Computer Sciences Commons^™

Transformnet: Self-Supervised Representation Learning Through Predicting Geometric Transformations, Hashim Sayed, Muhammad Ali

Student Publications

Deep neural networks need a big amount of training data, while in the real world there is a scarcity of data available for training purposes. To resolve this issue unsupervised methods are used for training with limited data. In this report, we describe the unsupervised semantic feature learning approach for recognition of the geometric transformation applied to the input data. The basic concept of our approach is that if someone is unaware of the objects in the images, he/she would not be able to quantitatively predict the geometric transformation that was applied to them. This self supervised scheme is based …

Jamming Detection And Classification In Ofdm-Based Uavs Via Feature- And Spectrogram-Tailored Machine Learning, Y. Li, J. Pawlak, J. Price, K. Al Shamaileh, Q. Niyaz, S. Paheding, V. Devabhaktuni

Michigan Tech Publications, Part 1

In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers with applications to unmanned aerial vehicles (UAVs). Using software-defined radio (SDR), four types of jamming attacks; namely, barrage, protocol-aware, single-tone, and successive-pulse are launched and investigated. Each type is qualitatively evaluated considering jamming range, launch complexity, and attack severity. Then, a systematic testing procedure is established by placing an SDR in the vicinity of a UAV (i.e., drone) to extract radiometric features before and after a jamming attack is launched. Numeric features that include signal-to-noise ratio (SNR), …

Hippocampal Ensembles Represent Sequential Relationships Among An Extended Sequence Of Nonspatial Events, Babak Shahbaba, Lingge Li, Forest Agostinelli, Mansi Saraf, Keiland W. Cooper, Derenik Haghverdian, Gabriel A. Elias, Pierre Baldi, Norbert J. Fortin

Faculty Publications

The hippocampus is critical to the temporal organization of our experiences. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain unclear. Here we recorded hippocampal activity as rats remembered an extended sequence of nonspatial events unfolding over several seconds, as in daily life episodes in humans. We then developed statistical machine learning methods to analyze the ensemble activity and discovered forms of sequential organization and coding important for order memory judgments. Specifically, we found that hippocampal ensembles provide significant temporal coding throughout nonspatial event sequences, differentiate distinct types of task-critical information sequentially within events, …

Intelligent Fault-Tolerant Mechanism For Data Centers Of Cloud Infrastructure, Satish Kumar T, Madhusudhan H S, S. M. F. D. Syed Mustapha, Punit Gupta, Rajan Prasad Tripathi

All Works

Fault tolerance in cloud computing is considered as one of the most vital issues to deliver reliable services. Checkpoint/restart is one of the methods used to enhance the reliability of the cloud services. However, many existing methods do not focus on virtual machine (VM) failure that occurs due to the higher response time of a node, byzantine fault, and performance fault, and existing methods also ignore the optimization during the recovery phase. This paper proposes a checkpoint/restart mechanism to enhance reliability of cloud services. Our work is threefold: (1) we design an algorithm to identify virtual machine failure due to …

Comparing Online Surveys For Cybersecurity: Sona And Mturk, Anne Wagner, Anna Bakas, Shelia Kennison, Eric Chan-Tin

Computer Science: Faculty Publications and Other Works

People have many accounts and usually need to create a password for each. They tend to create insecure passwords and re-use passwords, which can lead to compromised data. This research examines if there is a link between personality type and password security among a variety of participants in two groups of participants: SONA and MTurk. Each participant in both surveys answered questions based on password security and their personality type. Our results show that participants in the MTurk survey were more likely to choose a strong password and to exhibit better security behaviors and knowledge than participants in the SONA …

The Effect Of Using The Gamification Strategy On Academic Achievement And Motivation Towards Learning Problem-Solving Skills In Computer And Information Technology Course Among Tenth Grade Female Students, Mazyunah Almutairi, Prof. Ahmad Almassaad

