Digital Commons Network^™

Predicting Phishing Victimization: Comparing Prior Victimization, Cognitive, And Emotional Styles, And Vulnerable Or Protective E-Mail Strategies, Loretta Stalans, Eric Chan-Tin, Anna Hart, Madeline Moran, Shelia Kennison

Computer Science: Faculty Publications and Other Works

Phishing victimization is prevalent and results in theft of personal identifiable information (PII) or installing malware to steal PII. Drawing upon social psychological and criminological theories, we conducted a prospective study to assess three groups of predictors to being phished or not: a) prior victimization; b) protective or vulnerable habitual strategies, and c) emotional and cognitive decision-making styles. Students (N = 236) completed a survey assessing these predictors and then about 4 weeks later received a phishing e-mail using the university’s phishing testing system. The e-mail requested that they click on a link and enter their student ID to avoid …

Human-Ai Collaboration For Smart Education: Reframing Applied Learning To Support Metacognition, James Hutson, Daniel Plate

Faculty Scholarship

This chapter investigates the profound influence of intelligent virtual assistants (IVAs) on the educational domain, specifically in the realm of individualized learning and the instruction of writing abilities and content creation. IVAs, incorporating generative AI technologies such as ChatGPT and Stable Diffusion, hold the potential to bring about a paradigm shift in educational programs, emphasizing the enhancement of advanced metacognitive capacities rather than the fundamentals of communication. The subsequent recommendations stress the need to cultivate enduring proficiencies and ascertain tailored learning approaches for each learner, which will be indispensable for success in the evolving job market. In this context, prompt …

Exploiting Association Rules Mining To Inform The Use Of Non-Manual Features In Sign Language Processing, Robert G. Smith

Other Resources

In recent years, the use of virtual assistants and voice user interfaces has become a latent part of modern living. Unseen to the user are the various artificial intelligence and natural language processing technologies, the vast datasets, and the linguistic insights that underpin such tools. The technologies supporting them have chiefly targeted widely used spoken languages, leaving sign language users at a disadvantage. One important reason why sign languages are unsupported by such tools is a requirement of the underpinning technologies for a comprehensive description of the language. Sign language processing technologies endeavour to bridge this technology inequality.

Recent approaches …

Commonsense Based Text Mining On Urban Policy, Manish Puri, Aparna S. Varde, Gerard De Melo

School of Computing Faculty Scholarship and Creative Works

Local laws on urban policy, i.e., ordinances directly affect our daily life in various ways (health, business etc.), yet in practice, for many citizens they remain impervious and complex. This article focuses on an approach to make urban policy more accessible and comprehensible to the general public and to government officials, while also addressing pertinent social media postings. Due to the intricacies of the natural language, ranging from complex legalese in ordinances to informal lingo in tweets, it is practical to harness human judgment here. To this end, we mine ordinances and tweets via reasoning based on commonsense knowledge so …

Exploring The Educational Potential Of Ai Generative Art In 3d Design Fundamentals: A Case Study On Prompt Engineering And Creative Workflows, James Hutson, Bryan Robertson

Faculty Scholarship

AI will be increasingly integrated into artistic practices and creative workflows with prompt engineering assuming an increasingly important role in the process. With readilyavailable generative AI, such as Midjourney, DALL-E 2, and Craiyon (formerly DALLE-mini), anyone can seemingly create "art,” prompting questions about the future necessity of art and design education. However, whereas the ease with which content can be created has seen an outcry from the traditional artmaking community, fears over widespread adoption replacing the need for a firm foundation in art and design principles and fundamentals is unfounded. Instead, these tools should be seen and adopted as other …

Predicting Location And Training Effectiveness (Plate), Erik Rolf Bruenner

Master's Theses

Abstract Predicting Location and Training Effectiveness (PLATE)
Erik Bruenner

Physical activity and exercise have been shown to have an enormous impact on many areas of human health and can reduce the risk of many chronic diseases. In order to better understand how exercise may affect the body, current kinesiology studies are designed to track human movements over large intervals of time. Procedures used in these studies provide a way for researchers to quantify an individual’s activity level over time, along with tracking various types of activities that individuals may engage in. Movement data of research subjects is often collected through …

