Digital Commons Network^™

Ur-62 Deep Learning Approach To Network Anomaly Detection, Rene P Lisasi, Jamia M Jackson

C-Day Computing Showcase

A model of network anomaly detection capable of detecting a multitude of network attacks. This model is based on the hypothesis that by studying a system’s network records for irregular patterns during system usage, network anomalies can be identified. This model contains information about the type of attacks and metrics. This model is to be used in any type of distributed environment. The general purpose of this model is to detect when an attack is or has happened using deep learning techniques to optimize the training speed, accuracy and robustness of attack detection. This is done to stop the epidemic …

Semantic Segmentation Of Point Cloud Sequences Using Point Transformer V3, Marion Sisk

Master's Theses

Semantic segmentation of point clouds is a basic step for many autonomous systems including automobiles. In autonomous driving systems, LiDAR sensors are frequently used to produce point cloud sequences that allow the system to perceive the environment and navigate safely. Modern machine learning techniques for segmentation have predominately focused on single-scan segmentation, however sequence segmentation has often proven to perform better on common segmentation metrics. Using the popular Semantic KITTI dataset, we show that by providing point cloud sequences to a segmentation pipeline based on Point Transformer v3, we increase the segmentation performance between seven and fifteen percent when compared …

A System Of Communication Between Two Computers Using Novel Frequency Shift Keying Techniques, Jared Reyes

Honors Thesis

Frequency shift keying (FSK) is an old but powerful form of modulation that powered much of the early modems of the 1960’s, and the author felt inspired to make his own version of audio binary FSK modulation. He researched the general history and legacy of the Bell 103, a modem using FSK that defined telecommunication for the next few decades. Using research of the most common English characters of recent emails to determine which English characters should have the shortest bit length, a novel character encoding standard was created using variable bit rate. In addition, he has created a modulation …

Designing For Deployable, Secure, And Generic Machine Learning Systems, Li-Yun Wang

Dissertations and Theses

Machine learning systems have catalyzed numerous image-centric applications owing to the significant achievements of machine learning algorithms and models. While these systems have showcased the efficacy of machine learning models, certain challenges persist, such as machine learning system design and security vulnerabilities inherent in deep neural networks. Moreover, the deployment of deep neural network models remains a significant hurdle. This dissertation introduces a multimedia prototyping framework tailored for visual analytical applications, improving the reusability of video analysis software tools with minimal performance overhead. Furthermore, we present novel image-processing techniques designed to bolster the robustness of deep neural networks and propose …

Creating With Generative Ai, Gudrun Hall, Zeitun Abdinoor

Student Academic Conference

CustomGPT is a framework that utilizes advanced natural language processing techniques to generate customized versions of ChatGPT, adapting conversational agents to various contexts, domains, or individual preferences. This project demonstrates how ChatGPT serves as a powerful tool for creating chatbots with actionable features and dynamic mobile games. By showcasing its versatility in empowering users to innovate and drive creative content generation, we highlight ChatGPT's potential to shape the future of interactive mobile experiences.

Drone-Based Bug Detection In Orchards With Nets: A Novel Orienteering Approach, Francesco Betti Sorbelli, Federico Coró, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti

Computer Science Faculty Research & Creative Works

The Use of Drones for Collecting Information and Detecting Bugs in Orchards Covered by Nets is a Challenging Problem. the Nets Help in Reducing Pest Damage, But They Also Constrain the Drone's Flight Path, Making It Longer and More Complex. to Address This Issue, We Model the Orchard as an Aisle-Graph, a Regular Data Structure that Represents Consecutive Aisles Where Trees Are Arranged in Straight Lines. the Drone Flies Close to the Trees and Takes Pictures at Specific Positions for Monitoring the Presence of Bugs, But its Energy is Limited, So It Can Only Visit a Subset of Positions. to …

Maximizing Data Optimization: Analysis, Retention, And Website Accessibility, Selena Cade

Masters Projects

As a graduate assistant at Grand Valley, there is significant involvement with a program that assists middle and high school students with learning about college. As a preface, the information presented in this document will remain anonymous and confidential for the program's benefit.

