Computer Sciences Commons^™

Mimic: Ai And Ar-Enhanced Multi-Modal, Immersive, Relative Instruction Comprehension, Dhanuja Wanniarachchi, Archan Misra

Research Collection School Of Computing and Information Systems

We present a multimodal instruction comprehension framework, called MImIC, that utilizes visual sensing (including LIDAR and 2D RGB sensing) & AI spatial reasoning capabilities to support more seamless and immersive interaction between humans and AI-driven situated assistive agents. MImIC's key new capability is to support disambiguation of a wider set of relative spatial references that users naturally employ while issuing spatially-situated instructions. To support enhanced visual grounding via a combination of both fully-qualified and relative attribute references, MImIC uses (a) a fine-tuned transformer-based language translation DNN to accurately convert natural verbal commands into a structured set of machine understandable constraints …

Identifying Human Factor Causes Of Remotely Piloted Aircraft System Safety Occurrences In Australia, John Murray, Steven Richardson, Keith Joiner, Graham Wild

Research outputs 2022 to 2026

Remotely piloted aircraft are a fast-emerging sector of the aviation industry. Although technical failures have been the largest cause of accident occurrences for Remotely Piloted Aircraft Systems (RPASs), if they are to follow the path of conventionally crewed aviation, Human Factors (HFs) will increasingly contribute to accidents as the technology of RPASs improves. Examining an RPAS accident database from 2008–2019 for HF-caused accidents and coding to the Human Factors Analysis and Classification System (HFACS) taxonomy, an exploration of RPAS HFs is carried out and the predominant HF issues for RPAS pilots identified. The majority of HF accidents were coded to …

Pathways To Chronic Disease Detection And Prediction: Mapping The Potential Of Machine Learning To The Pathophysiological Processes While Navigating Ethical Challenges, Ebenezer Afrifa-Yamoah, Eric Adua, Emmanuel Peprah-Yamoah, Enoch O. Anto, Victor Opoku-Yamoah, Emmanuel Acheampong, Michael J. Macartney, Rashid Hashmi

Research outputs 2022 to 2026

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these “omics” studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for …

Advances In Natural Fiber Polymer And Pla Composites Through Artificial Intelligence And Machine Learning Integration, Md Helal Uddin, Mohammed Huzaifa Mulla, Tarek Abedin, Abreeza Manap, Boon Kar Yap, Reji Kumar Rajamony, Kiran Shahapurkar, T. M.Yunus Khan, Manzoore Elahi M. Soudagar, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

Natural Fibre Polymer (NFP) and Polylactic Acid (PLA) composites have received a lot of interest in a variety of sectors because they are environmentally friendly, renewable, and sustainable. Over the last decade, researchers have investigated the aspects of NFP/PLA composite development and optimization for a wide range of applications, including packaging materials, automotive components, construction materials, textile and apparel, biomedical devices, agricultural and horticultural applications, electronics, and consumer electronics. Furthermore, using Artificial Intelligence (AI) and Machine Learning (ML) methodologies has increased these polymer materials and associated technologies in their search for new potential ways to further progress in NFP and …

Enhancing Virtual Reality Usability With A Ml Based Dynamic Adaptive System, Ananth Ramaseri-Chandra, Hassan Reza

Computer Science Posters and Presentations

Virtual reality (VR) holds tremendous potential, but cybersickness degrades the user experience. Since individuals vary, a one-size-fits-all design is insufficient. Our work introduces a dynamic adaptive system that personalizes VR experiences by learning individual cybersickness profiles from head-tracking data and sickness questionnaires while adjusting settings such as field of view and foveated rendering strength. Early results show that our system reduces post-exposure sickness scores, enhancing the user experience and highlighting the importance of personalizing VR.

What's The Art In Artificial Intelligence?, Emily Verla Bovino

Open Educational Resources

This workbook learns from Black and Indigenous artists working with Artificial Intelligence to confront issues of ethics and aesthetics in its technologies. It features guided learning activities with links to publicly available video lectures and online articles, and includes options for experiential learning through both a tutorial in Midjourney and a visit to the public art collection at York College in Jamaica, Queens. Featured artists include: American Artist, Rizvana Bradley, Beth Coleman, Denise Ferreira da Silva, Suzanne Kite, Sondra Perry, Mimi Onuoha and Alisha B. Wormsley. Works by Martin Puryear and Maren Hassinger are explored in the Midjourney exercise.

