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Full-Text Articles in Computer Sciences

Comparative Analyses Of De Novo Transcriptome Assembly Pipelines For Diploid Wheat, Natasha Pavlovikj May 2022

Comparative Analyses Of De Novo Transcriptome Assembly Pipelines For Diploid Wheat, Natasha Pavlovikj

School of Computing: Dissertations, Theses, and Student Research

Gene expression and transcriptome analysis are currently one of the main focuses of research for a great number of scientists. However, the assembly of raw sequence data to obtain a draft transcriptome of an organism is a complex multi-stage process usually composed of pre-processing, assembling, and post-processing. Each of these stages includes multiple steps such as data cleaning, error correction and assembly validation. Different combinations of steps, as well as different computational methods for the same step, generate transcriptome assemblies with different accuracy. Thus, using a combination that generates more accurate assemblies is crucial for any novel biological discoveries. Implementing …

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Toward A Labeled Dataset Of Iot Malware Features, Stian Hagboe Olsen May 2022

Toward A Labeled Dataset Of Iot Malware Features, Stian Hagboe Olsen

Theses and Dissertations

IoT malware has accompanied the rapid growth of embedded devices over the last decade. The last few years have seen increased work on static and dynamic detection and classification techniques for IoT malware. However, this work requires a very diverse and fine-grained set of malware-specific characteristics. This paper takes a step toward constructing a large-scale, diverse, and open-source IoT malware dataset. To demonstrate the depth of the dataset, we propose an approach for recovering symbol tables and detecting the intent of stripped IoT malware binaries using function signature libraries and 14 defining Linux malware features with corresponding regular expressions. We …

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Identifying Functional And Non-Functional Software Requirements From User App Reviews And Requirements Artifacts, Dev Jayant Dave May 2022

Identifying Functional And Non-Functional Software Requirements From User App Reviews And Requirements Artifacts, Dev Jayant Dave

Theses, Dissertations and Culminating Projects

This thesis proposes and evaluates Machine Learning (ML) based data models to identify and isolate software requirements from datasets containing user app review statements. The ML models classify user app review statements into Functional Requirements (FRs), Non-Functional Requirements (NFRs), and Non-Requirements (NRs). This proposed approach consisted of creating a novel hybrid dataset that contains software requirements from Software Requirements Specification (SRS) documents and user app reviews. The Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), and Random Forest (RF) ML algorithms combined with the term frequency-inverse document frequency (TF-IDF) natural language processing (NLP) technique were implemented on the hybrid dataset. …

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Exposing Gan-Generated Faces Using Deep Neural Network, Hui Guo May 2022

Exposing Gan-Generated Faces Using Deep Neural Network, Hui Guo

Legacy Theses & Dissertations (2009 - 2024)

Generative adversarial network (GAN) generated high-realistic human faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts.In this work, we explore a universal physiological cue of the eye, namely the pupil shape consistency, to identify GAN-generated faces reliably. We show that GAN-generated faces can be exposed via irregular pupil shapes. This phenomenon is caused by the lack of physiological constraints in the GAN models. We demonstrate that such artifacts exist widely in high-quality GAN-generated faces. We design an automatic method to segment …

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Exploring Methods To Persuade Users To Watch Social Problem Films Through The Case Study Of Race-Related Films, Clay Troyer '22 May 2022

Exploring Methods To Persuade Users To Watch Social Problem Films Through The Case Study Of Race-Related Films, Clay Troyer '22

Honor Scholar Theses

The present study reports on the success of persuasive techniques and recommendation engine on persuading participants to choose a race-related movie. The study used a technical component as well as an experimental study to see how users respond to certain persuasive techniques. The technical component included a content-based filtering recommendation engine to recommend lists of movies to participants based on films they already enjoy. Persuasive techniques were then applied to those lists and results were measured and analyzed. This project aims to explore the relationship that recommendation engines and persuasive techniques have on users and their willingness to branch out …

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The Quest For New Music: A Recommendation Algorithm For Spotify Users, Ian Curtis May 2022

The Quest For New Music: A Recommendation Algorithm For Spotify Users, Ian Curtis

Honors Projects

Music is one of the rare forms of communication that can be understood on a profound level by anyone; it has the power to cause significant emotional effects, to spark inspiration, to ignite change, to spread knowledge, and more, even regardless of song language. A popular subject of research in music pertains to recommendations; determining a song a listener would enjoy is not an easy task. Moreover, certain factors may influence a user's satisfaction with recommended songs and their likelihood to continue using a service. Focusing on the major streaming service Spotify, we build a K-Means clustering algorithm to recommend …

