Digital Commons Network^™

Automated Cinematographer For Vr Viewing Experiences, Zihan Wu

Dartmouth College Master’s Theses

As the virtual reality (VR) industry continues to evolve, the question of how to effectively capture VR experiences for an audience remains a challenge. The predominant method of showcasing VR applications through first-person recordings lacks cinematic interest, failing to capture other viewpoints and the essence of the moment. Meanwhile, manually setting up cameras and editing videos requires technical expertise on behalf of the user. In this paper, we propose the use of machine learning (ML) to automatically select the most compelling predefined viewpoint in a VR environment, at any given moment. Our models, trained on actor motion and voice volume, …

An Empirical Study On The Efficacy Of Llm-Powered Chatbots In Basic Information Retrieval Tasks, Naja Faysal

Electronic Theses, Projects, and Dissertations

The rise of conversational user interfaces (CUIs) powered by large language models (LLMs) is transforming human-computer interaction. This study evaluates the efficacy of LLM-powered chatbots, trained on website data, compared to browsing websites for finding information about organizations across diverse sectors. A within-subjects experiment with 165 participants was conducted, involving similar information retrieval (IR) tasks using both websites (GUIs) and chatbots (CUIs). The research questions are: (Q1) Which interface helps users find information faster: LLM chatbots or websites? (Q2) Which interface helps users find more accurate information: LLM chatbots or websites?. The findings are: (Q1) Participants found information significantly faster …

Code For Care: Hypertension Prediction In Women Aged 18-39 Years, Kruti Sheth

Electronic Theses, Projects, and Dissertations

The longstanding prevalence of hypertension, often undiagnosed, poses significant risks of severe chronic and cardiovascular complications if left untreated. This study investigated the causes and underlying risks of hypertension in females aged between 18-39 years. The research questions were: (Q1.) What factors affect the occurrence of hypertension in females aged 18-39 years? (Q2.) What machine learning algorithms are suited for effectively predicting hypertension? (Q3.) How can SHAP values be leveraged to analyze the factors from model outputs? The findings are: (Q1.) Performing Feature selection using binary classification Logistic regression algorithm reveals an array of 30 most influential factors at an …

Leveraging Blockchain Technology To Revamp The Vehicle Electrification Journey: Perspectives Of Accountability And Economic Circularity, Eva Escobar Brown

Electronic Theses, Projects, and Dissertations

The automotive industry is undergoing a significant transition accelerated by global emission regulations for a phase out of internal combustion engines (ICEs) and a transition toward the adoption of electric vehicles (EVs). While regulatory measures and incentivized adoption for EVs presents opportunities for reducing emissions and promoting sustainability, it also poses complex challenges. The EV industry faces potential production challenges, particularly in the sourcing, manufacturing, and lifecycle management of critical minerals and raw materials for electric vehicle batteries (EVBs). With a heavy reliance on a steady and diversified supply of critical minerals such as lithium, cobalt and rare earth elements, …

Crash Detecting System Using Deep Learning, Yogesh Reddy Muddam

Electronic Theses, Projects, and Dissertations

Accidents pose a significant risk to both individual and property safety, requiring effective detection and response systems. This work introduces an accident detection system using a convolutional neural network (CNN), which provides an impressive accuracy of 86.40%. Trained on diverse data sets of images and videos from various online sources, the model exhibits complex accident detection and classification and is known for its prowess in image classification and visualization.

CNN ensures better accident detection in various scenarios and road conditions. This example shows its adaptability to a real-world accident scenario and enhances its effectiveness in detecting early events. A key …

Detection Of Jamming Attacks In Vanets, Thomas Justice

Undergraduate Honors Theses

A vehicular network is a type of communication network that enables vehicles to communicate with each other and the roadside infrastructure. The roadside infrastructure consists of fixed nodes such as roadside units (RSUs), traffic lights, road signs, toll booths, and so on. RSUs are devices equipped with communication capabilities that allow vehicles to obtain and share real-time information about traffic conditions, weather, road hazards, and other relevant information. These infrastructures assist in traffic management, emergency response, smart parking, autonomous driving, and public transportation to improve roadside safety, reduce traffic congestion, and enhance the overall driving experience. However, communication between the …

The Effects Of Using Machine Translators On The Performance Of Second Language Learners, Kasey Myer

University Honors College

With the rise of technology has also come the development of various online language translators and artificial intelligence that are often utilized by individuals learning a second language. However, there is a wide range of quality between the different machine translation tools, and many people tend to be under the impression that it is inferior to the quality of interpretations provided by human translators. This paper considers the positives and negatives of machine translation as a tool for second language learning. Variations between the input and output languages on a grammatical and cultural level are analyzed. Machine translation is compared …

