Computer Sciences Commons^™

An Improved Atomic Search Algorithm, Jianfeng Li, Di Lu, Hexiang Li

Journal of System Simulation

Abstract: The atom search algorithm (ASO) is a new optimization algorithm proposed by imitating the movement of atoms in the natural world. An improved atomic search algorithm (IASO) is proposed to address the problems of prematureness and slow convergence of ASO in solving complex functions. IASO adds the binding force generated by the historical optimal solution of individual atoms to correct the acceleration of ASO and enhance the global search capability. The two multiplier coefficients are adaptively updated to coordinate the algorithm's global search and local development capabilities. The Gaussian mutation strategy is used to re-update the atomic position and …

Research On The Simulation Method Of Urban Rail Transit Feedback Assignment, Jianpeng Hu, Xia Luo

Journal of System Simulation

Abstract: Based on the characteristics of a large number of transfer routes in rail transit network, an improved depth first search algorithm is proposed to get the effective travel time of transfer routes between stations. Based on passenger entry and exit timing obtained from the automatic fare collection (AFC) data, the connect relationship between passengers and trains in time and route is obtained from the arrival time and route selection behavior of passengers. Considering the difference of route choice behavior between departure passenger and transfer passenger, the two are distinguished from each other. The dynamically updated travel …

Simulation Of Robust Optimal Synchronization Control For Direct Drive H-Type Motion Platform, Limei Wang, Hongyan Yao, Kang Zhang

Journal of System Simulation

Abstract: Cell manufacturing is an important organizational form of modern production systems. In scheduling of cell manufacturing systems, machine failures or interruptions are very common in practice, meanwhile the waste due to energy consumption during machine idle time cannot be ignored. Hence the relevant research is with strong significance. This paper considers the problems of machine interruption and energy consumption in cell scheduling, and developed an integer programming model to minimize the makespan as well as the cost of energy consumption during machine idling and the interruption cost. A mixed optimization method is proposed based on improved wolf pack algorithm …

Modeling Time Series Using Multi-Modality Fuzzy Cognitive Maps, Guoliang Feng, Wei Lu, Jianhua Yang

Journal of System Simulation

Abstract: A multi-modality modeling method for time series data based on fuzzy cognitive maps is proposed to address the problem that a single model is difficult to accurately reflect the multi-modal characteristics of time series.The bootstrap method is used to select multiple sub-sequences from the original time serieswhich contain the diverse modality in the original time series. The fuzzy cognitive map sub-models are constructed on each sub-sequencesrespectively. The formed sub-models are further merged by means of granular computing method and the merging performance with different weighting strategies is analyzed. The developed multi-modal model not only has prediction abilities at …

Research On Information Flow Integrated M&S Method For Project Type Manufacturing Process, Mindong Liu, Longjun Wu, Mingchao Tang, Mei Meng

Journal of System Simulation

Abstract: The project type manufacturing process is quite common in shipbuilding and construction industries. Aiming at the problem that the existing discrete manufacturing system modeling and simulation method for flow shop cannot effectively express and imitate this process, taking shipyard dock shop hoisting process as an example, we analyze its components structure, propose a working network-centric, information flow and work flow integrated simulation modelling method, and design a dedicated simulation algorithm with the task process interactive idea. A prototype simulation program is developed with Python language, and the effectiveness of the proposed modelling and simulation method is verified through …

Research On Optimization Of Network Resource Utilization In Static Segment Of Flexray Bus, Xinhang He, Erkang Li, Hongchao Zhang

Journal of System Simulation

Abstract: In order to improve the utilization of the network resources of the FlexRay bus, the network is optimized for static segment scheduling. The FlexRay communication mechanism is analyzed, the message model is established and the calculation method of bandwidth loss is derived, while considering the protocol overhead and network idling, taking the number of static frames and the length of the static frame payload as design variables, the overall optimal packaging scheme is obtained by solving this multi-objective optimization problem. This solution is finally applied to the vehicle chassis integrated control system for simulation analysis and verification. The results …

Research On Joint Optimization Of Energy-Saving Distributed Manufacturing And Preventive Maintenance For Semiconductor Wafers, Jun Dong, Chunming Ye

