Artificial Intelligence and Robotics Commons^™

Interactive Example-Based Explanations To Improve Health Professionals’ Onboarding With Ai For Human-Ai Collaborative Decision Making, Min Hun Lee, Renee Bao Xuan Ng, Silvana Xinyi Choo, Shamala Thilarajah

Research Collection School Of Computing and Information Systems

A growing research explores the usage of AI explanations on user’s decision phases for human-AI collaborative decision-making. However, previous studies found the issues of overreliance on ‘wrong’ AI outputs. In this paper, we propose interactive example-based explanations to improve health professionals’ onboarding with AI for their better reliance on AI during AI-assisted decision-making. We implemented an AI-based decision support system that utilizes a neural network to assess the quality of post-stroke survivors’ exercises and interactive example-based explanations that systematically surface the nearest neighborhoods of a test/task sample from the training set of the AI model to assist users’ onboarding with …

Self-Adaptive Fine-Grained Multi-Modal Data Augmentation For Semi-Supervised Muti-Modal Coreference Resolution, Li Zheng, Boyu Chen, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Donghong Ji

Research Collection School Of Computing and Information Systems

Coreference resolution, an essential task in natural language processing, is particularly challenging in multi-modal scenarios where data comes in various forms and modalities. Despite advancements, limitations due to scarce labeled data and underleveraged unlabeled data persist. We address these issues with a self-adaptive fine-grained multi-modal data augmentation framework for semi-supervised MCR, focusing on enriching training data from labeled datasets and tapping into the untapped potential of unlabeled data. Regarding the former issue, we first leverage text coreference resolution datasets and diffusion models,to perform fine-grained text-to-image generation with aligned text entities and image bounding boxes. We then introduce a self-adaptive selection …

Brushbuds: Toothbrushing Tracking Using Earphone Imus, Qiang Yang, Yang Liu, Jake Stuchbury-Wass, Kayla-Jade Butkow, Dong Ma, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Inadequate toothbrushing habits are a leading cause of oral health problems such as tooth decay. Many individuals are uncertain if they are brushing effectively or over-focusing on specific areas. While high-end electric toothbrushes can address these concerns, manual toothbrushes remain widely used due to their simplicity and affordability. In this paper, we introduce BrushBuds, an earphone-based toothbrushing monitoring system aimed at tracking brushing areas, which leverages the ubiquitous presence of earphones to enhance manual toothbrushing. BrushBuds utilizes Inertial Measurement Units (IMUs) in earphones to detect subtle head movements incurred by toothbrushing. By capturing distinct motion patterns specific to brushing for …

An Empirical Study To Evaluate Aigc Detectors On Code Content, Jian Wang, Shangqing Liu, Xiaofei Xie, Yi Li

Research Collection School Of Computing and Information Systems

Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with Large Language Models (LLMs), like ChatGPT, emerging as a leading AIGC model that produces high-quality responses across various applications, including software development and maintenance. Despite its potential, the misuse of LLMs, especially in security and safetycritical domains, such as academic integrity and answering questions on Stack Overflow, poses significant concerns. Numerous AIGC detectors have been developed and evaluated on natural language data. However, their performance on code-related content generated by LLMs remains unexplored. To fill this gap, in this paper, we present an empirical study evaluating …

Risurconv : Rotation Invariant Surface Attention-Augmented Convolutions For 3d Point Cloud Classification And Segmentation, Zhiyuan Zhang, Licheng Yang, Xiang Zhiyu

Research Collection School Of Computing and Information Systems

Despite the progress on 3D point cloud deep learning, most prior works focus on learning features that are invariant to translation and point permutation, and very limited efforts have been devoted for rotation invariant property. Several recent studies achieve rotation invariance at the cost of lower accuracies. In this work, we close this gap by proposing a novel yet effective rotation invariant architecture for 3D point cloud classification and segmentation. Instead of traditional pointwise operations, we construct local triangle surfaces to capture more detailed surface structure, based on which we can extract highly expressive rotation invariant surface properties which are …

A Guideline For Open-Source Tools To Make Medical Imaging Data Ready For Artificial Intelligence Applications: A Society Of Imaging Informatics In Medicine (Siim) Survey, Sanaz Vahdati, Bardia Khosravi, Elham Mahmoudi, Kuan Zhang, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Aylin Tahmasebi, Katherine Andriole, Peter Chang, Keyvan Farahani, Mona Flores, Les Folio, Sina Houshmand, Maryellen Giger, Judy Gichoya, Bradley Erickson

Department of Radiology Faculty Papers

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora …

Can Federated Learning Solve Ai’S Data Privacy Problem?: A Legal Analysis, Warren B. Chik, Florian Gamper

