scheme in place. There have been many successful authentication schemes for a smartphones such as Password/PIN, bio-metric approach(e.g. fingerprint, face recognition), etc directly used on smartwatches. However, these approaches are not quite suitable for smartwatches due to its constraints in size and limited computation power. To address this issue, we propose TaPIN that allows users to authenticate themselves by playing out the …

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Mln-Subdue: Decoupling Approach-Based Substructure Discovery In Multilayer Networks (Mlns), Anish Rai May 2020

Mln-Subdue: Decoupling Approach-Based Substructure Discovery In Multilayer Networks (Mlns), Anish Rai

Computer Science and Engineering Theses - Archive

Substructure discovery is well-researched for single graphs (both simple and attribute) as it is an important component of knowledge discovery for many applications such as finding the core substructure in a protein, important concept in a large graph, etc. However, multilayer networks or MLNs (instead of attribute graphs) have been shown to be better for modeling complex data sets that have multiple entity and feature types. This model provides more clarity on semantics of data sets as well as the ability to use an arbitrary subset of layers for analysis. However, the challenge is that many algorithms such as community …

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Randomized And Evolutionary Approaches To Dataset Characterization, Feature Weighting, And Sampling In K-Nearest Neighbors, Suryoday Basak May 2020

Randomized And Evolutionary Approaches To Dataset Characterization, Feature Weighting, And Sampling In K-Nearest Neighbors, Suryoday Basak

Computer Science and Engineering Theses - Archive

K-Nearest Neighbors (KNN) has remained one of the most popular methods for supervised machine learning tasks. However, its performance often depends on the characteristics of the dataset and on appropriate feature scaling. In this thesis, characteristics of a dataset that make it suitable for being used within KNN are explored. As part of this, two new measures for dataset dispersion, called mean neighborhood target variance (MNTV), and mean neighborhood target entropy (MNTE) are developed to help determine the performance we expect while using KNN regressors and classifiers, respectively. It is empirically demonstrated that these measures of dispersion can be indicative …

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3d Skeleton Construction From Multiple Camera Views For Quantifying Gait Parameters, Saket Gupta May 2020

3d Skeleton Construction From Multiple Camera Views For Quantifying Gait Parameters, Saket Gupta

Computer Science and Engineering Theses - Archive

Research has shown that human gait characteristics permit inference with respect to different personal and health characteristics and can thus be used as a diagnostic tool. To do this automatically it is important to be able to extract them from senor information. This thesis work is aimed at doing this from multiple camera views and for this focuses on construction of a 3D body skeleton from multiple viewpoint video (MVV) and then quantifying a number of gait characteristics such as swing time, step time, cadence, stride length, single support, or double support. The method introduced here uses a marker-less approach …

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Intrinsic Curiosity In Reinforcement Learning By Improving Next State Prediction, Paul Lewis Lobo May 2020

Intrinsic Curiosity In Reinforcement Learning By Improving Next State Prediction, Paul Lewis Lobo

Computer Science and Engineering Theses - Archive

In Reinforcement Learning, an agent receives feedback from the environment in the form of an extrinsic reward. It learns to take actions that maximize this extrinsic reward. However, to start learning, the agent needs to be able to get feedback from the environment by using random actions. This works in environments with frequent rewards, however, in environments where the rewards are sparse the probability of reaching any reward even once becomes very low. One way to explore an environment efficiently is for the agent to generate its own intrinsic reward by using the prediction error from a model that is …

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Azul - Using Multimodal Sensors From Mobile And Wearable Devices For Assessment And Monitoring Of Depression Symptoms, Manu Srivastava May 2020

Azul - Using Multimodal Sensors From Mobile And Wearable Devices For Assessment And Monitoring Of Depression Symptoms, Manu Srivastava

Computer Science and Engineering Theses - Archive

With the ubiquitous presence of mobile phones, there is too much data that can be collected for different purposes which can be correlated to monitor multiple more user information. If used responsibly, this data can be used to assess and monitor various mental health conditions. For example, fitness sensors in wearable devices collect step counts, sleep-related data and heart rate; GPS sensors in smartphones continually collect device location, and Android on-device services collect smartphone usage habits of the user. All the data from the previous example relate to exhibited depression symptoms in users. This data, when used in conjugation, has …

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Mr_Qp: A Scalable Approach To Query Processing On Arbitrary-Size Graphs Using The Map/Reduce Framework, Harshit Ashokkumar Modi May 2020