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The Acm/Ais Is2020 Competency Model For Undergraduate Programs In Information Systems: A Joint Acm/Ais Task Force Report, Paul Leidig, Hannu Salmela May 2022

The Acm/Ais Is2020 Competency Model For Undergraduate Programs In Information Systems: A Joint Acm/Ais Task Force Report, Paul Leidig, Hannu Salmela

Peer-Reviewed Publications

The Association of Computing Machinery (ACM) and the Association of Information Systems (AIS) along with the ISCAP EDSIG, recently released a joint taskforce report IS2020: A Competency Model for Undergraduate Programs in Information Systems. In this paper, the co-chairs of IS2020, the latest Information Systems curriculum guidelines, provide their insight on the problems presented with existing guidelines, illustrate the issue, and share their opinions that led to the release of these latest guidelines.

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How Hard Is Bribery In Elections With Randomly Selected Voters, Liangde Tao, Lin Chen, Lei Xu, Weidong Shi, Ahmed Sunny, Md Mahabub Uz Zaman May 2022

How Hard Is Bribery In Elections With Randomly Selected Voters, Liangde Tao, Lin Chen, Lei Xu, Weidong Shi, Ahmed Sunny, Md Mahabub Uz Zaman

Computer Science Faculty Publications

Many research works in computational social choice assume a fixed set of voters in an election and study the resistance of different voting rules against electoral manipulation. In recent years, however, a new technique known as random sample voting has been adopted in many multi-agent systems. One of the most prominent examples is blockchain. Many proof-of-stake based blockchain systems like Algorand will randomly select a subset of participants of the system to form a committee, and only the committee members will be involved in the decision of some important system parameters. This can be viewed as running an election where …

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Game-Theoretic Deception Modeling For Distracting Network Adversarie, Mohammad Sujan Miah May 2022

Game-Theoretic Deception Modeling For Distracting Network Adversarie, Mohammad Sujan Miah

Open Access Theses & Dissertations

In this day and age, adversaries in the cybersecurity space have become alarmingly capable of identifying network vulnerabilities and work out various targets to attack where deception is becoming an increasingly crucial technique for the defenders to delay these attacks. For securing computer networks, the defenders use various deceptive decoy objects to detect, confuse, and distract attackers. By trapping the attackers, these decoys gather information, waste their time and resources, and potentially prevent future attacks. However, we have to consider that an attacker with the help of smart techniques may detect the decoys and avoid them. One of the well-known …

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Designing And Evaluating Accessible E-Learning For Students With Visual Impairments In K-12 Computing Education, Earl W. Huff Jr May 2022

Designing And Evaluating Accessible E-Learning For Students With Visual Impairments In K-12 Computing Education, Earl W. Huff Jr

All Dissertations

This dissertation explores the pathways for making K-12 computing education more accessible for blind or visually impaired (BVI) learners. As computer science (CS) expands into K-12 education, more concerted efforts are required to ensure all students have equitable access to opportunities to pursue a career in computing. To determine their viability with BVI learners, I conducted three studies to assess current accessibility in CS curricula, materials, and learning environments. Study one was interviews with visually impaired developers; study two was interviews with K-12 teachers of visually impaired students; study three was a remote observation within a computer science course. My …

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Using Ai To Examine Nocturnal Moth Pollination, Conor Moriarty May 2022

Using Ai To Examine Nocturnal Moth Pollination, Conor Moriarty

Biological Sciences Undergraduate Honors Theses

The importance of insect-mediated agriculture cannot be understated especially in the wake of pollinator population declines. Most research of insect-mediated pollination is focused on diurnal pollinators like the honeybee (Apis mellifera). Deepening our understanding of how other pollinators, such as nocturnal pollinators, can benefit agriculture and ecosystems will be very important as honeybee populations decline. I explored nocturnal moth interactions with flowering apple flowers during their pollination season to better understand how these nocturnal pollinators interact with the plants. To accomplish this, I used a University of Arkansas farm area with a dedicated apple orchard. I gathered my raw data …

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Computational Approaches To Understanding Subduction Zone Geodynamics, Surface Heat Flow, And The Metamorphic Rock Record, Buchanan C. Kerswell May 2022

Computational Approaches To Understanding Subduction Zone Geodynamics, Surface Heat Flow, And The Metamorphic Rock Record, Buchanan C. Kerswell

