Computer Sciences Commons^™

Topics And Challenges In Teaching Computer Science For Gifted And Talented Students, Tom Meyer, Namrata Pandya

Faculty Publications & Research

No abstract provided.

An Introduction To Seshat: Global History Databank, Peter Turchin, Harvey Whitehouse, Pieter François, Daniel Hoyer, Abel Alves, John Baines, David Baker, Marta Bartkowiak, Jennifer Bates, James Bennett, Julye Bidmead, Peter Bol, Alessandro Ceccarelli, Kostis Christakis, David Christian, Alan Covey, Franco De Angelis, Timothy K. Earle, Neil R. Edwards, Gary Feinman, Stephanie Grohmann, Philip B. Holden, Árni Júlíusson, Andrey Korotayev, Axel Kristinsson, Jennifer Larson, Oren Litwin, Victor Mair, Joseph G. Manning, Patrick Manning, Arkadiusz Marciniak, Gregory Mcmahon, John Miksic, Juan Carlos Moreno Garcia, Ian Morris, Ruth Mostern, Daniel Mullins, Oluwole Oyebamiji, Peter Peregrine, Cameron Petrie, Johannes Preiser-Kapeller, Peter Rudiak-Gould, Paula Sabloff, Patrick Savage, Charles Spencer, Miriam Stark, Barend Ter Haar, Stefan Thurner, Vesna Wallace, Nina Witoszek, Liye Xie

Religious Studies Faculty Articles and Research

This article introduces the Seshat: Global History Databank, its potential, and its methodology. Seshat is a databank containing vast amounts of quantitative data buttressed by qualitative nuance for a large sample of historical and archaeological polities. The sample is global in scope and covers the period from the Neolithic Revolution to the Industrial Revolution. Seshat allows scholars to capture dynamic processes and to test theories about the co-evolution (or not) of social scale and complexity, agriculture, warfare, religion, and any number of such Big Questions. Seshat is rapidly becoming a massive resource for innovative cross-cultural and cross-disciplinary research. Seshat is …

Multimodal Data Fusion And Attack Detection In Recommender Systems, Mehmet Aktukmak

USF Tampa Graduate Theses and Dissertations

The commercial platforms that use recommender systems can collect relevant information to produce useful recommendations to the platform users. However, these sources usually contain missing values, imbalanced and heterogeneous data, and noisy observations. Such characteristics render the process of exploiting the information nontrivial, as one should carefully address them during the data fusion process. In addition to the degenerative characteristics, some entries can be fake, i.e., they can be the outcomes of malicious intents to manipulate the system. These entries should be eliminated before incorporation to any recommendation task. Detecting such malicious attacks quickly and accurately and then mitigating them …

Unifying Security Policy Enforcement: Theory And Practice, Shamaria Engram

USF Tampa Graduate Theses and Dissertations

Security policies stipulate restrictions on the behaviors of systems to prevent themfrom behaving in harmful ways. One way to ensure that systems satisfy the constraints of a security policy is through the use of security enforcement mechanisms. To understand the fundamental limitations of such mechanisms, formal methods are employed to prove properties and reason about their behaviors. The particular formalism employed, however, typically depends on the time at which a mechanism operates.

Mechanisms operating before a program's execution are static mechanisms, and mechanisms operating during a program's execution are dynamic mechanisms. Static mechanisms are fundamentally limited in the types of …

Pembangunan Laman Sesawang Tatabahasa Tamil Sekolah Menengah, Alagesan Ambikapathy

Student Works (2020-2029)

Isu kurang minat dan pencapaian rendah murid dalam aspek tatabahasa menyebabkan perlunya bahan pengajaran tatabahasa Tamil yang berinovasi. Dengan itu, kajian ini bertujuan membangunkan laman sesawang tatabahasa Tamil bagi meningkatkan minat dan pencapaian murid dalam tatabahasa Tamil. Kaedah reka bentuk dan pembangunan yang dipelopori oleh Richey dan Klein (2014) telah digunakan dalam kajian ini yang melibatkan tiga fasa kajian, iaitu fasa analisis keperluan, reka bentuk dan pembangunan dan penilaian. Data fasa analisis keperluan dikumpul dengan menggunakan borang soal selidik yang melibatkan 152 murid-murid Tingkatan Empat dan temu bual separa berstruktur dengan empat guru bahasa Tamil. Data soal selidik dianalisis secara …

Digital Identity: A Human-Centered Risk Awareness Study, Toufic N. Chebib

USF Tampa Graduate Theses and Dissertations

Cybersecurity threats and compromises have been at the epicenter of media attention; their risk and effect on people’s digital identity is something not to be taken lightly. Though cyber threats have affected a great number of people in all age groups, this study focuses on 55 to 75-year-olds, as this age group is close to retirement or already retired. Therefore, a notable compromise impacting their digital identity can have a major impact on their life.