Students receive a campus tour, eat at the dining halls, and obtain information about college. Before students arrive, a pre-visit survey is sent to gauge their college knowledge. After the visit, a post-visit survey with the same questions is sent out to determine if they gained more knowledge. This data has rarely been viewed in the past …

Scalable Relational Analysis Via Relational Bound Propagation, Clay Stevens, Hamid Bagheri

School of Computing: Faculty Publications

Bounded formal analysis techniques (such as bounded model checking) are incredibly powerful tools for today’s software engineers. However, such techniques often suffer from scalability challenges when applied to large-scale, real-world systems. It can be very difficult to ensure the bounds are set properly, which can have a profound impact on the performance and scalability of any bounded formal analysis. In this paper, we propose a novel approach—relational bound propagation—which leverages the semantics of the underlying relational logic formula encoded by the specification to automatically tighten the bounds for any relational specification. Our approach applies two sets of semantic rules to …

Social Order In Age Of Artificial Intelligence, Yongnian Zheng

Bulletin of Chinese Academy of Sciences (Chinese Version)

While the rapid development of artificial intelligence is empowering society, it is also posing serious threats to the continuation of the traditional social order. The "de-intellectualization" function of AI has given rise to the phenomenon of "artificial ignorance", referring to the self-inflicted intellectual harm caused by the uncontrolled and excessive misuse of AI-related tools, thus causing a profound impact on the social order. The technical structural characteristics and governance structural characteristics of AI contributed to the emergence of the phenomenon of "artificial ignorance". The technical structural characteristics of AI can be summarized as highly concentrated capabilities, highly centralized control, highly …

Sign-A-Mander: A Mobile App That Enhances Asl Learning With Computer Vision, Sandrine Adap

Honors Theses

Several machine learning researchers have developed algorithms recognizing American Sign Language (ASL), but few have applied the algorithms to real-world situations, such as with portable ASL learning applications. This project develops a beta version of a mobile application designed to allow beginner ASL learners to practice basic ASL vocabulary and receive feedback about their signing accuracy. Building on Dongxu Li et al.’s I3D sign language recognition algorithm and 2000-word dataset, the app seeks to determine whether the I3D algorithm can sufficiently recognize a user’s motions when recorded from a mobile device and accurately classify whether or not the user signed …

A Research Proposal Investigating The Geopolitical And Economic Impact Resulting From The Development Of Artificial General Intelligence (Agi), Tobia Donadon, Kristen Migliano

Student Publications and Presentations

Artificial General Intelligence (AGI) represents the next step in the development of AI, a huge step in the quest for artificial intelligence. AGI aims to create machines capable of understanding, learning, and applying knowledge across various tasks, much like humans.

This research proposal focuses on advancements and potential effects, with an objective to bridge the gap between narrow AI applications and the broader cognitive capabilities of AGI. This study explores the transformative potential of AGI while addressing the societal impacts. The research looks into the relationship between the development of AGI and geopolitical affairs.

Drawing on recent discoveries in mathematical …

Use Of Deep Learning In Content-Based Image Retrieval (Cbir), Angelina Das

ATU Scholars Symposium

In the world of computer vision and data retrieval, a crucial task is finding images within a database based on their visual content. This is known as content-based image retrieval (CBIR). As the number of digital images explodes across fields like online shopping, healthcare, and social media, the need for powerful and precise CBIR systems becomes ever more critical. Early CBIR methods depended on features crafted by hand, like color distributions, texture descriptions, and shape characteristics. However, these techniques often have difficulty capturing the true meaning of an image and might not handle very large datasets effectively. With the rise …

Anomaly Detection With Spiking Neural Networks (Snn), Shruti Bhandari, Vyshnavi Gogineni

ATU Scholars Symposium

Abstract:

Anomaly detection, the identification of rare or unusual patterns that deviate from normal behavior, is a fundamental task with wide-ranging applications across various domains. Traditional machine learning techniques often struggle to effectively capture the complex temporal dynamics present in real-world data streams. Spiking Neural Networks (SNNs), inspired by the spiking nature of biological neurons, offer a promising approach by inherently modeling temporal information through precise spike timing. In this study, we investigate the use of Spiking Neural Networks (SNNs) for detecting anomalies or unusual patterns in data. We propose an SNN model that can learn what constitutes normal …

Innovating Inventory And Alert Systems With Object Tracking, Juan Harmse, Esther Peden

Campus Research Month

Security system users require safeguarding inventory from potential theft while reducing manual tracking of physical objects. Our contribution harnesses the power of artificial intelligence and computer vision with YOLO to automate the process of tracking inventory items. The system sends alerts to the inventory manager when it detects particular events. Our approach was evaluated with KernProf profiling, interference, and orientation tests. The results were overall positive in these testing areas.

Providing Beginners With Interactive Exploration Of Error Messages In Clojure, John Walbran, Elena Machkasova

Undergraduate Research Symposium 2024

Programmers are imperfect, and will often make mistakes when programming and create a program error, for example, attempting to divide by zero. When a computer tries to run a program with an error, the program will halt and present the details of the error to the user in the form of an error message. These error messages are often very jargon-heavy, and are not designed to be palatable to a novice programmer. This creates significant friction for new programmers trying to learn programming languages. This work is a part of an ongoing project (called Babel) led by Elena Machkasova in …

A Holistic And Collaborative Behavioral Health Detection Framework Using Sensitive Police Narratives, Martin Keagan Wynne Brown