Boise State University Theses and Dissertations

Pressure-temperature (PT) estimates from exhumed high-pressure (HP) metamorphic rocks and global surface heat flow observations evidently encode information about subduction zone thermal structure and the nature of mechanical and chemical processing of subducted materials along the interface between converging plates. Previous work demonstrates the possibility of decoding such geodynamic information by comparing numerical geodynamic models with empirical observations of surface heat flow and the metamorphic rock record. However, ambiguous interpretations can arise from this line of inquiry with respect to thermal gradients, plate coupling, and detachment and recovery of subducted materials. This dissertation applies a variety of computational techniques to …

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Video Games, Grief, And The Character Link System, Nam Nguyen May 2022

Video Games, Grief, And The Character Link System, Nam Nguyen

LSU New Orleans Theses and Dissertations

Grief can encompass more than just the loss of real-life people. It can be felt with the loss of a pet, changes in daily structure, and even the loss of video game characters. The topic of grief related to video games and video game characters comes at a time when games as a service (GaaS) continue to increase in popularity and the phenomenon where these games also inevitably terminate service. To combat this unique form of grief, the Character LINK System was created as a tool that uses simple natural language processing (NLP) techniques to offer support to the bereaved …

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Levee Seepage Identification From Aerial Images Using Machine Learning, Sofiane Benkara May 2022

Levee Seepage Identification From Aerial Images Using Machine Learning, Sofiane Benkara

LSU New Orleans Theses and Dissertations

Levees protect from natural disasters that can threaten human health, infrastructure, and biological systems by protecting low-lying lands near or below sea level from flooding. However, seepage in those levees undermines their structural integrity, leading to failures. Today the United States has approximately over a hundred thousand miles of levee, many of which are reaching or have surpassed their initial design life. Given the concern, there is a need to develop reliable, rapid, and non-intrusive levee monitoring systems to detect the presence of seepage. This study explores the use of Deep Convolutional Neural Network (DCNN) integrated with Discrete Cosine Transform …

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One Ubiquitous Digital, But Social, Identity, Byron Moore May 2022

One Ubiquitous Digital, But Social, Identity, Byron Moore

Theses/Capstones/Creative Projects

Creating a digital identity in the Solana ecosystem is composed of creating accounts across multiple platforms with similar pseudo-names. Using public ledger technology (blockchain), digital identities can be developed so they are seamlessly applied across applications. Creating one such identity enables the individual to express themselves and build their personal brand. Blockchain technology automates this process by creating snapshots of an individual’s digital footprint within the scope of the ecosystem and enables a user to create a digital profile inspired by facets of self-sovereign identity principles. A digital identity should be controlled by the individual, but also allow for the …

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Optimized Damage Assessment And Recovery Through Data Categorization In Critical Infrastructure System., Shruthi Ramakrishnan May 2022

Optimized Damage Assessment And Recovery Through Data Categorization In Critical Infrastructure System., Shruthi Ramakrishnan

Graduate Theses and Dissertations

Critical infrastructures (CI) play a vital role in majority of the fields and sectors worldwide. It contributes a lot towards the economy of nations and towards the wellbeing of the society. They are highly coupled, interconnected and their interdependencies make them more complex systems. Thus, when a damage occurs in a CI system, its complex interdependencies make it get subjected to cascading effects which propagates faster from one infrastructure to another resulting in wide service degradations which in turn causes economic and societal effects. The propagation of cascading effects of disruptive events could be handled efficiently if the assessment and …

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A Novel Data Lineage Model For Critical Infrastructure And A Solution To A Special Case Of The Temporal Graph Reachability Problem, Ian Moncur May 2022

A Novel Data Lineage Model For Critical Infrastructure And A Solution To A Special Case Of The Temporal Graph Reachability Problem, Ian Moncur

Graduate Theses and Dissertations

Rapid and accurate damage assessment is crucial to minimize downtime in critical infrastructure. Dependency on modern technology requires fast and consistent techniques to prevent damage from spreading while also minimizing the impact of damage on system users. One technique to assist in assessment is data lineage, which involves tracing a history of dependencies for data items. The goal of this thesis is to present one novel model and an algorithm that uses data lineage with the goal of being fast and accurate. In function this model operates as a directed graph, with the vertices being data items and edges representing …

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The Executive’S Guide To Getting Ai Wrong, Jerrold Soh May 2022

The Executive’S Guide To Getting Ai Wrong, Jerrold Soh

Asian Management Insights

This article explores how we see AI and argues that we mostly get it wrong. In the process, it explains the reasons backed by social science research on why we tend to get AI wrong and illustrates the dangers of doing so from a managerial and law-making perspective. Some readers may also find the article useful as a guide on how and when to manipulate portrayals of AI in your favour.

