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arXiv 2025年

作者： Yang, Kai-Cheng Goel, Pranav Quintana-Mathé, Alexi Horgan, Luke McCabe, Stefan D. Grinberg, Nir Joseph, Kenneth Lazer, David Network Science Institute Northeastern University BostonMA United States Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Be’er-Sheva Israel Computer Science and Engineering Department University at Buffalo BuffaloNY United States

Social media play a pivotal role in disseminating web content, particularly during elections, yet our understanding of the association between demographic factors and political discourse online remains limited. Here, we introduce a unique dataset, DomainDemo, linking domains shared on Twitter (X) with the demographic characteristics of associated users, including age, gender, race, political affiliation, and geolocation, from 2011 to 2022. This new resource was derived from a panel of over 1.5 million Twitter users matched against their U.S. voter registration records, facilitating a better understanding of a decade of information flows on one of the most prominent social media platforms and trends in political and public discourse among registered U.S. voters from different sociodemographic groups. By aggregating user demographic information onto the domains, we derive five metrics that provide critical insights into over 129,000 websites. In particular, the localness and partisan audience metrics quantify the domains’ geographical reach and ideological orientation, respectively. These metrics show substantial agreement with existing classifications, suggesting the effectiveness and reliability of DomainDemo’s approach. © 2025, CC BY.

关键词： Tweets

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Deep Reinforcement Learning based SAR Image Pre-Processing Algorithm with Finite Buffer LEO Satellite networks 13

Deep Reinforcement Learning based SAR Image Pre-Processing A...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Kim, Tae-Yoon Kim, Kyeongrok Kim, Jae-Hyun Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Satellite System 1 Team Hanwha Systems Yongin17121 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

As space technology advances, launching low Earth orbit (LEO) satellites become easier and LEO satellites are being used in various fields. In particular, LEO synthetic aperture radar (SAR) system is in the spotlight with many advantages, e.g., regardless of weather condition, 24 hour operation. SAR system can be used in various fields such as object detection and disaster observation. However, SAR image has speckling noise, so image pre-processing must be required. There are many researches on the SAR image processing, however, few publications are considering a buffer status. Therefore, in this paper, we suggest the optimal SAR image pre-processing in LEO SAR satellites and a ground station with finite buffer based on deep reinforcement learning (DRL). As a result of DRL simulation, while changing the buffer size of the LEO SAR satellites, efficiency of buffer was improved by selecting the optimal filter size according to the state of the buffer. © 2022 IEEE.

关键词： Synthetic aperture radar

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Modeling and Designing Non-Pharmaceutical Interventions in Epidemics: A Submodular Approach

arXiv

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arXiv 2024年

作者： Cheng, Shiyu Niu, Luyao Ramasubramanian, Bhaskar Clark, Andrew Poovendran, Radha Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States Department of Electrical and Computer Engineering University of Washington SeattleWA United States Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA United States Electrical and Computer Engineering Program Western Washington University BellinghamWA United States

This paper considers the problem of designing non-pharmaceutical intervention (NPI) strategies, such as masking and social distancing, to slow the spread of a viral epidemic. We formulate the problem of jointly minimizing the infection probabilities of a population and the cost of NPIs based on a Susceptible-Infected-Susceptible (SIS) propagation model. To mitigate the complexity of the problem, we consider a steady-state approximation based on the quasi-stationary (endemic) distribution of the epidemic, and prove that the problem of selecting a minimum-cost strategy to satisfy a given bound on the quasi-stationary infection probabilities can be cast as a submodular optimization problem, which can be solved in polynomial time using the greedy algorithm. We carry out experiments to examine effects of implementing our NPI strategy on propagation and control of epidemics on a Watts-Strogatz small-world graph network. We find the NPI strategy reduces the steady state of infection probabilities of members of the population below a desired threshold value. © 2024, CC BY.

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Collaborative Human-AI Decision-Making systems with Numerical Channels 12

Collaborative Human-AI Decision-Making Systems with Numerica...