International Journal for Research in Education

Abstract

This study aimed to identify the effect of using the gamification strategy on academic achievement and motivation towards learning problem-solving skills in computer and information technology course. A quasi-experimental method was adopted. The study population included tenth-grade female students in Al-Badi’ah schools in Riyadh. The sample consisted of 54 students divided into two equal groups: control group and experimental group. The study tools comprised an achievement test and the motivation scale. The results showed that there were statistically significant differences between the two groups in the academic achievement test in favor of the experimental group, with a large effect …

Experimental Study To Assess The Impact Of Timers On User Susceptibility To Phishing Attacks, Amy E. Antonucci, Yair Levy, Laurie P. Dringus, Martha Snyder

Journal of Cybersecurity Education, Research and Practice

Social engineering costs organizations billions of dollars. It exploits the weakest link of information systems security, the users. It is well-documented in literature that users continue to click on phishing emails costing them and their employers significant monetary resources and data loss. Training does not appear to mitigate the effects of phishing much; other solutions are warranted. Kahneman introduced the concepts of System-One and System-Two thinking. System-One is a quick, instinctual decision-making process, while System-Two is a process by which humans use a slow, logical, and is easily disrupted. The key aim of our experimental field study was to investigate …

Faculty And Advisor Advice For Cybersecurity Students: Liberal Arts, Interdisciplinarity, Experience, Lifelong Learning, Technical Skills, And Hard Work, Brian K. Payne, Bria Cross, Tancy Vandecar-Burdin

Journal of Cybersecurity Education, Research and Practice

The value of academic advising has been increasingly emphasized in higher education. In this study, attention is given to the most significant types of advice that a sample of cybersecurity faculty and advisors from the Commonwealth of Virginia recommend giving to cybersecurity students. The results show that faculty and advisors recommended that students be aware of six different aspects of cybersecurity education including the value of experience, the need for lifelong learning, the importance of hard work, the need to develop technical skills, the interdisciplinary nature of cybersecurity, and the need to develop liberal arts or professional/soft skills. Implications of …

Subject Matter Experts’ Feedback On Experimental Procedures To Measure User’S Judgment Errors In Social Engineering Attacks, Tommy Pollock, Yair Levy, Wei Li, Ajoy Kumar

Journal of Cybersecurity Education, Research and Practice

Distracted users can fail to correctly distinguish the differences between legitimate and malicious emails or search engine results. Mobile phone users can have a more challenging time identifying malicious content due to the smaller screen size and the limited security features in mobile phone applications. Thus, the main goal of this research study was to design, develop, and validate a set of field experiments to assess user’s judgment when exposed to two types of simulated social engineering attacks: phishing and Potentially Malicious Search Engine Results (PMSER), based on the interaction of the environment (distracting vs. non-distracting) and type of device …

A Taxonomy Of Cyberattacks Against Critical Infrastructure, Miloslava Plachkinova, Ace Vo

Journal of Cybersecurity Education, Research and Practice

The current study proposes a taxonomy to organize existing knowledge on cybercrimes against critical infrastructure such as power plants, water treatment facilities, dams, and nuclear facilities. Routine Activity Theory is used to inform a three-dimensional taxonomy with the following dimensions: hacker motivation (likely offender), cyber, physical, and cyber-physical components of any cyber-physical system (suitable target), and security (capable guardian). The focus of the study is to develop and evaluate the classification tool using Design Science Research (DSR) methodology. Publicly available data was used to evaluate the utility and usability of the proposed artifact by exploring three possible scenarios – Stuxnet, …

The 2020 Twitter Hack – So Many Lessons To Be Learned, Paul D. Witman, Scott Mackelprang

Journal of Cybersecurity Education, Research and Practice

In mid-July 2020, the social media site Twitter had over 100 of its most prominent user accounts start to tweet requests to send Bitcoin to specified Bitcoin wallets. The requests promised that the Bitcoin senders would receive their money back doubled, as a gesture of charity amidst the COVID-19 pandemic. The attack appears to have been carried out by a small group of hackers, leveraging social engineering to get access to internal Twitter support tools. These tools allowed the hackers to gain full control of the high-profile user accounts and post messages on their behalf. The attack provides many paths …

Editorial Vol 2021, No 2, Herbert J. Mattord, Michael E. Whitman, Hossain Shahriar

Journal of Cybersecurity Education, Research and Practice

Welcome to the Winter 2021 edition of the Journal for Cybersecurity Education, Research, and Practice.

