Artificial Intelligence and Robotics Commons^™

Anogat-Sparse-Tl: A Hybrid Framework Combining Sparsification And Graph Attention For Anomaly Detection In Attributed Networks Using The Optimized Loss Function Incorporating The Twersky Loss For Improved Robustness., Nadhem Ebrahim, Wasim Khan

University Research

In recent years, the identification of abnormalities in attributed networks has become essential for applications including social media analysis, cybersecurity, and financial fraud detection. Unsupervised graph anomaly detection techniques seek to recognize infrequent and anomalous patterns in graph-structured data without the necessity of labelled instances. Conventional methods employing Graph Neural Networks (GNNs) frequently encounter difficulties, especially due to the transmission of noisy edges and the intrinsic intricacy of node interrelations. To overcome these restrictions, we introduce ANOGAT-Sparse-TL, an innovative hybrid framework that integrates graph sparsification and Graph Attention Networks (GAT) with autoencoder-based reconstruction for anomaly detection in attributed networks. The …

A Review Of Artificial Intelligence, Algorithms, And Robots Through The Lens Of Stakeholder Theory, Michael J. Matthews, Su Runkun, Lindsey Yonish, Shawn Mcclean, Joel Koopman, Kai Chi Yam

Management Faculty Publications

With the arrival of the Fourth Industrial Revolution, intelligent machines are affecting the daily lives of multiple organizational stakeholders. However, despite the continued expansion of intelligent machines in society, management scholarship has generally lagged, and current frameworks are under-equipped to offer meaningful guidance regarding the intersection of intelligent machines and organizations. We address this issue via a multidisciplinary review and a novel framework of intelligent machines and value creation. First, we discuss the characteristics of intelligent machines (i.e., autonomy, learning, inscrutability, and materiality) and how variation in these characteristics impacts their affordances and, subsequently, the value offered to stakeholders. We …

Accelerated Multiobjective Calibration Of Fused Deposition Modeling 3d Printers Using Multitask Bayesian Optimization And Computer Vision, Craig S. Ganitano, Benji Maruyama, Gilbert L. Peterson

Faculty Publications

Proper process parameter calibration is critical to the success of fused deposition modeling (FDM) three-dimensional (3D) printing, but is time-consuming and requires expertise. While existing systems for autonomous calibration have demonstrated success in calibrating for a single objective, users may need to balance multiple conflicting objectives. Herein, an easily deployable, camera-based system for autonomous calibration of FDM printers that optimizes for both part quality and completion time is presented. Autonomous calibration is achieved through a novel, multifaceted computer vision characterization and a multitask learning extension to Bayesian optimization. The system is demonstrated on four popular filament types using two distinct …

Plc-Controlled Intelligent Conveyor System With Ai-Enhanced Vision Of Efficient Waste Sorting, Natheer Almtireen, Nathir Rawashdeh, Viraj Reddy, Max Sutton, Alexander Nedvidek, Caden Karn, Et. Al.

Michigan Tech Publications

Current waste sorting mechanisms, particularly those relying on manual processes, semi-automated systems, or technologies without Artificial Intelligence (AI) integration, are hindered by inefficiencies, inaccuracies, and limited scalability, reducing their effectiveness in meeting growing waste management demands. This study introduces a prototype waste sorting machine that integrates an AI-driven vision system with a Programmable Logic Controller (PLC) for high-accuracy automated waste sorting. The system, powered by the YOLOv8 deep learning model, achieved sorting accuracies of 88% for metal cans, 75% for paper, and 91% for plastic bottles, with an overall precision of 90%, a recall of 80%, and a mean average …

Confronting Catastrophic Risk: The International Obligation To Regulate Artificial Intelligence, Bryan Druzin, Anatole Boute, Michael Ramsden

Michigan Journal of International Law

While artificial intelligence (“AI”) holds enormous promise, many experts in the field are warning that there is a non-trivial chance that the development of AI poses an existential threat to humanity. Existing regulatory initiatives do not address this threat but instead merely focus on discrete AI-related risks such as consumer safety, cybersecurity, data protection, and privacy. In the absence of regulatory action to address the possible risk of human extinction by AI, the question arises: What obligations, if any, does public international law impose on states to regulate its development?