Research Collection Yong Pung How School Of Law

Federated learning (FL) is a method of training AI systems on different datasets without sharing data. The promise of FL is to enable AI systems to be trained on data, including personal data, while preserving data privacy and confidentiality, and thus, inter alia, facilitate compliance with data protection legislation. FL has generated a considerable interest amongst the computer science community, yet there is a dearth of legal analysis of FL. This is a problem because the question of whether FL facilitates compliance with data protection legislation is a legal question. This article will fill this lacuna by providing a comprehensive …

Trust And Robotics: A Multi-Staged Decision-Making Approach To Robots In Community, Wenxi Zhang, Willow Wong, Mark Findlay

Research Collection Yong Pung How School Of Law

With the desired outcome of social good within the wider robotics ecosystem, trust is identified as the central adhesive of the human–robot interaction (HRI) interface. However, building trust between humans and robots involves more than improving the machine’s technical reliability or trustworthiness in function. This paper presents a holistic, community-based approach to trust-building, where trust is understood as a multifaceted and multi-staged looped relation that depends heavily on context and human perceptions. Building on past literature that identifies dispositional and learned stages of trust, our proposed decision to trust model considers more extensively the human and situational factors influencing how …

The Promise And Perils Of Smart (City) Bots As Educational Tools, Thomas Menkhoff, Siew Ning Kan, Shaohui Foong

Research Collection Lee Kong Chian School Of Business

Written from an educator’s perspective, the paper aims to examine the increasing use of chatbot technology capable of answering user queries in various fields such as commerce, public service delivery, urban areas, higher education etc. by simulating human conversation through text messages or voice commands. We analyse why smart bots have become popular technologies in the context smart cities; shed light on different types of bots (chatbots, large language models such as ChatGPT, flying bots); and explain how we use bots as teaching tool in an introductory AI for business course. A key argument putting forward is that smart bots …

Leveraging Imitation Learning In Agricultural Robotics: A Comprehensive Survey And Comparative Analysis, Siavash Mahmoudi, Amirreza Davar, Pouya Sohrabipour, Ramesh Bahadur Bist, Yang Tao, Dongyi Wang

Biological and Agricultural Engineering Faculty Publications and Presentations

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control processes. This paper presents an exhaustive insight focusing on the implementation of imitation learning techniques in agricultural robotics. The survey rigorously examines varied research endeavors utilizing imitation learning to address pivotal agricultural challenges. Methodologically, this survey comprehensively investigates multifaceted aspects of imitation learning applications in agricultural robotics. The survey encompasses the identification of agricultural tasks that can potentially be addressed through imitation learning, detailed analysis of specific models …

Robust And Generalizable Representations In The Hippocampus, Hung-Tu Chen

Dartmouth College Ph.D Dissertations

An intelligent system must balance generalizing across similar experiences with maintaining the distinctiveness of each experience. This thesis explores how the hippocampus manages this balance through its neural representations to support adaptive behavior. In Chapter 1, I provide an overview of key hippocampal phenomena that contribute to this process, including remapping, splitter signal, and replay. In Chapter 2, I challenge the concept of random remapping by showing that it is possible to predict, better than chance, how a given experience will be encoded in the hippocampus across different subjects. This suggests that encoding of related experiences, which was previously thought …

Privacy Risks And Regulatory Challenges In Smart Grids And Renewable Energy Systems, Mikołaj Rajca

internetowy Kwartalnik Antymonopolowy i Regulacyjny (internet Quarterly on Antitrust and Regulation)

Smart grid technologies are central to the global shift towards a more efficient and sustainable energy infrastructure, integrating advanced digital systems with traditional power networks. While these technologies offer significant benefits, including enhanced energy management and the seamless integration of renewable energy sources, they also introduce complex privacy challenges. The extensive data collection and real-time communication capabilities inherent in smart grids raise concerns over consumer privacy, data breaches, and cybersecurity threats. This paper critically examines these privacy risks within the context of evolving regulatory frameworks such as the GDPR, NIS2 Directive, and the forthcoming EU AI Act. The discussion emphasizes …

A Framework For Human Evaluation Of Large Language Models In Healthcare Derived From Literature Review, Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor, Alisa V Stolyar, Katelyn Polanska, Karleigh R Mccarthy, Hunter Osterhoudt, Xizhi Wu, Shyam Visweswaran, Sunyang Fu, Piyush Mathur, Giovanni E Cacciamani, Cong Sun, Yifan Peng, Yanshan Wang

Faculty, Staff and Student Publications

With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we …

Architecting Standards: Leveraging Ia To Drive Emerging Technology Standards​, Noreen Y. Whysel

Publications and Research

In this talk for the annual meeting of the W3C Information Architecture Community Group, I discuss how standards bodies can learn from the Information Architecture field to improve accessibility and usability. By adopting user-centered design principles and engaging in iterative feedback loops, standards organizations can create more relevant and practical standards that align with real-world applications. Emphasizing collaboration with industry experts and end-users will help ensure that standards evolve along with technological advancements.