Dissertations

Identifying behavioral health is paramount for law enforcement officers to provide appropriate follow-up community care. In the current practice, law enforcement offices manually identify these behavioral health cases to allow the designation of the relevant follow-up resources. Police reports generated by officers' response to 911 calls remain an untapped resource for identifying such incidents. Therefore, we advocate for the incorporation of manual annotations from experts, natural language processing (NLP), active learning, advanced machine learning, and ensemble techniques to detect behavioral health cases within police reports. In this dissertation, we develop tools and frameworks to automatically detect behavioral health cases from …

Kalamazoo Nature Center Mobile Application, Jacob Tebben

Honors Theses

This project aimed to address the challenge of enhancing visitor engagement and information dissemination at the Kalamazoo Nature Center (KNC) through the development of an integrated mobile and desktop application system. This initiative arose due to the limitations posed by traditional mobile applications which often become outdated and need to be updated by a dedicated software team. This project was designed for any user of the KNC desktop app to be able to update content on the mobile app, without the need of a dedicated software team.

The mobile application was designed for visitor use, enabling them to access up-to-date …

The Vulnerabilities Of Artificial Intelligence Models And Potential Defenses, Felix Iov

Cybersecurity Undergraduate Research Showcase

The rapid integration of artificial intelligence (AI) into various commercial products has raised concerns about the security risks posed by adversarial attacks. These attacks manipulate input data to disrupt the functioning of AI models, potentially leading to severe consequences such as self-driving car crashes, financial losses, or data breaches. We will explore neural networks, their weaknesses, and potential defenses. We will discuss adversarial attacks including data poisoning, backdoor attacks, evasion attacks, and prompt injection. Then, we will explore defense strategies such as data protection, input sanitization, and adversarial training. By understanding how adversarial attacks work and the defenses against them, …

Dynamic Path Planning For Mobile Robot Based On Rrt* And Dynamic Window Approach, Rui Zhang, Li Zhou, Zhengyang Liu

Journal of System Simulation

Abstract: A dynamic path planning method combining RRT* and dynamic window approach(DWA) is proposed to realize the obstacle avoidance of mobile robot in complex environment of dynamic obstacles. Improved RRT* algorithm is used to generate the global optimal safe path based on the known environment information. By eliminating the dangerous nodes generated by RRT* algorithm, the security of global path is ensured. Greedy algorithm is used to remove the redundant nodes in the path to reduce the length of global path. DWA is used to track along the global optimal path planned by the improved RRT* algorithm. When static obstacles …

Comprehensive Question And Answer Generation With Llama 2, Matous Hybl

MS in Computer Science Theses

Since the introduction of transformers, large language models have proven capable in many natural language processing fields. However, existing systems still face challenges in generating high-quality extractive questions. Base models and public chatbots fall short if the question source or quantity are critical. Our contribution is a question and answer generator for generating comprehensive, extractive questions and answers. This approach includes fine-tuning a LLaMA 2 base model for answer extraction (AE) and question generation (QG). We evaluate the resulting system using common automated metrics and a manual evaluation. We find that our system is comparable to the latest research and …

Towards Erasing The Distinction Between The Computational And Syntactic Accounts Of Scientific Theories, Timothy Luft

Theses

One of the main goals of philosophy of science is to give a proper account of scientific theories and their structure. One way that accounts of the structure of scientific theories can be distinguished is by the mathematical or logical structures that they involve. For instance, syntactic accounts of scientific theories hold that theories are axioms in a logical framework, whereas semantic accounts are more liberal in the range of mathematical and logical structures they take as pertinent to the structure of scientific theories. Paul Thagard (1988) offers a computational account of scientific theories, which holds that theories are complex …

Classifying Patient Handling Techniques To Reduce Risk Of Musculoskeletal Injury In Nursing Students, Giovanni Battaglia, Emanuel Sanchez

23rd Annual A. Paul and Carol C. Schaap Celebration of Undergraduate Research and Creative Activity (2024)

Nurses suffer musculoskeletal injuries at a higher proportion than the general population due to physical strain and poor posture during patient-handling tasks; studies show that back injuries occur at a rate of 28.9 cases per 10,000 registered nurses. The purpose of this study is to apply multivariate time series classifiers (MTSCs) to classify six patient-handling tasks and the quality of subject posture (good, poor, or neutral) during these tasks. Manikins weighing 44 lbs, 66 lbs, and 110 lbs simulated patients. In this proof-of-concept study the XCM, ResNet, and MiniRocket MTSCs were trained with data collected from four non-nursing students using …

Individualized Learning As An Ai Tool: A Technical Report, Petsimnan Blessing Dayit, Kasen Holt, Nuala Roper

