Computer Sciences Commons^™

Data-Driven Surgical Duration Prediction Model For Surgery Scheduling: A Case-Study For A Practice-Feasible Model In A Public Hospital, Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Sean Shao Wei Lam

Research Collection School Of Computing and Information Systems

Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and …

Successful Shot Locations And Shot Types Used In Ncaa Men’S Division I Basketball, Olivia D. Perrin

All NMU Master's Theses

The primary purpose of the current study was to investigate the effect of court location (distance and angle from basket) and shot types used on shot success in NCAA Men’s DI basketball during the 2017-18 season. A secondary purpose was to further expand the analysis based on two additional factors: player position (guard, forward, or center) and team ranking. All statistical analyses were completed in RStudio and three binomial logistic regression analyses were performed to evaluate factors that influence shot success; one for all two and three point shot attempts, one for only two point attempts, and one for only …

Language And Robotics: Complex Sentence Understanding, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Existing robotic systems can take actions based on natural language commands but they tend to be only simple commands. On the other hand, in the domain of Natural Language Processing (NLP), complex sentences are processed, but this NLP domain does not make close contact with robotics. The beginning of computer processing of natural language, when traced back to a system such as Winograd’s SHRUDLU, conceived in 1973, actually aimed to address the issues of Natural Language Understanding (NLU) of relatively complex sentences by a robotic system which in turn takes actions accordingly based on the natural language input. NLU, in …

Industry 4.0: Challenges And Opportunities In Different Countries, Keng Siau, Yingrui Xi, Cui Zou

Research Collection School Of Computing and Information Systems

Along with the rapid development of artificial intelligence (AI), cyber-physical systems (CPSs), big data analytics, and cloud computing, Industry 4.0 — a subset of the fourth Industrial Revolution — has started to emerge and take root in many countries. Many expect that Industry 4.0 will be transformative and revolutionary for multiple industries and countries. Its impact will be much more significant than those of Industry 1.0, 2.0, and 3.0. Most studies and papers on Industry 4.0 have examined its impact on various industries, jobs, and organizations. In this article, we investigate the impact of Industry 4.0 on countries and groups …

Deepstellar: Model-Based Quantitative Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Deep Learning (DL) has achieved tremendous success in many cutting-edge applications. However, the state-of-the-art DL systems still suffer from quality issues. While some recent progress has been made on the analysis of feed-forward DL systems, little study has been done on the Recurrent Neural Network (RNN)-based stateful DL systems, which are widely used in audio, natural languages and video processing, etc. In this paper, we initiate the very first step towards the quantitative analysis of RNN-based DL systems. We model RNN as an abstract state transition system to characterize its internal behaviors. Based on the abstract model, we design two …

Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin

Computer Science Faculty Research & Creative Works

Production innovations are occurring faster than ever. Manufacturing workers thus need to frequently learn new methods and skills. In fast changing, largely uncertain production systems, manufacturers with the ability to comprehend workers' behavior and assess their operation performance in near real-time will achieve better performance than peers. Action recognition can serve this purpose. Despite that human action recognition has been an active field of study in machine learning, limited work has been done for recognizing worker actions in performing manufacturing tasks that involve complex, intricate operations. Using data captured by one sensor or a single type of sensor to recognize …

Cybersecurity Education In Utah High Schools: An Analysis And Strategy For Teacher Adoption, Cariana June Cornel

Theses and Dissertations

The IT Education Specialist for the USBE, Brandon Jacobson, stated:I feel there is a deficiency of and therefore a need to teach Cybersecurity.Cybersecurity is the “activity or process, ability or capability, or state whereby information and communications systems and the information contained therein are protected from and/or defended against damage, unauthorized use or modification, or exploitation” (NICE, 2018). Practicing cybersecurity can increase awareness of cybersecurity issues, such as theft of sensitive information. Current efforts, including but not limited to, cybersecurity camps, competitions, college courses, and conferences, have been created to better prepare cyber citizens nationwide for such cybersecurity occurrences. In …

Rcrab: An R Analytics Tool To Visualize And Analyze The Movement Of Horseshoe Crabs In Long Island Sound, Ismael Youssef, Samah Senbel, Jo-Marie Kasinak, Jennifer Mattei