---

Go to article

Automatic Scoring Of Speeded Interpersonal Assessment Center Exercises Via Machine Learning: Initial Psychometric Evidence And Practical Guidelines, Louis Hickman, Christoph N. Herde, Filip Lievens, Louis Tay

Research Collection Lee Kong Chian School Of Business

Assessment center (AC) exercises such as role-plays have established themselves as valuable approaches for obtaining insights into interpersonal behavior, but they are often considered the “Rolls Royce” of personnel assessment due to their high costs. The observation and rating process comprises a substantial part of these costs. In an exploratory case study, we capitalize on recent advances in natural language processing (NLP) by developing NLP-based machine learning (ML) models to investigate the possibility of automatically scoring AC exercises. First, we compared the convergent-related validity and contamination with word count of ML scores based on models that used different NLP methods …

Scanet: Self-Paced Semi-Curricular Attention Network For Non-Homogeneous Image Dehazing, Yu Guo, Yuan Gao, Ryan Wen Liu, Yuxu Lu, Jingxiang Qu, Shengfeng He, Ren Wenqi

Research Collection School Of Computing and Information Systems

The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details. Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner. The crucial challenge of non-homogeneous dehazing is to effectively extract the non-uniform distribution features and reconstruct the details of hazy areas with high quality. In this paper, we propose a novel self-paced semi-curricular attention network, called SCANet, for non-homogeneous image dehazing that focuses on enhancing haze-occluded regions. Our approach consists of an attention generator network and a scene re-construction network. We use the luminance …

Life, Death, And Ai: Exploring Digital Necromancy In Popular Culture—Ethical Considerations, Technological Limitations, And The Pet Cemetery Conundrum, James Hutson, Jay Ratican

Faculty Scholarship

This article explores the rise of generative AI, particularly ChatGPT, and the combination of large language models (LLM) with robotics, exemplified by Ameca the Robot. It addresses the need to study the ethical considerations and potential implications of digital necromancy, which involves using AI to reanimate deceased individuals for various purposes. Reasons for desiring to engage with a disembodied or bodied replica of a person include the preservation of memories, emotional closure, cultural heritage and historical preservation, interacting with idols or influential figures, educational and research purposes, and creative expression and artistic endeavors. As such, this article examines historical examples …

Patient Engagement In A Multimodal Digital Phenotyping Study Of Opioid Use Disorder, Cynthia I. Campbell, Ching-Hua Chen, Sara R. Adams, Asma Asyyed, Ninad R. Athale, Monique B. Does, Saeed Hassanpour, Emily Hichborn, Melanie Jackson-Morris, Nicholas C. Jacobson, Heather K. Jones, David Kotz, Chantal A. Lambert-Harris, Zhiguo Li, Bethany Mcleman, Varun Mishra, Catherine Stanger, Geetha Subramaniam, Weiyi Wu, Christopher Zegers, Lisa A. Marsch

Dartmouth Scholarship

Background: Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD.

Objective: The aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD.

Methods: The study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media …

Utilizing Mixed Graphical Network Models To Explore Parent Psychological Symptoms And Their Centrality To Parent Mental Health In Households With High Child Screen Usage, Piper F. Stacey, Nicholas C. Jacobson, Damien Lekkas

Computer Science Senior Theses

Especially among adolescents, screens are being used more than ever. In conjunction with this trend, mental illness is increasingly prevalent among both adults and children, and parental psychological problems are shown to be associated with children's TV watching, video watching, and gaming (Pulkki-Råback et al., 2022). This study aims to approach parent mental illness symptom by symptom to explore which specific symptoms are most central to parent psychological problems in households where children show high screen time behaviors. We draw from the Adolescent Brain Cognitive Development Study (ABCD Study®), a nationwide sample of 11,875 children aged 10-13 collected by …

Identifying And Sharing Per-And Polyfluoroalkyl Substances Hot-Spot Areas And Exposures In Drinking Water, Sweta Ojha, P. Travis Thompson, Christian D. Powell, Hunter N. B. Moseley, Kelly G. Pennell

