Engineering Commons^™

Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre

Whittier Scholars Program

The introduction of PoetHQ, a mobile application, offers an economical strategy for colleges, potentially ushering in significant cost savings. These savings could be redirected towards enhancing academic programs and services, enriching the educational landscape for students. PoetHQ aims to democratize access to crucial software, effectively removing financial barriers and facilitating a richer educational experience. By providing an efficient software solution that reduces organizational overhead while maximizing accessibility for students, the project highlights the essential role of equitable education and resource optimization within academic institutions.

Vertical Free-Swinging Photovoltaic Racking Energy Modeling: A Novel Approach To Agrivoltaics, Koami Soulemane Hayibo, Joshua M. Pearce

Electrical and Computer Engineering Publications

To enable lower-cost building materials, a free-swinging bifacial vertical solar photovoltaic (PV) rack has been proposed, which complies with Canadian building codes and is the lowest capital-cost agrivoltaics rack. The wind force applied to the free-swinging PV, however, causes it to have varying tilt angles depending on the wind speed and direction. No energy performance model accurately describes such a system. To provide a simulation model for the free-swinging PV, where wind speed and direction govern the array tilt angle, this study builds upon the open-source System Advisor Model (SAM) using Python. After the SAM python model is validated, a …

Development Of User Interface And Testing Harness, Jacob Amezquita, William Albertini

College of Engineering Summer Undergraduate Research Program

No abstract provided.

Visualizing Transaction-Level Modeling Simulations Of Deep Neural Networks, Nataniel Farzan, Emad Arasteh

Engineering Technical Reports

The growing complexity of data-intensive software demands constant innovation in computer hardware design. Performance is a critical factor in rapidly evolving applications such as artificial intelligence (AI). Transaction-level modeling (TLM) is a valuable technique used to represent hardware and software behavior in a simulated environment. However, extracting actionable insights from TLM simulations is not a trivial task. We present Netmemvisual, an interactive, cross-platform visualization tool for exposing memory bottlenecks in TLM simulations. We demonstrate how Netmemvisual helps system designers rapidly analyze complex TLM simulations to find memory contention. We describe the project’s current features, experimental results with two state-of-the-art deep …

Multi-Scale Attention Networks For Pavement Defect Detection, Junde Chen, Yuxin Wen, Yaser Ahangari Nanehkaran, Defu Zhang, Adan Zeb

Engineering Faculty Articles and Research

Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning-based CNNs has shown competitive performance in image detection and classification. To detect pavement defects automatically and improve effects, a multi-scale mobile attention-based network, which we termed MANet, is proposed to perform the detection of pavement defects. The architecture of the encoder-decoder is used in MANet, where the encoder adopts the MobileNet as the backbone network to extract pavement defect features. …

The Characteristics Of Successful Military It Projects: A Cross-Country Empirical Study, Helene Berg, Jonathan D. Ritschel

Faculty Publications

No abstract provided.

Niche: A Curated Dataset Of Engineered Machine Learning Projects In Python, Ratnadira Widyasari, Zhou Yang, Ferdian Thung, Sheng Qin Sim, Fiona Wee, Camellia Lok, Jack Phan, Haodi Qi, Constance Tan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This …

Investigating The Use Of Recurrent Neural Networks In Modeling Guitar Distortion Effects, Caleb Koch, Scott Hawley, Andrew Fyfe

Belmont University Research Symposium (BURS)

Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to replicate and produce audio effects initially created by analog and digital effects units. Recurrent Neural Networks have proven to be exceptional at modeling audio effects …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Improving Developers' Understanding Of Regex Denial Of Service Tools Through Anti-Patterns And Fix Strategies, Sk Adnan Hassan, Zainab Aamir, Dongyoon Lee, James C. Davis, Francisco Servant

Department of Electrical and Computer Engineering Faculty Publications

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them …

An Empirical Study Of Pre-Trained Model Reuse In The Hugging Face Deep Learning Model Registry, Wenxin Jiang, Nicholas Synovic, Matt Hyatt, Taylor R. Schorlemmer, Rohan Sethi, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems.

In this work, we present the first empirical investigation of PTM reuse. …

Accelerating A Software Defined Satnav Receiver Using Multiple Parallel Processing Schemes, Logan Reich, Sanjeev Gunawardena, Michael Braasch

Faculty Publications

Excerpt: Satnav SDRs present many benefits in terms of flexibility and configurability. However, due to the high bandwidth signals involved in satnav SDR processing, the software must be highly optimized for the host platform in order to achieve acceptable runtimes. Modules such as sample decoding, carrier replica generation, carrier wipeoff, and correlation are computationally intensive components that benefit from accelerations.

