Comparing The Popularity Of Testing Careers Among Canadian, Indian, Chinese, And Malaysian Students,
2021
University of Western Ontario
Comparing The Popularity Of Testing Careers Among Canadian, Indian, Chinese, And Malaysian Students, Luiz Fernando Capretz, Pradeep Waychal, Jingdong Jia, Shuib Basri
Electrical and Computer Engineering Publications
This study attempts to understand motivators and de-motivators that influence the decisions of software students to take up and sustain software testing careers across four different countries, Canada, India, China, and Malaysia. Towards that end, we have developed a cross-sectional, but simple, survey-based instrument. In this study we investigated how software engineering and computer science students perceive and value what they do and their environmental settings. This study found that very few students are keen to take up software testing careers - why is this happening with such an important task in the software life cycle? The common advantages of …
Transfer-Learned Pruned Deep Convolutional Neural Networks For Efficient Plant Classification In Resource-Constrained Environments,
2021
Dakota State University
Transfer-Learned Pruned Deep Convolutional Neural Networks For Efficient Plant Classification In Resource-Constrained Environments, Martinson Ofori
Masters Theses & Doctoral Dissertations
Traditional means of on-farm weed control mostly rely on manual labor. This process is time-consuming, costly, and contributes to major yield losses. Further, the conventional application of chemical weed control can be economically and environmentally inefficient. Site-specific weed management (SSWM) counteracts this by reducing the amount of chemical application with localized spraying of weed species. To solve this using computer vision, precision agriculture researchers have used remote sensing weed maps, but this has been largely ineffective for early season weed control due to problems such as solar reflectance and cloud cover in satellite imagery. With the current advances in artificial …
The Forestecology R Package For Fitting And Assessing Neighborhood Models Of The Effect Of Interspecific Competition On The Growth Of Trees,
2021
Smith College
The Forestecology R Package For Fitting And Assessing Neighborhood Models Of The Effect Of Interspecific Competition On The Growth Of Trees, Albert Y. Kim, David N. Allen, Simon P. Couch
Statistical and Data Sciences: Faculty Publications
Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross-validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model. We demonstrate the package's functionality using data from the Smithsonian Conservation Biology Institute's large forest dynamics plot, part of the ForestGEO global network of research …
Facilitating Team-Based Data Science: Lessons Learned From The Dsc-Wav Project,
2021
University of Minnesota
Facilitating Team-Based Data Science: Lessons Learned From The Dsc-Wav Project, Chelsey Legacy, Andrew Zieffler, Benjamin S. Baumer, Valerie Barr, Nicholas J. Horton
Statistical and Data Sciences: Faculty Publications
While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their …
Masked Face Analysis Via Multi-Task Deep Learning,
2021
University of Dayton
Masked Face Analysis Via Multi-Task Deep Learning, Vatsa S. Patel, Zhongliang Nie, Trung-Nghia Le, Tam Van Nguyen
Computer Science Faculty Publications
Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to …
Internet Of Things Software And Hardware Architectures And Their Impacts On Forensic Investigations: Current Approaches And Challenges,
2021
University of Alabama, Huntsville
Internet Of Things Software And Hardware Architectures And Their Impacts On Forensic Investigations: Current Approaches And Challenges, Abel Alex Boozer, Arun John, Tathagata Mukherjee
Journal of Digital Forensics, Security and Law
The never-before-seen proliferation of interconnected low-power computing devices, patently dubbed the Internet of Things (IoT), is revolutionizing how people, organizations, and malicious actors interact with one another and the Internet. Many of these devices collect data in different forms, be it audio, location data, or user commands. In civil or criminal nature investigations, the data collected can act as evidence for the prosecution or the defense. This data can also be used as a component of cybersecurity efforts. When data is extracted from these devices, investigators are expected to do so using proven methods. Still, unfortunately, given the heterogeneity in …
Infer: An R Package For Tidyverse-Friendly Statistical Inference,
2021
Johns Hopkins University
Infer: An R Package For Tidyverse-Friendly Statistical Inference, Simon P. Couch, Andrew P. Bray, Chester Ismay, Evgeni Chasnovski, B. Baumer, Mine Cetinkaya-Rundel
Statistical and Data Sciences: Faculty Publications
infer implements an expressive grammar to perform statistical inference that adheres to the tidyverse design framework (Wickham et al., 2019). Rather than providing methods for specific statistical tests, this package consolidates the principles that are shared among common hypothesis tests and confidence intervals into a set of four main verbs (functions), supplemented with many utilities to visualize and extract value from their outputs.