Privacy Protection In Mobile Photography With Face Cloaking, Rithyka Heng

Computer Science and Computer Engineering Undergraduate Honors Theses

In a world of increasing connectivity, privacy is becoming ever-more difficult to maintain. People have little control over the capture of their image while in public and have even less control over the online sharing or posting of their image. This leaves many people vulnerable to being tracked or profiled via their image’s presence in other people’s photos. This thesis implements and evaluates an approach to privacy protection that involves the photographers protecting the privacy of bystanders. Because most photographs are now being taken by smartphones, a mobile application is decidedly the technology that would best achieve widespread adoption and …

The Quantitative Analysis And Visualization Of Nfl Passing Routes, Sandeep Chitturi

Computer Science and Computer Engineering Undergraduate Honors Theses

The strategic planning of offensive passing plays in the NFL incorporates numerous variables, including defensive coverages, player positioning, historical data, etc. This project develops an application using an analytical framework and an interactive model to simulate and visualize an NFL offense's passing strategy under varying conditions. Using R-programming and data management, the model dynamically represents potential passing routes in response to different defensive schemes. The system architecture integrates data from historical NFL league years to generate quantified route scores through designed mathematical equations. This allows for the prediction of potential passing routes for offensive skill players in response to the …

Automatic Extraction Of Vulnerability Information For Security Operators Using Gpt Models, Waeland Elder

Computer Science and Computer Engineering Undergraduate Honors Theses

Artificial intelligence has progressed rapidly in recent years, greatly revolutionizing the world and helping to automate increasingly complex tasks. However, there are still some disciplines where the problem of automation has not yet been thoroughly tackled, such as in software vulnerability management. Vulnerability management is a critical component of cybersecurity for an organization. Until recently, learning about a vulnerability required a security operator to manually search and read through online information and security advisories to find needed information. Doing so is a significantly time-consuming task, as these advisories tend to be quite lengthy and packed with information about various technologies. …

Generative Ai In Education From The Perspective Of Students, Educators, And Administrators, Aashish Ghimire

All Graduate Theses and Dissertations, Fall 2023 to Present

This research explores how advanced artificial intelligence (AI), like the technology that powers tools such as ChatGPT, is changing the way we teach and learn in schools and universities. Imagine AI helping to summarize thick legal documents into something you can read over a coffee break or helping students learn how to code by offering personalized guidance. We looked into how teachers feel about using these AI tools in their classrooms, what kind of rules schools have about them, and how they can make learning programming easier for students. We found that most teachers are excited about the possibilities but …

Instant3d: Instant Text-To-3d Generation, Ming Li, Pan Zhou, Jia-Wei Liu, Jussi Keppo, Shuicheng Yan, Xiangyu Xu

Research Collection School Of Computing and Information Systems

Text-to-3D generation has attracted much attention from the computer vision community. Existing methods mainly optimize a neural field from scratch for each text prompt, relying on heavy and repetitive training cost which impedes their practical deployment. In this paper, we propose a novel framework for fast text-to-3D generation, dubbed Instant3D. Once trained, Instant3D is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network. We achieve this remarkable speed by devising a new network that directly constructs a 3D triplane from a text prompt. The core …

The Impact Of Avatar Completeness On Embodiment And The Detectability Of Hand Redirection In Virtual Reality, Martin Feick, Andre Zenner, Simon Seibert, Anthony Tang, Antonio Krüger

Research Collection School Of Computing and Information Systems

To enhance interactions in VR, many techniques introduce offsets between the virtual and real-world position of users’ hands. Nevertheless, such hand redirection (HR) techniques are only effective as long as they go unnoticed by users—not disrupting the VR experience. While several studies consider how much unnoticeable redirection can be applied, these focus on mid-air floating hands that are disconnected from users’ bodies. Increasingly, VR avatars are embodied as being directly connected with the user’s body, which provide more visual cue anchoring, and may therefore reduce the unnoticeable redirection threshold. In this work, we studied more complete avatars and their effect …

Algorithms For Canvas-Based Attention Scheduling With Resizing, Yigong Hu, Ila Gokarn, Shengzhong Liu, Archan Misra, Tarek Adbelzaher

Research Collection School Of Computing and Information Systems

Canvas-based attention scheduling was recently pro-posed to improve the efficiency of real-time machine perception systems. This framework introduces a notion of focus locales, referring to those areas where the attention of the inference system should “allocate its attention”. Data from these locales (e.g., parts of the input video frames containing objects of interest) are packed together into a smaller canvas frame which is processed by the downstream machine learning algorithm. Compared with processing the entire input data frame, this practice saves resources while maintaining inference quality. Previous work was limited to a simplified solution where the focus locales are quantized …

