Computer Sciences Commons^™

Task Scheduling For Internet Of Vehicles Based On Deep Reinforcement Learning In Edge Computing, Xiang Ju, Shengchao Su, Chaojie Xu, Beibei He

Journal of System Simulation

Abstract: Aiming at the offloading and execution of delay-constrained computing tasks for internet of vehicles in edge computing, a task scheduling method based on deep reinforcement learning is proposed. In multi-edge server scenario, a software-defined network-aided internet of vehicles task offloading system is built. On this basis, the task scheduling model of vehicle computation offloading is given. According to the characteristics of task scheduling, a scheduling method based on an improved pointer network is designed. Considering the complexity of task scheduling and computing resource allocation, the deep reinforcement learning algorithm is used to train the pointer network. The vehicle offloading …

Airport Operational Efficiency Evaluation Based On Combined Weighting-Topsis Model, Jie Hu, Fan Bao

Journal of System Simulation

Abstract: In order to improve the scientificity and comprehensiveness of the airport operational efficiency evaluation, a new method based on the combined weighting-TOPSIS model is proposed. From 4 dimensions of stand operational efficiency, passenger boarding efficiency, aircraft taxiing efficiency, and coordination efficiency, a new airport operational efficiency evaluation system consisting of 11 indicators, such as flight approach rate, corridor bridge turnover rate, stand change ratio, etc., are constructed. G1 method and entropy weight method are implemented respectively to calculate the subjective and objective weights of the evaluation indicators, and the combined weights are calculated by minimizing the deviation of subjective …

Research On Network Public Opinion Propagation Model Of Major Epidemics Under Cross-Infection Of Double Emotions, Yaming Zhang, Yanyuan Su, Guiru Zhao, Xiaoyu Guo

Journal of System Simulation

Abstract: Major epidemics provoke a variety of netizens' emotions. To some degree, the interaction of netizens' intense emotions determine the development direction of public opinion. Considering the complexity and dual emotional contagion, the impact of emotional factors in network public opinion is quantified to three dimensions indicators, emotional enhancement, differences and conversion rates. SIPINR public opinion propagation model is constructed. The equilibrium points and the transmission threshold are estimated and the stability is proved. The law of network public opinion propagation during major epidemics is revealed through numerical simulation. The results show that the dual emotional contagion would lead to …

Automatic Target Recognition Of Substation 3d Scene For Digital Twin, Qian Tu, Jun Li, Dongliang Fan, Qi Kong, Jie Shen

Journal of System Simulation

Abstract: In order to improve the accuracy of automatic target recognition and promote the effect on substation operation and maintenance, automatic target recognition of substation 3D scene for digital twin is proposed. The automatic target recognition model for the three dimensional scene of the substation is constructed. The perception module of the model is used to collect the real-time status data of substation, and the communication module is used to transmit the data to digital twin modules. This module, based on the received data information, realizes the deep fusion and panoramic mapping of substation information through the knowledge base constructed …

Simulation On High-Speed Train Carriage Evacuation Considering Passengers Moving To Adjacent Carriages, Zuoan Hu, Tian Zeng, Yidong Wei, Yi Ma

Journal of System Simulation

Abstract: To study the influence of passengers moving to adjacent carriages on high-speed train carriages' evacuation, a cellular automata model considering export selection is established. Taking the CR400BF second-class carriage as the research object, some analytical indexes such as evacuation efficiency, number of conflicts and the congestion degree are used to study the effect of passengers moving to adjacent carriages on carriage evacuation, and the passenger seat distribution, the number of passengers transferred from adjacent carriages and the opening door modes of adjacent carriages are discussed. The simulation results show that the discretized seat distribution reduces the times of conflicts …

Application Of Improved Path Tracking Algorithm In Robot Slam, Qian Li, Ye Tao, Hui Li

Journal of System Simulation

Abstract: Mapping is an important part of automated logistics. At present, SLAM is widely used. However, in large-scale scenes, errors are accumulated because robots often repeatedly measure and scan the region edge, which makes it impossible to quickly build a high-precision and complete map. An autonomous mapping method based on auxiliary path tracking is proposed, in which the given initial sketch is grid denoised and the auxiliary path is fitted and improved by multi segment cubic polynomial. The improved pure pursuit algorithm is used to guide the robot to build the map and improve the total distance and time of …

Modeling And Simulation On Production Logistics Of Intelligent Workshop Manufacturing System Based On Efsm, Liuzhen Li, Chao Jin, Tingyu Lin, Yaoqin Zhu

