Computer Sciences Commons^™

Review On Finite Element Model For Anti-Riot Kinetic Projectile Thorax Blunt Impact, Wang Song, Renjun Zhan

Journal of System Simulation

Abstract: In order to promote the construction and verification of the finite element models of human thorax and non-lethal projectiles in the field of blunt ballistic impact, the detailed anatomical and analogous structures of the thorax finite element models are combed and five typical of them are compared and verified. The construction and verification methods of the typical non-lethal projectile models are refined and the impact injury assessment methods are summarized. The next research recommendations are given. In order to improve the accuracy and biological fidelity of the thorax model, it is necessary to establish the physical and finite …

Real-Time Leaf Recognition Method Based On Cnn Network And Multi-Task Loss Function, Xingquan Cai, Yuxin Tu, Yakun Ge, Zhe Yang

Journal of System Simulation

Abstract: Aiming at the problems that the traditional leaf recognition is susceptible to the environmental interference and is difficult to realize the multi-leaf real-time recognition in complex background, a real-time leaf recognition method based on CNN network and multi-task loss function is proposed. The CNN network is used to the extract image feature maps and input them into RPN network to generate regional proposals. On the basis of the feature maps and region proposals, the feature map is proposaled, the leaf classification and bounding box regression are performed respectively, and the leaf classification and position of the leaf prediction box …

Hyperspectral Image Anomaly Detection Based On Background Reconstruction, Xiaorui Song, Zou Ling, Lingda Wu, Wanpeng Xu

Journal of System Simulation

Abstract: In the anomaly detection of hyperspectral images (HSIs), aiming at the difficulty of distinguishing the abnormal target from the background and the low accuracy of background prediction, a new HSI anomaly detection algorithm based on background sparse reconstruction is proposed. An online dictionary learning method is used to estimate the background spectral dictionary. The estimated background image is sparse reconstructed by the learning dictionary. The estimated background image is subtracted from the origin image to get the residual image. The anomaly detection is achieved by using the local RX detector to traverse the residual image. The effectiveness of the …

Human Depth Maps Restoration Based On Guided Gan, Jingfang Yin, Dengming Zhu, Shi Min, Zhaoqi Wang

Journal of System Simulation

Abstract: The depth maps captured by a small depth camera on mobile devices suffer from the problem of severe holes. The Guided Generative Adversarial Network (Guided GAN) based on deep learning is proposed to restore human depth maps with above problems. The high-precision human segmentation features and depth class features are extracted from the monocular RGB image by the guider based on the stacked hourglass network. The holes in the human depth maps are filled by the special generator under the guidance of the extracted human features. In order to get the more realistic results, the discriminator is introduced …

Modeling And Visualization On Scalar Fields Of Meteorological Data, Shuoben Bi, Yucheng Gong, Mingyue Lu, Zhou Hao, Yuanxiang Mao

Journal of System Simulation

Abstract: There are still many deficiencies in efficiency, interactivity, and accuracy in the visualization of meteorological scalar fields. Aiming at these problems, a display method based on multi-level volume rendering technology is proposed, which processes the spatial field data to form a three-dimensional space grid. Through the dynamic mapping between the scalar values and the color values and transparency, the three-dimensional dynamic visualization effect of the meteorological scalar data is realized. Aiming at the interpolation method for the traditional MC (Marching Cubes) algorithm being not applicable to the meteorological data, and the connection method having ambiguity problem, the improvement method …

Preloading Mechanism Of Large-Scale Web3d Scene Based On Dr Prediction, Huijuan Zhang, Xinqi Guo, Dongqing Wang, Jinyuan Jia

Journal of System Simulation

Abstract: In order to improve the loading efficiency of large-scale Web3D scene, a mechanism based on DR prediction is proposed. It combines the DR track prediction algorithm and historical path-based interest path clustering algorithm to track the field. The medium path prediction and path clustering in the traffic domain are applied to the virtual scene loading field, and a preloading mechanism of the Web3D scene is proposed. Experiment results show that the preloading mechanism can significantly improve the data transmission efficiency, optimize the loading speed of large-scale Web3D scene, and effectively improve the roaming experience of users in Web3D scene. …

A Survey On Graph Representation And Visualization Techniques, Chen Yi, Menglu Zhang, Yuchai Wan

Journal of System Simulation

Abstract: A graph consists of nodes and edges, and represents the relationship between two or more entities. Graph-based analysis can help understand the structure and nature of the entity relationships and reveal the implicit relationships in graphs. The representation and visualization methods of graphs play an important role in the graph analysis. In graph visualization research, the accuracy of knowledge transfer and people's mental map, etc. should be considered first, then the graphs beauty, the time requirement, and the computer performance. The graph representation methods, graph layout algorithms, visualization methods based on node-link-graphs, adjacency matrices, and graph embedding are reviewed, …

Advanced Parallel Algorithms In Computational Electromagnetics, Shu Wang

Electrical and Computer Engineering ETDs

The rapid development of high performance computing has pushed the computational electromagnetic(CEM) towards high accuracy, high fidelity and extreme computational scales. There is a great need for existing CEM solvers to have enhanced parallelism and scaling capability. The purpose of this dissertation is to investigate advanced parallel algorithms for both frequency and time domain solvers.

