Computer Engineering Commons^™

Bytecode-Based Multiple Condition Coverage: An Initial Investigation, Srujana Bollina

Theses and Dissertations

Masking occurs when one condition prevents another condition from influencing the output of a Boolean expression. Logic-based adequacy criteria such as Multiple Condition Coverage (MCC) are designed to overcome masking at the within-expression level, but can offer no guarantees about masking in subsequent expressions. As a result, a Boolean expression written as a single complex statement will yield test cases that are more likely to overcome masking than when the expression is written as series of simple statements. Many approaches to automated analysis and test case generation for Java systems operate not on the source code representation of code, but …

Information Systems Continuance: Faculty Perceptions Of Canvas, Leila Halawi, Richard V. Mccarthy, James Farah

Publications

This research in process proposes a study of the theoretical background, motivation, and methods to examine factors that predict faculty perceived usefulness, ease of use, satisfaction and learning facilitation of the Canvas learning management system. We propose utilizing a model to predict continuance intention of use in an online class using Canvas and describe the methodology that will be used to test this model. The focus of this research is on continuance usage as it is the variable that enhances the teaching and learning experience with factors that are helpful in understanding the phenomenon.

An Application Of Natural Language Processing For Triangulation Of Cognitive Load Assessments In Third Level Education, Luis Alfredo Contreras

Dissertations

Work has been done to measure Mental Workload based on applications mainly related to ergonomics, human factors, and Machine Learning. The influence of Machine Learning is a reflection of an increased use of new technologies applied to areas conventionally dominated by theoretical approaches. However, collaboration between MWL and Natural Language Processing techniques seems to happen rarely. In this sense, the objective of this research is to make use of Natural Languages Processing techniques to contribute to the analysis of the relationship between Mental Workload subjective measures and Relative Frequency Ratios of keywords gathered during pre-tasks and post-tasks of MWL activities …

Can Threshold-Based Sensor Alerts Be Analysed To Detect Faults In A District Heating Network?, Liam Cantwell

Dissertations

Older IoT “smart sensors” create system alerts from threshold rules on reading values. These simple thresholds are not very flexible to changes in the network. Due to the large number of false positives generated, these alerts are often ignored by network operators. Current state-of-the-art analytical models typically create alerts using raw sensor readings as the primary input. However, as greater numbers of sensors are being deployed, the growth in the number of readings that must be processed becomes problematic. The number of analytic models deployed to each of these systems is also increasing as analysis is broadened. This study aims …

Elasticity Measurement In Caas Environments - Extending The Existing Bungee Elasticity Benchmark To Aws's Elastic Container Service, Nora Limbourg

Dissertations

Rapid elasticity and automatic scaling are core concepts of most current cloud computing systems. Elasticity describes how well and how fast cloud systems adapt to increases and decreases in workload. In parallel, software architectures are moving towards employing containerised microservices running on systems managed by container orchestration platforms. Cloud users who employ such container-based systems may want to compare the elasticity of different systems or system settings to ensure rapid elasticity and maintain service level objectives while avoiding over-provisioning. Previous research has established a variety of metrics to measure elasticity. Some existing benchmark tools are designed to measure elasticity in …

Use Of Hyperspectral Images (Hsi) And Convolutional Neural Network (Cnn) To Identify Normal, Precancerous And Cancerous Tissues, Pallavi Jain

Dissertations

Cancer detection has been a great topic of research for a long time, as early detection of cancer can help in increasing the survival rate of patients by providing on time better treatment. A robust system is required in order to detect early-stage cancer as its difficult to identify early-stage cancer from the normal clinical process. The computer vision techniques provide a new way to understand the challenges related to the medical image analysis. This thesis presents the medical image analysis using a combination of Convolutional Neural Network and Hyperspectral Images of cancer patient's tissues. The idea behind choosing the …

From Business Understanding To Deployment: An Application Of Machine Learning Algorithms To Forecast Customer Visits Per Hour To A Fast-Casual Restaurant In Dublin, Odunayo David Adedeji

Dissertations

This research project identifies the significant factors that affects the number of customer visits to a fast-casual restaurant every hour and proceeds to develop several machine learning models to forecast customer visits. The core value proposition of fast-casual restaurants is quality food delivered at speed which means they have to prepare meals in advance of customers visit but the problem with this approach is in forecasting future demand, under estimating demand could lead to inadequate meal preparation which would leave customers unsatisfied while over estimation of demand could lead to wastage especially with restaurants having to comply with food safety …