The Development Of Teaching Case Studies To Explore Ethical Issues Associated With Computer Programming,
2021
Technological University Dublin
The Development Of Teaching Case Studies To Explore Ethical Issues Associated With Computer Programming, Michael Collins, Damian Gordon, Dympna O'Sullivan
Conference papers
In the past decade software products have become pervasive in many aspects of people’s lives around the world. Unfortunately, the quality of the experience an individual has interacting with that software is dependent on the quality of the software itself, and it is becoming more and more evident that many large software products contain a range of issues and errors, and these issues are not known to the developers of these systems, and they are unaware of the deleterious impacts of those issues on the individuals who use these systems. The authors of this paper are developing a new digital …
An Empirical Study Of Thermal Attacks On Edge Platforms,
2021
Kennesaw State University
An Empirical Study Of Thermal Attacks On Edge Platforms, Tyler Holmes
Symposium of Student Scholars
Cloud-edge systems are vulnerable to thermal attacks as the increased energy consumption may remain undetected, while occurring alongside normal, CPU-intensive applications. The purpose of our research is to study thermal effects on modern edge systems. We also analyze how performance is affected from the increased heat and identify preventative measures. We speculate that due to the technology being a recent innovation, research on cloud-edge devices and thermal attacks is scarce. Other research focuses on server systems rather than edge platforms. In our paper, we use a Raspberry Pi 4 and a CPU-intensive application to represent thermal attacks on cloud-edge systems. …
Energy Saving On Edges: State-Of-The-Art And Future Directions,
2021
Kennesaw State University
Energy Saving On Edges: State-Of-The-Art And Future Directions, Kousalya Banka
Symposium of Student Scholars
Internet of Things (IoT) comprises a set of devices that are interconnected ranging from our daily used objects to advanced networked devices. It is a constantly evolving phenomenon as the number of devices owned by the regular user is increasing at a rapid rate. These devices are used for various reasons such as social networking, monitoring, performing complex operations and with the increase of advanced technologies, they demand more energy to perform such tasks. Cloud computing enables these communications to seamlessly perform complex tasks in a cloud environment but utilizing these resources properly to perform at the best is the …
Enhancing Microbiome Host Disease Prediction With Variational Autoencoders,
2021
Chapman University
Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter
Computational and Data Sciences (MS) Theses
Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …
Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques,
2021
Chapman University
Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques, Matthew J. Nwerem
Computational and Data Sciences (MS) Theses
Computational investigation of molecular structures and reactions of biological and pharmaceutical interests remains a grand scientific challenge due to the size and conformational flexibility of these systems. The work requires parsing and analyzing thousands of conformations in each molecular state for meaningful chemical information and subjecting the ensemble to costly quantum chemical calculations. The current status quo typically involves a manual process where the investigator must look at each conformation, separating each into structural families. This process is time-intensive and tedious, making this process infeasible in some cases, and limiting the ability of theoreticians to study these systems. However, the …
Verification Of Piecewise Deep Neural Networks: A Star Set Approach With Zonotope Pre-Filter,
2021
University of Nebraska
Verification Of Piecewise Deep Neural Networks: A Star Set Approach With Zonotope Pre-Filter, Hoang-Dung Tran, Neelanjana Pal, Diego Manzanas Lopez, Patrick Musau, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson
Computer Science Faculty Publications
Verification has emerged as a means to provide formal guarantees on learning-based systems incorporating neural network before using them in safety-critical applications. This paper proposes a new verification approach for deep neural networks (DNNs) with piecewise linear activation functions using reachability analysis. The core of our approach is a collection of reachability algorithms using star sets (or shortly, stars), an effective symbolic representation of high-dimensional polytopes. The star-based reachability algorithms compute the output reachable sets of a network with a given input set before using them for verification. For a neural network with piecewise linear activation functions, our approach can …
Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra,
2021
Missouri State University
Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra, Joshua D. Ellis
Graduate Theses/Dissertations