Journal of System Simulation

Abstract: Aiming at the joint optimization problem of energy-saving distributed manufacturing and preventive maintenance for semiconductor wafers, a two-stage green scheduling model considering both the manufacturing stage and the inspection and repair stage is established to minimize the makespan, the total carbon emissions and the total preventive maintenance cost. An improved hybrid multi-objective grey wolf optimization (IHMGWO)algorithm is proposed. The decoding schemes of factory allocation strategy, machine allocation strategy and synchronous scheduling maintenance strategy considering the flexibility of maintenance workers are designed in IHMGWO. By designing the initial population fusion strategy, predation behavior search strategy, and sub-population mutation strategy, the …

Research On The Construction Method Of Simulation Evaluation Index Of Operation Effectiveness Operation Concept Traction, Ziwei Zhang, Liang Li, Zhiming Dong, Yifei Wang, Li Duan

Journal of System Simulation

Abstract: Agents are difficult to be directly modeled and simulated due to the complexity of their own interaction and learning behaviors. Aiming at the common problems in the discrete simulation of the agent, the event transfer mechanism of the discrete event system specification (DEVS) atomic model is applied to express the interaction and learning of an agent. Through the interaction mode of the agent, the transfer control of multi-state external events, the port connection mode, as well as the introduction of reinforcement learning event transfer representation, a discrete simulation construction method of the agent based on the DEVS atomic model …

Path-Based Model For The Heterogeneous-Fleet Electric Vehicle Routing Problem With Partial Linear Recharging, Weiquan Wang, Ding Ding, Linsha Yan

Journal of System Simulation

Abstract: The heterogeneous-fleet electric vehicle routing problem with partial linear recharging is studied for realistic logistics distribution scenarios using multiple electric vehicle fleets with different transport capacities, driving ranges and acquisition costs. A path-based mixed integer linear model is proposed. The model enumerates the paths visited by all vehicle types between any non-charging nodes, eliminates the infeasible paths through capacity constraints and time window constraints, and eliminates the dominated paths by the dominance criterion. Compared with the traditional charging station replica-based model, this model eliminates the need to set the number of charging station replicas. The results show that the …

Confronting Barriers To Human-Robot Cooperation: Balancing Efficiency And Risk In Machine Behavior, Tim Whiting

Theses and Dissertations

In strategically rich settings in which machines and people do not fully share the same preferences, machines must learn to cooperate and compromise with people to establish mutually successful relationships. However, designing machines that effectively cooperate with people in these settings is difficult due to a variety of technical and psychological challenges. To better understand these challenges, we conducted a series of user studies in which we investigated human-human, robot-robot, and human-robot cooperation in a simple, yet strategically rich, resource-sharing scenario called the Block Dilemma, a game in which players must balance fairness, efficiency, and risk. While both human-human and …

Chatgpt Goes To Law School, Jonathan H. Choi, Kristin E. Hickman, Amy B. Monahan, Daniel Schwarcz

Journal of Legal Education

No abstract provided.

Diabetic Foot Exam System, Stephanie Trusty

Undergraduate Research Symposium

The diabetic foot exam system aims to perform certain aspects of the dermatological and musculoskeletal assessments that are typical to a 3-minute diabetic foot exam. Utilizing the RaspberryPi computer and camera module, the system seeks to capture a series of images of the patient’s foot. It then evaluates these images for calluses, blisters, and three types of deformities: claw toe deformities, hammertoe deformities, and bunions. This evaluation is performed using a trained TensorFlow image classification model, which categorizes the image as a callus, blister, or deformity. The system was tested using six different images: four callus images, a hammertoe deformity …

Covid-19 Classroom Occupancy Detection System, Stephanie Trusty

Undergraduate Research Symposium

The classroom occupancy detection system aims to limit the spread of COVID-19 and support mitigation efforts advised by national and international health organizations by enforcing social distancing in classroom environments. Utilizing the RaspberryPi computer and its compatible camera module, the system accomplishes this by capturing an overhead image of a classroom and assessing the image for violations. Here, violations are defined as the presence of adjacent occupied seats. As such, for an acceptable state to be detected, there must be at least one vacant seat between all students seated in the classroom. The system communicates the classroom’s state with two …

