Computer Sciences Commons^™

Modeling And Simulation Of Car Following In Fog Based On Cellular Automata, Zhanhong Liu, Xiujian Yang, Xiangji Wu, Huang Zhen

Journal of System Simulation

Abstract: In order to ensure the safety of driving in foggy weather, a microscopic model of car following and lane changing traffic flow is established based on cellular automata. In view of the instability of car following state in foggy weather, dynamic random acceleration is introduced to segment modeling. The simulation accuracy is improved by refining cell size and cell step size and adding position update allowance. The relationship curves of speed and vehicle dispersion with time and the speed density diagram are obtained. The results show that: the vehicle speed fluctuates greatly in medium fog; the dispersion …

Three-Dimensional Visual Dynamic Simulation Of Rice Leaf Cells, Deheng Zhao, Yingding Zhao, Hongyun Yang, Wenlong Yi

Journal of System Simulation

Abstract: To explore the evolution process of rice leaf cell morphology with the help of computer visualization technology, it is necessary to establish the biomechanical relationship between the cell geometry model and its deformation. A dynamic simulation method is proposed for the 3D visualization of leaf cells based on the physical-mechanical properties, in which the cytoskeleton topology is represented by a "half-edge" data structure, and the surface geometries are constructed using a bicubic Bézier surface. The control parameters of a deformed surface are determined through "vertex", "edge" or "face" topological unit query algorithms of the cytoskeleton, and the cell stress-strain …

Research On Simulation Data Mapping For Production Performance Digital Twin, Junfeng Wang, Yufan Zhang, Yaoqi Shao, Shiqi Li

Journal of System Simulation

Abstract: To fully use the real-time data of the physical workshop to realize the accurate mapping of the data to the virtual workshop and drive the simulation and optimization of the manufacturing system, a data mapping method for the digital twin simulation of production performance is proposed. The fusion process of simulation data and production logic model is analyzed from the aspects of simulation data modeling, storage and updating. Focusing on the production performance simulation requirements, the redundancy analysis, outlier analysis and elimination, statistics and fitting of the original production site sample data is preprocessed. The real-time mapping update process …

Modeling And Simulation Of Channel Passage Capacity Based On Cellular Automata, Zongyang Liu, Chunhui Zhou, Junnan Zhao, Jinli Xiao, Langxiong Gan

Journal of System Simulation

Abstract: In order to study the passage capacity of the seaport and explore the service level of the channel, the cellular automata theory is used to discretize and model the main channel of the seaport, and the movement rules of the cell in the channel are processed in sections. In addition, this study focuses on the existence of a traffic "warning zone" in the curved channel section. The ship motion rules of the straight channel section and the curved channel section are respectively constrained, through simulation experiments, the vessel traffic flow of the channel and the maximum ship passing …

Virtual And Real Fusion Experiment Simulation Technology Based On Gesture Interaction, Xuyan Zou, Hanwu He, Yueming Wu, Jingwei Deng

Journal of System Simulation

Abstract: In experimental teaching, some traditional experiments cannot be carried out due to limitations of experimental environment, experimental equipment, and faculty. In order to solve the above problems, the method of constructing virtual and real fusion simulation experiment using depth cameras is explored.. The Aruco labeling algorithm is used to achieve one-to-one registration of the virtual scene and the real scene, and the experimental environment of virtual and real fusion is constructed combining the colour images and depth images collected by the depth camera in real time. In order to achieve the purpose of operating virtual equipment, a gesture interaction …

Hybrid Approach For Resource Allocation In Cloud Infrastructure Using Random Forest And Genetic Algorithm, Madhusudhan H S, Satish Kumar T, S.M.F D Syed Mustapha, Punit Gupta, Rajan Prasad Tripathi

All Works

In cloud computing, the virtualization technique is a significant technology to optimize the power consumption of the cloud data center. In this generation, most of the services are moving to the cloud resulting in increased load on data centers. As a result, the size of the data center grows and hence there is more energy consumption. To resolve this issue, an efficient optimization algorithm is required for resource allocation. In this work, a hybrid approach for virtual machine allocation based on genetic algorithm (GA) and the random forest (RF) is proposed which belongs to a class of supervised machine learning …

