Physical Sciences and Mathematics Commons^™

Predicting Software Fault Proneness Using Machine Learning, Sanjay Ghanathey

Electronic Thesis and Dissertation Repository

Context: Continuous Integration (CI) is a DevOps technique which is widely used in practice. Studies show that its adoption rates will increase even further. At the same time, it is argued that maintaining product quality requires extensive and time consuming, testing and code reviews. In this context, if not done properly, shorter sprint cycles and agile practices entail higher risk for the quality of the product. It has been reported in literature [68], that lack of proper test strategies, poor test quality and team dependencies are some of the major challenges encountered in continuous integration and deployment.

Objective: The objective …

Partitioning And Offloading For Iot And Video Streaming Applications That Utilize Computing Resources At The Network Edge, Navid Bayat

Electronic Thesis and Dissertation Repository

The Internet of Things (IoT) is a concept in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. IoT applications connect physical objects for the purpose of decision making by sensing and analysing generated data from the embedded sensors in physical objects. IoT applications are growing rapidly as sensors become less expensive. Sensors generate large amounts of data that may meaningless unless the data is used to derive knowledge with in a certain period of time. Stream processing paradigm is used by IoT to provide requirements of IoT applications. In a stream …

Secured Data Masking Framework And Technique For Preserving Privacy In A Business Intelligence Analytics Platform, Osama Ali

Electronic Thesis and Dissertation Repository

The main concept behind business intelligence (BI) is how to use integrated data across different business systems within an enterprise to make strategic decisions. It is difficult to map internal and external BI’s users to subsets of the enterprise’s data warehouse (DW), resulting that protecting the privacy of this data while maintaining its utility is a challenging task. Today, such DW systems constitute one of the most serious privacy breach threats that an enterprise might face when many internal users of different security levels have access to BI components. This thesis proposes a data masking framework (iMaskU: Identify, Map, Apply, …

Complexity Results For Fourier-Motzkin Elimination, Delaram Talaashrafi

Electronic Thesis and Dissertation Repository

In this thesis, we propose a new method for removing all the redundant inequalities generated by Fourier-Motzkin elimination. This method is based on Kohler’s work and an improved version of Balas’ work. Moreover, this method only uses arithmetic operations on matrices. Algebraic complexity estimates and experimental results show that our method outperforms alternative approaches based on linear programming.

Computer Games Are Serious Business And So Is Their Quality: Particularities Of Software Testing In Game Development From The Perspective Of Practitioners, Ronnie Santos, Cleyton Magalhaes, Luiz Fernando Capretz, Jorge Correia-Neto, Fabio Q. B. Silva Dr., Abdelrahman Saher

Electrical and Computer Engineering Publications

Over the last several decades, computer games started to have a significant impact on society. However, although a computer game is a type of software, the process to conceptualize, produce and deliver a game could involve unusual features. In software testing, for instance, studies demonstrated the hesitance of professionals to use automated testing techniques with games, due to the constant changes in requirements and design, and pointed out the need for creating testing tools that take into account the flexibility required for the game development process. Goal. This study aims to improve the current body of knowledge regarding software …

Hpc For Predictive Models In Healthcare, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

Increasingly we are faced with complex health data, thus researchers are limited in their capacity to mine data in a way that accounts for the complex inter-relationships between health variables of interest. This research tackles the challenge of producing accurate health prediction models in order to overcome the limitations of simple multivariate regression techniques and the assumption of linear association, also known as algorithmic models, by combining it with a soft computing approach. Predictive models develop methods to enable healthcare researchers and professionals to predict the likelihood of an individual's proclivity to a disease and the likely effectiveness of possible …

High Performance Sparse Multivariate Polynomials: Fundamental Data Structures And Algorithms, Alex Brandt

Electronic Thesis and Dissertation Repository

Polynomials may be represented sparsely in an effort to conserve memory usage and provide a succinct and natural representation. Moreover, polynomials which are themselves sparse – have very few non-zero terms – will have wasted memory and computation time if represented, and operated on, densely. This waste is exacerbated as the number of variables increases. We provide practical implementations of sparse multivariate data structures focused on data locality and cache complexity. We look to develop high-performance algorithms and implementations of fundamental polynomial operations, using these sparse data structures, such as arithmetic (addition, subtraction, multiplication, and division) and interpolation. We revisit …

