Computer Sciences Commons^™

Research On Benefits Of Mixed Traffic Flow Of Intelligent Connected Vehicles, Mingwei Hu, Zhiming Zhang, Xiangsheng Chen

Journal of System Simulation

Abstract: In order to study the benefits of the mixed traffic flow Intelligent Connected Vehicles (ICV) and non-connected vehicles, aiming at the intelligent and connected characteristics of ICV. Through the microscopic traffic simulation software Vissim redevelopment, ICV having the function of intelligent connected vehicles traffic flow modeling is realized. The mixed traffic flow composed of intelligent connected vehicles and non-connected vehicles under different market penetration rates is simulated. By comparing the traffic benefits at different time periods and different penetration rates, it is found that with the increase of the market penetration rate of intelligent connected vehicles, the average speed …

Visual Analysis Method Of Tobacco Quality Data Based On Dimension Reduction, Tian Dong, Guihua Shan, Xuebin Chi, Yanling Zhang, Weihua Feng, Jianwei Wang, Aiguo Wang, Wang Rui

Journal of System Simulation

Abstract: In order to meet the requirements of tobacco leaf matching across regions in tobacco material selection, a visual analysis method of tobacco leaf quality data that incorporating dimension reduction and correlation analysis methods is developed. Through the dimension reduction algorithm, the comparison algorithm and the visual interaction method based on the classification of aroma area for tobacco leaf quality data, a visual analysis method for exploring space division and correlation analysis of tobacco leaf quality data is provided. National tobacco leaf quality data analysis cases and expert demonstrations show that the method can carry out the tobacco leaf quality …

Learning In Convolutional Neural Networks Accelerated By Transfer Entropy, Adrian Moldovan, Angel Caţaron, Răzvan Andonie

Computer Science Faculty Scholarship

Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. Our focus is on how to include the TE in the learning mechanisms of a Convolutional Neural Network (CNN) architecture. We introduce a novel training mechanism for CNN architectures which integrates the TE feedback connections. Adding the TE feedback parameter accelerates the training process, as fewer epochs are needed. On the flip side, it adds computational overhead to each epoch. …

Computer Science Principles With C++, Seth D. Bergmann

Open Educational Resources

This textbook is intended to be used for a first course in computer science, such as the College Board’s Advanced Placement course known as AP Computer Science Principles (CSP). This book includes all the topics on the CSP exam, plus some additional topics. It takes a breadth-first approach, with an emphasis on the principles which form the foundation for hardware and software. No prior experience with programming should be required to use this book. This version of the book uses the C++ programming language.

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

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 …

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.

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.

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.

Teachers’ Engagement And Self-Efficacy In A Pk–12 Computer Science Teacher Virtual Community Of Practice, Robert Schwarzhaupt, Feng Liu, Joseph Wilson, Fanny Lee, Melissa Rasberry

Journal of Computer Science Integration

Prekindergarten to 12th-grade teachers of computer science (CS) face many challenges, including isolation, limited CS professional development resources, and low levels of CS teaching self-efficacy that could be mitigated through communities of practice (CoPs). This study used survey data from 420 PK–12 CS teacher members of a virtual CoP, CS for All Teachers, to examine the needs of these teachers and how CS teaching self-efficacy, community engagement, and sharing behaviors vary by teachers’ instructional experiences and school levels taught. Results show that CS teachers primarily join the CoP to gain high-quality pedagogical, assessment, and instructional resources. The study also found …

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 …

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 …

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 …

Localized Learning: A Possible Alternative To Current Deep Learning Techniques, Javier Viana, Kelly Cohen, Anca Ralescu, Stephan Ralescu, Vladik Kreinovich

Departmental Technical Reports (CS)

At present, the most efficient deep learning technique is the use of deep neural networks. However, recent empirical results show that in some situations, it is even more efficient to use "localized" learning -- i.e., to divide the domain of inputs into sub-domains, learn the desired dependence separately on each sub-domain, and then "smooth" the resulting dependencies into a single algorithm. In this paper, we provide theoretical explanation for these empirical successes.

How ‘Human’ Should Robots Be?, Singapore Management University

Perspectives@SMU

Hotel guests like interaction with devices that look and sound like them, but they can spark displeasure after service failures, new CUHK study shows

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 …

What Is A Reasonable Way To Make Predictions?, Leonardo Orea Amador, Vladik Kreinovich

Departmental Technical Reports (CS)

Predictions are usually based on what is called laws of nature: many times, we observe the same relation between the states at different moments of time, and we conclude that the same relation will occur in the future. The more times the relation repeats, the more confident we are that the same phenomenon will be re-peated again. This is how Newton's laws and other laws came into being. This is what is called inductive reasoning. However, there are other reasonable approaches. For example, assume that a person speeds and is not caught. This may be repeated two times, three times …

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 …

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 …

How The Pavement's Lifetime Depends On The Stress Level: An Explanation Of The Empirical Formula, Edgar Daniel Rodriguez Velasquez, Vladik Kreinovich, Olga Kosheleva, Hoang Phuong Nguyen

Departmental Technical Reports (CS)

We show that natural invariance ideas explain the empirical dependence on the pavement's lifetime on the stress level.

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 …

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 …

As Complexity Rises, Meaningful Statements Lose Precision -- But Why?, Miroslav Svitek, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the motivations for Zadeh's development of fuzzy logic -- and one of the explanations for the success of fuzzy techniques -- is the empirical observation that as complexity rises, meaningful statements lose precision. In this paper, we provide a possible explanation for this empirical phenomenon.

Rotten Green Tests In Java, Pharo And Python, Vincent Aranega, Julien Delplanque, Matias Martinez, Andrew P. Black, Stéphane Ducasse, Anne Etien, Christopher Fuhrman, Guillermo Polito

Computer Science Faculty Publications and Presentations

Rotten Green Tests are tests that pass, but not because the assertions they contain are true: a rotten test passes because some or all of its assertions are not actually executed. The presence of a rotten green test is a test smell, and a bad one, because the existence of a test gives us false confidence that the code under test is valid, when in fact that code may not have been tested at all. This article reports on an empirical evaluation of the tests in a corpus of projects found in the wild. We selected approximately one hundred mature …

Enterprise Environment Modeling For Penetration Testing On The Openstack Virtualization Platform, Vincent Karovic Jr., Jakub Bartalos, Vincent Karovic, Michal Gregus

Journal of Global Business Insights

The article presents the design of a model environment for penetration testing of an organization using virtualization. The need for this model was based on the constantly increasing requirements for the security of information systems, both in legal terms and in accordance with international security standards. The model was created based on a specific team from the unnamed company. The virtual working environment offered the same functions as the physical environment. The virtual working environment was created in OpenStack and tested with a Linux distribution Kali Linux. We demonstrated that the virtual environment is functional and its security testable. Virtualizing …