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12th International Conference on Advanced computer Information Technologies, ACIT 2022

作者： Oksana, Mules Kotsipak, Mykola Dolgikh, Serge Bilak, Yurii Radivilova, Tamara Baranovskyi, Oleksii Uzhhorod National University Department of Software Systems Uzhhorod Ukraine Bratislava Slovakia Solana Networks Department of Network Engineering Ottawa Canada Kharkiv National University of Radio Electronics Department of V.V.Popovskyy Infocommunication Engineering Kharkiv Ukraine Blekinge Institute of Technology Department of Computer Science Blekinge Karlskrona Sweden

ISBN: (纸本)9781665466479

This work is devoted to the study of the features of functioning in Collaborative Human-AI Decision-Making systems with numerical channels. The system operates in automatic mode without external influences. The channels can be both artificial intelligence software and competent experts giving conclusions on the question under study. The cases considered are those in which an agreed solution must be numerical and logical. For each of these cases, decision rules for the system have been developed. The developed rules include the ability to take into account the reliability of the channels, which allows expanding their number in the system if necessary. © 2022 IEEE.

关键词： Decision making

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Blockchain in Environmental Sustainability Measures: a Survey

arXiv

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arXiv 2024年

作者： Vladucu, Maria-Victoria Wu, Hailun Medina, Jorge Salehin, Khondaker M. Dong, Ziqian Rojas-Cessa, Roberto The Network and Innovation Laboratory Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Computer Science University of Portland PortlandOR97203 United States The Networking and Intelligent Systems Laboratory Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research. © 2024, CC BY-NC-ND.

关键词： Circular economy

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A Comparative Evaluation of Ensemble Classifiers for Malicious Webpage Detection 18

A Comparative Evaluation of Ensemble Classifiers for Malicio...

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18th International Learning and Technology Conference, L and T 2021

作者： Subasi, Abdulhamit Balfaqih, Mohammed Balfagih, Zain Alfawwaz, Khaled Computer Science Department Effat University Jeddah Saudi Arabia Department of Computer and Network Engineering University of Jeddah Jeddah Saudi Arabia Department of Information Systems King Abdulaziz University Saudi Arabia

Malicious webpage is developed or manipulated to be used as attack tool where it is considered as one of the main reasons of Internet criminal activities. Thus, it is essential to detect such webpages and prevent end users form accessing it. The conventional malicious webpages detection techniques are based on searching through a blacklist that contains a list of webpages classified as malicious from the perspective of users. However, these techniques have high false-negative rates especially with aforesaid sophisticated attacks due to technical and computational limitations. Hence, machine learning techniques have been employed to classify webpages by systemically analyzing set of features that reflect the characteristics of a malicious webpage. This paper compares the prediction accuracy of several machine learning classification algorithms and ensemble techniques. A data set of 5000 instances of URLs, with 189 different features are used in the comparative study. The results show that the most accurate classification technique in MultiBoost and Adaboost is Support Vector Machine (SVM), while K-Nearest Neighbor (k-NN) technique in bagging and random subspace. © 2021 The Authors. Published by Elsevier B.V.

关键词： Face recognition

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Hospital Scheduling systems Current Challenges and Emerging Solutions

Hospital Scheduling Systems Current Challenges and Emerging ...

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Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)

作者： Nura Shifa Musa Saida Hafsa Rafique Elezabeth Mathew College of Engineering Al Ain University Al Ain UAE Department of Information Systems and Security United Arab Emirates University Al Ain UAE Department of Computer and Network Engineering United Arab Emirates University Al Ain UAE

ISBN: (数字)9798331531331

ISBN: (纸本)9798331531348

Hospital scheduling systems are integral components of healthcare management, addressing the complex challenge of optimizing appointment schedules. In the realm of healthcare research, the Appointment Scheduling Problem (ASP) has garnered significant attention due to its multifaceted nature. This problem is particularly pronounced in the outpatient setting, where effective scheduling systems can significantly impact patient satisfaction and overall health care efficiency. Recent comprehensive reviews highlighted the complexity of the Appointment Scheduling Problem within healthcare services, emphasizing the need for innovative solutions and proposing new scheduling models. This paper reviews literature on the topic of hospital scheduling systems, then looks into the current challenges prevalent in it and the emerging solutions that are discussed in literature. The review offers opportunities for innovation and improvements in scheduling procedures while highlighting difficulties with allocation of resources, appointment scheduling, and technology integration. Using advanced technologies into healthcare such as blockchain and AI-driven algorithms, are said to be integral to move in the direction of care that is patient-centric and efficient operations. The study highlights limitations in hospital scheduling systems, including scalability and generalizability, operational uncertainties, need for real-world implementation, and lack of consideration for emergency cases. To improve patient outcomes and the efficiency of healthcare scheduling, ongoing research and development, stakeholder involvement, user-centred design, and longitudinal research are essential. In summary, hospital scheduling systems represent a dynamic area of research, addressing the intricate interplay between patient satisfaction, resource optimization, and technological advancements.