Can Machine Learning Beat Physics At Modeling Car Crashes?, Gavin Byrne

Dissertations

This study aimed to look at a traditional method used for measuring the severity and principle direction of force of a car crash and see if it could be improved on using machine learning models. The data used was publicly available from the NHTSA database and included descriptions of the vehicle, test and sensors as well as the accelerometer data over the period of the crashes. The models built were SVM classifiers and multinomial regression models. Although the SVM and Regression models were built successfully and gave higher levels of accuracy than the momentum models in terms of the severity, …

Supervised Learning Models To Predict Stock Direction Within Different Sectors In A Bull And Bear Market, Tiffany Razy

Dissertations

Forecasting stock market price movement is a well researched and an alluring topic within the machine learning and financial realm. Supervised machine learning algorithms such as Random Forest (RF) and Support Vector Machines (SVM) have been used independently to gain insight on the market. With such volatility in the market the scope of this study will utilized the RF and SVM in a very volatility market to determine if these models will perform at a high level or outperform each other in both markets. This relative study is performed on 16 stocks in 4 different sectors over the bear market …

Identifying Expert Investors On Financial Microblog Via Artificial Neural Networks, Pierluca Del Buono

Dissertations

In the recent years, thanks to social media platform, a plethora of information has been available to financial investors, that were traditionally dependent from financial institutions advisors. Strategies are now shared among web users, performances of stocks are commented in web communities and hints and suggestions are travelling on the internet with a fast pace, in a way that was unthinkable few years before. Several attempts have been made in the recent past, to predict Market movements and trends from activity of Financial Social Networks participants, and to evaluate if contributions from individuals with high level of expertise distinguish themselves …

Forecasting Changes In Religiosity And Existential Security With An Agent-Based Model, Ross J. Gore, Carlos Lemos, F. Leron Shults, Wesley J. Wildman

VMASC Publications

We employ existing data sets and agent-based modeling to forecast changes in religiosity and existential security among a collective of individuals over time. Existential security reflects the extent of economic, socioeconomic and human development provided by society. Our model includes agents in social networks interacting with one another based on the education level of the agents, the religious practices of the agents, and each agent's existential security within their natural and social environments. The data used to inform the values and relationships among these variables is based on rigorous statistical analysis of the International Social Survey Programme Religion Module (ISSP) …

Ontology-Guided Pre-Release Inference Disruption, Mark Stephen Daniels

Theses and Dissertations

We investigate privacy violations occurring when non-confidential patient data is combined with medical domain ontologies to disclose a patient’s protected health information (PHI). We propose a framework that detects privacy violations and eliminates undesired inferences. Our inference channel removal process is based on controlling the release of the data items that lead to undesired inferences. These data items are either blocked from release or generalized to eliminate the disclosure of the PHI. We show that our method is sound and complete. Soundness means the only inference paths generated logically follow from released data and corresponding domain knowledge. Completeness means we …

การปรับปรุงการแยกฉากหลังบนพื้นหลังสีเขียวไม่สม่ำเสมอแบบทันที, วรายุ จริยาวัฒนรัตน์

Chulalongkorn University Theses and Dissertations (Chula ETD)

วัตถุประสงค์หลักของงานวิจัยนี้คือปรับปรุงการแยกฉากหลังบนพื้นหลังสีเขียวที่ไม่สม่ำเสมอแบบทันที ในวิธีการพื้นฐาน แต่ละพิกเซลจะถูกคำนวณว่าเป็นฉากหลังโดยใช้ค่าขีดจำกัดเดียวกันทั้งภาพเพียง 2 ค่า แต่ในความเป็นจริง วิธีการนี้มีปัญหาในหลาย ๆ กรณี เช่น พื้นหลังไม่ได้เป็นสีเดียวกันทั้งหมด หรือมีเงาของนักแสดงทอดลงไปที่พื้นหลัง เป็นต้น ดังนั้นการใช้ค่าขีดจำกัดเดียวกันหมดทั้งภาพจึงไม่เหมาะสมสำหรับกรณีดังกล่าว งานวิจัยนี้จึงใช้การประมาณความหนาแน่นเคอร์เนลมาช่วยในการหาค่าขีดจำกัดของแต่ละพิกเซลในภาพ เพื่อให้ทุก ๆ พิกเซลในภาพมีขีดจำกัดที่เหมาะสม และยังมีความเร็วในการประมวลผลที่สามารถออกอากาศสดได้ที่ 60 เฟรมต่อวินาที สำหรับความละเอียดระดับ Full HD

Epithelium Detection And Cervical Intraepithelial Neoplasia Classification In Digitized Histology Images, Sri Venkata Ravitej Addanki