Automatic Segmentation Of Sinkholes Using A Convolutional Neural Network, Muhammad Usman Rafique, Junfeng Zhu, Nathan Jacobs

Computer Science Faculty Publications

Sinkholes are the most abundant surface features in karst areas worldwide. Understanding sinkhole occurrences and characteristics is critical for studying karst aquifers and mitigating sinkhole-related hazards. Most sinkholes appear on the land surface as depressions or cover collapses and are commonly mapped from elevation data, such as digital elevation models (DEMs). Existing methods for identifying sinkholes from DEMs often require two steps: locating surface depressions and separating sinkholes from non-sinkhole depressions. In this study, we explored deep learning to directly identify sinkholes from DEM data and aerial imagery. A key contribution of our study is an evaluation of various ways …

Understanding The Decline In Successful Cattle Pregnancies, Andre Tu Nguyen

Research on Capitol Hill

USU junior Andre, a local Loganer, studies computer science and biology.He has been working in an animal science lab. Over time, we have seen a decline in successful dairy cattle pregnancies. This is a huge cause for concern for Utah, with milk sales at an estimated value of $405 million in 2020. Andre’s work has been in studying a certain protein in pregnant cattle; now that he has determined there is a decrease in this protein over the course of the pregnancy, he hopes to see whether that might impact its viability. Andre got involved in research in a high …

Automatic Segmentation Of Sinkholes Using A Convolutional Neural Network, Muhammad Usman Rafique, Junfeng Zhu, Nathan Jacobs

Faculty, Staff, and Affiliated Publications--KGS

Sinkholes are the most abundant surface features in karst areas worldwide. Understanding sinkhole occurrences and characteristics is critical for studying karst aquifers and mitigating sinkhole-related hazards. Most sinkholes appear on the land surface as depressions or cover collapses and are commonly mapped from elevation data, such as digital elevation models (DEMs). Existing methods for identifying sinkholes from DEMs often require two steps: locating surface depressions and separating sinkholes from non-sinkhole depressions. In this study, we explored deep learning to directly identify sinkholes from DEM data and aerial imagery. A key contribution of our study is an evaluation of various ways …

A New Agent-Based Model Offers Insight Into Population-Wide Adoption Of Prosocial Common-Pool Behavior, Garry Sotnik, Thaddeus Shannon, Wayne Wakeland

Complex Systems Faculty Publications and Presentations

New theoretical agent-based model of population-wide adoption of prosocial common-pool behavior with four parameters (initial percent of adopters, pressure to change behavior, synergy from behavior, and population density); dynamics in behavior, movement, freeriding, and group composition and size; and emergence of multilevel group selection. Theoretical analysis of model’s dynamics identified six regions in model’s parameter space, in which pressure-synergy combinations lead to different outcomes: extinction, persistence, and full adoption. Simulation results verified the theoretical analysis and demonstrated that increases in density reduce number of pressure-synergy combinations leading to population-wide adoption; initial percent of contributors affects underlying behavior and final outcomes, …

Land-Surface Parameters For Spatial Predictive Mapping And Modeling, Aaron E. Maxwell, Charles Shobe

Faculty & Staff Scholarship

Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and …

Numerical Treatment For Special Type Of Mixed Linear Delay Volterra Integro-Differential Equations, Atheer J. Kadhim

Emirates Journal for Engineering Research

The idea of research is a representation of the nonlinear pseudo-random generators using state-space equations that is not based on the usual description as shift register synthesis but in terms of matrices. Different types of nonlinear pseudo-random generators with their algorithms have been applied in order to investigate the output pseudo-random sequences. Moreover, two examples are given for conciliated the results of this representation.