From Large-Scale Molecular Clouds To Filaments And Cores : Unveiling The Role Of The Magnetic Fields In Star Formation, Sayantan Auddy

Electronic Thesis and Dissertation Repository

I present a comprehensive study of the role of strong magnetic fields in characterizing the structure of molecular clouds. We run three-dimensional turbulent non-ideal magnetohydrodynamic simulations (with ambipolar diffusion) to see the effect of magnetic fields on the evolution of the column density probability distribution function (PDF). Our results indicate a systematic dependence of the column density PDF of molecular clouds on magnetic field strength and turbulence, with observationally distinguishable outcomes between supercritical (gravity dominated) and subcritical (magnetic field dominated) initial conditions. We find that most cases develop a direct power-law PDF, and only the subcritical clouds with turbulence are …

Finding Nonlinear Relationships In Functional Magnetic Resonance Imaging Data With Genetic Programming, James Hughes

Electronic Thesis and Dissertation Repository

The human brain is a complex, nonlinear dynamic chaotic system that is poorly understood. When faced with these difficult to understand systems, it is common to observe the system and develop models such that the underlying system might be deciphered. When observing neurological activity within the brain with functional magnetic resonance imaging (fMRI), it is common to develop linear models of functional connectivity; however, these models are incapable of describing the nonlinearities we know to exist within the system.

A genetic programming (GP) system was developed to perform symbolic regression on recorded fMRI data. Symbolic regression makes fewer assumptions than …

Searching For Relevant Lessons Learned Using Hybrid Information Retrieval Classifiers: A Case Study In Software Engineering, Tamer Mohamed Abdellatif Mohamed, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

The lessons learned (LL) repository is one of the most valuable sources of knowledge for a software organization. It can provide distinctive guidance regarding previous working solutions for historical software management problems, or former success stories to be followed. However, the unstructured format of the LL repository makes it difficult to search using general queries, which are manually inputted by project managers (PMs). For this reason, this repository may often be overlooked despite the valuable information it provides. Since the LL repository targets PMs, the search method should be domain specific rather than generic as in the case of general …

Baseline Assisted Classification Of Heart Rate Variability, Elham Harirpoush

Electronic Thesis and Dissertation Repository

Recently, among various analysis methods of physiological signals, automatic analysis of Electrocardiogram (ECG) signals, especially heart rate variability (HRV) has received significant attention in the field of machine learning. Heart rate variability is an important indicator of health prediction and it is applicable to various fields of scientific research. Heart rate variability is based on measuring the differences in time between consecutive heartbeats (also known as RR interval), and the most common measuring techniques are divided into the time domain and frequency domain. In this research study, a classifier based on analysis of HRV signal is developed to classify different …

Analysing Popularity Of Software Testing Careers In Canada, Luiz Fernando Capretz, Pradeep Waychal, Sachin Pardeshi

Electrical and Computer Engineering Publications

Software testing is critical to prevent software failures. Therefore, research has been carried out in testing but that is largely limited to the processand technology dimensions and has not sufficiently addressed the human dimension. Even though there are reports about inadequacies of testing professionals and their skills, only a few studies have tackled the problem. Therefore, we decided to explore the human dimension. We started with the basic problem that plagues the testing profession, the shortage of talent, by asking why do students and professionals are reluctant to consider testing careers, what can be done about that, and is the …

Word Blending And Other Formal Models Of Bio-Operations, Zihao Wang

Electronic Thesis and Dissertation Repository

As part of ongoing efforts to view biological processes as computations, several formal models of DNA-based processes have been proposed and studied in the formal language literature. In this thesis, we survey some classical formal language word and language operations, as well as several bio-operations, and we propose a new operation inspired by a DNA recombination lab protocol known as Cross-pairing Polymerase Chain Reaction, or XPCR. More precisely, we define and study a word operation called word blending which models a special case of XPCR, where two words x w p and q w y sharing a non-empty overlap part …

Analysis Challenges For High Dimensional Data, Bangxin Zhao

Electronic Thesis and Dissertation Repository

In this thesis, we propose new methodologies targeting the areas of high-dimensional variable screening, influence measure and post-selection inference. We propose a new estimator for the correlation between the response and high-dimensional predictor variables, and based on the estimator we develop a new screening technique termed Dynamic Tilted Current Correlation Screening (DTCCS) for high dimensional variables screening. DTCCS is capable of picking up the relevant predictor variables within a finite number of steps. The DTCCS method takes the popular used sure independent screening (SIS) method and the high-dimensional ordinary least squares projection (HOLP) approach as its special cases.