In frequency domain, this work first develop the underpinnings of parallel preconditioning technique and high-order transmission condition in the context of multi-solver scheme. The result is a computing resource-aware and implementation wise compact solver. Then this work targeted at developing efficient algorithms for cases where …

Deep Learning For Identifying Breast Cancer, Yihong Li

Master of Science in Computer Science Theses

Medical images are playing an increasingly important role in the prevention and diagnosis of diseases. Medical images often contain massive amounts of data. Professional interpretation usually requires a long time of professional study and experience accumulation by doctors. Therefore, the use of super storage and computing power in deep learning as a basis can effectively process a large amount of medical data. Breast cancer brings great harm to female patients, and early diagnosis is the most effective prevention and treatment method, so this project will create a new optimized breast cancer auxiliary diagnosis model based on ResNet. Analyze and process, …

Deep Learning For Identifying Lung Diseases, Lin Wang

Master of Science in Computer Science Theses

Growing health problems, such as lung diseases, especially for children and the elderly, require better diagnostic methods, such as computer-based solutions, and it is crucial to detect and treat these problems early. The purpose of this article is to design and implement a new computer vision-based algorithm based on lung disease diagnosis, which has better performance in lung disease recognition than previous models to reduce lung-related health problems and costs . In addition, we have improved the accuracy of the five lung diseases detection, which helps doctors and doctors use computers to solve this problem at an early stage.

Automatic Recognition, Segmentation, And Sex Assignment Of Nocturnal Asthmatic Coughs And Cough Epochs In Smartphone Audio Recordings: Observational Field Study, Filipe Barata, Peter Tinschert, Frank Rassouli, Claudia Steurer-Stey, Elgar Fleisch, Milo Puhan, Martin Brutsche, David Kotz, Tobias Kowatsch

Dartmouth Scholarship

Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio …

Human Supremacy As Posthuman Risk, Daniel Estrada

The Journal of Sociotechnical Critique

Human supremacy is the widely held view that human interests ought to be privileged over other interests as a matter of ethics and public policy. Posthumanism is the historical situation characterized by a critical reevaluation of anthropocentrist theory and practice. This paper draws on animal studies, critical posthumanism, and the critique of ideal theory in Charles Mills and Serene Khader to address the appeal to human supremacist rhetoric in AI ethics and policy discussions, particularly in the work of Joanna Bryson. This analysis identifies a specific risk posed by human supremacist policy in a posthuman context, namely the classification of …

A Data Scientist Looks At Covid19 Part Ii: Mythbusting, Anthony Breitzman

College of Science & Mathematics Departmental Research

No abstract provided.

A Real-Time Feature Indexing System On Live Video Streams, Aditya Chakraborty, Akshay Pawar, Hojoung Jang, Shunqiao Huang, Sripath Mishra, Shuo-Han Chen, Yuan-Hao Chang, George K. Thiruvathukal, Yung-Hsiang Lu

Computer Science: Faculty Publications and Other Works

Most of the existing video storage systems rely on offline processing to support the feature-based indexing on video streams. The feature-based indexing technique provides an effec- tive way for users to search video content through visual features, such as object categories (e.g., cars and persons). However, due to the reliance on offline processing, video streams along with their captured features cannot be searchable immediately after video streams are recorded. According to our investigation, buffering and storing live video steams are more time-consuming than the YOLO v3 object detector. Such observation motivates us to propose a real-time feature indexing (RTFI) system …

Law, Technology, And Pedagogy: Teaching Coding To Build A “Future-Proof” Lawyer, Alfredo Contreras, Joe Mcgrath

Minnesota Journal of Law, Science & Technology

No abstract provided.

Not Intelligent: Encoding Gender Bias, Cara Tenenbaum

Minnesota Journal of Law, Science & Technology

No abstract provided.

Motivational Principles And Personalisation Needs For Geo-Crowdsourced Intangible Cultural Heritage Mobile Applications, Federica Lucia Vinella, Ioanna Lykourentzou, Konstantinos Papangelis

Presentations and other scholarship

Whether it’s for altruistic reasons, personal gains, or third party’s interests, users are influenced by different kinds of motivations when making use of mobile geo-crowdsourcing applications (geoCAs). These reasons, extrinsic and/or intrinsic, must be factored in when evaluating the use intention of these applications and how effective they are. A functional geoCA, particularly if designed for Volunteered Geographic Information (VGI), is the one that persuades and engages its users, by accounting for their diversity of needs across a period of time. This paper explores a number of proven and novel motivational factors destined for the preservation and collection of Intangible …