Subomiembed: Self-Supervised Representation Learning Of Multi-Omics Data For Cancer Type Classification, Sayed Hashim, Muhammad Ali, Karthik Nandakumar, Mohammad Yaqub

Computer Vision Faculty Publications

For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features and small number of available samples. Different types of omics data show various aspects of samples. Integration and analysis of multi-omics data give us a broad view of tumours, which can improve clinical decision making. Omics data, mainly DNA methylation and gene expression profiles are usually high dimensional data with a lot of molecular features. In recent years, variational autoencoders (VAE) [13] have been extensively used in embedding image and text data into …

Diagnosis Of Polypoidal Choroidal Vasculopathy From Fluorescein Angiography Using Deep Learning, Yu-Yeh Tsai, Wei-Yang Ling, Shih-Jen Chen, Paisan Ruamviboonsuk, Cheng-Ho King, Chia-Ling Tsai

Publications and Research

Purpose: To differentiate polypoidal choroidal vasculopathy (PCV) from choroidal neovascularization (CNV) and to determine the extent of PCV from fluorescein angiography (FA) using attention-based deep learning networks.

Methods: We build two deep learning networks for diagnosis of PCV using FA, one for detection and one for segmentation. Attention-gated convolutional neural network (AG-CNN) differentiates PCV from other types of wet age-related macular degeneration. Gradient-weighted class activation map (Grad-CAM) is generated to highlight important regions in the image for making the prediction, which offers explainability of the network. Attention-gated recurrent neural network (AG-PCVNet) for spatiotemporal prediction is applied for segmentation …

Concurrent Identification And Characterization Of Protein Structure And Continuous Internal Dynamics With Redcraft, Hanin Omar, Aaron Hein, Casey A. Cole, Homayoun Valafar

Faculty Publications

Internal dynamics of proteins can play a critical role in the biological function of some proteins. Several well documented instances have been reported such as MBP, DHFR, hTS, DGCR8, and NSP1 of the SARS-CoV family of viruses. Despite the importance of internal dynamics of proteins, there currently are very few approaches that allow for meaningful separation of internal dynamics from structural aspects using experimental data. Here we present a computational approach named REDCRAFT that allows for concurrent characterization of protein structure and dynamics. Here, we have subjected DHFR (PDB-ID 1RX2), a 159-residue protein, to a fictitious, mixed mode model of …

An Automated Zoom Class Session Analysis Tool To Improve Education, Jack Arlo Cannon Ii

Dissertations and Theses

The recent shift towards remote education has presented new challenges for instructors with respect to teaching evaluation. Students in traditional classrooms send signals to instructors which provide feedback for the effectiveness of a given lecture. Virtual learning environments lack some of these communication channels and require new ways of collecting feedback. This work presents a suite of analysis tools for the virtual instructor. Given the transcript and video files for a Zoom meeting, this tool summarizes student sentiment and speaking characteristics. Sentiment scores are derived using state of the art Natural Language Processing (NLP) models. The video file is used …

Ordered Weighted Averaging (Owa), Decision Making Under Uncertainty, And Deep Learning: How Is This All Related?, Vladik Kreinovich

Departmental Technical Reports (CS)

Among many research areas to which Ron Yager contributed are decision making under uncertainty (in particular, under interval and fuzzy uncertainty) and aggregation -- where he proposed, analyzed, and utilized ordered weighted averaging (OWA). The OWA algorithm itself provides only a specific type of data aggregation. However, it turns out that if we allow several OWA stages, one after another, we obtain a scheme with a universal approximation property -- moreover, a scheme which is perfectly equivalent to modern ReLU-based deep neural networks. In this sense, Ron Yager can be viewed as a (grand)father of ReLU-based deep learning. We also …

Motivations Do Not Decrease Procrastination, So What Can We Do?, Olga Kosheleva, Vladik Kreinovich, Christian Servin