Two methods …

Putting Fürer's Algorithm Into Practice With The Bpas Library, Linxiao Wang

Electronic Thesis and Dissertation Repository

Fast algorithms for integer and polynomial multiplication play an important role in scientific computing as well as other disciplines. In 1971, Schönhage and Strassen designed an algorithm that improved the multiplication time for two integers of at most n bits to O(log n log log n). In 2007, Martin Fürer presented a new algorithm that runs in O (n log n · 2 ^O(log* n)) , where log*n is the iterated logarithm of n. We explain how we can put Fürer’s ideas into practice for multiplying polynomials over a prime field Z/pZ, which characteristic is a Generalized Fermat prime of …

A Framework For Modelling User Activity Preferences, Roberto Barboza Junior

Electronic Thesis and Dissertation Repository

The availability of location data increases every day and brings the opportunity to mine these data and extract valuable knowledge about human behaviour. More specifically, these data may contain information about users’ activities, which can enable, for example, services to improve advertising campaigns or enhance the user experience of a mobile application. However, several techniques ignore the fact that users’ context other than location and time, such as weather conditions, influences their behaviour. Moreover, several studies focus only on a single data source, addressing either data collected without any type of user interaction, such as GPS data, or data spontaneously …

Computational Modelling Of Human Transcriptional Regulation By An Information Theory-Based Approach, Ruipeng Lu

Electronic Thesis and Dissertation Repository

ChIP-seq experiments can identify the genome-wide binding site motifs of a transcription factor (TF) and determine its sequence specificity. Multiple algorithms were developed to derive TF binding site (TFBS) motifs from ChIP-seq data, including the entropy minimization-based Bipad that can derive both contiguous and bipartite motifs. Prior studies applying these algorithms to ChIP-seq data only analyzed a small number of top peaks with the highest signal strengths, biasing their resultant position weight matrices (PWMs) towards consensus-like, strong binding sites; nor did they derive bipartite motifs, disabling the accurate modelling of binding behavior of dimeric TFs.

This thesis presents a novel …

Pelee: A Real-Time Object Detection System On Mobile Devices, Jun Wang

Electronic Thesis and Dissertation Repository

There has been a rising interest in running high-quality Convolutional Neural Network (CNN) models under strict constraints on memory and computational budget. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and NASNet-A. However, all these architectures are heavily dependent on depthwise separable convolution which lacks efficient implementation in most deep learning frameworks. Meanwhile, there are few studies that combine efficient models with fast object detection algorithms. This research tries to explore the design of an efficient CNN architecture for both image classification tasks and object detection tasks. We propose an efficient architecture named …

Using Computer Algorithms To Elucidate Zebra Finch Reproductive Behaviour, Tanya T. Shoot, Sophie C. Edwards, Robert J. Martin, Susan D. Healy, David F. Sherry, Mark J. Daley

Western Research Forum

Birds that experience variation in climatic conditions must maintain a stable nest temperature during incubation for successful hatching of offspring. Varying nest structure and incubation behaviour may be the methods birds use to regulate nest temperature. We used a modeling approach to investigate how birds adjust incubation behaviour to ambient temperature.

