Artificial Intelligence and Robotics Commons^™

Diabetes Technology Meeting 2023, Tiffany Tian, Rachel E Aaron, Ashley Y Dunova, Johan H Jendle, David Kerr, Eda Cengiz, Andjela Drincic, John C Pickup, Kong Y Chen, Naomi Schwartz, Douglas B Muchmore, Halis K Akturk, Carol J Levy, Signe Schmidt, Riccardo Bellazzi, Alan H B Wu, Elias K Spanakis, Bijan Najafi, James Geoffrey Chase, Jane Jeffrie Seley, David C Klonoff

Center on Aging Staff Publications

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Sound And Complete Witnesses For Template-Based Verification Of Ltl Properties On Polynomial Programs, Krishnendu Chatterjee, Amir Goharshady, Ehsan Goharshady, Mehrdad Karrabi, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We study the classical problem of verifying programs with respect to formal specifications given in the linear temporal logic (LTL). We first present novel sound and complete witnesses for LTL verification over imperative programs. Our witnesses are applicable to both verification (proving) and refutation (finding bugs) settings. We then consider LTL formulas in which atomic propositions can be polynomial constraints and turn our focus to polynomial arithmetic programs, i.e. programs in which every assignment and guard consists only of polynomial expressions. For this setting, we provide an efficient algorithm to automatically synthesize such LTL witnesses. Our synthesis procedure is both …

Text-Driven Video Prediction, Xue Song, Jingjing Chen, Bin Zhu, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Current video generation models usually convert signals indicating appearance and motion received from inputs (e.g., image and text) or latent spaces (e.g., noise vectors) into consecutive frames, fulfilling a stochastic generation process for the uncertainty introduced by latent code sampling. However, this generation pattern lacks deterministic constraints for both appearance and motion, leading to uncontrollable and undesirable outcomes. To this end, we propose a new task called Text-driven Video Prediction (TVP). Taking the first frame and text caption as inputs, this task aims to synthesize the following frames. Specifically, appearance and motion components are provided by the image and caption …

How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation, Cen Zhang, Yaowen Zheng, Mingqiang Bai, Yeting Li, Wei Ma, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Fuzz drivers are essential for library API fuzzing. However, automatically generating fuzz drivers is a complex task, as it demands the creation of high-quality, correct, and robust API usage code. An LLM-based (Large Language Model) approach for generating fuzz drivers is a promising area of research. Unlike traditional program analysis-based generators, this text-based approach is more generalized and capable of harnessing a variety of API usage information, resulting in code that is friendly for human readers. However, there is still a lack of understanding regarding the fundamental issues on this direction, such as its e ectiveness and potential challenges. To …

Genixer : Empowering Multimodal Large Language Models As A Powerful Data Generator, Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou

Research Collection School Of Computing and Information Systems

Multimodal Large Language Models (MLLMs) demonstrate exceptional problem-solving capabilities, but few research studies aim to gauge the ability to generate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce Genixer, a comprehensive data generation pipeline consisting of four key steps: (i) instruction data collection, (ii) instruction template design, (iii) empowering MLLMs, and (iv) data generation and filtering. Additionally, we outline two modes of data generation: task-agnostic and task-specific, enabling controllable output. We demonstrate that a synthetic VQA-like dataset trained with LLaVA1.5 enhances performance on 10 …

A Two-Stage Matheuristic For The Home Healthcare Routing And Scheduling Problem With Perishable Products, Aldy Gunawan, Nabila Yuraisyah Salsabila, Vincent F. Yu, Pham Kien Minh Nguyen

Research Collection School Of Computing and Information Systems

This study proposes a home healthcare routing and scheduling problem, where perishable products such as medicines, vaccines, or meals must be provided for some patients’ treatments. This problem is formulated as a mixed integer linear programming (MILP). A two-stage matheuristic is then developed as the solution approach. The first stage is a local search to solve the nurse routing problem, and the second stage is run as the relaxed MILP to solve the scheduling problem. The matheuristic is tested on newly generated instances and compared with the results of CPLEX. The proposed matheuristic is able to obtain CPLEX solutions within …

Quantum-Enhanced Simulation-Based Optimization For Newsvendor Problems, Monit Sharma, Hoong Chuin Lau, Rudy Raymond

