Artificial Intelligence and Robotics Commons^™

Exploring And Adapting Chinese Gpt To Pinyin Input Method, Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi

Research Collection School Of Computing and Information Systems

While GPT has become the de-facto method for text generation tasks, its application to pinyin input method remains unexplored. In this work, we make the first exploration to leverage Chinese GPT for pinyin input method. We find that a frozen GPT achieves state-of-the-art performance on perfect pinyin. However, the performance drops dramatically when the input includes abbreviated pinyin. A reason is that an abbreviated pinyin can be mapped to many perfect pinyin, which links to even larger number of Chinese characters. We mitigate this issue with two strategies, including enriching the context with pinyin and optimizing the training process to …

Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Software developers often use social media (such as Twitter) to shareprogramming knowledge such as new tools, sample code snippets,and tips on programming. One of the topics they talk about is thesoftware library. The tweets may contain useful information abouta library. A good understanding of this information, e.g., on thedeveloper’s views regarding a library can be beneficial to weigh thepros and cons of using the library as well as the general sentimentstowards the library. However, it is not trivial to recognize whethera word actually refers to a library or other meanings. For example,a tweet mentioning the word “pandas" may refer to …

Diffusion Of Ai Governance, Langtao Chen, Brenda Eschenbrenner, Fiona Fui-Hoon Nah, Keng Siau, Yuzhou Qian

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) has the potential to address social, economic, and environmental challenges. However, effective use of AI in organizations relies on the establishment of an AI governance framework. Although existing studies have discussed a variety of issues raised by AI-based systems and proposed AI governance frameworks to overcome those issues, organizations face challenges in adopting AI governance. Informed by innovation diffusion theory, this research evaluates the impact of internal and external influences on AI governance adoption between highly regulated and less regulated industries. We also assess the effect of adopting AI governance on organizational performance. Findings from this study …

Neighborhood Cooperative Multiagent Reinforcement Learning For Adaptive Traffic Signal Control In Epidemic Regions, Chengwei Zhang, Yu Tian, Zhibin Zhang, Wanli Xue, Xiaofei Xie, Tianpei Yang, Xin Ge, Rong Chen

Research Collection School Of Computing and Information Systems

Nowadays, multiagent reinforcement learning (MARL) have shared significant advances in the adaptive traffic signal control (ATSC) problems. For most of the researches, agents are all isomorphic, which disregards the situation in which isomerous intersections cooperative together in a real ATSC scenario, especially in epidemic regions where different intersections have quite different levels of importance. To this end, this paper models the ATSC problem as a networked Markov game (NMG), in which agents take into account information, including traffic conditions of it and its connected neighbors. A cooperative MARL framework named neighborhood cooperative hysteretic DQN (NC-HDQN) is proposed. Specifically, for each …

Software Engineering User Study Recruitment On Prolific: An Experience Report, Brittany Reid, Markus Wagner, Marcelo D’Amorim, Christoph Treude

Research Collection School Of Computing and Information Systems

Online participant recruitment platforms such as Prolific have been gaining popularity in research, as they enable researchers to easily access large pools of participants. However, participant quality can be an issue; participants may give incorrect information to gain access to more studies, adding unwanted noise to results. This paper details our experience recruiting participants from Prolific for a user study requiring programming skills in Node.js, with the aim of helping other researchers conduct similar studies. We explore a method of recruiting programmer participants using prescreening validation, attention checks and a series of programming knowledge questions. We received 680 responses, and …

Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw

Electrical & Computer Engineering Theses & Dissertations

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …

Development Of Modeling And Simulation Platform For Path-Planning And Control Of Autonomous Underwater Vehicles In Three-Dimensional Spaces, Sai Krishna Abhiram Kondapalli

Mechanical & Aerospace Engineering Theses & Dissertations

Autonomous underwater vehicles (AUVs) operating in deep sea and littoral environments have diverse applications including marine biology exploration, ocean environment monitoring, search for plane crash sites, inspection of ship-hulls and pipelines, underwater oil rig maintenance, border patrol, etc. Achieving autonomy in underwater vehicles relies on a tight integration between modules of sensing, navigation, decision-making, path-planning, trajectory tracking, and low-level control. This system integration task benefits from testing the related algorithms and techniques in a simulated environment before implementation in a physical test bed. This thesis reports on the development of a modeling and simulation platform that supports the design and …