At present there is no scientific consensus as to the …

Zombie-Auras: Genai And Hybrid Text Production, Joshua Nieubuurt

English Faculty Publications

This paper explores the iterative evolutions of textual production and their impact on the “aura” of texts, as conceptualized by Walter Benjamin. The study identifies three key phases of textual production: the natural, the mechanized, and the digitized, each progressively displacing the “cult value” of texts. This cult value is lost through increased ease of creation, reproduction, dissemination, and dislocation of creators and audiences in time and space. The advent of Generative AI (GenAI) marks the latest evolution, transforming the “aura” into a memetic “zombie” form—familiar yet opaque, evoking both the sublime and fear. By examining Benjamin’s notion of “aura” …

Chatgpt Didn’T Write This: Evaluating The Impact Of Llms With A Case Study In Grading Cuny Language Immersion Program Student Essays, Benjamin Inbar

Dissertations, Theses, and Capstone Projects

This study evaluates the capabilities and limitations of large language models (LLMs), specifically OpenAI’s ChatGPT-4o, in grading essays from students in the City University of New York’s Language Immersion Program. The program serves English language learners with diverse linguistic and demographic backgrounds, offering intensive language instruction to prepare students for academic success in college. Using a dataset of 30 pre- and post-program essays scored by program instructors and ChatGPT-4o under three paradigms, this research explores the alignment between human and AI-generated scores across five rubric-based competency areas. Findings reveal that ChatGPT-4o aligns moderately with human grading, with the strongest agreement …

Vaxbot-Hpv: A Gpt-Based Chatbot For Answering Hpv Vaccine-Related Questions, Yiming Li, Jianfu Li, Manqi Li, Evan Yu, Danniel Rhee, Muhammad Amith, Lu Tang, Lara S Savas, Licong Cui, Cui Tao

Faculty, Staff and Student Publications

OBJECTIVE: Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine.

METHODS: We constructed the knowledge base (KB) for VaxBot-HPV, which consists of 451 documents from biomedical literature and web sources on the HPV vaccine. We extracted 202 question-answer pairs from the KB and 39 questions generated by GPT-4 for training and …

Hotpatching On The Fly: Mitigating Drone Incidents Arising From Incorrect Configuration, Ruidong Han, Juanru Li, Zhuo Ma, David Lo, Arash Shaghaghi, Jianfeng Ma, Siqi Ma

Research Collection School Of Computing and Information Systems

Manufacturers offer adjustable control parameters for flight control systems to accommodate diverse environments and missions. To ensure flight safety, they also develop established boundaries, i.e., range specifications for parameter values. However, even when the configuration parameters fall within the prescribed manufacturer range, they could still lead to instability or even severe incidents like crashes, which are referred to as Range Specification Bugs. Prior research has suggested shrinking the range of parameter values to protect drones from the adverse effects of such bugs. However, narrowing the range of parameters may only reduce the probability of errors and could potentially limit the …

Human-Ai Synergy In Survey Development: Implications From Large Language Models In Business And Research, Ping Fan Ke, Ka Chung Ng

Research Collection School Of Computing and Information Systems

This study examines the novel integration of Large Language Models (LLMs) into the survey development process in business and research through the development and evaluation of the Behavioral Research ASSistant (BRASS) Bot. We first analyzed the traditional scale development process to identify tasks suitable for LLM integration, including both human-in-the-loop and automated LLM data collection methods. Following this analysis, we developed the details of BRASS Bot, incorporating design principles of falsifiability and reproducibility. We then conducted a comprehensive evaluation of the BRASS Bot across a diverse set of LLMs, including GPT, Claude, Gemini, and Llama, to assess its usability, validity, …

Bridging Expert Knowledge With Deep Learning Techniques For Just-In-Time Defect Prediction, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features from commit contents. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may …

Seven Hci Grand Challenges Revisited: Five-Year Progress, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Vincent G Duffy, Qin Gao, Waldemar Karwowski, Fiona Nah, Stavroula Ntoa, Pei-Luen Patrick Rau, Keng Siau, Jia Zhou

