Artificial Intelligence and Robotics Commons^™

23. Cake-Making Analogy For Setting Generative Ai Guidelines/Ethics, Maha Bali

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

This is a lesson plan that offers metaphor as an innovative approach to teaching about the ethical use of generative Al. The cake-making analogy equates different ways of acquiring a cake (baking from scratch, using a readymade mix from a box, buying from a bakery or buying preserved cake from a supermarket) with varying degrees of reliance on Al as a shortcut for tasks or assignments. The lesson invites participants (who may be students or teachers) to critically consider the implications of each mode, examining factors such as quality, time, cost, and personal investment. This analogy is then applied to …

7. Automated Aid Or Offloading Close Reading? Student Perspectives Of Ai Reading Assistants, Marc Watkins

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

Generative Al technologies offer new opportunities for enhancing student learning that go beyond chatbot interfaces like ChatGPT. This chapter presents reflections from a small study about the possible benefits and challenges posed by integrating Al-powered reading assistants in first-year writing courses. Careful integration of these tools suggests potential benefits that do not simply generate text on students' behalf. For example, reading assistants like Explainpaper and SciSpace are powered by large language models like OpenAl's GPT and can help students augment reading. This application of generative technology could aid non-native speakers, students with disabilities, and those struggling with reading comprehension. However, …

6. More Is Less?: Using Generative Ai For Idea Generation And Diversification In Early Writing Processes, Franziska Tsufim, Lainie Pomerleau

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

As writing teachers, we are strong proponents of process writing. At the same time, we are aware that early process work, especially in a group setting, can be time consuming and anxiety-inducing. Students may also self-censor when sharing work with peers especially if they are not confident in their ideas. Drawing on the process of nominal, electronic brainstorming, we created two different prompts that allowed students to incorporate generative Al into their idea generation process. This first activity improves the efficiency of individual idea generation, while the second exercise helps increase student confidence in their ideas in collaborative brainstorming situations. …

Digital Resurrection Of Historical Figures: A Case Study On Mary Sibley Through Customized Chatgpt, James Hutson, Paul Huffman, Jeremiah Ratican

Faculty Scholarship

This study investigates the emerging realm of digital resurrection, focusing on Mary Sibley (1800–1878), the esteemed founder of Lindenwood University. The core objective was to demonstrate the capability of advanced artificial intelligence, specifically a customized version of ChatGPT, in revitalizing historical figures for educational and engagement purposes. By integrating comprehensive diaries from Sibley with Claude 2.0, the research utilized a substantial autobiographical dataset to develop a GPT beta version that replicates her distinct voice and tone. The incorporation of her official portrait and diaries into the GPT Builder was pivotal, creating an interactive platform that accurately reflects her perspectives on …

Analysis Of Speech Recognition Systems And Error Correction Approaches, Saki Imai

Honors Theses

Despite significant advances in automatic speech recognition (ASR) accuracy, challenges remain. Naturally occurring conversation often involves multiple overlapping speakers, of different ages, accents and genders, as well as noisy environments and suboptimal audio recording equipment, all of which reduce ASR accuracy. In this study, we evaluate the accuracy of state of the art open source ASR systems across diverse conversational speech datasets, examining the impact of audio and speaker characteristics on WER. We then explore the potential of ASR ensembling plus post-ASR correction methods to improve transcription accuracy. Our findings underscore the need for robust error correction techniques and of …

Development Of A Two-Finger Haptic Robotic Hand With Novel Stiffness Detection And Impedance Control, Vahid Mohammadi, Ramin Shahbad, Mojtaba Hosseini, Mohammad Hossein Gholampour, Saeed Shiry Ghidary, Farshid Najafi, Ahad Behboodi

Mechanical & Aerospace Engineering Faculty Publications

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive …

Kinodynamic Motion Planning For A System With Squid Dynamics, Logan E. Beaver, Cong Wei, Wei-Kuo Yen

Mechanical & Aerospace Engineering Faculty Publications

This paper introduces a path planning algorithm for a system with squid dynamics in a cluttered environment. We capture the complex interactions of fin, arms, and body patterning by analyzing experimental data collected from observing squid motion. We extract nine motion primitives to build the control sequence for a time-optimal trajectory. This task is formulated as a mixed-integer program, and we generate the minimum-time trajectory using a sample-based approach. Numerical simulations illustrate the efficacy of this strategy and motivate ongoing and future efforts to exploration of squid motion features, improvement of the modeling, and experimental demonstrations of the motion planning …

Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart

Theses and Dissertations

Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …

Invoice Processing With Rpa, Maheen Sohail

MSCS Research Projects

This project aims to develop an automated invoice processing system leveraging Robotic Process Automation (RPA) and Optical Character Recognition (OCR) technologies to streamline invoice management, reduce manual effort, and minimize errors. The system captures invoice images via a mobile application and validates the vendor against a predefined vendor list. Recognized vendor’s invoices are uploaded to SharePoint and sent for further processing with OCR to extract data, while others are routed for approval before being processed further. This dual-path workflow ensures both speed and accuracy in handling invoices.