Mr_Qp: A Scalable Approach To Query Processing On Arbitrary-Size Graphs Using The Map/Reduce Framework, Harshit Ashokkumar Modi

Computer Science and Engineering Theses - Archive

The utility and widespread use of Relational Database Management Systems(RDBMSs) comes not only from its simple, easy-to-understand data model (a relation or a set) but mainly from the ability to write non-procedural queries and their optimization by the system. Queries produce exact answers that match the contents of the database. Query processing of RDBMSs has been researched for more than 4 decades and includes extensions to more complex analysis on data warehouses. In contrast, search has not been addressed by RDBMSs. As the use of other other data types (key-value store, column-store, and graphs to name a few) are becoming …

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Extractive Summarization And Simplification Of Scholarly Literature, Nilav Bharatkumar Vaghasiya May 2020

Extractive Summarization And Simplification Of Scholarly Literature, Nilav Bharatkumar Vaghasiya

Computer Science and Engineering Theses - Archive

Research papers and journals have always played a crucial role in the field of research and development. However, these research papers usually have a complex usage of language which limits the range of target readers. The language and the terms used in these literary works can make the concept or the topic tough to understand for a naive reader. The goal of this project is to simplify a complex piece of literature into something meaningful without sounding verbose. The idea is based upon Nobel Prize-winning physicist Richard Feynman’s learning technique known as the Feynman Technique that emphasizes the usage of …

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Using Graph Convolutional Network And Message Passing Neural Networks For Solving Unit Commitment And Economic Dispatch In A Day Ahead Energy Trading Market Based On Ercot Nodal Model., Pradnya S. Gaikwad May 2020

Using Graph Convolutional Network And Message Passing Neural Networks For Solving Unit Commitment And Economic Dispatch In A Day Ahead Energy Trading Market Based On Ercot Nodal Model., Pradnya S. Gaikwad

Computer Science and Engineering Theses - Archive

Various machine learning applications will pre-process graphical representations into a vector of real values which in turn loses information regarding graph structure. Graph Neural Networks (GNNs) are a combination of an information diffusion mechanism and neural networks, which represent a set of transition functions and a set of output functions. Graph Convolution Network (GCN) is based on the optimized variant of CNN which operates on graph and is a scalable approach for semi-supervised learning on structured graph data. Message Passing Neural Networks (MPNNs) summaries the cohesions between many of the existing Neural Network models for structured graph data. This thesis …

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Portable Electrochemical Sensing Platform: From Hardware To Software, Karthik Gangadhara May 2020

Portable Electrochemical Sensing Platform: From Hardware To Software, Karthik Gangadhara

Computer Science and Engineering Theses - Archive

Recent advances in the electrochemical biosensors is increasing the popularity of the point of care devices since the electrochemical sensor can provide low cost, portability, detectability, experimental simplicity, and capacity to provide real time monitoring. The point of care devices has been used in various biomedical applications such as blood glucose monitors, pregnancy tests, HIV tests, hemoglobin level tests etc. However, the existing portable devices are limited to a specific sensing mechanism due to the inability to include the various electrochemical sensing techniques into a compact formfactor. Thus, there is a need for miniaturized all-in-one electrochemical sensing platform for the …

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Using Chebconv And B-Spline Gnn Models For Solving Unit Commitment And Economic Dispatch In A Day Ahead Energy Trading Market Based On Ercot Nodal Model, Yashodhan Kumthekar May 2020

Using Chebconv And B-Spline Gnn Models For Solving Unit Commitment And Economic Dispatch In A Day Ahead Energy Trading Market Based On Ercot Nodal Model, Yashodhan Kumthekar

Computer Science and Engineering Theses - Archive

Spectral Convolutions and B-Spline Graph Neural Network techniques have been used in past to learn embeddings in various complex, multidimensional structured knowledge graphs like genetics, social networks, geometric shapes and more. Spectral graphs provide a way to apply fast and localized filters on graph data. B-Spline kernels provides a way to keep the computation time independent by due to the local support property of B-spline basis functions. This thesis aims at using each of these models to test their viability for solving the Unit Commitment (UC) and Economic Dispatch (ED) problem for the energy market. There have been multiple attempts …

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Big Data In Single Player Games, Mohammad Khaldoun Farhan Aldaboubi May 2020