Research Collection School Of Computing and Information Systems

Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence it is estimated through simulation. Exploiting the enhanced efficiency of Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we make use of a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical …

An Empirical Study Of Static Analysis Tools For Secure Code Review, Wachiraphan Charoenwet, Patanamon Thongtanunam, Van-Thuan Pham, Christoph Treude

Research Collection School Of Computing and Information Systems

Early identification of security issues in software development is vital to minimize their unanticipated impacts. Code review is a widely used manual analysis method that aims to uncover security issues along with other coding issues in software projects. While some studies suggest that automated static application security testing tools (SASTs) could enhance security issue identification, there is limited understanding of SAST’s practical effectiveness in supporting secure code review. Moreover, most SAST studies rely on synthetic or fully vulnerable versions of the subject program, which may not accurately represent real-world code changes in the code review process. To address this gap, …

Fintech Digital Transformation: Generative Ai, Humanoid Robots, Metaverse, Human-Ai Collaboration, And Industry 5.0, Yuxin Liu, Runyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

This paper discusses the transformative impact of emerging digital technologies on the digital transformation of the financial industry, focusing on integrating Generative AI (GenAI), humanoid robots, and the Metaverse within the framework of Industry 5.0. Industry 5.0 emphasizes a human-centric approach to technology, where human-AI collaboration plays a central role in reshaping financial services. By reviewing both academic research and practical applications, the paper highlights the current advancements in FinTech, particularly in AI technologies and the Metaverse, and their future potential, demonstrating how these innovations are driving growth, efficiency, and resilience in the financial sector. Further, the paper proposes a …

Federated Learning Systems For Mobile Sensing Data, Xiaopeng Jiang

Dissertations

Federated Learning (FL) has emerged as a new distributed Deep Learning (DL) paradigm that enables privacy-aware training and inference on mobile devices with help from the cloud. This dissertation presents a comprehensive exploration of FL with mobile sensing data, covering systems, applications, and optimizations.

First, a mobile-cloud FL system, FLSys, is designed to balance model performance with resource consumption, tolerate communication failures, and achieve scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. In addition, FLSys provides advanced privacy-preserving mechanisms and a common API for third-party app developers to …

Ai And Academic Integrity, Max Sparkman Research Instruction Librarian, Milne Library, Brandon West Head Of Research & Instruction, Milne Library

Artificial Intelligence, 2024-25

This short module introduces students to important concepts regarding the use of AI and academic integrity. Concepts covered include a brief overview of generative AI, whether or not their use is considered plagiarism, how to use generative AI tools responsibly, and potential use cases. The module ends with a quiz where students can apply concepts from the module to three scenarios.

Module: Ai And Value-Neutrality, Jonathan Auyer Ph.D., Department Of Philosophy

Artificial Intelligence, 2024-25

Artificial Intelligence is on the tips of everyone’s tongues these days – What exactly is it? What will can it be used for? What will it be used for in the future? What problems will it create or solve or exacerbate? This learning module aims to look at a specific facet of AI — the issue of value-neutrality — by having students look inward at capabilities necessary for human flourishing and then ask whether AI can cultivate (or inhibit) those capabilities. This will lead to a discussion of what values underlie AI and what this says about whether or not …

Chatgpt Can Write My Class Assignments, Right?: A Guided Classroom Activity For Teaching The Strengths And Weaknesses Of Generative-Ai Tools, Dr. Peter J. Kalenda Assistant Professor, Elementary Science & Math, School Of Education

Artificial Intelligence, 2024-25

This module is designed to support your undergraduate or graduate students with developing an understanding of:

• How to use generative-AI tools, like ChatGPT,
• The strengths and weaknesses of generative-AI tools for completing different tasks,
• How to design and revise a classroom presentation that is aligned with course topics and objectives by using generative-AI tools as a resource, and
• How to engage in prompt engineering and prompt revision for generative-AI tools.