Masters Theses

“Cervical cancer is one of the most deadly cancers faced by women. It is the second leading cause of cancer death in women aged 20 to 39 years. In order to detect cancer at early stages, pathologists analyze the epithelium region from the cervical histology images. These histology images have a pre-cervical cancer condition called cervical intraepithelial neoplasia (CIN) determined by pathologists. This study deals with automating the process of epithelium detection and epithelium CIN classification in digitized histology images. For epithelium detection, the objective is to detect epithelium regions in microscopy images from non-epithelium regions and background. convolutional neural …

Developing An Energy Efficient Real-Time System, Aamir Aarif Khan

Masters Theses

"Increasing number of battery operated devices creates a need for energy-efficient real-time operating system for such devices. Designing a truly energy-efficient system is a multi-staged effort; this thesis consists of three main tasks that address different aspects of energy efficiency of a real-time system (RTS).

The first chapter introduces an energy-efficient algorithm that alternates processor frequency using DVFS to schedule tasks on cores. Speed profiles is calculated for every task that gives information about how long a task would run for and at what processor speed. We pair tasks with similar speed profiles to give us a resultant merged speed …

Precise Energy Efficient Scheduling Of Mixed-Criticality Tasks & Sustainable Mixed-Criticality Scheduling, Sai Sruti

Masters Theses

"In this thesis, the imprecise mixed-criticality model (IMC) is extended to precise scheduling of tasks, and integrated with the dynamic voltage and frequency scaling (DVFS) technique to enable energy minimization. The challenge in precise scheduling of MC systems is to simultaneously guarantee the timing correctness for all tasks, hi and lo, under both pessimistic and optimistic (less pessimistic) assumptions. To the best of knowledge this is the first work to address the integration of DVFS energy conserving techniques with precise scheduling of lo-tasks of the MC model.

In this thesis, the utilization based schedulability tests and sufficient conditions for such …

Quantitative Fine-Grained Human Evaluation Of Machine Translation Systems: A Case Study On English To Croatian, Filip Klubicka, Antonio Toral, Victor Manuel Sanchez-Cartagena

Articles

This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant. We conduct a case study for English-to- Croatian, a language direction that involves translating into a morphologically rich language, for which we compare three MT systems belonging to different paradigms: pure phrase-based, factored phrase-based and neural. First, we design an MQM-compliant error taxonomy tailored to the relevant …

Using Moocs To Promote Digital Accessibility And Universal Design, The Moocap Experience, John Gilligan, W. Chen, J. Darzentas

Articles

The recently completed Massive Open Online Course for Accessibility Partnership project (MOOCAP), had the twin aims of establishing a strategic partnership around the promotion of Universal Design and Accessibility for ICT professionals and of developing a suite of Open Educational resources (OERs) in this domain. MOOCAP's eight university partners from Germany, Norway, Greece, Ireland, the UK and Austria have a significant history in developing and providing courses in the domains of Universal Design and Accessibility, as well as leading research and advocacy roles within Europe. The MOOCAP project consisted of two phases: the development of an introductory MOOC on Digital …

Examining The Effects Of A Virtual Character On Learning And Engagement In Serious Games, Vihanga Gamage, Cathy Ennis

Articles

Virtual characters have been employed for many purposes including interacting with players of serious games, with a purpose to increase engagement. These characters are often embodied conversational agents playing diverse roles, such as demonstrators, guides, teachers or interviewers. Recently, much research has been conducted into properties that affect the realism and plausibility of virtual characters, but it is less clear whether the inclusion of interactive agents in serious applications can enhance a user’s engagement with the application, or indeed increase efficacy. In a first step towards answering these questions, we conducted a study where a Virtual Learning Environment was used …

Investigating The Application Of Deep Convolutional Neural Networks In Semi-Supervised Video Object Segmentation, Jayadeep Sasikumar

Dissertations

This thesis investigates the different approaches to video object segmentation and the current state-of-the-art in the discipline, focusing on the different deep learning techniques used to solve the problem. The primary contribution of the thesis is the investigation of usefulness of Exponential Linear Units as activation functions for deep convolutional neural architectures trained to perform object semi-supervised segmentation in videos. Mask R-CNN was chosen as the base convolutional neural architecture, with the view of extending the image segmentation algorithm to videos. Two models were created, one with Rectified Linear Units and the other with Exponential Linear Units as the respective …

Is It Worth It? Budget-Related Evaluation Metrics For Model Selection, Filip Klubicka, Giancarlo Salton, John D. Kelleher

Conference papers

Projects that set out to create a linguistic resource often do so by using a machine learning model that pre-annotates or filters the content that goes through to a human annotator, before going into the final version of the resource. However, available budgets are often limited, and the amount of data that is available exceeds the amount of annotation that can be done. Thus, in order to optimize the benefit from the invested human work, we argue that the decision on which predictive model one should employ depends not only on generalized evaluation metrics, such as accuracy and F-score, but …

Mitigating The Effect Of Misspeculations In Superscalar Processors, Zhaoxiang Jin

Dissertations, Master's Theses and Master's Reports

Modern superscalar processors highly rely on the speculative execution which speculatively executes instructions and then verifies. If the prediction is different from the execution result, a misspeculation recovery is performed. Misspeculation recovery penalties still account for a substantial amount of performance reduction. This work focuses on the techniques to mitigate the effect of recovery penalties and proposes practical mechanisms which are thoroughly implemented and analyzed.