Journal of System Simulation

Abstract: The production logistics mode of manufacturing industry is developing rapidly, on which the modeling and simulation can provide the decision support for the design, analysis and transformation of manufacturing system. A description of the entity elements in intelligent workshop manufacturing system is given according to the classification of "human machine material environment rule". A production and logistics componentized EFSM model is created on the basis of EFSM and componentized modeling ideas. The modeling process for multi-job production in smart shop and the component model instantiation methodology are elaborated. The simulation running through the automatic conversion of EFSM-DEVS model and …

Research On 3d Object Detection Method With Cross-Module Attention, Renjie Xu, Xiaoming Zhang, Chen Wang, Peng Wu

Journal of System Simulation

Abstract: To address the issue of feature loss that occurs during the extraction and transmission of target features in 3D object detection tasks using point cloud data, this study proposes an object detection method based on cross-module attention. This method incorporates a channel attention module and a spatial attention module to enhance the crucial feature information. Through feature transformation, the features from different stages of the attention module are connected to mitigate the loss of features during the extraction and transmission process. To tackle the problem of inadequate detection performance in target detection networks for objects of different scales, a …

A Critical Computing Curriculum Design Case: Exploring Tribal Sovereignty For Middle School Students, Kristin A. Searle, Aubrey Rogowski, Colby Tofel-Grehl, Mengying Jiang

Journal of Computer Science Integration

We report on our efforts to design an integrated computing curriculum for middle school students in Montana that is in line with the Kapor Center’s focus on culturally sustaining-revitalizing pedagogies. Montana provides a unique context for doing this work because a state constitutional mandate requires all K-12 students to learn about tribal histories and cultures through Indian Education For All (IEFA). IEFA centers around seven essential understandings about Indigenous peoples in Montana that are integrated across content areas. In addition, implementation of Montana’s CS standards began in the 2021–2022 school year. In the curricular design, we sought to bring together …

Employing An Abolitionist, Critical Race Pedagogy In Cs: Centering The Voices, Experiences And Technological Innovations Of Black Youth, Tiera Tanksley

Journal of Computer Science Integration

This paper proposes a pedagogical extension of culturally responsive praxis called abolitionist, critical race pedagogy in CS. To showcase the power and potentiality of this pedagogy, this paper examines the experiences of 2 cohorts of Black high school students (n = 30) who participated in a critical race technology course that was taught during the dual pandemic of COVID-19 and anti-Black racism. The goal of this summer course was to employ an abolitionist, critical race pedagogy in CS to foster Black students’ ability to critically examine the ubiquity of anti-Black racism within the socio-technical architectures (e.g. code, data, algorithms and …

Guilty Machines: On Ab-Sens In The Age Of Ai, Dylan Lackey, Katherine Weinschenk

Critical Humanities

For Lacan, guilt arises in the sublimation of ab-sens (non-sense) into the symbolic comprehension of sen-absexe (sense without sex, sense in the deficiency of sexual relation), or in the maturation of language to sensibility through the effacement of sex. Though, as Slavoj Žižek himself points out in a recent article regarding ChatGPT, the split subject always misapprehends the true reason for guilt’s manifestation, such guilt at best provides a sort of evidence for the inclusion of the subject in the order of language, acting as a necessary, even enjoyable mark of the subject’s coherence (or, more importantly, the subject’s separation …

Culturally Responsive-Sustaining Computational Thinking: Enactment In Elementary Classrooms, Victoria Macann, Aman Yadav

Journal of Computer Science Integration

Technology has increasingly permeated many aspects of everyday life and this evolution raises the need for individuals to understand how the digital world works and what opportunities and risks it brings (Nouri, Zhang, Mannila & Norén, 2019). For this to be an experience for everyone, we need to rethink how we integrate computational thinking (CT) and provide teachers with tools to center their students’ identities, experiences, and cultures in the classroom. In this paper, we present two case studies of primary (elementary) teachers from a full primary (student ages 5–13) semi-rural school in the North Island of New Zealand that …

Tiny Machine Learning For Underwater Image Enhancement: Pruning And Quantizaition Approach, Dr Khaled Nagaty, The British University In Egypt, Andreas Pester Dr

Computer Science

Many people have expressed an interest in underwater image processing in a variety of fields, including underwater vehicle control, archaeology, marine biological studies, etc. Underwater exploration is becoming an increasingly important element of our lives, with applications ranging from underwater marine and creature research to pipeline and communication logistics, military use, touristic and entertainment use. Underwater images suffer from poor visibility, distortion, and poor quality for a variety of causes, including light propagation. The major issue arises when these images must be captured at depths greater than 500 feet and artificial lighting needs to be provided. Efficient algorithms and models …

Deep Learning For Microbiome-Based Integrative Modeling And Microbial Biomarkers Identification, Sen Yang

Statistical Science Theses and Dissertations

The human microbiome, comprising trillions of microorganisms, plays a pivotal role in modulating host physiology via molecular and metabolite exchanges. One of the major challenges in this field lies in the effective integration of microbiome and metabolomics data, an achievement that holds the promise of substantially enhancing the precision of disease prediction. However, many datasets prioritize microbiome data while neglecting paired metabolome information. Additionally, the prevalent analytical tools face challenges in effectively merging these intricate datasets, leading to possible misinterpretations and reduced prediction accuracies.