School of Computer Science & Engineering Faculty Publications

Mark-recapture programs are important for studying the ecology and population dynamics of wildlife. An R shiny analytics tool was developed to track the movement of horseshoe crabs in Long Island Sound based on tag and resight data. The crabs were tagged and recaptured by volunteers of Project Limulus, a community-based research program. The dataset contains tag and recapture location information for 14,065 horseshoe crabs over 18 years. The dataset was initially cleaned by removing records with missing, duplicate or incorrect data. A new data structure was developed to save the data and simplify processing: Three dimensions were used, one for …

Experiences With Implementing Parallel Discrete-Event Simulation On Gpu, Janche Sang, Che-Rung Lee, Vernon Rego, Chung-Ta King

Electrical and Computer Engineering Faculty Publications

Modern graphics processing units (GPUs) offer much more computational power than recent CPUs by providing a vast number of simple, data-parallel, multithreaded cores. In this study, we focus on the use of a GPU to perform parallel discrete-event simulation. Our approach is to use a modified service time distribution function to allow more independent events to be processed in parallel. The implementation issues and alternative strategies will be discussed in detail. We describe and compare our experience and results in using Thrust and CUB, two open-source parallel algorithms libraries which resemble the C++ Standard Template Library, to build our tool. …

Iot Ignorance Is Digital Forensics Research Bliss: A Survey To Understand Iot Forensics Definitions, Challenges And Future Research Directions, Tina Wu, Frank Breitinger, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

Interactions with IoT devices generates vast amounts of personal data that can be used as a source of evidence in digital investigations. Currently, there are many challenges in IoT forensics such as the difficulty in acquiring and analysing IoT data/devices and the lack IoT forensic tools. Besides technical challenges, there are many concepts in IoT forensics that have yet to be explored such as definitions, experience and capability in the analysis of IoT data/devices and current/future challenges. A deeper understanding of these various concepts will help progress the field. To achieve this goal, we conducted a survey which received 70 …

A Multimodal Approach To Sarcasm Detection On Social Media, Dipto Das

Graduate Theses/Dissertations

In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly …

Semantically Aligned Sentence-Level Embeddings For Agent Autonomy And Natural Language Understanding, Nancy Ellen Fulda

Theses and Dissertations

Many applications of neural linguistic models rely on their use as pre-trained features for downstream tasks such as dialog modeling, machine translation, and question answering. This work presents an alternate paradigm: Rather than treating linguistic embeddings as input features, we treat them as common sense knowledge repositories that can be queried using simple mathematical operations within the embedding space, without the need for additional training. Because current state-of-the-art embedding models were not optimized for this purpose, this work presents a novel embedding model designed and trained specifically for the purpose of "reasoning in the linguistic domain".Our model jointly represents single …

A Region-Based Deep Learning Algorithm For Detecting And Tracking Objects In Manufacturing Plants, Muhammad Monjurul Karim, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin

Computer Science Faculty Research & Creative Works

In today's competitive production era, the ability to identify and track important objects in a near real-time manner is greatly desired among manufacturers who are moving towards the streamline production. Manually keeping track of every object in a complex manufacturing plant is infeasible; therefore, an automatic system of that functionality is greatly in need. This study was motivated to develop a Mask Region-based Convolutional Neural Network (Mask RCNN) model to semantically segment objects and important zones in manufacturing plants. The Mask RCNN was trained through transfer learning that used a neural network (NN) pre-trained with the MS-COCO dataset as the …

Safedb: Spark Acceleration On Fpga Clouds With Enclaved Data Processing And Bitstream Protection, Han-Yee Kim, Rohyoung Myung, Boeui Hong, Heonchang Yu, Taeweon Suh, Lei Xu, Weidong Shi

Computer Science Faculty Publications

This paper proposes SafeDB: Spark Acceleration on FPGA Clouds with Enclaved Data Processing and Bitstream Protection. SafeDB provides a comprehensive and systematic hardware-based security framework from the bitstream protection to data confidentiality, especially for the cloud environment. The AES key shared between FPGA and client for the bitstream encryption is generated in hard-wired logic using PKI and ECC. The data security is assured by the enclaved processing with encrypted data, meaning that the encrypted data is processed inside the FPGA fabric. Thus, no one in the system is able to look into clients' data because plaintext data are not exposed …