In general, we can divide the misspeculation penalty into four parts: misspeculation detection delay; stale instruction elimination delay; state restoration delay and pipeline fill delay. This dissertation does not consider the detection delay, instead, we …

Applications Of Robot Operating System (Ros) To Mobile Microgrid Formation Outdoors, John Naglak

Dissertations, Master's Theses and Master's Reports

Application of mobile robots to microgrid formation has value for disaster response and service of forward operating bases. This thesis describes the development, testing and demonstration of broad effort across multiple disciplines to enable outdoor positioning and connection of mobile microgrids for the first time. This work includes an outdoor waypoint controller for a UGV agent, specifically the Clearpath Husky. It details sensor fusion of 2D LiDAR and stereo vision, and fusion of odometry sources using an Extended Kalman Filter. Development of these software tools entails integration of many of the packages available through the Robot Operating System (ROS), with …

ระบบนำทางหุ่นยนต์ด้วยหลายตัวนำทางย่อยและหลายแผนที่ย่อย, สุขุม สัตตรัตนามัย

Chulalongkorn University Theses and Dissertations (Chula ETD)

การใช้งานหุ่นยนต์อัตโนมัติในเขตเมืองที่มีความหลากหลายของลักษณะพื้นที่สูงเป็นงานท้าทายที่ต้องการโปรแกรมนำทางและรูปแบบแผนที่ที่แตกต่างกันในแต่ละบริเวณ การปฏิบัติงานของหุ่นยนต์ในโลกจริงต้องเจอกับสิ่งแวดล้อมที่มีการเปลี่ยนแปลงอย่างต่อเนื่อง เช่น ฝนตกหรือความคับคั่งของบริเวณที่ต้องนำทาง ส่งผลให้ต้องปรับแผนเส้นทาง ในงานวิจัยนี้นำเสนอระบบนำทางหุ่นยนต์ในโลกจริงซึ่งเป็นรูปแบบใหม่ของการสลับการทำงานระหว่างโปรแกรมนำทางหลายตัวอย่างเหมาะสม เพื่อให้สามารถจัดการกับพฤติกรรมของหุ่นยนต์และสภาพแวดล้อมที่ท้าทายได้ ระบบที่นำเสนอในงานวิจัยนี้เป็นผลลัพธ์ของการติดตั้งหุ่นยนต์จริงในระบบซึ่งมีผู้ใช้งานและสภาพแวดล้อมจริง

Matrix Effect Study And Immunoassay Detection Using Electrolyte-Gated Graphene Biosensor, Jianbo Sun, Yuxin Liu

Faculty & Staff Scholarship

Significant progress has been made on the development of electrolyte-gated graphene field effect transistor (EGGFET) biosensors over the last decade, yet they are still in the stage of proof-of-concept. In this work, we studied the electrolyte matrix effects, including its composition, pH and ionic strength, and demonstrate that variations in electrolyte matrices have a significant impact on the Fermi level of the graphene channel and the sensitivity of the EGGFET biosensors. This is attributed to the polarization-induced interaction between the electrolyte and the graphene at the interface which can lead to considerable modulation of the Fermi level of the graphene …

An Efficient Method For Online Identification Of Steady State For Multivariate System, Honglun None Xu

Open Access Theses & Dissertations

Most of the existing steady state detection approaches are designed for univariate signals. For multivariate signals, the univariate approach is often applied to each process variable and the system is claimed to be steady once all signals are steady, which is computationally inefficient and also not accurate. The article proposes an efficient online method for multivariate steady state detection. It estimates the covariance matrices using two different approaches, namely, the mean-squared-deviation and mean-squared-successive-difference. To avoid the usage of a moving window, the process means and the two covariance matrices are calculated recursively through exponentially weighted moving average. A likelihood ratio …

Knowing When Not To Answer: Positional Peptide Sequencing With Encoder-Decoder Networks, Korrawe Karunratanakul

Chulalongkorn University Theses and Dissertations (Chula ETD)