Concept Drift Adaptation With Incremental–Decremental Svm, Honorius Gâlmeanu, Răzvan Andonie

Computer Science Faculty Scholarship

Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a …

Recent Advances In Wearable Sensing Technologies, Alfredo J. Perez, Sherali Zeadally

Computer Science Faculty Publications

Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus …

Recent Advances In Wearable Sensing Technologies, Alfredo J. Perez, Sherali Zeadally

Information Science Faculty Publications

Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus …

Swift Trust And Sensemaking In Fast Response Virtual Teams, Xiaodan Yu, Yuanyanhang Shen, Deepak Khazanchi

Information Systems and Quantitative Analysis Faculty Publications

Fast-response virtual teams (FRVTs) have been developed as a response to emergent challenges faced by organizations that need to be addressed urgently. Even though FRVTs offer enormous potential in terms of their benefits, their success is not guaranteed. When used, the need for high performing FRVTs has become critical for organizational success. However, there is a lack of detailed understanding of how sensemaking can potentially influence FRVT performance. Drawing on social exchange theory, we identify swift trust as a potential antecedent of sensemaking. In this paper, we report the results of a study that examined the effects of swift trust …

Simplifying The Development Of Portable, Scalable, And Reproducible Workflows, Stephen Piccolo, Zachary E. Ence, Elizabeth C. Anderson, Jeffrey T. Chang, Andrea H. Bild

Faculty Publications

Command-line software plays a critical role in biology research. However, processes for installing and executing software differ widely. The Common Workflow Language (CWL) is a community standard that addresses this problem. Using CWL, tool developers can formally describe a tool’s inputs, outputs, and other execution details. CWL documents can include instructions for executing tools inside software containers. Accordingly, CWL tools are portable—they can be executed on diverse computers—including personal workstations, high-performance clusters, or the cloud. CWL also supports workflows, which describe dependencies among tools and using outputs from one tool as inputs to others. To date, CWL has been used …

Preparing School Leaders To Advance Equity In Computer Science Education, Julie Flapan, Jean J. Ryoo, Roxana Hadad, Joel Knudson

Journal of Computer Science Integration

Background and Context: Most large-scale statewide initiatives of the Computer Science for All (CS for All) movement have focused on the classroom level. Critical questions remain about building school and district leadership capacity to support teachers while implementing equitable computer science education that is scalable and sustainable.

Objective: This statewide research-practice partnership, involving university researchers and school leaders from 14 local education agencies (LEA) from district and county offices, addresses the following research question: What do administrators identify as most helpful for understanding issues related to equitable computer science implementation when engaging with a guide and workshop we …

Ufuzzer: Lightweight Detection Of Php-Based Unrestricted File Upload Vulnerabilities Via Static-Fuzzing Co-Analysis, Jin Huang, Junjie Zhang, Jialun Liu, Chuang Li

Computer Science and Engineering Faculty Publications

Unrestricted file upload vulnerabilities enable attackers to upload malicious scripts to a web server for later execution. We have built a system, namely UFuzzer, to effectively and automatically detect such vulnerabilities in PHP-based server-side web programs. Different from existing detection methods that use either static program analysis or fuzzing, UFuzzer integrates both (i.e., static-fuzzing co-analysis). Specifically, it leverages static program analysis to generate executable code templates that compactly and effectively summarize the vulnerability-relevant semantics of a server-side web application. UFuzzer then “fuzzes” these templates in a local, native PHP runtime environment for vulnerability detection. Compared to static-analysis-based methods, UFuzzer preserves …

Statistical Potentials For Rna-Protein Interactions Optimized By Cma-Es, Takayuki Kimura, Nobuaki Yasuo, Masakazu Sekijima, Brooke Lustig

Faculty Research, Scholarly, and Creative Activity

Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses.

Professional Responsibility, Legal Malpractice, Cybersecurity, And Cyber-Insurance In The Covid-19 Era, Ethan S. Burger

St. Mary's Journal on Legal Malpractice & Ethics

In response to the COVID-19 outbreak, law firms conformed their activities to the Centers for Disease Control and Prevention (CDC), Occupational Safety and Health Administration (OSHA), and state health authority guidelines by immediately reducing the size of gatherings, encouraging social distancing, and mandating the use of protective gear. These changes necessitated the expansion of law firm remote operations, made possible by the increased adoption of technological tools to coordinate workflow and administrative tasks, communicate with clients, and engage with judicial and governmental bodies.