About …

Human-Ai Collaboration In Writing: A Multidimensional Framework For Creative And Intellectual Authorship, James Hutson

Faculty Scholarship

The integration of AI technologies into the writing process has significantly altered traditional notions of authorship, creativity, and intellectual labor. Historically, writing was seen as a human-driven cognitive and creative exercise, but with the rise of generative AI tools such as ChatGPT and Claude, the line between human and AI contributions has become increasingly ambiguous. This paper addresses the limitations of the current sliding scale model, which views AI involvement as ranging from “none” to “complete”. In its place, we propose a new multidimensional framework that more accurately reflects the complexity of human-AI collaboration in writing. The model includes axes …

Beyond The Blue Skies: A Comprehensive Guide For Risk Assessment In Aviation, Leila Halawi, Mark Miller, Sam Holley

Publications

Risk assessment in aviation is a critical process that safeguards the safety and reliability of operations. Aviation operations encompass inherent risks, from mechanical failures to human errors and environmental factors. The significance of these risks may be severe, leading to accidents, injuries, and loss of life. Recognizing and mitigating risks is supreme in this dynamic environment, where emerging technologies and innovation constantly reshape this industry. This chapter includes an in-depth explanation of risk management and analysis, leading to the core elements of risk assessment specifically for aviation operations. We will describe the process and explore some of the applications and …

Using Ai To Make Accessible Accessible Content, Melinda Turner

Faculty Other Scholarly Works

This professional development session will explore how to leverage AI tools, specifically Google’s NotebookLM, to create accessible learning materials for college students. Participants will learn how to use NotebookLM to transform existing content into more accessible formats. The session will cover practical strategies for implementing AI to improve readability and comprehension for all learners including students with disabilities, non-native English speakers, and students with varying learning preferences. We will discuss how to create transcripts for audio/video files and ensure content is well-organized and easy to navigate. This session will also highlight the importance of evidence-based practices in content creation and …

A Review Of Racial Differences And Disparities In Ecg, Jianwei Zheng, Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei, Omolola Ogunyemi

Mathematics, Physics, and Computer Science Faculty Articles and Research

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major …

Bloom: Behavioral Learning And Outcome Observation In Microbes, Sean Sarwar Haque, Luke Compton Wharton, Ming Lin, Razvan Voicu

Symposium of Student Scholars

Understanding how pathogens respond to physical changes in their environment is crucial for developing effective treatments and preventative measures. Current research often relies on static models or experimental data that either fail to capture the dynamic interactions within cellular environments or are not generalizable to other types of pathogens. This project aims to address this gap by creating a comprehensive cell simulation that models pathogens and their response to chemical, physical, and physiological changes. The proposed solution is a simulation that integrates biological data and computational modeling to replicate the behavior of pathogens in real time as they are affected …

Quantifying The Role Of Active Listening And Reassurance In Virtual Health Coach Interactions, Ghulam Hussain, Brian Keegan, Robert Ross

Articles

Conversational Agents have the potential to support healthcare through coaching exercise routines, but are still lacking in demonstrating authentic social behaviours to support engagement. To this end, we present a series of experiments that we conducted in order to investigate how automated health care coaches can be more effective when their interaction style is tailored to demonstrate qualities associated with a good bedside manner, namely active listening and reassurance. To test this, we first developed a dataset of 135 dialogue excerpts from three distinct sources, i.e., original, handcrafted and LLMs, the latter two of which were tuned to demonstrate specific …

Ai Culture ‘Profiling’ And Anti-Money Laundering: Efficacy Vs Ethics, John W. Goodell, Cal B. Muckley, Parvati Neelakantan, Darragh Ryan

University Research

Using extensive transaction and money laundering detection data, at a globally important financial institution, we investigate the efficacy of including facets of national culture in formulating anti-money laundering predictions. For corporate and individual accounts, Hofstede individualism scores of the country in which a customer is resident, or from which a wire is sent/received, are of first-order importance in the detection of money laundering. When combined with account and transaction data; as well as even a proprietary institutional algorithm, individualism scores continue to determine the models’ predictive performances. The efficacy of cultural profiling in money laundering detection underscores the need for …