In the meantime, key standards are examined, such as ISO 9241, which provides guidelines for usability and user-centered design, and the W3C's Web Content Accessibility Guidelines (WCAG), …

The Limits Of Generative Ai In Administrative Law Research, Susan Azyndar

Journal Articles

The author recounts an administrative law classroom experience using generative AI. She considers the complexities of administrative law, AI training, professional responsibility, and traditional resources.

Revolutionizing Public Safety And Criminal Justice Through Ai, Alan Saquella

Publications

Artificial Intelligence (AI) is rapidly transforming public safety, criminal justice and security by fundamentally changing how crimes are committed, investigated and prevented. As AI tools become increasingly sophisticated, law enforcement and corporate security professionals are utilizing these advancements to enhance their capabilities. However, integrating AI into these sectors also brings significant challenges, including ethical concerns, recruitment difficulties, and the surge in crime rates. This article examines the transformative impact of AI, the ongoing efforts to unify AI applications across public safety and security sectors, and expert advice on overcoming the associated challenges.

Ai In The Health Professions, Heidi Monroe, Carrie Fry, Phillip Baker, Erika Busz

AI and the Future of Work

The aim of this track is to provide health professionals, and those interested in mental health and healthcare careers with an understanding of key aspects of AI use in healthcare. Participants will explore advantages of some recent AI developments and evaluate how they may be effectively leveraged to improve patient care, while addressing potential challenges, limitations, and ethical considerations.

Educating With Ai, Grace Seo, David Wicks

AI and the Future of Work

This conference track explores the integration of AI within teaching and learning, with a focus on practical approaches that leverage AI technologies to optimize teaching practices and enhance students’ learning experience. The topics include the essential AI literacies in educational contexts, collaborative learning with AI, and the use of AI for enhanced learning assessments.

A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground …

An Automated Machine Learning Approach To The Retrieval Of Daily Soil Moisture In South Korea Using Satellite Images, Meteorological Data, And Digital Elevation Model, Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence …

The Aimag Project: Using Machine Learning To Predict Crustal Magnetic Anomaly Values, Xavier Gobble, Marlie Mollett, Dr. Dawn King, Dr. Cory Reed, Erin Knese

Undergraduate Research Symposium

A detailed model of the Earth’s total magnetic field is important for acquiring the means for GPS-alternative, magnetic anomaly-based navigation. The Earth’s total magnetic field is an amalgam of 5 mechanisms: the geodynamo generated by the rotation of the Earth’s molten iron core, the fields induced by the flows of electric current in the atmosphere and oceans, the disturbance of the ionosphere by solar wind, and local anomalies attributable to ferromagnetic minerals present in the crust; the lattermost compose the crustal magnetic field. The EMAG2v3 dataset comprises a compilation of satellite, shipborne, and airborne magnetic measurements differenced from the Comprehensive …

Fostering Trust Through User Interface Design In Multi-Drone Search And Rescue, Johanna Ahlskog, Maria Theresa Bahodi, Artur Lugmayr, Timothy Merritt

Research outputs 2022 to 2026

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used in search and rescue (SAR) missions, with pilots transitioning from manual control of single drones to more collaborative tasks orchestrating semi-autonomous fleets. Designing user interfaces to support UAV pilots effectively is crucial to improving the success of search missions. We developed two versions of a multi-drone SAR system prototype to simulate SAR missions and evaluated them with professional UAV SAR pilots in Sweden. Both versions showed the flight paths of the UAVs, yet in one version, a heatmap was overlayed to provide information from a lost person model. We evaluated situational …

Research On Simulation Resource Management Based On Graph Association Organization, Zewei Liu, Yishan Ding, Tingyu Lin, Mingxing Ke, Liqing Guo, Yingying Xiao, Zhilong Zhao, Yan Li, Xuan Lü

Journal of System Simulation

Abstract: The simulation test and evaluation of intelligent system of systems, systems and single equipment is a complex system engineering, which requires effective management of multi-source, heterogeneous and distributed massive simulation resources scattered in cloud test centers and test sites of various units; and good control of dynamically generated tasks, assumptions, configurations, results, evaluations and other data and files. The traditional way of managing and querying simulation resources by category is inefficient and difficult to meet the requirements of large-scale intelligent simulation test and evaluation activities. An overall framework for simulation resource management based on graph association organization, defines a …

Path Planning Based On Improved A* And Dynamic Window Approach, Peng Ji, Xinyuan Zhang, Shuaixuan Gao, Shuorang Wei