Novel Technique To Analyze The Effects Of Cognitive And Non-Cognitive Predictors On Students Course Withdrawal In College, Mohammed Ali

Technology Faculty Publications and Presentations

A novel technique was applied to a college student database to identify the cognitive and non-cognitive factors that predict college students’ course withdrawal behaviors. Predictors such as high school grade point average (HSGPA), standardized test scores (ACT–American College Test or SAT-Scholastic Aptitude Test), number of credit hours enrolled, and age were analyzed in this study. Data mining software algorithms were used to study information about undergraduate students at a west-south-central state university in the United States. The study results revealed that two factors, number of enrolled credit hours, and a student’s age have the most effect on collegiate course withdrawal …

Being Human In Stem Csc 213x, Harrison Dekker

Library Impact Statements

No abstract provided.

Exploring Covid-19 Data Csc 292, Harrison Dekker

Library Impact Statements

No abstract provided.

Deep Learning Predictive Modeling With Data Challenges (Small, Big, Or Imbalanced), Renhao Liu

USF Tampa Graduate Theses and Dissertations

In the real world, data used to build machine learning models always has different sizes and characteristics. These size and characteristic features, including small datasets, big datasets, imbalanced datasets, often lead to different challenges when training machine learning models. Models trained on a small number of observations tend to overfit the training data and produce inaccurate results. When it comes to big data, efficiently learning from "huge" size data in a short time becomes important. With an imbalanced dataset, learning is usually biased towards the majority class in the data and appropriate measurements are needed to check model performance.

As …

Satc: Core: Medium: Collaborative: Baitbuster 2.0: Keeping Users Away From Clickbait, Mahdi Nasrullah Al-Ameen

Funded Research Records

No abstract provided.

Ieee Access Special Section Editorial: Machine Learning Designs, Implementations And Techniques, Shadi A. Aljawarneh, Oguz Bayat, Juan A. Lara, Robert P. Schumaker

Computer Science Faculty Publications and Presentations

IEEE access special section editorial.

Allosteric Regulation At The Crossroads Of New Technologies: Multiscale Modeling, Networks, And Machine Learning, Gennady M. Verkhivker, Steve Agajanian, Guang Hu, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the “second secret of life.” The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of …

Appliance Energy Prediction Using Data Analysis Pipeline And Machine Learning Algorithms, Rishav Hore

Manipal Institute of Technology, Manipal Theses and Dissertations

No abstract provided.

Implementation Of Neural Network On Low Resource Environment, Movin Dsouza

Manipal Institute of Technology, Manipal Theses and Dissertations

No abstract provided.

Recent Advances In Smart Contracts: A Technical Overview And State Of The Art, Victor Youdom Kemmoe, William Stone, Jeehyeong Kim, Daeyoung Kim, Junggab Son

School of Computing Faculty Scholarship and Creative Works

Smart contracts, as an added functionality to blockchain, have received increased attention recently. They are executable programs whose instance and state are stored in blockchain. Hence, smart contracts and blockchain enable a trustable, trackable, and irreversible protocol without the need for trusted third parties which generally constitute a single point of failure. If a user creates and distributes a smart contract, others will be able to interact with it while the underlying blockchain ensures a trustable execution. In this paper, we aim to introduce state-of-the-art technologies of the smart contract protocol. We firstly introduce the history of blockchain and smart …

Atmospheric Contrail Detection With A Deep Learning Algorithm, Nasir Siddiqui

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Aircraft contrail emission is widely believed to be a contributing factor to global climate change. We have used machine learning techniques on images containing contrails in hopes of being able to identify those which contain contrails and those that do not. The developed algorithm processes data on contrail characteristics as captured by long-term image records. Images collected by the United States Department of Energy’s Atmospheric Radiation Management user facility(ARM) were used to train a deep convolutional neural network for the purpose of this contrail classification. The neural network model was trained with 1600 images taken by the Total Sky Imager(TSI) …

Earth-Like Planet In A Binary Star System, Melissa Kamrowski

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper was written as a final lab report for a computer modeling class. It uses dynamical simulations to investigate the behavior of a planet with the mass and velocity magnitude of the Earth placed in a low mass binary star system, which was loosely based on the Sirius binary. The simulations are completed using gravitational force properties along with the Verlet algorithm, then the results were observed through a modeling program, in which each set was set as an animation to determine behavior. Results came from 40 strategic starting points, which were chosen to cover a large spread of …

Summed Batch Lexicase Selection On Software Synthesis Problems, Joseph Deglman

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Lexicase selection is one of the most successful parent selection methods in evolutionary computation. However, it has the drawback of being a more computationally involved process and thus taking more time compared to other selection methods, such as tournament selection. Here, we study a version of lexicase selection where test cases are combined into several composite errors, called summed batch lexicase selection; the hope being faster but still reasonable success. Runs on some software synthesis problems show that a larger batch size tends to reduce the success rate of runs, but the results are not very conclusive as the number …