Multiple Continuous Top-K Queries Over Data Stream, Rui Zhu, Yujin Jia, Xiaochun Yang, Baihua Zheng, Bin Wang, Chuanyu Zong

Research Collection School Of Computing and Information Systems

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Discovering Personalized Characteristic Communities In Attributed Graphs, Yudong Niu, Yuchen Li, Panagiotis Karras, Yanhao Wang, Zhao Li

Research Collection School Of Computing and Information Systems

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of …

Quantum Machine Learning For Credit Scoring, Nikolaos Schetakis, Davit Aghamalyan, Micheael Boguslavsky, Agnieszka Rees, Marc Rakotomalala, Paul Robert Griffin

Research Collection School Of Computing and Information Systems

This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs …

Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng, Lei Zhou, Xin Ning

Research Collection School Of Computing and Information Systems

Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is known during training. Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes. To tackle this combined ZSL and OSR problem, we consider the case of “Zero-Shot Open-Set Recognition” (ZS-OSR), where a model is trained under the ZSL …

Cmd: Co-Analyzed Iot Malware Detection And Forensics Via Network And Hardware Domains, Ziming Zhao, Zhaoxuan Li, Jiongchi Yu, Fan Zhang, Xiaofei Xie, Haitao Xu, Binbin Chen

Research Collection School Of Computing and Information Systems

With the widespread use of Internet of Things (IoT) devices, malware detection has become a hot spot for both academic and industrial communities. Existing approaches can be roughly categorized into network-side and host-side. However, existing network-side methods are difficult to capture contextual semantics from cross-source traffic, and previous host-side methods could be adversary-perceived and expose risks for tampering. More importantly, a single perspective cannot comprehensively track the multi-stage lifecycle of IoT malware. In this paper, we present CMD, a co-analyzed IoT malware detection and forensics system by combining hardware and network domains. For the network part, CMD proposes a tailored …

Enhancing Visual Grounding In Vision-Language Pre-Training With Position-Guided Text Prompts, Alex Jinpeng Wang, Pan Zhou, Mike Zheng Shou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Vision-Language Pre-Training (VLP) has demonstrated remarkable potential in aligning image and text pairs, paving the way for a wide range of cross-modal learning tasks. Nevertheless, we have observed that VLP models often fall short in terms of visual grounding and localization capabilities, which are crucial for many downstream tasks, such as visual reasoning. In response, we introduce a novel Position-guided Text Prompt ( PTP ) paradigm to bolster the visual grounding abilities of cross-modal models trained with VLP. In the VLP phase, PTP divides an image into N x N blocks and employs a widely-used object detector to identify objects …

Time-Controllable Keyword Search Scheme With Efficient Revocation In Mobile E-Health Cloud, Yinbin Miao, Feng Li, Xinghua Li, Zhiquan Liu, Jianting Ning, Hongwei Li, Kim-Kwang Raymond Choo, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Electronic health (e-health) systems may outsource data such as patient e-health records to mobile cloud servers for efficiency gains (e.g., minimizing local storage and computation costs). However, such a move may result in privacy implications in the presence of semi-honest cloud servers. Searchable Encryption (SE) can potentially facilitate privacy-preserving searches based on keywords for encrypted data stored in the mobile cloud, but most existing SE solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users for specified time ranges). Hence, in this paper we design a time-controllable keyword search scheme by using an attribute-based …

Secure Multiparty Protocols On Blockchain With Fairness And Scalability, Joshua Holmes

Boise State University Theses and Dissertations

Secure multiparty computation is a major field of research in modern cryptography. It allows for the creation of a protocol that maintains the privacy of the inputs and ensures that violation of the protocol results in no undue benefit to the violator or detriment to an honest party. These protocols can be used in many fields. In this dissertation, we explore the application of the mechanisms of secure party computation in the context of peer-to-peer lending, fair exchange with cryptocurrencies, consensus, and electronic voting. In all these areas, honesty of execution and fairness in the outcome should be assured or …

Best Practices For Offline Evaluation For Top-N Recommendation: Candidate Set Sampling And Statistical Inference, Ngozi Ihemelandu

Boise State University Theses and Dissertations

Evaluation of recommender systems is key to ensuring that we are making progress by only promoting proposed algorithms that actually outperform the state-of-the-art. Evaluation can be performed offline using logged historic data, or online such as A/B tests. Offline evaluation is the most popular evaluation paradigm used in recent research publications because of its accessibility. It has been an area of study since its earliest days, and is still currently an active area of research. Even though significant work has been done to improve the process and metrics of offline evaluation, it still faces fundamental difficulties. Given the significant role …