Limitations In Speech Recognition For Young Adults With Down Syndrome, Franceli L. Cibrian, Yingying 'Yuki' Chen, Kayla Anderson, Cecilia Marie Abrahamsson, Vivian Genaro Motti

Engineering Faculty Articles and Research

Speech recognition has the potential to make technology more accessible to users. However, the accuracy of speech recognition remains limited for users with disabilities, including those with Down Syndrome, and the types and frequencies of recognition errors are poorly understood. This paper characterizes these problems, focusing on errors occurring when recognizing Down Syndrome speech. We analyze the transcripts from six speech recognition algorithms (Google, IBM, Otter.ai, Microsoft, AssemblyAI, OpenAI) using the audio content of 15 individuals with Down Syndrome (331 dialogues; 3428 words). Our analysis shows: (1) significant difference in speech recognition accuracy for people with Down Syndrome compared to …

Self Supervised Artificial Intelligence Predicts Poor Outcome From Primary Cutaneous Squamous Cell Carcinoma At Diagnosis, Nicolas Coudray, Michelle C. Juarez, Maressa C. Criscito, Adalberto Claudio Quiros, Reason Wilken, Stephanie R. Jackson Cullison, Mary L. Stevenson, Nicole A. Doudican, Ke Yuan, Jamie D. Aquino, Daniel M. Klufas, Jeffrey P. North, Siegrid S. Yu, Fadi Murad, Emily Ruiz, Chrysalyne D. Schmults, Cristian D. Cardona Machado, Javier Cañueto, Anirudh Choudhary, Alysia N. Hughes, Alyssa Stockard, Zachary Leibovit-Reiben, Aaron R. Mangold, Aristotelis Tsirigos, John A. Carucci

Department of Dermatology and Cutaneous Biology Faculty Papers

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of …

Improved Bidirectional A* Quadratic Path Planning Algorithm For Mobile Robots, Jiongyi Li, Qiang Li, Xinwen Zhang, Myo Htet Zin, Yongbin Cai

Journal of System Simulation

Abstract: Aiming at the problems of the traditional A* algorithm, such as the unhoped intersection between the planned path and the obstacles, the planned path has many inflection points and the search time is long, an improved bidirectional A* quadratic path planning algorithm for the indoor environments is proposed. Through the expansion of the map, the intersection between the planned path and the obstacle is solved. By new heuristic functions and bidirectional expansion methods, the search speed and accuracy of the bidirectional A* algorithm are improved. Turning cost function and adaptive weight are introduced to reduce the number of turning …

Collaborative Optimization Problem Of Dynamic Pre-Maintenance And Green Scheduling, Yuyan Jiang, Ning Ma, Yan Li, Rumeijiang Gan, Fuyu Wang

Journal of System Simulation

Abstract: For the traditional flexible job shop scheduling problem, a joint optimization of machine dynamic pre-maintenance and green scheduling is considered to establish an integrated optimization model with the optimization objectives of minimizing maximum completion time, total carbon emissions, and total cost. An improved NSGA-II algorithm is proposed to solve the model. A three-layer encoding method based on process, machine, and pre maintenance is adopted to design a one-step decoding scheme that considers process allocation, machine selection, and machine pre-maintenance strategies. The algorithm improves the elitist retention strategy, designs an adaptive crossover mutation function with algebraic changes, and a mutation …

Trajectory Optimization Of Robotic Arm Based On Improved Simulated Annealing Genetic Algorithm, Qiang Xu, Jianlei Xu, Yanhai Hu, Haihui Chen, Xing Zhang, Zhaohui Xing

Journal of System Simulation

Abstract: To optimize the working trajectory of the robotic arm, a modified simulated annealing genetic algorithm is proposed. Comprehensively considering the operating requirements and performance characteristics of the robotic arm, the five-order polynomial interpolation method is used to plan a smooth motion trajectory in the joint space. The penalty function method is used to handle the individuals that do not meet the constraint conditions, and the fitness function is recalibrated by the dynamic linear calibration method. An adaptive adjustment mechanism for crossover probability and variation probability is set to modify the genetic algorithm. The cooling idea of the simulated annealing …