Big Data In Single Player Games, Mohammad Khaldoun Farhan Aldaboubi

Computer Science and Engineering Theses - Archive

Improving video games can be exponentially more efficient by utilizing big data. Big data plays a big part in modern gaming, especially for multiplayer games like poker, first person shooter games. Utilizing the data gathered from video games can be used in ways that will improve the player experience massively and can be eye-opening to find issues, player pattern, and improve the game in ways that will be hard to pin point without gathering data of how the players are playing the game. However, while big data is being utilized in multiplayer games, it’s not utilized as much in single …

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Comparative Analysis Of Metabolic Pathways Of Bacteria Used In Fermented Food, Keanu Hoang, Kiran Bastola May 2020

Comparative Analysis Of Metabolic Pathways Of Bacteria Used In Fermented Food, Keanu Hoang, Kiran Bastola

Theses/Capstones/Creative Projects

This study presents a novel methodology for analyzing metabolic pathways. Utilizing KEGG REST API through a Biopython package and file parser, data about whether or not a bacteria has an enzyme or not was extracted. The results found that differences in metabolic pathway enrichment values follow along the lines of genera and pathway type. In particular, bacteria found in food spoilage and commercial nitrogen fixing products had high values of enrichment.

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Feature Selection And Data Reconstruction Via Robust And Flexible Learning Models, Di Ming May 2020

Feature Selection And Data Reconstruction Via Robust And Flexible Learning Models, Di Ming

Computer Science and Engineering Dissertations - Archive

Feature selection and data reconstruction are very important topics in machine learning area. In today's big data environment, many data could have high dimensions and come with noise, corruption, etc. Thus, we develop robust and flexible learning models so as to select the relevant features from the high-dimensional data spaces and reconstruct the original clean data from the corrupted input data more efficiently and more effectively. To resolve the inflexibility of the widely used class-shared feature selection methods such as L21-norm, we derive LASSO from probabilistic selection on ridge regression which provides an independent point of view from the usual …

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Deep Representation Learning On Giga-Pixel Whole Slide Images, Xinliang Zhu May 2020

Deep Representation Learning On Giga-Pixel Whole Slide Images, Xinliang Zhu

Computer Science and Engineering Dissertations - Archive

I present my work towards solving the fundamental, challenging and valuable problem for automatically processing the giga-pixel level whole slide pathology images (WSIs): the representation of them. Specifically, I target on solving the combinations of three critical aspects of the problem: (1) it's not engineering feasible to directly fit them into existing convolutional neural networks because they are too large; (2) pre-trained parameters from other domains may not be effectively transferred to pathology images, and (3) both the image samples and annotations for those images are rarely available. To evaluate the effectiveness of the developed methods, I mainly focus on …

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Toward The Automatic Classification Of Self-Affirmed Refactoring, Mohamed Wiem Mkaouer, Eman Abdullah Alomar, Ali Ouni May 2020

Toward The Automatic Classification Of Self-Affirmed Refactoring, Mohamed Wiem Mkaouer, Eman Abdullah Alomar, Ali Ouni

Articles

The concept of Self-Affirmed Refactoring (SAR) was introduced to explore how developers document their refactoring activities in commit messages, i.e., developers explicit documentation of refactoring operations intentionally introduced during a code change. In our previous study, we have manually identified refactoring patterns and defined three main common quality improvement categories including internal quality attributes, external quality attributes, and code smells, by only considering refactoring-related commits. However, this approach heavily depends on the manual inspection of commit messages. In this paper, we propose a two-step approach to first identify whether a commit describes developer-related refactoring events, then to classify it according …

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How Do Developers Refactor Code To Improve Code Reusability?, Eman Abdullah Alomar, Philip T. Rodriguez, Jordan Bowman, Tianjia Wang, Benjamin Adepoju, Kevin Lopez, Christian D. Newman, Ali Ouni, Mohamed Wiem Mkaouer May 2020

How Do Developers Refactor Code To Improve Code Reusability?, Eman Abdullah Alomar, Philip T. Rodriguez, Jordan Bowman, Tianjia Wang, Benjamin Adepoju, Kevin Lopez, Christian D. Newman, Ali Ouni, Mohamed Wiem Mkaouer

Articles

. Refactoring is the de-facto practice to optimize software health. While there has been several studies proposing refactoring strategies to optimize software design through applying design patterns and removing design defects, little is known about how developers actually refactor their code to improve its reuse. Therefore, we extract, from 1,828 open source projects, a set of refactorings which were intended to improve the software reusability. We analyze the impact of reusability refactorings on state-of-the-art reusability metrics, and we compare the distribution of reusability refactoring types, with the distribution of the remaining mainstream refactorings. Overall, we found that the distribution of …

