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Building A Unified Data Falsification Threat Landscape For Internet Of Things/Cyberphysical Systems Applications, Shameek Bhattacharjee, Sajal K. Das Mar 2023

Building A Unified Data Falsification Threat Landscape For Internet Of Things/Cyberphysical Systems Applications, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

We Lay Out a Blueprint of a Complete and Parameterized Threat Landscape for Data Falsification/false Data Injection Attacks on Telemetry Data Collected from Internet of Things/cyberphysical Systems Applications under Zero-Trust Assumptions, Helping to Enable Better Validation of Anomaly-Based Attack Detection Methods.

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Of Inventorship And Patent Ownership: Examining The Intersection Between Artificial Intelligence And Patent Law, Cheng Lim Saw, Zheng Wen Samuel Chan Mar 2023

Of Inventorship And Patent Ownership: Examining The Intersection Between Artificial Intelligence And Patent Law, Cheng Lim Saw, Zheng Wen Samuel Chan

Research Collection Yong Pung How School Of Law

Artificial intelligence (“AI”) has garnered much attention in recent years, with capabilities spanning the operation of self-driving cars to the emulation of the great artistic masters of old. The field has now been ostensibly enlarged in light of the professed abilities of AI machines to autonomously generate patentable inventions. This article examines the present state of AI technology and the suitability of existing patent law frameworks in accommodating it. Looking ahead, the authors also offer two recommendations in a bid to anticipate and resolve the challenges that future developments in AI technology might pose to patent law. In particular, the …

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Exploring And Repairing Gender Fairness Violations In Word Embedding-Based Sentiment Analysis Model Through Adversarial Patches, Lin Sze Khoo, Jia Qi Bay, Ming Lee Kimberly Yap, Mei Kuan Lim, Chun Yong Chong, Zhou Yang, David Lo Mar 2023

Exploring And Repairing Gender Fairness Violations In Word Embedding-Based Sentiment Analysis Model Through Adversarial Patches, Lin Sze Khoo, Jia Qi Bay, Ming Lee Kimberly Yap, Mei Kuan Lim, Chun Yong Chong, Zhou Yang, David Lo

Research Collection School Of Computing and Information Systems

With the advancement of sentiment analysis (SA) models and their incorporation into our daily lives, fairness testing on these models is crucial, since unfair decisions can cause discrimination to a large population. Nevertheless, some challenges in fairness testing include the unknown oracle, the difficulty in generating suitable test inputs, and the lack of a reliable way of fixing the issues. To fill in these gaps, BiasRV, a tool based on metamorphic testing (MT), was introduced and succeeded in uncovering fairness issues in a transformer-based model. However, the extent of unfairness in other SA models has not been thoroughly investigated. Our …

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Investment And Risk Management With Online News And Heterogeneous Networks, Meng Kiat Gary Ang, Ee-Peng Lim Mar 2023

Investment And Risk Management With Online News And Heterogeneous Networks, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Stock price movements in financial markets are influenced by large volumes of news from diverse sources on the web, e.g., online news outlets, blogs, social media. Extracting useful information from online news for financial tasks, e.g., forecasting stock returns or risks, is, however, challenging due to the low signal-to-noise ratios of such online information. Assessing the relevance of each news article to the price movements of individual stocks is also difficult, even for human experts. In this article, we propose the Guided Global-Local Attention-based Multimodal Heterogeneous Network (GLAM) model, which comprises novel attention-based mechanisms for multimodal sequential and graph encoding, …

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Improving Rumor Detection By Promoting Information Campaigns With Transformer-Based Generative Adversarial Learning, Jing Ma, Jun Li, Wei Gao, Yang Yang, Kam-Fai Wong Mar 2023

Improving Rumor Detection By Promoting Information Campaigns With Transformer-Based Generative Adversarial Learning, Jing Ma, Jun Li, Wei Gao, Yang Yang, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Rumors can cause devastating consequences to individuals and our society. Analysis shows that the widespread of rumors typically results from deliberate promotion of information aiming to shape the collective public opinions on the concerned event. In this paper, we combat such chaotic phenomenon with a countermeasure by mirroring against how such chaos is created to make rumor detection more robust and effective. Our idea is inspired by adversarial learning method originated from Generative Adversarial Networks (GAN). We propose a GAN-style approach, where a generator is designed to produce uncertain or conflicting voices, further polarizing the original conversational threads to boost …

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Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick Mar 2023

Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick

Theses and Dissertations

This research trains, tests, and analyzes bot and troll classification models using publicly available, open source datasets. Specifically, it applies decision tree, random forest, feed forward neural networks, and long-short term memory neural networks with hyperparameters tuned via designed experiment to five labeled bot datasets created between 2011 and 2020 and one dataset labeling state-sponsored disinformation accounts or trolls. The first three models utilize account profile features, while the last model applies natural language processing techniques, specifically GloVe embedding, to analyze a user’s Tweet history. Results indicate that the random forest model outperforms the other three models with an average …

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A Study Of The Impact Of Data Intelligence On Software Delivery Performance, Yongdong Dong Mar 2023

A Study Of The Impact Of Data Intelligence On Software Delivery Performance, Yongdong Dong

Dissertations and Theses Collection (Open Access)

With the rise of big data and artificial intelligence, data intelligence has gradually become the focus of academia and industry. Data intelligence has two obvious characteristics: big data drive and application scene drive. More and more enterprises extract valuable patterns contained in data with prediction and decision analysis methods and technologies such as large-scale data mining, machine learning and deep learning and use them to improve the management and decision in complex practice, so as to promote changes of new business modes, organizational structures and even business strategies, and improve the operational efficiency of organizations. However, there are few studies …

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Air Force Digital Badges, Jacob Chan Mar 2023

Air Force Digital Badges, Jacob Chan

Theses and Dissertations

The Air Force talent management and force development systems are antiquated. Airmen records are often stored on different Air Force information systems. Existing records sometimes lack granularity and context to recognize Airmen skills. Digital badges are a newer technology utilized by academia and industry to recognize member skills. However, military badging research is sparse and existing studies do not provide sufficient evidence on the value of digital badging to the Air Force. The studies: (1) lack background research on badging; (2) do not provide quantitative data on the effects of badging; and (3) issued badges through commercial entities which may …

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Distributed Reconnaissance Deception Using Software-Defined Networking In A Dynamic Network Environment, Richard Hunter Feustel Mar 2023

Distributed Reconnaissance Deception Using Software-Defined Networking In A Dynamic Network Environment, Richard Hunter Feustel

Theses and Dissertations

This research outlines the design and implementation of a DRDS, which is a RDS distributed across multiple controllers that is capable of deploying reconnaissance deception across multiple switches to mitigate network enumeration by a compromised host. This research outlines the design and development of the DRDS as well as tests its functional abilities and routing performance when compared to a two other network routing solutions: a legacy network solution and centralized ONOS controller scheme deploying layer 2 forwarding. The functional tests proved the system can properly route traffic across 100% of the tested scenarios carrying traffic that includes IP, ARP, …

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The Electromagnetic Bayonet: Development Of A Scientific Computing Method For Aperture Antenna Optimization, Michael P. Ingold Mar 2023

The Electromagnetic Bayonet: Development Of A Scientific Computing Method For Aperture Antenna Optimization, Michael P. Ingold

Theses and Dissertations

The quiet zone of a radar range is the region over which a transmitted EM field approximates a uniform plane wave to within some finite error tolerance. Any target to be measured must physically fit within this quiet zone to prevent excess measurement error. Compact radar ranges offer significant operational advantages for performing RCS measurements but their quiet zone sizes are constrained by space limitations. In this work, a scientific computing approach is used to investigate whether equivalent-current transmitters can be designed that generate larger quiet zones than a conventional version at short range. A time-domain near-field solver, JefimenkoModels, was …

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Spectral Material Classification Of Orbital Objects - Applying Machine Learning To Visible And Near-Infrared Spectral Scenes, Stephen M. Stumpf Mar 2023

Spectral Material Classification Of Orbital Objects - Applying Machine Learning To Visible And Near-Infrared Spectral Scenes, Stephen M. Stumpf

Theses and Dissertations

MSI and HSI techniques allow users to determine the material composition of an object at range. To avoid labor-intensive manual classification, ML is used to determine the most likely material contained in a given pixel of a target image. Previous work primarily focuses on terrestrial applications; this paper extends these techniques into the low-illumination space situational awareness domain, which is of critical importance to national security. HSI datacubes are preprocessed with RL deconvolution as a means of reducing the effects of the optical PSF; then, statistical ML techniques, including k-NN, LDA, QDA, and SVMs are implemented as means of assigning …

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Responses To Sad Emotion In Autistic And Normal Developing Children: Is There A Difference?, Mohamed Basel Almourad, Emad Bataineh, Zelal Wattar Mar 2023

Responses To Sad Emotion In Autistic And Normal Developing Children: Is There A Difference?, Mohamed Basel Almourad, Emad Bataineh, Zelal Wattar