Developed with Microsoft Power Apps and automated using Microsoft Power Automate, the system …

Infusing Commonsense Via Knowledge Bases In Multipurpose Robotic Task Organization, Rafael Omar Hidalgo

Theses, Dissertations and Culminating Projects

This research explores the innovative integration of commonsense knowledge (CSK) within AI systems, with a particular focus on domestic robotics. At the heart of this study is the Robo- CSK-Organizer, a groundbreaking system that utilizes a classical knowledge base, namely ConceptNet, to enhance robotic decision-making through sophisticated object organization and classification. This system is contrasted with a ChatGPT-based organizer, examining their performance in terms of ambiguity resolution, consistency in object placement, adaptability to task classifications, and crucially, in explainability, a key aspect of XAI (Explainable AI). Through a combination of controlled experiments, quantitative and qualitative analysis, the study demonstrates that …

Natural Language Processing And Neurosymbolic Ai: The Role Of Neural Networks With Knowledge-Guided Symbolic Approaches, Emily Barnes, James Hutson

Faculty Scholarship

Neurosymbolic AI (NeSy AI) represents a groundbreaking approach in the realm of Natural Language Processing (NLP), merging the pattern recognition of neural networks with the structured reasoning of symbolic AI to address the complexities of human language. This study investigates the effectiveness of neurosymbolic AI in providing nuanced understanding and contextually relevant responses, driven by the need to overcome the limitations of existing models in handling complex linguistic tasks and abstract reasoning. Employing a hybrid methodology that combines multimodal contextual modeling with rule-governed inferences and memory activations, the research delves into specific applications like Named Entity Recognition (NER), where architectures …

Digitalization Of Railway Transportation Through Ai-Powered Services: Digital Twin Trains, Salih Sarp, Murat Kuzlu, Vukica Jovanovic, Zekeriya Polat, Ozgur Guler

Engineering Technology Faculty Publications

Digitalization is a key concept that transformed the various industries through technologies like Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin (DT). Although innovations provided by the advancement of digitalization have paved the way for more efficient operations and products for transportation, the rail transportation sector struggles to keep up with the rest of the transportation industry, since trains are designed to last for decades, and the insufficient infrastructure investment leads to multiple railroad derailments across the globe. Therefore, the primary aim is to transform current railway systems into human-centric, adaptable, sustainable and future-proof networks, aligning with Industry …

Federated Learning: Overview, Strategies, Applications, Tools And Future Directions, Betul Yurdem, Murat Kuzlu, Mehmet Kemal Gullu, Maliha Tabassum

Engineering Technology Faculty Publications

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications.

A Benchmark Framework For Data Visualization And Explainable Ai (Xai), Murat Kuzlu, Gokcen Ozdemir, Umut Ozdemir

Engineering Technology Faculty Publications

This research introduces a benchmark framework, called EDUMX, designed for machine learning (ML)-based forecasting and XAI tasks, leveraging the Streamlit open-source Python library. The framework offers a comprehensive suite of functionalities, including data loading, feature selection, relationship analysis, data preprocessing, model selection, metric evaluation, training, and real-time monitoring. Users can easily upload data in diverse formats, explore relationships between variables, preprocess data using various techniques, and assess the performance of the ML model using customizable metrics. With its user-friendly interface, this framework offers invaluable insights for forecasting tasks in various domains, catering to the evolving needs of predictive analytics. EDUMX …

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

Learning Optimal Inter-Class Margin Adaptively For Few-Shot Class-Incremental Learning Via Neural Collapse-Based Meta-Learning, Hang Ran, Weijun Li, Lusi Li, Songsong Tian, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class margins based on empirical observations, which can be suboptimal. The emerging Neural Collapse (NC) theory provides a theoretically optimal inter-class margin for classification, serving as a basis for adaptively computing the margin. Yet, it is designed for closed, balanced data, not for sequential or few-shot …

Identifying Patterns For Neurological Disabilities By Integrating Discrete Wavelet Transform And Visualization, Soo Yeon Ji, Sampath Jayarathna, Anne M. Perrotti, Katrina Kardiasmenos, Dong Hyun Jeong

Computer Science Faculty Publications

Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities …

Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2015 And 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive’s (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt’s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based …

Mosaic: A Prune-And-Assemble Approach For Efficient Model Pruning In Privacy-Preserving Deep Learning, Yifei Cai, Qiao Zhang, Rui Ning, Chunsheng Xin, Hongyi Wu

Computer Science Faculty Publications

To enable common users to capitalize on the power of deep learning, Machine Learning as a Service (MLaaS) has been proposed in the literature, which opens powerful deep learning models of service providers to the public. To protect the data privacy of end users, as well as the model privacy of the server, several state-of-the-art privacy-preserving MLaaS frameworks have also been proposed. Nevertheless, despite the exquisite design of these frameworks to enhance computation efficiency, the computational cost remains expensive for practical applications. To improve the computation efficiency of deep learning (DL) models, model pruning has been adopted as a strategic …

Identifying New Cancer Genes Based On The Integration Of Annotated Gene Sets Via Hypergraph Neural Networks, Chao Deng, Hong-Dong Li, Li-Shen Zhang, Yiwei Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation

Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.

Results

Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of …