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An Exploratory Study On How Software Reuse Is Discussed In Stack Overflow, Mohamed Wiem Mkaouer, Eman Abdullah Alomar, Jiaqian Liu, Ali Ouni, Christian D. Newman, Diego Barinas May 2020

An Exploratory Study On How Software Reuse Is Discussed In Stack Overflow, Mohamed Wiem Mkaouer, Eman Abdullah Alomar, Jiaqian Liu, Ali Ouni, Christian D. Newman, Diego Barinas

Articles

Software reuse is an important and crucial quality attribute in modern software engineering, where almost all software projects, open source or commercial, no matter small or ultra-large, source code reuse in one way or another. Although software reuse has experienced an increased adoption throughout the years with the exponentially growing number of available third-party libraries, frameworks and APIs, little knowledge exists to investigate what aspects of code reuse developers discuss. In this study, we look into bridging this gap by examining Stack Overflow to understand the challenges developers encounter when trying to reuse code. Using the Stack Overflow tags “code-reuse” …

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How We Refactor And How We Document It? On The Use Of Supervised Machine Learning Algorithms To Classify Refactoring Documentation, Eman Abdullah Alomar, Anthony Peruma, Mohamed Wiem Mkaouer, Christian D. Newman, Marouane Kessentini, Ali Ouni May 2020

How We Refactor And How We Document It? On The Use Of Supervised Machine Learning Algorithms To Classify Refactoring Documentation, Eman Abdullah Alomar, Anthony Peruma, Mohamed Wiem Mkaouer, Christian D. Newman, Marouane Kessentini, Ali Ouni

Articles

Refactoring is the art of improving the structural design of a software system without altering its external behavior. Today, refactoring has become a well-established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactoring strategies in other development-related activities that go beyond improving the design especially with the emerging challenges in contemporary software engineering. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. To cope with the above-mentioned limitations, …

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Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation., Arinjita Bhattacharyya May 2020

Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation., Arinjita Bhattacharyya

Electronic Theses and Dissertations

Generalized linear models have broad applications in biostatistics and sociology. In a regression setup, the main target is to find a relevant set of predictors out of a large collection of covariates. Sparsity is the assumption that only a few of these covariates in a regression setup have a meaningful correlation with an outcome variate of interest. Sparsity is incorporated by regularizing the irrelevant slopes towards zero without changing the relevant predictors and keeping the resulting inferences intact. Frequentist variable selection and sparsity are addressed by popular techniques like Lasso, Elastic Net. Bayesian penalized regression can tackle the curse of …

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A Software Source Code Recommendation System For Code Reuse From Private Repositories, Md Mazharul Islam May 2020

A Software Source Code Recommendation System For Code Reuse From Private Repositories, Md Mazharul Islam

Graduate Theses/Dissertations

Motivated by the idea of reusing existing source code from previous projects within a software company, in this thesis, I present a new source code recommendation technique to help programmers find relevant implementations or sample code based on software requirement specifications. My proposed technique assists programmers to search existing code repositories using natural language query. My approach summarizes the uploaded code into sentences or phrases to match them against user queries. This version of my proposed technique extracts and analyzes the content of Python code (such as variables, functions, docstrings, and comments) to generate code summary for each function which …

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Connecting The Dots For People With Autism: A Data-Driven Approach To Designing And Evaluating A Global Filter, Viseth Sean May 2020

Connecting The Dots For People With Autism: A Data-Driven Approach To Designing And Evaluating A Global Filter, Viseth Sean

Computational and Data Sciences (PhD) Dissertations

"Social communication is the use of language in social contexts. It encompasses social interaction, social cognition, pragmatics, and language processing” [3]. One presumed prerequisite of social communication is visual attention–the focus of this work. “Visual attention is a process that directs a tiny fraction of the information arriving at primary visual cortex to high-level centers involved in visual working memory and pattern recognition” [7]. This process involves the integration of two streams: the global and local streams; the global stream rapidly processes the scene, and the local stream processes details. This integration is important to social communication in that attending …

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Integrated Machine Learning And Bioinformatics Approaches For Prediction Of Cancer-Driving Gene Mutations, Oluyemi Odeyemi May 2020

Integrated Machine Learning And Bioinformatics Approaches For Prediction Of Cancer-Driving Gene Mutations, Oluyemi Odeyemi