Research Collection School Of Computing and Information Systems

Motivated by the rapid technological advancements achieved in the last five years, and the pervasiveness of Artificial Intelligence, the paper investigates the evolving role of Human-Computer Interaction and revisits the seven grand challenges outlined in 2019: human-technology symbiosis, human-environment interactions, ethics, privacy and security, well-being, health and eudaimonia, accessibility and universal access, learning and creativity, and social organization and democracy. Through literature analysis, the paper reevaluates the status of each challenge and highlights emerging requirements. Key findings reveal the widespread impact of Artificial Intelligence across all domains and emphasize the need for improved AI transparency, alignment with human values, and …

Ai As Your Ally: The Effects Of Ai-Assisted Venting On Negative Affect And Perceived Social Support, Meilan Hu, Xavier Cheng Wee Chua, Shu Fen Diong, K. T. A. Sandeeshwara Kasturiratna, Nadyanna M. Majeed, Andree Hartanto

Research Collection School of Social Sciences

In recent years, artificial intelligence (AI) chatbots have made significant strides in generating human-like conversations. With AI's expanding capabilities in mimicking human interactions, its affordability and accessibility underscore the potential of AI chatbots to facilitate negative emotional disclosure or venting. The study's primary objective is to highlight the potential benefits of AI-assisted venting by comparing its effectiveness to venting through a traditional journaling platform in reducing negative affect and increasing perceived social support. We conducted a pre-registered within-subject experiment involving 150 participants who completed both traditional venting and AI-assisted venting conditions with counterbalancing and a wash-out period of 1-week between …

Proactive Conversational Ai: A Comprehensive Survey Of Advancements And Opportunities, Yang Deng, Lizi Liao, Wenqiang Lei, Grace Hui Yang, Wai Lam, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system's response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior-a crucial aspect of intelligent conversations-is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general …

Loco: Low-Bit Communication Adaptor For Large-Scale Model Training, Xingyu Xie, Zhijie Lin, Kim-Chuan Toh, Pan Zhou

Research Collection School Of Computing and Information Systems

To efficiently train large-scale models, low-bit gradient communication compresses full-precision gradients on local GPU nodes into low-precision ones for higher gradient synchronization efficiency among GPU nodes. However, it often degrades training quality due to compression information loss. To address this, we propose the Low-bit Communication Adaptor (LoCo), which compensates gradients on local GPU nodes before compression, ensuring efficient synchronization without compromising training quality. Specifically, LoCo designs a moving average of historical compensation errors to stably estimate concurrent compression error and then adopts it to compensate for the concurrent gradient compression, yielding a less lossless compression. This mechanism allows it to …

A Causality-Aware Paradigm For Evaluating Creativity Of Multimodal Large Language Models, Zhongzhan Huang, Shanshan Zhong, Pan Zhou, Shanghua Gao, Marink Zitnik, Liang Lin

Research Collection School Of Computing and Information Systems

Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and data-scarce nature of creativity, especially in multimodal scenarios. In this paper, we consider the comprehensive pipeline for evaluating the creativity of multimodal LLMs, with a focus on suitable evaluation platforms and methodologies. First, we find the Oogiri game—a creativity-driven task requiring humor, associative thinking, and the ability to produce unexpected responses to text, images, or both. This game aligns well with the input-output structure of modern …

Towards Resource-Efficient Reactive And Proactive Auto-Scaling For Microservice Architectures, Hussain Ahmad, Christoph Treude, Markus Wagner, Claudia Szabo

Research Collection School Of Computing and Information Systems

Microservice architectures have become increasingly popular in both academia and industry, providing enhanced agility, elasticity, and maintainability in software development and deployment. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust the resources of microservices to accommodate fluctuating workloads. However, existing HPAs are not suitable for resource-constrained environments, as they make scaling decisions based on the individual resource capacities of microservices, leading to service unavailability, resource mismanagement, and financial losses. Furthermore, the inherent delay in initializing and terminating microservice pods hinders HPAs from timely responding to workload fluctuations, …

Human‑Ai And Human‑Robot Collaboration In The Age Of Generative Ai, Agentic Ai, And Artificial General Intelligence: Opportunities And Challenges, Keng Siau