Law firms’ increased use of these technological tools for carrying out legal and administrative activities has implications …

Masked Face Analysis Via Multi-Task Deep Learning, Vatsa S. Patel, Zhongliang Nie, Trung-Nghia Le, Tam Van Nguyen

Computer Science Faculty Publications

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to …

Human-Computer Interaction Research And Education--Crossing Boundaries Between Academic Research And Industry Practices, Yasushi Akiyama

Interface: The C3 Lab Knowledge Commons

In this paper, I will discuss my own experience and approaches to enhancing students' learning in Human-Computer Interaction (HCI) classes by adopting an interdisciplinary approach that integrates academic research and industry practice. Due to the inherent interdisciplinarity of HCI, and to foster a set of "soft" skills known in Interdisciplinary Studies as the "cognitive toolkit," I invite expertise from the other departments at my university as well as industry professionals to give lectures, facilitate workshops, and oversee projects. These collaborations have produced several insights and have had a positive impact on the participants.

Towards Generalizable Network Anomaly Detection Models, Md Arifuzzaman, Shafkat Islam, Engin Arslan

Computer Science Faculty Research & Creative Works

Finding the root causes of network performance anomalies is critical to satisfy the quality-of-service requirements. In this paper, we introduce machine learning (ML) models to process TCP socket statistics to pinpoint underlying reasons of performance issues such as packet loss and jitter. More importantly, we introduce a novel feature engineering method to transform network-dependent metrics (e.g., total packet count and round-trip time) in training datasets into network independent forms to be able to transfer the models to new network settings without requiring retraining them. Experimental results in various network settings show that the proposed feature engineering approach improves the performance …

D2gen: A Decentralized Device Genome Based Integrity Verification Mechanism For Collaborative Intrusion Detection Systems, Imran Makhdoom, Kadhim Hayawi, Mohammed Kaosar, Sujith Samuel Mathew, Pin-Han Ho

All Works

Collaborative Intrusion Detection Systems are considered an effective defense mechanism for large, intricate, and multilayered Industrial Internet of Things against many cyberattacks. However, while a Collaborative Intrusion Detection System successfully detects and prevents various attacks, it is possible that an inside attacker performs a malicious act and compromises an Intrusion Detection System node. A compromised node can inflict considerable damage on the whole collaborative network. For instance, when a malicious node gives a false alert of an attack, the other nodes will unnecessarily increase their security and close all of their services, thus, degrading the system’s performance. On the contrary, …

The Future Of Medicine Is Digital: Developing Educational Materials To Explore The Ethics Of Digital Pills., Dympna O'Sullivan, J. Paul Gibson, Yael Jacob, Ioannis Stavrakakis, Damian Gordon

Conference papers

Digital Pills are a drug-device technology that permit to combine traditional medications with a monitoring system that automatically records data about medication adherence and patients’ physiological data. They are a promising innovation in digital medicine, however their use has raised a number of ethical concerns. In this paper, we outline some of the main Digital Pills technologies and explore key ethical challenges surrounding their use. In this paper, we introduce educational materials we have developed that provide an insight into the technologies and ethical aspects that underpin Digital Pills.

Predicting Human–Pathogen Protein–Protein Interactions Using Natural Language Processing Methods, Nikhil Mathews, Tuan Tran, Banafsheh Rekabdar, Chinwe Ekenna

Computer Science Faculty Publications and Presentations

In this paper, we predict the interaction of proteins between Humans and Yersinia pestis via amino acid sequences. We utilize multiple Natural Language Processing (NLP) methods available in deep learning in a unique format and produce promising results. Our developed model gives a cross-validation AUC score of 0.92 and is comparable with other work that utilizes extensive biochemical properties i.e, network and sequence in conjunction. We achieve this by combining advanced tools in neural machine translation into an integrated end-to-end deep learning framework as well as methods of preprocessing that are novel to the field of bioinformatics. We show that …

Ai: Friend Or Foe? (And What Business Leaders Need To Know), Singapore Management University