All Works

This paper describes how the gazing pattern differ between the responses of Normal Developing (ND) and Autistic (AP) children to sad emotion. We employed an eye tracking technology to collect and track the participants’ eye movements by showing a dynamic stimulus (video) that showed a gradual transition from pale emotions to melancholy facial expressions in both female and male faces. The location of the child's gaze in the stimulus was the focus of our data analysis. We deduced that there was a distinction between the two groups based on this. ND children predominantly concentrated on the eyes and mouth region …

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Concept-Oriented Transformers For Visual Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw Mar 2023

Concept-Oriented Transformers For Visual Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In the richly multimedia Web, detecting sentiment signals expressed in images would support multiple applications, e.g., measuring customer satisfaction from online reviews, analyzing trends and opinions from social media. Given an image, visual sentiment analysis aims at recognizing positive or negative sentiment, and occasionally neutral sentiment as well. A nascent yet promising direction is Transformer-based models applied to image data, whereby Vision Transformer (ViT) establishes remarkable performance on largescale vision benchmarks. In addition to investigating the fitness of ViT for visual sentiment analysis, we further incorporate concept orientation into the self-attention mechanism, which is the core component of Transformer. The …

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Proactive Conversational Agents, Lizi Liao, Grace Hui Yang, Chirag Shah Mar 2023

Proactive Conversational Agents, Lizi Liao, Grace Hui Yang, Chirag Shah

Research Collection School Of Computing and Information Systems

Conversational agents, or commonly known as dialogue systems, have gained escalating popularity in recent years. Their widespread applications support conversational interactions with users and accomplishing various tasks as personal assistants. However, one key weakness in existing conversational agents is that they only learn to passively answer user queries via training on pre-collected and manually-labeled data. Such passiveness makes the interaction modeling and system-building process relatively easier, but it largely hinders the possibility of being human-like hence lowering the user engagement level. In this tutorial, we introduce and discuss methods to equip conversational agents with the ability to interact with end …

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Dminer: Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu Mar 2023

Dminer: Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

Dashboards, which comprise multiple views on a single display, help analyze and communicate multiple perspectives of data simultaneously. However, creating effective and elegant dashboards is challenging since it requires careful and logical arrangement and coordination of multiple visualizations. To solve the problem, we propose a data-driven approach for mining design rules from dashboards and automating dashboard organization. Specifically, we focus on two prominent aspects of the organization: arrangement , which describes the position, size, and layout of each view in the display space; and coordination , which indicates the interaction between pairwise views. We build a new dataset containing 854 …

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Effective Graph Kernels For Evolving Functional Brain Networks, Xinlei Wang, Jinyi Chen, Bing Tian Dai, Junchang Xin, Yu Gu, Ge Yu Mar 2023

Effective Graph Kernels For Evolving Functional Brain Networks, Xinlei Wang, Jinyi Chen, Bing Tian Dai, Junchang Xin, Yu Gu, Ge Yu

Research Collection School Of Computing and Information Systems

The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel …

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The Use Of Artificial Intelligence To Detect Students Sentiments And Emotions In Gross Anatomy Reflections, Krzysztof J. Rechowicz, Carrie A. Elzie Mar 2023

The Use Of Artificial Intelligence To Detect Students Sentiments And Emotions In Gross Anatomy Reflections, Krzysztof J. Rechowicz, Carrie A. Elzie

VMASC Publications

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to evaluating student writings are resource heavy and timely. To overcome this, natural language processing, a nascent field of artificial intelligence that uses computational techniques for the analysis and synthesis of text, was used to compare health professional students' reflections on the importance of various regions of the body to their own lives and those of the anatomical donor dissected. A total of 1365 anonymous writings (677 about a donor, 688 about self) were collected from 132 students. Binary and trinary …

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Graph Analysis On Social Networks, Shen Lu Feb 2023

Graph Analysis On Social Networks, Shen Lu

USF Tampa Graduate Theses and Dissertations

With the development of transportation network, social network, and communication network, there are many applications in streaming data. For example, traffic congestion happens between the origin and destination of daily trips. Traffic analysis can help plan the trips so that traffic congestion can be avoided. Social network and communication network represent the behaviors of the entire population. People build connections based on their hobbies, daily activities, photos, videos, simple messages, and even anonymous web surfing. All of these can be turned into commercial use, such as product marketing, business network building, and technology trending. Data science is about how to …

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Neutrosophic Critic Mcdm Methodology For Ranking Factors And Needs Of Customers In Product's Target Demographic In Virtual Reality Metaverse, Ahmed Sleem, Nehal Mostafa, Ibrahim Elhenawy Feb 2023