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

Video Games, Grief, And The Character Link System, Nam Nguyen

LSU New Orleans Theses and Dissertations

Grief can encompass more than just the loss of real-life people. It can be felt with the loss of a pet, changes in daily structure, and even the loss of video game characters. The topic of grief related to video games and video game characters comes at a time when games as a service (GaaS) continue to increase in popularity and the phenomenon where these games also inevitably terminate service. To combat this unique form of grief, the Character LINK System was created as a tool that uses simple natural language processing (NLP) techniques to offer support to the bereaved …

Levee Seepage Identification From Aerial Images Using Machine Learning, Sofiane Benkara

LSU New Orleans Theses and Dissertations

Levees protect from natural disasters that can threaten human health, infrastructure, and biological systems by protecting low-lying lands near or below sea level from flooding. However, seepage in those levees undermines their structural integrity, leading to failures. Today the United States has approximately over a hundred thousand miles of levee, many of which are reaching or have surpassed their initial design life. Given the concern, there is a need to develop reliable, rapid, and non-intrusive levee monitoring systems to detect the presence of seepage. This study explores the use of Deep Convolutional Neural Network (DCNN) integrated with Discrete Cosine Transform …

Computational Approaches To Facilitate Automated Interchange Between Music And Art, Rao Hamza Ali

Computational and Data Sciences (PhD) Dissertations

Recently, there has been a tremendous increase in generating and synthesizing music and art using various computational techniques. An area that is still under-researched, however, is how one medium can be converted into the other, while maintaining the overall aesthetics. Over the last few centuries, artists, composers, and scholars, have attempted to use substitute one form of art for the other: by proposing techniques where music notes are synonymous to colors, by inventing instruments that combine the aesthetics of music and visual art, and by incorporating the two media in live performances. A widely accepted computational approach, for the conversion, …

Data-Driven Models For Remaining Useful Life Estimation Of Aircraft Engines And Hard Disk Drives, Austin Coursey

Honors College Theses

Failure of physical devices can cause inconvenience, loss of money, and sometimes even deaths. To improve the reliability of these devices, we need to know the remaining useful life (RUL) of a device at a given point in time. Data-driven approaches use data from a physical device to build a model that can estimate the RUL. They have shown great performance and are often simpler than traditional model-based approaches. Typical statistical and machine learning approaches are often not suited for sequential data prediction. Recurrent Neural Networks are designed to work with sequential data but suffer from the vanishing gradient problem …

Using Machine Learning To Recognize Chronic Rhinosinusitis, Irene Liu '23

Student Publications & Research

Chronic Rhinosinusitis (CRS) is a nasal disease characterized by the inflammation of the mucosa and paranasal sinuses with a duration of at least 12 consecutive weeks. So, to diagnose CRS, one needs to keep a record of their symptoms for ~12 weeks before they are recommended to get a tomography which will allow physicians to classify them as a patient with CRS or without. This is a timely and costly process; thus, machine learning should be used to speed the process up. Since patients with CRS have more obstructed noses, the sound produced should be different than an individual without …

Identification Of Return-Oriented Programming Attacks Using Risc-V Instruction Trace Data, Daniel F. Koranek, Scott R. Graham, Brett J. Borghetti, Wayne C. Henry

Faculty Publications

An increasing number of embedded systems include dedicated neural hardware. To benefit from this specialized hardware, deep learning techniques to discover malware on embedded systems are needed. This effort evaluated candidate machine learning detection techniques for distinguishing exploited from non-exploited RISC-V program behavior using execution traces. We first developed a dataset of execution traces containing Return Oriented Programming (ROP) exploitation on the RISC-V Instruction Set Architecture (ISA) and then developed several deep learning bidirectional Long Short-Term Memory (LSTM) models capable of distinguishing exploited traces from non-exploited traces, each using subsets of features from the execution traces. An objective of this …

Self-Supervised Video Object Segmentation Via Cutout Prediction And Tagging, Jyoti Kini, Fahad Shahbaz Khan, Salman Khan, Mubarak Shah

