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International Conference on Web Research (ICWR)

作者： Ali Nazarizadeh Minoo Sayyadpour Omid Ebrahimkhani Amirmasoud Iravani Golnaz Ghannadan Shirazi Mojgan Behravan Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Dept. Math and Computer Science Kharazmi University Tehran Iran Department of English Translation Studies Faculty of Persian Literature and Foreign Languages Allameh Tabataba'i University Tehran Iran Department of Linguistics Faculty of Letters and Humanities Ferdowsi University of Mashhad Mashhad Iran Department of Engineering Faculty of Computer Engineering Alzahra University Tehran Iran Department of IT& Technology IU International University of Applied science Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Offensive language detection is essential for maintaining respectful and inclusive online environments. By implementing detection systems, platforms aim to protect users from harmful content, curb the spread of hate speech, and promote positive interactions within digital communities. Leveraging natural language processing (NLP) techniques, this area has become a key focus in computational linguistics research and development. To address this challenge, we created the ParsOffensive dataset, consisting of approximately 8,433 Persian comments labeled as Offensive or Neutral. These comments were sourced from Instagram and labeled by two linguists. Machine learning models were trained on this dataset, with the ParsBert model achieving 90% accuracy in detecting offensive speech in Persian.

关键词： Accuracy Social networking (online) Computational modeling Prevention and mitigation Hate speech Text categorization Natural language processing Web sites Multimedia communication Research and development

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Convergent regularization in inverse problems and linear plug-and-play denoisers

arXiv

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arXiv 2023年

作者： Hauptmann, Andreas Mukherjee, Subhadip Schönlieb, Carola-Bibiane Sherry, Ferdia The Research Unit of Mathematical Sciences University of Oulu Finland The Department of Computer Science University College London United Kingdom The Dept. of Electronics and Electrical Communication Engineering IIT Kharagpur India The Dept. of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom

Plug-and-play (PnP) denoising is a popular iterative framework for solving imaging inverse problems using off-the-shelf image denoisers. Their empirical success has motivated a line of research that seeks to understand the convergence of PnP iterates under various assumptions on the denoiser. While a significant amount of research has gone into establishing the convergence of the PnP iteration for different regularity conditions on the denoisers, not much is known about the asymptotic properties of the converged solution as the noise level in the measurement tends to zero, i.e., whether PnP methods are provably convergent regularization schemes under reasonable assumptions on the denoiser. This paper serves two purposes: first, we provide an overview of the classical regularization theory in inverse problems and survey a few notable recent data-driven methods that are provably convergent regularization schemes. We then continue to discuss PnP algorithms and their established convergence guarantees. Subsequently, we consider PnP algorithms with linear denoisers and propose a novel spectral filtering technique to control the strength of regularization arising from the denoiser. Further, by relating the implicit regularization of the denoiser to an explicit regularization functional, we rigorously show that PnP with linear denoisers leads to a convergent regularization scheme. More specifically, we prove that in the limit as the noise vanishes, the PnP reconstruction converges to the minimizer of a regularization potential subject to the solution satisfying the noiseless operator equation. The theoretical analysis is corroborated by numerical experiments for the classical inverse problem of tomographic image reconstruction. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

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Analyzing the function of smart security in the context of the internet of things

Analyzing the function of smart security in the context of t...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： K. Venkatagurunatham Naidu Anchu Krishnan Pillai C. E. Rajaprabha Rajesh Sharma R Atowar Ul Islam Shaik Khasimbee Dept. of Computer Science and Engineering Dr M.G.R Educational and Research Institute Chennai Applied Science and Humanities Xavier Institute of Engineering Mumbai Maharashtra Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore Department of Computer Science and Engineering School of Electrical Engineering and Computing Adama Science and Technology University Nazareth Ethiopia Department of Computer Science and Electronics University of Science and Technology Meghalaya Baridua Meghalaya Department of Computer Science and Engineering Aditya College of Engineering Surampalem Andhra Pradesh

Technology is getting better day by day and human beings are using the latest technology possible for their needs and safety. Many devices have been invented that use the internet and with this human safety issues are also taken care of up to a great extent. GPS tracking devices, SHE (Society Harnessing Equipment), AESHS (Advanced Electronic System for Human Safety), ILA Systems are one of the most useful applications and devices that are used for human safety. Human safety and especially the safety of girls have become an emerging and sensitive issue in the present world scenario. This article deals with the role of the Internet of Things in advancing smart security. The issues are taken care of with the help of these upgraded devices that are easily accessible from a remote location with an internet connection.

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Variations in Ground Motion Amplification in the Los Angeles Basin due to the 2019 M7.1 Ridgecrest Earthquake: Implications for the Long-Period Response of Infrastructure

Variations in Ground Motion Amplification in the Los Angeles...