An Algorithm For Cloud-Based Web Service Combination Optimization Through Plant Growth Simulation, Qiang Li, Huawei Qin, Bingqin Qiao, Ruifang Wu

Journal of System Simulation

Abstract: In order to improve the efficiency of cloud-based web services, an improved plant growth simulation algorithm scheduling model. This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources. Then, a lightinduced plant growth simulation algorithm was established. The performance of the algorithm was compared through several plant types, and the best plant model was selected as the setting for the system. Experimental results show that when the number of test cloud-based web services reaches 2 048, the model being 2.14 times faster than PSO, 2.8 times faster than the …

Research On Flexible Integrated Scheduling Under Stochastic Processing Times Based On Improved D3qn Algorithm, Xiang Li, Xiaoyu Ren, Yongbing Zhou, Jian Zhang

Journal of System Simulation

Abstract: Aiming at the problem of time uncertainty in discrete manufacturing workshops, we construct an integrated scheduling mathematical model with the optimization objective of minimizing the maximum completion time based on the consideration of equipment and process constraints, and propose an improved dual-competitive deep Q-network algorithm (ID3QN) to solve the flexible integrated scheduling problem under stochastic working hours. The levels of process, machine, and overall scheduling are designed as features. Eight composite scheduling rules are formed as the action space by combining process rules based on processing times, processing sequences, and process structure tree, along with machine rules relevant to …

Research On The Target Allocation Method For Air Defense And Anti-Missile Defense Of Naval Ships, Shuaidi Fei, Changlong Cai, Fei Liu, Minghui Chen, Xiaoming Liu

Journal of System Simulation

Abstract: To solve the problems of multiple types of state information and correlation of time-series state information encountered in the dynamic weapon target assignment problem, a dynamic weapon target assignment method based on an improved deep reinforcement learning algorithm is proposed. A multiinput assignment model of target missile-interceptor unit, interceptor unit, and defense unit under multiwave target and multi-phase is constructed. A multi-input state space is designed, and a Markov decision process is established in conjunction with the problem model. A feature extraction network combining multi-input information processing and gated recurrent network is designed, which improves the ability to extract …

Combat Effectiveness Evaluation Of Air Defense Missile Weapon System Based On Rbf Neural Network, Peng Zhang, Ke Feng, Jiancheng Gong, Xiaoqiang Yang, Jinxing Shen

Journal of System Simulation

Abstract: A combat effectiveness evaluation method based on RBF neural network is proposed to address the problems of high dimensionality, high complexity, and subjective evaluation methods in current air defense missile weapon systems. A combat effectiveness index system for air defense missile weapon systems has been constructed by analyzing the OODA environmental combat theory. The RBF neural network model simulation is implemented using MATLAB, and several methods such as BP, PCABP, and Elman neural network are compared and verified through simulation. The simulation results show that the predicted evaluation results of the RBF neural network model are closer to the …

Dynamic Loading Simulation Method For Large-Scale Spiking Neural Network, Jiawei Shen, Daye Cai, Guoqing Yang, Pan Lü, Hong Li

Journal of System Simulation

Abstract: To address the problem of high GPU memory requirements in large-scale spiking neural network simulation, a dynamic loading simulation method for large-scale spiking neural networks is proposed. This method uses data movement at the sub-network granularity and utilizes the host memory as a larger memory pool to reduce the limitation of GPU memory on the model simulation scale, enabling large-scale spiking neural network simulation on a single GPU computer. The pipeline acceleration technique is adopted to reduce the impact of data movement on simulation speed. The simulation of a million-scale neural network is achieved in a single GPU experimental …

Design And Simulation Analysis Of Guidance Law For Boost Phase Interceptor Missile, Xu Zhang, Peng Zeng, Xu Li, Xuehe Zheng, Jianheng Xue, Chao Wang

Journal of System Simulation

Abstract: The intercept time window in the boost phase is very short, which requires the missile to have high speed and high acceleration capability, at the same time, because the target ballistic missile is still accelerating in the boost phase, the guidance scheme of the interceptor missile needs to have the ability to cope with the characteristics of the booster phase interceptor missile. The trajectory simulation model is established, and the guidance law of the boost phase interceptor missile in the initial guidance, midguidance and final guidance stage are designed in detail according to the performance of the guidance mechanism …