Computer Vision Faculty Publications

We propose a novel self-supervised Video Object Segmentation (VOS) approach that strives to achieve better object-background discriminability for accurate object segmentation. Distinct from previous self-supervised VOS methods, our approach is based on a discriminative learning loss formulation that takes into account both object and background information to ensure object-background discriminability, rather than using only object appearance. The discriminative learning loss comprises cutout-based reconstruction (cutout region represents part of a frame, whose pixels are replaced with some constant values) and tag prediction loss terms. The cutout-based reconstruction term utilizes a simple cutout scheme to learn the pixel-wise correspondence between the current …

Data-Driven Design And Analysis Of Next Generation Mobile Networks For Anomaly Detection And Signal Classification With Fast, Robust And Light Machine Learning, Muhammed Furkan Küçük

USF Tampa Graduate Theses and Dissertations

This research focuses on machine (and deep) learning applications (including clustering,anomaly detection and signal classification) for self-organizing and next generation mobile networks in wireless communications. Specifically, this dissertation document will address the three different topics.

First, in the study titled “Performance analysis of neural network topologies and hyperparameters for deep clustering”, we explore the relationship between the clustering performance and network complexity. Deep learning found its initial footing in supervised applications such as image and voice recognition successes of which were followed by deep generative models across similar domains. In recent years, researchers have proposed creative learning representations to utilize …

Research On Discrete Workshop Task Assignment Based On Improved Water Filling Algorithm, Kaituan Feng, Jie Yuan

Journal of System Simulation

Abstract: Aiming at the unsatisfactory the results of the real-time dynamic task allocation in discrete workshops are not ideal, an improved water filling algorithm is proposed. Compared with the equal cost allocation of the water injection algorithm, the processing rate and cost factors are added to the improved water injection algorithm to coordinate the processing rate, the cost and the workpieces. The allocation of the different cost workpieces is realized and the result is adjusted, which can meet the requirements of discrete distribution. The improved water injection algorithm can dynamically allocate the newly added workpieces in real time. The proposed …

Virtual Scene Stereoscopic Panorama Generation And Viewport Rendering Algorithm, Haoxiang Li, Chunyi Chen, Xiaojuan Hu, Yunbiao Liu, Qiwei Xing

Journal of System Simulation

Abstract: Aiming at the nonuniform sampling in map projection and the redundancy in aspheric projection, spherical Fibonacci lattices is used to sample the visible spherical area to generate spherical Fibonacci lattice panorama with low-redundancy and high-quality. On the basis of panoramic stereo imaging model, the binocular ray direction generation algorithm for spherical stereo panorama is proposed. With Fibonacci grid, an adaptive filtering method of light-visibility map for panorama is designed to generate spherical panorama with approximate soft shadow. The nearest neighbor interpolation is used to render the viewport of panorama. Extensive experiments show that the frame frequency of the viewport …

Study On Near-Body Pressure Characteristics Of Bionic Robotic Fish Undulating In Near Wall Region, Ou Xie, Aiguo Song, Qixin Zhu

Journal of System Simulation

Abstract: To avoid unbalanced workload assignment, we studied the vehicle routing problem with refined oil secondary distribution considering workload balance. A bi-objectivemixed integer programming model was built to minimize the total distribution cost and the maximum difference in vehicle route length. A heuristic variable neighborhood tabu search algorithm was designed. An improved Solomon_I1 insertion algorithm was developed to generate afeasible initial solution such that the total distribution cost was as small as possible. Then, the variable neighborhood tabu search algorithm was used to improve the initial solution and thereby obtain the approximate optimal solution. The simulation results show that in …

Key Technology Research On Stall Spin Simulation Training System Of An Aircraft, Guangxu Xi, Yongyi Liu, Chong Wu, Junjie Zhang, Yinghao Chen

Journal of System Simulation

Abstract: In order to realize the simulation continuity of the multi-state evolution of traffic and to solve the problem that multi-state traffic can only be simulated by single state traffic through multiple times, the middleware model of continuous traffic event simulation is built by the secondary development interface of VISSIM simulation software, and the original two related traffic simulation events are jointly driven. The secondary development of VISSIM-com is carried out by C# and database. The survey data of the intersection of Ganghua Road and Baihua Road in the Yuzhong District of Chongqing is selected as the example, and the …