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arXiv 2023年

作者： Zhao, Wanying Guo, Fiona Lerman, Kristina Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University United States Department of Computer Science University of Southern California United States Information Sciences Institute University of Southern California United States Network Science Institute Indiana University BloomingtonIN United States

Narrative is a foundation of human cognition and decision making. Because narratives play a crucial role in societal discourses and spread of misinformation and because of the pervasive use of social media, the narrative dynamics on social media can have profound societal impact. Yet, systematic and computational understanding of online narratives faces critical challenge of the scale and dynamics;how can we reliably and automatically extract narratives from massive amount of texts? How do narratives emerge, spread, and die? Here, we propose a systematic narrative discovery framework that fill this gap by combining change point detection, semantic role labeling (SRL), and automatic aggregation of narrative fragments into narrative networks. We evaluate our model with synthetic and empirical data-two Twitter corpora about COVID-19 and 2017 French Election. Results demonstrate that our approach can recover major narrative shifts that correspond to the major events. © 2023, CC BY-NC-ND.

关键词： decision making

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Computationally Efficient Predictive Control of Photovoltaic Central NPC Inverter with Constant Switching Frequency

Computationally Efficient Predictive Control of Photovoltaic...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Alexander Dahlmann Venkata Yaramasu Samir Kouro Matias Aguirre School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff USA Department of Electronic Engineering Universidad Tecnica Federico Santa Maria Valparaiso Chile Department of Electronic Engineering Arturo Prat University and Energy Resource Research Center Iquique Chile

This paper proposes a computationally efficient modulated model predictive current control method for a three-phase neutral-point clamped (NPC) central inverter in the photovoltaic energy system. The proposed control method calculates the optimal sector number based on grid voltages, grid currents and maximum power point tracking and uses a subset of voltage vectors to reduce the number of calculations significantly, reducing the computational burden. The subset of voltage vectors are used to predict the future behavior of grid currents and DC-link capacitor voltages. A triangular-region based cost function chooses three optimal voltage vectors for a seven-segment switching method. The proposed control method provides low steady-state errors, fast transient response and a constant switching frequency. An 817 kW photovoltaic energy system is simulated to validate the proposed control method under transient and steady-state conditions.

关键词： Photovoltaic systems Switching frequency Switches Control systems Mathematical models Inverters Steady-state

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Predicting anti-Asian hateful users on Twitter during COVID-19

arXiv

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arXiv 2021年

作者： An, Jisun Kwak, Haewoon Lee, Claire Seungeun Jun, Bogang Ahn, Yong-Yeol School of Computing and Information Systems Singapore Management University School of Criminology and Justice Studies University of Massachusetts Lowell Department of Economics Inha University School of Informatics Computing and Engineering Indiana University Bloomington United States

We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19. We compare two user groups-those who posted anti-Asian slurs and those who did not-with respect to a rich set of features measured with data prior to COVID-19 and show that it is possible to predict who later publicly posted anti-Asian slurs. Our analysis of predictive features underlines the potential impact of news media and information sources that report on online hate and calls for further investigation into the role of polarized communication networks and news media. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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A Simulation Framework for Service Loss of Power Networks under Extreme Weather Events: A Case of Puerto Rico

A Simulation Framework for Service Loss of Power Networks un...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Thomaz Carvalhaes Alireza Inanlouganji Esther Boyle Petar Jevtić Giulia Pedrielli Agami Reddy School of Sustainable Engineering and the Built Environment School of Computing Informatics and Decision Systems Engineering ASU Tempe USA School of Mathematical Sciences and Statistics ASU Tempe USA

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Extreme weather events are increasingly common across the globe. As a result, it is becoming more important to design tools that can efficiently and dynamically enable the analysis of vulnerabilities across nodes and links of critical, possibly interconnected networks. In this paper, we focus on power systems, and we show a proof of concept of our Simulation-Driven approach for the loss Analysis of Critical Infrastructures (SACI). In particular, we show the potential of the proposed tool for the case of hurricanes hitting large scale power networks, specifically of Puerto Rico. The numerical analysis demonstrates the simulation analytics capabilities of SACI and its potential impact for the utility companies.

关键词： Wind speed Power systems Hurricanes Numerical models Generators

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Situating Network Infrastructure with People, Practices, and Beyond: A Community Building Workshop 25

Situating Network Infrastructure with People, Practices, and...