Research Collection School Of Computing and Information Systems

The advancement of Artificial Intelligence (AI) has been exponential, especially in the past few years. Most, if not all, of the AI systems we encounter and are exposed to at this point are Artificial Narrow Intelligence (ANI). ANI specializes in one area and solves problems in one area. Generative AI (GenAI) and Agentic AI (i.e., independent AI agent), at the current stage of development, are regarded as ANI. The race is currently on to develop Artificial General Intelligence (AGI). AGI refers to AI systems as smart as humans across a wide range of cognitive tasks. Recently, OpenAI’s o3 system received …

Negotiating With Gpt-4: Digital Doormat Or Skilful Counterpart?, Dorcas Quek Anderson

Research Collection Yong Pung How School Of Law

Large language models (LLMs) such as GPT-4 have been creatively harnessed in the conflict resolution arena as dialogue agents interacting with humans within negotiations, due to their capacity for in-context learning and giving human-like responses. In light of the burgeoning use of LLMs in conflict resolution training, a pilot study was conducted to ascertain the desirability of using dialogue agents built on GPT-4 in conducting simulations for students learning negotiation skills. This article discusses insights gained from the study on the reliability of LLM agents in following prompts for negotiation simulations; notable negotiation behaviour of the LLM agent; the degree …

Towards The Next Generation Of Geospatial Artificial Intelligence, Gengchen Mai, Yiqun Xie, Xiaowei Jia, Ni Lao, Jinmeng Rao, Qing Zhu, Zeping Liu, Yao-Yi Chiang, Jiao, Junfeng

Research Collection College of Integrative Studies

Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote …

Heartdj - Music Recommendation And Generation Through Biofeedback From Heart Rate Variability, Egemen Şahin

Dartmouth College Master’s Theses

This study investigates the integration of real-time physiological data with AI-generated music to enhance emotional well-being, stress regulation, and focus, using Heart Rate Variability (HRV) as a biomarker of autonomic function. Conducted in two phases—Stable Audio Open (SAO) and Suno (SUNO)—the research evaluates biofeedback-driven music interventions across varying daily music-listening habits.

In the SAO phase, short AI-generated instrumental tracks were compared with Spotify recommendations and guided meditation. Modest HRV improvements were observed in biofeedback conditions, but participants noted emotional limitations, citing short track lengths and abrupt transitions.

The SUNO phase addressed these limitations with longer, more complex AI-generated compositions combined …

A Robust Framework For Graph Construction In Vision Graph Neural Networks, Ismael Elsharkawi

Theses and Dissertations

In Computer Vision, the method of representing an image has a profound effect on the performance of a model. Traditionally speaking, an image is treated as a grid of pixels and can be processed via Convolution Neural Net- works (CNN). An image can also be treated as a sequence of patches. Vision Transformers and MLP-Mixers (Multi-Layer Perceptron Mixers) are two types of models that process an image as a sequence. A more generic representation than grids and sequences would be graphs. That is why Vision Graph Neural Network (ViG) construct a graph for an image and process the image as …

Deep Learning-Based Auto-Segmentation For Liver Yttrium-90 Selective Internal Radiation Therapy, Jun Li, Wookjin Choi, Rani Anne

Department of Radiation Oncology Faculty Papers

The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SIRT patients. DL auto-segmented liver contours were evaluated against physician manually-delineated contours. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated. The DL-model-generated contours were compared with the contours generated using an Atlas-based method. Ratio of volume (RV, the ratio of DL-model auto-segmented liver volume to manually-delineated liver volume), and ratio of activity (RA, …

Playing The Digital Dialectic Game: Writing Pedagogy With Generative Ai, Rebekah Shultz Colby

University Writing Program: Faculty Scholarship

This article explores teaching writing with generative AI as critical play where students and teachers engage in an ethically dialectical and aleatory game with generative AI. I qualitatively surveyed 24 writing teachers about how they teach writing with generative AI as well as its advantages and disadvantages. I discovered that teachers used generative AI to teach about the ethics of generative AI's design and rhetorical use to avoid plagiarism. Teachers also critically played with generative AI to teach the writing process of invention, drafting, revision, and editing. Specifically, the critical, dialectical interplay of human and machine invents in aleatory and …

