Computer Sciences Commons^™

Workers’ Attitudes Toward Increased Surveillance During And After The Covid-19 Pandemic, Jessica Vitak, Michael Zimmer

Computer Science Faculty Research and Publications

Amid the Covid-19 pandemic, the transition of many offices to remote work has led to new ways for employers to track workers’ movements, behavior, and productivity. Through their SSRC-funded research, Jessica Vitak and Michael Zimmer surveyed remote workers in the US about perceptions of current workplace monitoring practices. They argue that worker concerns about reductions in privacy and independence at work might have negative outcomes on worker productivity, satisfaction, and well-being.

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.

Uav-Assisted Data Dissemination Based On Network Coding In Vehicular Networks, Shidong Huang, Chuanhe Huang, Yabo Yin, Dongfang Wu, M. Wasim Abbas Ashraf, Bin Fu

Computer Science Faculty Publications and Presentations

Efficient and emergency data dissemination service in vehicular networks (VN) is very important in some situations, such as earthquakes, maritime rescue, and serious traffic accidents. Data loss frequently occurs in the data transition due to the unreliability of the wireless channel and there are no enough available UAVs providing data dissemination service for the large disaster areas. UAV with an adjustable active antenna can be used in light of the situation. However, data dissemination assisted by UAV with the adjustable active antenna needs corresponding effective data dissemination framework. A UAV-assisted data dissemination method based on network coding is proposed. First, …

Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers

Faculty Publications

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can …

Human Or Robot?: Investigating Voice, Appearance And Gesture Motion Realism Of Conversational Social Agents, Ylva Ferstl, Sean Thomas, Cédric Guiard, Cathy Ennis, Rachel Mcdonnell

Conference papers

Research on creation of virtual humans enables increasing automatization of their behavior, including synthesis of verbal and nonverbal behavior. As the achievable realism of different aspects of agent design evolves asynchronously, it is important to understand if and how divergence in realism between behavioral channels can elicit negative user responses. Specifically, in this work, we investigate the question of whether autonomous virtual agents relying on synthetic text-to-speech voices should portray a corresponding level of realism in the non-verbal channels of motion and visual appearance, or if, alternatively, the best available realism of each channel should be used. In two perceptual …

Generative Adversarial Networks For Classic Cryptanalysis, Deanne Charan

Master's Projects

The necessity of protecting critical information has been understood for millennia. Although classic ciphers have inherent weaknesses in comparison to modern ciphers, many classic ciphers are extremely challenging to break in practice. Machine learning techniques, such as hidden Markov models (HMM), have recently been applied with success to various classic cryptanalysis problems. In this research, we consider the effectiveness of the deep learning technique CipherGAN---which is based on the well- established generative adversarial network (GAN) architecture---for classic cipher cryptanalysis. We experiment extensively with CipherGAN on a number of classic ciphers, and we compare our results to those obtained using HMMs.

Accelerated Online Certificate In Quantum Computing, Joanna Burkhardt

Library Impact Statements

No abstract provided.

Digital Forensic Readiness Framework Based On Honeypot And Honeynet For Byod, Audrey Asante, Vincent Amankona

Journal of Digital Forensics, Security and Law

The utilization of the internet within organizations has surged over the past decade. Though, it has numerous benefits, the internet also comes with its own challenges such as intrusions and threats. Bring Your Own Device (BYOD) as a growing trend among organizations allow employees to connect their portable devices such as smart phones, tablets, laptops, to the organization’s network to perform organizational duties. It has gained popularity over the years because of its flexibility and cost effectiveness. This adoption of BYOD has exposed organizations to security risks and demands proactive measures to mitigate such incidents. In this study, we propose …

Efficient Neuromorphic Algorithms For Gamma-Ray Spectrum Denoising And Radionuclide Identification, Merlin Phillip Carson

Dissertations and Theses

Radionuclide detection and identification are important tasks for deterring a potentially catastrophic nuclear event. Due to high levels of background radiation from both terrestrial and extraterrestrial sources, some form of noise reduction pre-processing is required for a gamma-ray spectrum prior to being analyzed by an identification algorithm so as to determine the identity of anomalous sources. This research focuses on the use of neuromorphic algorithms for the purpose of developing low power, accurate radionuclide identification devices that can filter out non-anomalous background radiation and other artifacts created by gamma-ray detector measurement equipment, along with identifying clandestine, radioactive material.

A sparse …

Bone Quality And Fractures In Women With Osteoporosis Treated With Bisphosphonates For 1 To 14 Years, Hartmut H. Malluche, Jin Chen, Florence Lima, Lucas J. Liu, Marie-Claude Monier-Faugere, David A. Pienkowski

Internal Medicine Faculty Publications

Oral bisphosphonates are the primary medication for osteoporosis, but concerns exist regarding potential bone-quality changes or low-energy fractures. This cross-sectional study used artificial intelligence methods to analyze relationships among bisphosphonate treatment duration, a wide variety of bone-quality parameters, and low-energy fractures. Fourier transform infrared spectroscopy and histomorphometry quantified bone-quality parameters in 67 osteoporotic women treated with oral bisphosphonates for 1 to 14 years. Artificial intelligence methods established two models relating bisphosphonate treatment duration to bone-quality changes and to low-energy clinical fractures. The model relating bisphosphonate treatment duration to bone quality demonstrated optimal performance when treatment durations of 1 to 8 …