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Lifelines 2022 Conference: 1971 San Fernando Earthquake and Lifeline Infrastructure

作者： Kohler, Monica D. Filippitzis, Filippos Graves, Robert Massari, Anthony Heaton, Thomas Clayton, Robert Bunn, Julian Guy, Richard Mani Chandy, K. Division of Engineering and Applied Science Dept. of Mechanical and Civil Engineering California Institute of Technology PasadenaCA United States US Geological Survey PasadenaCA United States Dept. of Engineering Civil Environmental Geodetic Engineering Ohio State Univ ColumbusOH United States Division of Geological and Planetary Sciences Seismological Laboratory California Institute of Technology PasadenaCA United States Division of Physics Math and Astronomy Center for Data-Driven Discovery California Institute of Technology PasadenaCA United States Division of Engineering and Applied Science Dept. of Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States

ISBN: (数字)9780784484449

ISBN: (纸本)9780784484449

Coherent patterns and large variations in ground shaking amplification were observed in the Los Angeles basin during the 2019 M7.1 Ridgecrest earthquake. In particular, 3 s to 6 s responses showed variations due to shallow basin geological structure that have implications for the response to large earthquakes of mid-rises, high-rises, long-span bridges, and fuel storage tanks, even if epicentral distances are several hundred kilometers. The Ridgecrest strong-motion data were recorded by seismic stations from the spatially dense Community Seismic Network, the Southern California Seismic Network, and the California Strong Motion Instrumentation Program. The mainshock observations are compared at the same locations with ground motion simulations to examine the regions that experienced the largest shaking, and to investigate the geological sources of large-amplitude shaking. The simulations were computed for the two most commonly used regional community seismic velocity models, CVM-S4.26.M01 ('CVM-S') and CVM-H 15.1.0 ('CVM-H'). Both observations and simulations are used in dynamic analysis with a finite-element model of an existing high-rise with 6-s fundamental horizontal periods, located in downtown Los Angeles. The geographical variation in maximum story drift, story-level shear force, and story-level moment values suggest that the excitation of a hypothetical high-rise located in an area characterized by the largest 6-s PSA values could be significantly larger than in a downtown Los Angeles location. Ground motion simulations using the CVM-H velocity model more closely predict the long-period site amplifications in greater Los Angeles, particularly in the south-central San Fernando Valley, than simulations using CVM-S. © 2022 Lifelines 2022: 1971 San Fernando Earthquake and Lifeline Infrastructure - Selected Papers from the Lifelines 2022 Conference. All rights reserved.

关键词： Digital storage

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Graph2Graph learning with conditional autoregressive models

arXiv

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arXiv 2021年

作者： Wang, Guan Lauze, Francois Bernard Feragen, Aasa Dept. of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark Dept. of Computer Science University of Copenhagen Copenhagen Denmark

We present a graph neural network model for solving graph-to-graph learning problems. Most deep learning on graphs considers "simple" problems such as graph classification or regressing real-valued graph properties. For such tasks, the main requirement for intermediate representations of the data is to maintain the structure needed for output, i.e. keeping classes separated or maintaining the order indicated by the regressor. However, a number of learning tasks, such as regressing graph-valued output, generative models, or graph autoencoders, aim to predict a graph-structured output. In order to successfully do this, the learned representations need to preserve far more structure. We present a conditional auto-regressive model for graph-to-graph learning and illustrate its representational capabilities via experiments on challenging subgraph predictions from graph algorithmics;as a graph autoencoder for reconstruction and visualization;and on pretraining representations that allow graph classification with limited labeled data. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Solving an Urban Ridesharing Problem with Stochastic Travel Times: A Simheuristic Approach

Solving an Urban Ridesharing Problem with Stochastic Travel ...

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2021 Winter Simulation Conference, WSC 2021

作者： Martins, Leandro Do C. Juan, Angel A. Torres, Maria Perez-Bernabeu, Elena Corlu, Canan G. Faulin, Javier Universitat Oberta de Catalunya IN3-Computer Science Dept. Barcelona08018 Spain Universitat Politècnica de València Dept. of Applied Statistics and Operations Research Alcoy03801 Spain Metropolitan College Boston University BostonMA02215 United States Institute of Smart Cities Public University of Navarre Dept. Statistics Computer Science and Mathematics Pamplona31006 Spain

ISBN: (纸本)9781665433112

Ridesharing and carsharing concepts are redefining mobility practices in cities across the world. These concepts, however, also raise noticeable operational challenges that need to be efficiently addressed. In the urban ridesharing problem (URSP), a fleet of small private vehicles owned by citizens should be coordinated in order to pick up passengers on their way to work, hence maximizing the total value of their trips while not exceeding a deadline for reaching the destination points. Since this is a complex optimization problem, most of the existing literature assumes deterministic travel times. This assumption is unrealistic and, for this reason, we discuss a richer URSP variant in which travel times are modeled as random variables. Using random travel times also forces us to consider a probabilistic constraint regarding the duration of each trip. For solving this stochastic optimization problem, a simheuristic approach is proposed and tested via a series of computational experiments. © 2021 IEEE.

