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Articles 1 - 30 of 26319
Full-Text Articles in Physical Sciences and Mathematics
Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif
Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif
All Works
In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application …
Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda
Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda
All Works
Asthma is a prevalent respiratory condition that poses a substantial burden on public health in the United States. Understanding its prevalence and associated risk factors is vital for informed policymaking and public health interventions. This study aims to examine asthma prevalence and identify major risk factors in the U.S. population. Our study utilized NHANES data between 1999 and 2020 to investigate asthma prevalence and associated risk factors within the U.S. population. We analyzed a dataset of 64,222 participants, excluding those under 20 years old. We performed binary regression analysis to examine the relationship of demographic and health related covariates with …
Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He
Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He
Research Collection School Of Computing and Information Systems
Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two key components: ScratchNet and PatchNet. In detail, ScratchNet employs the parallel Multi-scale Partial Convolution Module to effectively repair scratches, learning from multi-scale local receptive fields. In contrast, the patch-misses necessitate the network to emphasize global information. To this end, we incorporate a transformer-based encoder and decoder …
Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang
Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang
Research Collection School Of Computing and Information Systems
In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that …
A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, Ahmed Al-Adaileh, Mousa Al-Kfairy, Mohammad Tubishat, Omar Alfandi
A Sentiment Analysis Approach For Understanding Users’ Perception Of Metaverse Marketplace, Ahmed Al-Adaileh, Mousa Al-Kfairy, Mohammad Tubishat, Omar Alfandi
All Works
This research explores the user perceptions of the Metaverse Marketplace, analyzing a substantial dataset of over 860,000 Twitter posts through sentiment analysis and topic modeling techniques. The study aims to uncover the driving factors behind user engagement and sentiment in this novel digital trading space. Key findings highlight a predominantly positive user sentiment, with significant enthusiasm for the marketplace's revenue generation and entertainment potential, particularly within the gaming sector. Users express appreciation for the innovative opportunities the Metaverse Marketplace offers for artists, designers, and traders in handling and trading digital assets. This positive outlook is tempered by notable concerns regarding …
On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov
On Coresets For Fair Clustering In Metric And Euclidean Spaces And Their Applications, Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov
Computer Science Faculty Publications and Presentations
Fair clustering is a constrained clustering problem where we need to partition a set of colored points. The fraction of points of each color in every cluster should be more or less equal to the fraction of points of this color in the dataset. The problem was recently introduced by Chierichetti et al. (2017) [1]. We propose a new construction of coresets for fair clustering for Euclidean and general metrics based on random sampling. For the Euclidean space Rd, we provide the first coreset whose size does not depend exponentially on the dimension d. The question of whether such constructions …
Can Ai Become An Information Literacy Ally? A Survey Of Library Instructor Perspectives On Chatgpt, Melissa S. Del Castillo, Hope Y. Kelly
Can Ai Become An Information Literacy Ally? A Survey Of Library Instructor Perspectives On Chatgpt, Melissa S. Del Castillo, Hope Y. Kelly
Works of the FIU Libraries
Libraries can play a role in navigating the AI era by integrating these tools into information literacy (IL) programs. To implement generative AI tools like ChatGPT effectively, it is important to understand the attitudes of library professionals involved in IL instruction toward this tool and their intention to use it for instruction. This study explored perceptions of ChatGPT using survey data that included acceptance factors and potential uses derived from the emerging literature. While some librarians saw potential, others found it too unreliable to be useful; yet the vast majority imagined utilizing the tool in the future.
Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng, Lei Zhou, Xin Ning
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 …
A Little Loud And A Little Alone: A Phenomenology Of Leadership Identity Construction Among Women In Higher Education Technology, Amy Barry
Department of Teaching, Learning, and Teacher Education: Dissertations, Theses, and Student Research
This qualitative study is an exploration of how women in higher education information technology (IT) positions navigate constructing their leadership identities. This includes the messy, personal, internal identity work that occurs prior to claiming their leadership identities on the public stage, followed by an examination of what the experience of attempting to claim and negotiate a leadership identity is like in the social context of their organizations. This educational and sociological study employs an Interpretative Phenomenological Analysis approach with a series of three interviews per participant that allowed the researcher to deeply explore the personal identity experiences of participants. Findings …
Agriculture 4.0 And Beyond: Evaluating Cyber Threat Intelligence Sources And Techniques In Smart Farming Ecosystems, Hang T. Bui, Hamed Aboutorab, Arash Mahboubi, Yansong Gao, Nazatul H. Sultan, Aufeef Chauhan, Mohammad Z. Parvez, Michael Bewong, Rafiqul Islam, Zahid Islam, Seyit A. Camtepe, Praveen Gauravaram, Dineshkumar Singh, M. A. Babar, Shihao Yan
Agriculture 4.0 And Beyond: Evaluating Cyber Threat Intelligence Sources And Techniques In Smart Farming Ecosystems, Hang T. Bui, Hamed Aboutorab, Arash Mahboubi, Yansong Gao, Nazatul H. Sultan, Aufeef Chauhan, Mohammad Z. Parvez, Michael Bewong, Rafiqul Islam, Zahid Islam, Seyit A. Camtepe, Praveen Gauravaram, Dineshkumar Singh, M. A. Babar, Shihao Yan
Research outputs 2022 to 2026
The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing cyber threat intelligence (CTI) techniques within smart farming infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored to the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified need for a virtual Chief Information …
Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang
Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang
Research Collection School Of Computing and Information Systems
Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …
On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao
On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao
Research Collection School Of Computing and Information Systems
Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods primarily emphasize structural dependencies and their temporal changes. However, these approaches often overlook detailed temporal aspects or struggle with long-term dependencies. Furthermore, many solutions overly complicate the process by emphasizing intricate module designs to capture dynamic evolutions. In this work, we harness the strength of the Transformer’s self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. Our approach offers a simple Transformer model, called SimpleDyG, tailored for dynamic graph modeling without complex modifications. We …
Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan
Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan
Research Collection School Of Computing and Information Systems
Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availability of task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the …
An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko
An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko
Research Collection School Of Computing and Information Systems
The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed …
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
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
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 …
An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo
An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo
Research Collection School Of Computing and Information Systems
With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient …
Ai And Advocacy: Maximizing Potential, Minimizing Risk, Matthew Salzano, Nicholas Fung, Ada Lin, Sofia Marchetta, Faith Colombo, Kaylah Davis, John Flynn, Carlos Fuentes, Fion Li, Malar Paavi Muthukumaran, Angelica Paramoshin, Chrisanne Pearce, Vianney Ramos, Charles St. Hilaire, Xi Zheng, Wei Zhuang
Ai And Advocacy: Maximizing Potential, Minimizing Risk, Matthew Salzano, Nicholas Fung, Ada Lin, Sofia Marchetta, Faith Colombo, Kaylah Davis, John Flynn, Carlos Fuentes, Fion Li, Malar Paavi Muthukumaran, Angelica Paramoshin, Chrisanne Pearce, Vianney Ramos, Charles St. Hilaire, Xi Zheng, Wei Zhuang
School of Communication and Journalism Faculty Publications
New Generative AI tools are revolutionizing writing and communication. This report focuses on AI and advocacy, the act of influencing public policy and resource allocation decisions within political, economic, and social systems and institutions. This report identifies three major opportunities and accompanying risks, plus one strong recommendation for advocates considering using AI. We argue that AI can be useful for advocates, but they must be careful to center human judgment and avoid risks that could distract from their important work or even contribute to societal harms.
Develop An Interactive Python Dashboard For Analyzing Ezproxy Logs, Andy Huff, Matthew Roth, Weiling Liu
Develop An Interactive Python Dashboard For Analyzing Ezproxy Logs, Andy Huff, Matthew Roth, Weiling Liu
Faculty Scholarship
This paper describes the development of an interactive dashboard in Python with EZproxy log data. Hopefully, this dashboard will help improve the evidence-based decision-making process in electronic resources management and explore the impact of library use.
Artificial Intelligence And Film: A Journey In Public Perception From 1960 To The Present Day, Kayla Anderson, Andrew Roggeman, Joseph Fuller
Artificial Intelligence And Film: A Journey In Public Perception From 1960 To The Present Day, Kayla Anderson, Andrew Roggeman, Joseph Fuller
Celebrating Scholarship and Creativity Day (2018-)
An analysis of accomplishments in film from the 1960s-2020s that feature Artificial Intelligence to give a full picture of how public perception has changed towards these technologies over time, supplemented by historical and technological context.
Combining Empirical And Physics-Based Models For Solar Wind Prediction, Rob Johnson, Soukaina Filali Boubrahimi, Omar Bahri, Shah Muhammad Hamdi
Combining Empirical And Physics-Based Models For Solar Wind Prediction, Rob Johnson, Soukaina Filali Boubrahimi, Omar Bahri, Shah Muhammad Hamdi
Computer Science Faculty and Staff Publications
Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to …
Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre
Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre
Whittier Scholars Program
The introduction of PoetHQ, a mobile application, offers an economical strategy for colleges, potentially ushering in significant cost savings. These savings could be redirected towards enhancing academic programs and services, enriching the educational landscape for students. PoetHQ aims to democratize access to crucial software, effectively removing financial barriers and facilitating a richer educational experience. By providing an efficient software solution that reduces organizational overhead while maximizing accessibility for students, the project highlights the essential role of equitable education and resource optimization within academic institutions.