关键词： Technological innovation Uncertainty Hospitals Reviews Scalability User centered design Dynamic scheduling Stakeholders Resource management Research and development

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Secured Cluster-Based Electricity Theft Detectors Against Blackbox Evasion Attacks

Secured Cluster-Based Electricity Theft Detectors Against Bl...

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Consumer Communications and networking Conference, CCNC IEEE

作者： Islam Elgarhy Ahmed T. El-Toukhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Maazen Alsabaan Hisham A. Kholidy Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Electrical Engineering College of Engineering Al-Azhar University Cairo Egypt Department of Network and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

In smart power grids, electricity theft causes huge economic losses to electrical utility companies. Machine learning (ML), especially deep neural network (DNN) models hold state-of-the-art performance in detecting electricity theft cyberattacks. However, DNN models are vulnerable to adversarial attacks, i.e., evasion attacks. In this work, we study the vulnerability of the DNN-based electricity theft detectors against evasion attacks and the influence of the model's regularization (generalization) on robustness. We cluster the power consumers and train a detector for each cluster, and compare the performance and robustness of this detector to a global detector that is trained on all the consumers, data. The results indicate that the cluster-based detector is not only more robust against evasion attacks but also enhances normal classification accuracy because its training data has more consumption pattern similarity compared to the training data of the global detector which requires higher level of regularization. Moreover, unlike the existing solutions that sacrifice the normal accuracy of the model to improve the robustness against evasion attacks, the proposed cluster-based detector holds state-of-the-art performance in both robustness and accuracy.

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A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

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computers, Materials & Continua 2022年第11期73卷 4291-4309页

作者： Kais Tissaoui Sahbi Boubaker Waleed Saud Alghassab Taha Zaghdoudi Jamel Azibi University of Ha’il Applied CollegeHail CitySaudi Arabia University of Tunis El Manar Faculty of Economic Sciences and Management of Tunisthe International Finance GroupTunisTunisia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Unit on Study of Systems and Renewable Energy National College of Engineering of MonastirUniversity of MonastirMonastirTunisia LAREQUAD&FSEGT University of Tunis El ManarTunisTunisia University of Jendouba Law FacultyManagement and Economic Sciences of JendoubaTunisia

The application of optimization methods to prediction issues is a continually exploring *** line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting *** complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown *** use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein *** indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,*** performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

关键词： Forecasting Cboe’s volatility index COVID-19 pandemic nonlinear polynomial hammerstein model hybrid particle swarm optimization

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Navigation Design to Improve Interaction Technique in Multi-Browser Virtual Environment 15

Navigation Design to Improve Interaction Technique in Multi-...

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15th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2024

作者： Ichsan, Mochammad Hannats Hanafi Sik-Lanyi, Cecilia Guzsvinecz, Tibor Department of Electrical Engineering and Information Systems Faculty of Information Technology University of Pannonia Egyetem u. 10 Veszprem8200 Hungary Department of Informatics Faculty of Computer Science Brawijaya University Malang Indonesia Hungarian Research Network Piarista u. 4 Budapest1052 Hungary Department of Information Technology and its Applications Faculty of Information Technology University of Pannonia Gasparich M. u. 18/A Zalaegerszeg8900 Hungary

ISBN: (纸本)9798350378245

Efficient, precise, and intuitive navigation in a Virtual Environment (VE) or in desktop Virtual Reality (VR) is critical to improving user experience. This research introduces three navigation designs for physical embodiment inside VE using a computer mouse for multi-browser VE in desktop VR based on navigation possibilities. Navigation is intended for the First-Person Shooter (FPS) perspective: Intuitive Navigation (IntuNav), Edge Shift Navigation (EdgeNav), and Twist Shift Navigation (TwistNav). IntuNav uses mouse x and y coordinate movements to move and rotate the camera. EdgeNav relies on the cursor positioning at the edge of the screen for camera rotation and movement. TwistNav uses a middle click and right mouse click for camera movement and rotation. The current navigation design has been implemented in Unity with C#. This research proposed three model designs based on feasible navigation possibilities, which shows that each navigation model has unique advantages that can be adapted to user needs and preferences. Based on the test results using the System Usability Scale, IntuNav and EdgeNav obtained good results and can be used for further development. © 2024 IEEE.

关键词： Virtual environments

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