To help guide this study, the following research question was formulated, “What are the risk perceptions of individuals, between the ages of 55 and 75 …

Coevolution, Dynamics And Allostery Conspire In Shaping Cooperative Binding And Signal Transmission Of The Sars-Cov-2 Spike Protein With Human Angiotensin-Converting Enzyme 2, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Binding to the host receptor is a critical initial step for the coronavirus SARS-CoV-2 spike protein to enter into target cells and trigger virus transmission. A detailed dynamic and energetic view of the binding mechanisms underlying virus entry is not fully understood and the consensus around the molecular origins behind binding preferences of SARS-CoV-2 for binding with the angiotensin-converting enzyme 2 (ACE2) host receptor is yet to be established. In this work, we performed a comprehensive computational investigation in which sequence analysis and modeling of coevolutionary networks are combined with atomistic molecular simulations and comparative binding free energy analysis of …

Proposal For The Development And Addition Of A Cybersecurity Assessment Section Into Technology Involving Global Public Health, Stanley Mierzwa, Saumya Ramarao, Jung Ah Yun, Bok Gyo Jeong

International Journal of Cybersecurity Intelligence & Cybercrime

This paper discusses and proposes the inclusion of a cyber or security risk assessment section during the course of public health initiatives involving the use of information and communication computer technology. Over the last decade, many public health research efforts have included information technologies such as Mobile Health (mHealth), Electronic Health (eHealth), Telehealth, and Digital Health to assist with unmet global development health needs. This paper provides a background on the lack of documentation on cybersecurity risks or vulnerability assessments in global public health areas. This study suggests existing frameworks and policies be adopted for public health. We also propose …

Book Review: Computer Capers: Tales Of Electronic Thievery, Embezzlement, And Fraud. By Thomas Whiteside, Brian Nussbaum

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Toward Mitigating, Minimizing, And Preventing Cybercrimes And Cybersecurity Risks, Claire Seungeun Lee

International Journal of Cybersecurity Intelligence & Cybercrime

Cybercrime and cybersecurity are emerging fields of research, shaped by technological developments. Scholars in these interconnected fields have studied different types of cybercrimes as well as victimization and offending. Increasingly, some of these scholars have focused on the ways in which cybercrimes can be mitigated, minimized, and even prevented. However, such strategies are often difficult to achieve in reality due to the human and technical factors surrounding cybercrimes. In this issue of the International Journal of Cybersecurity Intelligence and Cybercrime, three papers adequately address such challenges using college student samples and nationally representative samples, as well as a framework through …

Cyber-Situational Crime Prevention And The Breadth Of Cybercrimes Among Higher Education Institutions, Sinchul Back, Jennifer Laprade

International Journal of Cybersecurity Intelligence & Cybercrime

Academic institutions house enormous amounts of critical information from social security numbers of students to proprietary research data. Thus, maintaining up to date cybersecurity practices to protect academic institutions’ information and facilities against cyber-perpetrators has become a top priority. The purpose of this study is to assess common cybersecurity measures through a situational crime prevention (SCP) theoretical framework. Using a national data set of academic institutions in the United States, this study investigates the link between common cybersecurity measures, crime prevention activities, and cybercrimes. By focusing on the conceptualization of cybersecurity measures as SCP techniques, this study also offers the …

The Effects Of Self-Control On The Cyber Victim-Offender Overlap, Brooke Nodeland

International Journal of Cybersecurity Intelligence & Cybercrime

Increasingly, the overlap between victims and offenders has received empirical attention with regard to traditional forms of deviance. More recently, the growth of cyber offending has led to a need to examine whether traditional criminological theories can be used to explain these crimes in the same manner as traditional offenses. However, limited attention has been given to victim-offender overlap in cyber-offending. The current study uses a sample of American college students to examine the influence of self-control on cyber offending, cyber victimization, and the cyber victim-offender overlap. The results indicate that low self-control significantly predicts participation in cyber offending as …

Csci 49380/79526: Fundamentals Of Reactive Programming - Assignment 5, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

A Framework To Determine The Impact Of Poor Data Quality On The Reliability Of Iot Sensor-Based Real-Time Decision-Making, Arnold Rego

Theses and Dissertations

Today, more and more systems make real-time decisions based on the data received from sensors. But it is inevitable that sensors will fail, or send bad data – leading to faulty real-time decision-making. Using this poor-quality data in real-time decision-making can lead to deadly consequences. Traditional Data Quality (DQ) foundations call for “fixing” the poor DQ before the data can be used. However, time is often of the essence in a real-time decision-making environment, leaving little or no time to “fix” poor-quality data before it can be used in the decision-making process. The primary objective in real-time decision-making is not …

Cyber-Security Risks And Liabilities In Modern Marine Insurance., Thi Hong Hanh Hoang

World Maritime University Dissertations

No abstract provided.