To address these challenges, the first part of this research introduces the Microbiome-based Supervised Contrastive Learning …

Smart Applications And Resource Management In Internet Of Things, Zeinab Akhavan

Computer Science ETDs

Internet of Things (IoT) technologies are currently the principal solutions driving smart cities. These new technologies such as Cyber Physical Systems, 5G and data analytic have emerged to address various cities' infrastructure issues ranging from transportation and energy management to healthcare systems. An IoT setting primarily consists of a wide range of users and devices as a massive network interacting with different layers of the city infrastructure resulting in generating sheer volume of data to enable smart city services. The goal of smart city services is to create value for the entire ecosystem, whether this is health, education, transportation, energy, …

Simulation-Based Adaptive Interface For Personalized Learning Of Ai Fundamentals In Secondary School, Sara Guerreiro-Santalla, Dalila Duraes, Helen Crompton, Paulo Novais, Francisco Bellas

STEMPS Faculty Publications

This paper presents the first results on the validation of a new Adaptive E-learning System, focused on providing personalized learning to secondary school students in the field of education about AI by means of an adaptive interface based on a 3D robotic simulator. The prototype tool presented here has been tested at schools in USA, Spain, and Portugal, obtaining very valuable insights regarding the high engagement level of students in programming tasks when dealing with the simulated interface. In addition, it has been shown the system reliability in terms of adjusting the students’ learning paths according to their skills and …

An Empirical Study Of Machine Learning Techniques For Accurate Stock Price Forecasting, Daniel Paliulis, Hari Patchigolla

Honors Scholar Theses

This paper presents a comprehensive approach to predicting future stock prices of companies using machine learning and time series analysis. The research problem is centered around addressing the complexity and emotion-driven nature of stock investment decisions. To create an objective determinant in stock decisions, we propose a machine learning model utilizing time series data from major companies, including Amazon, Apple, Google, Nvidia, Meta, Tesla, Salesforce, Intel, and Microsoft. We explore the use of Long Short-Term Memory (LSTM) neural networks, to capture the temporal dynamics of stock prices. These models are designed to process sequential data, maintaining short term and long …

Ai Empire: Unraveling The Interlocking Systems Of Oppression In Generative Ai's Global Order, Jasmina Tacheva, Srividya Ramasubramanian

Media Studies - All Scholarship

As artificial intelligence (AI) continues to captivate the collective imagination through the latest generation of generative AI models such as DALL-E and ChatGPT, the dehumanizing and harmful features of the technology industry that have plagued it since its inception only seem to deepen and intensify. Far from a “glitch” or unintentional error, these endemic issues are a function of the interlocking systems of oppression upon which AI is built. Using the analytical framework of “Empire,” this paper demonstrates that we live not simply in the “age of AI” but in the age of AI Empire. Specifically, we show that …

Probing And Enhancing The Reliance Of Transformer Models On Poetic Information, Almas Abdibayev

Dartmouth College Ph.D Dissertations

Transformer models have achieved remarkable success in the widest variety of domains, spanning not just a multitude of tasks within natural language processing, but also those in computer vision, speech, and reinforcement learning. The key to this success is largely attributed to the self-attention mechanism, particularly its ability to scale in performance as it grows in the number of parameters. Extensive effort has been underway to study the major linguistic properties learned by these models during the course of their pretraining. However, the role of certain finer linguistic phenomena present in language and their utilization by Transformers has not been …

Deep Learning Uncertainty Quantification For Clinical Text Classification, Alina Peluso, Ioana Danciu, Hong-Jun Yoon, Jamaludin Mohd Yusof, Tanmoy Bhattacharya, Adam Spannaus, Noah Schaefferkoetter, Eric B. Durbin, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi, Shang Gao

School of Public Health Faculty Publications

INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated. METHOD: In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National …

Cm-Ii Meditation As An Intervention To Reduce Stress And Improve Attention: A Study Of Ml Detection, Spectral Analysis, And Hrv Metrics, Sreekanth Gopi