Energy-Based Modelling For Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher

Conference papers

The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including …

High Performance Computing Techniques To Better Understand Protein Conformational Space, Arpita Joshi

Graduate Doctoral Dissertations

This thesis presents an amalgamation of high performance computing techniques to get better insight into protein molecular dynamics. Key aspects of protein function and dynamics can be learned from their conformational space. Datasets that represent the complex nuances of a protein molecule are high dimensional. Efficient dimensionality reduction becomes indispensable for the analysis of such exorbitant datasets. Dimensionality reduction forms a formidable portion of this work and its application has been explored for other datasets as well. It begins with the parallelization of a known non-liner feature reduction algorithm called Isomap. The code for the algorithm was re-written in C …

Cybersecurity Education: The Quest To Building Bridge Skills, Andy Igonor, Raymond L. Forbes, Jonathan Mccombs

All Faculty and Staff Scholarship

Today's employers differ in what skills and abilities they believe make for a competent cybersecurity professional; however, they concur on the importance of technical and soft skills, which we collectively refer to as "bridge skills" - in other words, skills needed to bridge employer needs and what higher education teaches. Higher education, on the other hand favors producing a holistic and rounded graduate, with soft skills incorporated into the first one or two years of study. Somewhere between these two dichotomies is a missing link which currently manifests as higher education not meeting the needs of industry relative to cybersecurity …

How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy

Research Collection School Of Computing and Information Systems

Adding an ability for a system to learn inherently adds uncertainty into the system. Given the rising popularity of incorporating machine learning into systems, we wondered how the addition alters software development practices. We performed a mixture of qualitative and quantitative studies with 14 interviewees and 342 survey respondents from 26 countries across four continents to elicit significant differences between the development of machine learning systems and the development of non-machine-learning systems. Our study uncovers significant differences in various aspects of software engineering (e.g., requirements, design, testing, and process) and work characteristics (e.g., skill variety, problem solving and task identity). …

Design And Evaluation Of Convolutional Networks For Video Analysis Of Bee Traffic, Prateek Vats

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified.

A normal hive inspection can be very disruptive for the bee colony, as the hive needs to be disassembled to visually assess hive health from …

Query Autoawesome, Chetna Suryavanshi

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This research investigates how to improve legacy queries. Legacy queries are queries that programmers have coded and are used in applications. A database application typically has tens to hundreds of such queries. One way to improve legacy queries is to add new, interesting queries that are similar to or based on the set of queries. We propose Query AutoAwesome, a tool to generate new queries from legacy queries. The Query AutoAwesome philosophy is taken from Google’s AutoAwesomizer tool for photos, which automatically improves a photo uploaded to Google by animating the photo or adding special effects. In a similar …

Developing And Securing Software For Small Space Systems, Brandon L. Shirley

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The space systems industry is moving towards smaller multi-vendor satellites, known as Small Space. This shift is driven by economic and technological factors that necessitate hardware and software components that are modular, reusable, and secure. This research addresses two problems associated with the development of modular, reusable, and secure space systems: developing software for space systems (the Development Problem) and securing space systems (the Security Problem). These two problems are interrelated and this research addresses them together.

The Development Problem encompasses challenges that space systems developers face as they try to address the constraints induced by reduced budgets, …

Feature Selection And Analysis For Standard Machine Learning Classification Of Audio Beehive Samples, Chelsi Gupta

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The beekeepers need to inspect their hives regularly in order to protect them from various stressors. Manual inspection of hives require a lot of time and effort. Hence, many researchers have started using electronic beehive monitoring (EBM) systems to collect critical information from beehives, so as to alert the beekeepers of possible threats to the hive. EBM collects information by applying multiple sensors into the hive. The sensors collect information in the form of video, audio or temperature data from the hives.