Quadrotor Uav Path Planning Based On Rapidly-Exploration Directional Tree Algorithm, Shijun Hu, Hailiang Liu, Binglei Wang, Wenke Su

Journal of System Simulation

Abstract: Aiming at the problems of low planning success rate, slow convergence speed, and suboptimal paths in the RRT algorithm for quadrotor UAV path planning of in complex environments, a directional exploration tree algorithm is proposed, which uses a directional sampling strategy to improve the directionality of the tree expansion, and by introducing an adaptive target adjustment strategy and a branch expansion strategy, the tree can expand quickly towards the target point while avoiding obstacles. The redundant points in the initial path are removed by the pruning process, and then the trajectory correction and smoothing process are performed on the …

Research On Task Planning Methods For Space Robot Assisted Operation, Guoqiang Fang, Haitao Chang, Xing Liu, Zhengxiong Liu, Panfeng Huang

Journal of System Simulation

Abstract: In view of the difficulties caused by the complicated task process and numerous task constraints during the space robot assisted operation, a task planning method combining fast forward search algorithm and hierarchical network algorithm is proposed, in which the task planning process is divided into task planning and replanning. Based on the fast forward search task planning method of operation cost, the execution sequence of actions with minimum operation cost is obtained. The task adaptive replanning method based on hierarchical network corrects and compensates the problems according to the priority of compensation for the movement, grab and release actions. …

An Empirical Analysis Of New Perspectives For Strategy Solving In Intelligent Game-Theoretic Decision-Making, Jiongming Su, Junren Luo, Shaofei Chen

Journal of System Simulation

Abstract: With the development of artificial intelligence technology, especially the promotion of largescale pre-training model theory, some new perspectives of strategy solving for intelligent game-theoretic decision-making have gradually been widely concerned and discussed. This paper combines the development of artificial intelligence technology and the transformation of strategy solving paradigm for intelligent game-theoretic decision-making, takes Chess (two-player zero-sum perfect information game), diplomacy (multi-player general-sum imperfect information game), and StarCraft Multi-Agent Challenge (multi-agent Markov game) as the research object for empirical analysis on sequential decision-making, the new paradigm and new way of strategy solving are analyzed according to the new perspective of …

Intelligent Service Migration Towards Mec-Based Iov Systems, Sijin Huang, Jia Wen, Zheyi Chen

Journal of System Simulation

Abstract: To address the problem of QoS degradation during the vehicle movement, a novel service migration via convex-optimization-enabled deep reinforcement learning (SeMiR) method is proposed. The optimization problem is decomposed into two sub-problems and solved separately. For the service migration sub-problem, an improved deep reinforcement learning based service migration method is designed to explore the optimal migration policy. For the resource allocation sub-problem, a convex optimization based resource allocation method is developed to derive the optimal resource allocation for each MEC server under the given migration decisions, thereby improving the performance of service migration. Experimental results show that the SeMiR …

Intensity-Based Feature Filtering For Lidar-Based Slam, Weigang Li, Shaofeng Zou, Yongqiang Wang, Chuxiang Yu

Journal of System Simulation

Abstract: In order to solve the problem that an excessive influx of feature points into the point cloud registration phase can potentially lead to diminished algorithmic accuracy and suboptimal mapping outcomes, a novel laser SLAM algorithm predicated on the filtering of feature points through the utilization of intensity information is proposed. The intensity distribution near the feature points in the local map is calculated based on the point cloud intensity information, and each feature point within the local map is attributed an intensity distribution index. Through the application of an intensity threshold, feature points that exhibit substantial variations in intensity …

A Hybrid Heuristic Algorithm For Solving The Green Vrp With Priority Delivery, Huanhuan Cui, Lihe Guan

Journal of System Simulation

Abstract: This paper considers the problem that some customers' goods cannot be mixed in logistics distribution. Based on the traditional green vehicle routing problem with simultaneous pickup and delivery, customers are divided into three types: priority delivery, non-priority only pickup without delivery, and non-priority pickup with delivery. A single objective nonlinear optimization model is established to minimize the total cost. A hybrid heuristic method based on simulated annealing and adaptive large neighborhood search algorithm is designed. An improved saving algorithm is used to construct the initial solution. And 5 kinds of destruction operators and 2 kinds of repair operators are …