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25th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2022

作者： Jang, Esther Han Beol Bidwell, Nicola Liu, Jen Sengers, Phoebe Bagalkot, Naveen Verdezoto, Nervo Densmore, Melissa Vigil-Hayes, Morgan Hasan, Shaddi Paul G. Allen School for Computer Science and Engineering SeattleWA United States International University of Management Windhoek Namibia Cornell University Information Science IthacaNY United States Cornell University Information Science and Science & Technology Studies IthacaNY United States Srishti Manipal Institute of Art Design and Technology Karnataka Bengaluru India Cardiff University School of Computer Science and Informatics Cardiff United Kingdom University of Cape Town Cape Town South Africa Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Virginia Tech Department of Computer Science BlacksburgVA United States

ISBN: (纸本)9781450391900

Our world is now connected and even entangled in unprecedented ways through networked technologies. Yet pockets of unequal connectivity persist, and technical infrastructures for connectivity remain difficult to design and build even for experts. In this workshop we aim to bring together a global community of multi- and inter-disciplinary researchers and implementers working on infrastructure development and connectivity to explore the existing design challenges and opportunities for bringing technical dimensions of networked infrastructures in conversation with human-computer interaction (HCI) and the social science of infrastructure. We will share, assess and define research problems and resources for rethinking networked infrastructures from human-, community-, and society-centered perspectives, understanding them to be embedded with human values and biases. We particularly intend our collaborative work to support real-world connectivity initiatives, which have grown in critical importance over the pandemic years - especially projects in support of Global South communities. Concrete deliverables from the workshop will include: (1) an initial shared bibliography to help formalize the state of knowledge in our area, (2) an agenda of shared goals, challenges, and intentions in our field, (3) a compilation of resources to support future work, and (4) social and organizing infrastructures for continued communication and academic collaboration. © 2022 Owner/Author.

关键词： Human computer interaction

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Coupling Weap and Leap Models Using Interaction Modeling

Coupling Weap and Leap Models Using Interaction Modeling

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Spring Simulation Conference (SpringSim)

作者： Mostafa D. Fard Hessam S. Sarjoughian Arizona Center for Integrative Modeling & Simulation School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

ISBN: (数字)9781565553705

ISBN: (纸本)9781728163642

It is useful to model the interactions between the water and energy models separately. A coupled water and energy model using an interaction model lends itself to developing flexible, yet rigorous predictive simulation studies. A WEAP-LEAP interaction model is developed based on the Knowledge Interchange Broker modeling approach. A RESTful simulation framework is developed for coupling the Componentized-WEAP and Componentized-LEAP systems. Every interaction model is defined to have a set of modules where each module has its input and output ports with data transformation schemes. The input and output ports of the Componentized-WEAP and Componentized-LEAP models are coupled with those of the interaction model. A cyclic, synchronous execution protocol is defined for concurrent, bidirectional data transformations. The computational efficiency of this loosely coupled water-energy framework is acceptable compared with the tightly WEAP-LEAP system. This proposed framework shows understanding the water-energy system nexus is advantageous using interaction modeling.

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DEEPCRASHTEST: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving systems

arXiv

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arXiv 2020年

作者： Bashetty, Sai Krishna Amor, Heni Ben Fainekos, Georgios School of Computing Informatics and Decision Systems Engineering Arizona State University 660 S. Mill Ave TempeAZ85281

The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles. We use numerous dashcam crash videos uploaded on the internet to extract valuable collision data and recreate the crash scenarios in a simulator. We tackle the problem of extracting 3D vehicle trajectories from videos recorded by an unknown and uncalibrated monocular camera source using a modular approach. A working architecture and demonstration videos along with the open-source implementation are provided with the paper. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Hybrid K-Means and Firefly Algorithm-Based Load Balancer for Dynamic Task Scheduling in Fog computing for Postoperative Healthcare systems

Hybrid K-Means and Firefly Algorithm-Based Load Balancer for...

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Advanced computing Technologies (ICoACT), International Conference on

作者： N. Jagadish Kumar Rakesh Premkumar L. Maria Michael Visuwasam Gopalakrishnan Arjunan G. Yuyaraj C. Tharun Kumar Dept of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India School of Informatics and Computing Indiana University Bloomington USA Dept. of AI & DS RMK College of Engineering and Technology Puduvoyal Chennai India Accenture Bangalore India Dept of CSE (5 years Integrated) Sri Sai Ram Engineering College Chennai Tamil Nadu India Dept. of CSBS Sri Sai Ram Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

In fog computing for postoperative healthcare systems, efficient task scheduling and load balancing are crucial for improving response time, energy efficiency, makespan, and the overall performance of the system. This manuscript introduces a novel hybrid load balancing framework that amalgamates KMeans clustering with the Firefly Algorithm (FA) to tackle the dynamic task scheduling complexities inherent in fog-based healthcare environments. The K-Means methodology is employed to categorize tasks according to their computational necessities and resource requirements, thereby establishing clusters that facilitate optimized resource distribution. The Firefly Algorithm, recognized for its proficiency in addressing optimization challenges, is harnessed to ascertain the optimal scheduling arrangement within each task cluster by adeptly modifying task allocation among fog nodes in response to light intensity, which is directly proportional to the overall performance of the system. Empirical findings indicate that the proposed mechanism markedly surpasses conventional algorithms regarding the optimization of response time, makespan, cost, and energy consumption, thereby presenting a viable solution for the management of postoperative care within healthcare systems employing fog computing infrastructure. The results substantiate that our proposed model enhances response time by 18%, makespan by 17%, energy consumption by 15%, cost by 17%, and level of load balance by 18%.