This thesis involves the automatic classification of audio samples from a beehive into bee buzzing, cricket chirping and …

Latent Error Prediction And Fault Localization For Microservice Applications By Learning From System Trace Logs, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Dewei Liu, Qilin Xiang, Chuan He

Research Collection School Of Computing and Information Systems

In the production environment, a large part of microservice failures are related to the complex and dynamic interactions and runtime environments, such as those related to multiple instances, environmental configurations, and asynchronous interactions of microservices. Due to the complexity and dynamism of these failures, it is often hard to reproduce and diagnose them in testing environments. It is desirable yet still challenging that these failures can be detected and the faults can be located at runtime of the production environment to allow developers to resolve them efficiently. To address this challenge, in this paper, we propose MEPFL, an approach of …

Decision Making For Improving Maritime Traffic Safety Using Constraint Programming, Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Maritime navigational safety is of utmost importance to prevent vessel collisions in heavily trafficked ports, and avoid environmental costs. In case of a likely near miss among vessels, port traffic controllers provide assistance for safely navigating the waters, often at very short lead times. A better strategy is to avoid such situations from even happening. To achieve this, we a) formalize the decision model for traffic hotspot mitigation including realistic maritime navigational features and constraints through consultations with domain experts; and b) develop a constraint programming based scheduling approach to mitigate hotspots. We model the problem as a variant of …

Data-Driven Decision-Support For Process Improvement Through Predictions Of Bed Occupancy Rates, Kar Way Tan, Qi You Ng, Francis Ngoc Hoang Long Nguyen, Sean Shao Wei Lam

Research Collection School Of Computing and Information Systems

Managing bed utilization and ensuring the supply keeps up with the demand is not an easy task in a large public hospital with many medical disciplines. The bed managers who makes decisions on reserving and allocating beds centrally require high-dimensional data from several hospital information systems supporting emergency room, specialized clinics and bed management processes. In this work, we put together an automated process for cleaning, consolidating and integrating data from several hospital information systems to several reports required by the bed managers to analyse the bed occupancy situations across more than thirty medical disciplines. To prevent bed crunch situations …

Learning Multiple Maps From Conditional Ordinal Triplets, Duy Dung Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Ordinal embedding seeks a low-dimensional representation of objects based on relative comparisons of their similarities. This low-dimensional representation lends itself to visualization on a Euclidean map. Classical assumptions admit only one valid aspect of similarity. However, there are increasing scenarios involving ordinal comparisons that inherently reflect multiple aspects of similarity, which would be better represented by multiple maps. We formulate this problem as conditional ordinal embedding, which learns a distinct low-dimensional representation conditioned on each aspect, yet allows collaboration across aspects via a shared representation. Our geometric approach is novel in its use of a shared spherical representation and multiple …

Sar: Learning Cross-Language Api Mappings With Little Knowledge, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

To save effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, these approaches still require large amount of parallel corpora, ranging from pairs of APIs or code fragments that are functionally equivalent, to similar code comments. To minimize the need of parallel corpora, this paper aims at an automated approach that can map …

A Secure Iot Cloud Storage System With Fine-Grained Access Control And Decryption Key Exposure Resistance, Shengmin Xu, Guomin Yang, Yi Mu, Ximeng Liu

Research Collection School Of Computing and Information Systems

Internet of Things (IoT) cloud provides a practical and scalable solution to accommodate the data management in large-scale IoT systems by migrating the data storage and management tasks to cloud service providers (CSPs). However, there also exist many data security and privacy issues that must be well addressed in order to allow the wide adoption of the approach. To protect data confidentiality, attribute-based cryptosystems have been proposed to provide fine-grained access control over encrypted data in loT cloud. Unfortunately, the existing attributed-based solutions are still insufficient in addressing some challenging security problems, especially when dealing with compromised or leaked user …

Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang

Research Collection School Of Computing and Information Systems

The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional …

Who Should Make Decision On This Pull Request? Analyzing Time-Decaying Relationships And File Similarities For Integrator Prediction, Jing Jiang, David Lo, Jiateng Zheng, Xin Xia, Yun Yang, Li Zhang

Research Collection School Of Computing and Information Systems

In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests andintegrate code contributions. Ideally, pull requests are assigned to integrators and evaluated within a short time after their submissions. However, the volume of incoming pull requests is large in popular projects, and integrators often encounter difficulties inprocessing pull requests in a timely fashion. Therefore, an automatic integrator prediction approach is required to assign appropriate pull requests to integrators. In this paper, we propose an approach TRFPre which analyzes Time-decaying Relationships andFile similarities to predict integrators. We evaluate the effectiveness of TRFPre on 24 projects …