Analyzing Yankees And Red Sox Sentiment Over The Course Of A Season, Connor Koch

Honors Projects in Data Science

This paper investigates data collected on twitter which references the Yankees or Red Sox during the 2020 Major League Baseball (MLB) season. The objective is to analyze the sentiment of tweets referencing the Yankees and Red Sox over the course of the season. In addition, an investigation of the networks within the data and the topics that were prevalent will be conducted. The 2020 MLB season was started late because of the COVID-19 pandemic and was a season like no other. The expectation of a dataset revolving around baseball is that the topics discussed would be about baseball. The findings …

A Natural Formalization Of Changing-One's-Mind Leads To Square Root Of "Not" And To Complex-Valued Fuzzy Logic, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

We show that a natural formalization of the process of changing one's mind leads to such seemingly non-intuitive ideas as square root of "not" and complex-valued fuzzy degrees.

Set Operation Aided Network For Action Units Detection, Huiyuan Yang, Taoyue Wang, Lijun Yin

Computer Science Faculty Research & Creative Works

As a large number of parameters exist in deep model-based methods, training such models usually requires many fully AU-annotated facial images. This is true with regard to the number of frames in two widely used datasets: BP4D [31] and DISFA [18], while those frames were captured from a small number of subjects (41, 27 respectively). This is problematic, as subjects produce highly consistent facial muscle movements, adding more frames per subject would only adds more close points in the feature space, and thus the classifier does not benefit from those extra frames. Data augmentation methods can be applied to alleviate …

An Eeg-Based Multi-Modal Emotion Database With Both Posed And Authentic Facial Actions For Emotion Analysis, Xiaotian Li, Xiang Zhang, Huiyuan Yang, Wenna Duan, Weiying Dai, Lijun Yin

Computer Science Faculty Research & Creative Works

Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes. One behavioral change is facial expression, which has been studied extensively over the past few decades. Facial behavior varies with a person's emotion according to differences in terms of culture, personality, age, context, and environment. In recent years, physiological activities have been used to study emotional responses. A typical signal is the electroencephalogram (EEG), which measures brain activity. Most of existing EEG-based emotion analysis has overlooked the role of facial expression changes. There exits little research on the relationship between …

How To Find The Dependence Based On Measurements With Unknown Accuracy: Towards A Theoretical Justification For Midpoint And Convex-Combination Interval Techniques And Their Generalizations, Somsak Chanaim, Vladik Kreinovich

Departmental Technical Reports (CS)

In practice, we often need to find regression parameters in situations when for some of the values, we have several results of measuring this same value. If we know the accuracy of each of these measurements, then we can use the usual statistical techniques to combine the measurement results into a single estimate for the corresponding value. In some cases, however, we do not know these accuracies, so what can we do? In this paper, we describe two natural approaches to solving this problem. In addition to describing general techniques, our results also provide a theoretical explanation for several semi-heuristic …

A Framework For Identifying Host-Based Artifacts In Dark Web Investigations, Arica Kulm

Masters Theses & Doctoral Dissertations

The dark web is the hidden part of the internet that is not indexed by search engines and is only accessible with a specific browser like The Onion Router (Tor). Tor was originally developed as a means of secure communications and is still used worldwide for individuals seeking privacy or those wanting to circumvent restrictive regimes. The dark web has become synonymous with nefarious and illicit content which manifests itself in underground marketplaces containing illegal goods such as drugs, stolen credit cards, stolen user credentials, child pornography, and more (Kohen, 2017). Dark web marketplaces contribute both to illegal drug usage …

Literature Retrieval For Precision Medicine With Neural Matching And Faceted Summarization, Jiho Noh, Ramakanth Kavuluru

Institute for Biomedical Informatics Faculty Publications

Information retrieval (IR) for precision medicine (PM) often involves looking for multiple pieces of evidence that characterize a patient case. This typically includes at least the name of a condition and a genetic variation that applies to the patient. Other factors such as demographic attributes, comorbidities, and social determinants may also be pertinent. As such, the retrieval problem is often formulated as ad hoc search but with multiple facets (e.g., disease, mutation) that may need to be incorporated. In this paper, we present a document reranking approach that combines neural query-document matching and text summarization toward such retrieval scenarios. Our …

Semi-Analytical Model For Design And Analysis Of On-Orbit Servicing Architecture, Koki Ho, Hai Wang, Paul A. De Trempe, Tristan Sarton Du Jonchay, Kento Tomita