Perspectives@SMU

Artificial intelligence presents significant opportunities for business – as well as not insignificant threats to humanity – and governance frameworks are urgently needed to create a fair and equitable future under AI

Why Neural Networks In The First Place: A Theoretical Explanation, Jonatan Contreras, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Neural networks -- specifically, deep neural networks -- are, at present, the most effective machine learning techniques. There are reasonable explanations of why deep neural networks work better than traditional "shallow" ones, but the question remains: why neural networks in the first place? why not networks consisting of non-linear functions from some other family of functions? In this paper, we provide a possible theoretical answer to this question: namely, we show that of all families with the smallest possible number of parameters, families corresponding to neurons are indeed optimal -- for all optimality criteria that satisfy some reasonable requirements: : …

Why Daubechies Wavelets Are So Successful, Solymar Ayala Cortez, Laxman Bokati, Aaron Velasco, Vladik Kreinovich

Departmental Technical Reports (CS)

In many applications, including analysis of seismic signals, Daubechies wavelets perform much better than other families of wavelets. In this paper, we provide a possible theoretical explanation for the empirical success of Daubechies wavelets. Specifically, we show that these wavelets are optimal with respect to any optimality criterion that satisfies the natural properties of scale- and shift-invariance.

Uncertainty: Ideas Behind Neural Networks Lead Us Beyond Kl-Decomposition And Interval Fields, Michael Beer, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we know that there is a functional dependence between a quantity q and quantities a1, ..., an, but the exact form of this dependence is only known with uncertainty. In some cases, we only know the class of possible functions describing this dependence. In other cases, we also know the probabilities of different functions from this class -- i.e., we know the corresponding random field or random process. To solve problems related to such a dependence, it is desirable to be able to simulate the corresponding functions, i.e., to have algorithms that transform simple intervals or …

While, In General, Uncertainty Quantification (Uq) Is Np-Hard, Many Practical Uq Problems Can Be Made Feasible, Anderson Gray, Scott Ferson, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In general, many general mathematical formulations of uncertainty quantification problems are NP-hard, meaning that (unless it turned out that P = NP) no feasible algorithm is possible that would always solve these problems. In this paper, we argue that if we restrict ourselves to practical problems, then the correspondingly restricted problems become feasible -- namely, they can be solved by using linear programming techniques.

Crest Or Trough? How Research Libraries Used Emerging Technologies To Survive The Pandemic, So Far, Scout Calvert

University of Nebraska-Lincoln Libraries: Faculty Publications

Introduction

In the first months of the COVID-19 pandemic, it was impossible to tell if we were at the crest of a wave of new transmissions, or a trough of a much larger wave, still yet to peak. As of this writing, as colleges and universities prepare for mostly in-person fall 2021 semesters, case counts in the United States are increasing again after a decline that coincided with easier access to the COVID vaccine. Plans for a return to campus made with confidence this spring may be in doubt, as we climb the curve of what is already the second …

Excursions In Summation, Brock Erwin

Fall Showcase for Research and Creative Inquiry

Using polynomials from series representation of functions to approximate other functions on the closed interval from [-1,1].

Ethical Dilemma Of Self-Driving Cars: Conservative Solution, Christian Servin, Vladik Kreinovich, Shahnaz Shahbazova

Departmental Technical Reports (CS)

When designing software for self-driving cars, we need to make an important decision: When a self-driving car encounters an emergency situation in which either the car's passenger or an innocent pedestrian have a good change of being injured or even die, which option should it choose? This has been a subject of many years of ethical discussions -- and these discussions have not yet led to a convincing solution. In this paper, we propose a "conservative" (status quo) solution that does not require making new ethical decisions -- namely, we propose to limit both the risks to passengers and risks …

Effects Of Cloud Computing In The Workforce, Kevin Rossi Acosta

Cybersecurity Undergraduate Research Showcase

In recent years, the incorporation of cloud computing and cloud services has increased in many different types of organizations and companies. This paper will focus on the philosophical, economical, and political factors that cloud computing and cloud services have in the workforce and different organizations. Based on various scholarly articles and resources it was observed that organizations used cloud computing and cloud services to increase their overall productivity as well as decrease the overall cost of their operations, as well as the different policies that were created by lawmakers to control the realm of cloud computing. The results of this …