Fourier-transform infrared (FTIR) spectra of organic compounds can be used to compare and identify compounds. A mid-FTIR spectrum gives absorbance values of a compound over the 400-4000 cm-1 range. Spectral matching is the process of comparing the spectral signature of two or more compounds and returning a value for the similarity of the compounds based on how closely their spectra match. This process is commonly used to identify an unknown compound by searching for its spectrum’s closes match in a database of known spectra. A major limitation of this process is that it can only be used to identify …
Insights And Lessons Learned From The Design, Development And Deployment Of Pervasive Location-Based Mobile Systems “In The Wild”,
2021
Rochester Institute of Technology
Insights And Lessons Learned From The Design, Development And Deployment Of Pervasive Location-Based Mobile Systems “In The Wild”, Konstantinos Papangelis, Alan Chamberlain, Nicolas Lalone, Ting Cao
Presentations and other scholarship
This paper, based on a reflective approach, presents several insights and lessons learned from the design, development, and deployment of a location-based social network and a location-based game. These are analyzed and discussed against the life-cycle of our studies and range from engaging with the participants to dealing with technical issues while on the field. Overall, the insights and lessons learned illustrate that one should be prepared and flexible enough to accommodate any issues as they arise in a professional manner considering not only the results of the study but also the participants and the researchers involved.The aim of this …
Locating Identities In Time: An Examination Of The Impact Of Temporality On Presentations Of The Self Through Location-Based Social Networks,
2021
Rochester Institute of Technology
Locating Identities In Time: An Examination Of The Impact Of Temporality On Presentations Of The Self Through Location-Based Social Networks, Konstantinos Papangelis, Ioanna Lykourentzou, Vassilis-Javed Khan, Alan Chamberlain, Ting Cao, Micahel Saker, Nicolas Lalone
Articles
Studies of identity and location-based social networks (LBSN) have tended to focus on the performative aspects associated with marking one’s location. Yet, these studies often present this practice as being an a priori aspect of locative media. What is missing from this research is a more granular understanding of how this process develops over time. Accordingly, we focus on the first six weeks of 42 users beginning to use an LBSN we designed and named GeoMoments. Through our analysis of our users' activities, we contribute to understanding identity and LBSN in two distinct ways. First, we show how LBSN users …
Analysis Of The Slo Bay Microbiome From A Network Perspective,
2021
California Polytechnic State University, San Luis Obispo
Analysis Of The Slo Bay Microbiome From A Network Perspective, Lien Viet Nguyen
Master's Theses
Microorganisms are key players in the ecosystem functioning. In this thesis, we developed a framework to preprocess raw microbiome data, build a correlation network, and analyze co-occurrence patterns between microbes. We then applied this framework to a marine microbiome dataset. The dataset used in this study comes from a year-long time-series to characterize the microbial communities in our coastal waters off the Cal Poly Pier. In analyzing this dataset, we were able to observe and confirm previously discovered patterns of interactions and generate hypotheses about new patterns. The analysis of co-occurrences between prokaryotic and eukaryotic taxa is relatively novel and …
Montage Music Videos: Racial Utopianism Vs. Abstract Cowboys And The Question Of Cultural Montage,
2021
University of South Florida
Montage Music Videos: Racial Utopianism Vs. Abstract Cowboys And The Question Of Cultural Montage, Alan E. Blanchard
USF Tampa Graduate Theses and Dissertations
Along with the explosion of consumer goods in America over the past century came the human impulse to alter these objects to produce new meanings the manufacturers never intended: commercial products become amateur artists’ raw material. We see this with custom cars and the curious blending of clothes. Inevitably, digital commercial products, like music videos, would undergo a similar treatment as seen in DJ Cummerbund’s “mashup” videos “Old Staind Road” and “Blurry in the USA” where he is painting with audio tracks and sculpting with video clips to create new digital art with new meanings uncoupled from industry’s original intent …
A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking,
2021
California Institute of Technology
A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead
Mathematics, Physics, and Computer Science Faculty Articles and Research
In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …
Case Study Of Scrum Methodology As Used By A Capstone Team,
2021
Portland State University
Case Study Of Scrum Methodology As Used By A Capstone Team, Lilly I. Yeaton
University Honors Theses
Scrum is widely used in the software industry to manage all kinds of projects. This case study examines the way in which a capstone team used the methodology and models the specific project management processes they used over the course of their project. These models and the process modifications therein are then compared to the team’s velocity at different points in the project. The results of this analysis suggest a correlation between asynchronous daily meetings and sprint reviews and improved velocity.