Research Collection School Of Computing and Information Systems

Robotic on-orbit servicing (OOS) is expected to be a key technology and concept for future sustainable space exploration. This paper develops a novel semi-analytical model for OOS system analysis, responding to the growing needs and ongoing trend of robotic OOS. An OOS infrastructure system is considered whose goal is to provide responsive services to the random failures of a set of customer modular satellites distributed in space (e.g., at the geosynchronous orbit). The considered OOS architecture comprises a servicer that travels and provides module-replacement services to the customer satellites, an on-orbit depot to store the spares, and a series of …

Establishing Blockchain-Related Security Controls, Maitha Ali Al Ketbi

Theses

Blockchain technology is a secure and relatively new technology of distributed digital ledgers which is based on interlinked blocks of transactions. There is a rapid growth in the adoption of the blockchain technology in different solutions and applications and within different industries throughout the world, such as but not limited to, finance, supply chain, digital identity, energy, healthcare, real estate and government. Blockchain technology has great benefits such as decentralization, transparency, immutability and automation. Like any other emerging technology, the blockchain technology has also several risks and threats associated with its expected benefits which in turns could have a negative …

Using Commonsense Knowledge And Text Mining For Implicit Requirements Localization, Emebo Onyeka, Aparna S. Varde, Vaibhav Anu, Niket Tandon, Olawande Daramola

Department of Computer Science Faculty Scholarship and Creative Works

This paper addresses identification of implicit requirements (IMRs) in software requirements specifications (SRS). IMRs, as opposed to explicit requirements, are not specified by users but are more subtle. It has been noticed that IMRs are crucial to the success of software development. In this paper, we demonstrate a software tool called COTIR developed by us as a system that integrates Commonsense knowledge, Ontology and Text mining for early identification of Implicit Requirements. This relieves human software engineers from the tedious task of manually identifying IMRs in huge SRS documents. Our evaluation reveals that COTIR outperforms existing IMR tools. This demo …

Divide And Slide: Layer-Wise Refinement For Output Range Analysis Of Deep Neural Networks, Chao Huang, Jiameng Fan, Xin Chen, Wenchao Li, Qi Zhu

Computer Science Faculty Publications

In this article, we present a layer-wise refinement method for neural network output range analysis. While approaches such as nonlinear programming (NLP) can directly model the high nonlinearity brought by neural networks in output range analysis, they are known to be difficult to solve in general. We propose to use a convex polygonal relaxation (overapproximation) of the activation functions to cope with the nonlinearity. This allows us to encode the relaxed problem into a mixed-integer linear program (MILP), and control the tightness of the relaxation by adjusting the number of segments in the polygon. Starting with a segment number of …

Extending The Functional Subnetwork Approach To A Generalized Linear Integrate-And-Fire Neuron Model, Nicholas Szczecinski, Roger Quinn, Alexander J. Hunt

Mechanical and Materials Engineering Faculty Publications and Presentations

Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of non-spiking neurons, the “Functional subnetwork approach” (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network’s intended function, without the use of global optimization ormachine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuronmodel have fundamental similarities to those of …

Perceptions And Misperceptions Of Smartphone Use: Applying The Social Norms Approach, John Mcalaney, Mohamed Basel Almourad, Georgina Powell, Raian Ali

All Works

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The social norms approach is an established technique to bring about behaviour change through challenging misperceptions of peer behaviour. This approach is limited by a reliance on self-report and a lack of interactivity with the target population. At the same time, excessive use of digital devices, known as digital addiction, has been recognized as an emergent issue. There is potential to apply the social norms approach to digital addiction and, in doing so, address some of the limitations of the social norms field. In this study, we trialled a social norms …

Generating Routes For Autonomous Driving In Vehicle-To-Infrastructure Communications, Jianjun Yang, Tinggui Chen, Bryson Payne, Ping Guo, Yanping Zhang, Juan Guo

Computer Science Faculty Scholarship

The study of vehicular networks has attracted considerable interest in academia and the industry. In the broad area, connected vehicles and autonomous driving are technologies based on wireless data communication between vehicles or between vehicles and infrastructures. A Vehicle-to-Infrastructure (V2I) system consists of communications and computing over vehicles and related infrastructures. In such a system, wireless sensors are installed in some selected points along roads or driving areas. In autonomous driving, it is crucial for a vehicle to figure out the ideal routes by the communications between its equipped sensors and infrastructures then the vehicle is automatically moving along …

Cost-Sensitive Deep Forest For Price Prediction, Chao Ma, Zhenbing Liu, Zhiguang Cao, Wen Song, Jie Zhang, Weiliang Zeng

Research Collection School Of Computing and Information Systems

For many real-world applications, predicting a price range is more practical and desirable than predicting a concrete value. In this case, price prediction can be regarded as a classification problem. Although deep forest is recognized as the best solution to many classification problems, a crucial issue limits its direct application to price prediction, i.e., it treated all the misclassifications equally no matter how far away they are from the real classes, since their impacts on the accuracy are the same. This is unreasonable to price prediction as the misclassification should be as close to the real price range as possible …