Student Works

The purpose of the report’s research is to test and analyze whether Artificial Intelligence (AI) platforms can be used as beneficial tools for individualized learning at Lipscomb University without violating the Academic Integrity Policy. The methods section evaluates AI on the scopes of accuracy, analytical thinking, and adaptability. The results demonstrated how each platform responded to the prompts within the lines of the scope. The answers they gave were accurate, detailed, and contained various adaptations to make explanations clearer for the user. The team concluded that AI can be used at Lipscomb as a beneficial tool for students in their …

Immersive Japanese Language Learning Web Application Using Spaced Repetition, Active Recall, And An Artificial Intelligent Conversational Chat Agent Both In Voice And In Text, Marc Butler

MS in Computer Science Project Reports

In the last two decades various human language learning applications, spaced repetition software, online dictionaries, and artificial intelligent chat agents have been developed. However, there is no solution to cohesively combine these technologies into a comprehensive language learning application including skills such as speaking, typing, listening, and reading. Our contribution is to provide an immersive language learning web application to the end user which combines spaced repetition, a study technique used to review information at systematic intervals, and active recall, the process of purposely retrieving information from memory during a review session, with an artificial intelligent conversational chat agent both …

Mathematically Rigorous Deep Learning Paradigms For Data-Driven Scientific Modeling, Owen Nicholas Davis

Mathematics & Statistics ETDs

This dissertation explores the crucial role of data-driven modeling in science and engineering, with a focus on developing surrogate models to accelerate large-scale computational tasks, aiding in both outer-loop functions like uncertainty quantification and expensive inner-loop tasks within broader computational frameworks. Challenges arise with increased problem dimension and sparse, noisy training data, particularly significant when constructing surrogates for very expensive computational models where acquiring sufficient high-fidelity training data is unfeasible. In such scenarios, training surrogates from an ensemble of multifidelity information sources of varying accuracy and cost becomes essential. We emphasize neural network-based modeling paradigms, which are flexible in integrating …

Context-Aware Affective Behavior Modeling And Analytics, Md Taufeeq Uddin

USF Tampa Graduate Theses and Dissertations

Affective computing (AC) is a sub-domain of AI that has the potential to assist people by assessing mental states and making appropriate recommendations to patients, loved ones, caregivers, and domain experts. Humans usually produce an enormous amount of data (such as face videos) every day. One of the major challenges for affective computer vision is to efficiently deal with high volumes of data to facilitate automated model development. To cope with this challenge, we developed computer vision algorithms that measure the expressivity of the human face from video data. More precisely, the developed algorithms can map complex affect information from …

Assessing The Promise And Pitfalls Of Chatgpt For Automated Cs1-Driven Code Generation, Fawad Khan, Max Ramsdell

Student Research Symposium

Definition and Overview of Large Language Models:

• Large Language Models (LLMs) are advanced AI algorithms designed to understand, generate, and interact with human language at a vast scale.

The Evolution of LLMs:

• The development of LLMs has progressed
• 1960 - Basic LLM - basic predictive text functions
• 2017- Transformers - Core model powering ChatGPT introduced
• 2022 - ChatGPT context-aware systems

Game 'Make 24', Seunghyeok Jang

SACAD: Scholarly Activities

• Basic numerical skills are a must-have in today’s world. However, children are not picking up the four basic numerical skills adequately.

• To improve their mathematical skills, they need a way to learn the numerical skills easily.

• "Make 24" is a game for young children who are having a difficult time with basic numerical operations. The game helps children improve their numerical skills by playing this game.

Comparing Cognitive Theories Of Learning Transfer To Advance Cybersecurity Instruction, Assessment, And Testing, Daniel T. Hickey Ph.D., Ronald J. Kantor

Journal of Cybersecurity Education, Research and Practice

The cybersecurity threat landscape evolves quickly, continually, and consequentially. This means that the transfer of cybersecurity learning is crucial. We compared how different recognized “cognitive” transfer theories might help explain and synergize three aspects of cybersecurity education. These include teaching and training in diverse settings, assessing learning formatively & summatively, and testing & measuring achievement, proficiency, & readiness. We excluded newer sociocultural theories and their implications for inclusion as we explore those theories elsewhere. We first summarized the history of cybersecurity education and proficiency standards considering transfer theories. We then explored each theory and reviewed the most relevant cybersecurity education …

Performing Distributed Quantum Calculations In A Multi-Cloud Architecture Secured By The Quantum Key Distribution Protocol, Jose Luis Lo Huang, Vincent C. Emeakaroha

Department of Computer Science Publications

Quantum computing (QC) is an emerging area that yearly improves and develops more advances in the number of qubits and the available infrastructure for public users. Nowadays, the main cloud service providers (CSP) are implementing different mechanisms to support access to their quantum computers, which can be used to perform small experiments, test hybrid algorithms and prove quantum theories. Recent research work have discussed the low capacity of using quantum computers in a single CSP to perform quantum computation that are needed to solve different experiments for real world problems. Thus, there are needs for computing powers in the form …