Objective Measure Of Working Memory Capacity Using Eye Movements, James Owens, Gavindya Jayawardena, Yasasi Abeysinghe, Vikas G. Ashok, Sampath Jayarathna

Undergraduate Research Symposium

Human-autonomy teaming (HAT) has become an important area of research due to the autonomous systems being developed for different applications, such as remotely controlled aircraft. Many remotely controlled vehicles will be controlled by automated systems, with a human monitor that may be monitoring multiple vehicles simultaneously. The attention and working memory capacity of operators of remote-controlled vehicles must be maintained at appropriate levels during operation. However, there is currently no direct method of determining working memory capacity, which is important because it is a measure for how memory is being stored for a short term and interacting with long term …

Two-Stage Transfer Learning For Facial Expression Classification In Children, Gregory Hubbard, Megan Witherow, Khan Iftekharuddin

Undergraduate Research Symposium

Studying facial expressions can provide insight into the development of social skills in children and provide support to individuals with developmental disorders. In afflicted individuals, such as children with Autism Spectrum Disorder (ASD), atypical interpretations of facial expressions are well-documented. In computer vision, many popular and state-of-the-art deep learning architectures (VGG16, EfficientNet, ResNet, etc.) are readily available with pre-trained weights for general object recognition. Transfer learning utilizes these pre-trained models to improve generalization on a new task. In this project, transfer learning is implemented to leverage the pretrained model (general object recognition) on facial expression classification. Though this method, the …

Analyzing Decision-Making In Robot Soccer For Attacking Behaviors, Justin Rodney

USF Tampa Graduate Theses and Dissertations

In robotics soccer, decision-making is critical to the performance of a team’s SoftwareSystem. The University of South Florida’s (USF) RoboBulls team implements behavior for the robots by using traditional methods such as analytical geometry to path plan and determine whether an action should be taken. In recent works, Machine Learning (ML) and Reinforcement Learning (RL) techniques have been used to calculate the probability of success for a pass or goal, and even train models for performing low-level skills such as traveling towards a ball and shooting it towards the goal[1, 2]. Open-source frameworks have been created for training Reinforcement Learning …

Predicting The Number Of Objects In A Robotic Grasp, Utkarsh Tamrakar

USF Tampa Graduate Theses and Dissertations

Picking up the desired number of objects at once from a pile is still very difficult to dofor a robot. The main challenge is predicting the number of objects in the grasp. This thesis describes several deep-learning-based prediction models that predict the number of objects in the grasp of a Barrett hand using the tactile sensors on its fingers and palm and its joint angles and torque (strain gauge) readings. The deep learning models include various architectures using autoencoders and vision transformers. We evaluated the models with a dataset of grasping 0, 1, 2, 3, and 4 spheres. Then, we …

Fusionai, A Dna-Sequence-Based Deep Learning Protocol Reduces The False Positives Of Human Fusion Gene Prediction, Pora Kim, Hua Tan, Jiajia Liu, Himansu Kumar, Xiaobo Zhou

Faculty, Staff and Student Publications

Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).

A Deep Learning-Based Approach To Extraction Of Filler Morphology In Sem Images With The Application Of Automated Quality Inspection, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin

Engineering Faculty Articles and Research

Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system …

Interpretable Deep Learning For The Prediction Of Icu Admission Likelihood And Mortality Of Covid-19 Patients, Amril Nazir, Hyacinth Kwadwo Ampadu

All Works

The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such …

A Non-Invasive Interpretable Diagnosis Of Melanoma Skin Cancer Using Deep Learning And Ensemble Stacking Of Machine Learning Models, Iftiaz A. Alfi, Mahfuzur Rahman, Mohammad Shorfuzzaman, Amril Nazir

All Works

A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Malignant tumors are tumors that are cancerous, and benign tumors are non-cancerous. Malignant tumors have the ability to multiply and spread throughout the body at a much faster rate. The early detection of the cancerous skin lesion is crucial for the survival of the patient. Deep learning models and machine learning models play an essential role in …

Zero Trust And Advanced Persistent Threats: Who Will Win The War?, Bilge Karabacak, Todd Whittaker