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Symbolic Ns-3 For Efficient Exhaustive Testing, Jianfei Shao May 2022

Symbolic Ns-3 For Efficient Exhaustive Testing, Jianfei Shao

School of Computing: Dissertations, Theses, and Student Research

Exhaustive testing is an important type of simulation, where a user exhaustively simulates a protocol for all possible cases with respect to some uncertain factors, such as all possible packet delays or packet headers. It is useful for completely evaluating the protocol performance, finding the worst-case performance, and detecting possible design or implementation bugs of a protocol. It is, however, time consuming to use the brute force method with current NS-3, a widely used network simulator, for exhaustive testing. In this paper, we present our work on Sym-NS-3 for more efficient exhaustive testing, which leverages a powerful program analysis technique …

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New Debiasing Strategies In Collaborative Filtering Recommender Systems: Modeling User Conformity, Multiple Biases, And Causality., Mariem Boujelbene May 2022

New Debiasing Strategies In Collaborative Filtering Recommender Systems: Modeling User Conformity, Multiple Biases, And Causality., Mariem Boujelbene

Electronic Theses and Dissertations

Recommender Systems are widely used to personalize the user experience in a diverse set of online applications ranging from e-commerce and education to social media and online entertainment. These State of the Art AI systems can suffer from several biases that may occur at different stages of the recommendation life-cycle. For instance, using biased data to train recommendation models may lead to several issues, such as the discrepancy between online and offline evaluation, decreasing the recommendation performance, and hurting the user experience. Bias can occur during the data collection stage where the data inherits the user-item interaction biases, such as …

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Engaging Undergraduate Students In An Introductory A.I. Course Through A Knowledge-Based Chatbot Workshop, Thomas Menkhoff, Ying Teo Lydia Qian May 2022

Engaging Undergraduate Students In An Introductory A.I. Course Through A Knowledge-Based Chatbot Workshop, Thomas Menkhoff, Ying Teo Lydia Qian

Research Collection Lee Kong Chian School Of Business

In this paper we share interim results of an ongoing mixed method evaluative study of 43 students enrolled in an elective course “Doing Business with A.I.” at the Lee Kong Chian School of Business (LKCSB), Singapore Management University. A key component of the course design is an experiential chatbot workshop that provides non-STEM students with an opportunity to acquire basic skills to build a chatbot prototype using the ‘Dialogflow’ program. The workshop and the experiential learning activity were designed to impart students with relevant knowledge and skills such as conversation and user-centric design know how and know why that are …

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Rumorlens: Interactive Analysis And Validation Of Suspected Rumors On Social Media, Ran Wang, Kehan Du, Qianhe Chen, Yifei Zhao, Mojie Tang, Hongxi Tao, Shipan Wang, Yiyao Li, Yong Wang May 2022

Rumorlens: Interactive Analysis And Validation Of Suspected Rumors On Social Media, Ran Wang, Kehan Du, Qianhe Chen, Yifei Zhao, Mojie Tang, Hongxi Tao, Shipan Wang, Yiyao Li, Yong Wang

Research Collection School Of Computing and Information Systems

With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual …

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Sanitizable Access Control System For Secure Cloud Storage Against Malicious Data Publishers, Willy Susilo, Peng Jiang, Jianchang Lai, Fuchun Guo, Guomin Yang, Robert H. Deng May 2022

Sanitizable Access Control System For Secure Cloud Storage Against Malicious Data Publishers, Willy Susilo, Peng Jiang, Jianchang Lai, Fuchun Guo, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cloud computing is considered as one of the most prominent paradigms in the information technology industry, since it can significantly reduce the costs of hardware and software resources in computing infrastructure. This convenience has enabled corporations to efficiently use the cloud storage as a mechanism to share data among their employees. At the first sight, by merely storing the shared data as plaintext in the cloud storage and protect them using an appropriate access control would be a nice solution. This is assuming that the cloud is fully trusted for not leaking any information, which is impractical as the cloud …

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On The Transferability Of Pre-Trained Language Models For Low-Resource Programming Languages, Fuxiang Chen, Fatemeh H. Fard, David Lo, Timofey Bryksin May 2022

On The Transferability Of Pre-Trained Language Models For Low-Resource Programming Languages, Fuxiang Chen, Fatemeh H. Fard, David Lo, Timofey Bryksin

Research Collection School Of Computing and Information Systems

A recent study by Ahmed and Devanbu reported that using a corpus of code written in multilingual datasets to fine-tune multilingual Pre-trained Language Models (PLMs) achieves higher performance as opposed to using a corpus of code written in just one programming language. However, no analysis was made with respect to fine-tuning monolingual PLMs. Furthermore, some programming languages are inherently different and code written in one language usually cannot be interchanged with the others, i.e., Ruby and Java code possess very different structure. To better understand how monolingual and multilingual PLMs affect different programming languages, we investigate 1) the performance of …