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 …

Multi-Feature Data Repository Development And Analytics For Image Cosegmentation In High-Throughput Plant Phenotyping, Rubi Quiñones, Francisco Munoz-Arriola, Sruti Das Choudhury, Ashok Samal

School of Computing: Faculty Publications

Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants’ responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy …

Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms’ limitations plays a vital role in selecting the …

International Comparative Studies On The Software Testing Profession, Luiz Fernando Capretz, Pradeep Waychal, Jingdong Jia, Daniel Varona, Yadira Lizama

Electrical and Computer Engineering Publications

This work attempts to fill a gap by exploring the human dimension in particular, by trying to understand the motivation of software professionals for taking up and sustaining their careers as software testers. Towards that goal, four surveys were conducted in four countries—India, Canada, Cuba, and China—to try to understand how professional software engineers perceive and value work-related factors that could influence their motivation to start or move into software testing careers. From our sample of 220 software professionals, we observed that very few were keen to take up testing careers. Some aspects of software testing, such as the potential …

Procesi I Web Dizajnimit, Albin Ibrahimi

Theses and Dissertations

Numri i web sajteve në internet gjithnjë e më shumë po rritet, këto sajte zhvillohen nga programerë të ndryshëm duke përdorur gjuhë të ndryshme programuese. Ashtu që produkti final të jetë i suksesshëm, këto web sajte duhet të zhvillohen në bazë të një procesi, i cili do të shpjegohet në detaje dhe është objektiv kryesor i këtij punimi.

Procesi i tillë ndihmon në efikasitet më të lartë të produktit final, pasi që kërkesat kryesore ndahen në kërkesa më të vogla deri ne maksimum ashtu që krijohet një lloj hallke e cila mundëson planifikim shumë të mirë.

Në këtë proces përfshihen …

Ftth, Blerant Gashi

Theses and Dissertations

Rrjetet fibra për në shtepi (FTTH) i përkasin familjes së sistemeve të transmetimit FTT-x brenda botës së telekomunikacionit. Këto rrjete, të cilat konsiderohen broadband, kanë aftësinë për të transportuar sasi të mëdha të të dhënave dhe informacioneve me ritme shumë të larta të bitit. Teknologjia FTTH përfshin futjen e fibrave optike në rrjetin global, si operatori i rrjetit kryesor si linja e fundit e teknologjis. Rrjetet FTTH për kominikimin e dy pajisve ndermjet tyre perdor driten dhe si medium për transmetim të sinjalve përdor fibrin optikë, ku shpejtsia e transmetimin të të dhënave është Gigabitshte. Fibri optik ka shumë avantazhe …

Freedom Of Will, Physics, And Human Intelligence: An Idea, Miroslav Svitek, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

Among the main fundamental challenges related to physics and human intelligence are: How can we reconcile the free will with the deterministic character of physical equations? What is the physical meaning of extra spatial dimensions needed to make quantum physics consistent? and Why are we often smarter than brain-simulating neural networks? In this paper, we show that while each of these challenges is difficult to resolve on its own, it may be possible to resolve all three of them if we consider them together. The proposed possible solution is that human reasoning uses the extra spatial dimensions. This may sound …

Shall We Be Foxes Or Hedgehogs: What Is The Best Balance For Research?, Miroslav Svitek, Olga Kosheleva, Shahnaz Shahbazova, Vladik Kreinovich

Departmental Technical Reports (CS)

Some researchers have few main ideas that they apply to many different problems -- they are called hedgehogs. Other researchers have many ideas but apply them to fewer problems -- they are called foxes. Both approaches have their advantages and disadvantages. What is the best balance between these two approaches? In this paper, we provide general recommendations about this balance. Specifically, we conclude that the optimal productivity is when the time spent on generating new ideas is equal to the time spent on understanding new applications. So, if for a researcher, understanding a new problem is much easier than generating …

Why Rectified Linear Activation Functions? Why Max-Pooling? A Possible Explanation, Julio C. Urenda, Vladik Kreinovich

Departmental Technical Reports (CS)

At present, the most successful machine learning technique is deep learning, that uses rectified linear activation function (ReLU) s(x) = max(x,0) as a non-linear data processing unit. While this selection was guided by general ideas (which were often imprecise), the selection itself was still largely empirical. This leads to a natural question: are these selections indeed the best or are there even better selections? A possible way to answer this question would be to provide a theoretical explanation of why these selections are -- in some reasonable sense -- the best. This paper provides a possible theoretical explanation for this …

Why Normalized Difference Vegetation Index (Ndvi)?, Francisco Zapata, Eric Smith, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

Plants play a very important role in ecological systems -- they transform CO2 into oxygen. It is therefore very important to be able to estimate the overall amount of live green vegetation in a given area. The most efficient way to provide such a global analysis is to use remote sensing, i.e., multi-spectral photos taken from satellites, drones, planes, etc. At present, one of the most efficient ways to detect, based on remote sensing data, how much live green vegetation an area contains is to compute the value of the normalized difference vegetation index (NDVI). In this paper, we provide …