关键词： Processor scheduling Heuristic algorithms Clustering algorithms Medical services Optimal scheduling Dynamic scheduling Load management Time factors Resource management Edge computing

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An LMI-based Composite Nonlinear Controller Design for Robust Stabilization of a Knee Rehabilitation Exoskeleton Robot

An LMI-based Composite Nonlinear Controller Design for Robus...

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International Conference on Control, decision and Information Technologies (CoDIT)

作者： Sahar Jenhani Hassène Gritli Jyotindra Narayan Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia RISC Lab Department of Computing Imperial College London UK Chair of Digital Health Universität Bayreuth Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper introduces a novel robust control strategy specifically designed for a one-degree-of-freedom (1-DoF) knee rehabilitation exoskeleton robot, focusing on position control. Our approach addresses several key challenges, including state constraints, parameter uncertainties, solid and viscous frictions, and external disturbances. We suggest a composite controller that combines a nonlinear controller with a linear state feedback controller in order to successfully address these issues. By utilizing a quadratic Lyapunov function, we derive the expression of the nonlinear control law and establish the Linear Matrix Inequality (LMI) conditions for computing the matrix gain of the linear controller, ensuring robust stabilization of the exoskeleton robot to the desired position. These conditions are derived through the application of advanced mathematical techniques such as the matrix inversion lemma, the Young inequality, the Schur complement, and the S-procedure lemma. Numerical results show the effectiveness of our suggested control law, demonstrating the continuous convergence of the intended angular position to the real one, even when external disturbances and uncertainties are present. Overall, our research not only presents a novel methodology for robust control but also delivers promising results that underscore the efficacy of our approach in achieving stable and reliable performance for 1-DoF knee exoskeleton robots.

关键词： Robust control Uncertain systems State feedback Uncertainty Exoskeletons Position control Solids Linear matrix inequalities Reliability Robots

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Adaptive Optimization and Resource Allocation (AORA) Model for IoT-Edge computing Using Hybrid Newton-Raphson and Dolphin Echolocation Algorithm (HNR-DEA) Technique

Adaptive Optimization and Resource Allocation (AORA) Model f...

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Advanced computing Technologies (ICoACT), International Conference on

作者： N. Jagadish Kumar Rakesh Premkumar L. Maria Michael Visuwasam Gopalakrishnan Arjunan A. Shiny K. Dharani Dept of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India School of Informatics and Computing Indiana University Bloomington USA Dept. of AI & DS RMK College of Engineering and Technology Puduvoyal Chennai India Accenture Bangalore India Dept of CSE (5 years Integrated) Sri Sai Ram Engineering College Chennai Tamil Nadu India Dept. of CSBS Sri Sai Ram Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The swift expansion of Internet of Things (IoT) applications demands the implementation of real-time data processing and low-latency communication, thereby posing challenges to conventional cloud computing paradigms. This study confronts these issues by amalgamating edge computing with hybrid optimization methodologies, and introduces the AORA (Adaptive Optimization and Resource Allocation) framework. The AORA framework capitalizes on intelligent task scheduling, dynamic resource allocation, and energy-efficient approaches to augment the performance of edge *** research methodology encompasses the collection and preprocessing of data derived from IoT devices, feature extraction, prediction of resource demand, and the optimization of task offloading. It employs the Hybrid Newton Raphson and Dolphin Echolocation Algorithm (HNR-DEA) methodology, which synergizes Newton Raphson optimization technique for faster convergence rate around local optima with bio-inspired Dolphin Echolocation Algorithm (DEA) for comprehensive global *** Experimental outcomes reveal considerable advancements, including a 45% reduction in average latency, a 38% decrease in overall energy consumption, and a 23% enhancement in resource utilization. Furthermore, the model attained a 97% task completion rate alongside consistently elevated network throughput. Simulations were executed in CloudSim with 100 IoT devices generating dynamic workloads and edge servers equipped with multi-core processors. The experimental framework utilized Google Cluster Data to replicate workload variations, thereby ensuring scalability and robustness. These results underscore the AORA model's potential as a resilient and scalable solution for real-time IoT applications.

关键词： Adaptation models Computational modeling Scalability Prediction algorithms Real-time systems Internet of Things Resource management Newton method Optimization Dolphins

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