Computational and Data Sciences (PhD) Dissertations

Cancer arises from the accumulation of somatic mutations and genetic alterations in cell division checkpoints and apoptosis, this often leads to abnormal tumor proliferation. Proper classification of cancer-linked driver mutations will considerably help our understanding of the molecular dynamics of cancer. In this study, we compared several cancer-specific predictive models for prediction of driver mutations in cancer-linked genes that were validated on canonical data sets of functionally validated mutations and applied to a raw cancer genomics data. By analyzing pathogenicity prediction and conservation scores, we have shown that evolutionary conservation scores play a pivotal role in the classification of cancer …

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Interactions In Visualizations To Support Knowledge Activation, Kari Sandouka May 2020

Interactions In Visualizations To Support Knowledge Activation, Kari Sandouka

Masters Theses & Doctoral Dissertations

Humans have several exceptional abilities, one of which is the perceptual tasks of their visual sense. Humans have the unique ability to perceive data and identify patterns, trends, and outliers. This research investigates the design of interactive visualizations to identify the benefits of interacting with information. The research question leading the investigation is how does interacting with visualizations support analytical reasoning of emergent information to activate knowledge? The study uses the theory of distributed cognition and human-information interaction to apply the design science research framework. The motivation behind the research is to identify guidelines for interactive visualizations to enhance a …

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Faculty Perceptions Of Open Educational Resources In Cyber Curriculum: A Pilot Study, Alan Stines May 2020

Faculty Perceptions Of Open Educational Resources In Cyber Curriculum: A Pilot Study, Alan Stines

Masters Theses & Doctoral Dissertations

The cyber landscape is growing and evolving at a fast pace. Public and private industries need qualified applicants to protect and defend information systems that drive the digital economy. Currently, there are not enough candidates in the pipeline to fill this need in the workforce. The digital economy is still growing, thus presenting an even greater need for skilled workers in the future. The lack of a strong workforce in cybersecurity presents many challenges to safeguarding U.S. national security and citizens across the world. The William and Flora Hewlett Foundation defines Open Educational Resources (OER) as teaching, learning, and research …

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Learning Transferable Meta-Policies For Hierarchical Task Decomposition And Planning Composition, Predrag Djurdjevic May 2020

Learning Transferable Meta-Policies For Hierarchical Task Decomposition And Planning Composition, Predrag Djurdjevic

Computer Science and Engineering Dissertations - Archive

In real world scenarios where situated agents are faced with dynamic, high-dimensional, partially observable environments with action and reward uncertainty, the traditional states space Reinforcement Learning (RL) becomes easily prohibitively large for policy learning. In such scenarios, addressing the curse of dimensionality and eventual transfer to closely related tasks is one of the principal challenges and motivations for Hierarchical Reinforcement Learning (HRL). The prime appeal of hierarchal and particularly recursive approaches is in effective factored state, transition and reward representations which abstract out aspects that are not relevant to subtasks and allow potential transfer of skills which represent solutions to …

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Modelet E Vlerësimit Të Rrezikshmërisë Së Të Dhënave, Arbër Demhasaj May 2020

Modelet E Vlerësimit Të Rrezikshmërisë Së Të Dhënave, Arbër Demhasaj

Theses and Dissertations

Tema paraqet disa modele për të përmirësuar njohuritë tona për sigurinë e informacionit dhe menaxhimin e rrezikut në bizneset bashkëkohorë dhe në organizata të tjera. Në botën e sulmeve kibernetike të vazhdueshme në sistemet e informacionit, njohurit rreth menaxhimit të rrezikut janë duke u bërë një detyrë vendimtare për minimizimin e rreziqeve të mundshme që mund të bëjnë përpjekjet e tyre. Prandaj, kërkohet njohuri të mira në fushën e sigurisë së informacionit. Parandalimi i humbjeve të rënda që mund të ndodhin për shkak të sulmeve kibernetike dhe dështimeve të tjera në një organizatë zakonisht lidhet me njohuritë rreth investimeve të …

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Using Neural Networks To Predict Neurological Disorders, Gresa Gela May 2020

Using Neural Networks To Predict Neurological Disorders, Gresa Gela

Theses and Dissertations

The growth and development of Artificial Intelligence (AI) is fast becoming a powerful influence in our present day reality. In medicine there is a vast number of patients who suffers or even dies for the cause of the late detection of their disorders. Some of the biggest research institutes have been able to contribute in some areas of medicine by creating different complex systems. Even though this field is having a wide range of applications, there is still a lack in the field of neuroscience. In this thesis we will prove how efficiently ANNs can be used in early detection …

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