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A provably componentwise backward stable O(n2) QR algorithm for the diagonalization of colleague matrices

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arXiv 2021年

作者： Serkh, Kirill Rokhlin, Vladimir Dept. of Math. and Computer Science University of Toronto TorontoONM5S 2E4 Canada Dept. of Mathematics Yale University New HavenCT06511 United States

Advancements and challenges in Arabic sentiment analysis: A decade of methodologies, applications, and resource development

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Heliyon 2024年第21期10卷 e39786页

作者： Aladeemy, Amani A. Alzahrani, Ali Algarni, Mohammad H. Alsubari, Saleh Nagi Aldhyani, Theyazn H.H. Deshmukh, Sachin N. Khalaf, Osamah Ibrahim Wong, Wing-Keung Aqburi, Sameer Dept. of Computer Science & Information Technology Dr. Babasaheb Ambedkar Marathwada University Aurangabad India Faculty of Computer Sciences and Information Technology Taiz University Taiz Yemen Department of Computer Engineering King Faisal University Al-Alahsa 31982 Saudi Arabia Computer Science Department Al-Baha University Al-Baha 65779 Saudi Arabia Applied College in Abqaiq King Faisal University Al-Ahsa 31982 Saudi Arabia Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Asia University Taiwan Al-Kitab University College of Engineering Techniques Kirkuk Iraq

The exponential growth of digital information, particularly user-generated content on social media and blogging platforms, has underscored the importance of sentiment analysis (SA). Arabic language sentiment analysis (ASA) involves identifying the orientation of ideas, feelings, emotions, and attitudes within Arabic text to determine whether they convey a positive, negative, or neutral sentiment. This paper presents a comprehensive review of the past decade, focusing on the utilization of SA in the Arabic language. It examines various applications, methodologies, and challenges associated with ASA, highlighting gaps and limitations in existing approaches, lexicons, and annotated datasets. The primary objective of this review is to assist researchers in identifying these gaps and limitations while offering accessible annotated datasets, preprocessing techniques, and procedures. We defined specific criteria for selecting research publications from the last 10 years, including 150 papers in our review process, while excluding earlier publications. The review utilized multiple databases, including Google Scholar, Scopus, and Web of science. The inherent complexity of the Arabic language, due to its unique traits and diverse dialects, presents significant challenges in ASA. Moreover, the lack of annotated datasets, lexicon resources, and programming tools further complicates sentiment analysis in Arabic. The morphological variations within Arabic make it linguistically challenging. To address these issues, it is crucial to develop additional resources and construct new Arabic sentiment lexicons that account for the various dialects within Modern Standard Arabic (MSA). Our findings reveal that there is no standard public lexicon that adequately enhances the calculation of ASA across different domains, such as e-commerce, politics, public health, and marketing. © 2024

关键词： And lexical tools Arabic sentiment analysis Machine learning

A Learnable Prior Improves Inverse Tumor Growth Modeling

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arXiv 2024年

作者： Weidner, Jonas Ezhov, Ivan Balcerak, Michal Metz, Marie-Christin Litvinov, Sergey Kaltenbach, Sebastian Feiner, Leonhard Lux, Laurin Kofler, Florian Lipkova, Jana Latz, Jonas Rueckert, Daniel Menze, Bjoern Wiestler, Benedikt Dept. of Computer Science The Center for Translational Cancer Research TranslaTUM Technical University of Munich Germany Dept. of Neuroradiology School of Medicine and Health Technical University of Munich Germany School of Engineering and Applied Sciences Harvard University MA United States Dept. of Quantitative Biomedicine University of Zurich Zurich Switzerland Helmholtz AI Helmholtz Zentrum Munich Germany Center for Translational Cancer Research TranslaTUM Technical University of Munich Germany Department of Pathology University of California IrvineCA United States Department of Mathematics University of Manchester United Kingdom Department of Computing Biomedical Image Analysis Group Imperial College London United Kingdom Klinikum rechts der Isar Technical University Munich Germany

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

EDGEMIXUP: Improving Fairness for Skin Disease Classification and Segmentation

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arXiv 2022年

作者： Yuan, Haolin Hadzic, Armin Paul, William de Flores, Daniella Villegas mathew, Philip Aucott, John Cao, Yinzhi Burlina, Philippe Johns Hopkins University Applied Physics Laboratory LaurelMD United States Johns Hopkins University Dept. of Computer Science United States Johns Hopkins University Malone Center for Engineering in Healthcare United States Johns Hopkins University School of Medicine United States

Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assisting clinicians with prescreening patients. However, these models often learn biases inherent in training data, which can lead to a performance gap in the diagnosis of people with light and/or dark skin tones. To the best of our knowledge, limited work has been done on identifying, let alone reducing, model bias in skin disease classification and segmentation. In this paper, we examine DL fairness and demonstrate the existence of bias in classification and segmentation models for subpopulations with darker skin tones compared to individuals with lighter skin tones, for specific diseases including Lyme, Tinea Corporis and Herpes Zoster. Then, we propose a novel preprocessing, data alteration method, called EDGEMIXUP, to improve model fairness with a linear combination of an input skin lesion image and a corresponding a predicted edge detection mask combined with color saturation alteration. For the task of skin disease classification, EDGEMIXUP outperforms much more complex competing methods such as adversarial approaches, achieving a 10.99% reduction in accuracy gap between light and dark skin tone samples, and resulting in 8.4% improved performance for an underrepresented subpopulation. Copyright © 2022, The Authors. All rights reserved.

关键词： Diagnosis

PatrIoT: IoT automated interoperability and integration testing framework

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arXiv 2021年

作者： Bures, Miroslav Ahmed, Bestoun S. Rechtberger, Vaclav Klima, Matej Trnka, Michal Jaros, Miroslav Bellekens, Xavier Almog, Dani Herout, Pavel Dept. of Computer Science FEE Czech Technical University in Prague Prague Czech Republic Dept. of Math. & Computer Science Karlstad University Karlstad Sweden Red Hat Czech s.r.o. Brno Czech Republic Dept. of Electronic and Electrical Engineering University of Strathclyde Glasgow United Kingdom Software Engineering Dept. Shamoon College of Engineering Be'er Sheva Israel Dept. of CS and Engineering FAS University of West Bohemia Plzen Czech Republic

With the rapid growth of the contemporary Internet of Things (IoT) market, the established systems raise a number of concerns regarding the reliability and the potential presence of critical integration defects. In this paper, we present a PatrIoT framework that aims to provide flexible support to construct an effective IoT system testbed to implement automated interoperability and integration testing. The framework allows scaling from a pure physical testbed to a simulated environment using a number of predefined modules and elements to simulate an IoT device or part of the tested infrastructure. PatrIoT also contains a set of reference example testbeds and several sets of example automated tests for a smart street use case. Copyright © 2021, The Authors. All rights reserved.

关键词： Integration testing

Power Quality Enhancement of Solar Based Microgrids Using ML and AI

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Power Quality Enhancement of Solar Based Microgrids Using ML...

Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： K. Paul Joshua C. Vennila Kurikyala Prasad Yadav D. Kirubakaran Vartika Kulshrestha B.R. Praveen Department of Electronics and Communication Engineering St. Peter's College of Engineering and Technology Tamil Nadu India Department of Electrical and Electronics Engineering Alagappa Chettiar Government College of Engineering and Technology Tamil Nadu India Engineering Department University of Technology and Applied Sciences-Ibri (UTAS-Ibri) Dept. of Electrical and Electronics Eng. St. Joseph's Institute of Technology Tamil Nadu India Computer Science & Engineering ACED Alliance University-Central Campus Karnataka India Dept. of Electrical and Electronics Engineering Sai Vidya Institute of Technology Karnataka India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Solar-powered micro grids are gaining popularity as a cost-effective and environmentally friendly way to provide localized electricity. Microgrids struggle with solar power's fluctuation and intermittent nature. Sun-based microgrids' power quality is improved via machine learning (ML) and synthetic intelligence (AI). The recommended solution employs modern ML algorithms to predict solar power generation and micro grid energy output variations. ML models can accurately forecast changes in solar irradiance, temperature, and other crucial aspects by evaluating ancient and real-time data, enabling proactive micro grid power float management. The proposed ML and AI-based technology offers superior power best, proactive power control, and better predictions than standard management methods. Simulation and experimental results show that the technique reduces load interruptions, improves balance, and reduces solar energy oscillations on the micro grid. Its capabilities are excellent. to provide reliable and excellent power supply, large-scale renewable energy adoption, and sustainable power ecosystems with ML and AI in solar-based micrgarids.