Implementation Of Python Based High Voltage Tests For Gem Detectors, John Paul Hernandez
Implementation Of Python Based High Voltage Tests For Gem Detectors, John Paul Hernandez
Aerospace, Physics, and Space Science Student Publications
The Compact Muon Solenoid, CMS, and other detectors at LHC are in the process of being upgraded for the HL-LHC (High-Luminosity Large Hadron Collider) which will produce more than 5 times the particle interactions than of the current LHC. One upgrade to CMS is the introduction of new GEM detectors (Gaseous Electron Multiplier), GE2/1 and ME0 shown at right are new detectors to CMS and therefore must be tested thoroughly prior to being installed.
Rescape: Transforming Coral-Reefscape Images For Quantitative Analysis, Zachary Ferris, Eraldo Ribeiro, Tomofumi Nagata, Robert Van Woesik
Rescape: Transforming Coral-Reefscape Images For Quantitative Analysis, Zachary Ferris, Eraldo Ribeiro, Tomofumi Nagata, Robert Van Woesik
Ocean Engineering and Marine Sciences Faculty Publications
Ever since the first image of a coral reef was captured in 1885, people worldwide have been accumulating images of coral reefscapes that document the historic conditions of reefs. However, these innumerable reefscape images suffer from perspective distortion, which reduces the apparent size of distant taxa, rendering the images unusable for quantitative analysis of reef conditions. Here we solve this century-long distortion problem by developing a novel computer-vision algorithm, ReScape, which removes the perspective distortion from reefscape images by transforming them into top-down views, making them usable for quantitative analysis of reef conditions. In doing so, we demonstrate the …
Algorithmic Approaches For Object Tracking And Facial Detection Using Drones, Kareem Shahatta, Peter Savarese, Gina Egitto, Jongwook Kim
Algorithmic Approaches For Object Tracking And Facial Detection Using Drones, Kareem Shahatta, Peter Savarese, Gina Egitto, Jongwook Kim
Computer Science Student Work
Drones are unmanned aerial vehicles that have a variety of uses in many fields such as package delivery and search operations. Tello is a small, programmable drone designed for educational purposes. We developed algorithms using DJI Tello Py, an open-source Application Programming Interface, to command the movements of Tello for tracking a target object (i.e., human). Our algorithms utilize digital image processing techniques on Tello's live video stream to optimize the number of movements Tello needs to reach its target. Our poster presentation will explain our approaches to implement object-tracking and facial detection for Tello, discuss lessons we learned, and …
A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla
A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla
Electrical and Computer Engineering Faculty Research & Creative Works
Blockchain systems have been successful in discerning truthful information from interagent interaction amidst possible attackers or conflicts, which is crucial for the completion of nontrivial tasks in distributed networking. However, the state-of-the-art blockchain protocols are limited to resource-rich applications where reliably connected nodes within the network are equipped with significant computing power to run lottery-based proof-of-work (pow) consensus. The purpose of this work is to address these challenges for implementation in a severely resource-constrained distributed network with internet of things (iot) devices. The contribution of this work is a novel lightweight alternative, called weight-based reputation (wbr) scheme, to classify new …
Using Ai Chatbots As Ideation Machines, Brett Hawley, Naomi Hollans
Using Ai Chatbots As Ideation Machines, Brett Hawley, Naomi Hollans
Student Works
The team analyzed 3 popular chatbots and found that none of them could consistently produce idea-centered essay help responses. The team approached them with 3 separate prompts, one from each of three academic subjects. The team analyzed how each chatbot adapted to the addition of personal information from the “student” and to the phrase, “what are some ideas that could help me get started?” The goal with each interaction was to receive a response in which the chatbot did not produce any pre-written content. Overall, the team’s research did not suggest that AI is fully reliable as an ideation tool.
The Borderline Between Beneficial And Dishonest Ai: A Technical Report, Seth Richards, Katherine Shell, Seth Wright
The Borderline Between Beneficial And Dishonest Ai: A Technical Report, Seth Richards, Katherine Shell, Seth Wright
Student Works
Artificial Intelligence (AI) has been used since 1950 but it was largely overlooked by the public until 2022. Current discussions about AI center around academic integrity. This report seeks to understand if AI can be handled, used, or accepted in Lipscomb’s academic environment as a beneficial aid to writing and research, without actively doing these tasks for an individual. Generative AI is a neural network, which enables it to receive input, gather information from a database of existing content, and create new content [2]. Due to the nature of generative AI, its beneficial contributions to academia are extremely limited.
Individualized Learning As An Ai Tool: A Technical Report, Petsimnan Blessing Dayit, Kasen Holt, Nuala Roper
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 …