Interpretation And Robustness Of Black-Box Ml Models For Secure Cyber Space, Qudrat E. Alahy Ratul

Boise State University Theses and Dissertations

The advent of sophisticated Machine Learning (ML) models has revolutionized the cybersecurity landscape, offering unprecedented capabilities to address complex challenges. Despite the ML model's capabilities, such models are still perceived as black boxes, the critical aspects of interpretability and robustness within these models often remain underexplored, rendering them susceptible to adversarial attacks and hindering their integration into sensitive applications. This dissertation focuses on interpretability and robustness of black-box ML models, aiming to fortify the cyberinfrastructure. Through a meticulous investigation of advanced techniques and methodologies, this work contributes towards elucidating and enhancing the interpretability and robustness of ML models in cybersecurity …

Developing A Convolutional Neural Network (Cnn) Model For Facial Expression Recognition (Fer), Danrlei Martins, Leonardo Diesel

ICT

This Capstone Project focused on developing an accurate Facial Expression Recognition (FER) model by leveraging deep learning techniques, specifically Convolutional Neural Networks (CNNs). The objective was to explore, design, and implement custom architectures and evaluate their performance against existing work. The process involved several stages, such as data preprocessing, data augmentation, architecture design, hyperparameter tuning, and performance assessment using metrics like accuracy and F1-score while utilizing the FER-2013 dataset for training. The resulting FER model exhibited competitive accuracy levels and generalization capabilities, opening up opportunities for real-time implementation and application across various domains.

Using Predictive Analytics To Identify Risk Of Heart Disease Based On Lifestyle Factors And Health Metrics., Luiza Cavalcanti Albuquerque Brayner, Edgard Pacheco

ICT

In this project, we will report an innovative application, for the healthcare sector usage, which basically is a health tracking and disease prevention application. The application will enable users to log their daily meals, exercise routines, and lifestyle habits, providing a comprehensive overview of the user's health status. By making use of Machine Learning and data analytics, our solution offers a personalised and automated insight and predictive analytics, which empowers users to proactively manage their well-being.

Through a detailed data analysis, users will gain valuable insights of potential diseases development and risk. This report will explore the development process, implementation …

An Edge Computing System With Amd Xilinx Fpga Ai Customer Platform For Advanced Driver Assistance System, Tsun Kuang Chi, Tsung Yi Chen, Yu Chen Lin, Ting Lan Lin, Jun Ting Zhang, Cheng Lin Lu, Shih Lun Chen, Kuo Chen Li, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and …

Database And Machine Learning Model For Classifying Autism Spectrum Disorder From Smartphone Based Electroretinography, Rory Harris

Honors Scholar Theses

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that negatively affects a patient’s cognitive and communication aptitude and, therefore, can severely impact that patient’s quality of life. Because of this, early diagnosis is paramount. In recent studies, electroretinography (ERG), which is a measure of the retina’s electrical response to a brief flash of light into the eye, has shown promise in detecting ASD. Access to these scans can provide early diagnosis, improving well-being. Current ERG devices are very expensive due to their on board processing capabilities. This paper aims to create an ERG device using a smartphone as the main …

The Future Of Brain Tumor Diagnosis: Cnn And Transfer Learning Innovations, Shengyuan Wang

Mathematics, Statistics, and Computer Science Honors Projects

For the purpose of improving patient survival rates and facilitating efficient treatment planning, brain tumors need to be identified early and accurately classified. This research investigates the application of transfer learning and Convolutional Neural Networks (CNN) to create an automated, high-precision brain tumor segmentation and classification framework. Utilizing large-scale datasets, which comprise MRI images from open-accessible archives, the model exhibits the effectiveness of the method in various kinds of tumors and imaging scenarios. Our approach utilizes transfer learning techniques along with CNN architectures strengths to tackle the intrinsic difficulties of brain tumor diagnosis, namely significant tumor appearance variability and difficult …

Care-Teach: Proposing An Open-Source Approach To Personalized Learning, Jaime Augusto Alvarez Perez

Theses and Dissertations

Care-Teach is an algorithmic educational model that provides teachers and educators with the tools to create interactive, text-based lessons that address a student’s need for continuity and reinforcement. Care-Teach is built around two core components: a Student Behavior Profile and the Skill Tree. These models work together to give each student a personalized learning experience that reinforces their pre-existing strengths and inclinations. The Student Behavior Profile model keeps track of a learner’s inclinations and mood, utilizing metrics such as average response time and accuracy to categorize opportunities for educator involvement. The Skill Tree is an organizational …