Hidden Markov Models (HMM) have been used previously to predict the spatial distribution of animals based on the models’ ability to classify movement behaviour. We used a HMM to predict zebra finch (Taeniopygia guttata) incubation behaviour and nest structure from a nest temperature data set. The full …

Text Mining In Chinese Ancient Attires, Lu Wang

Western Research Forum

Starting from the Shang Dynasty (1600-1046 BCE) when writing system appeared in China, clothing was recorded as symbols to denote social statuses. The hierarchical signification of clothing remained in the following dynasties until the end of imperial China in 1911. The imperial period produced twenty-five official dynastic histories with rich corpuses on the subject of attire, documenting regulations and prohibitions of detailed dress code, a subject being scarcely studied and treated with assumptions today. This research will use text mining tools to identify descriptive words of clothing that reflect Chinese hierarchal ideology from the twenty-five histories. The method is to …

A Call To Promote Soft Skills In Software Engineering, Luiz Fernando Capretz, Fahem Ahmed

Electrical and Computer Engineering Publications

We have been thinking about other aspects of software engineering for many years; the missing link in engineering software is the soft skills set, essential in the software development process. Although soft skills are among the most important aspects in the creation of software, they are often overlooked by educators and practitioners. One of the main reasons for the oversight is that soft skills are usually related to social and personality factors, i.e., teamwork, motivation, commitment, leadership, multi-culturalism, emotions, interpersonal skills, etc. This editorial is a manifesto declaring the importance of soft skills in software engineering with the intention to …

Some Applications Of Higher-Order Hidden Markov Models In The Exotic Commodity Markets, Heng Xiong

Electronic Thesis and Dissertation Repository

The liberalisation of regional and global commodity markets over the last several decades resulted in certain commodity price behaviours that require new modelling and estimation approaches. Such new approaches have important implications to the valuation and utilisation of commodity derivatives. Derivatives are becoming increasingly crucial for market participants in hedging their exposure to volatile price swings and in managing risks associated with derivative trading. The modelling of commodity-based variables is an integral part of risk management and optimal-investment strategies for commodity-linked portfolios. The characteristics of commodity price evolution cannot be captured sufficiently by one-state driven models even with the inclusion …

Sol: Segmentation With Overlapping Labels, Karin Ng

Electronic Thesis and Dissertation Repository

Image segmentation is a fundamental problem in Computer Vision which involves segmenting an image into two or more segments. These segments usually correspond to objects of interest in the image, i.e. liver, kidney’s etc. The classic approach to this problem segments the image into mutually exclusive segments. However, this approach is not well-suited when segmenting overlapping objects, e.g. cells, or when segmenting a single object into multiple parts that are not necessarily mutually exclusive. Moreover, we show that optimization methods for multi-part object segmentation with different priors/constraints may better avoid local minima in case of a relaxation allowing parts to …

Feature Based Calibration Of A Network Of Kinect Sensors, Xiaoyang Li

Electronic Thesis and Dissertation Repository

The availability of affordable depth sensors in conjunction with common RGB cameras, such as the Microsoft Kinect, can provide robots with a complete and instantaneous representation of the current surrounding environment. However, in the problem of calibrating multiple camera systems, traditional methods bear some drawbacks, such as requiring human intervention. In this thesis, we propose an automatic and reliable calibration framework that can easily estimate the extrinsic parameters of a Kinect sensor network. Our framework includes feature extraction, Random Sample Consensus and camera pose estimation from high accuracy correspondences. We also implement a robustness analysis of position estimation algorithms. The …

Universality Of Egoless Behavior Of Software Engineering Students, Pradeep Waychal, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

Software organizations have relied on process and technology initiatives to compete in a highly globalized world. Unfortunately, that has led to little or no success. We propose that the organizations start working on people initiatives, such as inspiring egoless behavior among software developers. This paper proposes a multi-stage approach to develop egoless behavior and discusses the universality of the egoless behavior by studying cohorts from three different countries, i.e., Japan, India, and Canada. The three stages in the approach are self-assessment, peer validation, and action plan development. The paper covers the first stage of self-assssment using an instrument based on …

Energy Slices: Benchmarking With Time Slicing, Katarina Grolinger, Hany F. Elyamany, Wilson Higashino, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Benchmarking makes it possible to identify low-performing buildings, establishes a baseline for measuring performance improvements, enables setting of energy conservation targets, and encourages energy savings by creating a competitive environment. Statistical approaches evaluate building energy efficiency by comparing measured energy consumption to other similar buildings typically using annual measurements. However, it is important to consider different time periods in benchmarking because of differences in their consumption patterns. For example, an office can be efficient during the night, but inefficient during operating hours due to occupants’ wasteful behavior. Moreover, benchmarking studies often use a single regression model for different building categories. …