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Mapping Salt-Affected Land In The South-West Of Western Australia Using Satellite Remote Sensing, P A. Caccetta, John A. Simons, S Furby, Nicholas J. Wright, Richard J. George Dr May 2022

Mapping Salt-Affected Land In The South-West Of Western Australia Using Satellite Remote Sensing, P A. Caccetta, John A. Simons, S Furby, Nicholas J. Wright, Richard J. George Dr

Natural resources published reports

Dryland salinity is a pervasive form of land degradation that has resulted from the clearing of about 17 M ha of native vegetation and the introduction of predominately cereal and pasture-based farming systems in the South-West of Western Australia. The change in water balance caused by clearing deep rooted endemic woodlands increased recharge and resulted in rising groundwater levels. After a lag period, the regolith began filling and groundwater approached the soil surface, evaporating and depositing stored salts in the rootzone of salt sensitive crops. Groundwater levels also rise and affect areas of remnant native vegetation, streams, wet-lands and rural …

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Beyond Accuracy In Machine Learning., Aneseh Alvanpour May 2022

Beyond Accuracy In Machine Learning., Aneseh Alvanpour

Electronic Theses and Dissertations

Machine Learning (ML) algorithms are widely used in our daily lives. The need to increase the accuracy of ML models has led to building increasingly powerful and complex algorithms known as black-box models which do not provide any explanations about the reasons behind their output. On the other hand, there are white-box ML models which are inherently interpretable while having lower accuracy compared to black-box models. To have a productive and practical algorithmic decision system, precise predictions may not be sufficient. The system may need to have transparency and be able to provide explanations, especially in applications with safety-critical contexts …

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Modeling And Debiasing Feedback Loops In Collaborative Filtering Recommender Systems., Sami Khenissi May 2022

Modeling And Debiasing Feedback Loops In Collaborative Filtering Recommender Systems., Sami Khenissi

Electronic Theses and Dissertations

Artificial Intelligence (AI)-driven recommender systems have been gaining increasing ubiquity and influence in our daily lives, especially during time spent online on the World Wide Web or smart devices. The influence of recommender systems on who and what we can find and discover, our choices, and our behavior, has thus never been more concrete. AI can now predict and anticipate, with varying degrees of accuracy, the news article we will read, the music we will listen to, the movies we will watch, the transactions we will make, the restaurants we will eat in, the online courses we will be interested …

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Balancing Data- Vs. Art-Driven Decisions In Video Game Design, Jaden D. Goter May 2022

Balancing Data- Vs. Art-Driven Decisions In Video Game Design, Jaden D. Goter

Honors Program: Senior Projects (Public)

Video games, like software, need to be designed. Video game development studios tend to use data-driven or art-driven decision-making to design their games. Data-driven decision-making is where active and passive data is collected in order to make informed decisions about the design of a game. Art-driven decision-making is when designers use their artistic intuition to design games, potentially ignoring player data. This paper elaborates on the advantages and disadvantages of both approaches and provides case studies of games designed under both approaches. Based on these studies, for a game to be successful, a combined approach of data- and art-driven decision-making …

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Hci Education And Ux Practice: Highlights From Singapore, Tamas Makany, Dharani Perera-Schulz May 2022

Hci Education And Ux Practice: Highlights From Singapore, Tamas Makany, Dharani Perera-Schulz

Research Collection Lee Kong Chian School Of Business

This position paper highlights trends in education, practice, and support of HCI/UX in Singapore, a small city-state island in Southeast Asia. The paper was prepared for the 2022 Southeast Asia Computer-Human Interaction (SEACHI'22) virtual workshop on Apr 14, 2022, as part of the ACM CHI Conference on Human Factors in Computing Systems (CHI'22) international conference.

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A Machine Learning Approach To Stochastic Optimal Control, Pablo Ever Avalos May 2022

A Machine Learning Approach To Stochastic Optimal Control, Pablo Ever Avalos

Open Access Theses & Dissertations

Merton's portfolio optimization problem is a well-renowned problem in financial mathematics which seeks to optimize the investment decision for an investor. In the simplest situation, the market consists of a risk-less asset (i.e. a bond) that pays back a relatively low interest rate, and a risky asset (i.e. a stock) that follows a geometric Brownian motion. The optimal allocation strategy of the investor's wealth is found by optimizing the expected utility along the stochastic evolution of the market. This thesis focuses on several different applications of this optimization problem. We look at pre-constructed analytical solutions and showcase the results. We …

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