Softskip: Empowering Multi-Modal Dynamic Pruning For Single-Stage Referring Comprehension, Dulanga Weerakoon, Vigneshwaran Subbaraju, Tuan Tran, Archan Misra

Research Collection School Of Computing and Information Systems

Supporting real-time referring expression comprehension (REC) on pervasive devices is an important capability for human-AI collaborative tasks. Model pruning techniques, applied to DNN models, can enable real-time execution even on resource-constrained devices. However, existing pruning strategies are designed principally for uni-modal applications, and suffer a significant loss of accuracy when applied to REC tasks that require fusion of textual and visual inputs. We thus present a multi-modal pruning model, LGMDP, which uses language as a pivot to dynamically and judiciously select the relevant computational blocks that need to be executed. LGMDP also introduces a new SoftSkip mechanism, whereby 'skipped' visual …

Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv

Research Collection School Of Computing and Information Systems

Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. Such data isolation not only impairs user experience but also decreases the potential revenues of the platforms. In this paper, we advocate federated order dispatching for cross-platform ride hailing, where multiple platforms collaboratively make dispatching decisions without sharing their local data. Realizing this concept calls for new federated learning strategies that tackle the unique challenges on effectiveness, privacy and efficiency in the context of order …

Spotlight Report #6: Proffering Machine-Readable Personal Privacy Research Agreements: Pilot Project Findings For Ieee P7012 Wg, Noreen Y. Whysel, Lisa Levasseur

Publications and Research

What if people had the ability to assert their own legally binding permissions for data collection, use, sharing, and retention by the technologies they use? The IEEE P7012 has been working on an interoperability specification for machine-readable personal privacy terms to support this ability since 2018. The premise behind the work of IEEE P7012 is that people need technology that works on their behalf—i.e. software agents that assert the individual’s permissions and preferences in a machine-readable format.

Thanks to a grant from the IEEE Technical Activities Board Committee on Standards (TAB CoS), we were able to explore the attitudes of …

Monofacial Vs Bifacial Solar Photovoltaic Systems In Snowy Environments, Koami Soulemane Hayibo, Aliaksei Petsiuk, Pierce Mayville, Laura Brown, Joshua M. Pearce

Electrical and Computer Engineering Publications

There has been a recent surge in interest in the more accurate snow loss estimates for solar photovoltaic (PV) systems as large-scale deployments move into northern latitudes. Preliminary results show bifacial modules may clear snow faster than monofacial PV. This study analyzes snow losses on these two types of systems using empirical hourly data including energy, solar irradiation and albedo, and open-source image processing methods from images of the arrays in a northern environment in the winter. Projection transformations based on reference anchor points and snowless ground truth images provide reliable masking and optical distortion correction with fixed surveillance cameras. …

Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Software developers often use social media (such as Twitter) to shareprogramming knowledge such as new tools, sample code snippets,and tips on programming. One of the topics they talk about is thesoftware library. The tweets may contain useful information abouta library. A good understanding of this information, e.g., on thedeveloper’s views regarding a library can be beneficial to weigh thepros and cons of using the library as well as the general sentimentstowards the library. However, it is not trivial to recognize whethera word actually refers to a library or other meanings. For example,a tweet mentioning the word “pandas" may refer to …

Ad-Corre: Adaptive Correlation-Based Loss For Facial Expression Recognition In The Wild, Ali Pourramezan Fard, Mohammad H. Mahoor

Electrical and Computer Engineering: Faculty Scholarship

Automated Facial Expression Recognition (FER) in the wild using deep neural networks is still challenging due to intra-class variations and inter-class similarities in facial images. Deep Metric Learning (DML) is among the widely used methods to deal with these issues by improving the discriminative power of the learned embedded features. This paper proposes an Adaptive Correlation (Ad-Corre) Loss to guide the network towards generating embedded feature vectors with high correlation for within-class samples and less correlation for between-class samples. Ad-Corre consists of 3 components called Feature Discriminator, Mean Discriminator, and Embedding Discriminator. We design the Feature Discriminator component to guide …

Data-Driven Decarbonization Of Residential Heating Systems: An Equity Perspective., John Wamburu, Emma Grazier, David Irwin, Christine Crago, Prashant Shenoy

Publications

Since heating buildings using natural gas, propane and oil makes up a significant proportion of the aggregate carbon emissions every year, there is a strong interest in decarbonizing residential heating systems using new technologies such as electric heat pumps. In this poster, we conduct a data-driven optimization study to analyze the potential of replacing gas heating with electric heat pumps to reduce carbon emissions in a city-wide distribution grid. We seek to not only reduce the carbon footprint of residential heating, but also show how to do so equitably. Our results show that lower income homes have an energy usage …

Sustainable Computing - Without The Hot Air, Noman Bashir, David Irwin, Prashant Shenoy, Abel Souza