Markey Cancer Center Faculty Publications

Exposure to per- and polyfluoroalkyl substances (PFAS) in drinking water is widely recognized as a public health concern. Decision-makers who are responsible for managing PFAS drinking water risks lack the tools to acquire the information they need. In response to this need, we provide a detailed description of a Kentucky dataset that allows decision-makers to visualize potential hot-spot areas and evaluate drinking water systems that may be susceptible to PFAS contamination. The dataset includes information extracted from publicly available sources to create five different maps in ArcGIS Online and highlights potential sources of PFAS contamination in the environment in relation …

A Survey On Security Analysis Of Machine Learning-Oriented Hardware And Software Intellectual Property, Ashraful Tauhid, Lei Xu, Mostafizur Rahman, Emmett Tomai

Computer Science Faculty Publications

Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship (viz., literary and artistic works), emblems, brands, images, etc. This property is intangible since it is pertinent to the human intellect. Therefore, IP entities are indisputably vulnerable to infringements and modifications without the owner’s consent. IP protection regulations have been deployed and are still in practice, including patents, copyrights, contracts, trademarks, trade secrets, etc., to address these challenges. Unfortunately, these protections are insufficient to keep IP entities from being changed or stolen without permission. As for this, some IPs require hardware IP protection mechanisms, and others require software …

Tree-Based Unidirectional Neural Networks For Low-Power Computer Vision, Abhinav Goel, Caleb Tung, Nick Eliopoulos, Amy Wang, Jamie C. Davis, George K. Thiruvathukal, Yung-Hisang Lu

Computer Science: Faculty Publications and Other Works

This article describes the novel Tree-based Unidirectional Neural Network (TRUNK) architecture. This architecture improves computer vision efficiency by using a hierarchy of multiple shallow Convolutional Neural Networks (CNNs), instead of a single very deep CNN. We demonstrate this architecture’s versatility in performing different computer vision tasks efficiently on embedded devices. Across various computer vision tasks, the TRUNK architecture consumes 65% less energy and requires 50% less memory than representative low-power CNN architectures, e.g., MobileNet v2, when deployed on the NVIDIA Jetson Nano.

Stream-Evolving Bot Detection Framework Using Graph-Based And Feature-Based Approaches For Identifying Social Bots On Twitter, Eiman Alothali

Dissertations

This dissertation focuses on the problem of evolving social bots in online social networks, particularly Twitter. Such accounts spread misinformation and inflate social network content to mislead the masses. The main objective of this dissertation is to propose a stream-based evolving bot detection framework (SEBD), which was constructed using both graph- and feature-based models. It was built using Python, a real-time streaming engine (Apache Kafka version 3.2), and our pretrained model (bot multi-view graph attention network (Bot-MGAT)). The feature-based model was used to identify predictive features for bot detection and evaluate the SEBD predictions. The graph-based model was used to …

Digital Transformation In Local Governments: A Case Study Of Abu Dhabi Municipality Transport Department, Alia Sahmi Al Ahbabi

Theses

The new generation is rapidly adapting to the digital era, where government and private services are being transformed into electronic services, commonly known as Eservices. Cities are leveraging digitalization to streamline their business processes and business services. This digitalization has improved service delivery time and quality for individuals. With digitalization, business processes align with technology, enhancing performance and customer satisfaction. However, there are challenges associated with digitalization, particularly people working in various municipality departments who find it challenging to adapt to digitization. Employees may take time to adjust to the new techniques and technologies, which may hamper the actions of …

Generalizing Graph Neural Networks Across Graphs, Time, And Tasks, Zhihao Wen

Dissertations and Theses Collection (Open Access)

Graph-structured data are ubiquitous across numerous real-world contexts, encompassing social networks, commercial graphs, bibliographic networks, and biological systems. Delving into the analysis of these graphs can yield significant understanding pertaining to their corresponding application fields.Graph representation learning offers a potent solution to graph analytics challenges by transforming a graph into a low-dimensional space while preserving its information to the greatest extent possible. This conversion into low-dimensional vectors enables the efficient computation of subsequent graph algorithms. The majority of prior research has concentrated on deriving node representations from a single, static graph. However, numerous real-world situations demand rapid generation of representations …