关键词： Solar irradiance Renewable energy sources Temperature distribution Statistical analysis Power quality Microgrids Real-time systems Solar power generation Standards Systematic literature review

Towards Service Ontology for Web Services Storage and Discovery 4

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Towards Service Ontology for Web Services Storage and Discov...

4th International Symposium on Informatics and its Applications, ISIA 2020

作者： Kaouan, Moussa Bouchiha, Djelloul Benslimane, Sidi Mohamed Boukli-Hacene, Sofiane Ctr Univ Naama EEDIS Lab SBA Dept. Math. and Comp. Science Algeria Ecole Superieure en Informatique Sidi Bel-Abbes Algeria EEDIS Lab SBA Department of Computer Science Algeria

ISBN: (纸本)9781728196527

The ontology has marked its presence in several research fields in order to address issues in knowledge modeling' Artificial Intelligence, classification, clustering and more specifically in knowledge engineering. In the Web Service field, ontology is used principally to describe the Web Service interface for discovering, storing and composing Web services. This is well known as semantic Web services, such as OWL-S, WSMO and WSDL-S. Unfortunately, work in this field remain insufficient till now;so, supporting this description by adding a shared ontology (knowledge base) can improve the performance of the Web Service technology. Manual construction of ontology is a tedious and expensive task which requires domain expert intervention;an automatic ontological construction process will be interesting and less costly solution in terms of time and money. In this paper, we propose to use clustering algorithms to create an ontological network (Service Ontology (SO)) from a semantic Web Services corpus. The created SO can be used for storage and discovery of Web Services in a distributed and intelligent environment. © 2020 IEEE.

关键词： Web services

Simulation vs. Testbed: Small Scale Experimental Validation of an Open-Source LTE-A Model

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Simulation vs. Testbed: Small Scale Experimental Validation ...

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Lars Wischhof Fabian Schaipp Stefan Schuhbäck Gerta Köster Dept. of Computer Science and Mathematics Munich University of Applied Sciences Munich Germany

ISBN: (数字)9781728144900

ISBN: (纸本)9781728144917

A majority of scientific publications on wireless networking is (partly) based on network simulation in order to evaluate the presented concepts, algorithms and architectures. In research related to 4G and 5G cellular communication, the system-level simulation SimuLTE is a widely used model. Despite its popularity, no results regarding an experimental validation of this model are publicly available up to now. This paper presents results of a first step in this direction: A small-scale completely shielded cellular testbed based on OpenAirInterface is set up and packet delay and inter-arrival time are measured in several low and high load scenarios. Afterwards, the identical scenarios are modeled in SimuLTE. The comparison of simulation and measurement results indicate a good accuracy of the model for low-load scenarios. However, in particular in overload conditions involving more than one packet source the results differ significantly. Subsequently, several adaptations to the model are proposed which lead to a better agreement of simulation and measurement results in the investigated scenarios.

关键词： Wireless communication Load modeling Computational modeling Measurement 5G mobile communication Adaptation models Protocols

SOLAS: Superpositioning an Optical Lens in Automotive Simulation

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arXiv 2025年

作者： Jakab, Daniel Barthel, Julian Braun, Alexander Mohandas, Reenu Deegan, Brian Michael Kumbham, Mahendar Molloy, Dara Collins, Fiachra Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy Co. Limerick V94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems Tuam GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

Automotive Simulation is a potentially cost-effective strategy to identify and test corner case scenarios in automotive perception. Recent work has shown a significant shift in creating realistic synthetic data for road traffic scenarios using a video graphics engine. However, a gap exists in modeling realistic optical aberrations associated with cameras in automotive simulation. This paper builds on the concept from existing literature to model optical degradations in simulated environments using the Python-based ray-tracing library KrakenOS. As a novel pipeline, we degrade automotive fisheye simulation using an optical doublet with +/-2◦ Field of View(FOV), introducing realistic optical artifacts into two simulation images from SynWoodscape and Parallel Domain Woodscape. We evaluate KrakenOS by calculating the Root Mean Square Error (RMSE), which averaged around 0.023 across the RGB light spectrum compared to Ansys Zemax OpticStudio, an industrial benchmark for optical design and simulation. Lastly, we measure the image sharpness of the degraded simulation using the ISO12233:2023 Slanted Edge Method and show how both qualitative and measured results indicate the extent of the spatial variation in image sharpness from the periphery to the center of the degradations. © 2025, CC BY.

关键词： Ray tracing