Neutrosophic Critic Mcdm Methodology For Ranking Factors And Needs Of Customers In Product's Target Demographic In Virtual Reality Metaverse, Ahmed Sleem, Nehal Mostafa, Ibrahim Elhenawy

Neutrosophic Systems with Applications

Affective design has come to place a premium on customer-centric creativity, which ultimately results in the creation of a product tailored to the requirements of a certain demographic. Designing a fresh and original item that appeals to clients remains challenging, despite the abundance of literature on customer-centric creativity and impact product design. This is so because optimizing technological and aesthetical design aspects and variables for a group of consumers is highly complicated, and for the same reasons that it is hard to know a customer's choice, enable the product's functioning, etc. One of the most important parts of creating cutting-edge …

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Comparing Boys’ And Girls’ Attitudes Toward Computer Science, Danielle Scott, Amiee Zou, Sharin Rawhiya Jacob, Debra Richardson, Mark Warschauer Feb 2023

Comparing Boys’ And Girls’ Attitudes Toward Computer Science, Danielle Scott, Amiee Zou, Sharin Rawhiya Jacob, Debra Richardson, Mark Warschauer

Journal of Computer Science Integration

Women are severely underrepresented in computer science (CS) degrees and careers. While student interest is a key predictor of success, little is known about how elementary students from underserved groups, such as girls, develop their interest in CS. To address this issue, we examined the differences in attitudes between upper elementary girls and boys towards CS after participating in a yearlong, inquiry-based CS curriculum designed for diverse learners. Pre-and-post surveys on students’ attitudes towards CS (n = 108) were delivered before and after student participation in the curriculum. Results from the survey showed only two demonstrated significant differences between boys …

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Generative Ai As A Tool For Environmental Health Research Translation., Lauren B. Anderson, Dhiraj Kanneganti, Mary Bentley Houk, Rochelle H. Holm, Ted Smith Feb 2023

Generative Ai As A Tool For Environmental Health Research Translation., Lauren B. Anderson, Dhiraj Kanneganti, Mary Bentley Houk, Rochelle H. Holm, Ted Smith

Faculty and Staff Scholarship

One valuable application for generative artificial intelligence (AI) is summarizing research studies for non-academic readers. We submitted five articles to Chat Generative Pre-trained Transformer (ChatGPT) for summarization, and asked the article's author to rate the summaries. Higher ratings were assigned to more insight-oriented activities, such as the production of eighth-grade reading level summaries, and summaries highlighting the most important findings and real-world applications. The general summary request was rated lower. For the field of environmental health science, no-cost AI technology such as ChatGPT holds the promise to improve research translation, but it must continue to be improved (or improve itself) …

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Classifying Emotions And Toxicity On Audio To Text Signals From Videos On Youtube, Connice Trimmingham Feb 2023

Classifying Emotions And Toxicity On Audio To Text Signals From Videos On Youtube, Connice Trimmingham

Theses and Dissertations

Recent technological advancements have pushed humans past the boundaries of a computer screen. They have facilitated human-computer-interaction that were once inconceivable including emotional and audio engagement. Users are now able to utilize technology in more human-like ways and instantaneously transfer depth of opinions and emotions. This global prominence of modern technology, specifically with social media, has spawned a new norm, in which it is now a reasonable expectation of encountering high online toxicity on social media platforms. In addition, the complexities of human emotions make it challenging to determine whether toxic comments trigger certain emotions or vice versa. This study …

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The Rising Trend Of Metaverse In Education: Challenges, Opportunities, And Ethical Considerations, Sanaa Kaddoura, Fatima Al Husseiny Feb 2023

The Rising Trend Of Metaverse In Education: Challenges, Opportunities, And Ethical Considerations, Sanaa Kaddoura, Fatima Al Husseiny

All Works

Metaverse is invading the educational sector and will change human-computer interaction techniques. Prominent technology executives are developing novel ways to turn the Metaverse into a learning environment, considering the rapid growth of technology. Since the COVID-19 outbreak, people have grown accustomed to teleworking, telemedicine, and numerous other forms of distance interaction. Recently, the Metaverse has been the focus of many educators. With Facebook’s statement that it was rebranding and promoting itself as Meta, this field saw a surge in interest in the areas of computer science and education. There is a literature gap in studying the Metaverse’s role in education. …

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Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle Feb 2023

Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

Recently there has been high demand for the representation learning of graphs. Graphs are a complex data structure that contains both topology and features. There are first several domains for graphs, such as infectious disease contact tracing and social media network communications interactions. The literature describes several methods developed that work to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor’s features to create the embedding. Another method, Walklets, takes advantage of the topological information stored in a graph …