Publications

The demand for computing is continuing to grow exponentially. This growth will translate to exponential growth in computing's energy consumption unless improvements in its energy-efficiency can outpace increases in its demand. Yet, after decades of research, further improving energy-efficiency is becoming increasingly challenging, as it is already highly optimized. As a result, at some point, increases in computing demand are likely to outpace increases in its energy-efficiency, potentially by a wide margin. Such exponential growth, if left unchecked, will position computing as a substantial contributor to global carbon emissions. While prominent technology companies have recognized the problem and sought to …

Sok: Analysis Of Software Supply Chain Security By Establishing Secure Design Properties, Chinenye Okafor, Taylor R. Schorlemmer, Santiao Torres-Arias, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain: transparency, validity, and separation. The paper describes current security approaches and maps them to the proposed security properties, including research ideas and case studies of supply chains in practice. It discusses the strengths and weaknesses of current approaches relative to known attacks and details the various security frameworks put out to ensure the security of the software supply chain. Finally, the paper highlights potential gaps in actor and operation-centered …

Reflecting On Recurring Failures In Iot Development, Dharun Anandayuvaraj, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

As IoT systems are given more responsibility and autonomy, they offer greater benefits, but also carry greater risks. We believe this trend invigorates an old challenge of software engineering: how to develop high-risk software-intensive systems safely and securely under market pressures? As a first step, we conducted a systematic analysis of recent IoT failures to identify engineering challenges. We collected and analyzed 22 news reports and studied the sources, impacts, and repair strategies of failures in IoT systems. We observed failure trends both within and across application domains. We also observed that failure themes have persisted over time. To alleviate …

Exploiting Input Sanitization For Regex Denial Of Service, Efe Barlas, Xin Du, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Web services use server-side input sanitization to guard against harmful input. Some web services publish their sanitization logic to make their client interface more usable, e.g., allowing clients to debug invalid requests locally. However, this usability practice poses a security risk. Specifically, services may share the regexes they use to sanitize input strings — and regex-based denial of service (ReDoS) is an emerging threat. Although prominent service outages caused by ReDoS have spurred interest in this topic, we know little about the degree to which live web services are vulnerable to ReDoS.

In this paper, we conduct the first black-box …

Discrepancies Among Pre-Trained Deep Neural Networks: A New Threat To Model Zoo Reliability, Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz, Chengjun Guo, Wenxin Jiang, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Training deep neural networks (DNNs) takes significant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model zoos.collections of PTNNs; yet, the reliability of model zoos remains unexamined. In the absence of an industry standard for the implementation and performance of PTNNs, engineers cannot confidently incorporate them into production systems. As a first step, discovering potential discrepancies between PTNNs across model zoos would reveal a threat to model zoo reliability. Prior works indicated existing variances in deep learning systems in terms of accuracy. However, broader measures of reliability for PTNNs from …

An Empirical Study On The Impact Of Deep Parameters On Mobile App Energy Usage, Qiang Xu, James C. Davis, Y Charlie Hu, Abhilash Jindal

Department of Electrical and Computer Engineering Faculty Publications

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these "deep" parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. …

Reflections On Software Failure Analysis, Paschal C. Amusuo, Aishwarya Sharma, Siddharth R. Rao, Abbey Vincent, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Failure studies are important in revealing the root causes, behaviors, and life cycle of defects in software systems. These studies either focus on understanding the characteristics of defects in specific classes of systems or the characteristics of a specific type of defect in the systems it manifests in. Failure studies have influenced various software engineering research directions, especially in the area of software evolution, defect detection, and program repair.

In this paper, we reflect on the conduct of failure studies in software engineering. We reviewed a sample of 52 failure study papers. We identified several recurring problems in these studies, …

Novel Architecture Of Onem2m-Based Convergence Platform For Mixed Reality And Iot, Seungwoon Lee, Woogeun Kil, Byeong Hee Roh, Si-Jung Kim, Jin Suk Kang

College of Engineering Faculty Research

There have been numerous works proposed to merge augmented reality/mixed reality (AR/MR) and Internet of Things (IoT) in various ways. However, they have focused on their specific target applications and have limitations on interoperability or reusability when utilizing them to different domains or adding other devices to the system. This paper proposes a novel architecture of a convergence platform for AR/MR and IoT systems and services. The proposed architecture adopts the oneM2M IoT standard as the basic framework that converges AR/MR and IoT systems and enables the development of application services used in general-purpose environments without being subordinate to specific …

Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this paper, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for …

Messiness: Automating Iot Data Streaming Spatial Analysis, Christopher White, Atilio Barreda Ii

Publications and Research

The spaces we live in go through many transformations over the course of a year, a month, or a day; My room has seen tremendous clutter and pristine order within the span of a few hours. My goal is to discover patterns within my space and formulate an understanding of the changes that occur. This insight will provide actionable direction for maintaining a cleaner environment, as well as provide some information about the optimal times for productivity and energy preservation.

Using a Raspberry Pi, I will set up automated image capture in a room in my home. These images will …

Cleanpage: Fast And Clean Document And Whiteboard Capture, Jane Courtney

Articles

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and …