Unveiling The Potential Of Generative Artificial Intelligence: A Multidimensional Journey Into The Future, Keng Boon Ooi, Alex Koohang, Eugene Cheng Xi Aw, Tat Huei Cham, Cihan Cobanoglu, Charles Dennis, Yogesh K. Dwivedi, Jun Jie Hew, Heather Linton Kelly, Laurie Hughes, Chieh Yu Lin, Anubhav Mishra, Ian Phau, Ramakrishnan Raman, Marianna Sigala, Yun Chia Tang, Lai Wan Wong, Garry Wei Han Tan

Research outputs 2022 to 2026

Purpose: The launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas. Design/methodology/approach: Through an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare. Findings: The subject …

Incorporating Visual Information Into Natural Language Processing, Maxwell Mbabilla Aladago

Dartmouth College Ph.D Dissertations

Natural language describes entities in the world, some real and some abstract. It is also common practice to complement human learning of natural language with visual cues. This is evident in the heavily graphical nature of children’s literature which underscores the importance of visual cues in language acquisition. Similarly, the notion of “visual learners” is well recognized, reflecting the understanding that visual signals such as illustrations, gestures, and depictions effectively supplement language. In machine learning, two primary paradigms have emerged for training systems involving natural language. The first paradigm encompasses setups where pre-training and downstream tasks are exclusively in natural …

The Year Of Ai: Raising Campus Awareness Through Art, Exhibits, And Community Engagement, Essraa Nawar

Library Articles and Research

This poster highlights the Leatherby Libraries’ leadership in advancing AI literacy through creative, inclusive, and interdisciplinary approaches. As part of Chapman University’s “Year of AI,” the library launched initiatives such as Beyond the Lens and AI: The Next Chapter, blending art, ethics, and education to inspire campus-wide engagement. Through collaboration with IS&T, Town & Gown, and academic departments, the library positioned itself as a hub for ethical dialogue and innovation. The poster shares replicable models for how libraries can foster AI awareness through community partnerships, exhibitions, and experiential learning.

Sequential Convex Programming Using Safe Flight Corridor For Trajectory Planning Of Uavs, Zhu Wang, Zhenpeng Zhang, Mengtong Zhang, Guangtong Xu

Journal of System Simulation

Abstract: To address the issues of sensitivity to initial values and weak convergence of sequential convex programming(SCP) based time-optimal trajectory planning for UAVs, a SCP method using safety flight corridor, denoted as SFC-SCP(safe flight corridor-sequential convex programming) is proposed. According to the obstacle avoidance path obtained from the front-end path planning, a safe flight corridor is constructed by forming a convex polygon safe flight area without obstacles for each trajectory point. The non-convex obstacle avoidance constraint is converted into linear inequality constraints to improve convergence ability. The rear-end SCP method is used to transform the nonlinear trajectory optimization problem under …

Task Reallocation Method For Unmanned Swarm Under Adversarial Conditions, Lun Zhang, Mei Yang, Tuo Zhao, Shuiku Zhang, Jian Huang

Journal of System Simulation

Abstract: Heterogeneous unmanned swarms have important potential applications in future wars. However, during the high-intensity confrontation in the battlefield, how to efficiently and quickly redistribute the tasks carried by the damaged agents so that the swarms could successfully complete the mission is a difficult problem that must be addressed in the combat application of unmanned swarms. This paper proposes a task reallocation method named improved CNP-HA (contract net protocol-Hungarian algorithm). Through the allocation mechanism and the bidding mechanism, the method realizes the task reallocation of damaged agents with lower communication cost and faster speed comparing with baseline methods. In the …

Moving Target Velocity Measurement Method Based On Multi-View Observation Optimization Of Uav Image, Yuxin Wu, Zhilong Zhang, Aoxu Liu, Jiangwei Zou, Chuwei Li

Journal of System Simulation

Abstract: Measuring the position and velocity of moving targets is an important requirement for drone video analysis. In this paper, a moving target localization and velocity estimation algorithm based on least square optimization of UAV multi-view observation images is proposed: the video and corresponding pose parameters obtained by the airborne optoelectronic system are used to establish a line-of-sight model at multiple observation times, it is unified to the WGS-84 coordinate system by coordinate transformation, the position and velocity of the moving target are estimated based on the least squares algorithm. This algorithm does not require laser ranging information between the …