Tracing The Twenty-Year Evolution Of Developing Ai For Eye Screening In Singapore: A Master Chronology Of Sidrp, Selena+ And Eyris, Steven M. Miller

Research Collection School Of Computing and Information Systems

This working paper is entirely comprised of a timeline table that begins in 2002 and runs through mid-2023. Across these two decades, this timeline traces the evolutionary development of the following:

• The early Singapore R&D efforts to apply software-based image analysis algorithms and methods to analyse eye retina images for diabetic retinopathy and other eye diseases. This was based on a collaboration between the Singapore Eye Research Institute (SERI) and its parent organization, the Singapore National Eye Centre (SNEC), with faculty from the School of Computing at National University of Singapore.
• The establishment and operation of the Singapore Integrated Diabetic …

Freestyle Layout-To-Image Synthesis, Han Xue, Zhiwu Huang, Qianru Sun, Li Song, Wenjun Zhang

Research Collection School Of Computing and Information Systems

Typical layout-to-image synthesis (LIS) models generate images for a close set of semantic classes, e.g., 182 common objects in COCO-Stuff. In this work, we explore the freestyle capability of the model, i.e., how far can it generate unseen semantics (e.g., classes, attributes, and styles) onto a given layout, and call the task Freestyle LIS (FLIS). Thanks to the development of large-scale pre-trained language-image models, a number of discriminative models (e.g., image classification and object detection) trained on limited base classes are empowered with the ability of unseen class prediction. Inspired by this, we opt to leverage large-scale pre-trained text-to-image diffusion …

Strategic Planning For Flexible Agent Availability In Large Taxi Fleets, Rajiv Ranjan Kumar, Pradeep Varakantham, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

In large scale multi-agent systems like taxi fleets, individual agents (taxi drivers) are self interested (maximizing their own profits) and this can introduce inefficiencies in the system. One such inefficiency is with regards to the "required" availability of taxis at different time periods during the day. Since a taxi driver can work for limited number of hours in a day (e.g., 8-10 hours in a city like Singapore), there is a need to optimize the specific hours, so as to maximize individual as well as social welfare. Technically, this corresponds to solving a large scale multi-stage selfish routing game with …

Preference-Aware Delivery Planning For Last-Mile Logistics, Qian Shao, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Optimizing delivery routes for last-mile logistics service is challenging and has attracted the attention of many researchers. These problems are usually modeled and solved as variants of vehicle routing problems (VRPs) with challenging real-world constraints (e.g., time windows, precedence). However, despite many decades of solid research on solving these VRP instances, we still see significant gaps between optimized routes and the routes that are actually preferred by the practitioners. Most of these gaps are due to the difference between what's being optimized, and what the practitioners actually care about, which is hard to be defined exactly in many instances. In …

Livoauth: Liveness Detection In Voiceprint Authentication With Random Challenges And Detection Modes, Rui Zhang, Zheng Yan, Xueru Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Voiceprint authentication provides great convenience to users in many application scenarios. However, it easily suffers from spoofing attacks including speech synthesis, speech conversion, and speech replay. Liveness detection is an effective way to resist these attacks. But existing methods suffer from many disadvantages, such as extra deployment costs due to precise data collection, environmental disturbance, high computational overhead, and operational complexity. A uniform platform that can offer voiceprint authentication as a service (VAaS) over the cloud is also lacked. Hence, it is imperative to design an economic and effective method for liveness detection in voiceprint authentication. In this article, we …

Evading Deepfake Detectors Via Adversarial Statistical Consistency, Yang Hou, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds, more and more DeepFake detection techniques have been proposed. These methods typically rely on detecting statistical differences between natural (i.e., real) and DeepFake-generated images in both spatial and frequency domains. In this work, we propose to explicitly minimize the statistical differences to evade state-of-the-art DeepFake detectors. To this end, we propose a statistical consistency attack (StatAttack) against DeepFake detectors, which contains two main parts. First, we select several statistical-sensitive natural degradations (i.e., exposure, blur, and noise) and add them to the fake images …

Ifundit: Visual Profiling Of Fund Investment Styles, Rong Zhang, Bon Kyung Ku, Yong Wang, Xuanwu Yue, Siyuan Liu, Ke Li, Huamin Qu