Your students are being tasked with creating an engaging presentation they will deliver to their peers that aligns with the topics and learning objectives of your course. However, they will …

As Soon As [A]I Speak[S], Lytton Smith Professor Of Poetry, Department Of English

Artificial Intelligence, 2024-25

This guide facilitates the delivery of a two-workshop sequence allowing students to understand how an Artificial Intelligence (AI) Large Language Model (LLM) composes poetry, and to use that knowledge to compose their own poem for optional display in both electronic and physical form. Students may participate in either/both workshops, and in each will engage in reflective work to understand how AI impacts them and vice versa. This guide and curriculum aligns with the "Contemporary Global Challenges, Creativity and Innovation" Participation in a Global Society outcome within the Geneseo GLOBE Curriculum.

Creating A Business In 50 Minutes With Ai, Mark A. Rider Vanarsdale Chair In Entrepreneurship, School Of Business

Artificial Intelligence, 2024-25

This lesson plan explores using artificial intelligence (AI) to enhance the process of business idea generation and validation. Over a dynamic 50-minute workshop, students will engage with AI tools, particularly Microsoft's Copilot, to develop viable business concepts aligned with their personal interests and market needs.

The session begins with the Ikigai exercise, guiding students to identify their passions, strengths, and potential economic opportunities. Following this, students will use AI to engage in divergent thinking, generating a wide range of business ideas and refining them through iterative prompts until they find promising concepts.

In the validation phase, students will employ AI …

Using Ai In Higher Ed: Is It Cheating?, David Levy Associate Professor & Chair Of Philosophy

Artificial Intelligence, 2024-25

Students generate a course-based or college-wide policy regarding the use of Generative AI, based on assigned readings, discussion, practice using the tools on writing assignments.

Psychology And The Digital Everywhere: Artificial Intelligence, Cassie Van Stolk Assistant Professor Of Psychology, Department Of Psychology

Artificial Intelligence, 2024-25

This module within the PSYC 390: Psychology and the Digital Everywhere course investigates the the implications of AI on human experiences using a biopsychosocial lens. Topics covered include an exploration of AI as a tool versus as an autonomous mind, ethical considerations of AI usage, and the promises and pitfalls of AI as a tool within the field of psychology. This module aligns with the "Contemporary Global Challenges, Creativity and Innovation" Participation in a Global Society outcome within the Geneseo GLOBE Curriculum.

Heterogeneous Multi-Robot Person-Following In Constrained Environments, Ori A. Miller

Electronic Theses and Dissertations

Maintaining visibility of a person requires effective systems. Security cameras or ground robots might be ideal, but they often fail in uncontrolled or unknown environments. A single ground robot struggles to navigate and track an agent at the same time. This work addresses the challenge by developing a multi-robot system with a slow ground robot and an agile aerial robot. Three methods are evaluated: FORWARD-PF, RL-Person Following (RL), and a baseline closed-loop method. FORWARD-PF proved the most reliable, completing all nine paths and reaching targets nearly twice as fast as RL. Despite completing seven paths, RL faltered on complex tasks. …

Robotic Odor Source Localization Using Vision And Olfaction Sensing, Sunzid Hassan

Master's Theses

Robotic Odor Source Localization (ROSL) technology allows autonomous agents like robots to find an odor source in unknown environments. A successful odor source location depends crucially on an effective navigation algorithm that directs the robot towards the odor source. This thesis is a combination of three projects. First, we detail development of a versatile multi-modal robotic platform for ROSL real-world ROSL experimentation and discussed real-world validation of a traditional olfactionbased ROSL algorithm. Secondly, we introduced vision in ROSL by proposing a fusion navigation algorithm that integrates deep-learning enabled vision and olfaction-based navigation. This hybrid approach tackles challenges such as turbulent …

Supervised Classification Modeling On Louisiana Medicaid Data: A Comparative Study, Yead Rahman

Master's Theses

This thesis systematically optimizes and compares state-of-the-art supervised classification models for Louisiana Medicaid data targeting clinical services, COVID-19 infection, and tobacco use. These target variables are critically important as they represent key health outcomes and behaviors among Medicaid enrollees in Louisiana, a population often characterized by poverty and limited access to education. This study applies advanced machine learning techniques to identify the best model for multinomial and binary classification tasks. These include models such as Logistic Regression, XGBoost, AdaBoost, Random Forest, Decision Tree, Artificial Neural Networks, and Naïve Bayes. Extensive tuning of the hyperparameters and optimization of each classifier were …