Master of Science in Computer Science Theses

Students frequently face heightened stress due to academic and social pressures, particularly in de- manding fields like computer science and engineering. These challenges are often associated with serious mental health issues, including ADHD (Attention Deficit Hyperactivity Disorder), depression, and an increased risk of suicide. The average student attention span has notably decreased from 21⁄2 minutes to just 47 seconds, and now it typically takes about 25 minutes to switch attention to a new task (Mark, 2023). Research findings suggest that over 95% of individuals who die by suicide have been diagnosed with depression (Shahtahmasebi, 2013), and almost 20% of students …

The Transformative Integration Of Artificial Intelligence With Cmmc And Nist 800-171 For Advanced Risk Management And Compliance, Mia Lunati

Cybersecurity Undergraduate Research Showcase

This paper explores the transformative potential of integrating Artificial Intelligence (AI) with established cybersecurity frameworks such as the Cybersecurity Maturity Model Certification (CMMC) and the National Institute of Standards and Technology (NIST) Special Publication 800-171. The thesis argues that the relationship between AI and these frameworks has the capacity to transform risk management in cybersecurity, where it could serve as a critical element in threat mitigation. In addition to addressing AI’s capabilities, this paper acknowledges the risks and limitations of these systems, highlighting the need for extensive research and monitoring when relying on AI. One must understand boundaries when integrating …

Designing An Artificial Immune Inspired Intrusion Detection System, William Hosier Anderson

Theses and Dissertations

The domain of Intrusion Detection Systems (IDS) has witnessed growing interest in recent years due to the escalating threats posed by cyberattacks. As Internet of Things (IoT) becomes increasingly integrated into our every day lives, we widen our attack surface and expose more of our personal lives to risk. In the same way the Human Immune System (HIS) safeguards our physical self, a similar solution is needed to safeguard our digital self. This thesis presents the Artificial Immune inspired Intrusion Detection System (AIS-IDS), an IDS modeled after the HIS. This thesis proposes an architecture for AIS-IDS, instantiates an AIS-IDS model …

Scalable And Explainable Self-Supervised Motif Discovery In Temporal Data, Somayeh Bakhtiari Ramezani

Theses and Dissertations

The availability of a scalable and explainable rule extraction technique via motif discovery is crucial for identifying the health states of a system. Such a technique can enable the creation of a repository of normal and abnormal states of the system and identify the system’s state as we receive data. In complex systems such as ECG, each activity session can consist of a long sequence of motifs that form different global structures. As a result, applying machine learning algorithms without first identifying the local patterns is not feasible and would result in low performance. Thus, extracting unique local motifs and …

Study Of Augmentations On Historical Manuscripts Using Trocr, Erez Meoded

Theses and Dissertations

Historical manuscripts are an essential source of original content. For many reasons, it is hard to recognize these manuscripts as text. This thesis used a state-of-the-art Handwritten Text Recognizer, TrOCR, to recognize a 16^th-century manuscript. TrOCR uses a vision transformer to encode the input images and a language transformer to decode them back to text. We showed that carefully preprocessed images and designed augmentations can improve the performance of TrOCR. We suggest an ensemble of augmented models to achieve an even better performance.

Phenotyping Cotton Compactness Using Machine Learning And Uas Multispectral Imagery, Joshua Carl Waldbieser

Theses and Dissertations

Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

Is Your Computer Lying? Ai And Deception, Noreen L. Herzfeld

Reuter Professorship of Science and Religion Publications

Recent developments in AI, especially the spectacular success of Large Language models, have instigated renewed questioning of what remains distinctively human. As AI stands poised to take over more and more human tasks, what is left that distinguishes humans? One way we might identify a humanlike intelligence would be when we detect it telling lies. Yet AIs lack both the intention and the motivation to truly tell lies, instead producing merely bullshit. With neither emotions, embodiment, nor the social awareness that leads to a theory of mind, AIs lack the internal referents on which to judge truth or falsity. When …

Overcoming Foreign Language Anxiety In An Emotionally Intelligent Tutoring System, Daneih Ismail

College of Computing and Digital Media Dissertations

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning.

Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct …

The Holistic Archival Personality Profiling Model (Happm): Comprehensive Data Integration For Personality Analysis, James Hutson, Pace Ellsworth

Faculty Scholarship

The traditional approach to biographical profiling, predominantly reliant on limited and fragmented datasets, has frequently resulted in superficial personality understandings. This is largely due to an overemphasis on official records and notable events, neglecting the rich tapestry of everyday experiences and personal interactions that significantly shape personalities. To address this shortcoming, this article introduces a multi-disciplinary methodology, The Holistic Archival Personality Profiling Model (HAPPM), which integrates a diverse array of archival materials, including personal correspondences, social media footprints, and family memorabilia. This approach involves digitizing various data forms, including handwritten documents, into machine-readable text, and then semantically classifying this data …