关键词： Travel time

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Active Disturbance Rejection Control for 3D Crane Systems 11

Active Disturbance Rejection Control for 3D Crane Systems

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Roman, Raul-Cristian Precup, Radu-Emil Petriu, Emil M. Politehnica University of Timisoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 Timisoara300223 Romania Center for Fundamental and Advanced Tech. Res. Romanian Academy - Timisoara Br. Bd. Mihai Viteazu 24 Timisoara300223 Romania University of Ottawa School of Electrical Engineering and Computer Science 800 King Eduard OttawaONK1N 6N5 Canada

Active Disturbance Rejection Control (ADRC) is a data-driven algorithm that offers a promising approach for robust control design. This paper investigates the effectiveness of first-order and second-order ADRC for 3D cranes. To compare the results fairly and reduce the heuristics in parameter tuning, the ADRC parameters were optimally computed via the African Vultures Optimization Algorithm. Both controllers based on ADRC are validated using experiments on a 3D crane laboratory system and their design and performance evaluation are done by minimizing the sum of squared errors. The results demonstrate the robustness of the ADRCs. This comparative analysis highlights the advantages and limitations of both control strategies, providing valuable insights for selecting the appropriate ADRC order for specific applications. © 2024 The Authors.

关键词： Disturbance rejection

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PsychBERT: A Mental Health Language Model for Social Media Mental Health Behavioral Analysis

PsychBERT: A Mental Health Language Model for Social Media M...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Vajre, Vedant Naylor, Mitch Kamath, Uday Shehu, Amarda Stone Bridge High School Ashburn United States Applied Machine Learning Smarsh Nashville United States George Mason University Dept. of Computer Science Fairfax United States

ISBN: (纸本)9781665401265

Mental health behaviors are now recognized as primary factors contributing to suicide. This paper puts forth a novel mental health language model to address mental health and makes several contributions. First, it proposes a taxonomy and puts forth a comprehensive dataset of social media text. Second, it proposes a two-stage framework, first discriminating text relevant to mental health from non-relevant text and then carrying out multi-class classification for detection of mental health behaviors. Third, it proposes a novel mental health language model, PsychBERT, which is pretrained on a large corpus of biomedical literature on mental health and social media data. Fourth, the framework additionally incorporates components that enhance its explainability. Our evaluation shows that the proposed framework is outperforms state-of-the-art methods and is interpretable. Pre-trained PsychBERT is made publicly available for the community at https://***/mnaylor/psychbert-cased. © 2021 IEEE.

关键词： Health

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Comparative Analysis of Machine Learning Algorithms and Datasets for Detecting Cyberbullying on Social Media Platforms

Comparative Analysis of Machine Learning Algorithms and Data...

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Computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： Anusha Palagati Santhosh Kumar Balan S Arun Joe Babulo Laxmi Raja K.K. Natarajan Rajkumar Kalimuthu Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering SMIEEE Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications K.S.R College of Arts and Science for Women Tiruchengode Tamilnadu India Department of Cyber Security Karpagam Academy of Higher Education Coimbatore Tamilnadu India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Sultanate of Oman Department of Cyber Security Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

Cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naϊve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying.

关键词： Support vector machines computers Logistic regression Machine learning algorithms Supervised learning Optical character recognition Cyberbullying Bayes methods Object recognition Random forests

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Solving the Multi-Allocation p-hub Median Problem with Stochastic Travel Times: A Simheuristic Approach 23

Solving the Multi-Allocation p-hub Median Problem with Stoch...

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Proceedings of the Winter Simulation Conference

作者： Niklas Jost Aleksandra Grochala Christin Schumacher Majsa Ammouriova Angel A. Juan Institute of Transport Logistics TU Dortmund University Dortmund GERMANY Computer Science Department Universitat Oberta de Catalunya Barcelona SPAIN Dept. of Applied Statistics and Operations Research Universitat Politècnica de València Alcoy SPAIN

ISBN: (纸本)9798350369663

The p-hub median problems (pHMPs) are a well-researched topic within the fields of Operations Research and Industrial Engineering. These problems have been found to have a wide range of practical applications in various areas such as logistics, retailing, and Internet computing. These applications have made pHMPs an important area of study, leading to numerous research efforts aimed at solving different variations of the problem. This paper presents a simheuristic algorithm for solving the uncapacitated version of the pHMP with stochastic travel times. The proposed approach combines simulation with biased-randomized heuristics to generate high-quality solutions quickly. The proposed method is validated by testing it on huge benchmark instances, which include stochastic travel times. The results demonstrate the efficiency of the proposed approach for this particular problem variation. The simulation-optimization approach provides a promising solution to a practical problem that arises in many real-world applications.

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