Research Collection School Of Computing and Information Systems

Mutual funds are becoming increasingly popular with the emergence of Internet finance. Clear profiling of a fund's investment style is crucial for fund managers to evaluate their investment strategies, and for investors to understand their investment. However, it is challenging to profile a fund's investment style as it requires a comprehensive analysis of complex multi-dimensional temporal data. In addition, different fund managers and investors have different focuses when analysing a fund's investment style. To address the issue, we propose iFUNDit, an interactive visual analytic system for fund investment style analysis. The system decomposes a fund's critical features into performance attributes …

Gnnlens: A Visual Analytics Approach For Prediction Error Diagnosis Of Graph Neural Networks., Zhihua Jin, Yong Wang, Qianwen Wang, Yao Ming, Tengfei Ma, Huamin Qu

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis …

Generative Ai Tools In Art Education: Exploring Prompt Engineering And Iterative Processes For Enhanced Creativity, James Hutson, Peter Cotroneo

Faculty Scholarship

The rapid development and adoption of generative artificial intelligence (AI) tools in the art and design education landscape have introduced both opportunities and challenges. This timely study addresses the need to effectively integrate these tools into the classroom while considering ethical implications and the importance of prompt engineering. By examining the iterative process of refining original ideas through multiple iterations, verbal expansion, and the use of OpenAI’s DALL E2 for generating diverse visual outcomes, researchers gain insights into the potential benefits and pitfalls of these tools in an educational context. Students in the digital at case study were taught prompt …

Automated Approaches To Enable Innovative Civic Applications From Citizen Generated Imagery, Hye Seon Yi

USF Tampa Graduate Theses and Dissertations

Smart governance is an area, that is increasingly becoming important, not only in advanced countries, but all across the globe. Thanks to global scale network connectivity, permeance of smart-devices of various form-factors, and overall improvement in digital literary, we are now seeing "smartness" everywhere, or if not, the general public is expecting the same. Ultimately, the goal of smart governance is to facilitate state-of-the-art technologies to improve citizens’ lives. With the ubiquity of smart phone technologies today, citizens more readily participate in collaboration with public officials for improved quality of life and their communities. By utilizing optimal tools, public officials …

Interpreting Business Strategy And Market Dynamics: A Multi-Method Ai Approach, Lobna Jbeniani

Computer Science Senior Theses

This research paper presents an integrated approach that combines Long Short-Term Memory (LSTM), Q-Learning, Monte Carlo methods, and Text-to-Text Transfer Transformer (T5) to analyze and evaluate the business strategies of public companies. Leveraging a large and diverse dataset sourced from multiple reliable sources, the study examines corporate strategies and their impact on market dynamics. LSTM and Q-Learning are employed to process sequential data, enabling informed decision-making in simulated market environments and providing insights into potential outcomes of different strategies. The Monte Carlo method manages uncertainty, allowing for a comprehensive analysis of risks and rewards associated with specific strategies. T5 interprets …

Investigating English-Language Dialect-Adjusted Models, Samiha Datta

Computer Science Senior Theses

This thesis describes several approaches to better understand how large language models interpret different dialects of the English language. Our goal is to consider multiple contexts of textual data and to analyze how English-language dialects are realized in them, as well as how a variety of machine learning techniques handle these differences. We focus on two genres of text data: news and social media. In the news context, we establish a dataset covering news articles from five countries and four US states and consider language modeling analysis, topic and sentiment distributions, and manual analysis before performing nine experiments and evaluating …

Rapid Assessment Of Fish Freshness For Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy And Fusion-Based Artificial Intelligence, Hossein Kashani Zadeh, Mike Hardy, Mitchell Sueker, Yicong Li, Angelis Tzouchas, Nicholas Mackinnon, Gregory Bearman, Simon A Haughey, Alireza Akhbardeh, Insuck Baek, Chansong Hwang, Jianwei Qin, Amanda M Tabb, Rosalee S Hellberg, Shereen Ismail, Hassan Reza, Fartash Vasefi, Moon Kim, Kouhyar Tavakolian, Christopher T Elliott

Faculty, Staff and Student Publications

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and …