Journal of System Simulation

Abstract: In response to the low efficiency, redundant turning points, and collision issues of the traditional A* algorithm, a smart vehicle path planning algorithm that integrates an improved A* algorithm with a dynamic window approach has been proposed. The algorithm has enhanced the search point selection method, optimized the evaluation function, selected key turning points based on the slope values between turning points, and removed redundant turning points. Between every two optimized key turning points, a dynamic window approach that balances speed and safety is used for local obstacle avoidance. Experiments show that compared to the traditional A* algorithm, this …

Research On Digital Twin System Of Rockshaft Hoist, Baiting Zhao, Jianguo Shi, Xiaofen Jia

Journal of System Simulation

Abstract: In order to solve the problem of low intelligence and digitization of the current mine hoisting system, a method based on DT for digital modeling, 3D visualization, and virtual real interaction of shaft hoisting machines is proposed. Aiming at the rockshaft hoist system, based on the digital twin five dimensional model framework, we analyze the operating mechanism of the equipment, and model the fully physical digital system of the rockshaft hoist. By constructing multidimensional multi-scale models and multidimensional heterogeneous data models, twin digital scenes are constructed, and virtual real mapping technology is combined to achieve dynamic mapping and virtual …

A Multimodal Residual Spatial-Temporal Fusion Model Based On Automatic Sleep Classification, Yecai Guo, Shuang Tong

Journal of System Simulation

Abstract: Highly accurate sleep staging plays a crucial role in correctly assessing sleep conditions. Aiming at the problem that the existing convolutional network cannot obtain the topological characteristics of physiological signals, a sleep staging algorithm based on multi-modal residual spatio-temporal fusion is proposed. Time-frequency images and spatio-temporal images are obtained using short-time Fourier transform and adaptive map convolution, which are converted into high-dimensional feature vectors; lightweight interaction of feature information flow is realized through time-frequency feature and spatiotemporal feature extraction modules; the feature enhancement fusion module fuses feature information to outputs sleep staging results. The results show that the model …

Research On Autonomous Decision-Making In Air-Combat Based On Improved Proximal Policy Optimization, Dianwei Qian, Hongmin Qi, Zhen Liu, Zhiming Zho, Jianqiang Yi

Journal of System Simulation

Abstract: To address the problems of high information redundancy and slow convergence speed of traditional reinforcement learning in air-combat autonomous decision-making applications, a proximal policy optimization air-combat autonomous decision-making method, based on dual observation and composite reward is proposed. A dual observation space, which contains interaction information as the main information and individual feature information as a supplement, was designed to reduce the influence of redundant battlefield information on the training efficiency of the decision model. A composite reward function combining result reward and process reward was designed to improve convergence speed. The generalized advantage estimator was applied in the …

An Intelligent Adversaries Behavior Simulation Technology Based On Improved Behavior Trees, Fang Zhou, Bo Fan, Xiaoyi Liu, Yishan Ding, Ningxin Zhang, Yachao Shao, Xiaoyu Zhai

Journal of System Simulation

Abstract: Intelligent algorithm/intelligent platform/intelligent system intelligence capability testing and evaluation need to solve high-level intelligent opponent simulation problems, an intelligent opponent behavior simulation technology based on improved behavior tree is proposed. Four types of behavior tress nodes are designed, including behavior control, combat tasks, behavior actions, and execution condition node. Five atomic behavior actions and parameters are established, including maneuver, reconnaissance and early warning, command and decision-making, firepower strike, and electronic interference node. Five atomic condition nodes are provided, including target selection, weapon launch, and incoming weapon judgment node. The intelligent adversarial behavior simulation system is designed, including a behavior …

Study On Invulnerability Of Urban Rail Network Considering Sum Of The Neighbors Degree, Shuqing Li, Yixiao Song, Guojian Zhong

Journal of System Simulation

Abstract: In order to solve the problem of network cascade paralysis caused by urban rail station or line failure, considering the influence of the first-order neighborhood of network nodes, the load distribution impedance coefficient is proposed based on the nonlinear capacity load model, and a nonlinear capacity load optimization model considering the sum of the neighbors degree is constructed. By optimizing load structure, the alternative probability of nodes during load redistribution is adjusted to reduce the number of node failures in the cascading process, thereby the rail network invulnerability is improved. Taking Chongqing rail network as an example, the rail …

Multi-Step Information Aided Q-Learning Path Planning Algorithm, Yuelong Wang, Songyan Wang, Tao Chao

Journal of System Simulation

Abstract: To improve the path planning capability of mobile robots in a static environment and solve the problem of slow convergence of the traditional Q-learning algorithm in path planning, this paper proposes a multi-step information-aided Q-learning improvement algorithm. Using the multi-step information of greedy action in ε -greedy strategy and length of the historical optimal path to update the eligibility traces, which makes the effective eligibility traces work continuously in the iteration of the algorithm and solves the loop traps that may fall into with the preserved multi-step information; using the local multiflower pollination algorithm to initialize the Q-value table …