All Faculty and Staff Scholarship

Advanced Persistent Threats (APTs) are state-sponsored actors who break into computer networks for political or industrial espionage. Because of the nature of cyberspace and ever-changing sophisticated attack techniques, it is challenging to prevent and detect APT attacks. 2020 United States Federal Government data breach once again showed how difficult to protect networks from targeted attacks. Among many other solutions and techniques, zero trust is a promising security architecture that might effectively prevent the intrusion attempts of APT actors. In the zero trust model, no process insider or outside the network is trusted by default. Zero trust is also called perimeterless …

Symbolic Semantic Memory In Transformer Language Models, Robert Kenneth Morain

Theses and Dissertations

This paper demonstrates how transformer language models can be improved by giving them access to relevant structured data extracted from a knowledge base. The knowledge base preparation process and modifications to transformer models are explained. We evaluate these methods on language modeling and question answering tasks. These results show that even simple additional knowledge augmentation leads to a reduction in validation loss by 73%. These methods also significantly outperform common ways of improving language models such as increasing the model size or adding more data.

Split Classification Model For Complex Clustered Data, Katherine Gerot

Honors Program: Senior Projects (Public)

Classification in high-dimensional data has generated tremendous interest in a multitude of fields. Data in higher dimensions often tend to reside in non-Euclidean metric space. This prevents Euclidean-based classification methodologies, such as regression, from reliably modeling the data. Many proposed models rely on computationally-complex embedding to convert the data to a more usable format. Others, namely the Support Vector Machine, rely on kernel manipulation to implicitly describe the "feature space" to arrive at a non-linear decision boundary. The proposed methodology in this paper seeks to classify complex data in a relatively computationally-simple and explainable manner.

Pitfalls And Guidelines For Using Time-Based Git Data, Samuel W. Flint, Jigyasa Chauhan, Robert Dyer

School of Computing: Faculty Publications

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents an extended survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of …

Deeprobot: A Hybrid Deep Neural Network Model For Social Bot Detection Based On User Profile Data, Kadhim Hayawi, Sujith Mathew, Neethu Venugopal, Mohammad M. Masud, Pin Han Ho

All Works

Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the …

A High Precision Machine Learning-Enabled System For Predicting Idiopathic Ventricular Arrhythmia Origins, Jianwei Zheng, Guohua Fu, Daniele Struppa, Islam Abudayyeh, Tahmeed Contractor, Kyle Anderson, Huimin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, …

Robustness Analysis Of Classification Using Recurrent Neural Networks With Perturbed Sequential Input, Guangyi Liu, Arash Amini, Martin Takac, Nader Motee

Machine Learning Faculty Publications

For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices. The sequential inputs can be perturbed in various ways, e.g., streaming images can be deformed due to robot motion or imperfect camera lens. Using the notion of the Voronoi diagram and Lipschitz properties of stable RNNs, we provide a thorough analysis and characterize the maximum allowable perturbations while guaranteeing the full accuracy of the classification task. We illustrate and validate our theoretical results using a map dataset with clouds …

Developing Reinforcement Learning Algorithms For Robots To Aim And Pour Solid Objects, Haoxuan Li

USF Tampa Graduate Theses and Dissertations

Pouring is one of the most commonly executed tasks in a variety of environments. Thereis less attention paid to pouring solid objects and avoiding spillage. Learning the dynamics for pouring solid objects can be a challenge because the collisions and static frictions between objects make their trajectories less predictable than liquid. Nonetheless, pouring solid objects is an important task in real life. In this work, we propose a solution to help robots aim and pour solid objects. The agents will learn how to interact with the environment and identify the optimal pouring trajectories, then manipulate the arm to aim at …

Pad Beyond The Classroom: Integrating Pad In The Scrum Workplace, Jade S. Weiss

USF Tampa Graduate Theses and Dissertations

Purpose: The “story” format used in Scrum ticket writing is confusing to developers and leadsto insufficient ticket content, which lends to miscommunication between team members and administrators, and disrupts workflow from the bottom up. A burgeoning methodology in Technical Writing, Purpose, Audience, Design (PAD) is an alternative ticket format that is easier to teach developers and improves the aforementioned conditions than the existing “story” format. The goal of this paper is to lay out why and how PAD can benefit developers on smaller Scrum teams who are tasked with writing their own tickets. This paper does not offer solutions for …