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A Proposed Meta-Reality Immersive Development Pipeline: Generative Ai Models And Extended Reality (Xr) Content For The Metaverse, Jay Ratican, James Hutson, Andrew Wright Feb 2023

A Proposed Meta-Reality Immersive Development Pipeline: Generative Ai Models And Extended Reality (Xr) Content For The Metaverse, Jay Ratican, James Hutson, Andrew Wright

Faculty Scholarship

The realization of an interoperable and scalable virtual platform, currently known as the “metaverse,” is inevitable, but many technological challenges need to be overcome first. With the metaverse still in a nascent phase, research currently indicates that building a new 3D social environment capable of interoperable avatars and digital transactions will represent most of the initial investment in time and capital. The return on investment, however, is worth the financial risk for firms like Meta, Google, and Apple. While the current virtual space of the metaverse is worth $6.30 billion, that is expected to grow to $84.09 billion by the …

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Human Saliency-Driven Patch-Based Matching For Interpretable Post-Mortem Iris Recognition, Aidan Boyd, Daniel Moreira, Andrey Kuehlkamp, Kevin Bowyer, Adam Czajka Feb 2023

Human Saliency-Driven Patch-Based Matching For Interpretable Post-Mortem Iris Recognition, Aidan Boyd, Daniel Moreira, Andrey Kuehlkamp, Kevin Bowyer, Adam Czajka

Computer Science: Faculty Publications and Other Works

Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in their efforts to identify deceased persons. As a machine learning-based technique in a predominantly human-controlled task, forensic recognition serves as “back-up” to human expertise in the task of post-mortem identification. As such, the machine learning model must be (a) interpretable, and (b) post-mortem-specific, to account for changes in decaying eye tissue. In this work, we propose a method that satisfies both requirements, and that approaches the creation of a post-mortem-specific feature extractor in …

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Motif Mining: Finding And Summarizing Remixed Image Content, William Theisen, Daniel Gonzalez Cedre, Zachariah Carmichael, Daniel Moreira, Tim Weninger, Walter Scheirer Feb 2023

Motif Mining: Finding And Summarizing Remixed Image Content, William Theisen, Daniel Gonzalez Cedre, Zachariah Carmichael, Daniel Moreira, Tim Weninger, Walter Scheirer

Computer Science: Faculty Publications and Other Works

On the Internet, images are no longer static; they have become dynamic content. Thanks to the availability of smartphones with cameras and easy-to-use editing software, images can be remixed (i.e., redacted, edited, and re-combined with other content) on-the-fly, allowing a world-wide audience to repeat the process many times. From digital art to memes, the evolution of images through time is now an important topic of study for digital humanists, social scientists, and media forensics specialists. However, because typical data sets in computer vision are composed of static content, there has been limited development of automated algorithms for analyzing remixed content. …

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Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox Feb 2023

Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox

Faculty Publications

Emotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. This research found a statistical significance in the performance of …

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Multimodal Image Retrieval Combining Image And Text, Md Imran Sarker Feb 2023

Multimodal Image Retrieval Combining Image And Text, Md Imran Sarker

Theses and Dissertations

Retrieving visual or textual similarities from an image query and vice versa has drawn much interest in computer vision. With the growth of the E-commerce marketplace, image retrieval provides excellent competitive opportunities for vendors and customers through a robust recommendation system. Feature integration has always been an essential task for multimodal-based image retrieval approaches. However, different existing matching techniques have been used separately for visual and text similarity. Still, researchers are looking for new methods when it comes to multimodal Image Retrieval. In this paper, I study the image retrieval task, where the input query is an image plus text …

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Gamesmanship In Modern Discovery Tech, Neel Guha, Peter Henderson, Diego A. Zambrano Feb 2023

Gamesmanship In Modern Discovery Tech, Neel Guha, Peter Henderson, Diego A. Zambrano

Faculty Scholarship

This chapter explores the potential for gamesmanship in technology-assisted discovery. Attorneys have long embraced gamesmanship strategies in analog discovery, producing reams of irrelevant documents, delaying depositions, or interpreting requests in a hyper-technical manner. The new question, however, is whether machine learning technologies can transform gaming strategies. By now it is well known that technologies have reinvented the practice of civil litigation and, specifically, the extensive search for relevant documents in complex cases. Many sophisticated litigants use machine learning algorithms – under the umbrella of “Technology Assisted Review” (TAR) – to simplify the identification and production of relevant documents in discovery. …

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