Departmental Technical Reports (CS)

Students often start working on their assignments late and, as a result, turn them in late. This procrastination makes grading more difficult. It also delays posting correct solutions that could help students understand their mistakes – and this hinders the students’ progress in studying following topics. At first glance, motivation seems to be a solution to all pedagogical problems: a motivated student eagerly collaborates with the instructor to learn more. Motivation indeed increases students’ knowledge, but, unfortunately, it does not decrease procrastination. So what can we do? We can institute heavy penalties for late submissions, but this would unfairly punish …

Unexpected Economic Consequence Of Cloud Computing: A Boost To Algorithmic Creativity, Francisco Zapata, Eric Smith, Vladik Kreinovich

Departmental Technical Reports (CS)

While theoreticians have been designing more and more efficient algorithms, in the past, practitioners were not very interested in this activity: if a company already owns computers that provide computations in required time, there is nothing to gain by using faster algorithms. We show the situation has drastically changed with the transition to cloud computing: many companies have not yet realized this, but with the transition to cloud computing, any algorithmic speed up leads to immediate financial gain. This also has serious consequences for the whole computing profession: there is a need for professionals better trained in subtle aspects of …

Unreachable Statements Are Inevitable In Software Testing: Theoretical Explanation, Francisco Zapata, Eric Smith, Vladik Kreinovich

Departmental Technical Reports (CS)

Business gurus recommend that an organization should have, in addition to clearly described realistic goals, also additional aspirational goals -- goals for which we may not have resources and which most probably will not be reached at all. At first glance, adding such a vague goal cannot lead to a drastic change in how the company operates, but surprisingly, for many companies, the mere presence of such aspirational goals boosts the company's performance. In this paper, we show that a simple geometric model of this situation can explain the unexpected success of aspirational goals.

A Natural Causality-Motivated Description Of Learning, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Teaching is not easy. One of the main reasons why it is not easy is that the existing descriptions of the teaching process are not very precise -- and thus, we cannot use the usual optimization techniques, techniques which require a precise model of the corresponding phenomenon. It is therefore desirable to come up with a precise description of the learning process. To come up with such a description, we notice that on the set of all possible states of learning, there is a natural order s ≤ s' meaning that we can bring the student from the state s …

Why Gaussian Copulas Are Ubiquitous In Economics: Fuzzy-Related Explanation, Chon Van Le, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many real-life situations, deviations are caused by a large number of independent factors. It is known that in such situations, the distribution of the resulting deviations is close to Gaussian, and thus, that the copulas -- that describe the multi-D distributions as a function of 1-D (marginal) ones -- are also Gaussian. In the past, these conclusions were also applied to economic phenomena, until the 2008 crisis showed that in economics, Gaussian models can lead to disastrous consequences. At present, all economists agree that the economic distributions are not Gaussian -- however, surprisingly, Gaussian copulas still often provide an …

Video Or Text? Bullets Or No Bullets? Why Not Both?, Olga Kosheleva, Vladik Kreinovich, Christian Servin

Departmental Technical Reports (CS)

Some students – which are, in terms of pop-psychology – more left-brain – prefer linear exposition, others – more right-brain ones – prefer 2-D images and texts with visual emphasis (e.g., with bullets). At present, instructors try to find a middle grounds between these two audiences, but why not prepare each material in two ways, aimed at both audiences?

Computing The Range Of A Function-Of-Few-Linear-Combinations Under Linear Constraints: A Feasible Algorithm, Salvador Robles, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we need to find the range of a given function under interval uncertainty. For nonlinear functions -- even for quadratic ones -- this problem is, in general, NP-hard; however, feasible algorithms exist for many specific cases. In particular, recently a feasible algorithm was developed for computing the range of the absolute value of a Fourier coefficient under uncertainty. In this paper, we generalize this algorithm to the case when we have a function of a few linear combinations of inputs. The resulting algorithm also handles the case when, in addition to intervals containing each input, we …