การถอดรหัสเปปไทด์นั้นเป็นองค์ประกอบสำคัญสำหรับการศึกษาโปรตีน โดยทั่วไปแล้วการวิเคราะห์ข้อมูล mass spectrum นั้นจะศึกษาเพียงสายของกรดอะมิโนที่ปรากฏอยู่ในฐานข้อมูลเท่านั้น ทำให้การค้นหาสายเปปไทด์แบบใหม่ที่อาจเกิดจากการกลายพันธุ์นั้นทำได้ยาก วิถีการถอดรหัสด้วยดีโนโวแก้ไขข้อจำกัดนี้ด้วยการถอดรหัสสายเปปไทด์โดยตรงจากข้อมูล mass spectrum โดยใช้ความรู้เกี่ยวกับกระบวนการแตกตัวของไอออน ทำให้ไม่จำเป็นต้องใช้ฐานข้อมูลโปรตีนช่วย อย่างไรก็ดี วิธีดังกล่าวยังมีข้อจำกัดด้านความแม่นยำและต้องการการตรวจทานโดยผู้เชี่ยวชาญ วิทยานิพนธ์ฉบับนี้นำเสนอวิธีการถอดรหัสเปปไทด์ด้วยวิธีการดีโนโวแบบใหม่ชื่อ SMSNet โดยใช้โมเดล deep learning เข้าช่วย โดยยังสามารถทำนายกรดอะมิโนได้อย่างครอบคลุมในระดับความแม่นยำของกรดอะมิโนที่ 95% งานฉบับนี้เสนอขั้นตอน ถอดรหัส ตัดออก และสืบค้น เพื่อตัดผลทำนายในตำแหน่งที่มีความกำกวมออกและใช้ข้อมูลจากฐานข้อมูลโปรตีนช่วยเพื่อให้ทำนายสายเปปไทด์ได้ถูกต้องทั้งเส้น นอกจากนี้ งานนี้ได้นำเสนอการใช้ rescorer ในการแก้ไขคะแนนความมั่นใจสำหรับผลทำนายในแต่ละตำแหน่ง ซึ่งส่งผลให้สามารถแยกกลุ่มคะแนนความมั่นใจสำหรับคำตอบที่ถูกต้องและคำตอบที่ผิดได้ดียิ่งขึ้น เมื่อประกอบทุกขั้นตอนวิธีในงานวิจัยฉบับนี้เข้าด้วยกันพบว่า SMSNet สามารถทำนายสายเปปไทด์ได้ในประสิทธิภาพที่ใกล้เคียงกับการทำนายด้วยฐานข้อมูลในการทดลองจริง

Human Performance Modelling For Adaptive Automation, Maria Chiara Leva, M. Wilkins, F. Coster

Conference papers

The relentless march of technology is increasingly opening new possibilities for the application of automation and new horizons for human machine interaction. However there is insufficient scientific evidence on human factors for modern socio-technical systems supporting the guidelines currently used to design Human Machine Interfaces (HMI) (ISA 2014). This dearth of knowledge presents a particular risk in safety critical industries. The continuing 60–90% of accidents currently that are rooted in Human Factors (HF) and the rapid developments in the Internet of Things (IoT) and its novel automation archetypes means that the requirements for new interfaces are becoming more demanding, and …

A Stochastic Petri Net Reverse Engineering Methodology For Deep Understanding Of Technical Documents, Giorgia Rematska

Browse all Theses and Dissertations

Systems Reverse Engineering has gained great attention over time and is associated with numerous different research areas. The importance of this research derives from several technological necessities. Security analysis and learning purposes are two of them and can greatly benefit from reverse engineering. More specifically, reverse engineering of technical documents for deeper automatic understanding is a research area where reverse engineering can contribute a lot. In this PhD dissertation we develop a novel reverse engineering methodology for deep understanding of architectural description of digital hardware systems that appear in technical documents. Initially, we offer a survey on reverse engineering of …

Building An Abstract-Syntax-Tree-Oriented Symbolic Execution Engine For Php Programs, Jin Huang

Browse all Theses and Dissertations

This thesis presents the design, implementation, and evaluation of an abstract-syntax-tree-oriented symbolic execution engine for the PHP programming language. As a symbolic execution engine, our system emulate the execution of a PHP program by assuming that all inputs are with symbolic rather than concrete values. While our system inherits the basic definition of symbolic execution, it fundamentally differs from existing symbolic execution implementations that mainly leverage intermediate representation (IRs) to operate. Specifically, our system directly takes the abstract syntax tree (AST) of a program as input and subsequently interprets this AST. Performing symbolic execution using AST offers unique advantages. First, …